Miracle-dz/UniFAR-FTUs huggingface dataset:
Stopping Criteria for SVMs

The support vector machine has been shown to be comparable to th state of the art for classification. In certain situation s however, the convergence to a global optimum of the optimiza tion problem can be very slow. It is shown that solutions suboptimal in terms of the optimization problem can still ha ve very good performance. By using a heuristic stopping criterion (termedHERMES) these solutions can be found in substantially less time tha n is required to attain a global optimum of the optimization problem.
Optimizing code through iterative specialization

Code specialization is a way to obtain significant improvement in the performance of an application. It works by exposing values of different parameters in source code. The availability of these specialized values enables the compilers to generate better optimized code. Although most of the efficient source code implementations contain specialized code to benefit from these optimizations, the real impact of specialization may however vary depending upon the value of the specializing parameter. In this paper, we suggest the specialization of code to acquire an iterative approach. For some specialized code, we search for a better version of code by re-specializing the code, followed by a low-level code analysis. The specialized versions fulfilling the required criteria are then transformed to generate another equivalent version of the original specialized code. The approach has been tested on Itanium-II architecture using icc compiler. The results show significant improvement in the performance of different behchmarks.
Game Theoretic Study on Channel-Based Authentication in MIMO Systems

In this paper, we investigate the authentication based on radio channel information in multiple-input multiple-output (MIMO) systems and formulate the interactions between a receiver with multiple antennas and a spoofing node as a zero-sum physical (PHY)-layer authentication game. In this game, the receiver chooses the test threshold of the hypothesis test to maximize its Bayesian risk-based utility in the spoofing detection, while the adversary chooses its attack rate, i.e., how often a spoofing signal is sent. We derive the Nash equilibrium (NE) of the static PHY-layer authentication game and present the condition that the NE exists, showing that both the spoofing detection error rates and the spoofing rate decrease with the number of transmit and receive antennas. We propose a PHY-layer spoofing detection algorithm for MIMO systems based on Q-learning, in which the receiver applies the reinforcement learning technique to achieve the optimal test threshold via trials in a dynamic game without knowing the system parameters, such as the channel time variation and spoofing cost. We also use Dyna architecture and prioritized sweeping (Dyna-PS) to improve the spoofing detection in time-variant radio environments. The proposed authentication algorithms are implemented over universal software radio peripherals and evaluated via experiments in an indoor environment. Experimental results show that the Dyna-PS-based spoofing detection algorithm further reduces the spoofing detection error rates and increases the utility of the receiver compared with the Q-learning-based algorithm, and both performances improve with more number of transmit or receive antennas.
Are There Dimensions in the Brain ?

If individual cells do not code dimensions, is it still possible to find representations of dimensions in the brain? It is suggested that perceptual quality dimensions are represented in the brain as a distributed activity pattern at multiple scales. It is shown that categories learned using such representations fulfil a soft convexity criterion when no counterexamples to convexity are presented. A multi-scale representation of properties also allows the same population of cells to support multiple representational spaces that can be selected by a priming mechanism.
Real-Time Image Smoothing Based on True Edges

are as close as possible to the center of true edges. This gives us an opportunity to judge how well edges are preserved for an edge-preserving smoothing filter. We therefore propose a nonlinear edge-preserving interpolation technique guided by the Canny true edges in an image. Our new edge-preserving smoothing algorithm smoothes high-contrast details and, at the same time, effectively preserves the true edges in the input image. In order to achieve a real-time performance, we further implement the algorithm using NVIDIA CUDA by taking advantage of the high parallelism of modern graphics hardware. The proposed algorithm smoothes low-contrast regions while effectively preserving true edges, and can also be used to produce vivid abstracted effects from realistic photographs.
Using Spectrum-Based Fault Localization for Test Case Grouping

Model-based test case generation allows one to derive almost arbitrary numbers of test cases from models. If resulting test suites are executed against real implementations, there are often huge numbers of failed test cases. Thus, the analysis of the test execution, i.e. the identification of failures for error reporting, becomes a tedious and time consuming task. In this paper we investigate a technique for grouping test runs that most likely reveal the same failure. This reduces the post analysis time and enables the generation of small regression test suites. The test case grouping is implemented by means of spectrum-based fault localization at the level of the specification. We calculate the grouping by relating the spectra of the test cases. Besides a brief discussion of our approach we present results of applying our approach to the Session Initiation Protocol.
Receptor‐mediated regulation of calcium channels and neurotransmitter release

