Title: Hierarchy-based Image Embeddings for Semantic Image Retrieval

Abstract: Deep neural networks trained for classification have been found to learn powerful image representations, which are also often used for other tasks such as comparing images w.r.t. their visual similarity. However, visual similarity does not imply semantic similarity. In order to learn semantically discriminative features, we propose to map images onto class embeddings whose pair-wise dot products correspond to a measure of semantic similarity between classes. Such an embedding does not only improve image retrieval results, but could also facilitate integrating semantics for other tasks, e.g., novelty detection or few-shot learning. We introduce a deterministic algorithm for computing the class centroids directly based on prior world-knowledge encoded in a hierarchy of classes such as WordNet. Experiments on CIFAR-100, NABirds, and ImageNet show that our learned semantic image embeddings improve the semantic consistency of image retrieval results by a large margin.


Title: 2nd Place Solution to Google Universal Image Embedding

Abstract: Image representations are a critical building block of computer vision applications. This paper presents the 2nd place solution to the Google Universal Image Embedding Competition, which is part of the ECCV2022 instance-level recognition workshops. We use the instance-level fine-grained image classification method to complete this competition. We focus on data building and processing, model structure, and training strategies. Finally, the solution scored 0.713 on the public leaderboard and 0.709 on the private leaderboard.

Title: Obj-GloVe: Scene-Based Contextual Object Embedding

Abstract: Recently, with the prevalence of large-scale image dataset, the co-occurrence information among classes becomes rich, calling for a new way to exploit it to facilitate inference. In this paper, we propose Obj-GloVe, a generic scene-based contextual embedding for common visual objects, where we adopt the word embedding method GloVe to exploit the co-occurrence between entities. We train the embedding on pre-processed Open Images V4 dataset and provide extensive visualization and analysis by dimensionality reduction and projecting the vectors along a specific semantic axis, and showcasing the nearest neighbors of the most common objects. Furthermore, we reveal the potential applications of Obj-GloVe on object detection and text-to-image synthesis, then verify its effectiveness on these two applications respectively.


Title: Text to Image Generation: Leaving no Language Behind

Abstract: One of the latest applications of Artificial Intelligence (AI) is to generate images from natural language descriptions. These generators are now becoming available and achieve impressive results that have been used for example in the front cover of magazines. As the input to the generators is in the form of a natural language text, a question that arises immediately is how these models behave when the input is written in different languages. In this paper we perform an initial exploration of how the performance of three popular text-to-image generators depends on the language. The results show that there is a significant performance degradation when using languages other than English, especially for languages that are not widely used. This observation leads us to discuss different alternatives on how text-to-image generators can be improved so that performance is consistent across different languages. This is fundamental to ensure that this new technology can be used by non-native English speakers and to preserve linguistic diversity.

Title: Image search using multilingual texts: a cross-modal learning approach between image and text

Abstract: Multilingual (or cross-lingual) embeddings represent several languages in a unique vector space. Using a common embedding space enables for a shared semantic between words from different languages. In this paper, we propose to embed images and texts into a unique distributional vector space, enabling to search images by using text queries expressing information needs related to the (visual) content of images, as well as using image similarity. Our framework forces the representation of an image to be similar to the representation of the text that describes it. Moreover, by using multilingual embeddings we ensure that words from two different languages have close descriptors and thus are attached to similar images. We provide experimental evidence of the efficiency of our approach by experimenting it on two datasets: Common Objects in COntext (COCO) [19] and Multi30K [7].


Title: Using Terminological Knowledge Representation Languages to Manage Linguistic Resources

Abstract: I examine how terminological languages can be used to manage linguistic data during NL research and development. In particular, I consider the lexical semantics task of characterizing semantic verb classes and show how the language can be extended to flag inconsistencies in verb class definitions, identify the need for new verb classes, and identify appropriate linguistic hypotheses for a new verb's behavior.

Title: Diverse and Aligned Audio-to-Video Generation via Text-to-Video Model Adaptation

Abstract: We consider the task of generating diverse and realistic videos guided by natural audio samples from a wide variety of semantic classes. For this task, the videos are required to be aligned both globally and temporally with the input audio: globally, the input audio is semantically associated with the entire output video, and temporally, each segment of the input audio is associated with a corresponding segment of that video. We utilize an existing text-conditioned video generation model and a pre-trained audio encoder model. The proposed method is based on a lightweight adaptor network, which learns to map the audio-based representation to the input representation expected by the text-to-video generation model. As such, it also enables video generation conditioned on text, audio, and, for the first time as far as we can ascertain, on both text and audio. We validate our method extensively on three datasets demonstrating significant semantic diversity of audio-video samples and further propose a novel evaluation metric (AV-Align) to assess the alignment of generated videos with input audio samples. AV-Align is based on the detection and comparison of energy peaks in both modalities. In comparison to recent state-of-the-art approaches, our method generates videos that are better aligned with the input sound, both with respect to content and temporal axis. We also show that videos produced by our method present higher visual quality and are more diverse.


Title: Ultrafast Image Categorization in Biology and Neural Models

Abstract: Humans are able to categorize images very efficiently, in particular to detect the presence of an animal very quickly. Recently, deep learning algorithms based on convolutional neural networks (CNNs) have achieved higher than human accuracy for a wide range of visual categorization tasks. However, the tasks on which these artificial networks are typically trained and evaluated tend to be highly specialized and do not generalize well, e.g., accuracy drops after image rotation. In this respect, biological visual systems are more flexible and efficient than artificial systems for more general tasks, such as recognizing an animal. To further the comparison between biological and artificial neural networks, we re-trained the standard VGG 16 CNN on two independent tasks that are ecologically relevant to humans: detecting the presence of an animal or an artifact. We show that re-training the network achieves a human-like level of performance, comparable to that reported in psychophysical tasks. In addition, we show that the categorization is better when the outputs of the models are combined. Indeed, animals (e.g., lions) tend to be less present in photographs that contain artifacts (e.g., buildings). Furthermore, these re-trained models were able to reproduce some unexpected behavioral observations from human psychophysics, such as robustness to rotation (e.g., an upside-down or tilted image) or to a grayscale transformation. Finally, we quantified the number of CNN layers required to achieve such performance and showed that good accuracy for ultrafast image categorization can be achieved with only a few layers, challenging the belief that image recognition requires deep sequential analysis of visual objects.

Title: Cats vs Dogs, Photons vs Hadrons

Abstract: In gamma ray astronomy with Cherenkov telescopes, machine learning models are needed to guess what kind of particles generated the detected light, and their energies and directions. The focus in this work is on the classification task, training a simple convolutional neural network suitable for binary classification (as it could be a cats vs dogs classification problem), using as input uncleaned images generated by Montecarlo data for a single ASTRI telescope. Results show an enhanced discriminant power with respect to classical random forest methods.


is late interaction better for multi-modal workloads tan single vector models

Title: Improving Out-of-Distribution Generalization of Neural Rerankers with Contextualized Late Interaction

Abstract: Recent progress in information retrieval finds that embedding query and document representation into multi-vector yields a robust bi-encoder retriever on out-of-distribution datasets. In this paper, we explore whether late interaction, the simplest form of multi-vector, is also helpful to neural rerankers that only use the [CLS] vector to compute the similarity score. Although intuitively, the attention mechanism of rerankers at the previous layers already gathers the token-level information, we find adding late interaction still brings an extra 5% improvement in average on out-of-distribution datasets, with little increase in latency and no degradation in in-domain effectiveness. Through extensive experiments and analysis, we show that the finding is consistent across different model sizes and first-stage retrievers of diverse natures and that the improvement is more prominent on longer queries.

Title: Evaluating Embedding APIs for Information Retrieval

Abstract: The ever-increasing size of language models curtails their widespread availability to the community, thereby galvanizing many companies into offering access to large language models through APIs. One particular type, suitable for dense retrieval, is a semantic embedding service that builds vector representations of input text. With a growing number of publicly available APIs, our goal in this paper is to analyze existing offerings in realistic retrieval scenarios, to assist practitioners and researchers in finding suitable services according to their needs. Specifically, we investigate the capabilities of existing semantic embedding APIs on domain generalization and multilingual retrieval. For this purpose, we evaluate these services on two standard benchmarks, BEIR and MIRACL. We find that re-ranking BM25 results using the APIs is a budget-friendly approach and is most effective in English, in contrast to the standard practice of employing them as first-stage retrievers. For non-English retrieval, re-ranking still improves the results, but a hybrid model with BM25 works best, albeit at a higher cost. We hope our work lays the groundwork for evaluating semantic embedding APIs that are critical in search and more broadly, for information access.


paper to apply RL directly on real robot

Title: On the intermittency exponent of the turbulent energy cascade

Abstract: We consider the turbulent energy dissipation from one-dimensional records in experiments using air and gaseous helium at cryogenic temperatures, and obtain the intermittency exponent via the two-point correlation function of the energy dissipation. The air data are obtained in a number of flows in a wind tunnel and the atmospheric boundary layer at a height of about 35 m above the ground. The helium data correspond to the centerline of a jet exhausting into a container. The air data on the intermittency exponent are consistent with each other and with a trend that increases with the Taylor microscale Reynolds number, R_\lambda, of up to about 1000 and saturates thereafter. On the other hand, the helium data cluster around a constant value at nearly all R_\lambda, this being about half of the asymptotic value for the air data. Some possible explanation is offered for this anomaly.

Title: Thinking While Moving: Deep Reinforcement Learning with Concurrent Control

Abstract: We study reinforcement learning in settings where sampling an action from the policy must be done concurrently with the time evolution of the controlled system, such as when a robot must decide on the next action while still performing the previous action. Much like a person or an animal, the robot must think and move at the same time, deciding on its next action before the previous one has completed. In order to develop an algorithmic framework for such concurrent control problems, we start with a continuous-time formulation of the Bellman equations, and then discretize them in a way that is aware of system delays. We instantiate this new class of approximate dynamic programming methods via a simple architectural extension to existing value-based deep reinforcement learning algorithms. We evaluate our methods on simulated benchmark tasks and a large-scale robotic grasping task where the robot must "think while moving".


