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I'm trying to insert an element node (that has some children) from one XML into another in java.
What I'm trying (which isn't working) looks like this...
Node foo = document1.getChildNodes().item(3).cloneNode(true);
I'm getting an exception that complains that i'm trying to use a node created by one document in another.
Is there an alternative, short of recursing through doc1's node and creating the whole thing manually in doc2?


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Detroit Oppo Meet! Bump
Like strange hoopties? Are you a beatnik who's into jalopys? Join Jalopnik's David Tracy and I at the Concours d'LeMons next weekend, July 30th.
Like strange hoopties? Are you a beatnik who's into jalopys? Join Jalopnik's David Tracy and I at the Concours d'LeMons next weekend, July 30th.
The engine from this:
There is a LeMons road rally that goes from Montery, through Death Valley and Vegas in August. I feel a need to do this. I have started looking at Craig's List.
On the road tonight. At the track in the AM. Excited for a weekend of "racing" with two teams (I'm in demand!). Hopefully both manage to make it the whole weekend as they both have "new" motors.
Do you see the car above? That looks like a LeMons car (especially being pushed into a pitbox). See what's wrong with the car below?
I've been searching locally and saw this today:
My Yamaha R1-carbed, 20/22 Hybrid-in-a-73 Celica sort-of-a-lemons-car-sort-of-not LIVES!
We have relatively nice weather today, so I thought I'd start removing things from the Saturn in earnest. I pulled the headliner yesterday, and today I'd quite like to get the carpet out. The passenger side floor is SOAKED.
1984 Mazda RX-7 - $500 (Shelby)
1984 mazda rx7 tries to fire not getting spark could be as simple as plugs and wires needs some body work still a cool car a good little project for some need room in my garage 500 obo call or text
After running our 1991 Escort in 6 Lemons races we have decided to upgrade. Behold our new 240SX. It was being built as a Drift car so it is already caged and it came with a bunch of interesting bits and pieces. If anyone wants to buy a fully prepped Lemons Escort let me know!
Now that I'm in Detroit, I've got several friends that want to help me build my LeMons car. From the magical place of craigslist comes this gem. This would be awesome, and it's only $1000.
Here's a little trailer for our upcoming 24 Hours of LeMons video. Full video coming soon (hopefully before XMas). This time we managed to finish the weekend!
The answer is always Miata.
After I bent up the front end on the back of a Miata, the hood pins failed for our next driver. This is coming over the crest into the carousel at Sonoma Raceway. Good times!
Any Jalops/Opponauts going to be at the LeMons race in Sonoma this weekend? Stop by and say hello. The car will probably be in the above position all weekend. Still hoping for no rain, but it's looking unlikely.
American Endurance Racing is the new kid on the block for low-buck motorsports. Based around simple rules and a "race what ya got" mentality, it's got lots of appeal to anyone who's ever raced 24 Hours of LeMons or Chumpcar. On an ass-clenchingly cold late November weekend, AER stormed into historic Lime Rock Park…
If anyone here remembers the Brooklyn Bomb Squad's not-so-triumphant LeMons race at New Jersey Motorsports park in the spring, it's time to grab a hankie and wipe away those tears. For we have returned from the Halloween Hooptiefest in New Hampshire with glory and dignity! No, we didn't win (HAAAAA! HAA!! No.) Nor…
I have no intention of entering a LeMons race (logistics are a little tricky up in the Great White North) but hearing the guys on The Smoking Tire talk about the events and experiencing my first taste of track driving, I wanted to see what kind of cars might be available. Interesting choices.

subset: commoncrawl


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I spent the night at this hotel recently when I have to go see my son at Franklin and Marshall and give a presentation. I was pleasantly surprised how large the room was spacious the bathroom was compared to other hotels. The staff was very accommodating when I checked it. Parking was east with a first floor parking garage attached to the hotel. My only complaint was that room 400, was facing out to the main road, which was very busy during the evening. I specifically requested a very quiet room since I have trouble sleeping, and this is the room the assigned me. Additionally, the walls seem to be particularly thin, as I could hear the person next to me talking on phone in their own room. Pleasant surprise was the bar adjoining the main lobby. Steve the bartender was particularly verse in the single malt scotches and set me up with excellent flights of scotch and local cheese. Made for a fun ending to the night. The bar surprisingly is open to midnight although it was very quiet on a monday night.
We have stayed at some other local hotels when we need to see our son at F&M and I will give this another try. Just need to get a room facing away of the main road.
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Hotels in Rhodopes (region), Bulgaria
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- Astera Bansko Hotel & Spafrom $25
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Trending now in Rhodopes (region)
- Most bookedPremier Luxury Mountain Resort
200 people looked at this hotel in the last hour
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"Great hotel, friendly staff, breakfast buffet was excellent. Rooms could do with a thorough clean, wi-fi is free however operates slowly in the room."
A Traveller, Mar 2016, GB
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"This was a family skiing holiday and the Kempinski is known as the top hotel in Bansko, and it delivered. It was exceptional in every way. The rooms were amazing, the service and staff exceptional, location is unbeatable, you can buy the VIP ski passes that beat the lift queues ONLY available to ..."
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"whole hotel is unbelievably hot, in winter. there was no aircon in the bedroom of our apartment - only in the lounge area"
A Traveller, Feb 2016, GB
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"I stayed at this hotel for 3 nights with my cousin in early fall. The hotel staff is fully bilingual (Bulgarian and English), polite and very helpful. The hotel is made up of 3-4 separate buildings. The main building houses the reception, restaurant and other facilities while the other buildings ..."
A Traveller, Sep 2015, CA
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"nice hotel, close to the gondola. Good spa and service"
A Traveller, Jan 2015, US
18 people looked at this hotel in the last hour
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"Great location, very helpful staff. (One minor thing: the outdoor pool closed "for the winter season" on 7 Sep when the temperature was 33C :-) )"
A Traveller, Sep 2015, GB
18 people looked at this hotel in the last hour
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"Overall the experience was fine. The only issue we had was with the water draining from the shower. Many Bulgarian hotels have drains in the floor for overflow but this one didn't seem to work efficiently."
A Traveller, Apr 2016, IE
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"VERY nice place in the woods. Quiet and beautiful. Staff very friendly and eager to please."
