Miracle-dz/UniFAR-FTUs huggingface dataset:
EMG processing to interpret a neural tension‐limiting mechanism with fatigue

Introduction: Surface electromyography (sEMG) amplitude increases with constant muscle tension during fatiguing sub‐maximum efforts. The purpose of this study was to determine if extreme highpass filtering and/or autoregressive whitening would result in a more consistent sEMG‐to‐moment ratio than a standard bandpass filter (20–500 Hz) during repeated, dynamic maximal efforts of the quadriceps. Methods: We collected sEMG and knee extensor moment from 16 participants during the concentric and eccentric phases of repeated, maximal knee extensor efforts. Results: The alternative processing methods provided more consistent vastus medialis and lateralis sEMG‐to‐moment ratios. A neural tension‐limiting mechanism appeared to exist and was magnified during the eccentric phase, particularly with fatigue. Conclusions: There appears to be a difference in how the central nervous system controls concentric and eccentric efforts as the quadriceps fatigues, and this is more apparent with the alternative EMG processing methods we used. Muscle Nerve 50: 384–392, 2014
Night-time activity forecast by season and weather in a longitudinal design – natural light effects on three years’ rest-activity cycles in nursing home residents with dementia

ABSTRACT Backround: Night-time agitation is a frequent symptom of dementia. It often causes nursing home admission and has been linked to circadian rhythm disturbances. A positive influence of light interventions on night-time agitation was shown in several studies. The aim of our study was to investigate whether there is a long-term association between regional weather data (as indicator for daylight availability) and 24-hour variations of motor activity. Methods: Motor activity of 20 elderly nursing home residents living with dementia was analyzed using recordings of continuously worn wrist activity monitors over a three-year period. The average recording duration was 479 ± 206 days per participant (mean ± SD). Regional cloud amount and day length data from the local weather station (latitude: 52°56′N) were included in the analysis to investigate their effects on several activity variables. Results: Nocturnal rest, here defined as the five consecutive hours with the least motor activity during 24 hours (L5), was the most predictable activity variable per participant. There was a significant interaction of night-time activity with day length and cloud amount (F 1,1174 = 4.39; p = 0.036). Night-time activity was higher on cloudy short days than on clear short days (p = 0.007), and it was also higher on cloudy short days than on cloudy long days (p = 0.032). Conclusions: The need for sufficient zeitgeber (time cue) strength during winter time, especially when days are short and skies are cloudy, is crucial for elderly people living with dementia. Activity forecast by season and weather might be a valuable approach to anticipate adequately complementary use of electrical light and thereby foster lower night-time activity.
Ambiguous Risk Measures and Optimal Robust Portfolios

This paper deals with a problem of guaranteed (robust) financial decision-making under model uncertainty. An efficient method is proposed for determining optimal robust portfolios of risky financial instruments in the presence of ambiguity (uncertainty) on the probabilistic model of the returns. Specifically, it is assumed that a nominal discrete return distribution is given, while the true distribution is only known to lie within a distance $d$ from the nominal one, where the distance is measured according to the Kullback-Leibler divergence. The goal in this setting is to compute portfolios that are worst-case optimal in the mean-risk sense, that is, to determine portfolios that minimize the maximum with respect to all the allowable distributions of a weighted risk-mean objective. The analysis in the paper considers both the standard variance measure of risk and the absolute deviation measure.
Signaling and Regulation EWS / FLI 1 Regulates EYA 3 in Ewing Sarcoma via Modulation of miRNA-708 , Resulting in Increased Cell Survival and Chemoresistance

