Miracle-dz/UniFAR-FTUs huggingface dataset:
Bus-Aware Multicore WCET Analysis through TDMA Offset Bounds

In the domain of real-time systems, the analysis of the timing behavior of programs is crucial for guaranteeing the schedulability and thus the safeness of a system. Static analyses of the WCET (Worst-Case Execution Time) have proven to be a key element for timing analysis, as they provide safe upper bounds on a program's execution time. For single-core systems, industrial-strength WCET analyzers are already available, but up to now, only first proposals have been made to analyze the WCET in multicore systems, where the different cores may interfere during the access to shared resources. An important example for this are shared buses which connect the cores to a shared main memory. The time to gain access to the shared bus may vary significantly, depending on the used bus arbitration protocol and the access timings. In this paper, we propose a new technique for analyzing the duration of accesses to shared buses. We implemented a prototype tool which uses the new analysis and tested it on a set of real world benchmarks. Results demonstrate that our analysis achieves the same precision as the best existing approach while drastically outperforming it in matters of analysis time.
Spectral analysis and unsupervised SVM classification for skin hyper-pigmentation classification

Data reduction procedures and classification via support vector machines (SVMs) are often associated with multi or hy-perspectral image analysis. In this paper, we propose an automatic method with these two schemes in order to perform a classification of skin hyper-pigmentation on multi-spectral images. We propose a spectral analysis method to partition the spectrum as a tool for data reduction, implemented by projection pursuit. Once the data is reduced, an SVM is used to differentiate the pathological from the healthy areas. As SVM is a supervised classification method, we propose a spatial criterion for spectral analysis in order to perform automatic learning.
Rapid Microbial Sample Preparation from Blood Using a Novel Concentration Device

Appropriate care for bacteremic patients is dictated by the amount of time needed for an accurate diagnosis. However, the concentration of microbes in the blood is extremely low in these patients (1–100 CFU/mL), traditionally requiring growth (blood culture) or amplification (e.g., PCR) for detection. Current culture-based methods can take a minimum of two days, while faster methods like PCR require a sample free of inhibitors (i.e., blood components). Though commercial kits exist for the removal of blood from these samples, they typically capture only DNA, thereby necessitating the use of blood culture for antimicrobial testing. Here, we report a novel, scaled-up sample preparation protocol carried out in a new microbial concentration device. The process can efficiently lyse 10 mL of bacteremic blood while maintaining the microorganisms’ viability, giving a 30‑μL final output volume. A suite of six microorganisms (Staphylococcus aureus, Streptococcus pneumoniae, Escherichia coli, Haemophilus influenzae, Pseudomonas aeruginosa, and Candida albicans) at a range of clinically relevant concentrations was tested. All of the microorganisms had recoveries greater than 55% at the highest tested concentration of 100 CFU/mL, with three of them having over 70% recovery. At the lowest tested concentration of 3 CFU/mL, two microorganisms had recoveries of ca. 40–50% while the other four gave recoveries greater than 70%. Using a Taqman assay for methicillin-sensitive S. aureus (MSSA)to prove the feasibility of downstream analysis, we show that our microbial pellets are clean enough for PCR amplification. PCR testing of 56 spiked-positive and negative samples gave a specificity of 0.97 and a sensitivity of 0.96, showing that our sample preparation protocol holds great promise for the rapid diagnosis of bacteremia directly from a primary sample.
A Novel Method to Mask the Bitter Taste of Berberine Hydrochloride: Powder Surface Modification

Background: Berberine hydrochloride (BH) is widely used as a nonprescription drug to treat diarrhea without drug resistance and side effects worldwide. However, its drastic bitterness affects patient compliance severely. Hence, it is essential to mask the bitter taste of BH. Objective: Powder surface modification technology is attempted to mask the bitterness of BH through changing the surface properties in vibromill. The purpose of this study was to apply this technology to mask the bitterness of BH and improve the patient compliance. Materials and Methods: Initially, to prepare the modifier-BH composites, some parameters were optimized, including type of modifiers, ratio between BH and modifiers, and composite time. Then, the contact angles, scanning electron microscopy, and infrared (IR) spectroscopy were utilized to evaluate the microstructure of composites. Moreover, electronic tongue measurement, animal performance test, and bitterness evaluation methods were applied to evaluate the masking effect. Results: Based on the results of bitter taste evaluations, mannitol was chosen as the best modifier, and the optimal ratio of BH and mannitol was 6:4 with grinding together for 2 min in vibromill. For the composites prepared by this process, the IR spectroscopy and surface properties were similar with that of mannitol, and the microstructure was also demonstrated that small particles of mannitol successfully coated on the surface of BH. Special structure of the composites decreased the contact area between BH and external media and finally inhibited the bitterness. This effect was confirmed by three different kinds of methods. Conclusion: Our study provides a novel method to mask the bitter taste of drugs. It will be of great interest to pharmaceutical experts and pharmacists. Abbreviations used: BH:Berberine hydrochloride; CDI: Clostridium difficile infection; ODT: Orally disintegrating tablets; HPLC: High-performance liquid chromatography; CAs: Contact angles; SEM: Scanning electron microscopy; IR: Infrared spectrogram.
Pre-Fortis Shortening in Czech English: A Production and Reaction-Time Study

