Miracle-dz/UniFAR-FTUs huggingface dataset:
Articulation of English vowels in running speech: A real-time MRI study

16,105 vowels produced by three speakers of American English were examined using real-time MRI, to provide further insights into tongue shaping and articulatory contrast in stressed and unstressed positions in fluent speech. High front vowels were found to maintain characteristic lingual postures in prosodically weak environments. Non-high vowels were articulated with a more raised dorsum and different pharyngeal constrictions in unstressed positions, compared to their stressed counterparts. The data reveal complex patterns of reduction, influenced by individual speaker vocal tract morphology, that resist simple characterization as ‘centralization’.
Package 'pcalg' Title Methods for Graphical Models and Causal Inference

March 19, 2015 Version 2.0-10 Date 2015-03-18 Author Diego Colombo, Alain Hauser, Markus Kalisch, Martin Maechler Maintainer Markus Kalisch <kalisch@stat.math.ethz.ch> Title Methods for Graphical Models and Causal Inference Description Functions for causal structure learning and causal inference using graphical models. The main algorithms for causal structure learning are PC (for observational data without hidden variables), FCI and RFCI (for observational data with hidden variables), and GIES (for a mix of data from observational studies (i.e. observational data) and data from experiments involving interventions (i.e. interventional data) without hidden variables). For causal inference the IDA algorithm and the generalized backdoor criterion is implemented. Depends R (>= 3.0.2) LinkingTo Rcpp (>= 0.11.0), RcppArmadillo, BH Imports graphics, utils, methods, abind, graph, RBGL, igraph, ggm, corpcor, robustbase, vcd, Rcpp, bdsmatrix, sfsmisc Suggests MASS, Matrix, Rgraphviz, mvtnorm ByteCompile yes NeedsCompilation yes Encoding UTF-8 License GPL (>= 2)
Entropy-based features for robust place recognition

In this paper, an appearance-based modeling of the environment is presented for the sake of mobile robot localization. The model allows perception and recognition within a topological context. Highly descriptive SIFT is used to extract local features from visual data acquired from an indoor environment. A method is developed to select those features, which are best for localization using a probabilistic modeling and an entropy measure. The impact of feature selection on the localization performance is more than 60% reduction in the storage and recognition time overhead. The methodology insures the recognition of different places with 96% precision, in spite of perceptual aliasing and image variability.
Primary care randomised controlled trial of a tailored interactive website for the self-management of respiratory infections (Internet Doctor)

Objective To assess an internet-delivered intervention providing advice to manage respiratory tract infections (RTIs). Design Open pragmatic parallel group randomised controlled trial. Setting Primary care in UK. Participants Adults (aged ≥18) registered with general practitioners, recruited by postal invitation. Intervention Patients were randomised with computer-generated random numbers to access the intervention website (intervention) or not (control). The intervention tailored advice about the diagnosis, natural history, symptom management (particularly paracetamol/ibuprofen use) and when to seek further help. Outcomes Primary: National Health Service (NHS) contacts for those reporting RTIs from monthly online questionnaires for 20 weeks. Secondary: hospitalisations; symptom duration/severity. Results 3044 participants were recruited. 852 in the intervention group and 920 in the control group reported 1 or more RTIs, among whom there was a modest increase in NHS direct contacts in the intervention group (intervention 37/1574 (2.4%) versus control 20/1661 (1.2%); multivariate risk ratio (RR) 2.25 (95% CI 1.00 to 5.07, p=0.048)). Conversely, reduced contact with doctors occurred (239/1574 (15.2%) vs 304/1664 (18.3%); RR 0.71, 0.52 to 0.98, p=0.037). Reduction in contacts occurred despite slightly longer illness duration (11.3 days vs 10.7 days, respectively; multivariate estimate 0.60 days longer (−0.15 to 1.36, p=0.118) and more days of illness rated moderately bad or worse illness (0.52 days; 0.06 to 0.97, p=0.026). The estimate of slower symptom resolution in the intervention group was attenuated when controlling for whether individuals had used web pages which advocated ibuprofen use (length of illness 0.22 days, −0.51 to 0.95, p=0.551; moderately bad or worse symptoms 0.36 days, −0.08 to 0.80, p=0.105). There was no evidence of increased hospitalisations (risk ratio 0.25; 0.05 to 1.12; p=0.069). Conclusions An internet-delivered intervention for the self-management of RTIs modifies help-seeking behaviour, and does not result in more hospital admissions due to delayed help seeking. Advising the use of ibuprofen may not be helpful. Trial registration number ISRCTN91518452.
Does availability of informal care within the household impact hospitalisation?