Title: Parametric instability induced scalar gravitational waves from a model pulsating neutron star

Abstract: We identify a new dynamical mechanism for a strong scalar gravitational field effect. To illustrate this mechanism, we investigate the parametric excitation and emission of scalar gravitational waves by a radially pulsating model neutron star.

Title: Multiplicity one theorems over positive characteristic

Abstract: In [AGRS] a multiplicity one theorem is proven for general linear groups, orthogonal groups and unitary groups ($GL, O,$ and $U$) over $p$-adic local fields. That is to say that when we have a pair of such groups $G_n\subseteq G_{n+1}$, any restriction of an irreducible smooth representation of $G_{n+1}$ to $G_n$ is multiplicity free. This property is already known for $GL$ over a local field of positive characteristic, and in this paper we also give a proof for $O,U$, and $SO$ over local fields of positive odd characteristic. These theorems are shown in [GGP] to imply the uniqueness of Bessel models, and in [CS] to imply the uniqueness of Rankin-Selberg models. We also prove simultaniously the uniqeuness of Fourier-Jacobi models, following the outlines of the proof in [Sun]. By the Gelfand-Kazhdan criterion, the multiplicity one property for a pair $H\leq G$ follows from the statement that any distribution on $G$ invariant to conjugations by $H$ is also invariant to some anti-involution of $G$ preserving $H$.


is colbert good for multi-modal workloads in its original form

Title: Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions using Enhanced Reduction

Abstract: Recent progress in neural information retrieval has demonstrated large gains in effectiveness, while often sacrificing the efficiency and interpretability of the neural model compared to classical approaches. This paper proposes ColBERTer, a neural retrieval model using contextualized late interaction (ColBERT) with enhanced reduction. Along the effectiveness Pareto frontier, ColBERTer's reductions dramatically lower ColBERT's storage requirements while simultaneously improving the interpretability of its token-matching scores. To this end, ColBERTer fuses single-vector retrieval, multi-vector refinement, and optional lexical matching components into one model. For its multi-vector component, ColBERTer reduces the number of stored vectors per document by learning unique whole-word representations for the terms in each document and learning to identify and remove word representations that are not essential to effective scoring. We employ an explicit multi-task, multi-stage training to facilitate using very small vector dimensions. Results on the MS MARCO and TREC-DL collection show that ColBERTer can reduce the storage footprint by up to 2.5x, while maintaining effectiveness. With just one dimension per token in its smallest setting, ColBERTer achieves index storage parity with the plaintext size, with very strong effectiveness results. Finally, we demonstrate ColBERTer's robustness on seven high-quality out-of-domain collections, yielding statistically significant gains over traditional retrieval baselines.

Title: WhisBERT: Multimodal Text-Audio Language Modeling on 100M Words

Abstract: Training on multiple modalities of input can augment the capabilities of a language model. Here, we ask whether such a training regime can improve the quality and efficiency of these systems as well. We focus on text--audio and introduce Whisbert, which is inspired by the text--image approach of FLAVA (Singh et al., 2022). In accordance with Babylm guidelines (Warstadt et al., 2023), we pretrain Whisbert on a dataset comprising only 100 million words plus their corresponding speech from the word-aligned version of the People's Speech dataset (Galvez et al., 2021). To assess the impact of multimodality, we compare versions of the model that are trained on text only and on both audio and text simultaneously. We find that while Whisbert is able to perform well on multimodal masked modeling and surpasses the Babylm baselines in most benchmark tasks, it struggles to optimize its complex objective and outperform its text-only Whisbert baseline.


how can i adpat colbert to new domains without labels

Title: A Study on Token Pruning for ColBERT

Abstract: The ColBERT model has recently been proposed as an effective BERT based ranker. By adopting a late interaction mechanism, a major advantage of ColBERT is that document representations can be precomputed in advance. However, the big downside of the model is the index size, which scales linearly with the number of tokens in the collection. In this paper, we study various designs for ColBERT models in order to attack this problem. While compression techniques have been explored to reduce the index size, in this paper we study token pruning techniques for ColBERT. We compare simple heuristics, as well as a single layer of attention mechanism to select the tokens to keep at indexing time. Our experiments show that ColBERT indexes can be pruned up to 30\% on the MS MARCO passage collection without a significant drop in performance. Finally, we experiment on MS MARCO documents, which reveal several challenges for such mechanism.

Title: Unsupervised Domain Adaptation of Contextualized Embeddings for Sequence Labeling

Abstract: Contextualized word embeddings such as ELMo and BERT provide a foundation for strong performance across a wide range of natural language processing tasks by pretraining on large corpora of unlabeled text. However, the applicability of this approach is unknown when the target domain varies substantially from the pretraining corpus. We are specifically interested in the scenario in which labeled data is available in only a canonical source domain such as newstext, and the target domain is distinct from both the labeled and pretraining texts. To address this scenario, we propose domain-adaptive fine-tuning, in which the contextualized embeddings are adapted by masked language modeling on text from the target domain. We test this approach on sequence labeling in two challenging domains: Early Modern English and Twitter. Both domains differ substantially from existing pretraining corpora, and domain-adaptive fine-tuning yields substantial improvements over strong BERT baselines, with particularly impressive results on out-of-vocabulary words. We conclude that domain-adaptive fine-tuning offers a simple and effective approach for the unsupervised adaptation of sequence labeling to difficult new domains.


Title: Gpt-4: A Review on Advancements and Opportunities in Natural Language Processing

Abstract: Generative Pre-trained Transformer 4 (GPT-4) is the fourth-generation language model in the GPT series, developed by OpenAI, which promises significant advancements in the field of natural language processing (NLP). In this research article, we have discussed the features of GPT-4, its potential applications, and the challenges that it might face. We have also compared GPT-4 with its predecessor, GPT-3. GPT-4 has a larger model size (more than one trillion), better multilingual capabilities, improved contextual understanding, and reasoning capabilities than GPT-3. Some of the potential applications of GPT-4 include chatbots, personal assistants, language translation, text summarization, and question-answering. However, GPT-4 poses several challenges and limitations such as computational requirements, data requirements, and ethical concerns.

Title: Testing GPT-4 with Wolfram Alpha and Code Interpreter plug-ins on math and science problems

Abstract: This report describes a test of the large language model GPT-4 with the Wolfram Alpha and the Code Interpreter plug-ins on 105 original problems in science and math, at the high school and college levels, carried out in June-August 2023. Our tests suggest that the plug-ins significantly enhance GPT's ability to solve these problems. Having said that, there are still often "interface" failures; that is, GPT often has trouble formulating problems in a way that elicits useful answers from the plug-ins. Fixing these interface failures seems like a central challenge in making GPT a reliable tool for college-level calculation problems.


Research on enhancing few-shot learning for text classification using task reformulation.

Title: The New Rewriting Engine of Dedukti

Abstract: Dedukti is a type-checker for the $\lambda$$\Pi$-calculus modulo rewriting, an extension of Edinburgh's logicalframework LF where functions and type symbols can be defined by rewrite rules. It thereforecontains an engine for rewriting LF terms and types according to the rewrite rules given by the user.A key component of this engine is the matching algorithm to find which rules can be fired. In thispaper, we describe the class of rewrite rules supported by Dedukti and the new implementation ofthe matching algorithm. Dedukti supports non-linear rewrite rules on terms with binders usinghigher-order pattern-matching as in Combinatory Reduction Systems (CRS). The new matchingalgorithm extends the technique of decision trees introduced by Luc Maranget in the OCamlcompiler to this more general context.

Title: TART: Improved Few-shot Text Classification Using Task-Adaptive Reference Transformation

Abstract: Meta-learning has emerged as a trending technique to tackle few-shot text classification and achieve state-of-the-art performance. However, the performance of existing approaches heavily depends on the inter-class variance of the support set. As a result, it can perform well on tasks when the semantics of sampled classes are distinct while failing to differentiate classes with similar semantics. In this paper, we propose a novel Task-Adaptive Reference Transformation (TART) network, aiming to enhance the generalization by transforming the class prototypes to per-class fixed reference points in task-adaptive metric spaces. To further maximize divergence between transformed prototypes in task-adaptive metric spaces, TART introduces a discriminative reference regularization among transformed prototypes. Extensive experiments are conducted on four benchmark datasets and our method demonstrates clear superiority over the state-of-the-art models in all the datasets. In particular, our model surpasses the state-of-the-art method by 7.4% and 5.4% in 1-shot and 5-shot classification on the 20 Newsgroups dataset, respectively.


Study on ADReSS challenge dataset analyzing the effectiveness of BERT for Alzheimer's detection.

Title: To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detection

Abstract: Research related to automatically detecting Alzheimer's disease (AD) is important, given the high prevalence of AD and the high cost of traditional methods. Since AD significantly affects the content and acoustics of spontaneous speech, natural language processing and machine learning provide promising techniques for reliably detecting AD. We compare and contrast the performance of two such approaches for AD detection on the recent ADReSS challenge dataset: 1) using domain knowledge-based hand-crafted features that capture linguistic and acoustic phenomena, and 2) fine-tuning Bidirectional Encoder Representations from Transformer (BERT)-based sequence classification models. We also compare multiple feature-based regression models for a neuropsychological score task in the challenge. We observe that fine-tuned BERT models, given the relative importance of linguistics in cognitive impairment detection, outperform feature-based approaches on the AD detection task.


