Miracle-dz/UniFAR-FTUs huggingface dataset:
A Mouse Model of Visual Perceptual Learning Reveals Alterations in Neuronal Coding and Dendritic Spine Density in the Visual Cortex

Visual perceptual learning (VPL) can improve spatial vision in normally sighted and visually impaired individuals. Although previous studies of humans and large animals have explored the neural basis of VPL, elucidation of the underlying cellular and molecular mechanisms remains a challenge. Owing to the advantages of molecular genetic and optogenetic manipulations, the mouse is a promising model for providing a mechanistic understanding of VPL. Here, we thoroughly evaluated the effects and properties of VPL on spatial vision in C57BL/6J mice using a two-alternative, forced-choice visual water task. Briefly, the mice underwent prolonged training at near the individual threshold of contrast or spatial frequency (SF) for pattern discrimination or visual detection for 35 consecutive days. Following training, the contrast-threshold trained mice showed an 87% improvement in contrast sensitivity (CS) and a 55% gain in visual acuity (VA). Similarly, the SF-threshold trained mice exhibited comparable and long-lasting improvements in VA and significant gains in CS over a wide range of SFs. Furthermore, learning largely transferred across eyes and stimulus orientations. Interestingly, learning could transfer from a pattern discrimination task to a visual detection task, but not vice versa. We validated that this VPL fully restored VA in adult amblyopic mice and old mice. Taken together, these data indicate that mice, as a species, exhibit reliable VPL. Intrinsic signal optical imaging revealed that mice with perceptual training had higher cut-off SFs in primary visual cortex (V1) than those without perceptual training. Moreover, perceptual training induced an increase in the dendritic spine density in layer 2/3 pyramidal neurons of V1. These results indicated functional and structural alterations in V1 during VPL. Overall, our VPL mouse model will provide a platform for investigating the neurobiological basis of VPL.
Early Childhood Lower Respiratory Illness and Air Pollution

Background Few studies of air pollutants address morbidity in preschool children. In this study we evaluated bronchitis in children from two Czech districts: Teplice, with high ambient air pollution, and Prachatice, characterized by lower exposures. Objectives Our goal was to examine rates of lower respiratory illnesses in preschool children in relation to ambient particles and hydrocarbons. Methods Air monitoring for particulate matter < 2.5 μm in diameter (PM2.5) and polycyclic aromatic hydrocarbons (PAHs) was conducted daily, every third day, or every sixth day. Children born May 1994 through December 1998 were followed to 3 or 4.5 years of age to ascertain illness diagnoses. Mothers completed questionnaires at birth and at follow-up regarding demographic, lifestyle, reproductive, and home environmental factors. Longitudinal multivariate repeated-measures analysis was used to quantify rate ratios for bronchitis and for total lower respiratory illnesses in 1,133 children. Results After adjustment for season, temperature, and other covariates, bronchitis rates increased with rising pollutant concentrations. Below 2 years of age, increments in 30-day averages of 100 ng/m3 PAHs and of 25 μg/m3 PM2.5 resulted in rate ratios (RRs) for bronchitis of 1.29 [95 % confidence interval (CI), 1.07–1.54] and 1.30 (95% CI, 1.08–1.58), respectively; from 2 to 4.5 years of age, these RRs were 1.56 (95% CI, 1.22–2.00) and 1.23 (95% CI, 0.94–1.62), respectively. Conclusion Ambient PAHs and fine particles were associated with early-life susceptibility to bronchitis. Associations were stronger for longer pollutant-averaging periods and, among children > 2 years of age, for PAHs compared with fine particles. Preschool-age children may be particularly vulnerable to air pollution–induced illnesses.
A new method for mobile robot navigation in dynamic environment: Escaping algorithm

