Miracle-dz/UniFAR-FTUs huggingface dataset:
Facial Biometric Templates and Aging: Problems and Challenges for Artificial Intelligence

The performance of face recognition and/or authentication systems is greatly affected by within-person variations encountered in human faces. Within person facial variations distort the appearance of faces leading to inconsistencies between facial features stored in templates and features derived from a face, of the corresponding subject, captured at a different instance. For this reason a consider- able amount of effort has been devoted to the development of methods for elimi- nating within person-variations during face recognition/authentication. Among all types of within-person variations encountered, aging-related variations display unique characteristics that make the process of dealing with this type of variation a challenging task. In this paper we describe experiments that enable the quantifica- tion of the effects of aging on the performance of face recognition systems. We al- so review typical approaches that aim to eliminate the effects of aging in face rec- ognition and outline future research directions for this area.
Persistence of lower dimensional tori of general types in Hamiltonian systems

This work is a generalization to a result of J. You (1999) We study the persistence of lower dimensional tori of general type in Hamiltonian systems of general normal forms. By introducing a modified linear KAM iterative scheme to deal with small divisors, we shall prove a persistence result, under a Melnikov type of non-resonance condition, which particularly allows multiple and degenerate normal frequencies of the unperturbed lower dimensional tori.
Spatiotemporal power spectra of motion parallax: the case of cluttered 3D scenes

We examine the spatiotemporal power spectra of image sequences that depict dense motion parallax, namely the parallax seen by an observer moving laterally in a cluttered 3D scene. Previous models of the spatiotemporal power have accounted for effects such as a static 1/f spectrum in each image frame, a spreading of power at high spatial frequencies in the direction of motion, and a bias toward either lower or higher image speeds depending on the 3D density of objects the scene. Here we use computer graphics to generate a parameterized set of image sequences and qualitatively verify the main features of these models. The novel contribution is to discuss how failures of 1/f scaling can occur in cluttered scenes. Such failures have been described for the spatial case, but not for the spatiotemporal case. We find that when objects in the cluttered scene are visible over a wide range of depths, and when the image size of objects is smaller than the image width, failures of 1/f scaling tend to occur at certain critical frequencies, defined by a correspondence between object size and object speed.
Towards minimizing delay in MHWN based on activity estimation

Delay is a critical QoS performance metric for Multi-hop Wireless Network (MHWN). It is well known that schemes that give a higher data rate to the link with larger queue length can achieve high throughput performance. However, this queue-length-based approach may potentially suffer from large (even infinite) packet delays due to the subsequent contention problem caused by the next-hop arriving packets. In this paper, we present a novel method for minimizing multi-hop transmission delay by optimally setting the data rate along a selected path in MHWN based on multi-hop activity aware delay analysis. We develop an analytic model that enables us to obtain closed form expressions for delay in terms of multi-hop activity variables. Then a measuring deviation algorithm is used to derive the number of saturated nodes over a given set of topology. Numerical results and simulations are presented for different network load scenarios and compared with traditional studies.
Low-Wage Employment Subsidies in a Labor-Turnover Model of the 'Natural Rate'

This paper models two kinds of wage subsidy in a model of the natural rate having a continuum of workers ranked by their productivity - a flat wage subsidy and a graduated wage subsidy, each financed by a proportional payroll tax. In the small open economy case, with the graduation as specified, we show that both schemes expand employment throughout the distribution; for those whose productivity is sufficiently far below the mean, take-home pay is unambiguously up, though the tax financing lowers take-home pay at the mean and above.
Adaptation of the CHARM DNA methylation platform for the rat genome reveals novel brain region-specific differences