World War II

The causes of World War II included unresolved tensions in the aftermath of World War I and the rises of fascism in Europe and militarism in Japan, and it was preceded by events including the Japanese invasion of Manchuria, Spanish Civil War, outbreak of the Second Sino-Japanese War, and German annexations of Austria and the Sudetenland. World War II is generally considered to have begun on 1 September 1939, when Nazi Germany, under Adolf Hitler, invaded Poland. The United Kingdom and France declared war on Germany on 3 September. Under their Molotov–Ribbentrop Pact, Germany and the Soviet Union had partitioned Poland and marked out "spheres of influence" across Eastern Europe; in 1940, the Soviets annexed the Baltic states and parts of Finland and Romania. After the fall of France in June 1940, the war continued primarily between Germany and the British Empire, with campaigns in North and East Africa and the Balkans, the aerial Battle of Britain and the Blitz of the UK, and the naval Battle of the Atlantic. By mid-1941, through a series of campaigns and treaties, Germany occupied or controlled much of continental Europe and had formed the Axis alliance with Italy, Japan, and other countries. In June 1941, Germany led the European Axis in an invasion of the Soviet Union, opening the Eastern Front.

Causes of World War II

The causes of World War II have been given considerable attention by historians. The immediate precipitating event was the invasion of Poland by Nazi Germany on September 1, 1939, and the subsequent declarations of war on Germany made by Britain and France, but many other prior events have been suggested as ultimate causes. Primary themes in historical analysis of the war's origins include the political takeover of Germany in 1933 by Adolf Hitler and the Nazi Party; Japanese militarism against China, which led to the Japanese invasion of Manchuria and the Second Sino-Japanese War; Italian aggression against Ethiopia, which led to the Second Italo-Ethiopian War; or military uprising in Spain, which led to the Spanish Civil War.
During the interwar period, deep anger arose in the Weimar Republic over the conditions of the 1919 Treaty of Versailles, which punished Germany for its role in World War I with heavy financial reparations and severe limitations on its military that were intended to prevent it from becoming a military power again. The demilitarisation of the Rhineland, the prohibition of German unification with Austria, and the loss of its overseas colonies as well as some 12% of its pre-war land area and population all provoked strong currents of revanchism in German politics.


When did the genre of science fiction first emerge?

History of science fiction

Several stories within the One Thousand and One Nights (Arabian Nights, 8th–10th centuries CE) also feature science fiction elements. One example is "The Adventures of Bulukiya", where the protagonist Bulukiya's quest for the herb of immortality leads him to explore the seas, journey to the Garden of Eden and to Jahannam (Islamic hell), and travel across the cosmos to different worlds much larger than his own world, anticipating elements of galactic science fiction; along the way, he encounters societies of jinn, mermaids, talking serpents, talking trees, and other forms of life.
In "Abdullah the Fisherman and Abdullah the Merman", the protagonist gains the ability to breathe underwater and discovers an underwater submarine society that is portrayed as an inverted reflection of society on land, in that the underwater society follows a form of primitive communism where concepts like money and clothing do not exist.
Other Arabian Nights tales deal with lost ancient technologies, advanced ancient civilizations that went astray, and catastrophes which overwhelmed them. "The City of Brass" features a group of travellers on an archaeological expedition across the Sahara to find an ancient lost city and attempt to recover a brass vessel that the biblical King Solomon once used to trap a jinn, and, along the way, encounter a mummified queen, petrified inhabitants, lifelike humanoid robots and automata, seductive marionettes dancing without strings, and a brass robot horseman who directs the party towards the ancient city.

Science fiction

History Some scholars assert that science fiction had its beginnings in ancient times, when the line between myth and fact was blurred. Written in the 2nd century CE by the satirist Lucian, A True Story contains many themes and tropes characteristic of modern science fiction, including travel to other worlds, extraterrestrial lifeforms, interplanetary warfare, and artificial life. Some consider it the first science fiction novel. Some of the stories from The Arabian Nights, along with the 10th-century The Tale of the Bamboo Cutter and Ibn al-Nafis's 13th-century Theologus Autodidactus, are also argued to contain elements of science fiction.
Written during the Scientific Revolution and the Age of Enlightenment, Johannes Kepler's Somnium (1634), Francis Bacon's New Atlantis (1627), Athanasius Kircher's Itinerarium extaticum (1656), Cyrano de Bergerac's Comical History of the States and Empires of the Moon (1657) and The States and Empires of the Sun (1662), Margaret Cavendish's "The Blazing World" (1666), Jonathan Swift's Gulliver's Travels (1726), Ludvig Holberg's Nicolai Klimii Iter Subterraneum (1741) and Voltaire's Micromégas (1752) are sometimes regarded as some of the first true science-fantasy works. Isaac Asimov and Carl Sagan considered Somnium the first science fiction story; it depicts a journey to the Moon and how the Earth's motion is seen from there. Kepler has been called the "father of science fiction".


Can ChatGPT assist in creating effective Boolean queries for systematic literature reviews?

Title: Properly colored Hamilton cycles in Dirac-type hypergraphs

Abstract: We consider a robust variant of Dirac-type problems in $k$-uniform hypergraphs. For instance, we prove that if $H$ is a $k$-uniform hypergraph with minimum codegree at least $(1/2 + \gamma )n$, $\gamma >0$, and $n$ is sufficiently large, then any edge coloring $\phi$ satisfying appropriate local constraints yields a properly colored tight Hamilton cycle in $H$. Similar results for loose cycles are also shown.

Title: Assessing the Ability of ChatGPT to Screen Articles for Systematic Reviews

Abstract: By organizing knowledge within a research field, Systematic Reviews (SR) provide valuable leads to steer research. Evidence suggests that SRs have become first-class artifacts in software engineering. However, the tedious manual effort associated with the screening phase of SRs renders these studies a costly and error-prone endeavor. While screening has traditionally been considered not amenable to automation, the advent of generative AI-driven chatbots, backed with large language models is set to disrupt the field. In this report, we propose an approach to leverage these novel technological developments for automating the screening of SRs. We assess the consistency, classification performance, and generalizability of ChatGPT in screening articles for SRs and compare these figures with those of traditional classifiers used in SR automation. Our results indicate that ChatGPT is a viable option to automate the SR processes, but requires careful considerations from developers when integrating ChatGPT into their SR tools.


A study demonstrating the use of self-attention mechanisms to improve robot cooperation in dense crowds

Title: Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning

Abstract: Mobility in an effective and socially-compliant manner is an essential yet challenging task for robots operating in crowded spaces. Recent works have shown the power of deep reinforcement learning techniques to learn socially cooperative policies. However, their cooperation ability deteriorates as the crowd grows since they typically relax the problem as a one-way Human-Robot interaction problem. In this work, we want to go beyond first-order Human-Robot interaction and more explicitly model Crowd-Robot Interaction (CRI). We propose to (i) rethink pairwise interactions with a self-attention mechanism, and (ii) jointly model Human-Robot as well as Human-Human interactions in the deep reinforcement learning framework. Our model captures the Human-Human interactions occurring in dense crowds that indirectly affects the robot's anticipation capability. Our proposed attentive pooling mechanism learns the collective importance of neighboring humans with respect to their future states. Various experiments demonstrate that our model can anticipate human dynamics and navigate in crowds with time efficiency, outperforming state-of-the-art methods.


Hendrick Motorsports in the NASCAR Cup Series

In late 2014, it was announced that Nationwide Series Champion Chase Elliott would drive several races in a fifth Hendrick car in 2015, according to Hendrick Motorsports general manager Doug Duchardt. The car was officially announced as the No. 25 on January 29, 2015, and Elliott drove the car in five races with Xfinity Series sponsor NAPA Auto Parts, preparing to take over the No. 24 car in 2016.
Car No. 25 results
Car No. 48 history
Jimmie Johnson (2001–2020)
Hendrick Motorsports' No. 48 team began Cup Series competition in 2001 when Hendrick signed Jimmie Johnson, a second-year Busch Series driver for Herzog-Jackson Motorsports. The team is co-owned by Hendrick and Jeff Gordon. Johnson made his debut at the fall Charlotte race, qualifying 15th and finishing 39th after crashing out. Johnson competed in two other races that year before moving to the Cup Series full-time in 2002, making Hendrick Motorsports a 4-car operation. The No. 48 team took over old cars from the No. 24 team, which built new cars for the 2002 season. Johnson won three races and finished fifth in points, behind only Ryan Newman among rookies. He won three more races in 2003 and finished second in points.

Zane Smith (racing driver)

Zane Michael Smith (born June 9, 1999) is an American professional stock car racing driver. He competes full-time in the NASCAR Cup Series, driving the No. 71 Chevrolet Camaro ZL1 for Spire Motorsports in an alliance with Trackhouse Racing, and part-time in the NASCAR Craftsman Truck Series, driving the No. 91 Chevrolet Silverado for McAnally-Hilgemann Racing. He won the 2022 NASCAR Camping World Truck Series championship driving for Front Row Motorsports.
Smith previously drove full-time in the Craftsman Truck Series for GMS Racing, finishing second in the championship in 2020 and 2021. He also placed runner-up in the 2018 ARCA Menards Series standings for MDM Motorsports. Part-time stints have come in the NASCAR Xfinity Series for JR Motorsports and Stadium Super Trucks.
Racing career
Early career
First racing BMX, Smith moved to go-kart racing in 2004 and won championships for five consecutive years before moving up to legends car racing. He raced super late models in 2015, recording victories in the World Series of Asphalt, Pro All Stars Series and the CARS Tour, as well as garnering a runner-up finish in the Snowball Derby.
ARCA


Celine Dion

Céline Marie Claudette Dion  ( ; born 30 March 1968) is a Canadian singer. Referred to as the "Queen of Power Ballads", she is noted for her powerful and technically skilled vocals. Her music has incorporated genres such as pop, rock, R&B, gospel, and classical music. Her recordings have been mainly in English and French, although she has also sung in several other languages.

