Miracle-dz/UniFAR-FTUs huggingface dataset:
Sparse Solution of Underdetermined Systems of Linear Equations by Stagewise Orthogonal Matching Pursuit

Finding the sparsest solution to underdetermined systems of linear equations y = Φx is NP-hard in general. We show here that for systems with “typical”/“random” Φ, a good approximation to the sparsest solution is obtained by applying a fixed number of standard operations from linear algebra. Our proposal, Stagewise Orthogonal Matching Pursuit (StOMP), successively transforms the signal into a negligible residual. Starting with initial residual r0 = y, at the s -th stage it forms the “matched filter” ΦTrs-1, identifies all coordinates with amplitudes exceeding a specially chosen threshold, solves a least-squares problem using the selected coordinates, and subtracts the least-squares fit, producing a new residual. After a fixed number of stages (e.g., 10), it stops. In contrast to Orthogonal Matching Pursuit (OMP), many coefficients can enter the model at each stage in StOMP while only one enters per stage in OMP; and StOMP takes a fixed number of stages (e.g., 10), while OMP can take many (e.g., n). We give both theoretical and empirical support for the large-system effectiveness of StOMP. We give numerical examples showing that StOMP rapidly and reliably finds sparse solutions in compressed sensing, decoding of error-correcting codes, and overcomplete representation.
A Visual Data Mining Approach to Find Overlapping Communities in Networks

Communities in social networks may overlap, with some hub nodes belonging to multiple communities. They may also have outliers, which are nodes that belong to no community. The criterion to locate hubs or outliers is network dependent. Previous methods usually require this information as input parameters, e.g., an expected number of communities, with no intuition or assistance. Here we present a visual data mining approach, which first helps the user to make appropriate parameter selections by observing initial data visualizations, and then finds and extracts overlapping community structures from the network. Experimental results verify the scalability and accuracy of our approach on real network data and show its advantages over previous methods.
Relating interesting quantitative time series patterns with text events and text features

In many application areas, the key to successful data analysis is the integrated analysis of heterogeneous data. One example is the financial domain, where time-dependent and highly frequent quantitative data (e.g., trading volume and price information) and textual data (e.g., economic and political news reports) need to be considered jointly. Data analysis tools need to support an integrated analysis, which allows studying the relationships between textual news documents and quantitative properties of the stock market price series. In this paper, we describe a workflow and tool that allows a flexible formation of hypotheses about text features and their combinations, which reflect quantitative phenomena observed in stock data. To support such an analysis, we combine the analysis steps of frequent quantitative and text-oriented data using an existing a-priori method. First, based on heuristics we extract interesting intervals and patterns in large time series data. The visual analysis supports the analyst in exploring parameter combinations and their results. The identified time series patterns are then input for the second analysis step, in which all identified intervals of interest are analyzed for frequent patterns co-occurring with financial news. An a-priori method supports the discovery of such sequential temporal patterns. Then, various text features like the degree of sentence nesting, noun phrase complexity, the vocabulary richness, etc. are extracted from the news to obtain meta patterns. Meta patterns are defined by a specific combination of text features which significantly differ from the text features of the remaining news data. Our approach combines a portfolio of visualization and analysis techniques, including time-, cluster- and sequence visualization and analysis functionality. We provide two case studies, showing the effectiveness of our combined quantitative and textual analysis work flow. The workflow can also be generalized to other application domains such as data analysis of smart grids, cyber physical systems or the security of critical infrastructure, where the data consists of a combination of quantitative and textual time series data.
Sorting of soluble ER proteins in yeast.

In animal cells, luminal endoplasmic reticulum (ER) proteins are prevented from being secreted by a sorting system that recognizes the C‐terminal sequence KDEL. We show that yeast has a similar sorting system, but it recognizes HDEL, rather than KDEL: derivatives of the enzyme invertase that bear the HDEL signal fail to be secreted. An invertase fusion protein that is retained in the cells is partially modified by outer‐chain mannosyl transferases, which reside in the Golgi element. This supports the view, based on studies in animal cells, that ER targeting is achieved by continuous retrieval of proteins from the Golgi. We have used an invertase fusion gene to screen for mutants that are defective in this sorting system. Over 60 mutants were obtained; eight of these are alleles of a single gene, erd1. The mutant strains grow normally at 30 degrees C, but instead of retaining the fusion protein in the cells, they secrete it.
The monrningness-eveningness questionnaire in Korean version and Its relations with sleep-wake habits

