Miracle-dz/UniFAR-FTUs huggingface dataset:
Database independent human emotion recognition with Meta-Cognitive Neuro-Fuzzy Inference System

Facial emotions are the most expressive way to display emotions. Many algorithms have been proposed which employ a particular set of people (usually a database) to both train and test their model. This paper focuses on the challenging task of database independent emotion recognition, which is a generalized case of subject-independent emotion recognition. The emotion recognition system employed in this work is a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS). McFIS has two components, a neuro-fuzzy inference system, which is the cognitive component and a self-regulatory learning mechanism, which is the meta-cognitive component. The meta-cognitive component, monitors the knowledge in the neuro-fuzzy inference system and decides on what-to-learn, when-to-learn and how-to-learn the training samples, efficiently. For each sample, the McFIS decides whether to delete the sample without being learnt, use it to add/ prune or update the network parameter or reserve it for future use. This helps the network avoid over-training and as a result improve its generalization performance over untrained databases. In this study, we extract pixel based emotion features from well-known (Japanese Female Facial Expression) JAFFE and (Taiwanese Female Expression Image) TFEID database. Two sets of experiment are conducted. First, we study the individual performance of both databases on McFIS based on 5-fold cross validation study. Next, in order to study the generalization performance, McFIS trained on JAFFE database is tested on TFEID and vice-versa. The performance The performance comparison in both experiments against SVM classifier gives promising results.
Brief Communication: SIR-2.1-dependent lifespan extension of Caenorhabditis elegans by oxyresveratrol and resveratrol

Resveratrol (RES) has been studied for its effects on the lifespan extension of Caenorhabditis elegans, but controversy still remains on its mechanism related with SIR-2. In this study, longevity assay was performed to confirm SIR-2-dependent lifespan extension of C. elgeans with RES and oxyresveratrol (OXY), an isomer of hydroxylated RES using loss-of-function mutants of C. elegans including sir-2.1 mutant. The results showed that OXY and RES significantly (P < 0.05) extended the lifespan of C. elegans compared with the control. OXY and RES also significantly (P < 0.05) increased the mRNA expression levels of sir-2.1 and aak-2 in a dose-dependent manner and increased the protein expression levels of SIR-2.1. OXY and RES treatment extended the lifespan in daf-16 loss-of-function mutants, which suggested that lifespan extension was not occurring via the activation of DAF-16. However, OXY and RES failed to extend the lifespan in loss-of-function mutants of sir-2.1 and aak-2. Therefore, OXY and RES extend the lifespan of C. elegans by overexpression of SIR-2.1, which is related to lifespan extension through calorie restriction and the AMP-activated protein kinase (AMPK) pathway, although this process is independent of the FOXO/DAF-16 pathway.
Motion Prediction in Gesture-based Collaborative Design Environments

A collaborative design environment (CDE) is a shared environment over some communication networks. It allows remote users to interact with each other to perform some design tasks. To support collaborative design, the human hand is a very natural and convenient tool as it is capable of accomplishing diverse tasks such as pointing, gripping, twisting and tearing. However, there is not much work that considers using the human hand as input in CDEs, in particular over the Internet. The main reasons for this are that the Internet suffers from high network latency, which affects the interaction, and the human hand has many degrees of freedom, which adds additional challenges to synchronizing the collaboration. In this paper, we propose a prediction method specifically designed for human hand motion to address the network latency. Through a thorough analysis of human finger motion, we have identified various finger motion constraints. By considering these motion constraints, we propose a constraint-based motion prediction method for hand motion. We present a number of experiments to demonstrate the performance of our prediction method and the dead reckoning algorithm based on the proposed predictor.
Distributed approximate spectral clustering for large-scale datasets