I Am: Celine Dion (soundtrack)

I Am: Celine Dion is the soundtrack album to the 2024 documentary film, I Am: Celine Dion directed by Irene Taylor, which centers around Canadian singer Celine Dion. It was released by Columbia Records on 21 June 2024. The album includes 13 of Dion's essential hits, along with seven original score tracks mainly composed by Redi Hasa and produced by Alberto Fabris. In Francophone countries, the album was titled Je suis: Céline Dion. The album topped the chart in Quebec, and reached top ten on the compilation charts in the United Kingdom and Ireland.


what is the square root of 5 a natural number?

Math Stackexchange

Q: How to solve "$4\sqrt5$ is the same as which square root?"? What is the right method for solving a problem like this:
”$4\sqrt{5}$ is the same as which square root?"
Possible answers are:

*

*$\sqrt{20}$

*$\sqrt{10}$

*$\sqrt{40}$

*$\sqrt{80}$

I have been informed that $\sqrt{80}$ is the right answer, but I do not understand why.

A: Because $4=\sqrt{16}$. Then $\sqrt{16}\cdot \sqrt{5} = \cdots$

Math Stackexchange

Q: Is $\sqrt{n!}$ a natural number? I'm new here (on Mathematics Stack Exchange).
Also, I'm a 10th grade student not a math expert.
So, my question is whether,
$$\sqrt {n!} $$
comes in the set of the Natural Numbers.
There were some answers when I tried to search on Google. But, it was out of my knowledge, that is, I didn't read/learnt the methods, functions, etc. they were using. So, if possible give a detailed explanation.
And pardon me if there is some good answer there and if if it's there then please share a link.

A: For any $n \gt 1$ there will be some prime in the range $(n/2,n]$ which will only occur once in the factorization of $n!$ by Bertrand's Postulate.  This will ensure that $\sqrt{n!}$ is not an integer.


Paper introducing a method using control codes to manage and direct the behavior of large-scale language models.

Title: ControlLLM: Augment Language Models with Tools by Searching on Graphs

Abstract: We present ControlLLM, a novel framework that enables large language models (LLMs) to utilize multi-modal tools for solving complex real-world tasks. Despite the remarkable performance of LLMs, they still struggle with tool invocation due to ambiguous user prompts, inaccurate tool selection and parameterization, and inefficient tool scheduling. To overcome these challenges, our framework comprises three key components: (1) a \textit{task decomposer} that breaks down a complex task into clear subtasks with well-defined inputs and outputs; (2) a \textit{Thoughts-on-Graph (ToG) paradigm} that searches the optimal solution path on a pre-built tool graph, which specifies the parameter and dependency relations among different tools; and (3) an \textit{execution engine with a rich toolbox} that interprets the solution path and runs the tools efficiently on different computational devices. We evaluate our framework on diverse tasks involving image, audio, and video processing, demonstrating its superior accuracy, efficiency, and versatility compared to existing methods. The code is at https://github.com/OpenGVLab/ControlLLM.

Title: CTRL: A Conditional Transformer Language Model for Controllable Generation

Abstract: Large-scale language models show promising text generation capabilities, but users cannot easily control particular aspects of the generated text. We release CTRL, a 1.63 billion-parameter conditional transformer language model, trained to condition on control codes that govern style, content, and task-specific behavior. Control codes were derived from structure that naturally co-occurs with raw text, preserving the advantages of unsupervised learning while providing more explicit control over text generation. These codes also allow CTRL to predict which parts of the training data are most likely given a sequence. This provides a potential method for analyzing large amounts of data via model-based source attribution. We have released multiple full-sized, pretrained versions of CTRL at https://github.com/salesforce/ctrl.


Stackoverflow Stackexchange

Q: r_basicprofile for V2 api According to the documents listed below r_basicprofile should be available for the V2 api. I have tested using both endpoints:
https://api.linkedin.com/v2/me
and
https://api.linkedin.com/v2/people/(id:{my id})
and both of these return with an error message of
{
    "serviceErrorCode": 100,
    "message": "Not enough permissions to access: GET /***",
    "status": 403
}
If I use just the r_liteprofile permission, then I do get back the first name, last name and Id. as listed in the docs.
Has anyone else had success retrieving the basicprofile back from the V2 linkedin API.
According to the docs only the r_fullprofile permission needs special permission.
https://learn.microsoft.com/en-us/linkedin/shared/references/v2/profile/basic-profile?context=linkedin/consumer/context
https://learn.microsoft.com/en-us/linkedin/shared/references/v2/profile/full-profile?context=linkedin/consumer/context
thanks

English Stackexchange

Q: What is the difference between a cafe and a diner? I recently ate at an establishment called The Park Cafe and Diner. How could a debate not ensue? What is the difference between a Cafe and Diner? Apparently, this place is both.
I have read various opinions online that discuss the visibility of the kitchen, the presence of counter-seating and such. But nothing authoritative.
Is/was there a commonly understood difference, or have they been relatively interchangeable and perhaps regionally variable?

A: To make it as simple as possible: they are quite similar, but with a different focus.
The connotations are going to vary from region to region and person to person, so adding a great deal of detail is kind of pointless. Here's the general gist, which would be accurate everywhere I've ever been in the US:
You go to a cafe for coffee, and might get some food while you're there.
You go to a diner to eat a meal (dine), which will probably involve a beverage, which could be coffee.


Zimbabwe

Zimbabwe (; ), officially the Republic of Zimbabwe, is a landlocked country in Southern Africa, between the Zambezi and Limpopo Rivers, bordered by South Africa to the south, Botswana to the southwest, Zambia to the north, and Mozambique to the east. The capital and largest city is Harare, and the second largest is Bulawayo.
A country of roughly 15 million people as per 2022 census, Zimbabwe's largest ethnic group are the Shona, who make up 80% of the population, followed by the Northern Ndebele and other smaller minorities. Zimbabwe has 16 official languages, with English, Shona, and Ndebele the most common. Zimbabwe is a member of the United Nations, the Southern African Development Community, the African Union, and the Common Market for Eastern and Southern Africa.

Harare

Harare ( ), formerly known as Salisbury ( ), is the capital and largest city of Zimbabwe. The city proper has an area of , a population of 1,849,600 as of the 2022 census and an estimated 2,487,209 people in its metropolitan province. The city is situated in north-eastern Zimbabwe in the country's Mashonaland region. Harare is a metropolitan province which also incorporates the municipalities of Chitungwiza and Epworth. The city sits on a plateau at an elevation of  above sea level, and its climate falls into the subtropical highland category.


Zimbabwe

Beginning in the 9th century, during its late Iron Age, the Bantu people (who would become the ethnic Shona) built the city-state of Great Zimbabwe; the city-state became one of the major African trade centres by the 11th century but was abandoned by the mid 15th century. From there, the Kingdom of Zimbabwe was established, followed by the Rozvi and Mutapa empires. The British South Africa Company of Cecil Rhodes demarcated the Rhodesia region in 1890 when they conquered Mashonaland and later in 1893 Matabeleland after the First Matabele War. Company rule ended in 1923 with the establishment of Southern Rhodesia as a self-governing British colony. In 1965, the white minority government unilaterally declared independence as Rhodesia. The state endured international isolation and a 15-year guerrilla war with black nationalist forces; this culminated in a peace agreement that established de jure sovereignty as Zimbabwe in April 1980.
Robert Mugabe became Prime Minister of Zimbabwe in 1980, when his ZANU–PF party won the general election following the end of white minority rule and has remained the country's dominant party since. He was the President of Zimbabwe from 1987, after converting the country's initial parliamentary system into a presidential one, until his resignation in 2017. Under Mugabe's authoritarian regime, the state security apparatus dominated the country and was responsible for widespread human rights violations. From 1997 to 2008 the economy experienced consistent decline (and in the latter years, hyperinflation), though it has since seen rapid growth after the use of currencies other than the Zimbabwean dollar was permitted. In 2017, in the wake of over a year of protests against his government as well as Zimbabwe's rapidly declining economy, a coup d'état resulted in Mugabe's resignation. Emmerson Mnangagwa has since served as Zimbabwe's president.


Harare

Harare ( ), formerly known as Salisbury ( ), is the capital and largest city of Zimbabwe. The city proper has an area of , a population of 1,849,600 as of the 2022 census and an estimated 2,487,209 people in its metropolitan province. The city is situated in north-eastern Zimbabwe in the country's Mashonaland region. Harare is a metropolitan province which also incorporates the municipalities of Chitungwiza and Epworth. The city sits on a plateau at an elevation of  above sea level, and its climate falls into the subtropical highland category.

Zimbabwe

By 1220, the Kingdom of Zimbabwe eclipsed Mapungubwe. This Shona state further refined and expanded upon Mapungubwe's stone architecture. From  1450 to 1760, the Kingdom of Mutapa ruled much of the area of present-day Zimbabwe, plus parts of central Mozambique. It is known by many names including the Mutapa Empire, also known as Mwene Mutapa or Monomotapa as well as "Munhumutapa", and was renowned for its strategic trade routes with the Arabs and Portugal. The Portuguese sought to monopolise this influence and began a series of wars which left the empire in near collapse in the early 17th century.
As a direct response to increased European presence in the interior a new Shona state emerged, known as the Rozwi Empire. Relying on centuries of military, political and religious development, the Rozwi (meaning "destroyers") expelled the Portuguese from the Zimbabwean plateau in 1683. Around 1821 the Zulu general Mzilikazi of the Khumalo clan successfully rebelled against King Shaka and established his own clan, the Ndebele. The Ndebele fought their way northwards into the Transvaal, leaving a trail of destruction in their wake and beginning an era of widespread devastation known as the Mfecane. When Dutch trekboers converged on the Transvaal in 1836, they drove the tribe even further northward, with the assistance of Tswana Barolong warriors and Griqua commandos. By 1838 the Ndebele had conquered the Rozwi Empire, along with the other smaller Shona states, and reduced them to vassaldom.


