Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis and Synthesis of Sign Language Gestures : from Meaning to Movement Production

The ever growing use of gestures in advanced technologies, such as augmented or virtual reality environments, requires more and more understanding of the different levels of representation of gestures, from meanings to motion involving causal physical and biological phenomenon. This is even more true for gestures used for artistic expression, such as dance or musical gestures, or for communicative gestures such as sign language gestures, both involving high level semiotic and cognitive representations and requiring extreme rapidity, accuracy, and physical engagement with the environment. We first highlight in this paper the tight connection between the high-level and low-level processes, and provide a general outline for motion control of skilled gestures, where different sensory data are used to determine the action to perform in a global action-perception loop. We then propose a methodology for analyzing and synthesizing human expressive and semiotic gestures. We focus on data-driven approaches, using jointly captured motion and annotated videos. Motion capture allows us to find relevant representations that encode the main spatio-temporal characteristics of gestures. Annotations provide a way to introduce significant labels indicating the morphosyntactic nature of elements composing gestures, but also expressive features that are specific to some subject and articulation effects. The nature of the different types of information encoded in and by gestures makes it necessary to store data in two different structures, namely a semantic database for textual annotations, and a raw database for motion capture data. From these coupled databases, it becomes then possible to statistically extract some gesture invariants and to use them for resynthesis. The databases are also exploited for driving our simulations. We believe that data-driven synthesis methods significantly improve the quality and the credibility of the synthesized motion, and also provide significant clues for constraining the produced movements. In the remaining of the paper, we describe several challenges that are involved in the case study of sign language analysis and synthesis. 54
Translational Research in Acute Lung Injury and Pulmonary Fibrosis Animal models of bronchopulmonary dysplasia . The term rat models

O’Reilly M, Thébaud B. Animal models of bronchopulmonary dysplasia. The term rat models. Am J Physiol Lung Cell Mol Physiol 307: L948–L958, 2014. First published October 10, 2014; doi:10.1152/ajplung.00160. 2014. —Bronchopulmonary dysplasia (BPD) is the chronic lung disease of prematurity that affects very preterm infants. Although advances in perinatal care have enabled the survival of infants born as early as 23–24 wk of gestation, the challenge of promoting lung growth while protecting the ever more immature lung from injury is now bigger. Consequently, BPD remains one of the most common complications of extreme prematurity and still lacks specific treatments. Progress in our understanding of BPD and the potential of developing therapeutic strategies have arisen from large (baboons, sheep, and pigs) and small (rabbits, rats, and mice) animal models. This review focuses specifically on the use of the rat to model BPD and summarizes how the model is used in various research studies and the advantages and limitations of this particular model, and it highlights recent therapeutic advances in BPD by using this rat model.
Toward Enhancing Block I/O Performance for Virtualized Hadoop Cluster

Virtualization offers many advantages by providing server consolidation, isolation, and live migration. In cloud computing environments, virtual machine monitor is an essential part for efficient management of physical resources. Recently, processing big data is important to both most cloud service providers and users. In this paper, the performance of Xen based Hadoop cluster is analyzed, especially related to block device I/O. In addition, the overhead transferring I/O requests are introduced with Xen I/O ring mechanism, and the experimental result of our optimization for Hadoop workloads is presented. The proposed approach reduces CPU utilization by a third during I/O operations and improve throughput of Hadoop applications.
MIP3S: algorithms for power-conserving multicasting in static wireless ad hoc networks

In a static ad hoc wireless network, given a distinguished source node, and a subset of nodes called multicast group members, the minimum-energy multicast problem is to assign appropriate power levels to nodes in the network so that all group members are reachable from the source, and that the total power usage is as small as possible. In the centralized version of the problem, one finds such power assignment given the entire network topology. In the distributed version, a power assignment is found by exchanging information between neighboring nodes. In this paper, we proposed new algorithms based on the idea of multicast incremental power with potential power saving (MIP3S). Simulations show that the new algorithms work better than all known algorithms. Different versions of this idea, when made distributive, are of different time and message complexities, imposing an interesting trade-off in total saving in power and other complexity measures.
MAC Location Services for IEEE802.11 Networks

This paper presents an IEEE802.11 MAC extension to include localization services in the wireless LAN networks, which lack of any support for such services neither at hardware level nor at network level. Independently from the method at the PHY layer used to estimate the distance, we propose an effective location management policy procedure at the MAC level. The algorithm is based on the distance estimation performed by, at least, three Base Stations to calculate the mobile user position using the well known triangulation process. We propose an innovative and efficient method based on both the IEEE802.11 PCF and DCF phases to track the user position at a constant rate according to the user speed. A complete simulation model has been developed in order to assess the maximum number of users traceable and to provide our algorithm efficiency and performance level.
Farm Fatalities to Youth 1995-1997: A Comparison by Age Groups