Data-intensive applications are becoming important in many science and engineering fields, because of the high rates in which data are being generated and the numerous opportunities offered by the sheer amount of these data. Large-scale datasets, however, are challenging to process using many of the current machine learning algorithms due to their high time and space complexities. In this paper, we propose a novel approximation algorithm that enables kernel-based machine learning algorithms to efficiently process very large-scale datasets. While important in many applications, current kernel-based algorithms suffer from a scalability problem as they require computing a kernel matrix which takes O(N2) in time and space to compute and store. The proposed algorithm yields substantial reduction in computation and memory overhead required to compute the kernel matrix, and it does not significantly impact the accuracy of the results. In addition, the level of approximation can be controlled to tradeoff some accuracy of the results with the required computing resources. The algorithm is designed such that it is independent of the subsequently used kernel-based machine learning algorithm, and thus can be used with many of them. To illustrate the effect of the approximation algorithm, we developed a variant of the spectral clustering algorithm on top of it. Furthermore, we present the design of a MapReduce-based implementation of the proposed algorithm. We have implemented this design and run it on our own Hadoop cluster as well as on the Amazon Elastic MapReduce service. Experimental results on synthetic and real datasets demonstrate that significant time and memory savings can be achieved using our algorithm.
An Intrusion-Detection System Based on the Teiresias Pattern- Discovery Algorithm

This paper addresses the problem of creating a pattern table that can be used to model the normal behavior of a given process. The model can be used for intrusiondetection purposes. So far, most of the approaches proposed have been based on fixed-length patterns, although variable-length patterns seem to be more naturally suited to model the normal process behavior. We have developed a novel technique to build tables of variable-length patterns. This technique is based on Teiresias, an algorithm initially developed for the discovery of rigid patterns in unaligned biological sequences. We evaluate the quality of our technique in a testbed environment and compare it with techniques based on fixed-length patterns. EICAR Proceedings 1999
The site of destruction of autologous 111In‐labelled platelets and the efficiency of splenectomy in children and adults with idiopathic thrombocytopenic purpura: a study of 578 patients with 268 splenectomies

The indication for splenectomy in chronic idiopathic thrombocytopenic purpura (ITP) remains a controversial subject. The mortality rate of persistent thrombocytopenia is very low, except in severe cases. Conversely, the risks of splenectomy are significant (in the present series, morbidity: 4.1% mortality: 1.4%), with a success rate of only 60–75%. It is therefore useful to define a parameter able to predict the efficacy or failure of splenectomy. An analysis of 578 cases of chronic ITP, where the site of platelet destruction has been determined, is presented. 268 of these cases had been splenectomized. When platelet destruction was splenic, 96% of subjects aged 5–30 years and 91% of cases over the age of 30 years obtained a remission. Conversely, when platelet destruction was hepatic or diffuse, failure or incomplete results were observed in 92% of cases. The site of platelet destruction therefore constitutes a parameter which can help the clinician to make the decision to perform splenectomy.
Modulation of aqueous humor outflow by ionic mechanisms involved in trabecular meshwork cell volume regulation.

PURPOSE Trabecular meshwork (TM) cell shape, volume, contractility and their interactions with extracellular matrix determine outflow facility. Because cell volume seems essential to TM function, this study was conducted to investigate further the ionic channels and receptors involved in regulatory volume decrease and their roles in modulating outflow facility. METHODS Primary cultures of bovine TM cells were used. K(+) and Cl(-) currents were studied with whole-cell patch clamping. Swelling was induced by hypotonic shock. [Ca(2+)](i) was measured in TM cells loaded with fura-2. Bovine anterior segments were perfused at constant pressure to measure outflow facility. RESULTS Hypotonic media activated both the high-conductance Ca(2+)-activated K(+) channel (BK(Ca)) and swelling-activated Cl(-) channel (Cl(swell)) currents and induced release of adenosine 5'-triphosphate (ATP) from TM cells. ATP activated P2Y(2) receptors with the following profile: ATP = uridine 5'-triphosphate (UTP) > adenosine 5'-O-(3-thiotriphosphate) (ATP-gamma S) > adenosine 5'-diphosphate (ADP) = uridine 5'-diphosphate (UDP), and increased BK(Ca) current. Hypotonic medium initially decreased outflow facility in perfused anterior segments, which recovered with time to baseline levels. Addition of tamoxifen or iberiotoxin (Cl(swell) and BK(Ca) blockers, respectively) lengthened the recovery phase, which implies that these channels participate in cell volume regulation. In contrast, an activator of BK(Ca)s (NS1619) produced the opposite effect. CONCLUSIONS Cell swelling activates a regulatory volume decrease mechanism that implies activation of K(+) and Cl(-) currents and participation of P2Y(2) receptors. Because previous studies have shown that intracellular volume of TM cells is an important determinant of outflow facility, it seems feasible that cell volume regulation would be part of the homeostatic mechanisms of the TM, to regulate the outflow pathway.
Prediction of outcome after hypoxic-ischemic encephalopathy: a prospective clinical and electrophysiologic study.