Abstract This study assesses the effect of having informal support available at home on inpatient care use in Switzerland. The main contributions are to consider the availability of care regardless of its source, measured by multiple-adult living arrangements, and to examine this effect by type of inpatient care and source of potential support. A two-part model with region and time fixed effects is estimated to determine the impact of informal care availability on the likelihood of hospitalisation and length of stay, conditional on hospitalisation. The analysis is conducted on a sample of individuals aged 18+ from four waves of the Swiss Household Panel survey (2004–2007). Overall, availability of informal care has no impact on the likelihood of hospitalisation but does significantly reduce length of stay by 1.9 days. Available support has no effect on the shortest stays (up to 10 days), but has a significant impact on acute care stays up to 30 days and longer stays. Additionally, the effect does not significantly vary whether the source of informal support is a spouse only, a spouse and other adults, or other adults only. These results indicate that social changes leading to an expansion in the proportion of one-person households may increase future inpatient care use.
Enhancing diversity, coverage and balance for summarization through structure learning

Document summarization plays an increasingly important role with the exponential growth of documents on the Web. Many supervised and unsupervised approaches have been proposed to generate summaries from documents. However, these approaches seldom simultaneously consider summary diversity, coverage, and balance issues which to a large extent determine the quality of summaries. In this paper, we consider extract-based summarization emphasizing the following three requirements: 1) diversity in summarization, which seeks to reduce redundancy among sentences in the summary; 2) sufficient coverage, which focuses on avoiding the loss of the document's main information when generating the summary; and 3) balance, which demands that different aspects of the document need to have about the same relative importance in the summary. We formulate the extract-based summarization problem as learning a mapping from a set of sentences of a given document to a subset of the sentences that satisfies the above three requirements. The mapping is learned by incorporating several constraints in a structure learning framework, and we explore the graph structure of the output variables and employ structural SVM for solving the resulted optimization problem. Experiments on the DUC2001 data sets demonstrate significant performance improvements in terms of F1 and ROUGE metrics.
Deguelin inhibits non-small cell lung cancer via down-regulating Hexokinases II-mediated glycolysis

Hexokinases II (HK2) is a hub in the regulation of cancer cell glycolysis. Here we reported deguelin, a natural compound which has been studied in various tumor types, has a profound anti-tumor effect on human non-small cell lung cancer (NSCLC) via directly down-regulating of glycolysis. In NSCLC cell lines and primary NSCLC tissue, we found HK2 is overexpressed. Deguelin treatment markedly inhibited anchorage-dependent and independent growth of NSCLC cell lines. We revealed that deguelin exposure impaired glucose metabolism by inhibiting Akt-mediated Hexokinase II expression, overexpression of constitutively activated Akt1 substantially rescued deguelin-induced glycolysis suppression. Moreover, deguelin suppressed HK2 presence on mitochondrial outer membrane and induced apoptosis. The in vivo data indicated that deguelin prominently restrained tumor development in a xenograft mouse model. Thus, deguelin appears to be a promising new therapeutic agent for lung cancer and may be considered for further studies in other animal models and in clinical trials.
Becoming the center of attention in social anxiety disorder: startle reactivity to a virtual audience during speech anticipation.