Zimbabwe

Zimbabwe (; ), officially the Republic of Zimbabwe, is a landlocked country in Southern Africa, between the Zambezi and Limpopo Rivers, bordered by South Africa to the south, Botswana to the southwest, Zambia to the north, and Mozambique to the east. The capital and largest city is Harare, and the second largest is Bulawayo.
A country of roughly 15 million people as per 2022 census, Zimbabwe's largest ethnic group are the Shona, who make up 80% of the population, followed by the Northern Ndebele and other smaller minorities. Zimbabwe has 16 official languages, with English, Shona, and Ndebele the most common. Zimbabwe is a member of the United Nations, the Southern African Development Community, the African Union, and the Common Market for Eastern and Southern Africa.


Zimbabwe

By 1220, the Kingdom of Zimbabwe eclipsed Mapungubwe. This Shona state further refined and expanded upon Mapungubwe's stone architecture. From  1450 to 1760, the Kingdom of Mutapa ruled much of the area of present-day Zimbabwe, plus parts of central Mozambique. It is known by many names including the Mutapa Empire, also known as Mwene Mutapa or Monomotapa as well as "Munhumutapa", and was renowned for its strategic trade routes with the Arabs and Portugal. The Portuguese sought to monopolise this influence and began a series of wars which left the empire in near collapse in the early 17th century.
As a direct response to increased European presence in the interior a new Shona state emerged, known as the Rozwi Empire. Relying on centuries of military, political and religious development, the Rozwi (meaning "destroyers") expelled the Portuguese from the Zimbabwean plateau in 1683. Around 1821 the Zulu general Mzilikazi of the Khumalo clan successfully rebelled against King Shaka and established his own clan, the Ndebele. The Ndebele fought their way northwards into the Transvaal, leaving a trail of destruction in their wake and beginning an era of widespread devastation known as the Mfecane. When Dutch trekboers converged on the Transvaal in 1836, they drove the tribe even further northward, with the assistance of Tswana Barolong warriors and Griqua commandos. By 1838 the Ndebele had conquered the Rozwi Empire, along with the other smaller Shona states, and reduced them to vassaldom.


What's the purpose of `if __name__ == '__main__'`?

Stackoverflow Stackexchange

Q: What is the purpose of __name__? What does __name__ do? I have only seen it paired with __main__ and nothing else.
I know that the classic if __name__ == __main__: defines the behavior when acting as a package vs running as stand-alone.
However what other usages are there for __name__?

A: __name__ is "__main__" if you're executing the script directly. If you're importing a module, __name__ is the name of the module.
foo.py:
print(__name__)

bar.py
import foo

Run the scripts:
$ python foo.py
__main__
$ python bar.py
foo

Stackoverflow Stackexchange

Q: html dangerous tags to avoid while developing a chat application I am developing a chat application using PHP and jQuery... all messages sent by the chat pair are appended to a <div>. While doing this, I found that tags such as <script> can be entered by users. What tags should I avoid while appending the user's message?

A: Avoid tags that will inject content into the page:

*

*applet

*script

*style

*link

*iframe

Other tags may be necessary to remove, as well. Maybe you don't want to embed images. These are things that you will need to determine. At the bare minimum, allow <p> and <br />.


Stackoverflow Stackexchange

Q: Reading h264 stream in Python I am trying to read a video stream from a Parrot Bebop 2 drone.
The video stream is written as "socket" to a file as H264 stream.
$ ffmpeg -i [STREAM]
Input #0, h264, from 'stream_h264':
  Duration: N/A, bitrate: N/A
  Stream #0:0: Video: h264 (Constrained Baseline), 1 reference frame, yuv420p(progressive, left), 1280x720, 23.98 fps, 23.98 tbr, 1200k tbn, 47.95 tbc

Reading the video stream in MPlayer is not a problem using the parameters below. Playing it using VLC or ffmpeg should also not be too hard. For MPlayer the following works:
mplayer -fs -demuxer h264es -benchmark stream_h264

This plays the stream in high-res. However my goal is to perform image processing on the frames using Python (mostly OpenCV). Therefore, I would like to read the frames into NumPy arrays. I have already considered using cv2.VideoCapture but this does not seem to work for my stream. Other (somewhat easy) to use options I am not aware of, therefore my question is whether someone recommend me how to read the video frames in Python?
All recommendations are more than welcome!

Stackoverflow Stackexchange

Q: What is the purpose of __name__? What does __name__ do? I have only seen it paired with __main__ and nothing else.
I know that the classic if __name__ == __main__: defines the behavior when acting as a package vs running as stand-alone.
However what other usages are there for __name__?

A: __name__ is "__main__" if you're executing the script directly. If you're importing a module, __name__ is the name of the module.
foo.py:
print(__name__)

bar.py
import foo

Run the scripts:
$ python foo.py
__main__
$ python bar.py
foo


Stackoverflow Stackexchange

Q: What does `if name == "__main__"` mean in Python?
Possible Duplicate:
What does <if name==“main”:> do?

I have wrote scripts in Python for quite a while now and I study more of Python as I need it. When reading other people's code I meet if name == "__main__": construct quite often.
What is it good for?

A: Basically,
There's a distinction between the "main" script file and external files which were imported or referenced in another way. If the script is the "main" script then the special variable __name__ will equal "__main__".
You can use this to protect the "execution" code from the classes and variables the script has. This enables you to import a script and use classes and variables without actually running that script's main code if it has any.
See also: What does if name == “main”: do?

Stackoverflow Stackexchange

Q: Why does it do this ? if - __name__ == '__main__'
Duplicate of:
What does if __name__== "__main__" do?

Consider this code:
if __name__ == '__main__':
    import pdb
    pdb.run("interact()\n")

What does the following line mean?
if(__name__=='__main__')

I fainted.

A: That is a check to see if you are directly running the script or if it is included in a library.
When you run a python script like this:
python myScript.py

It sends a parameter, telling you to run the programs first method, which is widely called "main", so when __name__ is __main__ you know that the program was executed from a command line or double clicked.


Stackoverflow Stackexchange

Q: What's the point of a main function and/or __name__ == "__main__" check in Python? I occasionally notice something like the following in Python scripts:
if __name__ == "__main__":
    # do stuff like call main()

What's the point of this?

A: This allows a python script to be imported or run standalone is a sane way.
If you run a python file directly, the __name__ variable will contain __main__. If you import the script that will not be the case. Normally, if you import the script you want to call functions or reference classes from the file.
If you did not have this check, any code that was not in a class or function would run when you import.

Stackoverflow Stackexchange

Q: Reading h264 stream in Python I am trying to read a video stream from a Parrot Bebop 2 drone.
The video stream is written as "socket" to a file as H264 stream.
$ ffmpeg -i [STREAM]
Input #0, h264, from 'stream_h264':
  Duration: N/A, bitrate: N/A
  Stream #0:0: Video: h264 (Constrained Baseline), 1 reference frame, yuv420p(progressive, left), 1280x720, 23.98 fps, 23.98 tbr, 1200k tbn, 47.95 tbc

Reading the video stream in MPlayer is not a problem using the parameters below. Playing it using VLC or ffmpeg should also not be too hard. For MPlayer the following works:
mplayer -fs -demuxer h264es -benchmark stream_h264

This plays the stream in high-res. However my goal is to perform image processing on the frames using Python (mostly OpenCV). Therefore, I would like to read the frames into NumPy arrays. I have already considered using cv2.VideoCapture but this does not seem to work for my stream. Other (somewhat easy) to use options I am not aware of, therefore my question is whether someone recommend me how to read the video frames in Python?
All recommendations are more than welcome!


Stackoverflow Stackexchange

Q: What is the difference between Freidman mse and mse? I'm looking into a GradientBoostingClassifier in sklearn. Then, I found there are 3 kind of criterion. Friedman mse, mse, mae.
the descriptions provided by sklearn are:

The function to measure the quality of a split. Supported criteria are “friedman_mse” for the mean squared error with improvement score by Friedman, “mse” for mean squared error, and “mae” for the mean absolute error. The default value of “friedman_mse” is generally the best as it can provide a better approximation in some cases.

I can't understand what is different?
Who's gonna let me know?
thanks!

A: I've provided a full answer in this link due to the convenience of writing TeX. However, it resumes in the fact that this splitting criterion allow us to take the decision not only on how close we're to the desired outcome (which is what MSE does), but also based on the probabilities of the desired k-class that we're going to find in the region l or in the region r (by considering a global weight w1*w2 / (w1 + w2)). I strongly recommend you to check the above link for a full explanation.


Stackoverflow Stackexchange

Q: html dangerous tags to avoid while developing a chat application I am developing a chat application using PHP and jQuery... all messages sent by the chat pair are appended to a <div>. While doing this, I found that tags such as <script> can be entered by users. What tags should I avoid while appending the user's message?

A: Avoid tags that will inject content into the page:

*

*applet

*script

*style

*link

*iframe

Other tags may be necessary to remove, as well. Maybe you don't want to embed images. These are things that you will need to determine. At the bare minimum, allow <p> and <br />.


Stackoverflow Stackexchange

Q: Why does it do this ? if - __name__ == '__main__'
Duplicate of:
What does if __name__== "__main__" do?

Consider this code:
if __name__ == '__main__':
    import pdb
    pdb.run("interact()\n")

What does the following line mean?
if(__name__=='__main__')

I fainted.

A: That is a check to see if you are directly running the script or if it is included in a library.
When you run a python script like this:
python myScript.py

It sends a parameter, telling you to run the programs first method, which is widely called "main", so when __name__ is __main__ you know that the program was executed from a command line or double clicked.


Find me a planet near α 23h 06m 29.368s and δ −05° 02′ 29.04″ that could be habitable.