Ewing sarcoma is an aggressive pediatric cancer of the bone and soft tissue, in which patients whose tumors have a poor histologic response to initial chemotherapy have a poor overall prognosis. Therefore, it is important to identify molecules involved in resistance to chemotherapy.Herein, we show that theDNA repair protein and transcriptional cofactor, EYA3, is highly expressed in Ewing sarcoma tumor samples and cell lines compared with mesenchymal stem cells, the presumed cell-of-origin of Ewing sarcoma, and that it is regulated by the EWS/FLI1 fusion protein transcription factor.We further show that EWS/FLI1mediates upregulation of EYA3 via repression of miR-708, a miRNA that targets the EYA3 30-untranslated region, rather than by binding the EYA3 promoter directly. Importantly, we show that high levels of EYA3 significantly correlate with low levels of miR-708 in Ewing sarcoma samples, suggesting that this miR-mediated mechanism of EYA3 regulation holds true in human cancers. Because EYAproteins are important for cell survival during development, we examine, and show, that loss of EYA3 decreases survival of Ewing sarcoma cells. Most importantly, knockdown of EYA3 in Ewing sarcoma cells leads to sensitization to DNA-damaging chemotherapeutics used in the treatment of Ewing sarcoma, and as expected, after chemotherapeutic treatment, EYA3 knockdown cells repair DNA damage less effectively than their control counterparts. These studies identify EYA3 as a novel mediator of chemoresistance in Ewing sarcoma and define the molecular mechanisms of both EYA3 overexpression and of EYA3-mediated chemoresistance.Mol Cancer Res; 10(8); 1098–108. 2012 AACR.
Extracting and Re-Rendering Structured Auditory Scenes from Field Recordings

We present an approach to automatically extract and re-render a structured auditory scene from field recordings obtained with a small set of microphones, freely positioned in the environment. From the recordings and the calibrated position of the microphones, the 3D location of various auditory events can be estimated together with their corresponding content. This structured description is reproduction-setup independent. We propose solutions to classify foreground, well-localized sounds and more diffuse background ambiance and adapt our rendering strategy accordingly. Warping the original recordings during playback allows for simulating smooth changes in the listening point or position of sources. Comparisons to reference binaural and B-format recordings show that our approach achieves good spatial rendering while remaining independent of the reproduction setup and offering extended authoring capabilities.
Characteristics of wood-plastic composites reinforced with organo-nanoclays

Nanomer and Cloisite are the popular commercial nanoclays (NC) available in the market. The mechanical properties of wood—plastic composite (WPC) based on linear low density polyethylene with or without wood fiber are compared for different added nanoclays. In addition, the moisture uptakes at 23°C and 45% RH and water uptake/loss after immersion in water are evaluated. I.34TCN type nanoclay as well as Cloisite Na+ lead to composites with superior mechanical properties and better water resistance behaviors compared to the other kinds of nanoclays. It should be noted that the discoloration occurs in WPC reinforced with unmodified NC.
An area-restriction based localization method for wireless sensor networks using a mobile anchor

Localization is a very important issue to wireless sensor networks. The sensor node requires accurate location information in order to achieve the purpose of real-time monitoring and transmission of information. The more the sensor nodes and GPS modules are used in localization, the higher the localization accuracy can achieve, but employing more sensor nodes also leads to high costs. Thus, how to improve the localization accuracy while solving the problem of high costs has garnered attention. In this paper, an area-restriction based localization (ARBL) algorithm is proposed. The ARBL algorithm uses both range-based and range-free schemes, which combines area restriction setting and the sampling points from Monte Carlo localization (MCL) to improve the localization accuracy. The simulation results show that the ARBL algorithm proposed in this paper outperforms DuRT, MMRL and RL algorithms that employ a mobile anchor node, regardless of the number of the sensor nodes and the moving speed of the anchor node.
The GridWay Framework for Adaptive Scheduling and Execution on Grids

Many research and engineering fields, like Bioinformatics or Particle Physics, are confident about the development of Grid technologies to provide the huge amounts of computational and storage resources they require. Although several projects are working on creating a reliable infrastructure consisting of persistent resources and services, the truth is that the Grid will be a more and more dynamic entity as it grows. In this paper, we present a new tool that hides the complexity and dynamicity of the Grid from developers and users, allowing the resolution of large computational experiments in a Grid environment by adapting the scheduling and execution of jobs to the changing Grid conditions and application dynamic demands.
The use of large shipboard barrels and drifters to study the effects of coastal upwelling on phytoplankton dynamics1, 2