OBJECTIVE A detailed understanding of how individuals diagnosed with social anxiety disorder (SAD) respond physiologically under social-evaluative threat is lacking. Our aim was to isolate the specific components of public speaking that trigger fear in vulnerable individuals and best discriminate between SAD and healthy individuals. METHOD Sixteen individuals diagnosed with SAD (DSM-IV-TR criteria) and 16 healthy individuals were enrolled in the study from December 2005 to March 2008. Subjects were asked to prepare and deliver a short speech in a virtual reality (VR) environment. The VR environment simulated standing center stage before a live audience and allowed us to gradually introduce social cues during speech anticipation. Startle eye-blink responses were elicited periodically by white noise bursts presented during anticipation, speech delivery, and recovery in VR, as well as outside VR during an initial habituation phase, and startle reactivity was measured by electromyography. Subjects rated their distress at 4 timepoints in VR using a 0-10 scale, with anchors being "not distressed" to "highly distressed. " State anxiety was measured before and after VR with the Spielberger State-Trait Anxiety Inventory. RESULTS Individuals with SAD reported greater distress and state anxiety than healthy individuals across the entire procedure (P values < .005). Analyses of startle reactivity revealed a robust group difference during speech anticipation in VR, specifically as audience members directed their eye gaze and turned their attention toward participants (P < .05, Bonferroni-corrected). CONCLUSIONS The VR environment is sufficiently realistic to provoke fear and anxiety in individuals highly vulnerable to socially threatening situations. Individuals with SAD showed potentiated startle, indicative of a strong phasic fear response, specifically when they perceived themselves as occupying the focus of others' attention as speech time approached. Potentiated startle under social-evaluative threat indexes SAD-related fear of negative evaluation.
The Dynamics of Information Exchange: Reevaluating the Strategic Timing of Position Taking in Congress

This paper attempts to examine the information dissemination process within legislatures and its potential effects on public policy making in the United States Congress. I propose an examination of economic models of “informational cascades” as a suitable blueprint for analyzing information dissemination in legislatures. The model is sufficiently political in nature and captures the fundamental elements of the transmission process. I empirically examine the effects of information dissemination by reevaluating the logic behind the strategic timing of position taking by members of Congress. Specifically, I propose that the timing of publicly announced endorsements of legislation is not simply a reaction to public pressure to demonstrate representational agreement (Box- Steffensmeier, Arnold, and Zorn 1997; Mayhew 1974). Rather, this behavior also stands as vehicle by which members of Congress hope to influence one another. The evidence offered clearly demonstrates that – even on a highly salient issue such as NAFTA – the influential signals of other legislators are sometimes paramount to the preferences of a legislator's constituency.
Knowledge reuse in genetic programming applied to visual learning

We propose a method of knowledge reuse for an ensemble of genetic programming-based learners solving a visual learning task. First, we introduce a visual learning method that uses genetic programming individuals to represent hypotheses. Individuals-hypotheses process image representation composed of visual primitives derived from the training images that contain objects to be recognized. The process of recognition is generative, i.e., an individual is supposed to restore the shape of the processed object by drawing its reproduction on a separate canvas. This canonical method is extended with a knowledge reuse mechanism that allows a learner to import genetic material from hypotheses that evolved for the other decision classes (object classes). We compare the performance of the extended approach to the basic method on a real-world tasks of handwritten character recognition, and conclude that knowledge reuse leads to significant convergence speedup and, more importantly, significantly reduces the risk of overfitting.
Analyzing the Evolution of Large-Scale Software Systems Using Design Structure Matrices and Design Rule Theory: Two Exploratory Cases

Designers have long recognized the value of modularity, but important software modularity principles have remained informal. According to Baldwin and Clark's (2000) design rule theory (DRT) , modular architectures add value to system designs by creating options to improve the system by substituting or experimenting on individual modules. In this paper, we examine the design evolution of two software product platforms through the modeling lens of DRT and design structure matrices (DSMs). We show that DSM models and DRT precisely explain how real- world modularization activities in one case allowed for different rates of evolution in different software modules and in another case conferred distinct strategic advantages on a firm by permitting substitution of an at-risk software module without substantial change to the rest of the system. Our results provide positive evidence that DSM and DRT can inform important aspects of large-scale software structure and evolution, having the potential to guide software architecture design activities.
Automated Synthesis of Semantic Malware Signatures using Maximum Satisfiability