Morningness-Eveningness Questionnaire (MEQ) of Horne & Ostberg(1976) was translated into a Korean version, and MEQ and life habit inventory was administrated with approximately 900 university students. The distribution of MEQ score was normal. Arising time and bedtine for weekdays and holidays came in the order of morning type, intermediate and evening-type. And the deviation for the evening-type was greatest. Also, the deviation of sleep length for the evening-type was greater than the morning- type. There were tendencies that the bed time and arising time of the male were slightly later than those of the female, and the sleep length of the male was a little longer than that of the female. It also appeared that the arising time was later in the male who completed the military services than those who did not in the chronotype. And the sleep length of the non-served male was longer than that of the served one. Such tendencies noticeably appeared especially the evening-type. The evening-type was subject to heavy variability in bedtime. arising time, and sleep length. The sleep latency of the evening- type was longer and mood on arising was worse. And the ratio of nap or light sleep was higher in these subjects than in others. These results suggested that evening-type students had more irregular and/or flexible sleep-wake habits than the morning-type.
An extension of Chaitin's halting probability Ω to a measurement operator in an infinite dimensional quantum system

This paper proposes an extension of Chaitin's halting probability Ω to a measurement operator in an infinite dimensional quantum system. Chaitin's Ω is defined as the probability that the universal self‐delimiting Turing machine U halts, and plays a central role in the development of algorithmic information theory. In the theory, there are two equivalent ways to define the program‐size complexity H (s) of a given finite binary string s. In the standard way, H (s) is defined as the length of the shortest input string for U to output s. In the other way, the so‐called universal probability m is introduced first, and then H (s) is defined as –log2 m (s) without reference to the concept of program‐size.
Kernel methods for predicting protein-protein interactions

MOTIVATION Despite advances in high-throughput methods for discovering protein-protein interactions, the interaction networks of even well-studied model organisms are sketchy at best, highlighting the continued need for computational methods to help direct experimentalists in the search for novel interactions. RESULTS We present a kernel method for predicting protein-protein interactions using a combination of data sources, including protein sequences, Gene Ontology annotations, local properties of the network, and homologous interactions in other species. Whereas protein kernels proposed in the literature provide a similarity between single proteins, prediction of interactions requires a kernel between pairs of proteins. We propose a pairwise kernel that converts a kernel between single proteins into a kernel between pairs of proteins, and we illustrate the kernel's effectiveness in conjunction with a support vector machine classifier. Furthermore, we obtain improved performance by combining several sequence-based kernels based on k-mer frequency, motif and domain content and by further augmenting the pairwise sequence kernel with features that are based on other sources of data. We apply our method to predict physical interactions in yeast using data from the BIND database. At a false positive rate of 1% the classifier retrieves close to 80% of a set of trusted interactions. We thus demonstrate the ability of our method to make accurate predictions despite the sizeable fraction of false positives that are known to exist in interaction databases. AVAILABILITY The classification experiments were performed using PyML available at http://pyml.sourceforge. net. Data are available at: http://noble.gs. washington.edu/proj/sppi.
A polynomial method for blind identification of MIMO channels

In this paper, the blind identification problem for a multiple-input multiple-output (MIMO) channel is investigated by applying the spectral factorization method. The existing conditions on blind identification from signal's second order statistics (SOS) are relaxed by introducing an MIMO pre-filter in the transmitting side of the channel. A design criterion of the pre-filter in the frequency domain is given. Based on the properties of this pre-filter, a blind identification algorithm from SOS is developed. Simulation results show that this algorithm is effective in modelling MIMO FIR channels.
A Pattern Learning Method Based on Kernel Function

Entity relation extraction is one of the most important tasks of information extraction. In the process of semi-supervised relation extraction, how to add thereliable relation patterns to the seed set is one of the key factors that determines the reliability of the relation classification. Aiming at this problem, a novel kernel function based on the shortest dependency tree kernel functionis proposed in this paper, which greatly improves the reliability of the new joining patterns and effectively alleviates the problem of semanticdrift in the semi-supervised relation extraction,making the results better in subsequent predictions. The experimental result shows that the proposed method is superior to the traditional kernel function method, the recall rate and F1 value are greatly improved.
Differentiating the frontal variant of Alzheimer's disease

Individuals with a clinical diagnosis of Alzheimer's disease (AD) may have prominent features of executive dysfunction and language impairment as well as behavioral abnormalities early in the disease (‘high frontality’). When this occurs differentiation from frontotemporal dementia (FTD) is difficult. It is hypothesized that AD patients with high frontality may have clinical and pathological features that distinguish them from less frontal AD patients.
Molybdenum-Containing Nicotine Hydroxylase Genes in a Nicotine Degradation Pathway That Is a Variant of the Pyridine and Pyrrolidine Pathways

