Miracle-dz/UniFAR-FTUs huggingface dataset:
- ary Clustering with Optimal Leaf Ordering for Gene Expression Data

Motivation: A major challenge in gene expression analysis is effective data organization and visualization. One of the most popular tools for this task is hierarchical clustering. Hierarchical clustering allows a user to view relationships in scales ranging from single genes to large sets of genes, while at the same time providing a global view of the expression data. However, hierarchical clustering is very sensitive to noise, it usually lacks of a method to actually identify distinct clusters, and produces a large number of possible leaf orderings of the hierarchical clustering tree. In this paper we propose a new hierarchical clustering algorithm which reduces susceptibility to noise, permits up to siblings to be directly related, and provides a single optimal order for the resulting tree. Results: We present an algorithm that efficiently constructs a -ary tree, where each node can have up to children, and then optimally orders the leaves of that tree. By combining clusters at each step our algorithm becomes more robust against noise and missing values. By optimally ordering the leaves of the resulting tree we maintain the pairwise relationships that appear in the original method, without sacrificing the robustness. Our -ary construction algorithm runs in regardless of and our ordering algorithm runs in . We present several examples that show that our -ary clustering algorithm achieves results that are superior to the binary tree results in both global presentation and cluster identification. To whom correspondence should be addressed Availability: We have implemented the above algorithms in C++ on the Linux operating system. Source code is available upon request from zivbj@mit.edu.
Blocking connexin 43 accelerates corneal healing and improves tissue remodeling during the healing of diabetic rat corneas: A histological and immunohistochemical study

Connexin 43 (Cx43) is a potential target for accelerating wound healing. This study aimed at evaluating the therapeutic efficiency of topical application of Gap27, a Cx43 mimetic peptide, on corneal tissue reorganization during wound healing in streptozocin-induced Diabetes in albino rats and its effect on the infiltration of inflammatory cells. Fifty adult male albino Wistar rats were divided equally into two groups: non-diabetic and diabetic. Twenty rats from each group were subjected to corneal injury: 10 untreated and 10 treated with Gap27. The remaining five rats from each group served as negative controls (intact corneas). All rats were sacrificed 3 days after injury. Histological studies were performed to assess signs of cell degeneration, the infiltration of inflammatory cells. Histomorphometric studies were performed to quantify the expression of Cx43. Gap27 promoted corneal wound healing in non-diabetic and diabetic rats. It reduced mononuclear cell infiltration and improved corneal tissue remodeling. However, minor structural changes were still seen in diabetic corneas after treatment with Gap27. Blocking Cx43 was a valuable tool to restore corneal tissue structure, reduce the infiltration of inflammatory cells in non-diabetic and diabetic rats.
STPR: A Personalized Next Point-of-Interest Recommendation Model with Spatio-Temporal Effects Based on Purpose Ranking

The problem of personalized next point-of-interest (POI) recommendation is significant and of practical value in location-based social networks (LBSNs). Due to the sparsity of data in regard to check-ins, POI recommendations remain a challenging problem. Previous work developed recommendation models by taking into consideration the properties of user’s mobility, for example, spatio-temporal information and the similarity of movement rules. However, they ignore the influence of trip-purpose on user’s mobility, and the memory effect of historical check-in behavior at the same location. To cope with this problem, a POI recommendation model with Spatio-Temporal effects based on Purpose Ranking (STPR) is proposed. STPR contains two important phases: (1) Purpose prediction: classifying the POIs in the spatio-temporal database into four categories. Each category corresponds to a purpose. Then, a purpose ranking model is constructed to model the selection of user’s intended purpose for the trip. (2) The scoring of each candidate POI: the properties of spatial and temporal information are taken into consideration when calculating the score. For the spatial property, the kernel density estimation is used to estimate the visiting probability of a certain POI. For the temporal property, we focus on the time interval and the POI exploration mechanism. We find that POI exploration mechanism can reflect a user’s mobility of visiting new POIs. Furthermore, a method based on Bayesian personalized ranking, called BPR, is proposed to estimate the time interval for unvisited POIs. Extensive experiments were conducted on two real datasets, and the experimental results demonstrate that the recommendation accuracy of the STPR model outperforms the state-of-the-art POI recommendation models with good runtime performance.
Exploiting location awareness for scalable location-independent object ids

We are building a wide-area location service that tracks the current location of mobile and replicated objects. The location service should support up to 1012 objects on a worldwide scale. To support this huge number of objects, the workload of the location service is distributed over multiple hosts. Our load distribution method is unique in that it is aware of the (geographical) location of the hosts it uses. By using this location knowledge when distributing the workload, the distribution mechanism enforces locality of operations in the location service. Enforcing locality minimizes the use of global network resources by the location service and thereby enhances its scalability. We also show how this location-aware load distribution mechanism can be implemented.
Multi-objective Estimation of Distribution Algorithm based on Voronoi and local search

