Miracle-dz/UniFAR-FTUs huggingface dataset:
Face Recognition Using A Radial Basis Function Classifier

Face recognition is probably the most natural way to perform a biometric authentication between human beings. However, the available technology for automatic systems still presents some drawbacks and is far away from human performance. In this paper we use the same DCT feature extraction approach presented in previous ICCST03 and ICCST'05. However, we improve the experimental results using a radial basis function (RBF) neural network in combination with the coding of the recognized class. We explain why the RBF, do not have the limitations of other classifiers such as the MLP. We also propose a method for dealing with the high number of classes associated to the task of face recognition which takes into account the limitations of the RBF as classifiers, and discuss the weakness of these methods when the number of training samples is limited. We have performed an exhaustive study about the neural network architecture and parameters, which has let us to establish relevant conclusions about the optimal configuration
Complete genome of the multidrug-resistant Acinetobacter baumannii strain KBN10P02143 isolated from Korea

Acinetobacter baumannii, a strictly aerobic, non-fermentative, Gram-negative coccobacillary rod-shaped bacterium, is an opportunistic pathogen in humans. We recently isolated a multidrug-resistant A. baumannii strain KBN10P02143 from the pus sample drawn from a surgical patient in South Korea. We report the complete genome of this strain, which consists of 4,139,396 bp (G + C content, 39.08%) with 3,868 protein-coding genes, 73 tRNAs and six rRNA operons. Identification of the genes related to multidrug resistance from this genome and the discovery of a novel conjugative plasmid will increase our understanding of the pathogenicity associated with this species.
Rendering the Actually Existing Sharing Economy Visible:Home-Grown Food and the Pleasure of Sharing

Despite the unprecedented attention paid to the sharing economy and despite the growing interest in household food production, the non-market and non-monetised sharing of home-grown food – a social practice at the intersection of these two concerns – has so far largely escaped scholars’ attention. The goal of the article is twofold. First, drawing on a large-scale survey (2058 respondents) and four focus groups conducted in the Czech Republic in 2015, the article shows that in the Global North the sharing of home-grown food is a surprisingly widespread and economically and environmentally significant practice. Second, the article to some extent aims to break with the research tradition that deems studies conducted in the periphery of the Global North lacking in potential to produce more generally valid insights. It therefore seeks to counter the scripting of Eastern Europe on the margins of the geographies of knowledge production. The article contests the causal link between economic hardship and informal food practices, views these practices as sustainability by outcome rather than intention, and suggests they are compatible with the tenets of alternative food networks. While not perceived as sites of outright resistance to capitalism, these spaces are viewed by practitioners as constituting valuable domains of socially and culturally motivated human interactions, driven by the desire for fresh and healthy food, fulfilling personal hobbies, and the development of enjoyable social ties.
On power sharing and stability in autonomous inverter-based microgrids

We consider the problem of voltage and frequency stability for an autonomous inverter-based microgrid. An LMI-based decentralized feedback control design is derived that stabilizes the system under the consideration of droop-like controllers aiming to achieve power sharing among the different generation units. We provide a design procedure that accounts for uncertainties in line impedances and loads while guaranteeing zero steady-state frequency deviation.
Disclosing secrets in watermarking and data hiding

Watermarking security can be seen as the difficulty of estimating the secret keys used in the embedding and detection processes. This paper considers the problem of watermarking security emphasizing the point of view of parameter estimation. The main theoretical and practical results obtained by the authors in this regard are reviewed and discussed. In some cases, the problem posed by secret key estimation resembles other classical estimation problems, but sometimes it is fundamentally different, demanding for new estimation tools or the adaptation of existing techniques.
Peer Victimization and Dating Violence Among LGBTQ Youth

This study examined the moderating role of school violence and peer victimization on the association between sexual orientation and mental health. The sample consisted of 11,794 high school students (Mage = 16, SD = 1.23; female assigned at birth = 51%; 1.8% identified as transgender) across 23 schools. Participants completed a self-report survey that assessed sexual orientation, ethnicity, gender identity, victimization experiences (e.g., peer and dating), perceptions of school violence and crime, as well as anxiety and suicidality. Multilevel analyses indicated that lesbian, gay, bisexual, questioning, and transgender (LGBTQ) individuals with lower rates of victimization had significantly lower rates of suicidality compared to LGBTQ individuals with higher rates of victimization. LGBTQ individuals in schools with high student perceptions of school violence and crime had higher suicidality than their non-LGBTQ counterparts. LGBTQ youth in schools with lower school violence and crime levels had lower rates of suicidality than their non-LGBTQ counterparts. Interventions need to consider multiple forms of victimization and school environment as potential risk and protective factors for LGBTQ youth.
Reversible image watermarking using integer transform on Lifting wavelet coefficients

