Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel tactile display for softness and texture rendering in tele-operation tasks

Softness and texture high-frequency information represent fundamental haptic properties for every day life activities and environment tactual exploration. While several displays have been produced to convey either softness or high-frequency information, there is no or little evidence of systems that are able to reproduce both these properties in an integrated fashion. This aspect is especially crucial in medical tele-operated procedures, where roughness and stiffness of human tissues are both important to correctly identify given pathologies through palpation (e.g. in tele-dermatology). This work presents a fabric yielding display (FYD-pad), a fabric-based tactile display for softness and texture rendering. The system exploits the control of two motors to modify both the stretching state of the elastic fabric for softness rendering and to convey texture information on the basis of accelerometer-based data. At the same time, the measurement of the contact area can be used to control remote or virtual robots. In this paper, we discuss the architecture of FYD-pad and the techniques used for softness and texture reproduction as well as for synthesizing probe-surface interactions from real data. Tele-operation examples and preliminary experiments with humans are reported, which show the effectiveness of the device in delivering both softness and texture information.
Low-Order Stochastic Mode Reduction for a Prototype Atmospheric GCM

Abstract This study applies a systematic strategy for stochastic modeling of atmospheric low-frequency variability to a three-layer quasigeostrophic model. This model climate has reasonable approximations of the North Atlantic Oscillation (NAO) and Pacific–North America (PNA) patterns. The systematic strategy consists first of the identification of slowly evolving climate modes and faster evolving nonclimate modes by use of an empirical orthogonal function (EOF) decomposition in the total energy metric. The low-order stochastic climate model predicts the evolution of these climate modes a priori without any regression fitting of the resolved modes. The systematic stochastic mode reduction strategy determines all correction terms and noises with minimal regression fitting of the variances and correlation times of the unresolved modes. These correction terms and noises account for the neglected interactions between the resolved climate modes and the unresolved nonclimate modes. Low-order stochastic models w.
Exploiting the Internet Inter-ORB Protocol Interface to Provide CORBA with Fault Tolerance

The Eternal system is a CORBA 2.0-compliant system that provides, in addition to the location transparency and the interoperability inherent in the CORBA standard, support for replicated objects and thus fault tolerance. Eternal exploits the Internet Inter-ORB Protocol (IIOP) interface to "attach" itself transparently to objects operating over a commercial CORBA Object Request Broker (ORB). The Eternal Interceptor captures the IIOP system calls of the objects, and the Eternal Replication Manager maps these system calls onto a reliable totally ordered multicast group communication system. No modification to the internal structure of the ORB is necessary, and fault tolerance is provided in a manner that is transparent to both the application and the ORB.
Querying dynamic communities in online social networks

Online social networks (OSNs) offer people the opportunity to join communities where they share a common interest or objective. This kind of community is useful for studying the human behavior, diffusion of information, and dynamics of groups. As the members of a community are always changing, an efficient solution is needed to query information in real time. This paper introduces the Follow Model to present the basic relationship between users in OSNs, and combines it with the MapReduce solution to develop new algorithms with parallel paradigms for querying. Two models for reverse relation and high-order relation of the users were implemented in the Hadoop system. Based on 75 GB message data and 26 GB relation network data from Twitter, a case study was realized using two dynamic discussion communities: #musicmonday and #beatcancer. The querying performance demonstrates that the new solution with the implementation in Hadoop significantly improves the ability to find useful information from OSNs.
Outcome of conventional IVF and ICSI on sibling oocytes in moderate ligoasthenozoospermia