A Traveller, May 2016, US
5 people looked at this hotel in the last hour
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"It was great altogether. Fabulous location! Clean sheets, big room, ok staff. And a broken shower head."
A Traveller, Sep 2015, IE
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"Our stay at the Balkan Jewel Resort was an amazing experience, so relaxing and so delightful! The hotel is very modern, spotlessly clean and filled with all the amenities you need. We stayed at the Amber building and had a marvelous view of the Pirin mountains and our room was very nicely decorat..."
A Traveller, Oct 2015, US
Map of Rhodopes (region) hotels
- Borovets Ski Resort
- Borovets
- Bansko Ski Resort
- Rila Monastery
- Plovdiv Amphitheatre
- Pirin National Park
- Pamporovo Ski Resort
- Pamporovo
- Pirin Golf and Country Club
- Plovdiv International Fair Grounds
- Lake Batak
- Smolyan Ski Resort
- Kardzali Central Park
- Velingrad Museum of History
- Saint Marina Church
- Bachinovo Park
- Ethnographic Museum
- History Museum of Kardzhali
- Hristo Botev Stadium
- Baba Vanga House
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- MPM Sport Hotel
- Melanya Mountain Retreat
- Pirin Place Complex Apartments
- Grand Royale Apartment Complex & Spa
- Hotel Pirin
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If you are looking to score the best discounts and deals, use the filter to sort by price from low to high. You can book a hotel in Rhodopes (region) for as low as $20 per night. If you are looking to cut down on expenses, look for hotels that include free breakfast and parking. Some hotels even offer free shuttle service to and from nearby places. You can also sort by star rating, proximity, and guest rating.
Before you book, check out user reviews. Hotels.com has 645 reviews in Rhodopes (region). We show reviews from Expedia and TripAdvisor so you know that you are reading genuine, reliable reviews. Be sure to download the Hotels.com app so you can book your reservations on the go, view amenities, and save the hotels you want to view and compare later.


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How to Get a Green Cardby Ilona Bray, Loida Nicolas Lewis
View All Available Formats & Editions
Fortunately, How to Get a Green Card provides everything you need to understand whether you're eligible, and then make it through the bureaucratic/i>
The U.S. immigration system is an enormous bureaucracy. It's vital that you understand all the requirements for getting a green card before starting your application -- making a mistake can ruin your chances.
Fortunately, How to Get a Green Card provides everything you need to understand whether you're eligible, and then make it through the bureaucratic maze. Find out how to work with U.S. officials, how to prepare and present the right documents, and what to expect every step of the way.
Learn the quickest way to get a green card through:
- parents, siblings and adult children
- spouses and fiancés
- green card lotteries
- political asylum or refugee status
- and other categories
The 7th edition is completely revised to reflect the latest application procedures, laws and fees. It also shows how to find the latest required forms online, and provides filled-out sample forms.
Editorial Reviews
"Green card seekers: Look no further . . ." Asian Week.
"A definitive book that is also easy to read." Fort Worth Star Telegram
"Highly recommended." Library Journal
Product Details
- 9781413308525
- Publisher:
- Publication date:
- 05/25/2008
- Edition description:
- Eighth Edition
- Pages:
- 326
- Product dimensions:
- 8.30(w) x 10.80(h) x 0.90(d)
Meet the Author
Ms. Bray is an author and legal editor at Nolo. Her specialties include real estate, immigration law, and nonprofit fundraising. Ms. Bray also edits a number of Nolo's small business and small claims court books. Ms. Bray's working background includes solo practice, nonprofit, and corporate stints, as well as long periods of volunteering, including an internship at Amnesty International's main legal office in London. She received her law degree and a Masters degree in East Asian (Chinese) Studies from the University of Washington. When she's not at work she enjoys hiking the East Bay hills, attempting to cook Asian noodle dishes, and going to open houses.
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Recognizing direct relationships between variables connected in a network is a pervasive problem in biological, social and information sciences as correlation-based networks contain numerous indirect relationships. Here we present a general method for inferring direct effects from an observed correlation matrix containing both direct and indirect effects. We formulate the problem as the inverse of network convolution, and introduce an algorithm that removes the combined effect of all indirect paths of arbitrary length in a closed-form solution by exploiting eigen-decomposition and infinite-series sums. We demonstrate the effectiveness of our approach in several network applications: distinguishing direct targets in gene expression regulatory networks; recognizing directly-interacting amino-acid residues for protein structure prediction from sequence alignments; and distinguishing strong collaborations in co-authorship social networks using connectivity information alone.
Network science has been widely adopted in recent years in diverse settings, including molecular and cell biology1, social sciences2, information science3, document mining4 and other data mining applications. Networks provide an efficient representation for variable interdependencies, represented as weighted edges between pairs of nodes, with the edge weight typically corresponding to the confidence or the strength of a given relationship. Given a set of observations relating the values that elements of the network take in different conditions, a network structure is typically inferred by computing the pairwise correlation, mutual information or other similarity metrics between each pair of nodes.
The resulting edges include numerous indirect dependencies owing to transitive effects of correlations. For example, if there is a strong dependency between nodes 1 and 2, and between nodes 2 and 3 in the true (direct) network, high correlations will also be visible between nodes 1 and 3 in the observed (direct and indirect) network, thus inferring an edge from node 1 to node 3, even though there is no direct information flow between them (Fig. 1a). Moreover, even if a true relationship exists between a pair of nodes, its strength may be over-estimated owing to additional indirect relationships, and distinguishing the convolved direct and indirect contributions is a daunting task. As the size of networks increases, a very large number of indirect edges may be due to second-order, third-order and higher-order interactions, resulting in diffusion of the information contained in the direct network, and leading to inaccurate network structures and network weights in many applications1,5–11.
Several approaches have been proposed to infer direct dependencies among variables in a network. For example, partial correlations have been used to characterize conditional relationships among small sets of variables12–14, and probabilistic approaches, such as maximum entropy models, have been used to identify informative network edges10,15,16. Other works use graphical models and message passing algorithms to characterize direct information flows in a network17,18, or variations of Granger causality19 to capture the dynamic relationships among variables20–22. Alternative approaches formulated the problem of separating direct from indirect dependencies as a general feature-selection problem23–25, using Bayesian networks26–28, or using an information-theoretic approach to eliminate indirect information flow in the network29. These methods are limited to relatively low-order interaction terms29, or are computationally very expensive12–14, or are designed for specific applications10,15–17,30,31, thus limiting their applicability.