OBJECTIVES To evaluate and compare the predictive value of history, clinical examination, and biologic and electrophysiologic data regarding the prognosis of children with acute hypoxic-ischemic encephalopathy (HIE). STUDY DESIGN Prospective cohort of 57 consecutive children who were mechanically ventilated for HIE throughout a 3-year period in a tertiary pediatric intensive care unit at a university hospital in France. RESULTS At 24 hours after admission, 12 patients had died, 3 were awake and 42 showed impaired consciousness or were in a coma, of whom 38% had a favorable outcome. In this group, an initial cardiopulmonary resuscitation duration longer than 10 minutes and a Glasgow Coma Scale <5 at 24 hours after admission were associated with an unfavorable outcome (positive predictive value [PPV] 91%, 100%; sensitivity 50%, 54%). A discontinuous electroencephalogram (EEG), the presence of spikes or epileptiform discharges were associated with an unfavorable outcome (PPV 100% for the 2 criteria; sensitivity 27%, 54%). The bilateral absence of the N20 wave on short-latency sensory evoked potentials (SEPs) had a PPV for unfavorable outcome of 100% (sensitivity 63%). CONCLUSIONS The clinical assessment combined with EEG and SEPs allow an early prediction of the prognosis of children with HIE.
Study on the Robust Wavelet Threshold Technique for Heavy-tailed Noises

Interesting signals are often contaminated by heavy-tailed noise that has more outliers than Gaussian noise. Under the introduction of probability model for heavy-tailed noises, a robust wavelet threshold based on the minimax description length principle is derived in the e-contaminated normal family for maximizing the entropy. The performance and their measurement criterion for the robust wavelet threshold are studied in this paper. By the proposed performance measurement criterion, several kinds of noisy signals are processed with the wavelet thresholding techniques. Compared with classical threshold based on Gaussian assumption, the robust threshold can eliminate the heavy-tailed noise better, even if the precise value of e is unknown, which shows its robustness. The further experiment shows that soft threshold is more suitable than hard threshold for robust wavelet threshold technique. Finally, the robust threshold technique is applied to denoise the practically measured gas sensor dynamic signals. Results show its good performances.
Oscillatory activity in the monkey hippocampus during visual exploration and memory formation

Primates explore the visual world through the use of saccadic eye movements. Neuronal activity in the hippocampus, a structure known to be essential for memory, is modulated by this saccadic activity, but the relationship between visual exploration through saccades and memory formation is not well understood. Here, we identify a link between theta-band (3–12 Hz) oscillatory activity in the hippocampus and saccadic activity in monkeys performing a recognition memory task. As monkeys freely explored novel images, saccades produced a theta-band phase reset, and the reliability of this phase reset was predictive of subsequent recognition. In addition, enhanced theta-band power before stimulus onset predicted stronger stimulus encoding. Together, these data suggest that hippocampal theta-band oscillations act in concert with active exploration in the primate and possibly serve to establish the optimal conditions for stimulus encoding.
AlphaScreen HTS and Live-Cell Bioluminescence Resonance Energy Transfer (BRET) Assays for Identification of Tau–Fyn SH3 Interaction Inhibitors for Alzheimer Disease

