Scheduled Commit

data/clustering_individual-35e094d9-c3d4-447e-b2f4-7dd3f5d1d585.jsonl

@@ -62,3 +62,27 @@
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data/retrieval_individual-35e094d9-c3d4-447e-b2f4-7dd3f5d1d585.jsonl

@@ -237,3 +237,5 @@
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 {"tstamp": 1723439209.8552, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1723439207.3258, "finish": 1723439209.8552, "ip": "", "conv_id": "1729d13475704dbc8f01e85196039bca", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "Advanced GRAPH RAG approaches", "output": [["Advanced GRAPH RAG approaches", "Title: GRAPE for Fast and Scalable Graph Processing and random walk-based Embedding\n\nAbstract: Graph Representation Learning (GRL) methods opened new avenues for addressing complex, real-world problems represented by graphs. However, many graphs used in these applications comprise millions of nodes and billions of edges and are beyond the capabilities of current methods and software implementations. We present GRAPE, a software resource for graph processing and embedding that can scale with big graphs by using specialized and smart data structures, algorithms, and a fast parallel implementation of random walk-based methods. Compared with state-of-the-art software resources, GRAPE shows an improvement of orders of magnitude in empirical space and time complexity, as well as a competitive edge and node label prediction performance. GRAPE comprises about 1.7 million well-documented lines of Python and Rust code and provides 69 node embedding methods, 25 inference models, a collection of efficient graph processing utilities and over 80,000 graphs from the literature and other sources. Standardized interfaces allow seamless integration of third-party libraries, while ready-to-use and modular pipelines permit an easy-to-use evaluation of GRL methods, therefore also positioning GRAPE as a software resource to perform a fair comparison between methods and libraries for graph processing and embedding."]], "corpus": "arxiv"}
 {"tstamp": 1723439209.8552, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1723439207.3258, "finish": 1723439209.8552, "ip": "", "conv_id": "7d535ded43ec4f44a8dfa04baf617a02", "model_name": "text-embedding-3-large", "prompt": "Advanced GRAPH RAG approaches", "output": [["Advanced GRAPH RAG approaches", "Title: rags2ridges: A One-Stop-Shop for Graphical Modeling of High-Dimensional Precision Matrices\n\nAbstract: A graphical model is an undirected network representing the conditional independence properties between random variables. Graphical modeling has become part and parcel of systems or network approaches to multivariate data, in particular when the variable dimension exceeds the observation dimension. rags2ridges is an R package for graphical modeling of high-dimensional precision matrices. It provides a modular framework for the extraction, visualization, and analysis of Gaussian graphical models from high-dimensional data. Moreover, it can handle the incorporation of prior information as well as multiple heterogeneous data classes. As such, it provides a one-stop-shop for graphical modeling of high-dimensional precision matrices. The functionality of the package is illustrated with an example dataset pertaining to blood-based metabolite measurements in persons suffering from Alzheimer's Disease."]], "corpus": "arxiv"}
+{"tstamp": 1723439374.6691, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1723439372.4437, "finish": 1723439374.6691, "ip": "", "conv_id": "bcf477eeb8d840e0be410dfe130344f9", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "molecular generation", "output": [["molecular generation", "Title: Molecule Generation Experience: An Open Platform of Material Design for Public Users\n\nAbstract: Artificial Intelligence (AI)-driven material design has been attracting great attentions as a groundbreaking technology across a wide spectrum of industries. Molecular design is particularly important owing to its broad application domains and boundless creativity attributed to progresses in generative models. The recent maturity of molecular generative models has stimulated expectations for practical use among potential users, who are not necessarily familiar with coding or scripting, such as experimental engineers and students in chemical domains. However, most of the existing molecular generative models are Python libraries on GitHub, that are accessible for only IT-savvy users. To fill this gap, we newly developed a graphical user interface (GUI)-based web application of molecular generative models, Molecule Generation Experience, that is open to the general public. This is the first web application of molecular generative models enabling users to work with built-in datasets to carry out molecular design. In this paper, we describe the background technology extended from our previous work. Our new online evaluation and structural filtering algorithms significantly improved the generation speed by 30 to 1,000 times with a wider structural variety, satisfying chemical stability and synthetic reality. We also describe in detail our Kubernetes-based scalable cloud architecture and user-oriented GUI that are necessary components to achieve a public service. Finally, we present actual use cases in industrial research to design new photoacid generators (PAGs) as well as release cases in educational events."]], "corpus": "arxiv"}