In this paper, we formulate the problem of network deconvolution in a graph-theoretic framework. Our goal is a systematic method for inferring the direct dependencies in a network, corresponding to true interactions, and removing the effects of transitive relationships that result from indirect effects. When the matrix of direct dependencies is known, all transitive relationships can be computed by summing this direct matrix and all its powers, corresponding to the transitive closure of a weighted adjacency matrix, which convolves all direct and indirect paths at all lengths (Fig. 1b). Given an observed matrix of correlations that contains both direct and indirect effects, our task is to recover the original direct matrix that gave rise to the observed matrix. For a weighted network where edge weights represent the confidence, mutual information or correlation strength relating two elements in the network, the inverse problem seeks to recognize the fraction of the weight of each edge attributable to direct vs. indirect contributions, rather than to keep or remove unit-weight edges. This inverse problem is dramatically harder than the forward problem of transitive closure, as the original matrix is not known.
We introduce an algorithm for Network Deconvolution (ND) that can efficiently solve the inverse problem of transitive closure of a weighted adjacency matrix, by use of decomposition principles of eigenvectors and eigenvalues, and by exploiting the closed form solution of infinite Taylor series. We demonstrate the effectiveness of this approach and our algorithm in several large-scale networks from different domains and with different properties (Supplementary Table S1). First, we seek to distinguish likely direct targets in gene regulatory networks as a post-processing step for diverse gene network inference methods, and show that ND improves both global and local network quality. Second, we show effectiveness of network deconvolution in distinguishing directly-interacting amino-acid residues based on pairwise mutual information data in multi-species protein alignments. Third, we apply ND to a social network setting using a co-authorship network that contains solely connectivity information, and show that the resulting edge weights are able to distinguish strong and weak ties independently inferred based on the number of joint papers and additional co-authors. The wide applicability of ND suggests that such a closed-form solution is not only of important theoretical use in reversing the effect of matrix transitive closure, but also of wide practical applicability in a diverse set of real-world networks.
Mathematically, we model the weights of an observed network Gobs whose diagonal is set to zero as the sum of both direct weights in the true network Gdir, and indirect weights due to indirect paths of increasing length in , , etc (Fig. 1a). The inverse problem of inferring the direct network from the observed network is seemingly intractable, as the direct information has now diffused through the observed network beyond recognition. However, expressing Gobs as an infinite sum of the exponentially-decreasing contributions of increasingly-indirect paths leads to a closed form solution for Gobs as a function of Gdir using an infinite-series summation (Fig. 1b). Moreover, by decomposing the observed network into its eigenvalues and eigenvectors, we can express each eigenvalue of the direct matrix as a function of the corresponding eigenvalue of the observed matrix (Fig. 1c). This decomposition leads to a simple closed-form solution for Gdir and provides a framework for an efficient globally-optimal algorithm to deconvolve the contributions of direct and indirect edges given an observed network (Methods and Supplementary Note S1).
The resulting Network Deconvolution (ND) algorithm can be viewed as a nonlinear filter over eigenvalues of a locally observed network to compute global edge significance, for each eigenvalue computing the inverse of a Taylor series expansion. This results in the decrease of large positive eigenvalues of the observed dependency matrix that are inflated owing to indirect effects. The eigenvalue/eigenvector matrix decomposition holds for all symmetric matrices, including all correlation or information-based matrices, and also for some asymmetric input matrices as we show in Supplementary Note S1.4.1. For non-decomposable matrices, we present an iterative conjugate gradient descent approach for network deconvolution that converges to a globally optimal solution by convex optimization (Supplementary Note S1.4.2 and Fig. S2).
Our formulation of network deconvolution has two underlying modeling assumptions: first that indirect flow weights can be approximated as the product of direct edge weights, and second, that observed edge weights are the sum of direct and indirect flows. When these assumptions hold, network deconvolution provides an exact closed-form solution for completely removing all indirect flow effects and inferring all direct interactions and weights exactly (Fig. 1d). We show that ND performs well even when these assumptions do not hold, by inclusion of non-linear effects through simulations when the direct edges are known (Fig. 1e and Supplementary Note S1.3) and by application to diverse real-world biological and social networks where additional properties can be independently evaluated. Our Taylor series closed-form solution assumes that all eigenvalues of the direct dependency matrix are between -1 and 1, which leads to a geometric decrease in the contributions of indirect paths of increasing lengths (Supplementary Note S1.2). This assumption can be achieved for any matrix by scaling the observed input network by a function of the magnitude of its eigenvalues (Supplementary Note S1.6).
We also provide a useful generalization of network deconvolution when the observation dependency matrix is itself noisy (Supplementary Note S1.5). Although direct dependency weights cannot be recovered exactly from the noisy observations, we show that the resulting estimates are close to true weights for moderate noise levels in the input datasets (Fig. S3). We also present two extensions of the network deconvolution algorithm (Supplementary Note S1.7) that make it scalable to very large networks: the first exploits the sparsity of eigenvalues of low rank networks, and the second parallelizes network deconvolution over potentially-overlapping subgraphs of the network (Fig. S5).
We next apply our network deconvolution approach to three settings of inferring gene regulatory networks, inferring protein structural constraints and inferring weak and strong ties in social networks..
We first apply our network deconvolution algorithm to gene regulatory networks, which are pervasively used in molecular biology to describe regulatory relationships between transcription factors (regulators) and their target genes1. Regulatory network inference from high-throughput gene expression data1,6,32, or by integrating complementary types of datasets33–35, is a well-studied problem in computational molecular biology26,29,36,37, enabling us to benefit from available datasets and community efforts for direct method comparisons1,6. Perhaps the largest such comparison is the recently published network inference challenge part of the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project5.
