Miracle-dz/UniFAR-FTUs huggingface dataset:
Utilizing common substructures to speedup tensor factorization for mining dynamic graphs

In large and complex graphs of social, chemical/biological, or other relations, frequent substructures are commonly shared by different graphs or by graphs evolving through different time periods. Tensors are natural representations of these complex time-evolving graph data. A factorization of a tensor provides a high-quality low-rank compact basis for each dimension of the tensor, which facilitates the interpretation of frequent substructures of the original graphs. However, the high computational cost of tensor factorization makes it infeasible for conventional tensor factorization methods to handle large graphs that evolve frequently with time. To address this problem, in this paper we propose a novel iterative tensor factorization (ITF) method whose time complexity is linear in the cardinalities of all dimensions of a tensor. This low time complexity means that when using tensors to represent dynamic graphs, the computational cost of ITF is linear in the size (number of edges/vertices) of graphs and is also linear in the number of time periods over which the graph evolves. More importantly, an error estimation of ITF suggests that its factorization correctness is comparable to that of the standard factorization method. We empirically evaluate our method on publication networks and chemical compound graphs, and demonstrate that ITF is an order of magnitude faster than the conventional method and at the same time preserves factorization quality. To the best of our knowledge, this research is the first work that uses important frequent substructures to speed up tensor factorizations for mining dynamic graphs.
Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Paths

Relation classification is an important research arena in the field of natural language processing (NLP). In this paper, we present SDP-LSTM, a novel neural network to classify the relation of two entities in a sentence. Our neural architecture leverages the shortest dependency path (SDP) between two entities; multichannel recurrent neural networks, with long short term memory (LSTM) units, pick up heterogeneous information along the SDP. Our proposed model has several distinct features: (1) The shortest dependency paths retain most relevant information (to relation classification), while eliminating irrelevant words in the sentence. (2) The multichannel LSTM networks allow effective information integration from heterogeneous sources over the dependency paths. (3) A customized dropout strategy regularizes the neural network to alleviate overfitting. We test our model on the SemEval 2010 relation classification task, and achieve an $F_1$-score of 83.7\%, higher than competing methods in the literature.
Comprehensively Characterizing the Thioredoxin Interactome In Vivo Highlights the Central Role Played by This Ubiquitous Oxidoreductase in Redox Control*

Thioredoxin (Trx) is a ubiquitous oxidoreductase maintaining protein-bound cysteine residues in the reduced thiol state. Here, we combined a well-established method to trap Trx substrates with the power of bacterial genetics to comprehensively characterize the in vivo Trx redox interactome in the model bacterium Escherichia coli. Using strains engineered to optimize trapping, we report the identification of a total 268 Trx substrates, including 201 that had never been reported to depend on Trx for reduction. The newly identified Trx substrates are involved in a variety of cellular processes, ranging from energy metabolism to amino acid synthesis and transcription. The interaction between Trx and two of its newly identified substrates, a protein required for the import of most carbohydrates, PtsI, and the bacterial actin homolog MreB was studied in detail. We provide direct evidence that PtsI and MreB contain cysteine residues that are susceptible to oxidation and that participate in the formation of an intermolecular disulfide with Trx. By considerably expanding the number of Trx targets, our work highlights the role played by this major oxidoreductase in a variety of cellular processes. Moreover, as the dependence on Trx for reduction is often conserved across species, it also provides insightful information on the interactome of Trx in organisms other than E. coli.
An New Approach for Incremental Speaker Adaptation

Speaker-dependent (SD) systems yield much higher recognition rates than speaker independent (SI) ones. However for many applications, it is not feasible to gather enough data from one speaker to train the system. To overcome the mismatch, speaker adaptation techniques are widely used to achieve recognition rates that come close to SD systems but only use a fraction of speaker dependent data. At the same time, speaker adaptation techniques can also be used to modify the system parameters to better match variations in microphone, transmission channel and environment noise.
The role of ventral and dorsal striatum mGluR5 in relapse to cocaine‐seeking and extinction learning