Kepler-62f

Kepler-62f (also known by its Kepler Object of Interest designation KOI-701.04) is a super-Earth exoplanet orbiting within the habitable zone of the star Kepler-62, the outermost of five such planets discovered around the star by NASA's Kepler spacecraft.  It is located about  from Earth in the constellation of Lyra.

Habitable zone

K2-18b is an exoplanet 124 light-years away, orbiting in the habitable zone of the K2-18, a red dwarf. This planet is significant for water vapor found in its atmosphere; this was announced on September 17, 2019.
In September 2020, astronomers identified 24 superhabitable planet (planets better than Earth) contenders, from among more than 4000 confirmed exoplanets at present, based on astrophysical parameters, as well as the natural history of known life forms on the Earth.
Habitability outside the HZ
Liquid-water environments have been found to exist in the absence of atmospheric pressure and at temperatures outside the HZ temperature range. For example, Saturn's moons Titan and Enceladus and Jupiter's moons Europa and Ganymede, all of which are outside the habitable zone, may hold large volumes of liquid water in subsurface oceans.
Outside the HZ, tidal heating and radioactive decay are two possible heat sources that could contribute to the existence of liquid water. Abbot and Switzer (2011) put forward the possibility that subsurface water could exist on rogue planets as a result of radioactive decay-based heating and insulation by a thick surface layer of ice.
With some theorising that life on Earth may have actually originated in stable, subsurface habitats, it has been suggested that it may be common for wet subsurface extraterrestrial habitats such as these to 'teem with life'. On Earth itself, living organisms may be found more than  below the surface.


Why did the machines in The Matrix keep humans around?

Stackoverflow Stackexchange

Q: using wakelock in 4.9 kernel driver I'm trying to port a touchscreen driver that was written in 2014 for a device running Android Q (Hikey 960 on AOSP). The module won't compile because of:
#include <linux/wakelock.h>

which isn't there in the Linaro 4.9 kernel that the Hikey 960 board uses with AOSP.
Apparently, the functionality is there in kernel/power/wakelock.c and kernel/power/power.h, but I don't know how to compile that into my module. Or if I'm still even supposed to make use of wakelocks in driver modules like this? There must be a good reason the wakelock.h is not there any longer?

A: Found a solution myself, posting for future reference: https://www.linaro.org/blog/converting-code-implementing-suspend-blockers/

Scifi Stackexchange

Q: Why use full brained humans as batteries?
Possible Duplicate:
Is the basic premise of humans as a power source in The Matrix reasonable?
Why Did the Machines Even Bother With a Matrix?

The machines in The Matrix series use humans as batteries. That's OK. But why did the machines not alter their livestock such that the matrix itself would be unnecessary?
Consider the recently proposed headless chicken farm. Something similar for the human batteries would seem to be an ideal solution for logical machines. No longer would they need to spend resources maintaining a virtual reality for their batteries. The energy, physical hardware and programs running the matrix could then be put to who knows what other uses.

A: The explanation for the use of human brains would seem to be the fact that our complex thoughts lead to higher level of brain activity, thus resulting in exponentially more synapses firing (i.e. more electrical activity to harvest).
That makes human brains far more efficient than the brains of animals, even with the overhead of maintaining the matrix.  Plus, that movie would have really sucked... dumb cows and chickens revolt against the machines!  ;)


Scifi Stackexchange

Q: Do the Machines in "The Matrix" Adhere to the Three Laws of Robotics? In the "real world" of the Matrix, do the machines follow Asimov's Three Rules? Were they even programmed with them?
EDIT: I realize that killing people goes against the 1st law, but by keeping people in the Matrix (which the machines view as a symbiosis) rather than simply killing off these "annoying" creatures, I wonder if the killings can be seen as "preserving the greater good" or something along those lines; and thus justify violating the laws.

A:
A robot may not injure a human being or, through inaction, allow a
  human being to come to harm.
A robot must obey the orders given to it by human beings, except where
  such orders would conflict with the First Law.
A robot must protect its own existence as long as such protection does
  not conflict with the First or Second Laws.

No, no, and no.


Scifi Stackexchange

Q: Why do the machines allow/initiate the building of a new Zion? In the Matrix Reloaded, the Architect says:

The function of the One is now to return to the Source, allowing a temporary dissemination of the code you carry, reinserting the prime program. After which, you will be required to select from the Matrix 23 individuals - 16 female, 7 male - to rebuild Zion. Failure to comply with this process will result in a cataclysmic system crash, killing everyone connected to the Matrix, which, coupled with the extermination of Zion, will ultimately result in the extinction of the entire human race.

Why do the machines initiate the re-building of Zion?

A: As was discussed elsewhere on the site, the machines are not interested in humans being wiped out, they (as shown in Animatrix) just want to co-exist with humans.
As such, the machines allow the building of new Zion so that humans would not - as the quote you provided says - would not go extinct.


Gis Stackexchange

Q: Filling voids in DEM SRTM 1-arc second using QGIS by resampling and inserting another DEM I'd like to use the DEM SRTM 1-arc second (Version 4, 30m) from USGS for Europe. Unfortunately there are some voids. My idea is to fill those voids using the void-filled version 3 (90m) by resampling it to 30m and inserting the missing data in the version 4 DEM.
As I'm new to QGIS I don't know the proper steps. I already got a 30m map by reprojecting the dimensions times three but I'm not sure if that's the right thing to do and I have no idea how to fill the voids afterwards.


Please find me the paper on training code large language models to follow instructions via git commits.

Title: OctoPack: Instruction Tuning Code Large Language Models

Abstract: Finetuning large language models (LLMs) on instructions leads to vast performance improvements on natural language tasks. We apply instruction tuning using code, leveraging the natural structure of Git commits, which pair code changes with human instructions. We compile CommitPack: 4 terabytes of Git commits across 350 programming languages. We benchmark CommitPack against other natural and synthetic code instructions (xP3x, Self-Instruct, OASST) on the 16B parameter StarCoder model, and achieve state-of-the-art performance among models not trained on OpenAI outputs, on the HumanEval Python benchmark (46.2% pass@1). We further introduce HumanEvalPack, expanding the HumanEval benchmark to a total of 3 coding tasks (Code Repair, Code Explanation, Code Synthesis) across 6 languages (Python, JavaScript, Java, Go, C++, Rust). Our models, OctoCoder and OctoGeeX, achieve the best performance across HumanEvalPack among all permissive models, demonstrating CommitPack's benefits in generalizing to a wider set of languages and natural coding tasks. Code, models and data are freely available at https://github.com/bigcode-project/octopack.

Title: Search for gravitational waves from binary black hole inspirals in LIGO data

Abstract: We report on a search for gravitational waves from binary black hole inspirals in the data from the second science run of the LIGO interferometers. The search focused on binary systems with component masses between 3 and 20 solar masses. Optimally oriented binaries with distances up to 1 Mpc could be detected with efficiency of at least 90%. We found no events that could be identified as gravitational waves in the 385.6 hours of data that we searched.


Title: A Systematic Evaluation of Large Language Models of Code

Abstract: Large language models (LMs) of code have recently shown tremendous promise in completing code and synthesizing code from natural language descriptions. However, the current state-of-the-art code LMs (e.g., Codex (Chen et al., 2021)) are not publicly available, leaving many questions about their model and data design decisions. We aim to fill in some of these blanks through a systematic evaluation of the largest existing models: Codex, GPT-J, GPT-Neo, GPT-NeoX-20B, and CodeParrot, across various programming languages. Although Codex itself is not open-source, we find that existing open-source models do achieve close results in some programming languages, although targeted mainly for natural language modeling. We further identify an important missing piece in the form of a large open-source model trained exclusively on a multi-lingual corpus of code. We release a new model, PolyCoder, with 2.7B parameters based on the GPT-2 architecture, which was trained on 249GB of code across 12 programming languages on a single machine. In the C programming language, PolyCoder outperforms all models including Codex. Our trained models are open-source and publicly available at https://github.com/VHellendoorn/Code-LMs, which enables future research and application in this area.

Title: OctoPack: Instruction Tuning Code Large Language Models

Abstract: Finetuning large language models (LLMs) on instructions leads to vast performance improvements on natural language tasks. We apply instruction tuning using code, leveraging the natural structure of Git commits, which pair code changes with human instructions. We compile CommitPack: 4 terabytes of Git commits across 350 programming languages. We benchmark CommitPack against other natural and synthetic code instructions (xP3x, Self-Instruct, OASST) on the 16B parameter StarCoder model, and achieve state-of-the-art performance among models not trained on OpenAI outputs, on the HumanEval Python benchmark (46.2% pass@1). We further introduce HumanEvalPack, expanding the HumanEval benchmark to a total of 3 coding tasks (Code Repair, Code Explanation, Code Synthesis) across 6 languages (Python, JavaScript, Java, Go, C++, Rust). Our models, OctoCoder and OctoGeeX, achieve the best performance across HumanEvalPack among all permissive models, demonstrating CommitPack's benefits in generalizing to a wider set of languages and natural coding tasks. Code, models and data are freely available at https://github.com/bigcode-project/octopack.


Live Free or Die

By the time Stark wrote this, Vivre Libre ou Mourir ("Live free or die") was a popular motto of the French Revolution. A possible source of such mottoes is Patrick Henry's famed March 23, 1775, speech to the House of Burgesses (the legislative body of the Virginia colony), which contained the following phrase: "Is life so dear, or peace so sweet, as to be purchased at the price of chains and slavery? Forbid it, Almighty God! I know not what course others may take; but as for me, give me liberty or give me death!"
The motto was enacted at the same time as the New Hampshire state emblem, on which it appears.
Legal battle
In 1971, the New Hampshire state legislature mandated that the phrase "LIVE FREE OR DIE" appear on all non-commercial license plates, replacing "Scenic."
In 1977, the U.S. Supreme Court ruled in the case of Wooley v. Maynard, 430 U.S. 705, that the state of New Hampshire could not prosecute motorists who chose to hide part or all of the motto. That ruling came about because George Maynard, a Jehovah's Witness (albeit a disfellowshiped member), covered up "or die" from his plate. "By religious training and belief, I believe my 'government' – Jehovah's Kingdom – offers everlasting life. It would be contrary to that belief to give up my life for the state, even if it meant living in bondage." Pursuant to these beliefs, the Maynards began early in 1974 to cover up the motto on their license plates.