This paper addresses a new method for navigation in dynamic environment. The proposed method is based on force field method and it is supposed that the robot performs SLAM and autonomous navigation in dynamic environment without any predefined information about dynamic obstacles. The movement of dynamic obstacles is predicted by Kalman filter and is used for collision detection purpose. In the time of collision detection, a modifying force is added to repulsive and attractive forces corresponding to the static environment and leads robot to avoid collision. Moreover, a safe turning angle is defined to assure safe navigation of the robot. The performance of proposed method, named Escaping Algorithm, is verified through different simulation and experimental tests. The results show the proper performance of Escaping Algorithm in term of dynamic obstacle avoidance as a practical method for autonomous navigation.
COMPTEL observations of the γ-ray blazars 3C 454.3 and CTA 102 during the CGRO mission

We have investigated the MeV behaviour of the gamma-ray blazars 3C 454.3 and CTA 102 by analyzing all COMPTEL observations of this sky region during the complete CGRO mission. Both sources are detected by COMPTEL at the upper COMPTEL energies, although their flux estimates may be uncertain by possible minor contributions of nearby unidentified EGRET gamma-ray sources. While CTA 102 was only detected at energies above 10 MeV during the early mission, 3C 454.3 is most significantly detected in the COMPTEL 3- 10 MeV band in the sum of all data. Time-resolved analyses indicate a weak (near COMPTEL threshold) but likely steady 3- 10 MeV emission over years, being independent of the observed time variability at energies above 100 MeV as observed by EGRET. This energy-dependent variability behaviour suggests different emission mechanisms at work in the two bands. Putting the COMPTEL fluxes in multifrequency perspective (radio to gamma-rays) reveals for both sources the typical two-hump blazar spectrum, with a low-energy maximum around the IR and a high-energy maximum at MeV energies. The latter one dominates the energy output across the whole electro-magnetic spectrum. The results of our analyses are discussed in the framework of current blazar modeling.
Selection analysis and phylogeographic simulation study of worldwide spread of Zika virus

Zika is a mosquito borne virus from Flaviviridae family, which recently caused pandemic Zika fever that can lead to development of encephalitis. Even so, this virus is relatively new - it was first discovered in 1947. Recent Zika pandemic caused an interest on its sudden emergence as fatal human pathogen because previously, it was known as the viral pathogen of rhesus monkey. Why and how it crossed species barrier, how this virus spread throughout the world, how it gained the ability to create epidemic - these questions remained unsolved. In this study, selection pressure on two Zika viral protein encoding sequences, Env and M protein, was observed. Bayesian method was employed to create a phylogeographic simulation of its world-wide spread. Phylogenetic study based on maximum-likelihood has been done to reconstruct its ancestry. This study offers better understanding on the genetic and epidemic history of Zika virus in spatial-temporal dimension.
Towards a Higgs phase of gravity in string theory

We consider a braneworld scenario with a black brane parallel to our brane, aiming towards a Higgs phase of gravity in string theory. The existence of the black brane spontaneously breaks the Lorentz symmetry on our brane but preserves the rotational and translational invariance. If all moduli are stabilized then this should lead to a Higgs phase of gravity. We investigate moduli stabilization by using a KKLT-type moduli potential in the context of type IIB warped flux compactification.
Comparing between Maximum Likelihood Estimator and Non-linear Regression Estimation Procedures for NHPP Software Reliability Growth Modelling

Software Reliability Growth Models (SRGMs) have been used by engineers and managers for tracking and managing the reliability change of software to ensure required standard of quality is achieved before the software is released to the customer. SRGMs can be used during the project to help make testing resource allocation decisions and/ or it can be used after the testing phase to determine the latent faults prediction to assess the maturity of software artifact. A number of SRGMs have been proposed and to apply a given reliability model, defect inflow data is fitted to model equations. Two of the widely known and recommended techniques for parameter estimation are maximum likelihood and method of least squares. In this paper we compare between the two estimation procedures for their applicability in context of NHPP SRGMs. We also highlight a couple of practical considerations, reliability practitioners must be aware of when applying SRGMs.
Ensemble of Sparse Cross-Modal Metrics for Heterogeneous Face Recognition