Ca2+ influx into the nerve terminal is normally the trigger for the release of neurotransmitters. Many neurons possess presynaptic receptors whose activation results in changes in the quantity of neurotransmitter released by an action potential. This paper reviews studies that show that presynaptic receptors can regulate the activity of Ca2+ channels in the nerve terminal, resulting in changes in the influx of Ca2+ and in neurotransmitter release. Neurons possess several different types of voltage‐sensitive Ca2+ channels. Ca2+ influx through N‐type channels appears to trigger transmitter release in many instances. In other cases Ca2+ influx through L channels can influence transmitter release. Neurotransmitters can inhibit N channels through a G protein‐mediated transduction mechanism. The G proteins are frequently pertussis toxin substrates. Inhibition of N channels appears to involve changes in their voltage dependence. Neurotransmitters can also regulate neuronal K+ channels. Activation of these K+ channels can lead to a reduction in Ca2+ influx and neurotransmitter release; these effects are also mediated by G proteins. Thus neurotransmitters may often regulate both presynaptic Ca2+ and K+ channels. These two effects may be synergistic mechanisms for the regulation of Ca2+ influx and neurotransmitter release. — Miller, R. J. Receptor‐mediated regulation of calcium channels and neurotransmitter release. FASEB J. 4: 3291–3299; 1990.
Detection of Lactobacillus, Pediococcus, Leuconostoc, and Weissella Species in Human Feces by Using Group-Specific PCR Primers and Denaturing Gradient Gel Electrophoresis

ABSTRACT Denaturing gradient gel electrophoresis (DGGE) of DNA fragments generated by PCR with 16S ribosomal DNA-targeted group-specific primers was used to detect lactic acid bacteria (LAB) of the generaLactobacillus, Pediococcus, Leuconostoc, andWeissella in human feces. Analysis of fecal samples of four subjects revealed individual profiles of DNA fragments originating not only from species that have been described as intestinal inhabitants but also from characteristically food-associated bacteria such asLactobacillus sakei, Lactobacillus curvatus, Leuconostoc mesenteroides, and Pediococcus pentosaceus. Comparison of PCR-DGGE results with those of bacteriological culture showed that the food-associated species could not be cultured from the fecal samples by plating on Rogosa agar. On the other hand, all of the LAB species cultured from feces were detected in the DGGE profile. We also detected changes in the types of LAB present in human feces during consumption of a milk product containing the probiotic strainLactobacillus rhamnosus DR20. The analysis of fecal samples from two subjects taken before, during, and after administration of the probiotic revealed that L. rhamnosus was detectable by PCR-DGGE during the test period in the feces of both subjects, whereas it was detectable by culture in only one of the subjects.
Negative binomial analysis of intersection accident frequencies

Traffic accidents at urban intersections result in a huge cost to society in terms of death, injury, lost productivity, and property damage. Unfortunately, the elements that effect the frequency of intersection accidents are not well understood and, as a result, it is difficult to predict the effectiveness of specific intersection improvements that are aimed at reducing accident frequency. Using seven-yr accident histories from 63 intersections in Bellevue, Washington (all of which were targeted for operational improvements), this paper estimates a negative binomial regression of the frequency of accidents at intersection approaches. The estimation results uncover important interactions between geometric and traffic-related elements and accident frequencies. The findings of this paper provide exploratory methodological and empirical evidence that could lead to an approach to estimate the accident reduction benefits of various proposed improvements on operationally deficient intersections.
Effect of Proton Pump Inhibitors on the Continuous Real Time 13C-Urea Breath Test

OBJECTIVE:The aim of this study was to evaluate the accuracy of a new, continuous real time 13C-urea breath test, BreathID, for the diagnosis of Helicobacter pylori in patients taking proton pump inhibitors (PPIs). METHODS:Fifty-two consecutive patients, positive for H. pylori by BreathID, were prospectively evaluated. Patients were randomized to receive either omeprazole 20 mg/day or pantoprazole 40 mg/day for 14 days. A repeat breath test was performed on day 14 while patients received their last PPI pill. Patients were given a test drink containing 75 mg 13C-urea and 4.0 g citric acid. Real time, continuously sampled expired 13CO2, obtained within 6–20 min, was compared with measurement of expired 13CO2 by isotope ratio mass spectrometry (IRMS). RESULTS:A full set of test data was available for 43 patients. After 14 days of treatment with PPIs, false negative detection of H. pylori occurred in only 1/43 (2.3%) patients examined by continuous real time 13C-urea breath test compared with 2/43 (4.6%) patients examined by IRMS. With the exception of one case, complete agreement was observed between BreathID and the IRMS breath tests at both baseline and after PPI treatment. PPI treatment was associated with three different types of responses on UBT: 1) one third of the patients developed a significant decrease in the 13CO2/12CO2 excretion, 2) roughly one third developed a significant increase in the post-PPI breath test results, and 3) results did not change significantly in the remaining patients. Linear regression analysis of 43 H. pylori-positive subjects indicated a significant positive association between baseline and post-PPI Δ 13CO2/12CO2 excretion.CONCLUSIONS:The use of a single test drink containing 4.0 g citric acid in BreathID, resulted in a low number of false negative results associated with sustained PPI treatment. Although there were some differences between BreathID versus IRMS, the type of PPI and the sampling method used do not appear to play a critical role in the detection of H. pylori by BreathID. According to these results, BreathID is a reliable tool for testing H. pylori in patients taking PPIs.
The Role of the Exchange Rate in Inflation-Targeting Emerging Economies; Targeting Emerging Economies

