Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance of a Hybrid Receiver in the Downlink Multicell CDMA System

In this paper, we propose a novel low-complexity receiver, namely, a hybrid receiver (HR) for the downlink of a multicell code-division multiple-access (CDMA) system with a transmit delay diversity transmission scheme. The proposed receiver is designed by combining the merits of the decorrelating receiver (DR) and the conventional receiver (CR). Unlike most multiuser receivers, HR operates with the same information as CR. For a target performance metric (e.g., bit error probability (BEP)=10-2), the reduced-complexity HR significantly outperforms CR, DR, and minimum mean-square error (MMSE) receiver with estimated channel information. We also compare the performance of the reduced-complexity HR with a reduced-complexity MMSE receiver, which slightly outperforms the former at a price of higher complexity
Modeling the Contribution of Central Versus Peripheral Vision in Scene, Object, and Face Recognition

It is commonly believed that the central visual field is important for recognizing objects and faces, and the peripheral region is useful for scene recognition. However, the relative importance of central versus peripheral information for object, scene, and face recognition is unclear. In a behavioral study, Larson and Loschky (2009) investigated this question by measuring the scene recognition accuracy as a function of visual angle, and demonstrated that peripheral vision was indeed more useful in recognizing scenes than central vision. In this work, we modeled and replicated the result of Larson and Loschky (2009), using deep convolutional neural networks. Having fit the data for scenes, we used the model to predict future data for large-scale scene recognition as well as for objects and faces. Our results suggest that the relative order of importance of using central visual field information is face recognition>object recognition>scene recognition, and vice-versa for peripheral information.
Scheduling under Uncertainty for Single-Hub Intermodal Freight System

This paper examines the optimization of an intermodal system with freight transfers at a single hub by determining when departures should be scheduled on outbound routes, given information about the probabilistic arrivals of vehicles on inbound routes. The intermodal system is modeled with stochastic programming, and the schedule of outbound vehicles is optimized with a genetic algorithm. The model is designed to minimize the expected total cost of operating an intermodal system while considering all capacity constraints arising in the real world. This model allows the system performance to be computed numerically, without the approximations of alternative methods such as simulation. Although the model can be applied to the most general case, the model seems to be especially suitable for analyzing systems with a relatively small number of arrivals on inbound routes. In particular, the model can be successfully applied to situations where statistical or queuing analyses are not applicable because of the small number of events (vehicle arrivals). The authors specifically analyze an intermodal system consisting of multiple inbound truck routes and multiple outbound airline routes. However, the mathematical model developed in this paper is applicable to other combinations of transportation modes.
Parallel Branch and Bound Model Using Logarithmic Sampling (PBLS) for Symmetric Traveling Salesman Problem

1627 Abstract—Very Large and/or computationally complex optimization problems sometimes require parallel or highperformance computing for achieving a reasonable time for computation. One of the most popular and most complicate problems of this family is “Traveling Salesman Problem”. In this paper we have introduced a Branch & Bound based algorithm for the solution of such complicated problems. The main focus of the algorithm is to solve the “symmetric traveling salesman problem”. We reviewed some of already available algorithms and felt that there is need of new algorithm which should give optimal solution or near to the optimal solution. On the basis of the use of logarithmic sampling, it was found that the proposed algorithm produced a relatively optimal solution for the problem and results excellent performance as compared with the traditional algorithms of this series.
Cooperating Swarms: A Paradigm for Collective Intelligence and its Application in Finance

The control of nonlinear chaotic system and the estimation of parameters is a vital issue in nonlinear science. Studies on parameter estimation for chaotic systems have been investigated recently. A variant of Particle Swarm Optimization (PSO) known as Chaotic Multi Swarm Particle Swarm Optimization (CMS-PSO) is proposed which is inspired from the metaphor of ecological co-habitation of species. The generic PSO is modified with the chaotic sequences for multi-dimension parameter estimation and optimization by forming multiple cooperating swarms. Results demonstrate the effectiveness of the scheme in successfully estimating the unknown parameters of a new hyperchaotic finance system. Numerical results and comparison demonstrate that for the given parameters of the nonlinear system, CMS-PSO can identify the optimized parameters effectively to reach the pareto optimal solution and convergence speed. General Terms Swarm intelligence, parameter estimation, chaos theory, Lorenz system
Annual Changes in Day-length, Temperature, and Circulating Reproductive Hormones in Thoroughbred Stallions and Geldings