Abstract This study focuses on the production and perception of English words with a fortis vs. lenis obstruent in the syllable coda. The contrast is mostly cued by the duration of the preceding vowel, which is shorter before fortis than before lenis sounds in native speech. In the first experiment we analyzed the production of 10 Czech speakers of English and compared them to two native controls. The results showed that the Czech speakers did not sufficiently exploit duration to cue the identity of the word-final obstruent. In the second experiment we manipulated C and V durations in target words to transplant the native ratios onto the Czech-accented speech, enhancing the fortis-lenis contrast, and vice versa. 108 listeners took part in a word-monitoring task in which reaction times were measured. The hypothesized advantage to items in which the target word (with a fortis or lenis obstruent) was semantically congruent with the following context was not confirmed, and subsequent analyses showed that the words’ frequency of use and the collocations they enter into strongly affect speech processing and correlate to a large degree with the reaction times.
Discovering URLs through user feedback

Search engines rely upon crawling to build their Web page collections. A Web crawler typically discovers new URLs by following the link structure induced by links on Web pages. As the number of documents on the Web is large, discovering newly created URLs may take arbitrarily long, and depending on how a given page is connected to others, such a crawler may miss the pages altogether. In this paper, we evaluate the benefits of integrating a passive URL discovery mechanism into a Web crawler. This mechanism is passive in the sense that it does not require the crawler to actively fetch documents from the Web to discover URLs. We focus here on a mechanism that uses toolbar data as a representative source for new URL discovery. We use the toolbar logs of Yahoo! to characterize the URLs that are accessed by users via their browsers, but not discovered by Yahoo! Web crawler. We show that a high fraction of URLs that appear in toolbar logs are not discovered by the crawler. We also reveal that a certain fraction of URLs are discovered by the crawler later than the time they are first accessed by users. One important conclusion of our work is that web search engines can highly benefit from user feedback in the form of toolbar logs for passive URL discovery.
Online learning in biometrics: A case study in face classifier update

In large scale applications, hundreds of new subjects may be regularly enrolled in a biometric system. To account for the variations in data distribution caused by these new enrollments, biometric systems require regular re-training which usually results in a very large computational overhead. This paper formally introduces the concept of online learning in biometrics. We demonstrate its application in classifier update algorithms to re-train classifier decision boundaries. Specifically, the algorithm employs online learning technique in a 2ν-Granular Soft Support Vector Machine for rapidly training and updating face recognition systems. The proposed online classifier is used in a face recognition application for classifying genuine and impostor match scores impacted by different covariates. Experiments on a heterogeneous face database of 1,194 subjects show that the proposed online classifier not only improves the verification accuracy but also significantly reduces the computational cost.
Forever Young?

This study compares age identities of middle-aged and older adults in the United States and Germany. Differences between countries in social systems and cultural meanings of old age are expected to produce different age identities. Data are from respondents between ages 40 and 74 in the United States (MIDUS; n = 2,006) and Germany (German Aging Survey; n = 3,331). Americans and Germans tend to feel younger than their actual age, but the discrepancy is larger among Americans. The bias toward youthful identities is stronger at older ages, particularly among Americans. In both counties, persons with better health have younger identities and role losses are not related to age identities. The study shows that different social and cultural systems produce different subjective experiences of aging. As these differences exist within Western culture, the study makes clear that one should be careful in generalizing findings from aging research across countries.
Functional Implications of Neurotransmitter Expression during Axonal Regeneration: Serotonin, But Not Peptides, Auto-Regulate Axon Growth of an Identified Central Neuron

We studied the regenerative properties of one of two electrically coupled molluscan neurons, the serotonergic cerebral giant cells (CGCs) of Lymnaea stagnalis, after axotomy. The CGCs play a crucial role in feeding behavior, and when both cells are disconnected from their target neurons, animals no longer feed. When one CGC was permanently disconnected from its targets and the other was reversibly damaged by a nerve crush, the latter one regenerated over a period of 2 weeks to reform functional synapses with specific target neurons. At the same time, recovery of the feeding behavior was observed. After the crush, neuropeptide gene expression in the CGC was downregulated to ∼50%. Serotonin synthesis, on the other hand, remained unaffected, suggesting that serotonin might have an active role in regeneration. In primary neuron culture, CGCs failed to extend neurites in the presence of serotonin; in cells that extended neurites in the absence of serotonin, focally applied serotonin, but not neuropeptides, induced growth cone collapse. Using serotonin-sensitive sniffer cells, we show that CGC neurites and growth cones release serotonin in culture. Finally, both the spontaneous and stimulation-induced release of serotonin from CGCs in culture resulted in growth cone collapse responses that could be blocked by the serotonin receptor antagonist methysergide. Our data suggest that auto-released serotonin is inhibitory to CGC neurite outgrowth in vitro. During regeneration in vivo, serotonin release might fine-tune axon guidance and branching by inducing local collapse responses in extending neurites.
Efficient Collective Communication on Multidimensional Meshes with WormholeRouting