ABSTRACT Ochrobactrum sp. strain SJY1 utilizes nicotine as a sole source of carbon, nitrogen, and energy via a variant of the pyridine and pyrrolidine pathways (the VPP pathway). Several strains and genes involved in the VPP pathway have recently been reported; however, the first catalyzing step for enzymatic turnover of nicotine is still unclear. In this study, a nicotine hydroxylase for the initial hydroxylation step of nicotine degradation was identified and characterized. The nicotine hydroxylase (VppA), which converts nicotine to 6-hydroxynicotine in the strain SJY1, is encoded by two open reading frames (vppA S and vppA L [subunits S and L, respectively]). The vppA genes were heterologously expressed in the non-nicotine-degrading strains Escherichia coli DH5α and Pseudomonas putida KT2440; only the Pseudomonas strain acquired the ability to degrade nicotine. The small subunit of VppA contained a [2Fe-2S] cluster-binding domain, and the large subunit of VppA contained a molybdenum cofactor-binding domain; however, an FAD-binding domain was not found in VppA. Resting cells cultivated in a molybdenum-deficient medium had low nicotine transformation activity, and excess molybdenum was detected in the purified VppA by inductively coupled plasma-mass spectrometry analysis. Thus, it is demonstrated that VppA is a two-component molybdenum-containing hydroxylase.
Human Voice Recognition Depends on Language Ability

People with dyslexia have more difficulty than expected in recognizing familiar voices. The ability to recognize people by their voice is an important social behavior. Individuals differ in how they pronounce words, and listeners may take advantage of language-specific knowledge of speech phonology to facilitate recognizing voices. Impaired phonological processing is characteristic of dyslexia and thought to be a basis for difficulty in learning to read. We tested voice-recognition abilities of dyslexic and control listeners for voices speaking listeners’ native language or an unfamiliar language. Individuals with dyslexia exhibited impaired voice-recognition abilities compared with controls only for voices speaking their native language. These results demonstrate the importance of linguistic representations for voice recognition. Humans appear to identify voices by making comparisons between talkers’ pronunciations of words and listeners’ stored abstract representations of the sounds in those words.
Karyotype and reproduction mode of the rodent parasite Strongyloides venezuelensis

SUMMARY Strongyloides venezuelensis is a parasitic nematode that infects rodents. Although Strongyloides species described to date are known to exhibit parthenogenetic reproduction in the parasitic stage of their life cycle and sexual reproduction in the free-living stage, we did not observe any free-living males in S. venezuelensis in our strain, suggesting that the nematode is likely to depend on parthenogenetic reproduction. We confirmed by cytological analysis that S. venezuelensis produces eggs by parthenogenesis during the parasitic stage of its life cycle. Phylogenetic analysis using nearly the full length of 18S and D3 region of 28S ribosomal RNA gene suggested that S. venezuelensis is distantly related to another rodent parasite, namely Strongyloides ratti, but more closely related to a ruminant parasite, Strongyloides papillosus. Karyotype analysis revealed S. venezuelensis reproduces with mitotic parthenogenesis, and has the same number of chromosomes as S. papillosus (2n = 4), but differs from S. ratti (2n = 6) in this regard. These results, taken together, suggest that S. venezuelensis evolved its parasitism for rodents independently from S. ratti and, therefore, is likely to have a different reproductive strategy.
Classification of Mouth Action Units: Using Local Binary Patterns

Automated facial expression recognition is still a grand challenge in computer vision. The process of facial expression recognition consists of three main stages after image pre-processing and face acquisition. These stages are extracting the feature vector, which describes the facial image, reducing the dimensionality of that vector if possible, and finally, classifying test images into certain classes after training using the reduced dimensionality vector. A contribution to the first stage is chosen since the key issue in the analysis is finding a descriptive, representative, efficient, and easy to compute feature vector of the facial image. This work lays the ground for building a system that automatically, and in real-time classifies mouth action units (of the facial action coding system) with acceptable rates, and that is illumination tolerant. This is done by proposing the application of local binary patterns (LBPs) to the feature extraction stage of mouth action unit classification. LBPs require no manual pre-processing or initialization, contribute to a real-time system by being computationally simple, are illumination tolerant, and can be used in low-resolution images.
The Most Important Things Learned About Violence and Trauma in the Past 20 Years

In the past 2 decades, important insights have been gained regarding violence and trauma. Complications occur in how violence and trauma, their causes, and their effects on victims should be defined. Violence and abuse to women—physical, sexual, and emotional—are not rare events and are most often perpetrated by partners or acquaintances rather than strangers and occur in nonmarital as well as marital relationships, including same-sex relationships. A promising methodological innovation in the study of violence and trauma is the use of longitudinal designs. Innovations in treatments for victims such as evidence-based interventions have been slow to emerge; they include eye movement desensitization and reprocessing (EMDR) and the Seeking Safety group intervention for drug-abusingwomen with traumahistories. Future research should address increased understanding of variation in individual responses to violence and trauma, matching of treatment to different types of male offenders, better understanding of how culture affects violence perpetration and victimization, and evaluation of domestic violence interventions.
Bayesian Nonparametrics: Hierarchical Bayesian nonparametric models with applications