In the DREAM5 network inference challenge5, different methods were applied to reconstruct networks for the bacterium E. coli and the single-cellular eukaryote S. cerevisiae based on experimental datasets, and to reconstruct an in silico network based on simulated datasets (Supplementary Note S2.1 and Fig. S6). True positive interactions were defined as a set of experimentally validated interactions from the RegulonDB database for E. coli38, and a high-confidence set of interactions supported by genome-wide transcription-factor binding data (ChIP-chip) and evolutionarily conserved binding motifs for S. cerevisiae39. All methods were evaluated using the same four performance evaluation metrics: (a) the area under the precision-recall curve; (b) the area under the receiver operating characteristic curve; (c) a combined per-network score that utilizes both previous metrics for each individual network; and (d) an overall per-method score that summarizes the combined performance across all three networks (Methods and Supplementary Note S2.3). The DREAM5 challenge provides an ideal benchmark for evaluating ND, given the uniform benchmarks for network reconstruction used, and the participation of many of the research teams at the forefront of network inference research, with a total of 35 different prediction methods applied across a wide array of methodologies.
Given that ND is designed as a way to eliminate indirect edge weights in mutual information–based and correlation-based networks, we first applied it to the networks predicted by the top-scoring such methods, including CLR37, ARACNE29 and basic mutual information (Relevance networks)40. In all cases, we found that network deconvolution substantially improved the performance of each method according to all metrics used and for all networks tested in DREAM5 (Fig. 2a). The average per-method score increased by 59%, and the per-network scores increased by 53%, 78% and >300-fold in the in silico, E.coli and S. cerevisiae networks respectively (the strong S. cerevisiae improvements are due to low scores for all methods). It is notable that ARACNE, which seeks to remove transitive edges by studying feed-forward loops directly, showed a 75% improvement by network deconvolution, indicating that these indirect effects are not always detectable at the local level but instead require a global network deconvolution approach. As information theoretic methods are among the most widely-used network inference approaches5,6, their use in combination with ND can be of great general use.
We next applied ND to other top performing inference methods that are not based on mutual information or correlation. These include ANOVerence41 that uses a non-parametric non-linear similarity metric between transcription factors and target genes, GENIE323 that uses regression and a tree-based ensemble method, TIGRESS42 that uses a sparse regression formulation and feature selection, and Inferelator32 that uses regression and variable selection based on expression data. We found that network deconvolution was effective even when applied to these methods, leading to an overall performance increase of 11% on average. The performance was increased for three of the four methods, including for the top performing method (GENIE3), which increased by 13%. As GENIE3 was the overall top-performing method, this suggests that the combination of GENIE3 and ND provides the new top-performing method, outperforming all other 35 methods that were assessed in the DREAM5 challenge5. We also applied ND in combination with the community prediction method from DREAM55. We found that community prediction after ND showed 22% greater performance than community prediction on the original networks, suggesting that network deconvolution maintains the complementary aspects of these networks important in community prediction approaches. We note that the community prediction approach is not the best predictor here, with or without ND, likely owing to the insufficiently diverse nature of the original networks. Overall, these results suggest that despite the ability of even the best-performing methods to recover high-quality networks, strong indirect effects remain, which can be reduced by use of ND.
We next studied how ND affects the prediction of local network connectivity patterns. We specifically focused on the ability to correctly predict feed-forward loops, that truly contain both an indirect A→B→C path and a feed-forward A→C edge, and regulatory cascades, for which A and C are only connected through B (Supplementary Note S2.4). Consistent with previous studies5,43, we found that network inference methods tend to perform better on one or the other network motif, based on their approach for dealing with indirect information (Fig. 2b). For example, mutual information-based network inference (MI) is biased towards including feed-forward edges, leading to increased accuracy for feed-forward loops but many spurious transitive edges for cascades, while the Inferelator and ANOVerence are biased towards excluding feed-forward edges, leading to increased accuracy for cascades but many missing feed-forward edges in feed-forward loops. Notably, the ARACNE algorithm, which seeks to directly remove transitive edges, shows a decreased performance for feed forward loops relative to MI, highlighting the difficulty of distinguishing transitive edges from true feed-forward edges. If ND can accurately identify spurious indirect edges but preserve true feed-forward edges, we should expect substantially increased accuracy for cascades, and no decrease in accuracy for feed-forward edges. Indeed, we found that deconvolved networks lead to improved prediction accuracy for true cascades for each method, thus correctly eliminating spurious A→C edges (Fig. 2b). Importantly, the improved performance on cascades did not lead to an increased error rate on feed-forward loops, where prediction accuracy remained similar or improved in most deconvolved networks, with the exception of TIGRESS, which was also the only method where ND did not lead to an improved overall performance. Taken together, these results show that ND effectively distinguishes direct from indirect edges, improving the predictions of a wide range of gene regulatory network inference approaches.
We next applied ND to infer structural constraints between pairs of amino-acids for protein structure prediction44–46. Prior work used evolutionary information to reveal pairs of amino acid residues that are proximal in the three-dimensional protein structure. However, the pairwise evolutionary correlation matrix may contain many transitive relationships between pairs of residues7–10,17,31,47–50. For example, if two amino-acid residues both interact with an intermediate residue, but are not directly interacting with each other, they will show high mutual information owing to indirect effects. One approach to remove transitive noise is to use a probabilistic maximum entropy solution10 that is specifically designed for inferring directly interacting residues15,16,30. Our aim here is to demonstrate effectiveness of using network deconvolution as a general method to infer directly interacting residues over protein contact networks.
As strong clusters of high mutual information have been shown to hinder identification of directly-interacting residues, we reasoned that network deconvolution may be able to break up these clusters and reveal directly-interacting residues, by distinguishing those correlations that can be explained by transitive relationships. Here, we build on an approach which uses comparative genomics information of residue co-variation across evolutionarily-diverged species.
We applied network deconvolution to predict contact maps on fifteen proteins in different folding classes with sizes ranging from 50 to 260 residues15. In our input network, the nodes represent amino acid residues, and each edge between a pair of residues represents their co-variation across multiple sequence alignments spanning 2,000–72,000 sequences, quantified by their mutual information. Applying ND to a mutual information network leads to a systematic and substantial increase in the discovery rate of interacting amino-acids, based on non-adjacent amino-acid contact maps for known structures (Fig. 3a and Supplementary Figure S7). High mutual information residue pairs contain both physically-interacting residues and non-interacting residues, presumably owing to indirect interactions. Application of ND specifically reduces the scores of non-interacting pairs and enables distinguishing directly interacting ones (Fig. 3b).