Right to die

The right to die is a concept based on the opinion that human beings are entitled to end their life or undergo voluntary euthanasia. Possession of the right to die is often bestowed with the understanding that a person with a terminal illness, incurable pain, or without the will to continue living should be allowed to end their own life, use assisted suicide, or decline life-prolonging treatment. The question of who, if anyone, may be empowered to make this decision is often the subject of debate.
Religious views on suicide vary, from the Hindu and Jain practices of non-violent suicide through fasting (Prayopavesa and Santhara, respectively), to considering it a grave sin, as in Catholicism and Islam.


Aegon

Aegon Ltd. (stylized as AEGON) is a Dutch public company for life insurance, pensions and asset management. It is headquartered in The Hague, Netherlands and has 26,000 employees as of July 21, 2020. Aegon is listed on the Euronext Amsterdam and is a constituent of the AEX index. It operates a direct bank under the brand name "Knab" in the Netherlands. In October 2022, it was announced that AEGON's Dutch operations would be acquired by ASR. To the extent they had the AEGON trade name, it will become ASR; Knab and TKP will keep their names.
History
Aegon was founded in 1983 from the merger of AGO Holding N.V. (created by the merger of Algemeene Friesche, Groot-Noordhollandsche and Olveh (Onderlinge Verzekeringsmaatschappij Eigen Hulp) in 1968) and Ennia N.V. (formed by the merger of Eerste Nederlandsche and Nillmij (Nederlandsch-Indische Levensverzekering- en Lijfrente-Maatschappij) in 1969).
Aegon acquired Scottish Equitable in 1994. In 1998 it formed Stonebridge International Insurance Ltd to create and market a range of personal insurance products, providing accident, health and unemployment cover to its own customers and via business partners.
In 1999 it acquired the life assurance business of Guardian Royal Exchange. That year it also bought Transamerica Corporation.
On August 13, 2007, Aegon and Merrill Lynch announced a strategic business partnership in the areas of life insurance and investment products. As part of the relationship, Aegon acquired two of Merrill Lynch's life insurance companies for US$1.3 billion.
On April 23, 2008, Alex Wynaendts succeeded the retiring Donald J. Shepard as chairman of the executive board and CEO of Aegon N.V. following the group's annual general meeting of shareholders: Donald Shepard announced his retirement in November 2007, after six years as chairman.

Aegon Targaryen

Aegon Targaryen refers to multiple characters in George R. R. Martin's A Song of Ice and Fire fantasy franchise:
Aegon I Targaryen, the first Targaryen king in the backstory of A Song of Ice and Fire
Aegon II Targaryen, featured in Fire & Blood and its TV adaptation House of the Dragon
Aegon V Targaryen, also known as Aegon the Unlikely, featured in the Tales of Dunk and Egg stories
Aegon VI Targaryen, the son of Rhaegar Targaryen and Elia Martell in the backstory of A Song of Ice and Fire, and a character in A Dance with Dragons (2011)
Jon Snow (character), the secret son of Rhaegar Targaryen and Lyanna Stark in the TV adaptation Game of Thrones, born Aegon Targaryen


I am searching for a good and large-scale benchmark for testing the performance of text embeddings.

Title: MTEB: Massive Text Embedding Benchmark

Abstract: Text embeddings are commonly evaluated on a small set of datasets from a single task not covering their possible applications to other tasks. It is unclear whether state-of-the-art embeddings on semantic textual similarity (STS) can be equally well applied to other tasks like clustering or reranking. This makes progress in the field difficult to track, as various models are constantly being proposed without proper evaluation. To solve this problem, we introduce the Massive Text Embedding Benchmark (MTEB). MTEB spans 8 embedding tasks covering a total of 58 datasets and 112 languages. Through the benchmarking of 33 models on MTEB, we establish the most comprehensive benchmark of text embeddings to date. We find that no particular text embedding method dominates across all tasks. This suggests that the field has yet to converge on a universal text embedding method and scale it up sufficiently to provide state-of-the-art results on all embedding tasks. MTEB comes with open-source code and a public leaderboard at https://github.com/embeddings-benchmark/mteb.

Title: Text and Code Embeddings by Contrastive Pre-Training

Abstract: Text embeddings are useful features in many applications such as semantic search and computing text similarity. Previous work typically trains models customized for different use cases, varying in dataset choice, training objective and model architecture. In this work, we show that contrastive pre-training on unsupervised data at scale leads to high quality vector representations of text and code. The same unsupervised text embeddings that achieve new state-of-the-art results in linear-probe classification also display impressive semantic search capabilities and sometimes even perform competitively with fine-tuned models. On linear-probe classification accuracy averaging over 7 tasks, our best unsupervised model achieves a relative improvement of 4% and 1.8% over previous best unsupervised and supervised text embedding models respectively. The same text embeddings when evaluated on large-scale semantic search attains a relative improvement of 23.4%, 14.7%, and 10.6% over previous best unsupervised methods on MSMARCO, Natural Questions and TriviaQA benchmarks, respectively. Similarly to text embeddings, we train code embedding models on (text, code) pairs, obtaining a 20.8% relative improvement over prior best work on code search.


A Golden Crown

In Vaes Dothrak Daenerys is left unscathed after taking one of her dragon eggs from the fire. She undergoes a ritual with the Dosh Khaleen, eating a stallion's raw heart and proclaiming her unborn son will be the Khal to unite the entire world as one khalasar, and names him Rhaego, for Drogo and her brother Rhaegar, killed by Robert Baratheon. Viserys, angry at his sister's growing popularity among the Dothraki, tries to steal Daenerys' dragon eggs to fund a new army, but is confronted and stopped by Jorah.
A drunken Viserys threatens his sister at swordpoint, and Drogo agrees to give him the "golden crown" he desires; as his bloodriders restrain Viserys, Drogo pours molten gold on his head. Watching her brother burn to death while he begs for her to not let them kill him, Daenerys coldly remarks, "He was no dragon. Fire cannot kill a dragon."
Production
Writing
The teleplay for "A Golden Crown" was written by Jane Espenson, David Benioff and D. B. Weiss from a story by Benioff and Weiss, based on A Game of Thrones by George R. R. Martin. The finished teleplay would be Espenson's only credited writing contribution to the series.
The episode includes the book's chapters 38–41, 44-45 and 47 (Bran V, Tyrion V, Eddard X, Catelyn VII, Eddard XI, Sansa III, and Daenerys V). Chapter 42 (Jon V), dealing with Jon convincing Maester Aemon to allow Samwell to join the Watch as a steward, was removed from the series (although some of Ser Alliser's dialogue from the chapter was used), and chapter 43 (Tyrion VI) was moved to episode 8.

The Red Dragon and the Gold

"The Red Dragon and the Gold" is the fourth episode of the second season of the fantasy drama television series House of the Dragon, a prequel to Game of Thrones. Its title refers to Meleys and Sunfyre, the dragons of Rhaenys and Aegon, respectively. The episode was written by series co-creator and showrunner Ryan Condal and directed by executive producer Alan Taylor. It first aired on HBO and Max on July 7, 2024.
In the episode, Ser Criston continues to rally Greens allies and executes Black supporters. He rides to Rook's Rest to attack the last Black stronghold on the Blackwater Bay. Meanwhile, Rhaenyra returns to Dragonstone and learns about Criston's plan. She agrees to send Rhaenys with her dragon Meleys to confront Criston and his troops. Feeling his council disrespects him, especially after discovering Aemond's secret communications with Criston, Aegon decides to fly to Rook's Rest with his dragon Sunfyre. Unbeknownst to each other, Rhaenys, Aegon, and Aemond (with Vhagar) converge on Rook's Rest, leading to a climactic battle involving the three dragons.


A Golden Crown

In Vaes Dothrak Daenerys is left unscathed after taking one of her dragon eggs from the fire. She undergoes a ritual with the Dosh Khaleen, eating a stallion's raw heart and proclaiming her unborn son will be the Khal to unite the entire world as one khalasar, and names him Rhaego, for Drogo and her brother Rhaegar, killed by Robert Baratheon. Viserys, angry at his sister's growing popularity among the Dothraki, tries to steal Daenerys' dragon eggs to fund a new army, but is confronted and stopped by Jorah.
A drunken Viserys threatens his sister at swordpoint, and Drogo agrees to give him the "golden crown" he desires; as his bloodriders restrain Viserys, Drogo pours molten gold on his head. Watching her brother burn to death while he begs for her to not let them kill him, Daenerys coldly remarks, "He was no dragon. Fire cannot kill a dragon."
Production
Writing
The teleplay for "A Golden Crown" was written by Jane Espenson, David Benioff and D. B. Weiss from a story by Benioff and Weiss, based on A Game of Thrones by George R. R. Martin. The finished teleplay would be Espenson's only credited writing contribution to the series.
The episode includes the book's chapters 38–41, 44-45 and 47 (Bran V, Tyrion V, Eddard X, Catelyn VII, Eddard XI, Sansa III, and Daenerys V). Chapter 42 (Jon V), dealing with Jon convincing Maester Aemon to allow Samwell to join the Watch as a steward, was removed from the series (although some of Ser Alliser's dialogue from the chapter was used), and chapter 43 (Tyrion VI) was moved to episode 8.