This paper proposes a technique for automatically learning semantic malware signatures for Android from very few samples of a malware family. The key idea underlying our technique is to look for a maximally suspicious common subgraph (MSCS) that is shared between all known instances of a malware family. An MSCS describes the shared functionality between multiple Android applications in terms of inter-component call relations and their semantic metadata (e.g., data-flow properties). Our approach identifies such maximally suspicious common subgraphs by reducing the problem to maximum satisfiability. Once a semantic signature is learned, our approach uses a combination of static analysis and a new approximate signature matching algorithm to determine whether an Android application matches the semantic signature characterizing a given malware family. We have implemented our approach in a tool called ASTROID and show that it has a number of advantages over state-of-the-art malware detection techniques. First, we compare the semantic malware signatures automatically synthesized by ASTROID with manually-written signatures used in previous work and show that the signatures learned by ASTROID perform better in terms of accuracy as well as precision. Second, we compare ASTROID against two state-of-the-art malware detection tools and demonstrate its advantages in terms of interpretability and accuracy. Finally, we demonstrate that ASTROID's approximate signature matching algorithm is resistant to behavioral obfuscation and that it can be used to detect zero-day malware. In particular, we were able to find 22 instances of zero-day malware in Google Play that are not reported as malware by existing tools.
University of Sheffield - Lab Report for PAN at CLEF 2010

This paper describes the University of Sheffield entry for the 2nd international plagiarism detection competition (PAN 2010). Our system attempts to identify extrinsic plagiarism. A three-stage approach is used: pre-processing, candidate document selection (using word n-grams) and detailed analysis (using the Running Karp-Rabin Greedy String Tiling string matching algorithm). This approach achieved an overall performance of 0.20 in the official evaluation with a precision of 0.40, recall of 0.16 and granularity of 1.21.
Short length menger's theorem and reliable optical routing

We deal with a generalization of the Minimum path colouring problem, <i><i>k</i>-Edge disjoint path systems colouring</i>: given a graph <i>G</i> and a set of pairs of vertices of <i>G</i>, the task is to connect each pair by a system of <i>k</i>-edge disjoint paths (a <i>k</i>-<i>system</i>) and to colour the <i>k</i>-systems by minimal number of colours in such way that any two edge-intersecting <i>k</i>-systems have different colours. Multiple connecting paths between the same pair of vertices are motivated by a need for fault tolerant connections. We propose an <i>O(k<sup>2</sup> F)</i> approximation algorithm for this problem where <i>F</i> is the flow number of the graph. As a byproduct of our analysis we also show that any two <i>k</i>-connected vertices in <i>G</i> are connected by <i>k</i> edge disjoint paths of average length <i>O(k F)</i> which improves previously known bounds for many classes of graphs.
State of charge estimation considering OCV hysteresis in lithium iron phosphate battery for UPS applications

In recent years, there is growing interest in improving lithium-ion battery state monitoring. This is due to growing concern over ensuring reliable and safe operation of lithium-ion based energy storage system especially in automotive and stationary e.g. UPS in data center applications. In this paper, battery state of charge estimation based on Extended Kalman Filter (EKF) and improved battery model considering hysteresis is presented. Hysteresis in battery is complex phenomenon and normally not considered in SOC estimation. However, the consideration is crucial especially in lithium iron phosphate (LFP) battery. Simplification of hysteresis in open circuit voltage (OCV) in the battery leads to higher percentage error in SOC estimation. To consider and model hysteresis in OCV, discrete Preisach model (DPM) is utilized to provide satisfactory improvement in SOC estimation.
Study of Dct Coeecient Distributions

