Miracle-dz/UniFAR-FTUs huggingface dataset:
Suivi temporel de stimuli dynamiques interférants par marquage du plan temps-fréquence utilisant une statistique de passages par zéro

In a CASA context, a harmonicity-based time-frequency labeling method is described. This method exploits a statistics on first order inter-zeros interval lengths to perform the labeling; moreover, the time-frequency plane is labeled thanks to a reliabili ty c iterion, based on the standard deviation of interval lengths, which provides information about the presence (or not) of interference. After a description of the algorithm and its properties, we show that it can be used for tracking applications involving dynamical stimuli.
ECG features extraction using second-order dynamic system and regeneration using hybrid recurrent network

ECG signals show the heart's condition for each individual. ECG signal's characteristic can be extracted by using several methods such as P-wave conditions, RR-interval, fast-Fourier transform, wavelet transform, and etc. This study shows the relationship between features extraction of ECG signals by using second-order dynamic system (SODS) technique and ECG signals regeneration by using hybrid-recurrent network (HRN). HRN technique describes the mathematical proof of the algorithms used in SODS. The algorithm was developed by using Matlab software platform. Comparison was made and it was found that the ECG features extracted from SODS can be used to regenerate the ECG signals based on HRN technique. Therefore, the features extracted from SODS were valid to be used for further analysis of ECG signals.
Speed, Spatial, and Temporal Tuning of Rod and Cone Vision in Mouse

Rods and cones subserve mouse vision over a 100 million-fold range of light intensity (−6 to 2 log cd m−2). Rod pathways tune vision to the temporal frequency of stimuli (peak, 0.75 Hz) and cone pathways to their speed (peak, ∼12°/s). Both pathways tune vision to the spatial components of stimuli (0.064–0.128 cycles/°). The specific photoreceptor contributions were determined by two-alternative, forced-choice measures of contrast thresholds for optomotor responses of C57BL/6J mice with normal vision, Gnat2cpfl3 mice without functional cones, and Gnat1−/− mice without functional rods. Gnat2cpfl3 mice (threshold, −6.0 log cd m−2) cannot see rotating gratings above −2.0 log cd m−2 (photopic vision), and Gnat1−/− mice (threshold, −4.0 log cd m−2) are blind below −4.0 log cd m−2 (scotopic vision). Both genotypes can see in the transitional mesopic range (−4.0 to −2.0 log cd m−2). Mouse rod and cone sensitivities are similar to those of human. This parametric study characterizes the functional properties of the mouse visual system, revealing the rod and cone contributions to contrast sensitivity and to the temporal processing of visual stimuli.
Medical Ultrasound Video Coding with H.265/HEVC Based on ROI Extraction

High-efficiency video compression technology is of primary importance to the storage and transmission of digital medical video in modern medical communication systems. To further improve the compression performance of medical ultrasound video, two innovative technologies based on diagnostic region-of-interest (ROI) extraction using the high efficiency video coding (H.265/HEVC) standard are presented in this paper. First, an effective ROI extraction algorithm based on image textural features is proposed to strengthen the applicability of ROI detection results in the H.265/HEVC quad-tree coding structure. Second, a hierarchical coding method based on transform coefficient adjustment and a quantization parameter (QP) selection process is designed to implement the otherness encoding for ROIs and non-ROIs. Experimental results demonstrate that the proposed optimization strategy significantly improves the coding performance by achieving a BD-BR reduction of 13.52% and a BD-PSNR gain of 1.16 dB on average compared to H.265/HEVC (HM15.0). The proposed medical video coding algorithm is expected to satisfy low bit-rate compression requirements for modern medical communication systems.
Presynaptic inhibition balances the trade-off between differential sensitivity and reproducibility

