Miracle-dz/UniFAR-FTUs huggingface dataset:
Unbiased Decoding of Biologically Motivated Visual Feature Descriptors

Visual feature descriptors are essential elements in most computer and robot vision systems. They typically lead to an abstraction of the input data, images or video, for further processing such as clustering and machine learning. In clustering applications, the cluster center represents the prototypical descriptor of the cluster and estimating the corresponding signal value, such as color value or dominating flow orientation, by decoding the prototypical descriptor, is essential. Machine learning applications determine the relevance of respective descriptors and a visualization of the corresponding decoded information is very useful for the analysis of the learning algorithm. Thus decoding of feature descriptors is a relevant problem, frequently addressed in recent work. Also the human brain represents sensorimotor information at a suitable abstraction level through varying activation of neuron populations. Approximative computational models have been derived that confirm neurophysiological experiments on the representation of visual information by decoding the underlying signals. However, the represented variables have a bias towards centers or boundaries of the tuning curves. Despite the fact that feature descriptors in computer vision are motivated from neuroscience, the respective decoding methods have been derived largely independent. From first order principles, we derive unbiased decoding schemes for biologically motivated feature descriptors with a minimum amount of redundancy and suitable invariance properties. These descriptors establish a non-parametric density estimation of the underlying stochastic process with a particular algebraic structure. Based on the resulting algebraic constraints, we show formally how the decoding problem is formulated as an unbiased maximum likelihood estimator and we derive a recurrent inverse diffusion scheme to infer the dominating mode of the distribution. These methods are evaluated and compared to existing methods.
Stakeholder Analysis to Enrich the Systems Thinking and Modelling Methodology

A systematic analysis of stakeholders could enrich the systems thinking and modelling methodology. Among the five phases of the systems thinking and modelling framework, namely, problem structuring, causal loop modelling, dynamic modelling, scenario planning & modelling, and implementation & organisational learning, the importance of stakeholder analysis in the problem structuring and scenario planning & modelling phases is explained in this paper. A New Zealand transportation infrastructure case study is presented in this paper to illustrate the potential usefulness of stakeholder analysis in the systems thinking and modelling methodology.
Sharing training data among different activity classes

We propose a new activity recognition system for the daily activity by using a generative/discriminative hybrid model that can learn an activity classification model with small quantities of training data by sharing training data among different activity classes. Many existing activity recognition studies employ a supervised machine learning approach and thus require an end user's labeled training data, this approach places a large burden on the user. In this study, we assume that a user wears sensors (accelerometers) on several parts of the body such as the wrist, waist, and thigh, and by sharing sensor data obtained from only selected accelerometers (e.g., only waist and thigh sensors) among two different activity classes based on a sensor data similarity measure, the quantities of training data can be increased. For further reduction of the burden on the user, we also adopt semi-supervised approach to train the classifier in our study.
Efficient implementation of elliptic curve cryptography in wireless sensors

The deployment of cryptography in sensor networks is a challenging task, given the limited computational power and the resource-constrained nature of the sensoring devices. This paper presents the implementation of elliptic curve cryptography in the MICAz Mote, a popular sensor platform. We present optimization techniques for arithmetic in binary fields, including squaring, multiplication and modular reduction at two different security levels. Our implementation of field multiplication and modular reduction algorithms focuses on the reduction of memory accesses and appears as the fastest result for this platform. Finite field arithmetic was implemented in C and Assembly and elliptic curve arithmetic was implemented in Koblitz and generic binary curves. We illustrate the performance of our implementation with timings for key agreement and digital signature protocols. In particular, a key agreement can be computed in 0.40 seconds and a digital signature can be computed and verified in 1 second at the 163-bit security level. Our results strongly indicate that binary curves are the most efficient alternative for the implementation of elliptic curve cryptography in this platform.
Higher Order Triangular Finite Elements with Mass Lumping for the Wave Equation