Although a myriad of research illustrates the safety issues related to farm fatalities in youth populations, very little empirical evidence exists to describe farm fatalities to non-working youth and youth under 16 years of age. Research such as that conducted by Myers and Adekoya (2001) and Castillo et al. (1999) utilize surveillance systems such as the National Traumatic Occupational Fatalities (NTOF) Surveillance System and the Census of Fatal Occupational Injuries (CFOI) to provide information on occupational farm fatalities to youth. However, since youth are exposed to hazards both while working and playing on the farm, these studies exclude one critical component-non-occupational fatalities. Adekoya and Pratt (2001) utilize National Center for Health Statistics (NCHS) Mortality Data to address fatal farm injuries. However, the data do not allow for a delineation between occupational and non-occupational events. In addition, the coding system utilized with these data does not allow for inclusion of transportation or intentional fatalities, and fatalities occurring within the farm home were not included. To address both occupational and non-occupational deaths on farms, including transportation and intentional deaths, this research will utilize death certificate data collected by the National Institute for Occupational Safety and Health (NIOSH) from all 50 state vital statistics mortality registries. Records satisfying the following criteria were requested: underlying, immediate, or contributing cause of death from an external factor; age of the decedent is less than 20 years of age; and the indicated location of death is a farm. The results include information for 368 fatalities occurring in the years 1995 through 1997. In addition to providing annual fatality rates and description of the general cause of death, this research will further examine the variation between age groups. In particular, a distinction will be made between youth under 16 years of age and those 16 and over. Preliminary findings show an average annual fatality rate for all farm youth of 9.76 fatalities per 100,000 youth. The most prevalent causes of death are; machinery (94), drowning (59), motor vehicle (49), nature/environment (25) and suffocation (24). Of all youth fatalities occurring while at work, 34.0% (16) are to youth less than 16 years of age. This same age group accounts for 74.4% (221) of all non-work related fatalities. This paper will provide farm families and researchers more detailed information on farm hazards that contribute to the deaths of youth. As these youth may encounter hazards while working or playing in …
On Processing Extreme Data

Extreme Data is an incarnation of Big Data concept distinguished by the massive amounts of data that must be queried, communicated and analyzed in near real-time by using a very large number of memory or storage elements and exascale computing systems. Immediate examples are the scientific data produced at a rate of hundreds of gigabits-per-second that must be stored, filtered and analyzed, the millions of images per day that must be analyzed in parallel, the one billion of social data posts queried in real-time on an in-memory components database. Traditional disks or commercial storage nowadays cannot handle the extreme scale of such application data. Following the need of improvement of current concepts and technologies, we focus in this paper on the needs of data intensive applications running on systems composed of up to millions of computing elements (exascale systems). We propose in this paper a methodology to advance the state-of-the-art. The starting point is the definition of new programming paradigms, APIs, runtime tools and methodologies for expressing data-intensive tasks on exascale systems. This will pave the way for the exploitation of massive parallelism over a simplified model of the system architecture, thus promoting high performance and efficiency, offering powerful operations and mechanisms for processing extreme data sources at high speed and/or real time.
Eecient Learning Using Constrained Suucient Statistics

Learning Bayesian networks is a central problem for pattern recognition, density estimation and classiication. In this paper, we propose a new method for speeding up the computational process of learning Bayesian network structure. This approach uses constraints imposed by the statistics already collected from the data to guide the learning algorithm. This allows us to reduce the number of statistics collected during learning and thus speed up the learning time. We show that our method is capable of learning structure from data more eeciently than traditional approaches. Our technique is of particular importance when the size of the datasets is large or when learning from incomplete data. The basic technique that we introduce is general and can be used to improve learning performance in many settings where suf-cient statistics must be computed. In addition , our technique may be useful for alternate search strategies such as branch and bound algorithms.
Metabotropic Glutamate Receptor Activation Modulates Kainate and Serotonin Calcium Response in Astrocytes