A novel reversible watermarking scheme based on 2-D Lifting wavelet transform, LWT2, integer transform and block linking method is presented in the paper. LWT2 produces coefficient subbands whose values are in integer form. The integer transform is then applied for embedding watermark bits. In addition, the simple and fast block linking method is used for indicating the embedding location. Experimental results show that our proposed method can achieve high capacity for image watermarking while preserve good image quality.
Comparison of Artificial Neural Network and Regression Models in Software Effort Estimation

Good practices in software project management are basic requirements for companies to stay in the market, because the effective project management leads to improvements in product quality and cost reduction. Fundamental measurements are the prediction of size, effort, resources, cost and time spent in the software development process. In this paper, predictive Artificial Neural Network (ANN) and Regression based models are investigated, aiming at establishing simple estimation methods alternatives. The results presented in this paper compare the performance of both methods and show that artificial neural networks are effective in effort estimation.
Multi-Objective Game Theoretic Schedulingof Bag-of-Tasks Workflows on Hybrid Clouds

Scheduling multiple large-scale parallel workflow applications on heterogeneous computing systems like hybrid clouds is a fundamental NP-complete problem that is critical to meeting various types of QoS (Quality of Service) requirements. This paper addresses the scheduling problem of large-scale applications inspired from real-world, characterized by a huge number of homogeneous and concurrent bags-of-tasks that are the main sources of bottlenecks but open great potential for optimization. The scheduling problem is formulated as a new sequential cooperative game and propose a communication and storage-aware multi-objective algorithm that optimizes two user objectives (execution time and economic cost) while fulfilling two constraints (network bandwidth and storage requirements). We present comprehensive experiments using both simulation and real-world applications that demonstrate the efficiency and effectiveness of our approach in terms of algorithm complexity, makespan, cost, system-level efficiency, fairness, and other aspects compared with other related algorithms.
A unique requirement for the leukotriene B4 receptor BLT1 for neutrophil recruitment in inflammatory arthritis

Neutrophil recruitment into tissue plays an important role in host defense and disease pathogenesis, including the inflammatory arthritides. A multitude of diverse chemoattractants have been implicated in neutrophil recruitment, suggesting that they have overlapping functions in mediating this critical biological response. However, here we demonstrate a unique, non-redundant role for the leukotriene B4 receptor BLT1 in mediating neutrophil recruitment into the joint in the K/BxN mouse model of inflammatory arthritis. We demonstrate that neutrophil expression of BLT1 was absolutely required for arthritis generation and chemokine production in this model, and that specific BLT1 inhibition reversed established disease. Adoptive transfer of wild-type (WT) neutrophils restored arthritis and chemokine production in BLT1(-/-) mice. Surprisingly, the primary effect of the transferred WT neutrophils into BLT1(-/-) mice was to promote the entry of endogenous BLT1(-/-) neutrophils into the joints of these mice. However, continued joint inflammation was dependent on the presence of WT neutrophils, indicating an ongoing specific requirement for BLT1-activated neutrophils in mediating BLT1(-/-) neutrophil recruitment by other chemoattractants. These experiments demonstrate that neutrophil BLT1 functions in a novel and essential non-cell-autonomous manner to enable the recruitment of additional neutrophils not expressing this receptor, thereby amplifying the inflammatory response in autoantibody-induced arthritis.
Efficient power co-estimation techniques for system-on-chip design

We present efficient power estimation techniques for HW/SW System-On-Chip (SOC) designs. Our techniques are based on concurrent and synchronized execution of multiple power estimators that analyze different parts of the SOC (we refer to this as co-estimation), driven by a system-level simulation master. We motivate the need for power co-estimation, and demonstrate that performing independent power estimation for the various system components can lead to significant errors in the power estimates, especially for control-intensive and reactive embedded systems. We observe that the computation time for performing power co-estimation is dominated by: (i) the requirement to analyze/simulate some parts of the system at lower levels of abstraction in order to obtain accurate estimates of timing and switching activity information and (ii) the need to communicate between and synchronize the various simulators. Thus, a naive implementation of power co-estimation may be too inefficient to be used in an iterative design exploration framework. To address this issue, we present several acceleration (speedup) techniques for power co-estimation. The acceleration techniques are energy caching, software power macromodeling, and statistical sampling. Our speedup techniques reduce the workload of the power estimators for the individual SOC components, as well as their communication/synchronization overhead. Experimental results indicate that the use of the proposed acceleration techniques results in significant (8/spl times/ to 87/spl times/) speedups in SOC power estimation time, with minimal impact on accuracy. We also show the utility of our co-estimation tool to explore system-level power tradeoffs for a TCP/IP network interface card sub-system and an automotive controller.
Optimizing reward rate in two alternative choice tasks : Mathematical formalism