Alzheimer disease (AD) is the most common neurodegenerative disease, and with Americans’ increasing longevity, it is becoming an epidemic. There are currently no effective treatments for this disorder. Abnormalities of Tau track more closely with cognitive decline than the most studied therapeutic target in AD, amyloid-β, but the optimal strategy for targeting Tau has not yet been identified. On the basis of considerable preclinical data from AD models, we hypothesize that interactions between Tau and the Src-family tyrosine kinase, Fyn, are pathogenic in AD. Genetically reducing either Tau or Fyn is protective in AD mouse models, and a dominant negative fragment of Tau that alters Fyn localization is also protective. Here, we describe a new AlphaScreen assay and a live-cell bioluminescence resonance energy transfer (BRET) assay using a novel BRET pair for quantifying the Tau–Fyn interaction. We used these assays to map the binding site on Tau for Fyn to the fifth and sixth PXXP motifs to show that AD-associated phosphorylation at microtubule affinity regulating kinase sites increases the affinity of the Tau–Fyn interaction and to identify Tau–Fyn interaction inhibitors by high-throughput screening. This screen has identified a variety of chemically tractable hits, suggesting that the Tau–Fyn interaction may represent a good drug target for AD.
Mining multivariate spatiotemporal patterns from heterogeneous mobility data

Mobility data mining in the form of trajectory data mining has been extensively investigated in recent years. Predictive modeling and pattern discovery approaches have been proposed to predict movements and locations, and to extract useful trajectory and location patterns. Nowadays, mobility data consist of not only trajectory data. Mobility data from smart phones include measurements such as call duration/time, call type, digital media consumption, calendar information, apps usage, social interactions, and mobile browsing. These heterogeneous multivariate data allow one to discover interesting and more complex behavioral patterns and rules in terms of space and time. In this paper, we investigate spatiotemporal rule mining on heterogeneous multivariate mobility data. We propose a systematic approach consisting of three main steps: data fusion, frequent temporal multivariate-location extraction, and rule generation. In particular, we explore the task of extracting multivariate spatiotemporal patterns corresponding to the "where", "when", and "who" queries (and their combinations) related to phone call variables collected from smart phone users. Experimental results on the data from Nokia Mobile Data Challenge is used to show the feasibility and usefulness of our proposed approach.
PF2 fit: Polar Fast Fourier Matched Alignment of Atomistic Structures with 3D Electron Microscopy Maps

There continue to be increasing occurrences of both atomistic structure models in the PDB (possibly reconstructed from X-ray diffraction or NMR data), and 3D reconstructed cryo-electron microscopy (3D EM) maps (albeit at coarser resolution) of the same or homologous molecule or molecular assembly, deposited in the EMDB. To obtain the best possible structural model of the molecule at the best achievable resolution, and without any missing gaps, one typically aligns (match and fits) the atomistic structure model with the 3D EM map. We discuss a new algorithm and generalized framework, named PF2 fit (Polar Fast Fourier Fitting) for the best possible structural alignment of atomistic structures with 3D EM. While PF2 fit enables only a rigid, six dimensional (6D) alignment method, it augments prior work on 6D X-ray structure and 3D EM alignment in multiple ways: Scoring. PF2 fit includes a new scoring scheme that, in addition to rewarding overlaps between the volumes occupied by the atomistic structure and 3D EM map, rewards overlaps between the volumes complementary to them. We quantitatively demonstrate how this new complementary scoring scheme improves upon existing approaches. PF2 fit also includes two scoring functions, the non-uniform exterior penalty and the skeleton-secondary structure score, and implements the scattering potential score as an alternative to traditional Gaussian blurring. Search. PF2 fit utilizes a fast polar Fourier search scheme, whose main advantage is the ability to search over uniformly and adaptively sampled subsets of the space of rigid-body motions. PF2 fit also implements a new reranking search and scoring methodology that considerably improves alignment metrics in results obtained from the initial search.
State space exploration using feedback constraint generation and Monte-Carlo sampling

