Miracle-dz/UniFAR-FTUs huggingface dataset:
Saccadic performance character istics of ADHD-combined and ADHD-inattentive children

Objective:To analyze inhibition difference in subtypes of attention-deficit/hyperactivity disorder(ADHD)using saccade tasks.Methods:Forty-eight children aged 10～13 years,including 19 cases with ADHD-combined,11 cases with ADHD-inattentive,and 18 control subjects,were selected according to DSM-Ⅳ diagnosis,the Conners Teacher Rating Seale and Parent Symptom Questionnaire from 1500 pupils of 2 primary schools in Jinhua city.All performed 2 saccade tasks:prosaccade and antisaccade.Results: ⑴ Both ADHD-combined and ADHD-inattentive children exhibited more response suppression errors than children without ADHD on the antisaccade task[(37.97±13.14)% vs.(70.90±15.78)%,(84.04±12.84)%;P0.001]. Furthermore,in comparison with ADHD-combined children,errors were lower in ADHD-inattentive children(P=0.015). ⑵ Children without ADHD showed a higher correct rate than ADHD-combined[(95.39±7.49)% vs.(85.54±15.87)%,P0.05]and a shorter correction time than both of two ADHD groups(P0.05). ⑶ ADHD-combined showed a worse accuracy than children without ADHD on the antisaccade task[(5.47±1.63)° vs.(4.41±1.00)°,(4.23±1.31)°;P0.05]. ADHD children showed a higher rate of anticipatory saccades than children without ADHD on the prosaccade task[(3.11±4.55)% vs.(1.11±1.98)%,P0.05]. Conclusion:Integrative views of deficits in inhibition have been fond in ADHD-inattentive and ADHD-combined children,and the former maybe better response suppression than the latter.
Shear-Jamming in Two-Dimensional Granular Materials with Power-Law Grain-Size Distribution

Although substantial progress has been made in recent years in research onsheared granular matter, relatively few studies concentrate on the behavior of materials withvery strong polydispersity. In this paper, shear deformation of a two-dimensional granularmaterial composed of frictional disk-shaped grains with power-law size distribution isanalyzed numerically with a finite-difference model. The analysis of the results concentrateson those aspects of the behavior of the modeled system that are related to its polydispersity. Itis demonstrated that many important global material properties are dependent on the behaviorof the largest grains from the tail of the size distribution. In particular, they are responsiblefor global correlation of velocity anomalies emerging at the jamming transition. They alsobuild a skeleton of the global contact and force networks in shear-jammed systems, leadingto the very open, “sparse” structure of those networks, consisting of only ~ 35% of all grains.The details of the model are formulated so that it represents fragmented sea ice moving ona two-dimensional sea surface; however, the results are relevant for other types of stronglypolydisperse granular materials, as well.
A Prognostic Gene Expression Profile That Predicts Circulating Tumor Cell Presence in Breast Cancer Patients

The detection of circulating tumor cells (CTCs) in the peripheral blood and microarray gene expression profiling of the primary tumor are two promising new technologies able to provide valuable prognostic data for patients with breast cancer. Meta-analyses of several established prognostic breast cancer gene expression profiles in large patient cohorts have demonstrated that despite sharing few genes, their delineation of patients into “good prognosis” or “poor prognosis” are frequently very highly correlated, and combining prognostic profiles does not increase prognostic power. In the current study, we aimed to develop a novel profile which provided independent prognostic data by building a signature predictive of CTC status rather than outcome. Microarray gene expression data from an initial training cohort of 72 breast cancer patients for which CTC status had been determined in a previous study using a multimarker QPCR-based assay was used to develop a CTC-predictive profile. The generated profile was validated in two independent datasets of 49 and 123 patients and confirmed to be both predictive of CTC status, and independently prognostic. Importantly, the “CTC profile” also provided prognostic information independent of the well-established and powerful ‘70-gene’ prognostic breast cancer signature. This profile therefore has the potential to not only add prognostic information to currently-available microarray tests but in some circumstances even replace blood-based prognostic CTC tests at time of diagnosis for those patients already undergoing testing by multigene assays.
Model for Selecting an ERP system based on IIFWA operator under interval-valued intuitionistic fuzzy environment