Although metabotropic glutamate receptor (mGluR) modulation has been studied extensively in neurons, it has not been investigated in astrocytes. We studied modulation of glutamate-evoked calcium rises in primary astrocyte cultures using fura-2 ratiometric digital calcium imaging. Calcium plays a key role as a second messenger system in astrocytes, both in regulation of many subcellular processes and in long distance intercellular signaling. Suprachiasmatic nucleus (SCN) and cortical astrocytes showed striking differences in sensitivity to glutamate and to mGluR agonists, even after several weeks in culture. Kainate-evoked intracellular calcium rises were inhibited by concurrent application of the type I and II mGluR agonists quisqualate (10 μm),trans-(±)-1-amino-1,3-cyclopentanedicarboxylate (100–500 μm), and (2S-1′S-2′S)-2-(carboxycyclopropyl)glycine (L-CCG-I) (10 μm). Inhibition mediated by L-CCG-I had long-lasting effects (>45 min) in ∼30% of the SCN astrocytes tested. The inhibition could be mimicked by the L-type calcium channel blocker nimodipine (1 μm) as well as by protein kinase C (PKC) activators phorbol 12,13-dibutyrate (10 μm) and phorbol 12-myristate 13-acetate (500 nm), and blocked by the PKC inactivator (±)-1-(5-isoquinolinesulfonyl)-2-methylpiperazine (200 μm), suggesting a mechanism involving PKC modulation of L-type calcium channels. In contrast, mGluRs modulated serotonin (5HT)-evoked calcium rises through a different mechanism. The type III mGluR agonist l-2-amino-4-phosphonobutyrate consistently inhibited 5HT-evoked calcium rises, whereas in a smaller number of cells quisqualate and L-CCG-I showed both inhibitory and additive effects. Unlike the mGluR-kainate interaction, which required a pretreatment with an mGluR agonist and was insensitive to pertussis toxin (PTx), the mGluR modulation of 5HT actions was rapid and was blocked by PTx. These data suggest that glutamate, acting at several metabotropic receptors expressed by astrocytes, could modulate glial activity evoked by neurotransmitters and thereby influence the ongoing modulation of neurons by astrocytes.
Learning Solutions of Similar Linear Programming Problems using Boosting Trees

In many optimization problems, similar linear programming (LP) problems occur in the nodes of the branch and bound trees that are used to solve integer (mixed or pure, deterministic or stochastic) programming problems. Similar LP problems are also found in problem domains where the objective function and constraint coefficients vary due to uncertainties in the operating conditions. In this report, we present a regression technique for learning a set of functions that map the objective function and the constraints to the decision variables of such an LP system by modifying boosting trees, an algorithm we term the Boost-LP algorithm. Matrix transformations and geometric properties of boosting trees are utilized to provide theoretical performance guarantees on the predicted values. The standard form of the loss function is altered to reduce the possibility of generating infeasible LP solutions. Experimental results on three different problems, one each on scheduling, routing, and planning respectively, demonstrate the effectiveness of the Boost-LP algorithm in providing significant computational benefits over regular optimization solvers without generating solutions that deviate appreciably from the optimum values.
An analysis of edge based image steganography techniques in spatial domain

Steganography is a branch of information security. Steganography aims at hiding the existence of the actual communication. This aim is achieved by hiding the actual information into other information in such a way that intruder cannot detect it. A variety of carrier file formats can be used to carry out steganography e.g. images, text, videos, audio, radio waves etc. But mainly images are used for this purpose because of their high frequency on internet. Number of image steganography techniques has been introduced having some drawbacks and advantages. These techniques are evaluated on the basis of three parameters imperceptibility, robustness and capacity. In this paper we will review various edge based image steganography techniques. Main idea behind these techniques is that edges can bear more variation than smooth areas without being detected.
Abnormal Expression of Golgi Protein 73 in Clinical Values and Their Role in HBV-Related Hepatocellular Carcinoma Diagnosis and Prognosis

Background: The up-regulation of hepatic Golgi protein 73 (GP73) is associated with the progression of hepatocellular carcinoma (HCC). However, the exact mechanism and clinical values of its diagnosis and prognosis still need to be clarified. Objectives: To investigate the clinical values of abnormal liver or circulating GP73 expression and their effect on HCC diagnosis and prognosis. Materials and Methods: The expression of GP73 was investigated in 88 cancerous and self-control non-cancerous tissues using tissue microarrays with immunohisto- chemistry and was confirmed by Western blotting. Circulating GP73 levels were detected in the sera of 281 patients with liver diseases using enzyme-linked immunosorbent assay. Results: The levels of circulating GP73 expression in the HCC group were higher than those in any group of benign liver diseases or controls. No significant difference was found between GP73 expression and patients’ sex or age, tumor size, or AFP level except for those with hepatitis B virus (HBV) infection or distal metastasis (P < 0.05). The area under the receiver operating characteristic curve, sensitivity, and specificity for HCC diagnosis were 0.881, 78.34%, and 77.59% for GP73 levels over 70 μg/L or 0.754, 71.97%, and 84.48% for alpha-fetoprotein levels over 50 μg/L, respectively. The total incidence of GP73 plus alpha-fetoprotein was up to 87.26% for HCC. A positive GP73 result with brown particles was mainly located in the cytosol, with a few in the nucleus and none in the cell membrane, with abnormal expression in HCC tissues (480.7 ± 148.7) that was significantly higher (t = 10.730, P < 0.001) than those in their non-cancerous tissues (208.0 ± 66.1). The high GP73 expression in HCC was related to lymph node metastasis (χ2 = 6.940, P = 0.008), gross classification (χ2 = 6.311, P = 0.012), HBV (χ2 = 4.803, P = 0.028), tumor node metastasis staging (χ2 = 4.887, P = 0.027), and five-year survival (χ2 = 5.206, P = 0.023). Conclusions: Abnormality of hepatic or circulating GP73 expression should be regarded as an emerging biomarker for HCC diagnosis and prognosis.
Data Compression by Temporal and Spatial Correlations in a Body-Area Sensor Network: A Case Study in Pilates Motion Recognition