We also applied ND to a weighted interaction network based on direct information15. Although using ND over direct information led to a small but consistent improvement over the top predictions especially for non-redundant interacting pairs (Supplementary Figure S7), a robust performance assessment requires comparison of predicted proteins 3D structures which is beyond the scope of this manuscript.
We next applied our network deconvolution approach to a social network of co-authorship information51 to distinguish strong and weak collaborations, that can play different key roles in social networks11,52–54. Given the recent surge of social networks like Facebook or ResearchGate, recognizing weak and strong ties is increasingly important for recommending friends or colleagues, recognizing conflicts of interest or evaluating an author's contribution to a team. Previous approaches have defined strong ties using shared indirect contacts55, edges that increase network distance upon removal or edges connecting nodes within the same module53. In co-authorship networks, strong ties have been defined by using additional information beyond network connectivity (Supplementary Note S4), including the number of co-authored papers and the number of other co-authors of these papers51,56.
We used an unweighted input network of 1,589 scientists working in the field of network science51, in which two authors are connected by an edge if they have co-authored at least one paper. We then applied our network deconvolution approach directly on the edges provided by the co-authorship network, to recognize whether network connectivity information alone is sufficient to capture additional information about strong and weak ties previously computed on the same network. Our assumption is that edges resulting from indirect paths likely correspond to weak collaborations, diluted over many other co-authors, while edges with low indirect contributions are more likely to correspond to meaningful collaborations. Application of ND to this unit-weight network led to a weighted network whose transitive closure most closely captures the input network information, and whose weights represent the inferred strength of likely direct interactions. We then ranked all co-authorship edges according to the weight assigned to each by the ND approach.
We found that the resulting edge weights indeed capture co-authorship tie strengths previously computed by summing the number of co-authored papers and down-weighting each paper by the number of additional co-authors56. We defined true strong ties based on Newman's weight ≥ 0.5 (36% of edges) that incorporates additional publication information, and our predictions based on the network deconvolution weight corresponding to the same fraction of edges (ND weight ≥ 0.64). We found that network deconvolution correctly recovered 77% of strong co-authorship ties solely by use of the network topology, demonstrating that additional information about collaboration strength lies within network connectivity information, and that ND is very well-suited for discovering it (Fig. 4a). Beyond the binary classification of edges into strong and weak, we found a strong overall agreement between the rank obtained by the true collaboration strength and the rank provided by the ND weight (correlation coefficient R2=0.76, Fig. 4b). The exception was a population of edges that had strong collaboration scores but weak ND weights, likely due to the number of co-authored publications that factors in the collaboration score but is not available in the ND network input. Indeed, collaborators connected by a strong edge that were incorrectly predicted by ND had on average co-authored 6-fold more papers per author than collaborators correctly predicted as weak, suggesting a very strong additional bias beyond the information provided by the topology. With the wide-spread availability of social networks and the current interest in predicting strong and weak social ties, we expect that network deconvolution will be widely useful in many social network applications beyond co-authorship.
Network deconvolution provides a general framework for computing direct dependencies in a network by use of observed similarities. It can recognize and remove spurious transitive edges due to indirect effects, decrease edge weights that are overestimated due to indirect relationships, and assign edge weights corresponding to direct dependencies to the remaining edges. Thereby, network deconvolution can improve the quality of a broad range of observed networks that are tainted by indirect edge weights due to transitive effects. We introduced an efficient and scalable algorithm for deconvolving an observed network based on a nonlinear filter computing the inverse of a Taylor series expansion over each eigenvalue. We demonstrated that network deconvolution is effective for gene regulatory network inference, protein contact prediction based on protein sequence alignment and inference of collaboration strength from co-authorship social networks. In each case, even though we did not use domain-specific knowledge, ND was effective illustrating the generality and wide applicability of the approach.
The problem of indirect spurious edges has been widely recognized in network inference, but characterized mostly at the local level. In particular, even top-performing network inference methods have been shown to contain many false transitive edges in cascade network motifs, and efforts to remedy this situation lead to incorrect removal of true edges in feed-forward loops5. At this local level, we have shown that network deconvolution has the ability to correctly remove spurious transitive edges in true cascade network motifs, while maintaining true feed-forward edges in feed-forward network motifs. In contrast to previous methods that make well-documented tradeoffs in sensitivity vs. specificity for these transitive edges5, network deconvolution reduces the number of false positives on indirect interactions, while maintaining true positives in feed-forward loops.
However, network deconvolution has a much broader effect than simply removing local indirect edges. In contrast to previous approaches that study local patterns of dependencies to recognize potential indirect edges, network deconvolution takes a global approach by directly inverting the transitive closure of the true network. Previous algorithms29 have sought local approximations to the removal of indirect effects which have been limited to indirect paths of only limited lengths (typically of length 2), owing to the computational complexity of enumerating and evaluating all higher-order paths, and the lack of a systematic way to compute their combined effects. By exploiting eigenvector decomposition and Taylor series closed form solutions, network deconvolution provides four advantages over local approaches: (1) it leads to a much more computationally efficient solution, (2) it has the power to remove indirect effects over paths with arbitrary lengths, (3) it can remove the combined effects of arbitrarily many indirect paths between two nodes, and (4) it eliminates the need for iterative network refinement. These advantages are due to the fact that network deconvolution is essentially a single global operation to subtract the transitive effects of all powers of an adjacency matrix, rather than testing only pair-wise relationships or small network motifs one at a time.
Moreover, we showed that network deconvolution can be applied to networks with very different properties. The networks used here were of different size, density, clustering coefficient, or network centrality, showing that network deconvolution is robust to these parameters. The input networks were also based on different properties, including mutual information and correlation that network deconvolution was designed for, but also networks based on regression, tree-based ensemble methods, feature selection approaches, and other non-linear similarity metrics. We also applied network deconvolution to both weighted and unweighted networks, and used the results both for re-weighing of edges and for edge classification, demonstrating the discrete and continuous applications of the approach. More generally, network deconvolution is not just about edge inclusion or removal, but about probabilistic weighing of individual edges to reveal direct interactions based on observed relationships across the complete network.