In this article, we construct new higher order finite element spaces for the approximation of the two-dimensional (2D) wave equation. These elements lead to explicit methods after time discretization through the use of appropriate quadrature formulas which permit mass lumping. These formulas are constructed explicitly. Error estimates are provided for the corresponding semidiscrete problem. Finally, higher order finite difference time discretizations are proposed and various numerical results are shown.
EM and MAP Methods for Joint Path Delay and Complex Gain Estimation of a Slowly Varying Fading Channel for CPM Signals

This paper addresses the joint path delay and time-varying complex gain estimation for continuous phase modulation (CPM), over a time-selective slowly varying Rayleigh flat fading channel. We propose two estimation methods: an expectation-maximization (EM) algorithm for path delay estimation in a Kalman framework, and a Maximum a Posteriori (MAP) method for joint path delay and complex gain estimation. The time-varying complex gains are modeled by a first order autoregressive (AR) process. Such a modeling yields to the representation of the problem by a dynamic Bayesian system in a state-space model, which allows the use of the EM algorithm in the context of unobserved data for obtaining an estimate of the path delay, coupled with a Kalman smoother for the complex gain estimation. Also, the direct joint estimation using a MAP estimator over the observation block is given. We derive analytically a closed-form expression of the modified hybrid Cramer-Rao bound (MHCRB) for the path delay and complex gain estimation problem. Some numerical examples are presented to illustrate the performance of the proposed methods compared to both the conventional generalized correlation method and the MHCRB.
From Inclusion to Creativity Through Haptic Drawing: Unleashing the "Untouched" in Educational Contexts

Haptics, or the sense of active touch, is an underused modality in educational contexts. In this paper, we review current literature regarding the development of haptic processes and the underlying neurocognitive mechanisms. On the basis of previous research, we argue that exposure and guidance can increase the ability of individuals to employ haptic processes by recruiting areas of the visual cortex that are normally employed for visual processes, a process known as cross-modal plasticity. Cross-modal plasticity provides a plausible account for the counter-intuitive creativity displayed by blind individuals in producing drawings. We propose that further implementation of haptics in education may be used to promote creativity and inclusion, as well as facilitating other processes required for learning in educational settings. In particular, haptic drawing could be generalized as a tool for inclusive education to promote and enhance creativity at any age through cross-modal neurocognitive plasticity in students with and without visual impairment.
Long-term endothelin receptor antagonist administration improves alterations in expression of various cardiac genes in failing myocardium of rats with heart failure.

BACKGROUND We reported that long-term (3-month) treatment with the endothelin (ET) type A (ET(A)) receptor antagonist BQ-123 markedly improved survival in rats with chronic heart failure (CHF). However, it is not known whether long-term treatment with an ET receptor antagonist improves alterations in the expression of cardiac genes in failing hearts. METHODS AND RESULTS CHF rats and control sham-operated rats were treated with BQ-123, SB209670 (ET(A/B) dual receptor antagonist), or saline (vehicle) for 3 months. The survival of CHF rats was markedly higher in the BQ-123 or SB209670 treatment group than in the saline treatment group. The changes in the gene expression of classic molecular markers for failing hearts (mRNA levels of atrial natriuretic peptide and beta-myosin heavy chain) were greatly inhibited by BQ-123 or SB209670 treatment in CHF rats. Long-term BQ-123 treatment also normalized the alterations in the expression of functional molecular markers in failing hearts (eg, mRNA levels of ryanodine receptor, sarcoplasmic reticulum Ca(2+)-ATPase, angiotensin-converting enzyme, angiotensin II type 1 receptor, and prepro-ET-1). CONCLUSIONS We demonstrated for the first time that long-term (3-month) treatment with an ET receptor antagonist improves the alterations in the expression of various cardiac genes of classic molecular markers (eg, mRNA in atrial natriuretic peptide and beta-myosin heavy chain) and of functional molecular markers (eg, mRNA levels of ryanodine receptor, sarcoplasmic reticulum Ca(2+)-ATPase, angiotensin-converting enzyme, angiotensin II type 1 receptor, and prepro-ET-1) in the failing hearts of CHF rats, suggesting that the great improvement of survival in CHF rats by an ET blocker is partly attributed to the prevention of molecular changes in failing hearts.
Early predictors of responses and clinical outcomes of corticosteroid treatment for severe ulcerative colitis