In this paper we propose an Estimation of Distribution Algorithm (EDA) equipped with Voronoi and local search based on leader for multi-objective optimization. We introduce an algorithm that can keep the balance between the exploration and exploitation using the local information in the searched areas through the global estimation of distribution algorithm. Moreover, the probability model in EDA, receives special statistical information about the amount of the variables and their important dependency. The proposed algorithm uses the Voronoi diagram in order to produce the probability model. By using this model, there will be a selection based on the area instead of selection based on the individual, and all individual information could use to produce new solution. In the proposed algorithm, considering the simultaneous use of global information about search area, local information of the solutions and the Voronoi based probability model lead to produce more diverse solutions and prevent sticking in local optima. Also, in order to reduce the data dimension, the principle component analysis is proposed. Several benchmarks functions with different complexity like linear and non-linear relationship between the variables, the continues\-discontinues and convex\non-convex optima fronts use to show the algorithm performance.
A products integrated & collaborative design methodology based on virtual product prototypes

In this paper, a Product Integrated & Collaborative Design Methodology Based on Virtual Product Prototypes (PICD) is explored. In PICD, A Product Master Model (PMM) is used as a product shared information database, which is also integrated product information model of a Virtual Product Prototype (VPP). A VPP is used to view the product during design process before creation of any physical prototype, to detect the product's performance by simulating the product's working process and interactive using process. All the engineers related to developing this product do collaborative jobs by working on the VPP and PMM. By supporting of VPP and PMM, the product development cycle will considerably be shortened, the product quality and performance will also be ensured effectively. Finally, some application examples are given.
Psychoactive substance use and preventive trends in higher education institutions in Lithuania

Abstract This paper focuses on the analysis of psychoactive substance use situation and preventive trends within communities of HE institutions in Lithuania. The researchers used a questionnaire, thus applying the quantitative research method. The research results: strong alcohol drinks among students of HE institutions are not popular; the respondents used psychoactive substances for the first time while still at secondary school; more than one tenth of the respondents indicated having driven a vehicle while under alcoholic intoxication at least once over the last 12 months. The alcohol, tobacco and other psychoactive substance use situation among students could be related to psychosocial factors: the possibility to relax, to reduce stress, to experience pleasure, to overcome boredom and to communicate. The results of the survey reveal an imperative to start a discussion about the formation and implementation of preventive measures against psychoactive substance use within communities of Lithuanian HE institutions.
Optimal Dynamic XL Reinsurance

We consider a risk process modelled as a compound Poisson process. We find the optimal dynamic unlimited excess of loss reinsurance strategy to minimize infinite time ruin probability, and prove the existence of a smooth solution of the corresponding Hamilton-Jacobi-Bellman equation as well as a verification theorem. Numerical examples with exponential, shifted exponential, and Pareto claims are given.
Adaptive fuzzy control for wireless network

Compared with wired networks, the application of TCP over wireless networks is much different due to high bit error rates, fading and handoff. A new approach is proposed that improved transport control protocol (TCP) throughput in wireless networks, in which a fuzzy adaptive control algorithm based on BS buffer changing is adopted to calculate the congest window, and explicit feedback to TCP sender. As a result, the TCP sending window can respond to the load changes of networks rapidly, the loss of packets is avoided and the TCP flow becomes smoother. The simulation results show that this method can strengthen the adaptation and channel errors processing capability in real time and improve the throughput of the network, and this control scheme is more robust and adaptive than normal Snoop.
Privacy preserving Data Mining Algorithms without the use of Secure Computation or Perturbation

In our era knowledge is not "just" information any more, it is an asset. Data mining can be used to extract important knowledge from large databases. These days, it is often the case that such databases are distributed among several organizations who would like to cooperate in order to extract global knowledge, but at the same time, privacy concerns may prevent the parties from directly sharing the data among them. The two current main methods to perform data mining tasks without compromising privacy are: the perturbation method and the secure computation method. Many papers and published algorithms are based on those two methods. Yet, both have some disadvantages, like reduced accuracy for the first and increased overhead for the second. In this article we offer a new paradigm to perform privacy-preserving distributed data mining without using those methods, we present three algorithms for association rule mining which use this paradigm, and discuss their privacy and performance characteristics
Keyphrase extraction in biomedical publications using mesh and intraphrase word co-occurrence information