Hierarchical modeling is a fundamental concept in Bayesian statistics. The basic idea is that parameters are endowed with distributions which may themselves introduce new parameters, and this construction recurses. In this review we discuss the role of hierarchical modeling in Bayesian nonparametrics, focusing on models in which the infinite-dimensional parameters are treated hierarchically. For example, we consider a model in which the base measure for a Dirichlet process is itself treated as a draw from another Dirichlet process. This yields a natural recursion that we refer to as a hierarchical Dirichlet process. We also discuss hierarchies based on the Pitman-Yor process and on completely random processes. We demonstrate the value of these hierarchical constructions in a wide range of practical applications, in problems in computational biology, computer vision and natural language processing.
Multi-objective cost-sensitive attribute reduction

Cost-sensitive learning is both hot and difficult in data mining and machine learning applications. Some research considers only one type of cost. Others convert two or more types of cost into the same unit, and then deal with a single-objective optimization problem. However, in many cases different types of cost cannot be converted. In this paper, we define and tackle multi-objective attribute reduct problem with multiple types of test cost. First, we compute all reducts of a decision system. Then, we separately calculate the money cost and time cost of these reducts and compare them according to the two kinds of test cost. Finally, the worse ones are removed. The remaining reducts form a Pareto optimal solution set. We tested our algorithm with three representative cost distributions on four UCI datasets. Experimental results indicate that a Pareto optimal solution set is usually very small compared with the size of all reducts. Hence our approach is effective in filtering out worse solutions and helping users in scheme selection.
School-level intraclass correlation for physical activity in sixth grade girls.

PURPOSE The Trial for Activity in Adolescent Girls (TAAG) is a group-randomized trial (GRT) to reduce the usual decline in moderate to vigorous physical activity (MVPA) among middle school girls. We report the school-level intraclass correlation (ICC) for MVPA from the TAAG baseline survey of sixth grade girls and describe the relationship between the schedule of data collection and the ICC. METHODS Each of six sites recruited six schools and randomly selected 60 sixth grade girls from each school; 74.2% participated. Girls were grouped in waves defined by the date measurements began and asked to wear an Actigraph accelerometer for 6 d. Occasional missing data were replaced by imputation, and counts above 1500 per 30 s were treated as MVPA, converted into metabolic equivalents (METs), and summed over 6 a.m.-midnight to provide MET-minutes per 18-h day. Mixed-model regression was used to estimate ICC. RESULTS The school-level ICC were higher when estimated from a single wave compared with three waves (e.g., 0.057 vs 0.022) and across weekdays compared with weekend days (e.g., 0.024 vs 0.012). Power in a new trial would be greater with some schedules (e.g., 88% given three waves and 6 d) than with others (e.g., 23% given one wave and Tuesday only). CONCLUSIONS The schedule of data collection can have a dramatic effect on the ICC for MVPA. In turn, this can have a dramatic effect on the standard error for an intervention effect and on power. Investigators will need to consider the expected magnitude of the ICC and the validity of the MVPA estimates associated with their data collection schedule in planning a new study.
A phase locking test solution for MEMS devices

Physical defects in capacitive MEMS devices can change the nominal device capacitance by a few femtofarads. Detecting such small variations is challenging particularly in the presence of Process, Temperature, and supply Voltage (PVT) variations. A test solution utilizing a phase locking circuit is presented in this paper to minimize the effect of PVT variations on fault detection. A Delay Locked Loop (DLL) is modified to amplify small capacitance variation in order to detect structural faults of capacitive MEMS devices in the time domain. The measurement results on a prototype using 0.18-µm CMOS indicate that a physical defect changing the nominal capacitance of the device-under-test by 2 fF can be detected using the proposed method.
Finding Orientated Line Patterns in Digital Mammographic Images

In mammography the presence of subtle abnormalities such as stellate patterns and architectual distortions indicates possible malignancy. Radiologists do not always detect these abnormalities in screening mammograms; this has led to interest in computer‐aided mammographic interpretation where the radiologist is presented with computer‐generated ’prompts’ for abnormalities. A first step in this process is the detection of the “orientation”, “scale” and “strength” of linear structures in the mammograms. We discuss several generic methods for extracting this information from images and compare their per formance using synthetic images intended to simulate the appearance of mam mograms. We show significant differences in performance between the different methods. We also show results obtained for real mammograms.