In this report we collect and summarize mathematical results and formalism appropriate to describing one-dimensional drift-diffusion processes (stochastic ordinary differential equations) and related first passage and probability density evolution problems, governed by the backward and forward Kolmogorov (Fokker-Planck) equations respectively. We start by reviewing the Neyman-Pearson and Sequential Probability Ratio tests as optimal strategies for choosing between two alternative hypotheses in the presence of accumulating, noisy data. The continuum analog of both of these tests is a constant drift-diffusion process, and we give direct proofs of optimality with respect to reward rate of such a process in the broader class of Ornstein-Uhlenbeck processes, both in terms of first passages and density evolution. These correspond to the free response and interrogation protocols used in psychological testing. We end by considering the effects of variable gain on selected inputs to drift-diffusion and Ornstein-Uhlenbeck processes, and deriving optimal gain schedules for time-varying signalto-noise ratios. Parts of this report are an extended version of the appendix to [1]; others appear in the paper [2].
Sertraline for the prevention of relapse in detoxicated alcohol dependent patients with a comorbid depressive disorder: a randomized controlled trial.

AIMS We performed a double-blind, placebo-controlled randomized trial of sertraline in recently detoxified alcohol-dependent patients with current depressive symptoms. The objectives of the study were to evaluate the efficacy of sertraline at achieving stable abstinence, at ameliorating depressive symptoms and at improving quality of life in these patients. METHODS The study included 83 patients, who received either sertraline (50-150 mg/day) or placebo for 24 weeks. The primary outcome criteria were the rate of relapse into alcohol consumption and the rate of response on the Montgomery and Asberg Depression Rating Scale (MADRS). RESULTS At the end of the treatment period, relapse rates were 23.1% in the placebo group and 31.8% in the sertraline group. Responder rates for depression were 38.5% for the placebo group and 44.2% for the sertraline group. There was no significant difference between treatment groups with either variable. However, when patients were stratified into severe (MADRS score >or=26) and moderate (MADRS score <26) depression at inclusion, a significant treatment benefit with sertraline was observed in the former group. Quality of life, determined by the SF-36, improved in both groups, with more benefit observed for the sertraline group on mental health items. Sertraline was well tolerated, and the incidence of adverse events was similar in the two treatment groups. CONCLUSIONS The explanation for the overall good outcome in both treatment groups and for the inability to demonstrate a clear treatment effect may reside in the clinical features of the patients included.
Distributed Average Consensus using Probabilistic Quantization

In this paper, we develop algorithms for distributed computation of averages of the node data over networks with bandwidth/power constraints or large volumes of data. Distributed averaging algorithms fail to achieve consensus when deterministic uniform quantization is adopted. We propose a distributed algorithm in which the nodes utilize probabilistically quantized information to communicate with each other. The algorithm we develop is a dynamical system that generates sequences achieving a consensus, which is one of the quantization values. In addition, we show that the expected value of the consensus is equal to the average of the original sensor data. We report the results of simulations conducted to evaluate the behavior and the effectiveness of the proposed algorithm in various scenarios.
Power management strategy study for a multiple unit train

The use of multiple power sources in railway applications is common on modern rolling stock. Prime movers are usually distributed along the length of the train, and are able to provide distributed traction. If the primary power for such vehicles comes from individual diesel engines, or if there are non-linear efficiency characteristics within the drive train, then the supervisory controllers for each motive unit may be decoupled and operated independently. This can potentially save energy. This paper investigates two global optimisation algorithms. These are the dynamic programming method, and the greedy algorithm. Both of these two graphic searching algorithms help to find the optimum power distribution between engines for a typical Diesel Multiple Unit (DMU) train. It is found that the application of Dijkstra's Algorithm, in association with a Fibonacci heap (F-heap) based advanced data structure, can significantly improve the computing efficiency while maintaining the same optimisation precision as the dynamic programming method. In an optimisation process with M power requirements and n possible engine power states for each requirement, the time complexity will be reduced from O(M*n 2 ) to O(M*(n log(n) + n)).
The d-Level Nested Logit Model: Assortment and Price Optimization Problems