+{"tstamp": 1723439374.6691, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1723439372.4437, "finish": 1723439374.6691, "ip": "", "conv_id": "cc175b1a0f3442f8a9fadb43a99fce5d", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "molecular generation", "output": [["molecular generation", "Title: Generating equilibrium molecules with deep neural networks\n\nAbstract: Discovery of atomistic systems with desirable properties is a major challenge in chemistry and material science. Here we introduce a novel, autoregressive, convolutional deep neural network architecture that generates molecular equilibrium structures by sequentially placing atoms in three-dimensional space. The model estimates the joint probability over molecular configurations with tractable conditional probabilities which only depend on distances between atoms and their nuclear charges. It combines concepts from state-of-the-art atomistic neural networks with auto-regressive generative models for images and speech. We demonstrate that the architecture is capable of generating molecules close to equilibrium for constitutional isomers of C$_7$O$_2$H$_{10}$."]], "corpus": "arxiv"}

data/retrieval_side_by_side-35e094d9-c3d4-447e-b2f4-7dd3f5d1d585.jsonl

@@ -5,3 +5,4 @@
 {"tstamp": 1723412514.9147, "task_type": "retrieval", "type": "tievote", "models": ["text-embedding-004", "text-embedding-3-large"], "ip": "", "0_conv_id": "d3a6984a89be431bb491d7651af1be63", "0_model_name": "text-embedding-004", "0_prompt": "- how to increase the tensile capacity of concrete mix design?\n", "0_output": [["- how to increase the tensile capacity of concrete mix design?\n", "Title: Experimental and Numerical Analysis of Strain Gradient in Tensile Concrete Prisms Reinforced with Multiple Bars\n\nAbstract: This work is a continuation of the ongoing research on deformation behavior of reinforced concrete elements under tension. The previous studies have revealed that deformation behaviors of elements reinforced with multiple bars and the traditional prismatic members reinforced with a center bar are essentially different. The latter layout, though typical of laboratory specimens, could not represent the norm of structures in real-life. Thus, a new test methodology to investigate the strain distribution in concrete prismatic members reinforced with multiple bars subjected to axial tension is devised. Prismatic concrete specimens with different reinforcement configurations were fabricated and tested using the proposed setup. Deformation behavior of the specimens is modeled with a tailor-designed bond modeling approach for rigorous finite element analysis. It is revealed that the average deformations of the concrete could be different from the prevailing approach of average deformations of the steel, and are dependent on the reinforcement configurations. Therefore, the efficiency of concrete in tension should be carefully taken into account for rational design of structural elements. The study endorses promising abilities of finite element technique as a versatile analysis tool whose full potential is to be revealed with the advent of computer hardware."]], "0_corpus": "arxiv", "1_conv_id": "5b436baccb3b4009b76af55e5d40c772", "1_model_name": "text-embedding-3-large", "1_prompt": "- how to increase the tensile capacity of concrete mix design?\n", "1_output": [["- how to increase the tensile capacity of concrete mix design?\n", "Title: Experimental and Numerical Analysis of Strain Gradient in Tensile Concrete Prisms Reinforced with Multiple Bars\n\nAbstract: This work is a continuation of the ongoing research on deformation behavior of reinforced concrete elements under tension. The previous studies have revealed that deformation behaviors of elements reinforced with multiple bars and the traditional prismatic members reinforced with a center bar are essentially different. The latter layout, though typical of laboratory specimens, could not represent the norm of structures in real-life. Thus, a new test methodology to investigate the strain distribution in concrete prismatic members reinforced with multiple bars subjected to axial tension is devised. Prismatic concrete specimens with different reinforcement configurations were fabricated and tested using the proposed setup. Deformation behavior of the specimens is modeled with a tailor-designed bond modeling approach for rigorous finite element analysis. It is revealed that the average deformations of the concrete could be different from the prevailing approach of average deformations of the steel, and are dependent on the reinforcement configurations. Therefore, the efficiency of concrete in tension should be carefully taken into account for rational design of structural elements. The study endorses promising abilities of finite element technique as a versatile analysis tool whose full potential is to be revealed with the advent of computer hardware."]], "1_corpus": "arxiv"}