Heterogeneous face recognition aims to identify or verify person identity by matching facial images of different modalities. In practice, it is known that its performance is highly influenced by modality inconsistency, appearance occlusions, illumination variations and expressions. In this paper, a new method named as ensemble of sparse cross-modal metrics is proposed for tackling these challenging issues. In particular, a weak sparse cross-modal metric learning method is firstly developed to measure distances between samples of two modalities. It learns to adjust rank-one cross-modal metrics to satisfy two sets of triplet based cross-modal distance constraints in a compact form. Meanwhile, a group based feature selection is performed to enforce that features in the same position of two modalities are selected simultaneously. By neglecting features that attribute to "noise" in the face regions (eye glasses, expressions and so on), the performance of learned weak metrics can be markedly improved. Finally, an ensemble framework is incorporated to combine the results of differently learned sparse metrics into a strong one. Extensive experiments on various face datasets demonstrate the benefit of such feature selection especially when heavy occlusions exist. The proposed ensemble metric learning has been shown superiority over several state-of-the-art methods in heterogeneous face recognition.
Parents' perception about their preterm child's social interaction reaching school age

A key element in social development is interaction with others. Preterm infants have an increased risk for problems in this aspect. We aimed to gain insight into parents’ perception about their preterm child’s social interaction upon reaching school age. Twelve caregivers of preterm infants aged five to eight years participated in in-depth interviews. The children were born after a pregnancy of less than 36 weeks. All participants live in the northern part of the Netherlands. The level of education of the caregivers varied between lower vocational education to university. Parents informed us about their child being a little fighter, having issues of coping with their disabilities in social contexts, and how their child withdraws from situations that are overly stimulating. They also expressed their concerns about the future, how they encourage their child, and how they experience the transition to primary school. Parents’ experiences concerning the social interaction of their preterm child can be categorized into eight themes of processes in social interaction: child factors, self-regulation, realtime social interaction, long-term social interaction, parental factors, parenting, social environment, and social experiences. In supporting preterm children, it is recommended to focus on the relatedness between real-time social interaction experiences and long-term outcomes.
High speed precise control of robot arms with assigned speed under torque constraint by trajectory generation in joint co-ordinates

This paper presents a methodology for off-line trajectory generation that guarantees high-speed and precise end-effector point-to-point (positioning) control of industrial robot arms, within the bounds of the most relevant constraints. The constraints being addressed are the saturation of joint torque and the limit of Cartesian velocity. In the proposed methodology, shortest path of travel and maximum-joint torque/acceleration utility is guaranteed so that the true minimum-time trajectory is generated. The minimum-time trajectory is then compensated for the delay dynamics using a feedforward compensator. An illustrative implementation is presented using the Performer MK-3s robot arm, with which attractive results have been obtained. Being an off-line algorithm, the proposed method can be conveniently introduced into existing servo control systems of industrial robot arms, and to enhance their performance.
Popularity Is Everything: A New Approach to Protecting Passwords from Statistical-Guessing Attacks

We propose to strengthen user-selected passwords against statistical-guessing attacks by allowing users of Internet-scale systems to choose any password they want--so long as it's not already too popular with other users. We create an oracle to identify undesirably popular passwords using an existing data structure known as a count-min sketch, which we populate with existing users' passwords and update with each new user password. Unlike most applications of probabilistic data structures, which seek to achieve only a maximum acceptable rate false-positives, we set a minimum acceptable false-positive rate to confound attackers who might query the oracle or even obtain a copy of it.
Performance Improvement of Average Based Spatial Filters through Multilevel Preprocessing using Wavelets