With respect to multiple attribute decision making problems with interval-valued intuitionistic fuzzy information, some operational laws of interval-valued intuitionistic fuzzy numbers, score function and accuracy function of interval-valued intuitionistic fuzzy numbers are introduced. We utilize the interval-valued intuitionistic fuzzy weighted averaging (IIFWA) operator to aggregate the interval-valued intuitionistic fuzzy information corresponding to each alternative, and then rank the alternatives and select the most desirable one(s) according to the score function and accuracy function. Finally, an illustrative example about selecting an ERP system is given to verify the developed approach and to demonstrate its practicality and effectiveness.
In vitro generation of tumor-specific cytotoxic lymphocytes. Secondary allogeneic mixed tumor lymphocyte culture of normal murine spleen cells

In vivo or in vitro immunity to murine leukemia virus (MuLV)-induced leukemia cells which do not effectively produce virus, has been difficult to demonstrate. Because immunizations with allogeneic murine leukemia cells have been used to confer syngeneic tumor immunity to virus- producing cells, we attempted to generate lymphocytes, cytotoxic to syngeneic nonproducer leukemia cells, by stimulating normal murine spleen cells with allogeneic nonproducer leukemia cells in mixed tumor lymphocyte culture (MTLC) reactions in vitro. Secondary allogeneic MTLC of normal C57BL/6 or DBA/2 spleen cells effectively produced syngeneic tumor-specific cytotoxic lymphocytes. Target cells lysed in lymphocyte- mediated cytolysis (LMC) assays, included both Friend and Rauscher virus- induced syngeneic murine leukemia cells and chemically-induced hematopoietic tumor cells. Syngeneic tumor cells were lysed regardless of whether they produced infectious MuLV or expressed viral antigens gp-71, p-30, or p-12 at the cell surface. Syngeneic normal cells (thymus, lymph node, or Concanavalin A-stimulated spleen cells) used as targets in LMC assays were uneffected by lymphocytes harvested from secondary allogeneic MTLC. Several other in vitro culture treatments including secondary syngeneic MTLC and repetitive mixed lymphocyte culture stimulations were incapable of generating tumor-specific cytotoxic lymphocytes. Based upon these results, we propose that secondary MTLC stimulation of normal spleen cells with allogeneic nonproducer leukemia cells selects for the proliferation of two subpopulations of antigen-specific cytotoxic lymphocytes. The population capable of effecting syngeneic tumor cell lysis is directed against tumor-associated cell surface antigens which may be distinct from viral structural proteins or glycoproteins. The growth of these tumor-specific cytotoxic lymphocytes may be enhanced by a soluble allogeneic effect factor produced by the proliferation of the second subpopulation of lymphocytes generated in repetitive allogeneic MTLC, namely those lymphocytes with specificities directed against differing histocompatibility antigens.
A Meta-Heuristic Load Balancer for Cloud Computing Systems

This paper introduces a strategy to allocate services on a cloud system without overloading the nodes and maintaining the system stability with minimum cost. We specify an abstract model of cloud resources utilization, including multiple types of resources as well as considerations for the service migration costs. A prototype meta-heuristic load balancer is demonstrated and experimental results are presented and discussed. We also propose a novel genetic algorithm, where population is seeded with the outputs of other meta-heuristic algorithms.
The Research on the Effects of Large Intensity Exercises on Brain Electrical Activity

With the method of experiment research,the brain electrical activities of 20 students majored in sports were surveyed in the quiet state and one minute and three minutes after large intensity exercises immediately.The results of experiments were as follows: large intensity exercises affected motor column obviously.Large intensity exercises lead to the fatigue of the brain cells but recovery form fatigue is fast.Large intensity exercises can increase the level of the brain's functions.
Contingent Portfolio Programming for the Management of Risky Projects