Many image and video compression schemes perform the discrete cosine transform (DCT) to represent image data in frequency space. An analysis of a broad suite of images con rms previous nding that a Laplacian distribution can be used to model the luminance AC coe cients. This model is expanded and applied to color space (Cr/Cb) coe cients. In MPEG, the DCT is used to code interframe prediction error terms. The distribution of these coe cients is explored. Finally, the distribution model is applied to improve dynamic generation of quantization matrices.
The Relationship between Virtual Self Similarity and Social Anxiety

In virtual reality (VR), it is possible to embody avatars that are dissimilar to the physical self. We examined whether embodying a dissimilar self in VR would decrease anxiety in a public speaking situation. We report the results of an observational pilot study and two laboratory experiments. In the pilot study (N = 252), participants chose an avatar to use in a public speaking task. Trait public speaking anxiety correlated with avatar preference, such that anxious individuals preferred dissimilar self-representations. In Study 1 (N = 82), differences in anxiety during a speech in front of a virtual audience were compared among participants embodying an assigned avatar whose face was identical to their real self, an assigned avatar whose face was other than their real face, or embodied an avatar of their choice. Anxiety differences were not significant, but there was a trend for lower anxiety with the assigned dissimilar avatar compared to the avatar looking like the real self. Study 2 (N = 105) was designed to explicate that trend, and further investigated anxiety differences with an assigned self or dissimilar avatar. The assigned dissimilar avatar reduced anxiety relative to the assigned self avatar for one measure of anxiety. We discuss implications for theories of self-representation as well as for applied uses of VR to treat social anxiety.
An Equivalence Principle for the Incorporation of Favorable Mutations in Asexual Populations

Rapid evolution of asexual populations, such as that of cancer cells or of microorganisms developing drug resistance, can include the simultaneous spread of distinct beneficial mutations. We demonstrate that evolution in such cases is driven by the fitness effects and appearance times of only a small minority of favorable mutations. The complexity of the mutation-selection process is thereby greatly reduced, and much of the evolutionary dynamics can be encapsulated in two parameters—an effective selection coefficient and effective rate of beneficial mutations. We confirm this theoretical finding and estimate the effective parameters for evolving populations of fluorescently labeled Escherichia coli. The effective parameters constitute a simple description and provide a natural standard for comparing adaptation between species and across environments.
MANET-Evaluation of DSDV, AODV and DSR Routing Protocol

A mobile Ad-Hoc network is a collection of wireless nodes that can dynamically be set up anywhere and anytime without using any pre-existing network infrastructure. It is an autonomous system in which mobile hosts connected by wireless links are free to move randomly and often act as routers at the same time. Mobile ad-hoc network have the attributes such as wireless connection, continuously changing topology, distributed operation and ease of deployment. In this paper we have compared the performance of three MANET routing protocol DSDV, AODV and DSR by using NS-2. DSDV is proactive (Table driven routing Protocol) whereas AODV and DSR share similar On Demand behavior, but the protocol's internal mechanism leads to significant performance difference. A detailed simulation has been carried out in NS-2. The metrics used for performance analysis are Routing Overload, Delivery Ratio, And Average Delay.
Exercise increases hippocampal neurogenesis to high levels but does not improve spatial learning in mice bred for increased voluntary wheel running.

The hippocampus is important for the acquisition of new memories. It is also one of the few regions in the adult mammalian brain that can generate new nerve cells. The authors tested the hypothesis that voluntary exercise increases neurogenesis and enhances spatial learning in mice selectively bred for high levels of wheel running (S mice). Female S mice and outbred control (C) mice were housed with and without running wheels for 40 days. 5-Bromodeoxyuridine was used to label dividing cells. The Morris water maze was used to measure spatial learning. C runners showed a strong positive correlation between running distance and new cell number, as well as improved learning. In S runners, neurogenesis increased to high levels that reached a plateau, but no improvement in learning occurred. This is the first evidence that neurogenesis can occur without learning enhancement. The authors propose an alternative function of neurogenesis in the control of motor behavior.