Neural adaptation process needs to balance an ability to distinguish similar patterns (differential sensitivity) and an ability to learn new patterns while keeping old ones intact (incremental learning). In this paper, we used a striatal medium spiny neuron (MSN) model to assess the effects of presynaptic inhibition on the differential sensitivity/incremental learning trade-off problem. Differential sensitivity is positively correlated with the strength of long-term depression (LTD) because it emphasizes pattern difference by eliminating anti-causal weak responses. However, strong LTD interferes with incremental learning. We assumed that presynaptic lateral inhibition could emphasize the pattern difference without losing the previously formed synaptic weight pattern. Then, we applied spike timing dependent plasticity (STDP) rule to a simulation of the corticostriatal pathway with and without presynaptic inhibition of the MSNs. The result confirmed that the trade-off problem could be overcome by presynaptic lateral inhibition.
Information theoretic view-based and modular face detection

This paper describes information theoretic methods for the determination of the optimal subset of pixels for the problem of face detection in complex backgrounds. A view-based method is described which has limitations due to misalignments. This motivates the modular feature based method which minimizes the misalignment problem. Empirical comparisons between the view-based, modular, and sum of squared difference methods are made using four databases from three universities.
Contextual Search Using Ontology-Based User Profiles

Search engines, generally, return results without any regard for the concepts in which the user is interested. In this paper, we present our approach to personalizing search engines using ontology based contextual profiles. In contrast to long-term user profiles, we construct contextual user profiles that capture what the user is working on at the time they conduct a search. These profiles are used to personalize the search results to suit the information needs of the user at a particular instant of time. We present the results of experiments evaluating the effect of the original versus conceptual ranking and the use of multiple sources of information to build the contextual profile. We were able to achieve a 15% improvement over Google in the average rank of the result clicked by a user when contextual information extracted from open Word documents and Web pages was used to re-rank the results.
Novice Reviewers Retain High Sensitivity and Specificity of Posterior Segment Disease Identification with iWellnessExam™

Introduction. Four novices to Spectral Domain Optical Coherence Tomography (SD-OCT) image review were provided a brief lecture on the interpretation of iVue iWellnessExam™ findings (available on iVue® SD-OCT, Optovue, Inc., Fremont, CA). For a cohort of 126 (Confirmed) Normal, 101 (Confirmed) Disease subjects, iWellnessExam™ OD, OS, and OU reports were provided. Each novice independently reviewed and sorted the subjects into one of four categories: normal, retinal disease, optic nerve (ON) disease, and retinal + ON disease. Their accuracy is compared between the novices and with an expert reviewer. Results. Posterior segment disease was properly detected by novices with sensitivities of 90.6%, any disease; 84.3%, retinal disease; 88.0%, ON disease; expert sensitivity: 96.0%, 95.5%, and 90.0%, respectively; specificity: 84.3%, novices; 99.2%, expert. Novice accuracy correlates best with clinical exposure and amount of time spent reviewing each image set. The novices' negative predictive value was 92.0% (i.e., very few false negatives). Conclusions. Novices can be trained to screen for posterior segment disease efficiently and effectively using iWellnessExam™ data, with high sensitivity, while maintaining high specificity. Novice reviewer accuracy covaries with both clinical exposure and time spent per image set. These findings support exploration of training nonophthalmic technicians in a primary medical care setting.
Uncertainty avoidance as a moderator for influences on foreign resident dining out behaviors

Purpose The purpose of this study was to examine the moderating effect of uncertainty avoidance on the relationships among subjective knowledge, attitude toward Korean foods, and dining out behavioral intentions of foreign residents in Korea. Design/methodology/approach A total of 247 foreign residents in Korea were participated through a street intercept survey at several locations in metropolitan areas of South Korea. Subsequently, the samples were divided into two groups (a low uncertainty avoidance group and a high uncertainty avoidance group) for multiple group analysis to examine the moderating role of uncertainty avoidance. Findings The results of structural equation modeling showed that subjective knowledge and attitude toward Korean foods significantly influenced intention to visit Korean restaurants. Furthermore, multiple group analysis results showed that uncertainty avoidance had a significant moderating effect as a cultural dimension on the relationships between subjective knowledge and behav.
Bio-feedback system for rehabilitation based on a wireless body area network

