Miracle-dz/UniFAR-FTUs huggingface dataset:
A robust role for motor cortex

The role of motor cortex in non-primate mammals remains unclear. More than a century of stimulation, anatomical and electrophysiological studies has implicated neural activity in this region with all kinds of movement. However, following the removal of motor cortex, rats retain most of their adaptive behaviours, including previously learned skilled movements. Here we revisit these two conflicting views of motor cortex and present a new behaviour assay, challenging animals to respond to unexpected situations while navigating a dynamic obstacle course. Surprisingly, rats with motor cortical lesions show clear impairments facing an unexpected collapse of the obstacles, while showing no impairment with repeated trials in many motor and cognitive metrics of performance. We propose a new role for motor cortex: extending the robustness of sub-cortical movement systems, specifically to unexpected situations demanding rapid motor responses adapted to environmental context. The implications of this idea for current and future research are discussed.
The Novel Cross-Layer Design for Channel Access in Ad Hoc Cognitive Radio Network

In this paper, we discuss the framework of ad-hoc cognitive radio network (ad-hoc CRN), the network model and the dynamic spectrum access (DSA) for ad-hoc cognitive radio network first. Moreover, the motivation for cross-layer design in ad-hoc cognitive radio network is analysis. At last, we proposed a novel architecture in which the dynamic channel allocation is achieved by a cross-layer design between the PHY and MAC layers for ad-hoc cognitive radio network. Simulation indicated the performance of network is improved significantly.
Sub-threshold VLSI logic family exploiting unbalanced pull-up/down network, logical effort and inverse-narrow-width techniques

This paper presents a complete energy optimized sub-threshold standard cell library exploiting unbalanced pull-up/down (PU/PD) network, logical effort and inverse-narrow-width (INW) techniques. Individual logic cell is optimized for ultra-low-energy applications with low-to-moderate speed requirement. Three 14-tap 8-bit FIR filters are fabricated using a 0.18-μm CMOS technology, while one of them achieved the minimum energy/tap (0.0234 pJ) and 0.365 Figure-of-Merit (FoM) at 100 kHz, 0.31 V.
Debiasing the Becker – DeGroot – Marschak valuation mechanism

This experiment finds that the Becker-DeGroot-Marschak (BDM) (1964) valuation mechanism under-predicts the proportion of subjects choosing cash over an item. The extent of the divergence is increasing in risk aversion, which is consistent with reference dependent preferences. This suggests a reframing of the BDM to improve its performance. The modified BDM mechanism is found to better match choices at a single offer price (SOP).
Lightweight methods for large-scale product categorization

In this article, we present a study about classification methods for large-scale categorization of product offers on e-shopping web sites. We present a study about the performance of previously proposed approaches and deployed a probabilistic approach to model the classification problem. We also studied an alternative way of modeling information about the description of product offers and investigated the usage of price and store of product offers as features adopted in the classification process. Our experiments used two collections of over a million product offers previously categorized by human editors and taxonomies of hundreds of categories from a real e-shopping web site. In these experiments, our method achieved an improvement of up to 9% in the quality of the categorization in comparison with the best baseline we have found. © 2011 Wiley Periodicals, Inc.
Using the cognitive dimensions framework to measure the usability of a class library

In this paper we describe our attempts at using the Cognitive Dimensions framework to evaluate the usability of an object oriented (OO) application programming interface (API). The Cognitive Dimensions framework was originally designed as a tool to evaluate and describe the usability of a programming language but to our knowledge, no reports of it being used to evaluate an API are publicly available. Since many popular OO languages come with an existing set of APIs in the form of OO class libraries, it is important to ensure that the APIs developers use are usable, just as it is to ensure that a programming language is usable also. Thus, in order to measure the usability of an API, we have adapted the Cognitive Dimensions framework so that it better measures those aspects of an API that we feel have an impact on its usability. This paper describes these modifications and the reasons we chose to make them. In addition, using the framework in an industrial setting has presented some interesting challenges, and so we describe
On the Relevance of Sophisticated Structural Annotations for Disulfide Connectivity Pattern Prediction