Objective: To evaluate the outcomes of patients with moderate oligoasthenozoospermia treated with conventional in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI). Methods: A total of 99 couples with moderate oligoasthenozoospermia undergoing their first IVF/ICSI cycle were included in the study. Sibling oocytes were randomized to be inseminated either by conventional IVF or ICSI. Fertilization rate, cleavage rate, embryo quality, implantation rate, and clinical pregnancy rate were examined. Results: There was no difference in the fertilization rate, cleavage rate, implantation rate, and pregnancy rate between conventional IVF and ICSI (P>0.05). The good quality embryo rate was significant difference between after IVF and after ICSI (P<0.05). Conclusions: Couples with moderate oligoasthenozoospermia did not influence the major indices of IVF and the uncertainties concerning the safety of ICSI, couples with moderate oligoasthenozoospermia need not be subjected to ICSI.
Solutions to Burnout and Retention as Perceived by County Extension Agents of the Colorado State University Extension System

This study explored solutions to the issue of burnout and retention of Extension agents. Extension agents experience burnout for reasons such as long hours, stress, and organizational factors. As Extension administration addresses job satisfaction and performance of Extension employees, burnout and retention issues identified in this study can facilitate efforts to enhance the effectiveness of a statewide Extension program. Herzberg’s Motivation-Hygiene Theory was the theoretical framework for this study. Researchers used the constant-comparative method of analysis to identify recurring themes from the open-ended items of an online-administered survey. Twelve primary themes emerged, including (a) compensation, (b) hiring practices, (c) promotion and advancement within Extension, (d) organizational support regarding agent development, (e) organizational support regarding administration, (f) organizational support regarding colleagues, (g) reporting, (h) recognition, (i) resources, (j) personnel and staffing, (k) evaluation of administration and specialists, and (l) workload. Results suggest that Extension administration should focus on the maintenance factors of compensation, workload, and internal promotion and advancement, as well as motivating factors, to improve retention of Extension agents.
Comprehending Ajax Web Applications by the DynaRIA Tool

Thanks to Rich Internet Applications (RIAs) with their enhanced interactivity, responsiveness and dynamicity, the user experience in the Web 2.0 is becoming more and more appealing and user-friendly. The dynamic nature of RIAs and the heterogeneous technologies, frameworks, communication models used for implementing them negatively affect their analyzability and understandability. Consequently, specific software techniques and tools are needed for supporting RIA comprehension. This paper presents DynaRIA, a tool for the comprehension of RIAs implemented in Ajax that is based on dynamic analysis. It provides functionalities for recording and analyzing user sessions from several perspectives, and for producing various types of abstractions and visualizations about the run-time behavior of the application. In order to evaluate this tool, four case studies involving different comprehension tasks of Ajax applications have been executed. The experimental results showed the usefulness and effectiveness of the tool that provided a valid support for Ajax comprehension in reverse engineering, debugging, testing and quality assessment contexts.
Beach body work: Australian women’s experiences

Abstract This article examines Australian women’s complex relationship with the beach through a focus on affect and on what bodies do. Interviews with ten participants of diverse backgrounds and of different ages reveal that women understand the beach as a mediated and surveilled space where their bodies are foregrounded. In this environment, there is an intersection of women’s knowledge of the popular constructions of the archetypal Australian beach body, real women’s bodies, and interviewees’ experiences of the beach as a place of shame and pride. As a means of managing this affective landscape participants detail a range of bodily strategies enacted prior to going to the beach and once at the beach. This bodily labour demonstrates that for Australian women the beach is a dynamic and complicated site of both leisure and labour.
Appearance , Visual and Social Ensembles for Face Recognition in Personal Photo Collections