Image denoising filters intended to remove Gaussian noise, principally exploit a procedure called spatial averaging. Quite a lot of averaging approaches have been developed and numerous fall in the class of either pixel-based or patch-based or diffusion-based approach. While the designed filters get rid of the noise, the high frequency information will also be degraded, as the filters fit into a nature of integration. To preserve the high frequency information and hence the denoising performance, we propose a preprocessing filter designed in the wavelet domain, can be placed prior to the given existing spatial domain averaging filter. The proposed filter enhances high frequency information of given noisy image and obviously this enhanced information will also be degraded at some extent by the subsequent spatial domain filters. Accordingly, proposed preprocessing filter and existing average based spatial domain filter on a whole gives improved denoising performance. Simulation experiments have been conducted and it is proved that the proposed preprocessing filter certainly improves the denoising results of existing standard spatial domain filtering such as Anisotropic filtering, Bilateral filtering, Non local means filtering and recently proposed Probabilistic non local means filtering in terms of peak signal to noise ratio (PSNR) and structural similarity index (SSIM).
Packet level scheduling schemes for multi-user MIMO systems with beamforming

We investigate the packet-level scheduling for the downlink of multiple-input multiple-output (MIMO) multi-user systems using beamforming. We consider the traffic arrival process and different packet lengths. We tackle low-complex practical implementation that provides low average packet transmission delay and bit error rates (BER) to users. We propose a work-conserving scheduling scheme that considers different users guarantees (heterogeneous users). We implement and compare different MIMO schedulers at the packet level. Simulations show the low average packet transmission delay and bit error rate of our proposed scheduler.
User oriented trajectory search for trip recommendation

Trajectory sharing and searching have received significant attentions in recent years. In this paper, we propose and investigate a novel problem called User Oriented Trajectory Search (UOTS) for trip recommendation. In contrast to conventional trajectory search by locations (spatial domain only), we consider both spatial and textual domains in the new UOTS query. Given a trajectory data set, the query input contains a set of intended places given by the traveler and a set of textual attributes describing the traveler's preference. If a trajectory is connecting/close to the specified query locations, and the textual attributes of the trajectory are similar to the traveler'e preference, it will be recommended to the traveler for reference. This type of queries can bring significant benefits to travelers in many popular applications such as trip planning and recommendation. There are two challenges in the UOTS problem, (i) how to constrain the searching range in two domains and (ii) how to schedule multiple query sources effectively. To overcome the challenges and answer the UOTS query efficiently, a novel collaborative searching approach is developed. Conceptually, the UOTS query processing is conducted in the spatial and textual domains alternately. A pair of upper and lower bounds are devised to constrain the searching range in two domains. In the meantime, a heuristic searching strategy based on priority ranking is adopted for scheduling the multiple query sources, which can further reduce the searching range and enhance the query efficiency notably. Furthermore, the devised collaborative searching approach can be extended to situations where the query locations are ordered. The performance of the proposed UOTS query is verified by extensive experiments based on real and synthetic trajectory data in road networks.
Ant Colony Based Optimization Technique for Voltage Stability Control

This paper presents Ant Colony Optimization (ACO) technique for optimal reactive power dispatch (ORPD) in order to improve voltage stability condition along with transmission loss and voltage profile monitoring. ACO is a new cooperative agent's approach, which is inspired by the observation of the behaviours of real ant colonies on the topics of ant trial formation and foraging method. The set of cooperating agents called "ant" cooperate to find the optimal point of reactive power dispatch. Comparative studies presented with respect to Evolutionary Programming (EP) and Artificial Immune System (AIS) had indicated the merit of the proposed technique. Tests were conducted on the IEEE Reliability Test System producing promising results as compared to EP and AIS. The capability of developed ACO in solving continuous optimization problems rather that only limited to graphical optimization problems has been revealed as the added value in the algorithm.
Componentes del proceso de resiliencia comunitaria: conocimientos culturales, capacidades sociales y estrategias organizativas