In this paper we describe bio-WWS, a bio-feedback system for rehabilitation based on dedicated, wireless, sensor-network architecture. The sensor network is designed to be distributed on the user's body for balance monitoring and correction. The hardware and software architecture (communication protocols, power management policies and application-level control) have been tuned to optimize cost, battery autonomy and real-time performance required for this application. Bio-WWS is an example of complete vertical integration: the sensor network is fully integrated with processing and auditory feedback generation
Avoiding Policy-based Deadlocks in Business Processes

In the field of business process management, deadlocks describe a situation where a workflow execution is blocked and cannot be completed. We speak of policy-based deadlocks if such a situation is caused by unsatisfiable resource requirements due to security constraints specified as part of the business process. In this paper we propose a method to avoid policy-based deadlocks by analyzing a workflow's security constraints, determine the minimal required number of users, and provide an optimal user-activity assignment for a deadlock-free workflow execution. We will finally validate our proposed approach by applying it to a real-world scenario.
Multi-Layer Perceptrons and Conventional Adaptive Filters for Channel Estimation in CDMA System

In this paper, two different approaches multi-layer perceptron (MLP) neural network (NN) with back propagation (BP) learning algorithm and conventional adaptive filters are tested and compared in AWGN channel for code division multiple accesses (CDMA). In this work direct-sequence (DS) CDMA with binary phase shift keying (BPSK) modulation transmitted over complex base-band channel for real part of the matched-filter (MF) output and not the MF output itself should be use as sufficient statistics for further processing. Based on this observation, we drive novel improved bit error rate (BER) and mean square error (MSE) in MLP NN receiver. A pseudo noise (PN) has produced by stochastic statistical properties and PN is changed in duration of each symbol. Results show that MLP-NN has better performance than other proposed MFs in which training sequence is transmitted at the beginning of a session continuously and the initial estimation of the channel parameters is performed using the received data symbols.
State Complexity Advantages of Ultrametric Automata

Ultrametric automata have properties similar to the properties of probabilistic automata but the descriptional power of these types of automata can differ very much. In this paper, we compare ultrametric automata with deterministic, nondeterministic, probabilistic and alternating automata with various state complexities. We also show that two-way ultrametric automata can have a smaller state complexity than one-way ultrametric automata.
Comparing emotions using acoustics and human perceptual dimensions

Understanding the difference between emotions based on acoustic features is important for computer recognition and classification of emotions. We conducted a study of human perception of six emotions based on three perceptual dimensions and compared the human classification with machine classification based on many acoustic parameters. Results show that the six emotions cluster differently according to acoustic features and to perceptual dimensions. Acoustic features fail to characterize the perceptual dimension of valence. More research is needed to find acoustic features that have a close relation to human perception.
Active In-Hand Object Recognition on a Humanoid Robot

For any robot, the ability to recognize and manipulate unknown objects is crucial to successfully work in natural environments. Object recognition and categorization is a very challenging problem, as 3-D objects often give rise to ambiguous, 2-D views. Here, we present a perception-driven exploration and recognition scheme for in-hand object recognition implemented on the iCub humanoid robot. In this setup, the robot actively seeks out object views to optimize the exploration sequence. This is achieved by regarding the object recognition problem as a localization problem. We search for the most likely viewpoint position on the viewsphere of all objects. This problem can be solved efficiently using a particle filter that fuses visual cues with associated motor actions. Based on the state of the filter, we can predict the next best viewpoint after each recognition step by searching for the action that leads to the highest expected information gain. We conduct extensive evaluations of the proposed system in simulation as well as on the actual robot and show the benefit of perception-driven exploration over passive, vision-only processes at discriminating between highly similar objects. We demonstrate that objects are recognized faster and at the same time with a higher accuracy.
Using Health Care Utilization and Publication Patterns to Characterize the Research Portfolio and to Plan Future Research Investments