While modern research in face recognition has focused on new feature representations, alternate learning methods for fusion of features, most have ignored the issue of unmodeled correlations in face data when combining diverse features such as similar visual regions, attributes, appearance frequency, etc. Conventional wisdom is that by using sufficient data and machine, one can learn the systematic correlations and use the data to can form a more robust basis for core recognition tasks like verification, identification, and clustering. This however, takes large amounts of training data which is not really available for personal consumer photo collections. We address the fusion/correlation issue differently by proposing an ensemble-based approach that is built on different information sources such as facial appearance, visual context, and social (or co-appearance) information of samples in a dataset, to provide error diversity and higher classification accuracy for face recognition in consumer photo collections. To evaluate the utility of our ensembles and simultaneously generate stronger generic features, we perform two experiments (i) a verification experiment on the standard unconstrained LFW (Labeled Faces in the Wild) dataset where by using an ensemble of appearance related features we report state-of-the art results on protocol-1 that provides 2.9% better classification accuracy than the current best method, and (ii) an identification experiment on the Gallagher personal photo collection where we demonstrate at least 17% relative performance gain using visual and social ensembles.
Expression of a Yersinia pseudotuberculosis Type VI Secretion System Is Responsive to Envelope Stresses through the OmpR Transcriptional Activator

The Type VI secretion system (T6SS) is a macromolecular complex widespread in Gram-negative bacteria. Although several T6SS are required for virulence towards host models, most are necessary to eliminate competitor bacteria. Other functions, such as resistance to amoeba predation, biofilm formation or adaptation to environmental conditions have also been reported. This multitude of functions is reflected by the large repertoire of regulatory mechanisms shown to control T6SS expression, production or activation. Here, we demonstrate that one T6SS gene cluster encoded within the Yersinia pseudotuberculosis genome, T6SS-4, is regulated by OmpR, the response regulator of the two-component system EnvZ-OmpR. We first identified OmpR in a transposon mutagenesis screen. OmpR does not control the expression of the four other Y. pseudotuberculosis T6SS gene clusters and of an isolated vgrG gene, and responds to osmotic stresses to bind to and activate the T6SS-4 promoter. Finally, we show that T6SS-4 promotes Y. pseudotuberculosis survival in high osmolarity conditions and resistance to deoxycholate.
Advances in Brief An RNA-bindingProteinGene , TLSIFUS , Is FusedtoERG in HumanMyeloid Leukemia with t ( 16 ; 21 ) Chromosomal

Thet(16;21)(pll;q22)translocationIsa recurrentchromosomal abnor mality found in several types of myeloid leukemia. We have previously demonstrated that the breakpoints of this translocation are clustered in a s@fic intron of the ERG gene on chromosome 21, whIch has recently been reported to be involved in Ewing's sarcoma. We show here that the TLS/FUS gene on chromosome 16 is fused with the ERG gene to produce the TLSIFUS-ERGchimerictranscriptby this translocation.The TLS/ FUS gene has been identified as a translocated gene In myxoid liposar coma by the t(12;16)(q13;pll) translocation and encodes an RNA-binding protein that is highly homologous to the product ofthe EWS gene involved in Ewing's sarcoma. Thus, the TLS/FUS-ERG gene fusion in t(16;21) leukemia is predicted to produce a protein that is very similar to the EWS-ERG chimeric protein responsible for Ewing's sarcoma.
Chapter I Association Rules : An Overview

Association rules present one of the most versatile techniques for the analysis of binary data, with applications in areas as diverse as retail, bioinformatics, and sociology. In this chapter, the origin of association rules is discussed along with the functions by which association rules are traditionally characterised. Following the formal definition of an association rule, these functions – support, confidence and lift – are defined and various methods of rule generation are presented, spanning 15 years of development. There is some discussion about negations and negative association rules and an analogy between association rules and 2×2 tables is outlined. Pruning methods are discussed, followed by an overview of measures of interestingness. Finally, the post-mining stage of the association rule paradigm is put in the context of the preceding stages of the mining process.
Semantic Data Representation for Improving Tensor Factorization