Cocaine addiction is a chronic, relapsing disease characterized by an inability to regulate drug‐seeking behavior. Here we investigated the role of mGluR5 in the ventral and dorsal striatum in regulating cocaine‐seeking following both abstinence and extinction. Animals underwent 2 weeks of cocaine self‐administration followed by 3 weeks of home‐cage abstinence. Animals were then reintroduced to the operant chamber for a context‐induced relapse test, followed by 7–10 days of extinction training. Once responding was extinguished, cue‐primed reinstatement test was conducted. Both drug‐seeking tests were conducted in the presence of either mGluR5 negative allosteric modulator, MTEP or vehicle infused into either the nucleus accumbens (NA) core or dorsolateral striatum (dSTR). We found that MTEP infused in the NA core attenuated both context‐induced relapse following abstinence and cue‐primed reinstatement following extinction training. Blocking dSTR mGluR5 had no effect on context‐ or cue‐induced cocaine‐seeking. However, the intra‐dSTR MTEP infusion on the context‐induced relapse test day attenuated extinction learning for 4 days after the infusion. Furthermore, mGluR5 surface expression was reduced and LTD was absent in dSTR slices of animals undergoing 3 weeks of abstinence from cocaine but not sucrose self‐administration. LTD was restored by bath application of VU‐29, a positive allosteric modulator of mGluR5. Bath application of MTEP prevented the induction of LTD in dSTR slices from sucrose animals. Taken together, this data indicates that dSTR mGluR5 plays an essential role in extinction learning but not cocaine relapse, while NA core mGluR5 modulates drug‐seeking following both extinction and abstinence from cocaine self‐administration.
Working under Grey Skies: Information Systems Development and Organizational Identity in a Post-merger Context

This study pertains to the challenges encountered during the development of information systems aimed to support an organization created by the merger of previously independent entities. We draw from the concept of organizational identity and on the literature on knowledge sharing across boundaries to analyze three information systems development (ISD) projects in a large teaching healthcare centre resulting from the merger of five hospitals. We propose a multilevel model that suggests that the interaction of organizational identity and cross-boundary knowledge sharing may result in information systems with final functionality different from planned functionality. The model also suggests that organizational-level decisional events, such as the choice of integration approach between the merging entities, may influence how new information systems are developed at a group level, and how those organizational-level events, in turn, are shaped by group-level events.
The Performance Evaluation for Personal Identification using Palm print and Face Recognition

Biometrics helps to provide the identity of the user based on his/her physiological or behavioural characteristics. The physiological characteristics signifies using human body parts for authentication like fingerprint, iris, ear, palm print, face etc. The behavioural characteristics include action done using body parts like voice, signature and gait etc. Palm print is one of the relatively new physiological biometrics due to its stable and unique characteristics. The rich texture information of palm print offers one of the powerful means in personal identification and verification. Biometric palm print recognizes a person based on the principal lines, wrinkles and ridges on the surface of the palm. These line structures are stable and remain unchanged throughout the life of an individual. The proposed methodology is divided into four steps: image acquisition by a regular webcam, image pre-processing for image normalization, segmentation for biometric extraction and human interpretation. The goals of the face recognition system are 1) to create the fast recognition system for the face database 2) to predict how old the person is and to carry out the face recognition system based on this predicted age 3) to stop underage drinkers from entering bars, prevent minors from purchasing tobacco products from vending machines 4) deny children access to adult Web sites by predicting their age. The main advantage of this system is reduction of searching time and it requires small memory usage. According to the experiment result, this system is an effective age dependent face recognition system. The proposed system has been tested with different images. Experimental tests have proved that the proposed approach is not only robust but also quite efficient.
Critical points of the distance to an epsilon-sampling of a surface and flow-complex-based surface reconstruction

The distance function to surfaces in three dimensions plays a key role in many geometric modeling applications such as medial axis approximations, surface reconstructions, offset computations, feature extractions and others. In most cases, the distance function induced by the surface is approximated by a discrete distance function induced by a discrete sample of the surface. The critical points of the distance function determine the topology of the set inducing the function. However, no earlier theoretical result has linked the critical points of the distance to a sampling of geometric structures to their topological properties. We provide this link by showing that the critical points of the distance function induced by a discrete sample of a surface either lie very close to the surface or near its medial axis and this closeness is quantified with the sampling density. Based on this result, we provide a new flow-complex-based surface reconstruction algorithm that, given a tight ε-sampling of a surface, approximates the surface geometrically, both in Hausdorff distance and normals, and captures its topology.
Lung Biopsy

SIR,-Your leading article on lung biopsy (6 June, p. 555) cannot be allowed to pass without comment. To say that "owing to the indifferent contribution it has made in assisting diagnosis over the years the method has now been virtually abandoned" suggests that your leader writer cannot be familiar with either the paper by Steel and Winstanley' or with current practice in a number of hospitals all over the world. Quite contrary to the view quoted above, I believe that trephine biopsy has made a great contribution to the diagnosis of diffuse lung disease, and the procedure has converted me from being a keen advocate of open lung biopsy to an ardent supporter of the much simpler technique. To suggest that the procedure is more alarming or more liable to morbidity than a formal thoracotomy under general anaesthesia in an operating theatre seems to me to be pushing the objection of trephine biopsy beyond the limits of reason. -I am, etc., J. R. BELCHER. London Ghest Hospital, London E.2.
GIPSy: Geomagnetic indoor positioning system for smartphones