During the OPUS (Organization of Persistent Upwelling Structures) study of the upwelling center at Point Conception, California, large (380 liter) plastic shipboard enclosures (barrels) were tested as experimental tools to study phytoplankton processes in isolation from variability in circulation and grazing. Barrels were filled with water from the upwelling center during different stages of the upwelling cycle. Upwelling was simulated in one barrel series and the phytoplankton response to additional nutrients and shading was examined. Measurements of 15N uptake (VNO,-) and nitrate reductase activity (VNR) made in the barrels are reported in the context of a “conveyor-belt” hypothesis in which upwelled algae go through a “shift-up” in metabolism followed by a “shiftdown. ” The results showed the same trends as those obtained with Lagrangian sampling along drifter tracks initiated at the same time and place as the barrels. Our hypothesis that the initial nutrient concentration has an effect on the rate of shift-up (defined as acceleration of VNO,- or VNR) was substantiated by a positive correlation between acceleration of the rates, V/NO,- and VNR, and nitrate concentration. At the upwelling center at 15’S, Peru, a similar observation was based both on VNO,- acceleration from drogue and holdover experiments and on silicate concentration.
Biosynthesis of silver and gold nanoparticles by novel sundried Cinnamomum camphora leaf

The synthesis of nanocrystals is in the limelight in modern nanotechnology. Biosynthesis of nanoparticles by plant extracts is currently under exploitation. Not only could silver nanoparticles ranging from 55 to 80 nm in size be fabricated, but also triangular or spherical shaped gold nanoparticles could be easily modulated by reacting the novel sundried biomass of Cinnamomum camphora leaf with aqueous silver or gold precursors at ambient temperature. The marked difference of shape control between gold and silver nanoparticles was attributed to the comparative advantage of protective biomolecules and reductive biomolecules. The polyol components and the water-soluble heterocyclic components were mainly responsible for the reduction of silver ions or chloroaurate ions and the stabilization of the nanoparticles, respectively. The sundried leaf in this work was very suitable for simple synthesis of nanoparticles.
Licensed under Creative Commons Attribution Cc by Knowledge Transfer Using Cost Sensitive Online Learning Classification

A real assumption in numerous machine learning area and data mining algorithms is that the future data and training necessary to be in the similar feature space and have the similar distribution. Moreover, in numerous applications, this presumption may not hold. For illustration, we once in a while have task of a classification in one space of interest, yet we just have sufficient preparing data in an alternate area of interest, where the recent data may be in an alternate feature space or take after an alternate distribution of data. In such cases, knowledge transfer, if done effectively, would extraordinarily enhance the execution of learning by staying away from much costly data-naming efforts. Recently, transfer learning has developed as another learning structure to address this issue. Another machine learning structure called Online Transfer Learning (OTL) that plans to transfer information from some source space to an online learning assignment on a target domain. A famous methodology to cost-sensitive learning is to rescale the classes as per their mis-classification costs. In spite of the fact that this methodology is successful in managing with binary class issues, late studies demonstrate that it is frequently not all that supportive while being connected to multi-class issues specifically. In this paper, we have focus the survey on cost sensitive on machine learning and various methods used. This paper also focuses on online learning methods.
A Factor (or Mutator Gene) Influencing Mutation Rates in Escherichia Coli.