Comprehensive High-throughput Arrays for Relative Methylation (CHARM) was recently developed as an experimental platform and analytic approach to assess DNA methylation (DNAm) at a genome-wide level. Its initial implementation was for human and mouse. We adapted it for rat and sought to examine DNAm differences across tissues and brain regions in this model organism. We extracted DNA from liver, spleen, and three brain regions: cortex, hippocampus, and hypothalamus from adult Sprague Dawley rats. DNA was digested with McrBC, and the resulting methyl-depleted fraction was hybridized to the rat CHARM array along with a mock-treated fraction. Differentially methylated regions (DMRs) between tissue types were detected using normalized methylation log-ratios. In validating 24 of the most significant DMRs by bisulfite pyrosequencing, we detected large mean differences in DNAm, ranging from 33-59%, among the most significant DMRs in the across-tissue comparisons. The comparable figures for the hippocampus vs. hypothalamus DMRs were 14-40%, for the cortex vs. hippocampus DMRs, 12-29%, and for the cortex vs. hypothalamus DMRs, 5-35%, with a correlation of r2 = 0.92 between the methylation differences in 24 DMRs predicted by CHARM and those validated by bisulfite pyrosequencing. Our adaptation of the CHARM array for the rat genome yielded highly robust results that demonstrate the value of this method in detecting substantial DNAm differences between tissues and across different brain regions. This platform should prove valuable in future studies aimed at examining DNAm differences in particular brain regions of rats exposed to environmental stimuli with potential epigenetic consequences.
Strengthening the Gluteus Medius Using Various Bodyweight and Resistance Exercises

ABSTRACT THE GLUTEUS MEDIUS (GMED) IS AN IMPORTANT MUSCLE AND, IF WEAK, CAN CAUSE KNEE, HIP, OR LOWER-BACK PATHOLOGIES. THIS ARTICLE REVIEWS METHODS OF GMED STRENGTH ASSESSMENT, PROVIDES EXERCISES THAT TARGET THE GMED BASED ON ELECTROMYOGRAPHY, PRESENTS HOW TO IMPLEMENT GMED STRENGTHENING IN HEAVY RESISTANCE TRAINING PROGRAMS, AND EXPLAINS THE IMPORTANCE OF INCLUDING THESE EXERCISES IN THESE PROGRAMS.
Classifying internet one-way traffic

Internet background radiation (IBR) is a very interesting piece of Internet traffic as it is the result of attacks and misconfigurations. Previous work has primarily analyzed IBR traffic to large unused IP address blocks called network telescopes. In this work, we build new techniques for monitoring one-way traffic in live networks with the main goals of 1) expanding our understanding of this interesting type of traffic towards live networks as well as of 2) making it useful for detecting and analyzing the impact of outages. Our first contribution is a classification scheme for dissecting one-way traffic into useful classes, including one-way traffic due to unreachable services, scanning, peer-to-peer applications, and backscatter. Our classification scheme is helpful for monitoring IBR traffic in live networks solely based on flow level data. After thoroughly validating our classifier, we use it to analyze a massive data-set that covers 7.41 petabytes of traffic from a large backbone network to shed light into the composition of one-way traffic. We find that the main sources of one-way traffic are malicious scanning, peer-to-peer applications, and outages. In addition, we report a number of interesting observations including that one-way traffic makes a very large fraction, i.e., between 34% and 67%, of the total number of flows to the monitored network, although it only accounts for only 3.4% of the number of packets, which suggests a new conceptual model for Internet traffic in which IBR is dominant in terms of flows. Finally, we demonstrate the utility of one-way traffic of the particularly interesting class of unreachable services for monitoring network and service outages by analyzing the impact of interesting events we detected in the network of our university.
Using Loops in Decision-Theoretic Refinement Planners