This paper explores the role of exchange rates in emerging economies with inflation-targeting regimes, an issue that has become especially germane during the current episode of financial turmoil and volatile capital flows. Under inflation targeting, the interest rate is the main monetary policy tool for influencing activity and inflation, and there is little agreement about the appropriate role of the exchange rate.The exchange rate is a more important monetary policy tool for emerging economies that have adopted inflation targeting than it is for inflation-targeting advanced economies. Inflation-targeting emerging economies generally have less flexible exchange rate arrangements and intervene more frequently in the foreign exchange market than their advanced economy counterparts. The enhanced role of the exchange rate reflects these economies' greater vulnerability to exchange rate shocks and their less developed financial markets. However, their sharper focus on the exchange rate may cause some confusion about the commitment of their central banks to achieve the inflation target and may also complicate policy implementation. Global inflation pressures, greater exchange rate volatility, and the financial stresses from the global financial turmoil that began in mid-2007 are heightening these tensions.
High cell sensitivity to Helicobacter pylori VacA toxin depends on a GPI-anchored protein and is not blocked by inhibition of the clathrin-mediated pathway of endocytosis.

Helicobacter pylori vacuolating toxin (VacA) causes vacuolation in a variety of cultured cell lines, sensitivity to VacA differing greatly, however, among the different cell types. We found that the high sensitivity of HEp-2 cells to VacA was impaired by treating the cells with phosphatidylinositol-specific phospholipase C (PI-PLC) which removes glycosylphosphatidylinositol (GPI)-anchored proteins from the cell surface. Incubation of cells with a cholesterol-sequestering agent, that impairs both structure and function of sphingolipid-cholesterol-rich membrane microdomains ("lipid rafts"), also impaired VacA-induced cell vacuolation. Overexpression into HEp-2 cells of proteins inhibiting clathrin-dependent endocytosis (i.e., a dominant-negative mutant of Eps15, the five tandem Src-homology-3 domains of intersectin, and the K44A dominant-negative mutant of dynamin II) did not affect vacuolation induced by VacA. Nevertheless, F-actin depolymerization, known to block the different types of endocytic mechanisms, strongly impaired VacA vacuolating activity. Taken together, our data suggest that the high cell sensitivity to VacA depends on the presence of one or several GPI-anchored protein(s), intact membrane lipid rafts, and an uptake mechanism via a clathrin-independent endocytic pathway.
Photodynamic Quenched Cathepsin Activity Based Probes for Cancer Detection and Macrophage Targeted Therapy

Elevated cathepsins levels and activities are found in several types of human cancer, making them valuable biomarkers for detection and targeting therapeutics. We designed small molecule quenched activity-based probes (qABPs) that fluoresce upon activity-dependent covalent modification, yielding cell killing by Photodynamic Therapy (PDT). These novel molecules are highly selective theranostic probes that enable both detection and treatment of cancer with minimal side effects. Our qABPs carry a photosensitizer (PS), which is activated by light, resulting in oxidative stress and subsequent cell ablation, and a quencher that when removed by active cathepsins allow the PS to fluoresce and demonstrate PD properties. Our most powerful and stable PS-qABP, YBN14, consists of a selective cathepsin recognition sequence, a QC-1 quencher and a new bacteriochlorin derivative as a PS. YBN14 allowed rapid and selective non-invasive in vivo imaging of subcutaneous tumors and induced specific tumor macrophage apoptosis by light treatment, resulting in a substantial tumor shrinkage in an aggressive breast cancer mouse model. These results demonstrate for the first time that the PS-qABPs technology offers a functional theranostic tool, which can be applied to numerous tumor types and other inflammation-associated diseases.
Efficient Facial Attribute Recognition with a Spatial Codebook

There is a large number of possible facial attributes such as hairstyle, with/without glasses, with/without mustache, etc. Considering large number of facial attributes and their combinations, it is difficult to build attributes classifiers for all possible combinations needed in various applications, especially at the designing stage. To tackle this important and challenging problem, we propose a novel efficient facial attributes recognition algorithm using a learned spatial codebook. The Maximum Entropy and Maximum Orthogonality (MEMO) criterion is followed to learn the spatial codebook. With a spatial codebook constructed at the designing stage, attribute classifiers can be trained on demand with a small number of exemplars with high accuracy on the testing data. Meanwhile, up to 600 times speedup is achieved in the on-demand training process, compared to current state-of-the-art method. The effectiveness of the proposed method is supported by convincing experimental results.
Infrastructure-based location estimation in WLAN networks