Changes in follicle-stimulating hormone (FSH), luteinizing hormone (LH), prolactin, immunoreactive(ir)-inhibin, testosterone, estradiol-17β, and insulin-like growth factor (IGF)-I in Thoroughbred stallions along with changes in prolactin secretion in geldings were studied. The correlations of day-length with changes in the concentrations of these hormones were also studied. Five stallions and thirteen geldings were employed to draw blood samples in monthly basis and radioimmunoassay was performed to measure these hormones. All hormones showed a seasonal pattern, the levels being highest during the breeding season and lowest during the winter months. Most of the hormones were at their highest concentration during the month of April, the mid of spring in northern hemisphere. The concentration of circulating IGF-I also demonstrated seasonality, the peak lying on the month of April. The plasma concentration of prolactin also increased during the breeding season. This phenomenon was similar both in stallions and geldings although geldings had lower concentration than that of stallions. The changes in concentration of prolactin in stallions and geldings correlated more towards the day-length than towards the temperature. These results clearly indicate the seasonality of pituitary and gonadal hormones of Thoroughbred stallions, the activity being highest during the month of April and May of the breeding season.
Topology optimization for application-specific networks-on-chip

Compared to the well understood macro networks, networks-on-chip introduce novel design challenges. The characteristics of the system data flows and the knowledge of the required wire lengths can be exploited to optimize for speed and power consumption. A component library for flexible construction of interconnection architectures is being developed at the Tampere University of Technology to enable the creation of application development platforms. The overall design flow of these development platforms is reviewed in this paper. Network-on-chip topology optimization is addressed by describing the methodologies used by an effective design automation tool. The detailed cost functions of the tool capture the factors contributing to the speed and power consumption of asynchronous interconnections, while different abstraction level input information is supported. A case study into the application domain of industrial process control and monitoring is presented in order to evaluate the result quality.
Microscopic characterization of Pseudomonas Aeruginosa confines separate from clinical cases by testing RAPD-PCR method

Pseudomonas Aeruginosa is one of the leading opportunistic infectious agents especially in immune-suppressed patients such like burn victims. Carbapenems like Imipenem (IMP) and Meropenem (MEM) are the choice antimicrobial drugs, which used in the treatment of Penicillin and Aminoglycoside-resistant Pseudomonas Aeruginosa isolates. Based on the importance of the detection of infectious source and their resistance transfer algorithm have a main effect on the control of nosocomial infections this study designed for to consider the antibiotic sensitivity and the genetic pattern of Pseudomonas Aeruginosa isolate in burned patients hospitalized in Ghotbeddin Shirazi Hospital with RAPD-PCR technique. According to the Antibiogram results, the most sensitivity was related to the Gentamicin with 50% while the most resistant related to the Nalidixic Acid, Erythromycin, and Cefotaxime with 90% resistant to all. With RAPD-PCR technique used primers 272, 277 and 287 were detected 18, 15, and 11 algorithms respectively. RAPD-PCR is a genotyping method with a high efficiency and good for the study of bacterial epidemiology and polymorphism.
An Accurate Method To Voxelize Polygonal Meshes

The process of generating discrete surfaces in a volumetric representation, termed voxelization, is confronted with topological considerations as well as accuracy and efficiency requirements. We introduce a new method for voxelizing planar objects which, unlike existing methods, provides topological conformity through geometric measures. We extend our approach to provide, for the first time, an accurate and coherent method for voxelizing polygon meshes. This method eliminates common voxelization artifacts at edges and vertices. We prove the method's topological attributes and report performance of our implementation. Finally, we demonstrate that this approach forms a basis for a new set of voxelization algorithms by voxelizing an example cubic object.
Unequal wage of men and women in ICT in the Czech Republic?

Abstract This article analyzes inequality in the remuneration of ICT professionals in the Czech Republic during its evolution from 2000 to 2014. Data are analyzed by means of linear regression and the method of least squares for the entire period 2000–2014, and two periods 2000–2008 and 2009–2014, where we analyze de facto trends that emerged in the wages of ICT professionals after the economic crisis. We explore the evolution of wages and hence inequality between men and women from four perspectives: profession (ICT specialist – CZ ISCO 25 a ICT Technician CZ ISCO 35), education (secondary with maturita, bachelor’s, master’s, and doctoral degree), sphere of activity (wage sphere and salary sphere) and age (categories under 30 years, 30–50 years, and over 50 years). We find out that the trends in the equalization of wages between men and women are positive – a gradual equalization is taking place. What, however, we cannot regard as positive is the pace of equalization. The process is very slow and protracted. Another positive finding in terms of ICT professionals is the fact that the average amount of wage difference between men and women is approximately 17%, which is lower than in the entire Czech economy.
A Statistical Multiresolution Approach for Face Recognition Using Structural Hidden Markov Models