Classical AI planners use loops over subgoals to move a stack of blocks by repeatedly moving the top block. Probabilistic planners and reactive systems repeatedly try to pick up a block to increase the probability of success in an uncertain environment. These planners terminate a loop only when the goal is achieved or when the probability of success has reached some threshold. The tradeoff between the cost of repeating a loop and the expected benefit is ignored. Decision-theoretic refinement planners take this tradeoff into account, but to date, have been limited to considering only finite length plans. In this paper, we describe extensions to a decision-theoretic refinement planner, DRIPS, for handling loops. The extended planner terminates a loop when it can show that all plans with one or more additional iterations of the loop have lower utility. We give conditions under which optimal plans are finite and conditions under which the planner will find an optimal plan and terminate. With loops, a decision-theoretic refinement planner searches an infinite space of plans, making search control critical. We demonstrate how our sensitivity analysis-based search control technique provides effective search control without requiring the domain designer to hand-tune parameters, or otherwise provide search control information.
Self-Tuning PI Controller Based on Neural Network for Permanent Magnet Synchronous Motor

In servo motor drive applications, the variation of load inertia will degrade drive performance severely. Good dynamic and static performance of servo system requires controlling inertia robustly. In order to get the moment of rotational inertia, online identification methods based on model reference adaptive identification (MRAI) were developed in this paper. Then a well-trained neural network supplies the PI controller with suitable gain according to each operating condition pair (inertia, angular velocity error, and angular velocity) detected. To demonstrate the advantages of the proposed self-tuning PI control technique based on neural network, the simulation was executed in this research. The simulation results show that the method not only enhances the fast tracking performance, but also increases the robustness of the synchronous motor drive.
Prognostic implication of ABC transporters and cancer stem cell markers in patients with stage III colon cancer receiving adjuvant FOLFOX-4 chemotherapy

Cancer stem cell (CSC) and ATP-binding cassette (ABC) transporters are associated with treatment resistance and outcomes of patients with cancer. The present study investigated the prognostic implications of pre-therapeutic expression of ABC transporters and CSC markers in patients with colon cancer (CC) who received adjuvant 5-fluorouracil, leucovorin and oxaliplatin combination therapy (FOLFOX-4). The immunohistochemical expression of 3 ABC transporters, including ABC subfamily C member 2 (ABCC2), ABCC3 and ABC subfamily G member 2 (ABCG2), and 3 CSC markers, including sex determining region Y-box 2 (SOX2), leucine-rich repeat-containing G protein-coupled receptor 5 and aldehyde dehydrogenase 1, were determined in 164 CC tissues from patients with stage III CC, who underwent postoperative FOLFOX-4 chemotherapy. The association between the protein expression and patients' prognoses was statistically analyzed. ABCG2 was associated with favorable overall survival rate (OS; P=0.001), and ABCC2, ABCG2 and SOX2 were associated with increased disease-free survival rate (DFS; P=0.001, 0.002 and 0.013, respectively). In multivariate analyses, ABCG2 was an independent prognostic factor for OS [hazard ratio (HR)=2.877; P=0.046], and ABCC2 and SOX2 were independent prognostic factors for DFS (HR=2.831; P=0.014; HR=2.558, P=0.020, respectively). ABCC2, ABCG2 and SOX2 may be promising prognostic markers for patients with CC receiving FOLFOX-4 therapy.
All Negative Moods Are Not Equal: Motivational Influences of Anxiety and Sadness on Decision Making.

Affective states of the same valence may have distinct, yet predictable, influences on decision processes. Results from three experiments show that, in gambling decisions, as well as in job-selection decisions, sad individuals are biased in favor of high-risk/high-reward options, whereas anxious individuals are biased in favor of low-risk/low-reward options. We argue that these biases occur because anxiety and sadness convey distinct types of information to the decision-maker and prime different goals. While anxiety primes an implicit goal of uncertainty reduction, sadness primes an implicit goal of reward replacement. We offer that these motivational influences operate through an active process of feeling monitoring, whereby anxious or sad individuals think about the options and ask themselves, "What would I feel better about ellipsis? " Copyright 1999 Academic Press.
ANÁLISIS DE RESIDUOS DE LADRILLO COMO AGREGADO GRUESO PARA LA FABRICACIÓN DE CONCRETO