This work presents a methodology for cosntructing efficient collective communication libraries for mesh-based multicomputers with wormhole routing. Such libraries can be built from a small set of simple primitives. Specifically, two classes of routines are presented: short vector routines that minimize message startups and long vector routines that minimize data transmission. Routines from these two classes can then be hybridized, resulting in algorithms which are efficient over a wide range of machine performance and vector size parameters. Implementations of these basic routines are given, and the derivation of other routines from them is demonstrated. Furthermore, these methods are applicable to meshes in their full generality: There are no restrictions on the relative dimensions of the mesh or the number of nodes contained therein (e.g., they do not require power-of-two partition sizes). Also, the schemes presented here inherently support group communications as well as full-partition operations. Performance data from the Intel Paragon are included.
The Noncommutative bion core

We examine noncommutative solutions of the non-Abelian theory on the world-volume of N coincident D-strings. These solutions can be interpreted in terms of noncommutative geometry as funnels describing the non-Abelian D-string expanding out into an orthogonal D3-brane. These configurations are ``dual'' to the bion solutions in the Abelian world-volume theory of the D3-brane. In the latter, a charge N magnetic monopole describes N D-strings attached to the D3-brane with a spike deformation of the world volume. The noncommutative D-string solutions give a reliable account of physics at the core of the monopole, where the bion description is expected to break down. In the large N limit, we find good agreement between the two points of view, including the energy, couplings to background fields, and the shape of the funnel. We also study fluctuations traveling along the D-string, again obtaining agreement in the large N limit. At finite N, our results give a limit on the number of modes that can travel to infinity along the N D-strings attached to the D3-brane.
Synthesis tools for Mixed-Signal ICs: progress on frontend and backend strategies

Digital synthesis tools such as logic synthesis and semicustom layout have dramatically changed both the frontend (specification to netlist) and backend (netlist to mask) steps of the digital IC design process. In this tutorial, we look at the last decade's worth of progress on analog circuit synthesis and layout tools. We focus on the frontend and backend of analog and mixed-signal IC design flows. The tutorial summarizes the problems for which viable solutions are emerging, and those which are still unsolved.
A Study on the Appraisal Criteria of University Students' 24-stroke Taiji

According to the practicable conditions and the educational goal of the university students, the appraisal criteria of the 24-stroke Taiji in the P.E. curriculum are standardized with the formulation of 2 indices of class Ⅰ,10 indices of classⅡ,24 indices of class Ⅲ and the class and weight ratio of each index, thus making the appraisal criteria more scientific, standard, advanced, practical and operative.
Development of an adaptive observer algorithm using RMS signal

Because of recent progress of nano positioning technology, the feature of low vibration in controlling a stage has received great attention. In lithography process, the stage hunting directly affects the performance of the manufactured product. The stage perturbation results from various noise sources, and actually the statistical characteristics of the noises easily changes. This paper focused on the design of a novel state observer which filters out the random noises that cause stage vibration, and simultaneously maintains fast detection against external disturbances. The proposed algorithm was designed to use the RMS signal in the feedback loop of the position controller, and it continually measures the level of noises and adjusts the statistical parameters in the digital Kalman filter. The RMS signal is measured by a RMS convertor chip for fast filter performance, and an air levitated precision stage with linear voice coil motor (LVCM) was used for the experiment. The results show that the performance of the system is effectively improved by the proposed observer algorithm with a conventional PID controller.
Joint Decoding for Speech Recognition and Semantic Tagging

Most conversational understanding (CU) systems today employ a cascade approach, where the best hypothesis from automatic speech recognizer (ASR) is fed into spoken language understanding (SLU) module, whose best hypothesis is then fed into other systems such as interpreter or dialog manager. In such approaches, errors from one statistical module irreversibly propagates into another module causing a serious degradation in the overall performance of the conversational understanding system. Thus it is desirable to jointly optimize all the statistical modules together. As a first step towards this, in this paper, we propose a joint decoding framework in which we predict the optimal word as well as slot (semantic tag) sequence jointly given the input acoustic stream. On Microsoft’s CU system, we show 1.3% absolute reduction in word error rate (WER) and 1.2% absolute improvement in F measure for slot prediction when compared to a very strong cascade baseline comprising of the state-of-the-art recognizer followed by a slot sequence tagger.
Endocannabinoid Receptors Gene Expression in Morbidly Obese Women with Nonalcoholic Fatty Liver Disease

