Miracle-dz/UniFAR-FTUs huggingface dataset:
An Analysis of Different Approaches to Gait Recognition Using Cell Phone Based Accelerometers

Biometric gait authentication using Personal Mobile Device (PMD) based accelerometer sensors offers a user-friendly, unobtrusive, and periodic way of authenticating individuals on PMD. In this paper, we present a technique for gait cycle extraction by incorporating the Piecewise Linear Approximation (PLA) technique. We also present two new approaches to classify gait features extracted from the cycle-based segmentation by using Support Vector Machines (SVMs); a) pre-computed data matrix, b) pre-computed kernel matrix. In the first approach, we used Dynamic Time Warping (DTW) distance to compute data matrices, and in the later DTW is used for constructing an elastic similarity measure based kernel function called Gaussian Dynamic Time Warp (GDTW) kernel. Both approaches utilize the DTW similarity measure and can be used for classifying equal length gait cycles, as well as different length gait cycles. To evaluate our approaches we used normal walk biometric gait data of 51 participants. This gait data is collected by attaching a PMD to the belt around the waist, on the right-hand side of the hip. Results show that these new approaches need to be studied more, and potentially lead us to design more robust and reliable gait authentication systems using PMD based accelerometer sensor.
VANESA - A Software Application for the Visualization and Analysis of Networks in Systems Biology Applications

Summary VANESA is a modeling software for the automatic reconstruction and analysis of biological networks based on life-science database information. Using VANESA, scientists are able to model any kind of biological processes and systems as biological networks. It is now possible for scientists to automatically reconstruct important molecular systems with information from the databases KEGG, MINT, IntAct, HPRD, and BRENDA. Additionally, experimental results can be expanded with database information to better analyze the investigated elements and processes in an overall context. Users also have the possibility to use graph theoretical approaches in VANESA to identify regulatory structures and significant actors within the modeled systems. These structures can then be further investigated in the Petri net environment of VANESA. It is platform-independent, free-of-charge, and available at http://vanesa.sf. net.
Body mass index, weight loss, and mortality in community-dwelling older adults.

BACKGROUND The relationship between body mass index (BMI), weight loss, and mortality in older adults is not entirely clear. The purpose of this article is to evaluate the associations between BMI, weight loss (either intentional or unintentional), and 3-year mortality in a cohort of older adults participating in the University of Alabama at Birmingham (UAB) Study of Aging. METHODS This article reports on 983 community-dwelling older adults who were enrolled in the UAB Study of Aging, a longitudinal observational study of mobility among older African American and white adults. RESULTS In both raw and adjusted Cox proportional hazards models, unintentional weight loss and underweight BMI were associated with elevated 3-year mortality rates. There was no association with being overweight or obese on mortality, nor was there an association with intentional weight loss and mortality. CONCLUSIONS Our study suggests that undernutrition, as measured by low BMI and unintentional weight loss, is a greater mortality threat to older adults than is obesity or intentional weight loss.
A three-tier IDS via data mining approach

We introduced a three-tier architecture of intrusion detection system which consists of a blacklist, a whitelist and a multi-class support vector machine classifier. The first tier is the blacklist that will filter out the known attacks from the traffic and the whitelist identifies the normal traffics. The rest traffics, the anomalies detected by the whitelist, were then be classified by a multi-class SVM classifier into four categories: PROBE, DoS, R2L and U2R. Many data mining and machine learning techniques were applied here. We design this three-tier IDS based on the KDD'99 benchmark dataset. Our system has 94.71% intrusion detection rate and 93.52% diagnosis rate. The averag cost for each connection is 0.1781. All of these results are better than those of KDD'99 winner's. Our three-tier architecture design also provides the flexibility for the practical usage. The network system administrator can add the new patterns into the blacklist and allows to do fine tuning of the whitelist according to the environment of their network system and security policy.
Impact of transmission power on TCP performance in vehicular ad hoc networks

Vehicular ad hoc networks (VANET) are characterized by high velocities and relatively uniform mobility of vehicles. As a result, the dynamics of VANET are different from generic mobile ad hoc networks. The behavior of vehicles and their effect on the protocol performance has not been comprehensively investigated in the context of VANET. In this work, we analyze the performance of multi-hop TCP in a multi-lane highway environment where vehicles are configured as clients or routers trying to reach a fixed access point. In particular, we look at the effect of tuning transmission power in dense and sparse road scenarios on the TCP throughput and latency. To conduct our study, we have developed an integrated simulation tool that accurately models both the vehicular mobility and the networking protocol stack. Using our simulation tool we obtain detailed TCP statistics and correlate them with the location, velocity, and other properties of VANET nodes. We study scenarios with a single sender as well as multiple senders. In each case, results show that throughput significantly deteriorates as the number of hops from the sender to the access point increases. A higher number of hops results in higher losses due to interference and loss of connectivity, which causes TCP to throttle back the sending rate unnecessarily
Bleaching susceptibility and mortality of corals are determined by fine-scale differences in symbiont type