Methods for selecting a research and development (R&D) project portfolio have attracted considerable interest among practitioners and academics. This notwithstanding, the industrial uptake of these methods has remained limited, partly because of the difficulties of capturing relevant concerns in R&D portfolio management. Motivated by these difficulties, we develop contingent portfolio programming (CPP), which extends earlier approaches in that it (i) uses states of nature to capture exogenous uncertainties, (ii) models resources through dynamic state variables, and (iii) provides guidance for the selection of an optimal project portfolio that is compatible with the decision maker's risk attitude. Although CPP is presented here in the context of R&D project portfolios, it is applicable to a variety of investment problems where the dynamics and interactions of investment opportunities must be accounted for.
Information-Theoretic Feature Selection in Microarray Data Using Variable Complementarity

The paper presents an original filter approach for effective feature selection in microarray data characterized by a large number of input variables and a few samples. The approach is based on the use of a new information-theoretic selection, the double input symmetrical relevance (DISR), which relies on a measure of variable complementarity. This measure evaluates the additional information that a set of variables provides about the output with respect to the sum of each single variable contribution. We show that a variable selection approach based on DISR can be formulated as a quadratic optimization problem: the dispersion sum problem (DSP). To solve this problem, we use a strategy based on backward elimination and sequential replacement (BESR). The combination of BESR and the DISR criterion is compared in theoretical and experimental terms to recently proposed information-theoretic criteria. Experimental results on a synthetic dataset as well as on a set of eleven microarray classification tasks show that the proposed technique is competitive with existing filter selection methods.
Extraction of Linguistic Information with the AID of Acoustic Data to Build Speech Systems

Linguistic knowledge plays a major role in building high quality speech systems such as speech synthesizer and speech recognizer systems. Quality of such speech systems greatly depends on the availability of linguistic knowledge such as pronunciation dictionary, stress pattern, syllable structure and so on. However, it is very difficult to obtain these linguistic knowledge in the required format for most of the languages. In this paper an attempt has been made to design and develop tools for extraction of the core linguistic information (letter to sound rule) automatically with the aid of acoustic data to build improved quality speech systems. Acoustic evidences will be exploited with minimal language knowledge to deploy the vital linguistic resources for such systems. The resulting letter to sound (LTS) rule is tested on different test data on Amharic, Hindi and Tamil. The performance of the letter-to-sound rule is reported in this paper.
Facial Expression Recognition Using Wavelet Transform and Neural Network Ensemble

The paper presents a facial expression recognition approach based on wavelet transform features and neural network ensemble classifier for the six basic facial expressions from static images of the CMU-PITTSBURGH AU-Coded Face Expression Image Database. Because the facial expression information are mostly concentrate on facial expression information regions, so the mouth, eye and eyebrow regions are segmented from the facial expression images firstly. Then the low-dimension features using wavelet transform and Karhunen-Loeve transform are acquired. A neural network ensemble classifier based on Bagging is constructed finally. The proposed approach has demonstrated superior performance compared to neural networks. The neural network ensemble based classifier yielded an accuracy of 98.5%; the best accuracy obtained from all other neural network based classification schemes tested using the same database.
QB4OLAP: A Vocabulary for OLAP Cubes on the Semantic Web

On-Line Analytical Processing (OLAP) tools allow querying large multidimensional (MD) databases called data warehouses (DW). OLAP-style data analysis over the semantic web (SW) is gaining momentum, and thus SW technologies will be needed to model, manipulate, and share MD data. To achieve this, the definition of a vocabulary that adequately represents OLAP data is required. Unfortunately, so far, the proposals in this direction have followed different roads. On the one hand, the QB vocabulary (a proposal by the W3C Government Linked Data Working Group) follows a model initially devised for analyzing statistical data, but does not adequately support OLAP multidimensional data. Another recent proposal, the Open Cube vocabulary (OC) follows closely the classic MD models for OLAP and allows implementing OLAP operators as SPARQL queries, but does not provide a mechanism for reusing data already published using QB. In this work, we propose a new vocabulary, denoted QB4OLAP, which extends QB to fully support OLAP models and operators. We show how data already published in QB can be analyzed à la OLAP using the QB4OLAP vocabulary, and vice versa. To this end we provide algorithms that build the structures that allow performing both kinds of analysis, and show that compatibility between QB and QB4OLAP can be achieved at low cost, only adding dimensional information.
Dermaseptins and Magainins: Antimicrobial Peptides from Frogs' Skin—New Sources for a Promising Spermicides Microbicides—A Mini Review