Objective Government funders of biomedical research are under increasing pressure to demonstrate societal benefits of their investments. A number of published studies attempted to correlate research funding levels with the societal burden for various diseases, with mixed results. We examined whether research funded by the Department of Veterans Affairs (VA) is well aligned with current and projected veterans’ health needs. The organizational structure of the VA makes it a particularly suitable setting for examining these questions. Methods We used the publication patterns and dollar expenditures of VA-funded researchers to characterize the VA research portfolio by disease. We used health care utilization data from the VA for the same diseases to define veterans’ health needs. We then measured the level of correlation between the two and identified disease groups that were under- or over-represented in the research portfolio relative to disease expenditures. Finally, we used historic health care utilization trends combined with demographic projections to identify diseases and conditions that are increasing in costs and/or patient volume and consequently represent potential targets for future research investments. Results We found a significant correlation between research volume/expenditures and health utilization. Some disease groups were slightly under- or over-represented, but these deviations were relatively small. Diseases and conditions with the increasing utilization trend at the VA included hypertension, hypercholesterolemia, diabetes, hearing loss, sleeping disorders, complications of pregnancy, and several mental disorders. Conclusions Research investments at the VA are well aligned with veteran health needs. The VA can continue to meet these needs by supporting research on the diseases and conditions with a growing number of patients, costs of care, or both. Our approach can be used by other funders of disease research to characterize their portfolios and to plan research investments.
Capturing and stylizing hair for 3D fabrication

Recently, we have seen a growing trend in the design and fabrication of personalized figurines, created by scanning real people and then physically reproducing miniature statues with 3D printers. This is currently a hot topic both in academia and industry, and the printed figurines are gaining more and more realism, especially with state-of-the-art facial scanning technology improving. However, current systems all contain the same limitation - no previous method is able to suitably capture personalized hair-styles for physical reproduction. Typically, the subject's hair is approximated very coarsely or replaced completely with a template model. In this paper we present the first method for stylized hair capture, a technique to reconstruct an individual's actual hair-style in a manner suitable for physical reproduction. Inspired by centuries-old artistic sculptures, our method generates hair as a closed-manifold surface, yet contains the structural and color elements stylized in a way that captures the defining characteristics of the hair-style. The key to our approach is a novel multi-view stylization algorithm, which extends feature-preserving color filtering from 2D images to irregular manifolds in 3D, and introduces abstract geometric details that are coherent with the color stylization. The proposed technique fits naturally in traditional pipelines for figurine reproduction, and we demonstrate the robustness and versatility of our approach by capturing several subjects with widely varying hair-styles.
Statistical Estimation Of Average Power Dissipation In Sequential Circuits

In this paper, we present a new statistical techniquefor estimating average power dissipation in sequential circuits.Due to the feedback mechanism, in sequential circuitspower dissipation in consecutive clock cycles are temporallycorrelated, which violates the basic requirement ofstatistical mean inference procedures. We overcome thisproblem by using a randomness test and a sequential procedureto select a proper independence interval, which in turnis used to generate random power samples. A distribution-independentstopping criterion is applied to analyze thesample data and terminate the simulation upon achievementof the accuracy specification. The technique is successfullyapplied to a set of benchmark circuits.
Learning object affordances by imitation Research Progress Report 3

The report explores the role of imitation in learning object affordances. Since I intend to experiment with a robotic arm enabling only pushing objects, properties of objects will be the main factor affecting the overall complexity of this “toy world”. Learning in the toy world is a process of discovering objects’ affordances, as well as the kinds of actions and goals related to them. I intend to create an algorithm, where imitation can be thought of as a teacher-guided search, which will help a robot to constrain its own exploration to relevant “behavioural patterns”, accentuating either action plans or action goals. In effect, a robot which begins with very little knowledge about actions and objects, will be able not only to recognise and understand novel object affordances and goals, but also to learn how to generalise the acquired knowledge. Thus, a robot will demonstrate a high level of manipulation competence playing with entirely new objects “it will master the toy world”.
Automated goal detection from natural language constraints