Document keyphrases are used for various tasks such as indexing, clustering, and summarization. To documents without keyphrases, an automatic extraction method can be applied. In this paper, we propose an enhanced method of extracting keyphrases from biomedical papers, using MeSH (Medical Subject Headings) and intraphrase word co-occurrence information. MeSH terms assigned to biomedical papers can serve, not only as important features for keyphrase extraction, but also for expansion of keyphrase candidates. Intraphrase word co-occurrence information can be exploited for re-ranking keyphrase candidates. Through an experimental evaluation on 1,799 articles from three academic journals in the biomedical literature, we show that the candidate expansion and re-ranking steps of our approach are highly effective for improving the performance of keyphrase extraction.
Computing Semantic Text Similarity Using Rich Features

Semantic text similarity (STS) is an essential problem in many Natural Language Processing tasks, which has drawn a considerable amount of attention by research community in recent years. In this paper, our work focused on computing semantic similarity between texts of sentence length. We employed a Support Vector Regression model with rich effective features to predict the similarity scores between short English sentence pairs. Our model used WordNet-Based features, CorpusBased features, Word2Vec-based features, Alignment-Based feature and Literal-Based features to cover various aspects of sentences. And the experiment conducted on SemEval 2015 task 2a shows that our method achieved a Pearson correlation: 80.486% which outperformed the wining system (80.15%) by a small margin, the results indicated a high correlation with human judgments. Specially, among the five test sets which come from different domains used in the estimation, our method got better results than the top team on two of them whose domain-related data is available for training, while comparable results were achieved on the rest three unseen test sets. The experiments results indicated that our solution is more competitive when the domain-specific training data is available and our method still keeps good generalization ability on novel data.
I Love the Way You Lie: Investigating the Relationship Between Psychopathic Tendencies and Lying Behaviour

Lying is considered a common behaviour that individuals engage in on a daily basis. Prior research indicated that the presence of certain personality traits, such as psychopathy impacts the inclination to lie. I examined subclinical psychopathy, plus other variables, in predicting self-reported lying frequency, level of enjoyment received from lying, and motivations for lying in different contexts, in a sample of undergraduate (n = 91) and community (n = 61) participants. I hypothesized that individuals with subclinical psychopathy will have a greater tendency to lie across situations and enjoy it. Subclinical psychopathy was the only consistent predictor for all lying behaviour measures across samples. However, psychopathy did not predict lying behaviour across all contexts in which one could engage in deception. These findings enhance our understanding that specific features of psychopathy exist in subclinical, non-forensic populations, and they can predict behaviours such as lying. Keywords: psychopathy, lying behaviour
Identification of a Novel Bat Papillomavirus by Metagenomics

The discovery of novel viruses in animals expands our knowledge of viral diversity and potentially emerging zoonoses. High-throughput sequencing (HTS) technology gives millions or even billions of sequence reads per run, allowing a comprehensive survey of the genetic content within a sample without prior nucleic acid amplification. In this study, we screened 156 rectal swab samples from apparently healthy bats (n = 96), pigs (n = 9), cattles (n = 9), stray dogs (n = 11), stray cats (n = 11) and monkeys (n = 20) using a HTS metagenomics approach. The complete genome of a novel papillomavirus (PV), Miniopterus schreibersii papillomavirus type 1 (MscPV1), with L1 of 60% nucleotide identity to Canine papillomavirus (CPV6), was identified in a specimen from a Common Bent-wing Bat (M. schreibersii). It is about 7.5kb in length, with a G+C content of 45.8% and a genomic organization similar to that of other PVs. Despite the higher nucleotide identity between the genomes of MscPV1 and CPV6, maximum-likelihood phylogenetic analysis of the L1 gene sequence showed that MscPV1 and Erethizon dorsatum papillomavirus (EdPV1) are most closely related. Estimated divergence time of MscPV1 from the EdPV1/MscPV1 common ancestor was approximately 60.2–91.9 millions of years ago, inferred under strict clocks using the L1 and E1 genes. The estimates were limited by the lack of reliable calibration points from co-divergence because of possible host shifts. As the nucleotide sequence of this virus only showed limited similarity with that of related animal PVs, the conventional approach of PCR using consensus primers would be unlikely to have detected the novel virus in the sample. Unlike the first bat papillomavirus RaPV1, MscPV1 was found in an asymptomatic bat with no apparent mucosal or skin lesions whereas RaPV1 was detected in the basosquamous carcinoma of a fruit bat Rousettus aegyptiacus. We propose MscPV1 as the first member of the novel Dyolambda-papillomavirus genus.
STABILITY OF FUNCTIONAL DIFFERENTIAL EQUATIONS WITH OSCILLATING COEFFICIENTS AND DISTRIBUTED DELAYS