A low complexity and infrastructure free indoor positioning system is proposed, by tracking the users on a set of user profiles via Viterbi algorithm. A modification on the implementation is also proposed to further optimize the complexity of tracking, by using the sparsity of the state transition probability matrix. Our algorithm only requires users to hold a smartphone, in arbitrary orientation/gesture and walk in the building. Since we only use the sensors embedded on the smartphones, this approach does not require any installed infrastructure in the building. The algorithm is tested on real-case scenarios with successfully obtaining less than 2.5 meters of median error.
Next-generation sequencing: applications beyond genomes

The development of DNA sequencing more than 30 years ago has profoundly impacted biological research. In the last couple of years, remarkable technological innovations have emerged that allow the direct and cost-effective sequencing of complex samples at unprecedented scale and speed. These next-generation technologies make it feasible to sequence not only static genomes, but also entire transcriptomes expressed under different conditions. These and other powerful applications of next-generation sequencing are rapidly revolutionizing the way genomic studies are carried out. Below, we provide a snapshot of these exciting new approaches to understanding the properties and functions of genomes. Given that sequencing-based assays may increasingly supersede microarray-based assays, we also compare and contrast data obtained from these distinct approaches.
High yield production of myristoylated Arf6 small GTPase by recombinant N-myristoyl transferase

Small GTP-binding proteins of the Arf family (Arf GTPases) interact with multiple cellular partners and with membranes to regulate intracellular traffic and organelle structure. Understanding the underlying molecular mechanisms requires in vitro biochemical assays to test for regulations and functions. Such assays should use proteins in their cellular form, which carry a myristoyl lipid attached in N-terminus. N-myristoylation of recombinant Arf GTPases can be achieved by co-expression in E. coli with a eukaryotic N-myristoyl transferase. However, purifying myristoylated Arf GTPases is difficult and has a poor overall yield. Here we show that human Arf6 can be N-myristoylated in vitro by recombinant N-myristoyl transferases from different eukaryotic species. The catalytic efficiency depended strongly on the guanine nucleotide state and was highest for Arf6-GTP. Large-scale production of highly pure N-myristoylated Arf6 could be achieved, which was fully functional for liposome-binding and EFA6-stimulated nucleotide exchange assays. This establishes in vitro myristoylation as a novel and simple method that could be used to produce other myristoylated Arf and Arf-like GTPases for biochemical assays.
Consumer Health Information System

World Wide Web acts as one of the major sources of information for health related questions. However, often, there are multiple conflicting answers to a single question and it is hard to come up with “a single best correct answer”. Therefore, it is highly desirable to identify conflicting perspectives about a particular question (or topic). In this paper, we have described our participation in Consumer Health Information System(CHIS) task at FIRE 2016. There were two sub-tasks in this contest. The first sub-task deals with identifying if a particular answer is relevant to a given question. The second sub-task deals with detecting if a particular answer agrees or refuses the claim posed in a given question. We pose both these tasks as supervised pair classification tasks. We report our results for various document representations and classification algorithms.
Hierarchical Average Reward Reinforcement Learning