This paper introduces a novel methodology that combines the multiresolution feature of the discrete wavelet transform (DWT) with the local interactions of the facial structures expressed through the structural hidden Markov model (SHMM). A range of wavelet filters such as Haar, biorthogonal 9/7, and Coiflet, as well as Gabor, have been implemented in order to search for the best performance. SHMMs perform a thorough probabilistic analysis of any sequential pattern by revealing both its inner and outer structures simultaneously. Unlike traditional HMMs, the SHMMs do not perform the state conditional independence of the visible observation sequence assumption. This is achieved via the concept of local structures introduced by the SHMMs. Therefore, the long-range dependency problem inherent to traditional HMMs has been drastically reduced. SHMMs have not previously been applied to the problem of face identification. The results reported in this application have shown that SHMM outperforms the traditional hidden Markov model with a 73% increase in accuracy.
Partial FPGA bitstream encryption enabling hardware DRM in mobile environments

The concept of digital right management (DRM) has become extremely important in current mobile environments. This paper shows how partial bitstream encryption can allow the secure distribution of hardware applications resembling the mechanisms of traditional software DRM. Building on the recent developments towards the secure distribution of hardware cores, the paper demonstrates a prototypical implementation of a user mobile device supporting such distribution mechanisms. The prototype extends the Android operating system with support for hardware reconfigurability and showcases the interplay of novel security concepts enabled by hardware DRM, the advantages of a design flow based on high-level synthesis, and the opportunities provided by current software-rich reconfigurable Systems-on-Chips. Relying on this prototype, we also collected extensive quantitative results demonstrating the limited overhead incurred by the secure distribution architecture.
# NLP-1 Improving Classification in Phone-Based Language Recognition with Maximum Entropy Models April 2006

Phone-based automatic language recognition systems have recently been surpassed in accuracy by systems that make use of spectral information alone. Intuitively, it seems that the higher level knowledge employed by a phone-based approach should enable it to achieve higher levels of accuracy than spectralonly approaches. One purpose of this study is to show that the generative nature of the n-gram language models (LMs) used in phone-based approaches leads to modeling inefficiencies which ultimately degrade accuracy in language recognition systems. As a potential solution to this inefficiency, we present a discriminative approach to phone-based language recognition based on conditional maximum entropy models. We present results from our preliminary experiments with two types of back-end classifiers on a ten-language task using short files of varying durations from the handsegmented and labeled portion of the OGI_TS corpus. We also experiment with various normalization schemes. Our maximum entropy models yield results with a minimum possible decision cost function (DCF) value of 10.2% while that of the n-gram LMs is 12.8%. Consequently, the potential of the maximum entropy approach is much greater.
Proteomic Analyses of Human Cytomegalovirus Strain AD169 Derivatives Reveal Highly Conserved Patterns of Viral and Cellular Proteins in Infected Fibroblasts

Human cytomegalovirus (HCMV) particle morphogenesis in infected cells is an orchestrated process that eventually results in the release of enveloped virions. Proteomic analysis has been employed to reveal the complexity in the protein composition of these extracellular particles. Only limited information is however available regarding the proteome of infected cells preceding the release of HCMV virions. We used quantitative mass spectrometry to address the pattern of viral and cellular proteins in cells, infected with derivatives of the AD169 laboratory strain. Our analyses revealed a remarkable conservation in the patterns of viral and of abundant cellular proteins in cells, infected for 2 hours, 2 days, or 4 days. Most viral proteins increased in abundance as the infection progressed over time. Of the proteins that were reliably detectable by mass spectrometry, only IE1 (pUL123), pTRS1, and pIRS1 were downregulated at 4 days after infection. In addition, little variation of viral proteins in the virions of the different viruses was detectable, independent of the expression of the major tegument protein pp65. Taken together these data suggest that there is little variation in the expression program of viral and cellular proteins in cells infected with related HCMVs, resulting in a conserved pattern of viral proteins ultimately associated with extracellular virions.
On the dephasing of genetic oscillators

Significance Genetic oscillators are ubiquitous regulatory motifs in the molecular control circuits of living cells. Prominent examples include the cell cycle and cellular signaling. There are two primary sources of noise in these oscillators: the binary character of gene states and the low copy numbers of proteins. This molecular noise induces dephasing in oscillators in analogy to the way imperfections in clocks lead to disparities in time. We can study how these two distinct sources contribute to dephasing and how well it can be approximated by adding noise to a macroscopic model. Using the NFκB/IκB oscillator as an example, we find that gene noise leads to significant deviations from the often-used phenomenological models. The digital nature of genes combined with the associated low copy numbers of proteins regulating them is a significant source of stochasticity, which affects the phase of biochemical oscillations. We show that unlike ordinary chemical oscillators, the dichotomic molecular noise of gene state switching in gene oscillators affects the stochastic dephasing in a way that may not always be captured by phenomenological limit cycle-based models. Through simulations of a realistic model of the NFκB/IκB network, we also illustrate the dephasing phenomena that are important for reconciling single-cell and population-based experiments on gene oscillators.
Perceptual Video Quality and Blockiness Metrics for Multimedia Streaming Applications