We consider the scalar equation ˙ x(t)+ m ∑ j=1 aj(t) h 0 x(t −s)drj(s )= 0 (h = const > 0, ˙ x = dx/dt), where rj(s) are nondecreasing functions. Besides, we do not require that aj(t) are positive for all t 0. So the function z+ m ∑=1 aj(t) h 0 e −zs drj(s) can have zeros in the right-hand plane for some t 0. It is proved that the considered equation is exponentially stable, provided aj(t )= bj +cj(t) ,w herebj are positive constants, such that all the zeros of the function z+∑ m j=1bj h 0 e −zs drj(s) are in the open left-hand plane, and the integrals t 0 cj(s)ds (j = 1,.,m) are sufficiently small for all t > 0.
Subgoal Graphs for Optimal Pathfinding in Eight-Neighbor Grids

Grids are often used to represent maps in video games. In this paper, we propose a method for preprocessing eightneighbor grids to generate subgoal graphs and show how subgoal graphs can be used to find shortest paths fast. We place subgoals at the corners of obstacles (similar to visibility graphs) and add those edges between subgoals that are necessary for finding shortest paths, while ensuring that each edge connects only subgoals that are easily reachable from one another. We describe a method for finding shortest paths by first finding high-level paths through subgoals and then shortest low-level paths between consecutive subgoals on the highlevel path. Our method was one of ten entries in the Grid-Based Path Planning Competition 2012. Among all optimal path planners, ours was the fastest to find complete paths and required the least amount of memory.
Cloning and characterization of SOB1, a new testis-specific cDNA encoding a human sperm protein probably involved in oocyte recognition.

A human sperm-oocyte binding protein, SOB1, was purified by two dimensional gel electrophoresis and sequenced. This protein was selected because it was recognized by a monoclonal antibody that inhibited the binding of human sperm to zona-free hamster oocytes. The sequences of the tryptic peptides were used to design degenerate primers. These were used to amplify a specific fragment from human testis cDNA by the polymerase chain reaction. This 1233 bp fragment was extended in 3' and 5' by RACE to obtain the 3 kb full length SOB1 cDNA. Sequence analysis indicated that the deduced open reading frame encodes a 853 amino acid protein, with a molecular mass of 94. 7 kDa. This is a new testis-specific cDNA. It is 27, 32.8 and 34.4% homologous to three sperm proteins, HI, Fsc1 and AKAP82 respectively. A single 3kb transcript was demonstrated only in the testis by northern blot analysis. It is a single copy gene, well conserved among mammals and located on human chromosome 12 at band p13.
The Drosophila Transcription Factor Dimmed Affects Neuronal Growth and Differentiation in Multiple Ways Depending on Neuron Type and Developmental Stage

Growth of postmitotic neurons occurs during different stages of development, including metamorphosis, and may also be part of neuronal plasticity and regeneration. Recently we showed that growth of post-mitotic neuroendocrine cells expressing the basic helix loop helix (bHLH) transcription factor Dimmed (Dimm) in Drosophila could be regulated by insulin/IGF signaling and the insulin receptor (dInR). Dimm is also known to confer a secretory phenotype to neuroendocrine cells and can be part of a combinatorial code specifying terminal differentiation in peptidergic neurons. To further understand the mechanisms of Dimm function we ectopically expressed Dimm or Dimm together with dInR in a wide range of Dimm positive and Dimm negative peptidergic neurons, sensory neurons, interneurons, motor neurons, and gut endocrine cells. We provide further evidence that dInR mediated cell growth occurs in a Dimm dependent manner and that one source of insulin-like peptide (DILP) for dInR mediated cell growth in the CNS is DILP6 from glial cells. Expressing both Dimm and dInR in Dimm negative neurons induced growth of cell bodies, whereas dInR alone did not. We also found that Dimm alone can regulate cell growth depending on specific cell type. This may be explained by the finding that the dInR is a direct target of Dimm. Conditional gene targeting experiments showed that Dimm alone could affect cell growth in certain neuron types during metamorphosis or in the adult stage. Another important finding was that ectopic Dimm inhibits apoptosis of several types of neurons normally destined for programmed cell death (PCD). Taken together our results suggest that Dimm plays multiple transcriptional roles at different developmental stages in a cell type-specific manner. In some cell types ectopic Dimm may act together with resident combinatorial code transcription factors and affect terminal differentiation, as well as act in transcriptional networks that participate in long term maintenance of neurons which might lead to blocked apoptosis.
A dictionary to translate change tasks to source code

At the beginning of a change task, software developers spend a substantial amount of their time searching and navigating to locate relevant parts in the source code. Current approaches to support developers in this initial code search predominantly use information retrieval techniques that leverage the similarity between task descriptions and the identifiers of code elements to recommend relevant elements. However, the vocabulary or language used in source code often differs from the one used for describing change tasks, especially since the people developing the code are not the same as the ones reporting bugs or defining new features to be implemented. In our work, we investigate the creation of a dictionary that maps the different vocabularies using information from change sets and interaction histories stored with previously completed tasks. In an empirical analysis on four open source projects, our approach substantially improved upon the results of traditional information retrieval techniques for recommending relevant code elements.