Sexually transmitted infections (STIs) and human immunodeficiency virus (HIV), the causative agents of acquired immunodeficiency syndrome (AIDS), are two great concerns in the reproductive health of women. Thus, the challenge is to find products with a double activity, on the one hand having antimicrobial/antiviral properties with a role in the reduction of STI, and on the other hand having spermicidal action to be used as a contraceptive. In the absence of an effective microbicide along with the disadvantages of the most commonly used spermicidal contraceptive worldwide, nonoxynol-9, new emphasis has been focused on the development of more potential intravaginal microbicidal agents. Topical microbicides spermicides would ideally provide a female-controlled method of self-protection against HIV as well as preventing pregnancies. Nonoxynol-9, the only recommended microbicide spermicide, damages cervicovaginal epithelium because of its membrane-disruptive properties. Clearly, there is an urgent need to identify new compounds with dual potential microbicidal properties; antimicrobial peptides should be candidates for such investigations. Dermaseptins and magainins are two classes of cationic, amphipathic α-helical peptides that have been identified in the skin extracts of frogs Phyllomedusa sauvagei and Xenopus laevis. Regarding their contraceptive activities and their effect against various STI-causing pathogens, we believe that these two peptides are appropriate candidates in the evaluation of newer and safer microbicides spermicides in the future.
Boltzmann Machine Learning Using Mean Field Theory and Linear Response Correction

We present a new approximate learning algorithm for Boltzmann Machines, using a systematic expansion of the Gibbs free energy to second order in the weights. The linear response correction to the correlations is given by the Hessian of the Gibbs free energy. The computational complexity of the algorithm is cubic in the number of neurons. We compare the performance of the exact BM learning algorithm with first order (Weiss) mean field theory and second order (TAP) mean field theory. The learning task consists of a fully connected Ising spin glass model on 10 neurons. We conclude that 1) the method works well for paramagnetic problems 2) the TAP correction gives a significant improvement over the Weiss mean field theory, both for paramagnetic and spin glass problems and 3) that the inclusion of diagonal weights improves the Weiss approximation for paramagnetic problems, but not for spin glass problems.
A Multiple Hypothesis Walking Person Tracker with Switched Dynamic Model

This paper presents the first comprehensive model for a walking person in range data from a scanner mounted at leg height. Our model helps to distinguish people from other types of moving targets and provides good tracking robustness. The central assumption of the walking model is that at least one leg always remains stationary. The current implementation tracks a single person in a multiple hypothesis framework. We extend the multiple hypothesis framework to allow for both association uncertainty and a switched dynamic model depending on the currently moving leg. Furthermore, an occlusion model and non-stationary dynamic state transition probabilities are used in the evaluation of hypotheses to further improve tracking robustness. Experimental results demonstrate the robustness and efficiency of the proposed framework.
Is 3D integration an opportunity or just a hype?

Three-dimensional (3D) integration using through silicon via (TSV) is an emerging technology for integrated circuit designs. 3D integration technology provides numerous opportunities to designers looking for more cost-effective system chip solutions. In addition to stacking homogeneous memory dies, 3D integration technology supports heterogeneous integration of memories, logic, sensors, etc. It eases the interconnect performance limitation, provides higher functionality, results in small form factor, etc. On the other hand, there are challenges that should be overcome before volume production of TSV-based 3D ICs becomes possible, e.g., technological challenges, yield and test challenges, thermal and power challenges, infrastructure challenges, etc.
Osseoperception in Implants Supported Prosthesis - A Review