Teniendo en cuenta que la industria ladrillera esta clasificada por aspectos tecnicos y economicos, fueron seleccionadas dos muestras de residuos ceramicos de ladrillo de la industria tecnificada y dos de la industria artesanal, a fin de evaluar sus fases cristalinas mediante DRX (Difraccion de rayos X) y propiedades fisicas por medio de analisis granulometricos, determinacion de densidad, absorcion de agua, masas unitarias, forma y perdidas por calcinacion, basados en las normas tecnicas colombianas, para brindar una ruta de aprovechamiento a estos residuos, mediante su aplicabilidad como agregados gruesos en la fabricacion de concreto. Posteriormente, fue seleccionada una sola muestra de agregado reciclado, que correspondio a una industria tecnificada, la cual exhibio mejores propiedades para el diseno de una mezcla de concreto, variando su proporcion al 5%,10% y 20% en peso de agregado grueso natural por agregado reciclado, y asi establecer sus propiedades mecanicas, tales como la resistencia a compresion y la resistencia a flexion con respecto a una mezcla patron cuya totalidad de agregados son naturales. Considerando esto, tambien fueron evaluadas, la morfologia de las superficies de fractura mediante caracterizacion SEM (Microscopia electronica de barrido), colorimetria y propiedades fisicas del concreto en estado endurecido. Los resultados de los ensayos, indican que es viable utilizar triturado de ladrillo reciclado como agregado grueso en la elaboracion de concreto, siempre y cuando este no supere el 20% en peso del agregado grueso natural.
Dilated Residual Network with Multi-head Self-attention for Speech Emotion Recognition

Speech emotion recognition (SER) plays an important role in intelligent speech interaction. One vital challenge in SER is to extract emotion-relevant features from speech signals. In state-of-the-art SER techniques, deep learning methods, e.g, Convolutional Neural Networks (CNNs), are widely employed for feature learning and have achieved significant performance. However, in the CNN-oriented methods, two performance limitations have raised: 1) the loss of temporal structure of speech in the progressive resolution reduction; 2) the ignoring of relative dependencies between elements in suprasegmental feature sequence. In this paper, we proposed the combining use of Dilated Residual Network (DRN) and Multi-head Self-attention to alleviate the above limitations. By employing DRN, the network can retain high resolution of temporal structure in feature learning, with similar size of receptive field to CNN based approach. By employing Multi-head Self-attention, the network can model the inner dependencies between elements with different positions in the learned suprasegmental feature sequence, which enhances the importing of emotion-salient information. Experiments on emotional benchmarking dataset IEMOCAP have demonstrated the effectiveness of the proposed framework, with 11.7% to 18.6% relative improvement to state-of-the-art approaches.
Teaching Video NeuroImages: Frontal opercular seizures with jacksonian march

A 31-year-old man presented with a 3-month history of progressive dysarthria and 1 month of gradually worsening motor seizures predominantly affecting the right face. Examination was unremarkable except for a mild spastic dysarthria and slow, alternating tongue movements, probably due to a partial opercular syndrome. Seizures captured during EEG recording showed a jacksonian march starting over the opercular aspect of the motor homunculus,1,2 and anarthria and sialorrhea without EEG correlate (videos 1 and 2 on the Neurology® Web site at Neurology.org and figure 1). MRI brain showed a left opercular tumor (figure 2, A and B), found to be an anaplastic astrocytoma after resection. The patient has been seizure free for 12 months after treatment with carbamazepine and resective surgery but has residual dysarthria.
Finding Optimal Marking Probability to Reduce Convergence Time

On IP traceback, it is important to reduce the minimum number of packets required for path reconstruction (i.e., convergence time) for a probability packet marking (PPM) scheme. In this paper, we study the impact of the marking probability on the convergence time for Tabu Marking Scheme (TMS) and obtain its optimal marking probability. Both numerical and simulation results show that, the convergence time of TMS can be significantly reduced by using optimal marking probability. Moreover, the convergence time of TMS is robust subject to the deviation of the marking probability from the optimal one.
Matlab's solutions to reduce the PAPR on ISDB-T's OFDM