We consider assortment and price optimization problems under the d -level nested logit model. In the assortment optimization problem, the goal is to find the revenue-maximizing assortment of products to offer, when the prices of the products are fixed. Using a novel formulation of the d -level nested logit model as a tree of depth d , we provide an efficient algorithm to find the optimal assortment. For a d -level nested logit model with n products, the algorithm runs in O ( d n log n ) time. In the price optimization problem, the goal is to find the revenue-maximizing prices for the products, when the assortment of offered products is fixed. Although the expected revenue is not concave in the product prices, we develop an iterative algorithm that generates a sequence of prices converging to a stationary point. Numerical experiments show that our method converges faster than gradient-based methods, by many orders of magnitude. In addition to providing solutions for the assortment and price optimization problems, we give support for the d -level nested logit model by demonstrating that it is consistent with the random utility maximization principle and equivalent to the elimination by aspects model.
The retreat from overgeneralisation errors: A multiple-paradigm approach

This thesis examines children’s argument structure overgeneralisation errors (e.g. *Don’t giggle me! ). Errors of this kind arise from children observing that certain verbs can appear in more than one argument structure (e.g. The ball rolled/Homer rolled the ball). This pattern can be usefully generalised to allow children who have heard a verb produced in only one of these structures (e.g. The window opened) to produce it in the other (e.g. Marge opened the window). The ability to generalise patterns to new items is key to children becoming productive language users. However, if they overgeneralise this pattern, errors will result: Bart giggled is grammatical, but *Lisa giggled Bart (meaning Lisa made Bart giggle) is not. This thesis tested three hypotheses designed to explain how children retreat from such overgeneralisation errors, or, indeed, avoid making them altogether: the semantic verb class hypothesis (Pinker, 1989); the entrenchment hypothesis (Braine & Brooks, 1995); and the preemption hypothesis (Goldberg, 1995). Chapter 3 uses a novel-verb grammaticality judgment paradigm to investigate overgeneralisation errors in the locative construction (e.g. *Marge filled tea into the cup). Chapter 4 investigates overgeneralisation errors in the transitive and intransitive constructions, using a grammaticality judgment paradigm with known verbs, as well as a production priming paradigm designed to elicit errors from young children (e.g. *Homer swam the fish). Finally, in order to investigate the role of semantics in language development more generally, Chapter 5 moves beyond overgeneralisation errors to investigate children’s acquisition of the passive construction (e.g. Bart was helped by Lisa). This thesis adds to a growing body of work demonstrating that none of the individual theories (semantics, entrenchment, preemption) alone is able to explain children’s retreat from overgeneralisation, and that an integrated approach, such as that proposed by Ambridge and colleagues’ FIT account, is required to account for the data. The thesis moves our understanding forward by demonstrating both that this account can explain error patterns in production, and that the role of verb-in-construction semantic compatibility (a key aspect of the FIT account) can explain children’s acquisition of argument structure more widely.
On the Fairness of TCP Throughput Under Loss

There have been a number of efforts devoted to predicting or modeling TCP performance under the presence of various network defects with a statistic such as average TCP throughput. While the average TCP throughput is useful, it does not provide insight on the variability of results that may be seen across multiple end users. This paper reports on a simulation study to characterize the distribution of TCP throughput performance across multiple users sharing a bottleneck link using fairness as a metric. In particular, the sensitivity of the results is explored when considering variations in the numbers of clients sharing the bottleneck link and the effects of loss in the network experienced by an individual client.
Ownership Structure , and Managerial Decisions : Effects on R & D Expenditure

This study provides evidence that antitakeover amendments affect managerial behavior and provide long–term implications for the firm. Our study links the change in R&D expenditure after amendment adoption to board composition and ownership structure. Results are consistent with the hypothesis that the amendments provide an environment that allows managers to focus on long–term objectives. R&D expenditures significantly increased in the period subsequent to amendment adoption. Additionally, higher levels of R&D subsequent to amendment adoption are positively related to increasing levels of board representation as well as share ownership by directors who have some affiliation with the firm. * For further information, please contact: Victoria B. McWilliams; Chair, Department of Finance; College of Commerce and Finance; Villanova University; 800 Lancaster Ave.; Villanova, PA 19085; (610) 519–7395, (610) 519–6881 (fax), MCWILLIAM@CF_FACULTY. VILL.EDU (e–mail). We are grateful to Steve Ferris, Tom McWilliams, Marty Meznar, Afsaneh Nahavandi, and participants at workshops held at Drexel University and Villanova University for comments on earlier drafts of this manuscript. Antitakeover Amendments, Ownership Structure, and Managerial Decisions: Effects on R&D Expenditure