The systematic exploration of the space of all the behaviours of a software system forms the basis of numerous approaches to verification. However, existing approaches face many challenges with scalability and precision. We propose a framework for validating programs based on statistical sampling of inputs guided by statically generated constraints, that steer the simulations towards more "desirable" traces. Our approach works iteratively: each iteration first simulates the system on some inputs sampled from a restricted space, while recording facts about the simulated traces. Subsequent iterations of the process attempt to steer the future simulations away from what has already been seen in the past iterations. This is achieved by two separate means: (a) we perform symbolic executions in order to guide the choice of inputs, and (b) we sample from the input space using a probability distribution specified by means of previously observed test data using a Markov Chain Monte-Carlo (MCMC) technique. As a result, the sampled inputs generate traces that are likely to be significantly different from the observations in the previous iterations in some user specified ways. We demonstrate that our approach is effective. It can rapidly isolate rare behaviours of systems that reveal more bugs.
Evaluation of indoor air composition time variation in air-tight occupied spaces during night periods

This paper presents an easy-to-understand procedure for prediction of indoor air composition time variation in air-tight occupied spaces during the night periods. The mathematical model is based on the assumptions for homogeneity and perfect mixing of the indoor air, the ideal gas model for non-reacting gas mixtures, mass conservation equations for the entire system and for each species, a model for prediction of basal metabolic rate of humans as well as a model for prediction of O2 consumption rate and both CO2 and H2O generation rates by breathing. Time variation of indoor air composition is predicted at constant indoor air temperature for three scenarios based on the analytical solution of the mathematical model. The results achieved reveal both the most probable scenario for indoor air time variation in air-tight occupied spaces as well as the cause for morning tiredness after having a sleep in a modern energy efficient space.
On Low-Density Parity-Check Convolutional Codes 1

Abstract – In this paper a formal theory, construction methods, and a random ensemble approach for low-density parity-check convolutional codes (including turbo-codes) are presented. The principles of iterative decoding of low-density parity-check (LDPC) convolutional codes are given, and an iterative algorithm for decoding of homogeneous LDPC convolutional codes is described. Some simulation results and performance bounds are also presented.
Forest trees for on-line data

This paper presents an hybrid adaptive system for induction of forest of trees from data streams. The Ultra Fast Forest Tree system (UFFT) is an incremental algorithm, with constant time for processing each example, works online, and uses the Hoeffding bound to decide when to install a splitting test in a leaf leading to a decision node. Our system has been designed for continuous data. It uses analytical techniques to choose the splitting criteria, and the information gain to estimate the merit of each possible splitting-test. The number of examples required to evaluate the splitting criteria is sound, based on the Hoeffding bound. For multiclass problems, the algorithm builds a binary tree for each possible pair of classes, leading to a forest of trees. During the training phase the algorithm maintains a short term memory. Given a data stream, a fixed number of the most recent examples are maintained in a data-structure that supports constant time insertion and deletion. When a test is installed, a leaf is transformed into a decision node with two descendant leaves. The sufficient statistics of these leaves are initialized with the examples in the short term memory that will fall at these leaves. We study the behavior of UFFT in different problems. The experimental results shows that UFFT is competitive against a batch decision tree learner in large and medium datasets.
Unwanted traffic in 3G networks

The presence of "unwanted" (or background) traffic in the Internet is a well-known fact. In principle any network that has been engineered without taking its presence into account might experience troubles during periods of massive exposure to unwanted traffic, e.g. during large-scale infections. A concrete example was provided by the spreading of Code-Red-II in 2001, which caused several routers crashes worldwide. Similar events might take place in 3G networks as well, with further potential complications arising from their high functional complexity and the scarcity of radio resources. For example, under certain hypothetical network con guration settings unwanted traffic, and specifically scanning traffic from infected Mobile Stations, can cause large-scale wastage of logical resources, and in extreme cases even starvation. Unwanted traffic is present nowdays also in GPRS/UMTS, mainly due to the widespread use of 3G connect cards for laptops. We urge the research community and network operators to consider the issue of 3G robustness to unwanted traffic as a prominent research area.