Disulfide bridges strongly constrain the native structure of many proteins and predicting their formation is therefore a key sub-problem of protein structure and function inference. Most recently proposed approaches for this prediction problem adopt the following pipeline: first they enrich the primary sequence with structural annotations, second they apply a binary classifier to each candidate pair of cysteines to predict disulfide bonding probabilities and finally, they use a maximum weight graph matching algorithm to derive the predicted disulfide connectivity pattern of a protein. In this paper, we adopt this three step pipeline and propose an extensive study of the relevance of various structural annotations and feature encodings. In particular, we consider five kinds of structural annotations, among which three are novel in the context of disulfide bridge prediction. So as to be usable by machine learning algorithms, these annotations must be encoded into features. For this purpose, we propose four different feature encodings based on local windows and on different kinds of histograms. The combination of structural annotations with these possible encodings leads to a large number of possible feature functions. In order to identify a minimal subset of relevant feature functions among those, we propose an efficient and interpretable feature function selection scheme, designed so as to avoid any form of overfitting. We apply this scheme on top of three supervised learning algorithms: k-nearest neighbors, support vector machines and extremely randomized trees. Our results indicate that the use of only the PSSM (position-specific scoring matrix) together with the CSP (cysteine separation profile) are sufficient to construct a high performance disulfide pattern predictor and that extremely randomized trees reach a disulfide pattern prediction accuracy of on the benchmark dataset SPX, which corresponds to improvement over the state of the art. A web-application is available at http://m24.giga. ulg.ac. be:81/x3CysBridges.
The Effects of Deregulation on the Performance of Financial Institutions: The Case of Spanish Savings Banks

This paper examines the impact of regulatory reform on the performance of Spanish savings banks. To this end it uses panel data for the period 1986-1995 and a flexible variable profit function that incorporates time-varying technical efficiency. The focus is whether increased competition brought on by deregulation affected performance of banks over time. Bank performance, measured by the percentage change in profitability, ceteris paribus, is decomposed into technical change and change in technical efficiency both of which are defined in terms of the profit function. We also examine output technical efficiency, which is defined in terms of the production possibility frontier. Several alternative models with different specifications of technical efficiency are used to check robustness of the results. Empirical results show declining levels of output technical efficiency along with a significantly high rate of technical progress. In spite of declining technical efficiency during this period, we find evidence of an increasing trend in productivity growth.
A Novel Machine Vision Algorithm for a Fast Response Quality Control System

We are investigating the design of machine vision algorithms for real time inspection of filter components for quality assurance. Filter components are rigid 3D objects with predefined geometry so that a good deal of knowledge can be incorporated into the system design. However, while the objective is to reason about 3D structure, current machine vision techniques only allow the acquisition of 2D object descriptions and this is normally referred to as a 3D-2D correspondence problem. We have investigated geometrical techniques in 2D and in 3D and have developed a novel method to analyse rigid body transformations that have been successfully applied to machine vision calibration problems. In this paper, we first describe a geometrical analysis of image correspondence applied to 2D rigid body transformations. We then develop a novel calibration algorithm that, given a 3D model of an object and a set of 3D-2D image correspondence points allows the calibration of all transformation parameters of interest. The calibrated parameters can then be used to verify the physical dimensions of the object under inspection. For a comparative analysis, we also develop a calibration algorithm based on epipolar geometry applied to the same task. Experimental results have shown that our novel algorithm performs much better than the algorithm based on epipolar geometry and that it is well suited to the real time requirements of the task.
Design and Architectural Assessment of 3-D Resistive Memory Technologies in FPGAs

Emerging nonvolatile memories (eNVMs) such as phase-change random access memories (PCRAMs) or oxide-based resistive random access memories (OxRRAMs) are promising candidates to replace Flash and Static Random Access Memories in many applications. This paper introduces a novel set of building blocks for field-programmable gate arrays (FPGAs) using eNVMs. We propose an eNVM-based configuration point, a look-up table structure with reduced programming complexity and a high-performance switchbox arrangement. We show that these blocks yield an improvement in area and write time of up to 3× and 33×, respectively, versus a regular Flash implementation. By integrating the designed blocks in an FPGA, we demonstrate an area and delay reduction of up to 28% and 34%, respectively, on a set of benchmark circuits. These reductions are due to the eNVM 3-D integration and to their low on-resistance state value. Finally, we survey many flavors of the technologies and we show that the best results in terms of area and delay are obtained with Pt/TiO2/Pt stack, while the lowest leakage power is achieved by InGeTe stack.
Corporate Social Capital and Firm Performance