Abstract Background. Patients with severe ulcerative colitis (SUC) have a high risk of requiring colectomy or resorting to a second-line treatment. However, neither clinical outcomes nor factors predictive of poor response have been clearly established in the treatment of SUC. Objective. To assess prospectively the effects and predictors of corticosteroids (CS) use in clinical outcomes of SUC during 1 year of follow-up. Material and methods. Consecutive inpatients with SUC, who had been treated with intravenous CS, were enrolled. Patients were monitored by clinical, laboratory, and endoscopic examinations, and the data were recorded for 1 year. Univariate and multivariate analyses were performed at 1 week. Results. There were 22.6% (14/62) nonresponders at 7 days. Several predictors were associated with nonresponse to CS. These included Mayo Score at baseline (p = 0.007), partial Mayo Score, number of bowel movements, blood presence in stool, abdominal pain, and levels of C-reactive protein (CRP), hemoglobin (Hgb), platelet count (PLT), and erythrocyte sedimentation rate (ESR) on day 3 (p < 0.05). Multiple logistic regression analysis identified the Partial Mayo Score at day 3 as an independent predictor of outcome (p = 0.012). A total of 12 patients underwent colectomy within 1 year. The short-term response rates to intravenous cyclosporin (CsA) and infliximab (IFX) in SUC were 71.4% (5/7) and 77.8% (7/9), respectively. Conclusions. Many patients with SUC eventually became refractory to or dependent on CS. The Mayo score and laboratory characteristics were factors useful in predicting short-term outcome of CS treatment. Secondary medical therapy can help avoid emergency surgery.
Multidimensional Motor Sequence Learning Is Impaired in Older But Not Younger or Middle-Aged Adults

Background and Purpose: The purpose of this study was to identify which characteristics of a multidimensional sequence containing motor, spatial, and temporal elements would be most salient for motor sequence learning and whether age might differentially affect this learning. Subjects: Younger (n=11, mean age=26.0 years), middle-aged (n=13, mean age=50.7 years), and older (n=11, mean age=77.5 years) adults who were neurologically intact participated in the study. Methods: Participants practiced a sequencing task with repeated motor, spatial, and temporal dimensions for 2 days; on a separate third day, participants completed retention and interference tests designed to assess sequence learning and which elements of the sequence were learned. The mean median response time for each block of responses was used to assess motor sequence learning. Results: Younger and middle-aged adults demonstrated sequence-specific motor learning at retention testing via faster response times for repeated sequences than random sequences; both of these groups showed interference for the motor dimension. In contrast, older adults demonstrated nonspecific learning (ie, similar improvements in response time for both random and repeated sequences). These findings were shown by a lack of difference between random and repeated sequence performance in the older adult group both at retention testing and during interference tests. Conclusion and Discussion: Our data suggest that, when younger and middle-aged adults practice sequences containing multiple dimensions of movement, the motor element is most important for motor learning. The absence of sequence-specific change demonstrated by an older adult group that was healthy suggests an age-related impairment in motor learning that may have profound implications for rehabilitation.
Prohibitin is a novel regulator of antioxidant response that attenuates colonic inflammation in mice.