Coral bleaching has been identified as one of the major contributors to coral reef decline, and the occurrence of different symbionts determined by broad genetic groupings (clades A–H) is commonly used to explain thermal responses of reef-building corals. By using Stylophora pistillata as a model, we monitored individual tagged colonies in situ over a two-year period and show that fine level genetic variability within clade C is correlated to differences in bleaching susceptibility. Based on denaturing gradient gel electrophoresis of the internal transcribed spacer region 2, visual bleaching assessments, symbiont densities, host protein, and pulse amplitude modulated fluorometry, we show that subcladal types C78 and C8/a are more thermally tolerant than C79 and C35/a, which suffered significant bleaching and postbleaching mortality. Although additional symbiont types were detected during bleaching in colonies harboring types C79 and C35/a, all colonies reverted back to their original symbionts postbleaching. Most importantly, the data propose that the differential mortality of hosts harboring thermally sensitive versus resistant symbionts rather than symbiont shuffling/switching within a single host is responsible for the observed symbiont composition changes of coral communities after bleaching. This study therefore highlights that the use of broad cladal designations may not be suitable to describe differences in bleaching susceptibility, and that differential mortality results in a loss of both symbiont and host genetic diversity and therefore represents an important mechanism in explaining how coral reef communities may respond to changing conditions.
Human motion segmentation based on low-rank representation

We propose a robust and promising algorithm, named Low Rank Representation (LRR), for addressing human motion segmentation. LRR seeks the lowest rank representation among all the data jointly that represent all data vectors as the linear combination of the base in a dictionary. Given the human motion video, each frame can be regarded as an image, which is a representation of a collection of data vectors jointly. In many cases, the background variations are assumed to be low-rank, while the foreground human motion is sparse. The human motion part can be obtained by removing the low-rank part from the original image. Then the problem is converted to seek the Low Rank Representation of the image. This process is formulated as a convex optimization problem that minimizes a constrained combination of nuclear norm and ℓ2, 1-norm, which can be solved efficiently with Augmented Lagrange Multiplier (ALM) method. Compared to several methods for Human motion segmentation, the proposed method produces more reliable results, yet being more robust to noise and outliers. We do some experiments on the HumanEva human motion dataset. The results show that human motion segmentation by the proposed method is robust and promising.
Variational learning for Gaussian mixture models

This paper proposes a joint maximum likelihood and Bayesian methodology for estimating Gaussian mixture models. In Bayesian inference, the distributions of parameters are modeled, characterized by hyperparameters. In the case of Gaussian mixtures, the distributions of parameters are considered as Gaussian for the mean, Wishart for the covariance, and Dirichlet for the mixing probability. The learning task consists of estimating the hyperparameters characterizing these distributions. The integration in the parameter space is decoupled using an unsupervised variational methodology entitled variational expectation-maximization (VEM). This paper introduces a hyperparameter initialization procedure for the training algorithm. In the first stage, distributions of parameters resulting from successive runs of the expectation-maximization algorithm are formed. Afterward, maximum-likelihood estimators are applied to find appropriate initial values for the hyperparameters. The proposed initialization provides faster convergence, more accurate hyperparameter estimates, and better generalization for the VEM training algorithm. The proposed methodology is applied in blind signal detection and in color image segmentation
Anterograde Signaling by Nitric Oxide: Characterization andIn Vitro Reconstitution of an Identified Nitrergic Synapse

Nitric oxide (NO) is recognized as a signaling molecule in the CNS where it is a candidate retrograde neurotransmitter. Here we provide direct evidence that NO mediates slow excitatory anterograde transmission between the NO synthase (NOS)-expressing B2 neuron and an NO-responsive follower neuron named B7nor. Both are motoneurons located in the buccal ganglia of the snail Lymnaea stagnaliswhere they participate in feeding behavior. Transmission between B2 and B7nor is blocked by inhibiting NOS and is suppressed by extracellular scavenging of NO. Furthermore, focal application of NO to the cell body of the B7nor neuron causes a depolarization that mimics the effect of B2 activity. The slow interaction between the B2 and B7nor neurons can be re-established when the two neurons are cocultured, and it shows the same susceptibility to NOS inhibition and NO scavenging. In cell culture we have also examined spatial aspects of NO signaling. We show that before the formation of an anatomical connection, the presynaptic neuron can cause depolarizing potentials in the follower neuron at distances up to 50 μm. The strength of the interaction increases when the distance between the cells is reduced. Our results suggest that NO can function as both a synaptic and a nonsynaptic signaling molecule.
A Novel Gammaherpesvirus Associated with Genital Lesions in a Blainville's Beaked Whale (Mesoplodon densirostris)