This paper focuses on the development of an approach that can be used for automated detection of goals from software specifications text. Software specifications demonstration is the key analysis purpose of the provided analysis. Other analysis purpose is to examine that how goals are detected from business process texts. That is not only unambiguous and semantically limited but also machine processable. The execution of the provided strategy for company procedure to NL and growth of model device that can instantly execute convert feedback NL company guidelines into sensible types and find dependencies among these guidelines is the main participation of this paper.
KK/Ta Mice Administered Lactobacillus plantarum Strain No. 14 Have Lower Adiposity and Higher Insulin Sensitivity

Excess accumulation of white adipose tissue can lead to obesity-related metabolic abnormalities such as insulin resistance. We previously reported that intragastric administration of Lactobacillus plantarum No. 14 reduced adipocyte size in diet-induced obese C57BL/6 mice. The present study tested whether L. plantarum No. 14 affects adiposity and insulin sensitivity in an animal model of type-2 diabetes mellitus. Male KK/Ta mice were fed a normal-fat diet and intragastrically given L. plantarum No. 14 (108 CFU/mouse) or vehicle daily for 10 weeks. Interscapular brown adipose tissue and inguinal, mesenteric, and retroperitoneal white adipose tissue weights, serum leptin and insulin concentrations, and insulin resistance index (HOMA-IR) were significantly lower in L. plantarum No. 14-fed mice than in vehicle-fed mice. The sum of the inguinal, epididymal, mesenteric and retroperitoneal white adipose tissue weights correlated with serum leptin and non-esterified fatty acid concentrations and HOMA-IR. The mesenteric adipose tissue mRNA levels of monocyte chemoattractant protein-1 and tumor necrosis factor-α were significantly lower in L. plantarum No. 14-fed mice than in vehicle-fed mice. Mesenteric adipose tissue weight correlated with interleukin-6, monocyte chemoattractant protein-1, and tumor necrosis factor-α mRNA levels. HOMA-IR correlated with monocyte chemoattractant protein-1 and tumor necrosis factor-α mRNA levels. These data suggest that L. plantarum No. 14 prevents the development of insulin resistance, which is at least partly attributable to the prevention of obesity, in KK/Ta mice.
A Binary Translation System for Multithreaded Processors and its Preliminary Evaluation

Thread level parallelism (TLP) is a key technology to coming generation of high performance processors. Although it provides higher processing capability, the loss of compatibility with existing processors is a crucial issue. This research is motivated by the following two points: (1) TLP requires multithread programming which is rather di cult for ordinary programmers, or complexed compilation technologies that can exploit multithread parallelism, and (2) existing binary codes should be executed e ciently on multithreaded processors. In this paper, we rst propose a binary translation system, that translates existing binary codes to multithreaded ones and optimizes them dynamically during execution. The system inputs the original binary codes and translates them to internal RTL representation. It analyzes the structure of the program and applies multithreading to loop bodies in a thread pipelining manner. A pilot binary translator, that is a part of the proposed system, was built for the sake of preliminary evaluation. Evaluation results illustrate e ectiveness of the system.
Design, Synthesis, Antinociceptive and Anti-Inflammatory Activities of Novel Piroxicam Analogues

In this paper we report the design, synthesis, antinociceptive and anti-inflammatory activities of a series of benzothiazine N-acylhydrazones 14a–h, planned by structural modification of piroxicam (1), a non steroidal anti-inflammatory drug. Among the synthesized analogues, compounds 14f (LASSBio-1637) and 14g (LASSBio-1639) were identified as novel antinociceptive and anti-inflammatory prototypes, active by oral administration, acting by a mechanism of action that seems to be different from that of piroxicam, since they were inactive as an inhibitor of cyclooxygenase (COX-1 and COX-2) at concentrations of 10 μM.