We believe that the network deconvolution algorithm introduced here will serve as a foundational graph theoretic tool for computing direct dependencies in many problems in network science and other fields. Although the forward problem of repeated matrix multiplication, also known as network convolution or matrix interpolation in applied fields, has been a key graph theoretical tool, the inverse problem has received relatively little attention. Matrix interpolation has been used in protein-protein interaction networks to propagate functional information through the network57, in movies and shopping applications to make recommendations for users based on previous actions58, in social networks to make friend recommendations, etc. We similarly expect network deconvolution to lead to a rich set of applications in network science, molecular and cell biology and many other fields.
Network deconvolution framework is outlined in Figure 1 (full description in Supplementary Note S1). A perennial challenge to inferring networks is that, observed similarity weights are the sum of both direct and indirect relationships. A direct information flow modeled by an edge in Gdir, can give rise to two or higher level indirect flows. Such indirect flows are captured in Gindir:
where the power associated with each term in Gindir corresponds to the number of edges of indirect paths. Gdir + Gindir together capture both direct and indirect dependencies which in fact comprise the observed dependencies. Note that, the observed dependency matrix is linearly scaled so that the largest absolute eigenvalue of Gdir is smaller than 1. Therefore, the effects of indirect information flows decrease exponentially with the length of indirect paths (Supplementary Notes S1.2 and S1.6). Self-loops of observed dependency network are excluded by setting its diagonal components to zero.
Suppose Gobs represents the matrix of observed dependencies: a properly-scaled similarity matrix between variables (nodes in the network). Gobs can be derived by use of different pairwise similarity metrics such as correlation or mutual information, and scaled linearly based on the largest absolute eigenvalue of the un-scaled similarity matrix. The observed dependency matrix captures both direct and indirect effects; i.e., Gobs=Gdir + Gindir. Note that, the indirect dependency matrix, Gindir, is a function of another unknown Gdir. The main question is how to compute Gdir by using the tainted observed similarities Gobs.
Although Gindir may at first appear intractable because it is an infinite sum, one may note that, similarly to Taylor series expansions, under mild conditions (Supplementary Note S1.1 and S1.2) that are generally present in the setting that we consider, we have:
The above observation leads to a simple closed-form expression for Gdir (Fig. 1b):
For symmetric input matrices and some asymmetric ones, we show that, the observed dependency matrix Gobs can be decomposed to its eigenvalues and eigenvectors (Supplementary Note S1.4). Say U and Σobs represent the matrix of eigenvectors and a diagonal matrix of eigenvalues of matrix Gobs. The i-th diagonal component of the matrix Σobs represents the i-th eigenvalue of the observed dependency matrix Gobs. Then, by using the eigen decomposition principle, we have Gobs = UΣobsU−1.
In this framework, an optimal solution to compute direct dependencies can be computed in the following steps, which comprise the main parts of the proposed Network Deconvolution (ND) algorithm (Fig. 1c)
Decompose the observed dependency matrix Gobs to its eigenvalues and eigenvectors such that Gobs = UΣobsU−1.
Form a diagonal matrix Σdir whose i-th diagonal component is . Then, the output direct dependency matrix is Gdir = UΣdirU−1.
We show that this algorithm finds a globally optimal direct dependency matrix without error (Supplementary Note S1.2).
A detailed description of gene regulatory network performance metrics is given in Supplementary Note S2.3. Network predictions were evaluated as binary classification tasks where edges were predicted to be present or absent. Then, standard performance metrics from machine learning were used: precision-recall (PR) and receiver operating characteristic (ROC) curves. Similar to DREAM55, only the top 100,000 edge predictions were accepted. Then, AUROC and AUPR were separately transformed into p-values by simulating a null distribution for 25,000 random networks. To compute an overall score that summarizes the performance over the three networks with available gold standards (E. coli, S. cerevisiae and in silico), we used the same metric as in the DREAM5 project, which is defined as the mean of the (log-transformed) network specific p-values:
We thank Ben Holmes for suggestions on the initial aspects of this work, Mukul Bansal on the protein structural constraints, Yun Liu for initial analysis of DREAM5 networks and Robert Küffner for discussions and code used for network motif analysis. The work was supported by National Institutes of Health grants R01 HG004037 and HG005639 to M.K., a Swiss National Science Foundation fellowship to D. M., and National Science Foundation CAREER award 0644282 to M.K.
Author contributions: S.F. and M.K. developed the method, analyzed results and wrote the paper. D.M. contributed to DREAM5 datasets, gene network inference and network motif analysis. M.M. contributed to correctness proof and robustness analysis.
Competing financial interests: The authors declare no competing financial interests.
PubMed Central Canada is a service of the Canadian Institutes of Health Research (CIHR) working in partnership with the National Research Council's national science library in cooperation with the National Center for Biotechnology Information at the U.S. National Library of Medicine(NCBI/NLM). It includes content provided to the PubMed Central International archive by participating publishers.


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D.N.A.by Mario
View All Available Formats & Editions
Go was the most satisfying of Mario's first three albums, but it lacked a major crossover single on the level of "Let Me Love You." Although the sensitive ballad "Crying Out for Me" was big on R&B stations, it did not break the Top 30 of the Hot 100. The minimal Sean Garrett and Shondrae production "Break Up," however -- D.N.A.'s lead single, issued weeks before the album -- did not take long to become the singer's biggest pop hit since his breakthrough. And it does turn out to be his fourth album's greatest deviation from the back catalog, carrying a laggard and sparse pulse to back Mario's whiny swagger. "Get Out" is a close second, a buzzing and grinding production from Jim Jonson and Rico Love that mirrors the song's theme of emotional entrapment. One of the hardest beats Mario has had at his disposal, it pushes him into that tough guy mode (as heard occasionally in Go) where you can sense him forcing his jaw to tighten and his eyes to bug out: "This ain't real, so what the f*ck is we doin'?" Even with several new collaborators, including some of the aforementioned, Babyface, Carlos McKinney, the-Dream, and Tricky Stewart, D.N.A. is more a natural development than a series of drastic shifts, and while it will please the majority of the fan base, the material does not allow Mario -- a vocalist more versatile than many would like to admit -- to do much more than toggle between a Lothario and a softie.