There is now considerable evidence, particularly in maize and in Drosophila, for the existence of factors which influence the rates of mutation of genes.1 2 The interpretations which have been offered are varied and involve, for example, mutator genes, heterochromatin, or even infective, virus-like particles. Although much still remains to be done, the study of these phenomena constitutes one approach to the general problem of the nature of gene action. The present paper is concerned with the evidence for the existence of analogous effects in a micro6rganism, a variant of the well-known K12 strain of Escherichia coli.3 Our recognition of the special properties of this bacterial variant is not without its instructive, even amusing aspects: in the course of an investigation on possible changes in the nutritive requirements of bacteria after selection for drug resistance, we had observed that a number of streptomycin-resistant K12 stocks acquired a need for one or more amino acids before growth ensued. In one series a requirement for phenylalanine was indicated for a number of independent isolates, in a frequency somewhat beyond that expected by chance. To investigate further this apparent association, we then determined the resistance of a stock phenylalanine-requiring K12 auxotroph which had not previously been in contact with streptomycin. At the inoculum dilution used, the culture appeared completely resistant to streptomyein, inocula of 0.1 ml. of full-grown culture growing out in subcVltures containing 100 ,ug. or more streptomycin per milliliter, while other K12 stocks did not grow in drug concentrations beyond 5-10 jug. This appeared to be a verification of our working hypothesis concerning phenylalanine requirement and increased resistance to streptomycin. Further investigation, with greater dilutions of the culture, revealed, however, that we were dealing not with a high level of resistance for the average organism in the culture but rather with an unusually high rate of mutation to resistance, which resulted in an exceptional number of resistant organisms in an otherwise susceptible population. Of perhaps more general interest is our later observation that, as in the Drosophila material, this increased mutation rate obtains toward a considerable variety of agents, although the mutation rate is not increased uniformly to all states and in fact appears to be normal in at least one instaince. Finally, clear evidence has been obtained that the maintenance of this high rate of mutation is in no way related to a nutritional requirement for phenylalanine and that our original hypothesis in this regard, useful as it was in leading to further observations of interest, must be regarded as only a tentative working hypothesis, based on too small a series of initial observations and to be replaced in the light of additional experiments. Cultures. -The relationships of the principal K12 cultures used, which are of importance for the argument belowv, are best understood from the followA,ing chart:'
Reinforcement Learning of Behaviors in Mobile Robots Using Noisy Infrared Sensing

Multiple mobile robots are now being envisioned as being able to achieve a wide range of nonsupervised tasks, from being hospital gofors [Evans, 1994] to organizing themselves in a factory situation [Wallace, 1998]. The tasks are characterized by requiring some learning ability in the robot that enables it to improve its performance or acquire local important information related to the task. Many researchers have used the challenges of making mobile robots play soccer as a test bed [Kim et aI, 1997]. There are a variety of paradigms proposed for the learning phase such as fuzzy, neural, genetic [Zurada et aI, 1994], reinforcement [Sutton & Barto, 1998] and methods including combinations such as fuzzy-neural systems [Brown & Harris, 1994]. Work reported to date has been largely theoretical but the balance is changing as a range of versatile platforms become available with trade names such as Rug Warrior, Numbat, Pioneer and Helpmate, all of which have www-based access. We report research into ways of making mobile robots learn behaviors appropriate to tasks allotted to them via the reinforcement paradigm but with noisy infrared . sensors. This work is part of a more extensive exercise in comparing the practicality of various learning paradigms on the same platform. Of interest has been the learning rates and how they are affected by the richness of the environment, the need for some form of annealing to avoid local minimums, the overall specification of the behavior that is desired and the pro~g support offered by the platform. Results indicate that reinforcement learning for behaviors such as avoidance can be achieved ill periods of a few minutes with noisy sensors at the 68HCl1 computing power level chosen to work with.
Population Preferences of Undergoing Brachioplasty for Arm Laxity