This paper presents an overview of Brazilian Digital TV System (SBTVD), that is a modified version of the Terrestrial Integrated Service Digital Broadcasting (ISDB-T). It also presents the main properties of Orthogonal Frequency Division Multiplexing (OFDM) and the Peak-to-Average Power Ratio (PAPR). Large PAPR is one of drawbacks in OFDM system. Two techniques - Selected Mapping (SLM) and Partial Transmit Sequence (PTS) -- are shown to reduce the PAPR in the OFDM signal. The techniques were simulated on Matlab having as goal the reduction on the PAPR and consequently the battery consumption on the reception device. Finally, a comparison with other techniques are shown to prove the effectiveness of the SLM and PTS.
Hands-free gesture control with a capacitive textile neckband

We present a novel sensing modality for hands-free gesture controlled user interfaces, based on active capacitive sensing. Four capacitive electrodes are integrated into a textile neckband, allowing continuous unobtrusive head movement monitoring. We explore the capability of the proposed system for recognising head gestures and postures. A study involving 12 subjects was carried out, recording data from 15 head gestures and 19 different postures. We present a quantitative evaluation based on this dataset, achieving an overall accuracy of 79.1% for head gesture recognition and 40.4% for distinguishing between head postures (69.9% when merging the most adjacent positions), respectively. These results indicate that our approach is promising for hands-free control interfaces. An example application scenario of this technology is the control of an electric wheelchair for people with motor impairments, where recognised gestures or postures can be mapped to control commands.
Statistical sequential analysis for real-time video scene change detection on compressed multimedia bitstream

The increased availability and usage of multimedia information have created a critical need for efficient multimedia processing algorithms. These algorithms must offer capabilities related to browsing, indexing, and retrieval of relevant data. A crucial step in multimedia processing is that of reliable video segmentation into visually coherent video shots through scene change detection. Video segmentation enables subsequent processing operations on video shots, such as video indexing, semantic representation, or tracking of selected video information. Since video sequences generally contain both abrupt and gradual scene changes, video segmentation algorithms must be able to detect a large variety of changes. While existing algorithms perform relatively well for detecting abrupt transitions (video cuts), reliable detection of gradual changes is much more difficult. A novel one-pass, real-time approach to video scene change detection based on statistical sequential analysis and operating on a compressed multimedia bitstream is proposed. Our approach models video sequences as stochastic processes, with scene changes being reflected by changes in the characteristics (parameters) of the process. Statistical sequential analysis is used to provide an unified framework for the detection of both abrupt and gradual scene changes.
Egg Size as a Life History Character of Marine Invertebrates: Is It All It's Cracked Up to Be?

Egg size is one of the most important aspects of the life history of free-spawning marine organisms, and it is correlated with larval developmental mode and many other life-history characters. Egg size is simple to measure and data are available for a wide range of taxa, but we have a limited understanding of how large and small eggs differ in composition; size is not always the best measure of the characters under selection. Large eggs are generally considered to reflect increased maternal investment, but egg size alone can be a poor predictor of energetic content within and among taxa. We review techniques that have been used to measure the energetic content and biochemical makeup of invertebrate eggs and point out the strengths and difficulties associated with each. We also suggest a number of comparative and descriptive approaches to biochemical constituent analysis that would strengthen our understanding of how natural selection shapes oogenic strategies. Finally, we highlight recent empirical research on the intrinsic factors that drive intraspecific variation in egg size. We also highlight the relative paucity of these data in the literature and provide some suggestions for future research directions.
Understanding User's Behaviors in Coping with Security Threat of Mobile Devices Loss and Theft

As mobile devices widely used for personal and business purpose, mobile devices loss and theft have become one big security threat. In this paper, we analyze the potential risks of mobile devices loss and theft and summarize the countermeasures that can be used to cope with such risks. Based on protection motivation theory, we propose a research framework to investigate the factors that affect users' behavior to cope with the risk of mobile devices loss and theft.