We consider a body-area sensor network (BSN) consisting of multiple small, wearable sensor nodes deployed on a human body to track body motions. Concerning that human bodies are relatively small and wireless packets are subject to more serious contention and collision, this paper addresses the data compression problem in a BSN. We observe that, when body parts move, although sensor nodes in vicinity may compete strongly with each other, the transmitted data usually exist some levels of redundancy and even strong temporal and spatial correlations. Unlike traditional data compression approaches for large-scale and multihop sensor networks, our scheme is specifically designed for BSNs, where nodes are likely fully connected and overhearing among sensor nodes is possible. In our scheme, an offline phase is conducted in advance to learn the temporal and spatial correlations of sensing data. Then, a partial ordering of sensor nodes is determined to represent their transmission priorities so as to facilitate data compression during the online phase. We present algorithms to determine such partial ordering and discuss the design of the underlying MAC protocol to support our compression model. An experimental case study in Pilates exercises for patient rehabilitation is reported. The results show that our schemes reduce more than 70 percent of overall transmitted data compared with previous approaches.
Document Title: Rape Prevention Through Bystander Education: Bringing a Broader Community Perspective to Sexual Violence Prevention

The sexual violence prevention program we evaluated uses a community of responsibility model to teach women and men how to intervene safely and effectively in cases of sexual violence before, during, and after incidents with strangers, acquaintances, or friends. The program varies from other prevention programs in that it does not address men as potential perpetrators or women as potential victims. Rather it approaches both women and men as potential bystanders or witnesses to behaviors related to sexual violence. The program is grounded in recent research in social and community psychology on bystander intervention and community-focused solutions. The program draws upon findings from research on community change and prevention as well as more individually-focused studies of rape prevention programs and bystander behavior in emergency and crime situations. Three hundred and eighty-nine undergraduates participated and were randomly assigned to one of two treatment groups or a control group. Results from the research reveal that up to two months after participating in either a one or three session version of the program, participants in the treatment conditions showed improvements across measures of attitudes, knowledge, and behavior while the control group did not. Most program effects persisted at four and twelve month follow-ups. The program appeared to benefit women and men equally. Implications and future directions for research are
Skilled Immigration and the Employment Structures and Innovation Rates of U.S. Firms

We study the impact of skilled immigrants on the employment structures and innovation outcomes of U.S. rms using matched employer-employee data. We use the rm as the lens of analysis given that many skilled immigrant admissions are driven by rms subject to regulations and mandated caps (e.g., H-1B visa). OLS and IV speci cations nd rising overall employment with increased skilled immigrant employment by the rm; employment expansion is greater for younger natives than older natives. Departure rates for older workers appear higher for workers in STEM occupations. Skilled immigration expands rm innovation with little impact on the traits of patents led. JEL Classi cation: F15, F22, J44, J61, O31. Key Words: Employment, Age, Innovation, Research and Development, Patents, Scientists, Engineers, Inventors, H-1B, Immigration, Ethnicity. S. Kerr: Wellesley College; W. Kerr: Harvard and NBER; and W. Lincoln: John Hopkins SAIS. Comments are appreciated and can be sent to wkerr@hbs.edu. We especially thank Daniel Goro¤ and the Sloan Foundation for nancial support for this project; we thank Daron Acemoglu, George Borjas, Prithwiraj Choudhury, Jim Davis, Gilles Duranton, Lee Fleming, Bronwyn Hall, Larry Katz, Paul Oyer, and seminar participants at APPAM Fall Research Conference, Harvard University, Harvard Business School, John Hopkins SAIS, London Business School, London School of Economics, NBER Conference on High-Skill Immigration, NBER Productivity, Northeastern University, Research Institute of Industrial Economics, Richard Ivey School of Business, Rutgers Business School, University of California Berkeley Haas School, University of Pennsylvania Wharton School, and Wellesley College for very helpful comments; and we thank Alexis Brownell and Christine Huang for excellent research assistance. William Kerr is a research a¢ liate of the Bank of Finland and thanks the Bank for hosting him during a portion of this project. The research in this paper was conducted while the authors were Special Sworn Status researchers of the US Census Bureau at the Boston Census Research Data Center (BRDC). Support for this research from NSF grant ITR-0427889 is gratefully acknowledged. Research results and conclusions expressed are the authors and do not necessarily reect the views of the Census Bureau or NSF. This paper has been screened to ensure that no con dential data are revealed.