BACKGROUND & AIMS Increased free radicals and/or impaired antioxidant defenses have been shown to play a pathogenetic role in human and animal models of inflammatory bowel disease. Our previous studies showed that prohibitin (PHB) levels are decreased during colitis and that cultured intestinal epithelial cells overexpressing PHB are protected from oxidative stress. This study investigated the effect of intestinal epithelial cell-specific PHB overexpression on oxidative stress associated with experimental colitis and the potential mechanism by which PHB functions as an antioxidant using PHB transgenic mice. METHODS Colitis was induced using 2 established mouse models (Salmonella typhimurium and dextran sodium sulfate) in PHB transgenic mice and wild-type littermates. Oxidative stress was determined by measuring glutathione and protein carbonyl levels in the cecum or colon. Nuclear factor erythroid 2-related factor 2 (Nrf2), a transcriptional regulator of oxidant responses, expression, and activation, was assessed in colon mucosa and cultured intestinal epithelial cells overexpressing PHB. RESULTS Cells overexpressing PHB showed sustained Nrf2 nuclear accumulation and DNA binding during oxidant stress. PHB transgenic mice exhibited decreased oxidative stress and colitis and increased Nrf2 messenger RNA expression, nuclear protein translocation, and DNA binding compared with wild-type littermates during colitis. CONCLUSIONS These results show that PHB is a regulator of Nrf2 expression in intestinal epithelial cells during oxidative conditions and prevents inflammation-associated oxidative stress and injury through sustained activation of Nrf2. Our data show that PHB is a novel regulator of antioxidants and suggest that restoration of PHB levels represents a potential therapeutic approach in inflammatory bowel disease.
Hemagglutinin homologue from H17N10 bat influenza virus exhibits divergent receptor-binding and pH-dependent fusion activities

Bat influenza virus H17N10 represents a distinct lineage of influenza A viruses with gene segments coding for proteins that are homologs of the surface antigens, hemagglutinin (HA) and neuraminidase (NA). Our recent study of the N10 NA homolog revealed an NA-like structure, but with a highly divergent putative active site exhibiting little or no NA activity, and provided strong motivation for performing equivalent structural and functional analyses of the H17 HA protein. The overall structure of the H17 HA homolog from A/little yellow-shouldered bat/Guatemala/060/2010 at 3.18 Å resolution is very similar to other influenza HAs, with a putative receptor-binding site containing some conserved aromatic residues that form the base of the sialic acid binding site. However, the rest of the H17 receptor-binding site differs substantially from the other HA subtypes, including substitution of other conserved residues associated with receptor binding. Significantly, electrostatic potential analyses reveal that this putative receptor-binding site is highly acidic, making it unfavorable to bind any negatively charged sialylated receptors, consistent with the recombinant H17 protein exhibiting no detectable binding to sialylated glycans. Furthermore, the fusion mechanism is also distinct; trypsin digestion with recombinant H17 protein, when exposed to pH 4.0, did not degrade the HA1 and HA2, in contrast to other HAs. These distinct structural features and functional differences suggest that the H17 HA behaves very differently compared with other influenza HAs.
Regularized Latent Semantic Indexing: A New Approach to Large-Scale Topic Modeling

Topic modeling provides a powerful way to analyze the content of a collection of documents. It has become a popular tool in many research areas, such as text mining, information retrieval, natural language processing, and other related fields. In real-world applications, however, the usefulness of topic modeling is limited due to scalability issues. Scaling to larger document collections via parallelization is an active area of research, but most solutions require drastic steps, such as vastly reducing input vocabulary. In this article we introduce Regularized Latent Semantic Indexing (RLSI)---including a batch version and an online version, referred to as batch RLSI and online RLSI, respectively---to scale up topic modeling. Batch RLSI and online RLSI are as effective as existing topic modeling techniques and can scale to larger datasets without reducing input vocabulary. Moreover, online RLSI can be applied to stream data and can capture the dynamic evolution of topics. Both versions of RLSI formalize topic modeling as a problem of minimizing a quadratic loss function regularized by ℓ1 and/or ℓ2 norm. This formulation allows the learning process to be decomposed into multiple suboptimization problems which can be optimized in parallel, for example, via MapReduce. We particularly propose adopting ℓ1 norm on topics and ℓ2 norm on document representations to create a model with compact and readable topics and which is useful for retrieval. In learning, batch RLSI processes all the documents in the collection as a whole, while online RLSI processes the documents in the collection one by one. We also prove the convergence of the learning of online RLSI. Relevance ranking experiments on three TREC datasets show that batch RLSI and online RLSI perform better than LSI, PLSI, LDA, and NMF, and the improvements are sometimes statistically significant. Experiments on a Web dataset containing about 1.6 million documents and 7 million terms, demonstrate a similar boost in performance.
The osteoporosis risk assessment tool: establishing content validity through a panel of experts.