Product Details
- Release Date:
- 10/13/2009
- Label:
- J-Records
- 0886974965725
- catalogNumber:
- 749657
Album Credits
Performance CreditsMario Primary Artist,Background Vocals
Lee Blaske Strings
Babyface Guitar
Andre Bowman Bass
Ashanti Floyd Strings
Jessyca Wilson Background Vocals
Technical CreditsJermaine Jackson Composer
Eric Hudson Composer,Producer
Carlos McKinney Composer
Ronald Simmons Composer
Pat Thrall Engineer
Mike "Hitman" Wilson Engineer
Babyface Composer
Kuk Harrell Engineer
Paul Boutin Engineer
Christopher "Tricky" Stewart Composer,Producer
James Scheffer Composer
Stargate Producer
Andre Harris Composer,Producer
Kawan "KP" Prather Composer
Vidal Davis Composer,Producer
Peter Edge Producer
Andrew Davidson Composer
Tor Erik Hermanson Composer
Harold Lilly Composer,Producer
James Neng-Hsien Ho Composer
Ken Lewis drum programming
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Honoring Mr. Gault
The election of Bobby Gault to the leadership ladder of the Mississippi School Board Association brings recognition and honor not only to Mr. Gault, but also to the New Albany School District and to our city.
Gault, who has been a member of the New Albany School District Board of Trustees since 1976, was elected secretary-treasurer of the state school board association at the group's fall meeting.
In succeeding years he will become vice president, then president and finally past president of the association, capping a long career of involvement in education.
During his time on our local school board, he has been a part of two school bond issues, two major construction projects and the employment of our superintendent, Dr. Charles Garrett, who was recently named state superintendent of the year.
Because of his long tenure on the New Albany board, Mr. Gault's role on the board has evolved into one of providing perspective and historical insight into the workings of the board and its schools.
"Mr. Gault's experience and expertise are invaluable to our district," Garrett said.
We join the community in congratulating Mr. Gault for attaining this leadership position in the state association.
About Chris Elkins
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Are you planning in future this feature?
Meta Stack Exchange is where users like you discuss bugs, features, and support issues that affect the software powering all 158 Stack Exchange communities.
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What is meta?
Here's how it works:
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The above was out of date. You can selectively hide account associations now.
I do have an account on all 4 trilogy sites, but I am not very active on SF and SU. I would like to be able to select which accounts are shown on the public and which are shown on the private CV.
(Not that I am ashamed of anything I do on these sites, but it does not add to my CV to show that I have 101 rep on SF.)


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This affordable all-suite property is located approximately nine miles from downtown Minneapolis and four miles from Minneapolis-St. Paul International
Four Points Minneapolis Airport is the place to be with the convenience of 24-hour shuttle service to Minneapolis-St. Paul Airport (MSP), Mall of America
The AmericInn Bloomington East Airport is just a few miles from both the Mall of America and the Minneapolis Saint Paul International Airport (MSP).
The Embassy Suites Minneapolis Airport hotel in Bloomington MN is located one mile from the Mall of America, Nickelodeon Universe, Waterpark of America and the Minneapolis St. Paul International Airport - MSP. Two miles from historic Ft. Snelling.
Adjacent to the indoor Water Park of America, the Radisson is a half-mile from the Mall of America and 3 miles from the Minneapolis- Saint Paul International
Despite the cold winters, there is always summer fun inside the Holiday Inn Minneapolis NW Elk River Hotel.
A Minneapolis, MN hotel, the Comfort Suites is located in downtown Minneapolis.
DoubleTree Suites by Hilton Minneapolis is within one block of Nicollet Mall and Orchestra Hall and approximately 26 miles from Minneapolis-Saint Paul
The Holiday Inn is located in downtown St. Paul, approximately eight miles from the Minneapolis/St. Paul International Airport. Local attractions include
This LivInn Hotel is conveniently accessible to Highway 694, approximately seven miles from downtown Minneapolis and 22 miles from Minneapolis St. Paul
Select your destination in Richfield Minnesota. Browse thousands of listings for accommodations, adventures, tours and travel services.


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New product catalogue 2016 out now!
The new catalogue 2016 is out. » Open catalogue PDF
Catalogues in other languages »
New product: CDT2000-DC
CDT2000 carbon dioxide transmitter series is reinforced with CDT2000-DC transmitters with dual channel sensor module.read more »
Thank you for visiting us at the MCE 2016!
The MCE 2016 exhibition was held 15-18 March in Milan, Italy.read more »
Changes in management
After a long career, Managing Director Heikki Kalliomäki will leave his position as Managing Director of HK Instruments. Heikki will continue with the company in the role of Chairman of the Board. His successor will be the former Vice President Tuomo Kalliomäki who has been appointed CEO of HK Instruments.


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Stayed at the Eldorado on November 11th for a concert. Only reason i stay at the downtown casinos is becuase they are really close to the Reno events center. The front desk staff was professional not all that friendly but not rude. The room was clean and somewhat small. I requested a room near a elevator but instead was given a room as far away from the elevator as i think is possible. The hotel is older and is not geared towards family as much as the Atlantis casino or the Peppermill casino which are both located at the sound end of Reno. The price was right and came with one free buffett. If you have to stay downtown its not a bad place but all the downtown casions are showing their age.
- Official Description (provided by the hotel):
- Experience timeless luxury and friendly, personal service in the heart of Downtown Reno. Escape to the Eldorado Hotel Casino, named "Best Rooms and Suites" by Casino Player Magazine, where every detail has been considered in order to provide you with the stay of a lifetime. Lose yourself in the nonstop excitement and elegance of our luxurious Reno hotel casino. Choose from our nine award-winning Reno restaurants, including the world-renowned La Strada and Roxy. Browse for one-of-a-kind memorabilia in our shopping boutiques, or check out a Broadway-style performance in Reno's newest showroom. Whether you are traveling for business or pleasure, the Eldorado offers the ultimate choice among downtown Reno casinos. ... more less
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text:
Apple expected to debut iPad mini after September iPhone launchApple will release a slightly tweaked version of its existing iPad alongside a much-rumored 7-inch model after the sixth-generation iPhone launches in early September, says KGI Securities analyst Ming-Chi Kuo.