 {"tstamp": 1723413013.0564, "task_type": "retrieval", "type": "rightvote", "models": ["GritLM/GritLM-7B", "Salesforce/SFR-Embedding-2_R"], "ip": "", "0_conv_id": "63dca5c59a2d437593d3201df0f14fab", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "- what issues exist with using FRP in buildings? Other than mechanical failure", "0_output": [["- what issues exist with using FRP in buildings? Other than mechanical failure", "Title: An Experimental Study on Shear and Flexural Strengthening of Concrete Beams Using GFRP Composites\n\nAbstract: Maintaining and restoring buildings has nowadays gained special importance due to its high cost. Various different methods have been presented regarding this matter due to the above-mentioned reason and also the ever-growing demand for the engineers and the building-industry specialists for strengthening, restoring, and improving the concrete structures. The effect of FRP sheets on shear and flexural strengthening of simple concrete beams has been examined in this research. Four concrete beams containing concrete were constructed with a similar cross section and length and a common resistance level. They underwent the two-point flexural test and their exploitation conditions were studied. The results showed that if the FRP sheets are used appropriately and with proper bracing in strengthening the concrete beam samples, there will be a significant increase in their shear and flexural strength. Moreover, it was observed that relative deformation measured on the concrete surface and the GFRP sheet is proportional."]], "0_corpus": "arxiv", "1_conv_id": "41c064c029374fc383588bd7b2a4acfd", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "- what issues exist with using FRP in buildings? Other than mechanical failure", "1_output": [["- what issues exist with using FRP in buildings? Other than mechanical failure", "Title: Analytical Study of Reinforced Concrete Beams Strengthened by FRP Bars Subjected to Impact Loading Conditions\n\nAbstract: In this research, a new analytical model is developed to analyze structural members strengthened with FRP systems and subjected to impact loading conditions. ABAQUS based finite element code was used to develop the proposed model. The model was validated against nine beams built and tested with various configurations and loading conditions. Three sets of beams were prepared and tested under quasistatic and impact loadings by applying various impact height and Dynamic Explicit loading conditions. The first set consisted of two beams, where one of the beams was reinforced with steel bars and the other was externally reinforced with GFRP sheet. The second set consisted of six beams, with five of the beams were reinforced with steel bars and one of them wrapped by GFRP sheet. The last set was tested to validate the response of concrete beams reinforced by FRP bar. In addition, beams were reinforced with glass and carbon fiber composite bars tested under Quasi-Static and Impact loading conditions. The impact load was simulated by the concept of a drop of a solid hammer from various heights. The numerical results showed that the developed model can be an effective tool to predict the performance of retrofitted beams under dynamic loading condition. Furthermore, the model showed that FRP retrofitting of RC beams subjected to repetitive impact loads can effectively improve their dynamic performance and can slow the progress of damage."]], "1_corpus": "arxiv"}
 {"tstamp": 1723413025.7799, "task_type": "retrieval", "type": "rightvote", "models": ["GritLM/GritLM-7B", "Salesforce/SFR-Embedding-2_R"], "ip": "", "0_conv_id": "1904092c2c074090a373398203aa0615", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "- how to mitigate thermal degradation of FRPs in buildings?\n", "0_output": [["- how to mitigate thermal degradation of FRPs in buildings?\n", "Title: Radiative Cooling and Thermoregulation in the Earth's Glow\n\nAbstract: Passive radiative cooling involves a net radiative heat loss into the cold outer space through the atmospheric transmission windows. Due to its passive nature and net cooling effect, it is a promising alternative or complement to electrical cooling. For efficient radiative cooling of objects, an unimpeded view of the sky is ideal. However, the view of the sky is usually limited - for instance, the walls of buildings have >50% of their field of view subtended by the earth. Moreover, objects on earth become sources of heat under sunlight. Therefore, building walls with hot terrestrial objects in view experience reduced cooling or heating, even with materials optimized for heat loss into the sky. We show that by using materials with selective long-wavelength infrared (LWIR) emittances, vertical building facades experience higher cooling than achievable by using broadband thermal emitters like typical building envelopes. Intriguingly, this effect is pronounced in the summer and diminishes or even reverses during the winter, indicating a thermoregulation effect. The findings highlight a major opportunity to harness untapped energy savings in buildings."]], "0_corpus": "arxiv", "1_conv_id": "7442151372be497a8762b3a41f83180e", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "- how to mitigate thermal degradation of FRPs in buildings?