Traditional corporate social capital formulations have been based on a firm’s positioning within its network of market place alliances. This paper extends this model by incorporating additional firm status attributes into an integrated model for corporate social capital. An empirical study of some 155 firms in the global Information Technology sector was conducted, exploring the linkage between elements of corporate social capital and firm performance. A key contribution of this research is the specificity of the linkages identified between particular aspects of corporate social capital and firm performance. The results find that human capital is the most predictive of firm performance. Beyond human capital, a firm’s market network centrality and financial soundness were significant predictors for at least one firm performance measure. Surprisingly investments in internal capital and R&D were found in some circumstances to detract from a firm’s performance. Interestingly these effects were found to be moderated by financial soundness i.e. the negative effects were largely for firms with poor financial performance.
Coffman deadlocks in SCOOP

In this paper we address the deadlock detection problem in the context of SCOOP - an OO-programming model for concurrency, recently formalized in Maude. We present the integration of a deadlock detection mechanism on top of the aforementioned formalization and analyze how an abstract semantics of SCOOP based on a notion of "may alias expressions" can contribute to improving the deadlock detection procedure.
The Interphase Influences on the Particle-Reinforced Composites with Periodic Particle Configuration

This work improved upon an effective micromechanical method to analyze the mechanical properties of three-dimensional particle-reinforced composites (PRC) with consideration of the interfacial debonding. By incorporating the interfacial debonding model, Mises yield criterion, and failure theory, the effects of particle shape, particle volume fraction, and loading condition on the mechanical properties are studied. A comparison of simulation results obtained from the established method and published experimental data is presented. Good consistency can be found in this study. On this basis, the interfacial cohesive strength and particle shape effects on the biaxial failure strength of particle-reinforced composites with interfacial debonding were also studied. The results revealed that both interfacial strength and particle shape have significant effects on biaxial tensile failure strength. However, the different interfacial strength influence on failure envelope can hardly be discerned in biaxial compressive loading.
Two Genes on A/J Chromosome 18 Are Associated with Susceptibility to Staphylococcus aureus Infection by Combined Microarray and QTL Analyses

Although it has recently been shown that A/J mice are highly susceptible to Staphylococcus aureus sepsis as compared to C57BL/6J, the specific genes responsible for this differential phenotype are unknown. Using chromosome substitution strains (CSS), we found that loci on chromosomes 8, 11, and 18 influence susceptibility to S. aureus sepsis in A/J mice. We then used two candidate gene selection strategies to identify genes on these three chromosomes associated with S. aureus susceptibility, and targeted genes identified by both gene selection strategies. First, we used whole genome transcription profiling to identify 191 (56 on chr. 8, 100 on chr. 11, and 35 on chr. 18) genes on our three chromosomes of interest that are differentially expressed between S. aureus-infected A/J and C57BL/6J. Second, we identified two significant quantitative trait loci (QTL) for survival post-infection on chr. 18 using N2 backcross mice (F1 [C18A]×C57BL/6J). Ten genes on chr. 18 (March3, Cep120, Chmp1b, Dcp2, Dtwd2, Isoc1, Lman1, Spire1, Tnfaip8, and Seh1l) mapped to the two significant QTL regions and were also identified by the expression array selection strategy. Using real-time PCR, 6 of these 10 genes (Chmp1b, Dtwd2, Isoc1, Lman1, Tnfaip8, and Seh1l) showed significantly different expression levels between S. aureus-infected A/J and C57BL/6J. For two (Tnfaip8 and Seh1l) of these 6 genes, siRNA-mediated knockdown of gene expression in S. aureus–challenged RAW264.7 macrophages induced significant changes in the cytokine response (IL-1 β and GM-CSF) compared to negative controls. These cytokine response changes were consistent with those seen in S. aureus-challenged peritoneal macrophages from CSS 18 mice (which contain A/J chromosome 18 but are otherwise C57BL/6J), but not C57BL/6J mice. These findings suggest that two genes, Tnfaip8 and Seh1l, may contribute to susceptibility to S. aureus in A/J mice, and represent promising candidates for human genetic susceptibility studies.
Stability analysis and stabilization of switched nonlinear systems : a switched nonlinearity-dependent Lyapunov function

A class of switched non-quadratic Lyapunov functions is considered in this paper. The function is associated with discrete-time switched systems subject to mode-dependent cone bounded nonlinearities and saturation actuator. These Lyapunov functions depend on the switched nonlinearities and on the active mode, instead of the time-invariant Lur’e-type function. The only assumption considered here is the mode-dependent sector condition, without constraint related to the slope of the nonlinearities. The stability analysis and control design problems lead to linear matrix inequalities (LMI) conditions. Optimization problems are provided to cope with both issues in order to maximize the size of the basin of attraction estimate, which may be composed of disconnected sets. Numerical examples illustrate via comparisons that we improve the results of the literature.
Social simulation of commercial and financial behaviour for fraud detection research