Twenty-three items were initially developed for the Atkins Osteoporosis Risk Assessment Tool (ORAT) after a thorough examination of the literature. These items were reviewed for relevance to the domain of content by a panel of eight experts using Lynn's (1986) two-stage process for content validation. The Content Validity Index and the kappa coefficient of agreement were analyzed from panelists' quantitative ratings and 15 items were retained. Qualitative suggestions from the experts were also used to improve the final items in the ORAT.
Mining Web Query Hierarchies from Clickthrough Data

In this paper, we propose to mine query hierarchies from clickthrough data, which is within the larger area of automatic acquisition of knowledge from the Web. When a user submits a query to a search engine and clicks on the returned Web pages, the user's understanding of the query as well as its relation to the Web pages is encoded in the clickthrough data. With millions of queries being submitted to search engines every day, it is both important and beneficial to mine the knowledge hidden in the queries and their intended Web pages. We can use this information in various ways, such as providing query suggestions and organizing the queries. In this paper, we plan to exploit the knowledge hidden in clickthrough logs by constructing query hierarchies, which can reflect the relationship among queries. Our proposed method consists of two stages: generating candidate queries and determining "generalization/specialization" relatinns between these queries in a hierarchy. We test our method on some labeled data sets and illustrate the effectiveness of our proposed solution empirically.
Educational Software Features that Encourage and Discourage "Gaming the System"

Gaming the system, attempting to succeed in an interactive learning environment by exploiting properties of the system rather than by learning the material (for example, by systematically guessing or abusing hints), is prevalent across many types of educational software. Past research on why students choose to game has focused on student individual differences. Many student individual differences, including attitudes towards mathematics, have been shown to be associated with gaming, but generally with low correlation. In this paper, we investigate how individual differences between learning environments can increase or decrease the probability of gaming. We enumerate ways intelligent tutor lessons vary from each other, and use data mining to discover hypotheses about how differences in software design and content influence the choice to game the system. We discover a set of tutor features that explain 56% of the variance in gaming, over five times the degree of variance explained in any prior study of student individual differences and gaming. These results provide an important step towards developing prescriptions for designing intelligent tutor software that students game significantly less.
An Open Source performance tools software suite for scientific computing

With the rapid replacement of closed, homogeneous, proprietary HPC systems by heterogeneous, Linux‐MPI cluster systems, the state of performance monitoring and analysis tools has become a cause for concern. Proprietary systems, despite their drawbacks, provided consistent tools of high quality. Modern Linux cluster systems, on the other hand, benefit from a wide variety of Open Source tools in differing stages of evolution. Recognizing that Linux clusters are here to stay, SiCortex has taken a unique approach of integrating and enhancing Open Source tools into a production‐quality suite. Further, as a tribute to the unrewarded Open Source community developers and for more pragmatic reasons, such as long‐term sustainability, changes made to the tools are fed upstream to the original tool developers. In this paper, we present an overview of the SiCortex tools' suite, and some of the challenges and successes we had in the process of realizing it. Copyright © 2009 John Wiley & Sons, Ltd.
Modulated Nucleoside Kinases as Tools to Improve the Activation of Therapeutic Nucleoside Analogues

The use of nucleoside analogues in anticancer and antiviral treatments is often impaired by the slow intracellular activation of these drugs. This problem can be addressed by the modulation of rate‐limiting enzymes in the activation pathways of the nucleoside analogues. Therapeutic strategies based on the combination of optimized activating enzymes and established nucleoside drugs promise significant improvements to traditional chemotherapy.