El presente texto ofrece una aproximacion al proceso resiliente a escala comunitaria, a partir de un planteamiento teoretico que entrelaza tres componentes: los conocimientos culturales, las capacidades sociales, y las estrategias organizativas. Los conocimientos culturales y las capacidades sociales hacen referencia a mecanismos intersubjetivos y cognitivos colectivos, que dan cuenta de las pautas de interpretacion del mundo y de las herramientas psicologicas para resguardarse, enfrentarse y reconstruirse frente a las situaciones adversas. Las estrategias organizativas se refieren a la materializacion de las abstracciones en practicas concretas que implican negociaciones en ambitos formales e informales y relaciones de poder presentes en el contexto. Se defiende que al transitar hacia una comprension que articula dimensiones complejas, que integran diversas disciplinas academicas con el conocimiento de la propia gente y sus aspectos culturales, es posible comprender las caracteristicas propias de la capacidad colectiva de los grupos de resistir, sobreponerse y reconstruirse ante la adversidad. En ese sentido se genera un aporte a la comprension y posible intervencion en los procesos de resiliencia comunitaria, con fines de reconstruccion y fortalecimiento ante contextos hostiles y amenazantes sin que se pierdan las caracteristicas que involucran su particularidad cultural.
Reassessment of Expectations as a Comparison Standard in Measuring Service Quality: Implications for Further Research

The authors respond to concerns raised by Cronin and Taylor (1992) and Teas (1993) about the SERVQUAL instrument and the perceptions-minus-expectations specification invoked by it to operationalize service quality. After demonstrating that the validity and alleged severity of many of those concerns are questionable, they offer a set of research directions for addressing unresolved issues and adding to the understanding of service quality assessment.
ITERATIVE TURBO DECODING USING GIBBS SAMPLING

This paper discusses an iterative multiuser receiver for codedivision multiple access (CDMA) with forward error control coding. The receiver is derived from the maximum aposteriori (MAP) criterion for the joint received signal. A major drawback of the MAP receiver is its heavy computational cost that grows exponentially with the number of users. An alternative solution is proposed here based on a MonteCarlo approach where the MAP probabilities are computed via the Gibbs sampler. The computational cost of the MonteCarlo based method grows linearly with the number of users times the number of Monte-Carlo runs. In a second part of the paper, we present a ‘turbo’ equalization method that combines the previous decoding technique with an EM (Expectation Maximization) estimation technique for the blind or semi-blind identification of the channel parameters. Simulation results are finally presented to illustrate the performances of the proposed method.
A dynamic visual interface for news stream analysis

In this paper, we introduce a new visualization primitive called TextWheel, and present a visual analytics system for news streams which can bring multiple attributes of the news articles and the macro/micro relations between news streams and keywords into one coherent analytical context and meanwhile convey the dynamic natures of news streams. We use our system to analyze several large-scale news corpora related to some major companies and the results demonstrate the high potential of our method.
Robust active queue management control for TCP/IP networks

This paper is concerned with the robustness of AQM controller in internet congestion control. For the parameter variety and disturbance in TCP/IP network, a robust H∞ optimal AQM controller is designed based on uncertain time-delay model of the systems. The existence condition of the observer is obtained by applying Lyapunov method and matrix inequalities, and the design method of the feedback controller via the observe state is presented. The robust stability and the effect of interference restraint in the presence of network parameter variety and disturbance is validated and tested through simulations.
An Analytic Model for Predicting the Performance of Distributed Applications on Multicore Clusters

Computationally demanding applications can ben- efit from distributed processing. Distributed processing offers immerse computational power from collections of autonomous systems which can provide up to thousands of processing cores. Over the years, a considerable amount of research has been put towards developing performance prediction models for dis- tributed applications. Amdahl's law states that the speedup of any parallel program has an upper bound which is determined by the amount of time spent on the sequential fraction of the program, no matter how small and regardless of the number of processing nodes used. This paper discusses some of the short comings of this law in the current age. We propose a theoretical model for predicting the behavior of a distributed algorithm given the network restrictions of the cluster used. The paper focuses on the impact of latency and bandwidth which affect the cost of interprocessor communication and the number of processing nodes used to predict the performance. The model shows good accuracy in comparison to Amdahl's law.