BackgroundThe number of patients requesting surgical procedures performed for brachioplasty and massive weight loss is increasing. The authors set out to quantify the health state utility outcome assessment of living with arm deformity requiring brachioplasty. MethodsUtility assessments using the visual analog scale (VAS), time trade-off (TTO), and standard gamble (SG) were used to obtain utilities scores for arm deformity, monocular blindness, and binocular blindness from a sample of the general population and medical students. Linear regression and Student t test were used for statistical analysis. A P value less than 0.05 was deemed statistically significant. ResultsAll the measures for arm deformity of the 107 volunteers (VAS, 0.80 ± 0.14; TTO, 0.91 ± 0.12; SG, 0.94 ± 0.10) were significantly different (P < 0.001) from the corresponding measures for monocular blindness and binocular blindness. When compared to the sample of the general population, having a medical education demonstrated a statistical significance of being less likely to trade years of life and less likely to gamble risk of death for a procedure such as a brachioplasty. Race and sex were not statistically significant independent predictors of risk acceptance. ConclusionsWe have objectified the health state of living with upper arm deformity requiring brachioplasty. Utility outcome scores (VAS, 0.80 ± 0.14; TTO, 0.91 ± 0.12; SG, 0.94 ± 0.10) were comparable to living with health states such as aging neck needing rejuvenation, excess skin in the thighs necessitating thigh lift, and massive weight loss requiring panniculectomy based on previously reported studies.
Incentivized Outsourced Computation Resistant to Malicious Contractors

With the rise of Internet computing, outsourcing difficult computational tasks became an important need. Yet, once the computation is outsourced, the job owner loses control, and hence it is crucial to provide guarantees against malicious actions of the contractors involved. One may want to ensure that both the job itself and any inputs to it are hidden from the contractors, while still enabling them to perform the necessary computation. Furthermore, one would check that the computation was carried out correctly. In this paper, we are not concerned with hiding the job or the data, but our main task is to ensure that the job is computed correctly. We also observe that not all contractors are malicious; rather, majority are rational. Thus, our approach brings together elements from cryptography, as well as game theory and mechanism design. We achieve the following results: (1) We incentivize all the rational contractors to perform the outsourced job correctly, (2) we guarantee high fraction (e.g., 99.9 percent) of correct results even in the existence of a relatively large fraction (e.g., 33 percent) of malicious irrational contractors in the system, (3) and we show that our system achieves these while being almost as efficient as running the job locally (e.g., with only 3 percent overhead). Such a high correctness guarantee was not known to be achieved with such efficiency.
Exploiting Intra-Request Slack to Improve SSD Performance

With Solid State Disks (SSDs) offering high degrees of parallelism, SSD controllers place data and direct requests to exploit the maximum offered hardware parallelism. In the quest to maximize parallelism and utilization, sub-requests of a request that are directed to different flash chips by the scheduler can experience differential wait times since their individual queues are not coordinated and load balanced at all times. Since the macro request is considered complete only when its last sub-request completes, some of its sub-requests that complete earlier have to necessarily wait for this last sub-request. This paper opens the door to a new class of schedulers to leverage such slack between sub-requests in order to improve response times. Specifically, the paper presents the design and implementation of a slack-enabled re-ordering scheduler, called Slacker, for sub-requests issued to each flash chip. Layered under a modern SSD request scheduler, Slacker estimates the slack of each incoming sub-request to a flash chip and allows them to jump ahead of existing sub-requests with sufficient slack so as to not detrimentally impact their response times. Slacker is simple to implement and imposes only marginal additions to the hardware. Using a spectrum of 21 workloads with diverse read-write characteristics, we show that Slacker provides as much as 19.5%, 13% and 14.5% improvement in response times, with average improvements of 12%, 6.5% and 8.5%, for write-intensive, read-intensive and read-write balanced workloads, respectively.
Connections between neural networks and boolean functions

This report surveys some connections between Boolean functions and artificial neural networks. The focus is on cases in which the individual neurons are linear threshold neurons, sigmoid neurons, polynomial threshold neurons, or spiking neurons. We explore the relationships between types of artificial neural network and classes of Boolean function. In particular, we investigate the type of Boolean functions a given type of network can compute, and how extensive or expressive the set of functions so computable is.