Since the advent of Professor Per-Ingvar Branemark sensational revelation about Osseo integration, a whole new era has began where prosthetic rehabilitation of most of external parts of the body without much loss of function is possible. Osseoperception is the term given to the patient-reported with feeling of heightened perception of the environment with osseointegrated prostheses. In other words the implant placed in bone allows a person to perceive pressure, load, position and balance through a process called Osseo perception. The sensory – motor and tactile discriminative capabilities are improved with the implant supported prosthesis in comparison to the tissue born denture prosthesis yet these are less then the dentate status. However, it is also likely that an appropriately designed implant-supported restoration, being fixed to bone, more closely resembles the dental status before tooth loss, and this may more appropriately restore optimal motor and sensory function of the masticatory system. This brief review article is to give a general view about neurophysiological capability of Osseo integrated implants in medical and dental field.
RPM: Random Points Matching for Pair wise Face-Similarity

Matching face image pairs based on global features or local analysis on some points found using a key point or fiducial point detector becomes prohibitively difficult in realistic images when there are large pose, lighting, expressions and imaging differences. We develop a new approach that automatically and reliably finds well-matched and useful corresponding points, referred to as homologous points, from randomly initialized points on the two probe images under unrestricted image variations. The procedure obviates the need of using key or fiducial point detector and the over restrictive requirement of image alignment. We then propose a new pair-wise similarity metric that combines the strength of the useful parameters found during the random point matching and the similarity computed using a local descriptor around the homologous points. Our results in a face verification setting on two challenging datasets (‘Labelled Faces in the Wild’ and FacePix) under large pose, expression and imaging variations, show improved performance over the state-of-the-art methods for pair-wise similarity.
Blind speech separation using a joint model of speech production

We propose a new blind signal separation (BSS) technique, developed specifically for speech, that exploits a priori knowledge of speech production mechanisms. In our approach, the autoregressive (AR) structure and fundamental frequency (F0) production mechanisms of speech are jointly modeled. We compare the separation performance of our joint AR-F0 algorithm to existing BSS algorithms that model either speech's AR structure or F0 individually. Experimental results indicate that the joint algorithm demonstrates superior separation performance to both the individual AR algorithm (up to 77% improvement) and F0 (up to 50% improvement) algorithms. This suggests that speech separation performance is improved by employing a BSS model with a more realistic description of the speech production process.
The Attractor Structure of Rule 60 Cellular Automata

The analysis of Rule 90 CA by Martin, Odlyzko and Wolfram using diploynomials over finite fields is extended to Rule 60 CA with odd cylinder size. Two classes of such CA are recognized. Their maximum attractor lengths and transients characteristics are given. The basic building block, from which the entire state space can be produced, is identified for each type. An application of the results for diagnostic purpose is outlined.
Mechanisms of peripheral neuropathic pain: implications for musculoskeletal physiotherapy

Abstract Physiotherapy has long been part of the overall intervention for the attempted rehabilitation of patients with pain and disability following peripheral (and central) nerve damage. In musculoskeletal physiotherapy (a subspecialty), a movement-based assessment and treatment protocol has been devised that is guided by, among other things, therapists' perceptions of patients' responses to mechanical stimuli, including various tests of 'neural tension'. Recently, this process together with provocative tests of sensation has been employed to identify, and predict outcomes for, patients suspected of having a 'neural tissue' component to their pain and consequent disability (either fascicular damage or 'neuritis'). However, some of the syndromes involved are controversial, and uncertainty still surrounds the diagnosis, mechanisms and, therefore, effective treatment of the highly complex symptom, true neuropathic pain. In this review, the current basic scientific evidence for the proposed cause, and often intractable nature, of neuropathic pain is presented and discussed with reference to musculoskeletal therapy. It will be seen that peripheral nerve damage has the potential to create potentially irreversible changes in (peripheral and) central nervous system structure and function that have, to date, largely defied effective medical treatment. For musculoskeletal physiotherapy to discriminate accurately and, where appropriate, intervene (or not) responsibly, it would seem constructive to incorporate this (and other) mechanisms-related evidence into its clinical reasoning and decision-making process.