An adult male Blainville's beaked whale (Mesoplodon densirostris) was found stranded on the Atlantic coast of the USA on 28 January 2004. Necropsy revealed a focal papilloma-like penile lesion, the cells from which revealed single 4–6 μm basophilic intranuclear inclusions. Total DNA extracted from lesion material was tested using a pan-herpesvirus PCR assay that targets the DNA polymerase gene and found to be positive. When the amplified DNA fragment was cloned, sequenced, and compared to GenBank-deposited herpesvirus DNA polymerase sequences, the detected virus was determined to be a distinct member of the Gammaherpesvirinae subfamily of herpesviruses. This new virus, tentatively named Ziphiid herpesvirus type 1, was associated with but not determined to be the cause of genital disease in the Blainville's beaked whale.
Deblocking method using a percpetual recursive filter

A new method for deblocking is proposed. It aims to reduce the blocking artifacts in the compressed image by analyzing their visibility. A perceptual map is obtained using some Human Visual System (HVS) characteristics. This perceptual map is used as input to a recursive filter to reduce the blocking effect. The obtained results have been compared with a very recent efficient method considered.
The apoplastic antioxidant system in Prunus : response to long-term plum pox virus infection

This work describes, for the first time, the changes taking place in the antioxidative system of the leaf apoplast in response to plum pox virus (PPV) in different Prunus species showing different susceptibilities to PPV. The presence of p-hydroxymercuribenzoic acid (pHMB)-sensitive ascorbate peroxidase (APX) (class I APX) and pHMB-insensitive APX (class III APX), superoxide dismutase (SOD), peroxidase (POX), NADH-POX, and polyphenoloxidase (PPO) was described in the apoplast from both peach and apricot leaves. PPV infection produced different changes in the antioxidant system of the leaf apoplast from the Prunus species, depending on their susceptibility to the virus. In leaves of the very susceptible peach cultivar GF305, PPV brought about an increase in class I APX, POX, NADH-POX, and PPO activities. In the susceptible apricot cultivar Real Fino, PPV infection produced a decrease in apoplastic POX and SOD activities, whereas a strong increase in PPO was observed. However, in the resistant apricot cultivar Stark Early Orange, a rise in class I APX as well as a strong increase in POX and SOD activities was noticed in the apoplastic compartment. Long-term PPV infection produced an oxidative stress in the apoplastic space from apricot and peach plants, as observed by the increase in H2O2 contents in this compartment. However, this increase was much higher in the PPV-susceptible plants than in the resistant apricot cultivar. Only in the PPV-susceptible apricot and peach plants was the increase in apoplastic H2O2 levels accompanied by an increase in electrolyte leakage. No changes in the electrolyte leakage were observed in the PPV-inoculated resistant apricot leaves, although a 42% increase in the apoplastic H2O2 levels was produced. Two-dimensional electrophoresis analyses revealed that the majority of the polypeptides in the apoplastic fluid had isoelectric points in the range of pI 4–6. The identification of proteins using MALDI-TOF (matrix-assisted laser desorption/ionization-time of flight) and peptide mass fingerprinting analyses showed the induction of a thaumatin-like protein as well as the decrease of mandelonitrile lyase in peach apoplast due to PPV infection. However, most of the selected polypeptides showed no homology with known proteins. This fact emphasizes that, at least in Prunus, most of the functions of the apoplastic space remain unknown. It is concluded that long-term PPV infection produced an oxidative stress in the leaf apoplast, contributing to the deleterious effects produced by PPV infection in leaves of inoculated, susceptible Prunus plants.
Deep Portfolio Theory

We construct a deep portfolio theory. By building on Markowitz's classic risk-return trade-off, we develop a self-contained four-step routine of encode, calibrate, validate and verify to formulate an automated and general portfolio selection process. At the heart of our algorithm are deep hierarchical compositions of portfolios constructed in the encoding step. The calibration step then provides multivariate payouts in the form of deep hierarchical portfolios that are designed to target a variety of objective functions. The validate step trades-off the amount of regularization used in the encode and calibrate steps. The verification step uses a cross validation approach to trace out an ex post deep portfolio efficient frontier. We demonstrate all four steps of our portfolio theory numerically.
Visible-blind photodetector based on p–i–n junction GaN nanowire ensembles