Guaranteeing a certain level of quality for multimedia streaming applications is quite well understood in terms of network QoS, but it is much more tenuous in terms of perceptual quality as perceived by the user. In this paper, we classify video quality measurement schemes and review existing approaches with a focus on non-intrusive quality metrics, which do not require access to the reference video. In particular, we evaluate three different no-reference blockiness metrics and compare their performance.
Speech style and speaker recognition: a case study

This work presents an experimental evaluation of the effect of different speech styles on the task of speaker identification. Although the informal notion of a speaking style does not readily translate into operational procedures for eliciting speech in one style or another, we make use of willfully altered voice extracted from the CHAINS corpus and methodically assess the effect of its use in both testing and training a reference speaker identification system and a reference speaker verification system. In this work we contrast normal readings of text with two varieties of imitative styles and with the familiar, non-imitative, variant of fast speech. Furthermore, we test the applicability of a novel speech parameterization that has been suggested as a promising technique in the task of speaker identification: the pyknogram frequency estimate coefficients pykfec. The experimental evaluation indicates that both the reference verification and identification systems are affected by variations in style of the speech material used. Our case studies also indicates that the adoption of pykfec as speech encoding methodology has an overall favorable effect on the systems accuracy scores.
Supporting JFL Learners with User Customized Kanji Learning System Using Computer Graphics

This paper describes the development and evaluation of a user adaptive kanji learning system for JFL (Japanese as a foreign language) learners. Japanese language education for international students is an emergent issue in Japan. Especially learning kanji is a real challenge for them. Therefore we have tried to implement our newly developed kanji learning system, which received good reputations when introduced it into an elementary school, to a Japanese language class for non-native speakers. A pilot evaluation was conducted in order to find which aspects of our system to be modified to fit for JSL learners. The result of the pilot evaluation reveals that the participants' feedback was not good compared with that of elementary school children, especially about computer animations and fun factor. A user-unfriendliness about its navigation has been found. Our future works include the improvement of computer animations, the implementation of English navigation and the summary dashboards for reviewing.
Deterministic Shift-Reduce Parsing for Unification-Based Grammars by Using Default Unification

Many parsing techniques including parameter estimation assume the use of a packed parse forest for efficient and accurate parsing. However, they have several inherent problems deriving from the restriction of locality in the packed parse forest. Deterministic parsing is one of solutions that can achieve simple and fast parsing without the mechanisms of the packed parse forest by accurately choosing search paths. We propose (i) deterministic shift-reduce parsing for unification-based grammars, and (ii) best-first shift-reduce parsing with beam thresholding for unification-based grammars. Deterministic parsing cannot simply be applied to unification-based grammar parsing, which often fails because of its hard constraints. Therefore, it is developed by using default unification, which almost always succeeds in unification by overwriting inconsistent constraints in grammars.
Discovering molecular functions significantly related to phenotypes by combining gene expression data and biological information

MOTIVATION The analysis of genome-scale data from different high throughput techniques can be used to obtain lists of genes ordered according to their different behaviours under distinct experimental conditions corresponding to different phenotypes (e.g. differential gene expression between diseased samples and controls, different response to a drug, etc.). The order in which the genes appear in the list is a consequence of the biological roles that the genes play within the cell, which account, at molecular scale, for the macroscopic differences observed between the phenotypes studied. Typically, two steps are followed for understanding the biological processes that differentiate phenotypes at molecular level: first, genes with significant differential expression are selected on the basis of their experimental values and subsequently, the functional properties of these genes are analysed. Instead, we present a simple procedure which combines experimental measurements with available biological information in a way that genes are simultaneously tested in groups related by common functional properties. The method proposed constitutes a very sensitive tool for selecting genes with significant differential behaviour in the experimental conditions tested. RESULTS We propose the use of a method to scan ordered lists of genes. The method allows the understanding of the biological processes operating at molecular level behind the macroscopic experiment from which the list was generated. This procedure can be useful in situations where it is not possible to obtain statistically significant differences based on the experimental measurements (e.g. low prevalence diseases, etc.). Two examples demonstrate its application in two microarray experiments and the type of information that can be extracted.