In a note to investors shared with AppleInsider on Tuesday, Kuo said Apple is set to debut a next-generation iPhone in early September and will follow up with a smaller 7-inch iPad model shortly after.
Kuo notes that iPhone unit shipments shouldn't see a massive bump during the launch quarter, however, as limited component availability, a short on-sale time and a backlog of consumers holding out for the new handset will result in only a slight sales increase.
"iPhone 5 to debut in September. But due to in-cell touch panel and casing yield rate limits, ability to offset older models' shipments decrease will be moderate," Kuo writes as one of his bullet points for the upcoming second half of 2012.
Trailing the new iPhone's launch will be a rumored smaller iPad model, also seeing a likely announcement in September following a manufacturing ramp-up in August. While initial sales of the as-yet-unannounced device will be somewhat modest, Kuo sees an impressively strong performance in the holiday quarter and estimates sales of over 13 million units. If correct, the sales numbers would put the so-called "iPad mini" out in front of its bigger brother.
The iPad mini estimates are in line with recent reports that claim Apple's Chinese manufacturing partner Foxconn is already hiring extra staff in a build-up to mass production next month.
Adding to earlier reports which claimed Apple may be tweaking the internals of the third-generation iPad, Kuo's sources say the changes won't be aesthetic and are meant to cut component costs and improve thermal performance. The modified third-generation iPad is expected to hit shelves in October.
Source: KGI Research
While third quarter sales may be soft for the Cupertino-based company, Kuo sees huge gains going into quarter four thanks in large part to the introduction of new iPad and iPhone models. He pegs Apple's handset sales to hit 55 million in the fourth quarter of 2012 and iPad product line sales closing in on 24 million during the same period.
Also on the docket for this fall is a rumored redesign for Apple's iPod touch and iPod nano media players. At this point details are scarce, though Kuo claims the new devices will feature technology from both the sixth-generation iPhone like a larger screen as well as components from existing iOS products like the A5 processor found in the iPhone 4S.
On Topic: Future Hardware
- Apple patent filing shows future potential of Touch ID not tied to a button
- Apple bolsters 'Project Titan' self-driving software team with former QNX CEO
- Rumor: Apple to adopt USB Type-C in another upcoming MacBook, whether it's Pro or Air
- Apple shakes up electric vehicle team, places Bob Mansfield in charge of 'Project Titan' - report
- Intel's 7th-gen 'Kaby Lake' processors delivered to manufacturers


text:
Q & A Library
Does New Prostate Cancer Drug Really Work?
What are your thoughts and opinions on a drug called MDV3100 for prostate cancer elimination in advanced-stage patients?
Answer (Published 10/29/2009)
MDV3100 is an experimental drug that appears to hold great promise for treatment of advanced prostate cancer that resists current drugs. The majority of today's drugs block androgens, the male sex hormones that foster the cancer's growth and spread. Treatment results in "chemical castration." Advanced, resistant disease is called "castration-resistant prostate cancer" or CRPC.
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MDV3100 has been studied in cell cultures, where it worked well, and in mice, where it shrank tumors, but so far, it has been studied in only two human trials. The first enrolled 30 men with advanced CRPC. In 22 of the men, levels of prostate-specific antigen (PSA), a marker for tumor growth, declined with MDV3100 treatment. In 13 of those cases PSA levels fell by more than half. A second trial with 110 men tested higher doses, which reportedly were well tolerated. The company that produces MDV3100 announced in March, 2009, that it has FDA permission to launch a randomized, placebo-controlled, double-blind, multinational trial to assess overall survival with MDV3100. A total of 1,200 men with advanced CRPC will be enrolled.
If all goes well, it will be at least three to four years before MDV3100 could be ready for FDA evaluation and even longer before it becomes available for treatment.
If you're looking for ways to lower your risk of prostate cancer, here's what I recommend:
Andrew Weil, M.D.
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text: Research Overview Dr. Stack's research interests include the evaluation of resource utilization and standardization of care in the ED. Her recent work has focused on the development and implementation of evidence based guidelines for care in the pediatric emergency department. She is an expert in quality improvement. About Anne Stack Dr. Stack received her MD from Harvard Medical School. She completed residency in Pediatrics and fellowship in Pediatric Emergency Medicine at Boston Children's Hospital. Dr. Stack has recently served as Co-Chair of the PEM Special Interest Group of the Academic Pediatric Association. She is currently Co-Chair of the Evidence-Based Guideline Committee of the Committee on Quality Transformation of the Section of PEM of the American Academy of Pediatrics. Dr. Stack joined the faculty of the Division of Emergency Medicine at Children's Hospital in 1993. Selected Publications Akenroye AT, Thurm CW, Neuman MI, Alpern ER, Srivastava G, Spencer SP, Simon HK, Tejedor-Sojo J, Gosdin CH, Brennan E, Gottlieb LM, Gay JC, McClead RE, Shah SS, Stack AM. Prevalence and predictors of return visits to pediatric emergency departments. J Hosp Med. 2014 Dec; 9(12):779-87. Gallagher RA, Levy J, Vieira RL, Monuteaux MC, Stack AM. Ultrasound assistance for central venous catheter placement in a pediatric emergency department improves placement success rates. Acad Emerg Med. 2014 Sep; 21(9):981-6. Paul R, Melendez E, Stack A, Capraro A, Monuteaux M, Neuman MI. Improving adherence to PALS septic shock guidelines. Pediatrics. 2014 May; 133(5):e1358-66. Akenroye AT, Baskin MN, Samnaliev M, Stack AM. Impact of a bronchiolitis guideline on ED resource use and cost: a segmented time-series analysis. Pediatrics. 2014 Jan; 133(1):e227-34. Gallagher RA, Porter S, Monuteaux MC, Stack AM. Unscheduled return visits to the emergency department: the impact of language. Pediatr Emerg Care. 2013 May; 29(5):579-83. Volpe D, Harrison S, Damian F, Rachh P, Kahlon PS, Morrissey L, Mack J, Akenroye A, Stack AM. Improving timeliness of antibiotic delivery for patients with fever and suspected neutropenia in a pediatric emergency department. Pediatrics. 2012 Jul; 130(1):e201-10.


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