Hierarchical reinforcement learning (HRL) is a general framework for scaling reinforcement learning (RL) to problems with large state and action spaces by using the task (or action) structure to restrict the space of policies. Prior work in HRL including HAMs, options, MAXQ, and PHAMs has been limited to the discrete-time discounted reward semi-Markov decision process (SMDP) model. The average reward optimality criterion has been recognized to be more appropriate for a wide class of continuing tasks than the discounted framework. Although average reward RL has been studied for decades, prior work has been largely limited to flat policy representations. In this paper, we develop a framework for HRL based on the average reward optimality criterion. We investigate two formulations of HRL based on the average reward SMDP model, both for discrete-time and continuous-time. These formulations correspond to two notions of optimality that have been previously explored in HRL: hierarchical optimality and recursive optimality. We present algorithms that learn to find hierarchically and recursively optimal average reward policies under discrete-time and continuous-time average reward SMDP models. We use two automated guided vehicle (AGV) scheduling tasks as experimental testbeds to study the empirical performance of the proposed algorithms. The first problem is a relatively simple AGV scheduling task, in which the hierarchically and recursively optimal policies are different. We compare the proposed algorithms with three other HRL methods, including a hierarchically optimal discounted reward algorithm and a recursively optimal discounted reward algorithm on this problem. The second problem is a larger AGV scheduling task. We model this problem using both discrete-time and continuous-time models. We use a hierarchical task decomposition in which the hierarchically and recursively optimal policies are the same for this problem. We compare the performance of the proposed algorithms with a hierarchically optimal discounted reward algorithm and a recursively optimal discounted reward algorithm, as well as a non-hierarchical average reward algorithm. The results show that the proposed hierarchical average reward algorithms converge to the same performance as their discounted reward counterparts.
EMOTION‐RELATED REGULATORY DIFFICULTIES CONTRIBUTE TO NEGATIVE PSYCHOLOGICAL OUTCOMES IN ACTIVE‐DUTY IRAQ WAR SOLDIERS WITH AND WITHOUT POSTTRAUMATIC STRESS DISORDER

Data suggest military personnel involved in U.S. military initiatives in Iraq and Afghanistan are returning from deployment with elevated rates of mental health diagnoses, including posttraumatic stress disorder (PTSD). The aim of this study was to examine difficulties with emotion regulation as a potential contributory mechanism by which soldiers have poorer psychological outcomes, such as depression, dissociation, alcohol abuse, and interpersonal difficulties. Participants were 44 active‐duty male service members who comprised three groups, including those deployed with and without diagnosed PTSD and those prior to deployment. Participants in the PTSD group scored significantly higher on measures of self‐reported depression, trauma‐related dissociation, alcohol misuse, and social adjustment difficulties than did comparison groups. Importantly, difficulties with emotion regulation were found to partially mediate the relationship between PTSD and depression, poor social adjustment, and trauma‐related depersonalization but not alcohol misuse. Emotion‐regulation difficulties are important to consider in the relationship between PTSD and additional psychological outcomes in recently deployed personnel. Implications for treatment are briefly discussed.
EHPnet: Food, Drugs and More

Multiple sequence alignments are often used for the identification of key specificity-determining residues within protein families. We present a web server implementation of the Sequence Harmony (SH) method previously introduced. SH accurately detects subfamily specific positions from a multiple alignment by scoring compositional differences between subfamilies, without imposing conservation. The SH web server allows a quick selection of subtype specific sites from a multiple alignment given a subfamily grouping. In addition, it allows the predicted sites to be directly mapped onto a protein structure and displayed. We demonstrate the use of the SH server using the family of plant mitochondrial alternative oxidases (AOX). In addition, we illustrate the usefulness of combining sequence and structural information by showing that the predicted sites are clustered into a few distinct regions in an AOX homology model. The SH web server can be accessed at www.ibi. vu.nl/programs/
Accurate Changing Point Detection for ℓ1 Mean Filtering

It is often desirable to find the underlying trends in time series data. This is a well known signal processing problem that has many applications in areas such as financial data analysis, climatology, biological and medical sciences. Mean filtering finds a piece-wise constant trend in the data while trend filtering finds a piece-wise linear trend. When the signal is noisy, the main difficulty is finding the changing points in the data that mark the transition points when the mean or the trend changes. Previously proposed methods based on ${\ell _1}$ filtering suffer from the occurrence of false changing points in the estimate. This is known as the stair-case effect. The main contribution in this paper is incorporating a technique to remove these false changing points to a fast mean filtering algorithm, referred to as the taut-string method, resulting in an efficient procedure with accurate change point detection and thus the removal of the stair-case effect.
The Recovery System for Hadoop Cluster

Due to brisk growth of data volume in many organizations, large-scale data processing became a demanding topic for industry as well as for academic fields. Hadoop is widely adopted in Cloud Computing environment for unstructured data. Hadoop is an open source, a java based distributed computing framework, and supports large-scale distributed data processing. In the recent years, Hadoop Distributed File System (HDFS) is popular for huge data sets and streams of operation on it. Availability of Hadoop is the important factor in Cloud Computing. But, in HDFS, Namenode failure affects the performance of the Hadoop cluster. It can be a single point failure. In this paper, we analysed the behaviour of Namenode, what are effects of Namenode failure. This paper presents a scenario to overcome this failure. Our scenario replicates the Namenode on the other Datanode so that the availability of the metadata is increases which will reduce the loss of data as well as delay. Keywords— Hadoop; Cloud Computing; HDFS; Namenode; availability; failure.