This paper focuses on fundamental system deployment aspects of location estimation in 802.11-based wireless networks. We concentrate on adaptable infrastructure-based approaches, where sniffers measure received signal strength from clients to locate them. Our implementation experience and experimental results show that sniffer-based location estimation is feasible and works well provided some important rules are followed. By studying data over a 6-month period, we observe that adaptation of models is necessary for good location estimation, and we show that our techniques enable location estimation with minimal profiling. We also present an intriguing client-assisted approach for location estimation where a client, APs and sniffers collaborate to locate a terminal in enterprise (infrastructure-mode) wireless networks.
Maternal parity and its effect on adipose tissue deposition and endocrine sensitivity in the postnatal sheep

Maternal parity influences size at birth, postnatal growth and body composition with firstborn infants being more likely to be smaller with increased fat mass, suggesting that adiposity is set in early life. The precise effect of parity on fat mass and its endocrine sensitivity remains unclear and was, therefore, investigated in the present study. We utilised an established sheep model in which perirenal–abdominal fat mass (the major fat depot in the neonatal sheep) increases ∼10-fold over the first month of life and focussed on the impact of parity on glucocorticoid sensitivity and adipokine expression in the adipocyte. Twin-bearing sheep of similar body weight and adiposity that consumed identical diets were utilised, and maternal blood samples were taken at 130 days of gestation. One offspring from each twin pair was sampled at 1 day of age, coincident with the time of maximal recruitment of uncoupling protein 1 (UCP1), whilst its sibling was sampled at 1 month, when UCP1 had disappeared. Plasma leptin was lower in nulliparous mothers than in multiparous mothers, and offspring of nulliparous mothers possessed more adipose tissue with increased mRNA abundance of leptin, glucocorticoid receptor and UCP2, adaptations that persisted up to 1 month of age when gene expression for interleukin-6 and adiponectin was also raised. The increase in fat mass associated with firstborn status is therefore accompanied by a resetting of the leptin and glucocorticoid axis within the adipocyte. Our findings emphasise the importance of parity in determining adipose tissue development and that firstborn offspring have an increased capacity for adipogenesis which may be critical in determining later adiposity.
Inference Rules for Recognizing Textual Entailment

In this paper, we explore the application of inference rules for recognizing textual entailment (RTE). We start with an automatically acquired collection and then propose methods to refine it and obtain more rules using a hand-crafted lexical resource. Following this, we derive a dependency-based representation from texts, which aims to provide a proper base for the inference rule application. The evaluation of our approach on the RTE data shows promising results on precision and the error analysis suggests future improvements.
DelPHI: wormhole detection mechanism for ad hoc wireless networks

In mobile ad hoc networks, data transmission is performed within an untrusted wireless environment. Various kinds of attack have been identified and corresponding solutions have been proposed. Wormhole attack is one of the serious attacks which forms a serious threat in the networks, especially against many ad hoc wireless routing protocols and location-based wireless security system. We identify two types of wormhole attacks. In the first type, malicious nodes do not take part in finding routes, meaning that, legitimate nodes do not know their existence. In the second type, malicious nodes do create route advertisements and legitimate nodes are aware of the existence of malicious nodes, just do not know they are malicious. Some researchers have proposed detection mechanisms for the first type. In this paper, we propose an efficient detection method called delay per hop indication (DelPHI). By observing the delays of different paths to the receiver, the sender is able to detect both kinds of wormhole attacks. This method requires neither synchronized clocks nor special hardware equipped mobile nodes. The performance of DelPHI is justified by simulations.
Emotion Recognition on the Go : Providing Personalized Services Based on Emotional States

Personalized products and services are the basic need to attract more customers and to gain a competitive advantage as most of the products available in the market place are similar in technical characteristics, quality, and price. Currently, most of the products and services don’t consider the emotional state of the users before offering personalized services which is a big drawback. User emotional state acts as an important context in perception and decision making, and hence should be taken into consideration for offering personalized services. This applies particularly in mobile scenarios, during which people rapidly change places and environments and hence experience rapid changes of emotionally stimulating situations. Adaption to this rapid change of the emotional state promises to be an efficient way to offer personalized emotion-aware services. This paper reports about an ongoing project investigating the possibility of providing personalized services based on the emotional state of users. The model described in the paper uses emotion-related bio-data and physical activity as input to determine the likely emotional state of the user. Based on the computed emotional state, the model chooses an appropriate personalized service to provide. In order to evaluate the model, an experimental prototype is developed. Successful development and test of the model shows that emotional states can be determined with mobile technology and effectively utilized to offer better personalized products and services. The model can further lead to the possibility of developing emotionally intelligent interactive systems.