\n", "1_output": [["- how to mitigate thermal degradation of FRPs in buildings?\n", "Title: Thermal buckling of thin injection-molded FRP plates with fiber orientation varying over the thickness\n\nAbstract: The different thermo-elastic properties of glass fibers and polymer matrices can generate residual thermal stresses in injection-molded fiber-reinforced plastic (FRP) objects. During cooling from mold to room temperature, these stresses can be relaxed by large deformations resulting from an instability of the unwarped configuration (i.e., buckling). This article investigates the thermal buckling of thin FRP disks via an analytical formulation based on the Foppl-von Karman theory. Expanding on our previous work, cylindrical orthotropy with material parameters varying over the disk thickness is assumed in order to account for thickness dependency of the glass fiber orientation distribution. A disk parameter generalizing the thermal anisotropy ratio for homogeneous orthotropic disks is introduced and its relation with the occurrence and periodicity of buckling is discussed. This is done for a skin-coreskin model, for which the core-to-total thickness ratio is defined. For fiber orientation distributions typical of injection-molded disks, it is found that there exists a value of the thickness ratio for which no buckling occurs. It is also demonstrated that the periodicity of the first buckling mode is described by the generalized thermal anisotropy ratio, thus extending the results obtained for a homogeneous fiber orientation distribution. Improvements in the accuracy of the predictions for experimental data available in the literature when using the skin-core-skin model are shown. Finally, we study the relation between buckling temperature and disk thickness and propose an expression for the dependence of the normalized buckling temperature on the thermal anisotropy ratio. Results of FEM simulations are used to validate the proposed expression, proving its applicability and accuracy."]], "1_corpus": "arxiv"}
+{"tstamp": 1723439397.6826, "task_type": "retrieval", "type": "leftvote", "models": ["nomic-ai/nomic-embed-text-v1.5", "Alibaba-NLP/gte-Qwen2-7B-instruct"], "ip": "", "0_conv_id": "bcf477eeb8d840e0be410dfe130344f9", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "molecular generation", "0_output": [["molecular generation", "Title: Molecule Generation Experience: An Open Platform of Material Design for Public Users\n\nAbstract: Artificial Intelligence (AI)-driven material design has been attracting great attentions as a groundbreaking technology across a wide spectrum of industries. Molecular design is particularly important owing to its broad application domains and boundless creativity attributed to progresses in generative models. The recent maturity of molecular generative models has stimulated expectations for practical use among potential users, who are not necessarily familiar with coding or scripting, such as experimental engineers and students in chemical domains. However, most of the existing molecular generative models are Python libraries on GitHub, that are accessible for only IT-savvy users. To fill this gap, we newly developed a graphical user interface (GUI)-based web application of molecular generative models, Molecule Generation Experience, that is open to the general public. This is the first web application of molecular generative models enabling users to work with built-in datasets to carry out molecular design. In this paper, we describe the background technology extended from our previous work. Our new online evaluation and structural filtering algorithms significantly improved the generation speed by 30 to 1,000 times with a wider structural variety, satisfying chemical stability and synthetic reality. We also describe in detail our Kubernetes-based scalable cloud architecture and user-oriented GUI that are necessary components to achieve a public service. Finally, we present actual use cases in industrial research to design new photoacid generators (PAGs) as well as release cases in educational events."]], "0_corpus": "arxiv", "1_conv_id": "cc175b1a0f3442f8a9fadb43a99fce5d", "1_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "1_prompt": "molecular generation", "1_output": [["molecular generation", "Title: Generating equilibrium molecules with deep neural networks\n\nAbstract: Discovery of atomistic systems with desirable properties is a major challenge in chemistry and material science. Here we introduce a novel, autoregressive, convolutional deep neural network architecture that generates molecular equilibrium structures by sequentially placing atoms in three-dimensional space. The model estimates the joint probability over molecular configurations with tractable conditional probabilities which only depend on distances between atoms and their nuclear charges. It combines concepts from state-of-the-art atomistic neural networks with auto-regressive generative models for images and speech. We demonstrate that the architecture is capable of generating molecules close to equilibrium for constitutional isomers of C$_7$O$_2$H$_{10}$."]], "1_corpus": "arxiv"}