List of A Song of Ice and Fire characters

Aegon Targaryen Prince Aegon Targaryen, also known as Young Griff, is the only known son of Rhaegar Targaryen and Elia Martell. Fifteen years before the events of the series, the infant Aegon was reported to have been killed along with his mother and sister by Gregor Clegane.
In A Dance with Dragons, Varys claims to have switched Aegon with another, lowborn infant (known as the "Pisswater prince") and smuggled Aegon out of King's Landing prior to the capital's fall. During his exile in Essos, Tyrion Lannister (under the alias Hugor Hill) meets Young Griff, a well-trained teenage boy endorsed by Varys's associate Illyrio Mopatis, who claims to be the surviving Aegon and is under the guardianship of Rhaegar's close friend Jon Connington (under alias of Griff). He is to be used by Varys and Illyrio as a puppet king. Young Griff intends to travel to Slaver's Bay to visit Daenerys Targaryen and propose marriage, but Tyrion provokes him during a cyvasse game and convinces him to abandon the proposal and independently attack Westeros. Revealing himself as Aegon, Griff persuades the Golden Company to support his invasion, landing his army in the Stormlands and capturing several castles, while also planning to lay siege to the Baratheon seat of Storm's End.

The Burning Mill

In a brothel, a white-haired man claims to be a Dragonseed, a bastard of Valyrian blood, and the half-brother of the late King Viserys Targaryen and Prince Daemon. When Aegon and his retinue bring a new squire to the brothel to lose his virginity, they encounter a naked Aemond with Sylvi. Aegon mocks his brother until Aemond leaves.
At Harrenhal
Daemon arrives at Harrenhal on Caraxes before Criston's party reaches it. Ser Simon Strong, the castellan, instantly pledges his allegiance to Rhaenyra. He denounces his grand-nephew, Larys, claiming he had his own father, Lord Lyonel, and his brother, Ser Harwin, killed in the fire at Harrenhal. Later that night, an unseen force beckons Daemon to a weirwood tree. There, he experiences a vision of a young Rhaenyra sewing Jaehaerys' head back onto his decapitated body. He awakens before a cloaked woman who tells him he will die there.
At Dragonstone and the Crownlands
Erryk and Arryk Cargyll are buried together. Rhaenys suggests to Rhaenyra that Alicent might be persuaded against war. Rhaenyra sends her youngest sons Joffrey, Aegon, and Viserys away to the Eyrie to continue House Targaryen should the Blacks fail, with the Lady Jeyne Arryn taking Joffrey to ward. Rhaena is instructed to accompany them, but feels demotivated as she believes the reason to be her not having a dragon. She is mollified, however, when Rhaenyra entrusts four dragon eggs in her care.


Origin of Superman

Superman: The Movie The origin presented in Richard Donner's 1978 Superman film is considered to be the definitive origin story not only for Superman, but a template for origin stories in the super hero film genre. The film keeps the classic components intact, but explores more of Clark's boyhood in Smallville. Much of the film's groundwork is laid during the film's first sequences on the planet Krypton, with actor Marlon Brando portraying Superman's Kryptonian father, Jor-El. In this film, Jonathan Kent dies but Martha survives to see her adopted son become Superman.
Superman is divided into three basic sections, each with three distinct themes and visual styles. The first segment, set on Krypton, is meant to be typical of science fiction films, but also lays the groundwork for the Jesus Christ analogy that emerges in the relationship between Jor-El and Kal-El. The second segment, set in Smallville, is reminiscent of 1950s films, and its small-town atmosphere is meant to evoke a Norman Rockwell painting. The third (and largest) segment was an attempt to present the superhero story with as much realism as possible (what Donner called "verisimilitude"), relying on traditional cinematic drama and using only subtle humor instead of a campy approach.

Eve Teschmacher

Eve Teschmacher, or simply Miss Teschmacher, is the name of a fictional character created by Richard Donner and Mario Puzo who appears in DC Comics media as Lex Luthor's personal assistant and love interest.
Originally created for Superman (1978), in which she was portrayed by Valerie Perrine, Teschmacher has made further appearances in other Superman-related media, such as the Arrowverse series Supergirl, in which she was portrayed by Andrea Brooks, and the upcoming DC Universe film Superman (2025), portrayed by Sara Sampaio.
Film
Superman (1978) and Superman II
Created by Richard Donner and Mario Puzo for Superman and Superman II, Miss Teschmacher is Lex Luthor's assistant who frequently questions his schemes.
Superman Returns
A character loosely based on Eve Teschmacher named Kitty Kowalski appears in Superman Returns, portrayed by Parker Posey.
Teschmacher appears in the tie-in Superman Returns: Prequel Comic #3.
Comics
Eve Teschmacher appears in JLA: Earth 2 and Superman Family Adventures as Lex Luthor's secretary.


Spider-Man

Spider-Man is a superhero in American comic books published by Marvel Comics. Created by writer-editor Stan Lee and artist Steve Ditko, he first appeared in the anthology comic book Amazing Fantasy #15 (August 1962) in the Silver Age of Comic Books. He has been featured in comic books, television shows, films, video games, novels, and plays.


Commercial property

Once a PSA is executed, the buyer is commonly required to submit an escrow deposit, which may be refundable under certain conditions, to a title company office or held by a brokerage in escrow. The transaction moves to the due diligence phase, where the buyer makes a more detailed assessment of the property. Purchase and sale agreements will generally include clauses which require the seller to disclose certain information for buyer's review to determine if the terms of the agreement are still acceptable. The buyer may have the right to terminate the transaction and/or renegotiate the terms, often referred to as "contingencies". Many purchase agreements are contingent on the buyer's ability to obtain mortgage financing and buyer's satisfactory review of specific due diligence items. Common due diligence items include property financial statements, rent rolls, vendor contracts, zoning and legal uses, physical and environmental condition, traffic patterns and other relevant information to the buyer's purchase decision specified in the PSA. In competitive real estate markets, buyers may waive contingencies in order to make an offer more appealing to a buyer. The PSA will usually require the seller to provide due diligence information to the seller in a timely manner and limit the buyer's time to terminate the deal based on its due diligence review findings. If the buyer terminates the transaction within the due diligence timeframe, the escrow deposit is commonly returned to the buyer.  If the buyer has not terminated the agreement pursuant to the PSA contingencies, the escrow deposit becomes non-refundable and failure to complete the purchase will result in the escrow deposit funds to be transferred to the seller as a fee for failure to close. The parties will proceed to close the transaction in which funds and title are exchanged.
When a deal closes, post-closing processes may begin, including notifying tenants of an ownership change, transferring vendor relationships, and handing over relevant information to the asset management team.

Questionnaire

A questionnaire is a research instrument that consists of a set of questions (or other types of prompts) for the purpose of gathering information from respondents through survey or statistical study. A research questionnaire is typically a mix of close-ended questions and open-ended questions. Open-ended, long-term questions offer the respondent the ability to elaborate on their thoughts. The Research questionnaire was developed by the Statistical Society of London in 1838.


REAL ESTATE TRANSFER DISCLOSURE STATEMENT

Transfer

Transfer may refer to:
Arts and media
Transfer (2010 film), a German science-fiction movie directed by Damir Lukacevic and starring Zana Marjanović
Transfer (1966 film), a short film
Transfer (journal), in management studies
"The Transfer" (Smash), a television episode
The Transfer, a novel by Silvano Ceccherini
"Transfer", a song by Five for Fighting from the 2010 album Slice
Finance
Transfer payment, a redistribution of income and wealth by means of the government making a payment
Balance transfer, transfer of the balance (either of money or credit) in an account to another account
Money transfer (disambiguation)
Wire transfer, an international expedited bank-to-bank funds transfer
Science and technology
Learning and psychology
Transfer (propaganda), a method of psychological manipulation

Non-disclosure agreement

A non-disclosure agreement (NDA), also known as a confidentiality agreement (CA), confidential disclosure agreement (CDA), proprietary information agreement (PIA), or secrecy agreement (SA), is a legal contract or part of a contract between at least two parties that outlines confidential material, knowledge, or information that the parties wish to share with one another for certain purposes, but wish to restrict access to. Doctor–patient confidentiality (physician–patient privilege), attorney–client privilege, priest–penitent privilege and bank–client confidentiality agreements are examples of NDAs, which are often not enshrined in a written contract between the parties.


Disclaimer

A disclaimer is generally any statement intended to specify or delimit the scope of rights and obligations that may be exercised and enforced by parties in a legally recognized relationship. In contrast to other terms for legally operative language, the term disclaimer usually implies situations that involve some level of uncertainty, waiver, or risk.
A disclaimer may specify mutually agreed and privately arranged terms and conditions as part of a contract; or may specify warnings or expectations to the general public (or some other class of persons) in order to fulfill a duty of care owed to prevent unreasonable risk of harm or injury. Some disclaimers are intended to limit exposure to damages after a harm or injury has already been suffered. Additionally, some kinds of disclaimers may represent a voluntary waiver of a right or obligation that may be owed to the disclaimant.

Transaction

Transaction or transactional may refer to:
Commerce
Financial transaction, an agreement, communication, or movement carried out between a buyer and a seller to exchange an asset for payment
Debits and credits in a Double-entry bookkeeping system
Electronic funds transfer, the electronic exchange or transfer of money from one account to another
Real estate transaction, the process whereby rights in a unit of property is transferred between two or more parties
Transaction cost, a cost incurred in making an economic exchange
Transactional law, the practice of law concerning business and commerce
Computing
Transaction processing, information processing that is divided into individual, indivisible operations
Database transaction, a unit of work performed within a database management system
Atomic transaction, a series of database operations such that either all occur, or nothing occurs
Other uses
Transactions, the published proceedings of a learned society:
Transaction Publishers, a New Jersey-based publishing house that specializes in social sciences books
"Transaction" an episode of the Death Note anime series; see List of Death Note episodes
Transactional analysis, a psychoanalytic theory of psychology
Transactional interpretation, an interpretation of quantum mechanics
Transactional leadership, a leadership style described by James MacGregor Burns