Background. Recent reports suggest a role for the endocannabinoid system in the pathology of nonalcoholic fatty liver disease (NAFLD). The aim of this study was to investigate the relationship between liver expression of cannabinoid (CB) receptor subtypes, CB1 and CB2, in morbidly obese (MO) women with different histological stages of NAFLD. Methods. We analysed hepatic CB1 and CB2 mRNA expression, and the expression of genes involved in lipid metabolism in 72 MO women, subclassified by liver histology into MO with normal liver (NL, n = 16), simple steatosis (SS, n = 28), and nonalcoholic steatohepatitis (NASH, n = 28) by enzyme-linked immunosorbent assay and RT-PCR. Results. We found that CB1 mRNA expression was significantly higher in NASH compared with SS and correlated negatively with PPARα. Regarding CB2, CB2 mRNA expression correlated positively with ACC1, PPARγ, IL6, TNFα, resistin, and adiponectin. Conclusions. The increased expression of CB1 in NASH and the negative correlation with PPARα suggest a deleterious role of CB1 in NAFLD. Regarding CB2, its positive correlation with the anti-inflammatory molecule adiponectin and, paradoxically, with inflammatory genes suggests that this receptor has a dual role. Taken together, our results suggest that endocannabinoid receptors might be involved in the pathogenesis of NAFLD, a finding which justifies further study.
TendeR-Sims - Similarity Retrieval System for Public Tenders

TendeR-Sims (Tender Retrieval by Similarity) is a system that helps to search for satisfiable request for tender’s lots in a database by filtering irrelevant lots, so companies can easily discover the contracts they can win. The system implements the Similarity-aware Relational Division Operator in a commercial Relational Database Management System (RDBMS), and compares products by combining a path distance in a preprocessed ontology with a textual distance. Tender-Sims focuses on answering the following query: select the lots where a company has a similar enough item for each of all required items. We evaluated our proposed system employing a dataset composed of product catologs of Brazilian companies in the food market and real requests for tenders with known results. In the presented experiments, TendeR Sims achieved up to 66% cost reduction at 90% recall when compared to the ground truth.
Learning Behaviors Models for Robot Execution Control

Robust execution of robotic tasks is a difficult problem. In many situations, these tasks involve complex behaviors combining different functionalities (e.g. perception, localization, motion planning and motion execution). These behaviors are often programmed with a strong focus on the robustness of the behavior itself, not on the definition of a “high level” model to be used by a task planner and an execution controller. We propose to learn behaviors models as structured stochastic processes: Dynamic Bayesian Network. Indeed, the DBN formalism allows us to learn and control behaviors with controllable parameters. We experimented our approach on a real robot, where we learned over a large number of runs the model of a complex navigation task using a modified version of Expectation Maximization for DBN. The resulting DBN is then used to control the robot navigation behavior and we show that for some given objectives (e.g. avoid failure, optimize speed), the learned DBN driven controller performs much better than the programmed controller. We also show a way to achieve efficient incremental learning of the DBN. We believe that the proposed approach remains generic and can be used to learn complex behaviors other than navigation and for other autonomous systems.
Students' Social Presence in Online Learning System

The purposes of this study are to identify students' perceptions on social presence on online learning and to investigate students' social presence in online learning. Next, this study will also identify students' social presence in online learning in relation to their academic performance. The respondents for this study will be 33 undergraduate students who enrolled in Authoring Language course. One of the most highlighted issues in online learning is interaction process that takes place between either learners and learners or learners and teacher. As mentioned by Badrinathan and Gole [1], online learning requires the teacher or the instructor to be responsible in ensuring that the learners interact with one another. The interaction will encourage the students to voice out their thoughts and respond towards others' idea through computer mediated communication (CMC). Given that social presence is likely related to feeling rather than reason [2], the sense of connectedness among learners would stimulate them to become more interactive in online learning. Hence, rather than trying to acquire the knowledge individually, learners would have much experience in sharing and cooperating with their peers in the whole process of learning. Furthermore, social presence is considered as one of the keys to the success of online learning [3] when it has great influence in online interaction, user satisfaction, depth of online discussions, online language learning and critical thinking [4]. This study will be implementing quantitative approach with triangulation of data from questionnaire and students' online transcript. Quantitative data from questionnaires will be analyzed using SPSS (Statistical Package for the Social Science). As for the online transcript, the data will be analyzed through content analysis technique. With the findings of this study, it is hoped that students, teachers or instructors and the administrations of higher educational institutions will be more aware towards the importance of social presence in inculcating effective learning process via online system.