We report the synthesis, fabrication and extensive characterization of a visible-blind photodetector based on p–i–n junction GaN nanowire ensembles. The nanowires were grown by plasma-assisted molecular beam epitaxy on an n-doped Si(111) substrate, encapsulated into a spin-on-glass and processed using dry etching and metallization techniques. The detector presents a high peak responsivity of 0.47 A W − 1 at − 1 V. The spectral response of the detector is restricted to the UV range with a UV-to-visible rejection ratio of 2 × 102. The dependence on the incident power and the operation speed of the photodetector are discussed.
Cross-Domain Person Reidentification Using Domain Adaptation Ranking SVMs

This paper addresses a new person reidentification problem without label information of persons under nonoverlapping target cameras. Given the matched (positive) and unmatched (negative) image pairs from source domain cameras, as well as unmatched (negative) and unlabeled image pairs from target domain cameras, we propose an adaptive ranking support vector machines (AdaRSVMs) method for reidentification under target domain cameras without person labels. To overcome the problems introduced due to the absence of matched (positive) image pairs in the target domain, we relax the discriminative constraint to a necessary condition only relying on the positive mean in the target domain. To estimate the target positive mean, we make use of all the available data from source and target domains as well as constraints in person reidentification. Inspired by adaptive learning methods, a new discriminative model with high confidence in target positive mean and low confidence in target negative image pairs is developed by refining the distance model learnt from the source domain. Experimental results show that the proposed AdaRSVM outperforms existing supervised or unsupervised, learning or non-learning reidentification methods without using label information in target cameras. Moreover, our method achieves better reidentification performance than existing domain adaptation methods derived under equal conditional probability assumption.
Surveying Freedom: Folk Intuitions about free will and moral responsibility

Philosophers working in the nascent field of ‘experimental philosophy’ have begun using methods borrowed from psychology to collect data about folk intuitions concerning debates ranging from action theory to ethics to epistemology. In this paper we present the results of our attempts to apply this approach to the free will debate, in which philosophers on opposing sides claim that their view best accounts for and accords with folk intuitions. After discussing the motivation for such research, we describe our methodology of surveying people's prephilosophical judgments about the freedom and responsibility of agents in deterministic scenarios. In two studies, we found that a majority of participants judged that such agents act of their own free will and are morally responsible for their actions. We then discuss the philosophical implications of our results as well as various difficulties inherent in such research.
Dynamic Proteomic Analysis of Pancreatic Mesenchyme Reveals Novel Factors That Enhance Human Embryonic Stem Cell to Pancreatic Cell Differentiation

Current approaches in human embryonic stem cell (hESC) to pancreatic beta cell differentiation have largely been based on knowledge gained from developmental studies of the epithelial pancreas, while the potential roles of other supporting tissue compartments have not been fully explored. One such tissue is the pancreatic mesenchyme that supports epithelial organogenesis throughout embryogenesis. We hypothesized that detailed characterization of the pancreatic mesenchyme might result in the identification of novel factors not used in current differentiation protocols. Supplementing existing hESC differentiation conditions with such factors might create a more comprehensive simulation of normal development in cell culture. To validate our hypothesis, we took advantage of a novel transgenic mouse model to isolate the pancreatic mesenchyme at distinct embryonic and postnatal stages for subsequent proteomic analysis. Refined sample preparation and analysis conditions across four embryonic and prenatal time points resulted in the identification of 21,498 peptides with high-confidence mapping to 1,502 proteins. Expression analysis of pancreata confirmed the presence of three potentially important factors in cell differentiation: Galectin-1 (LGALS1), Neuroplastin (NPTN), and the Laminin α-2 subunit (LAMA2). Two of the three factors (LGALS1 and LAMA2) increased expression of pancreatic progenitor transcript levels in a published hESC to beta cell differentiation protocol. In addition, LAMA2 partially blocks cell culture induced beta cell dedifferentiation. Summarily, we provide evidence that proteomic analysis of supporting tissues such as the pancreatic mesenchyme allows for the identification of potentially important factors guiding hESC to pancreas differentiation.
Towards low-resource prosodic boundary detection

In this study we propose a method of prosodic boundary detection based only on acoustic cues which are easily extractable from the speech signal and without any supervision. Drawing a parallel between the process of language acquisition in babies and the speech processing techniques for under-resourced languages, we take advantage of the findings of several psycholinguistic studies relative to the cues used by babies for the identification of prosodic boundaries. Several durational and pitch cues were investigated, by themselves or in a combination, and relatively good performances were achieved. The best result obtained, a combination of all the cues, compares well against a previously proposed approach, without relying on any learning method or any lexical or syntactic cues. Index Terms Prosodic boundaries, acoustic cues, prosody recognition