We present a social simulation model that covers three main financial services: Banks, Retail Stores, and Payments systems. Our aim is to address the problem of a lack of public data sets for fraud detection research in each of these domains, and provide a variety of fraud scenarios such as money laundering, sales fraud (based on refunds and discounts), and credit card fraud. Currently, there is a general lack of public research concerning fraud detection in the financial domains in general and these three in particular. One reason for this is the secrecy and sensitivity of the customers data that is needed to perform research. We present PaySim, RetSim, and BankSim as three case studies of social simulations for financial transactions using agent-based modelling. These simulators enable us to generate synthetic transaction data of normal behaviour of customers, and also known fraudulent behaviour. This synthetic data can be used to further advance fraud detection research, without leaking sensitive information about the underlying data. Using statistics and social network analysis (SNA) on real data we can calibrate the relations between staff and customers, and generate realistic synthetic data sets. The generated data represents real world scenarios that are found in the original data with the added benefit that this data can be shared with other researchers for testing similar detection methods without concerns for privacy and other restrictions present when using the original data.
Sex and haemodynamics in pulmonary arterial hypertension

Female sex is a risk factor for pulmonary arterial hypertension (PAH), yet females have better survival than males. We sought to determine if sex was associated with baseline haemodynamics in subjects with PAH, and whether age modified these relationships. We conducted a pooled analysis from 11 randomised trials submitted to the US Food and Drug Administration. The study sample included 1211 subjects with idiopathic PAH, 25% of whom were males, and 489 subjects with connective tissue disease-associated PAH, 13% of whom were males. After multivariable adjustment, right atrial pressure was 1.36 mmHg higher (95% CI 0.44–2.27, p=0.004), cardiac index was -0.14 L·min−1·m−2 lower (95% CI -0.23–0.04, p=0.01) and pulmonary vascular resistance was 1.23 Wood units higher (95% CI 0.18–2.27, p=0.02) in males compared with females. Younger males had 5.43 mmHg (95% CI 2.20–8.66, p=0.001) higher mean pulmonary arterial pressures than younger females, but these relationships were attenuated after age 45 years. In the subgroup of connective tissue disease-associated PAH, males may have had higher right atrial pressure. These findings implicate age as a modifier and provide further evidence of sexual dimorphism in PAH. More evidence of sex-related differences in PVD; age implicated as a possible modifier of sexual dimorphism in PAH http://ow.ly/qJO3h
Instrument, Analysis, and Coaching Considerations with Velocity-Based Training

Instrument, Analysis, and Coaching Considerations with Velocity-Based Training Steven Mark Hirsch Master of Science Department of Exercise Science University of Toronto 2018 The purpose of this thesis was to assess: 1) The validity of commercial instruments to quantify barbell velocity, 2) The influence of an instruction to attain a specific target velocity in comparison to “move as fast as possible” on barbell velocity and subsequent repetition maximum test performance, and 3) The variability in load-velocity profiles and maximum velocity capabilities between athletes, and if the variability in training intensity is dependent on the absolute velocity target. GymAware was found to be valid across the widest range of velocities, followed by Tendo. PUSH was not valid. Participants moved faster when provided with a specific target velocity (p< 0.001) with no difference in subsequent repetition maximum test performance (p= 0.43). Finally, there was large variability in load-velocity profiles and maximum velocity capabilities between athletes. Faster absolute velocities also yielded greater variability in training intensity (p< 0.001).
Opportunistic Scheduling with Power Control in Cognitive Radio Networks

Cognitive Radio network (CRN) is a network composed of elements that, through learning and reasoning, dynamically adapt to varying network conditions in order to improve spectrum efficiency for wireless networking. To investigate opportunistic scheduling, this paper develops a methodology based on the jointly consideration of the power control and flow routing. The two-phrase cross-layer design with multiuser interference consideration is explained. The influences from the diverse transmission power control are identified. Using Bandwidth Foot Product (BFP) as a link metric, the power control granularity is predicted by a proposed strategy that considers radio resource management. To support user communication sessions, the Power Control with Decentralized Scheduling (PCDS) algorithm is proposed. Experimental and simulation results are provided as verification of the analyses and solutions. Keywordscognitive radio network; cross-layer design; power control; opportunistic scheduling.