Predicting human activities is important for improving recommender systems or analyzing social relationships among users. Those human activities are usually repre- sented as multi-object relationships (e.g. user’s tagging activities for items or user’s tweeting activities at some locations). Since multi-object relationships are naturally represented as a tensor, tensor factorization is becom- ing more important for predicting users’ possible ac- tivities. However, its prediction accuracy is weak for ambiguous and/or sparsely observed objects. Our so- lution, Semantic data Representation for Tensor Fac- torization (SRTF), tackles these problems by incorpo- rating semantics into tensor factorization based on the following ideas: (1) It first links objects to vocabu- laries/taxonomies and resolves the ambiguity caused by objects that can be used for multiple purposes. (2) It next links objects to composite classes that merge classes in different kinds of vocabularies/taxonomies (e.g. classes in vocabularies for movie genres and those for directors) to avoid low prediction accuracy caused by rough-grained semantics. (3) It then lifts sparsely observed objects into their classes to solve the sparsity problem for rarely observed objects. To the best of our knowledge, this is the first study that leverages seman- tics to inject expert knowledge into tensor factorization. Experiments show that SRTF achieves up to 10% higher accuracy than state-of-the-art methods.
Spontaneous number representation in semi–free–ranging rhesus monkeys

Previous research has shown that animals possess considerable numerical abilities. However, this work was based on experiments involving extensive training, a small number of captive subjects and relatively artificial testing procedures. We present the results of experiments on over 200 semi–free–ranging rhesus monkeys using a task which involves no training and mimics a natural foraging problem. The subjects observed two experimenters place pieces of apple, one at a time, into each of two opaque containers. The experimenters then walked away so that the subjects could approach. The monkeys chose the container with the greater number of apple slices when the comparisons were one versus two, two versus three, three versus four and three versus five slices. They failed at four versus five, four versus six, four versus eight and three versus eight slices. Controls established that it was the representation of number which underlay their successful choices rather than the amount of time spent placing apple pieces into the box or the volume of apple placed in the box. The failures at values greater than three slices stand in striking contrast to other animal studies where training was involved and in which far superior numerical abilities were demonstrated. The range of success achieved by rhesus monkeys in this spontaneous–number task matches the range achieved by human infants and corresponds to the range encoded in the syntax of natural languages.
Individual Differences in Infant Speech Segmentation: Achieving the Lexical Shift

We report a large-scale electrophysiological study of infant speech segmentation, in which over 100 English-acquiring 9-month-olds were exposed to unfamiliar bisyllabic words embedded in sentences (e.g., He saw a wild eagle up there), after which their brain responses to either the just-familiarized word (eagle) or a control word (coral) were recorded. When initial exposure occurs in continuous speech, as here, past studies have reported that even somewhat older infants do not reliably recognize target words, but that successful segmentation varies across children. Here, we both confirm and further uncover the nature of this variation. The segmentation response systematically varied across individuals and was related to their vocabulary development. About one-third of the group showed a left-frontally located relative negativity in response to familiar versus control targets, which has previously been described as a mature response. Another third showed a similarly located positive-going reaction (a previously described immature response), and the remaining third formed an intermediate grouping that was primarily characterized by an initial response delay. A fine-grained group-level analysis suggested that a developmental shift to a lexical mode of processing occurs toward the end of the first year, with variation across individual infants in the exact timing of this shift.
Location Based and Energy Efficient Anonymous Routing Protocol for MANET

— MANET (Mobile Ad-Hoc Network) are characterize self-organizing and independent infrastructures, having dynamic topology which make them an ideal choice for communication and information sharing in network. But MANETs are susceptible to malicious things which aim to tamper and analyze data and traffic analysis by communication snooping . For provide communication security ,in this paper we use anonymous routing protocol, which hide nodes identity ,nodes location and communication route. But only security is not enough for successful routing , routing lifetime is also important factor so for this we contribute energy of nodes.
Tamoxifen and breast density in women at increased risk of breast cancer.

BACKGROUND Although mammographic breast density is associated with the risk of breast cancer and is influenced by hormone levels, the effects of tamoxifen on breast density in healthy women and whether tamoxifen-induced density changes are associated with breast cancer risk are unclear. We investigated mammographic breast density in healthy women with an increased risk of breast cancer at baseline and during 54 months of tamoxifen treatment. METHODS Mammograms were reviewed from 818 breast cancer-free women (388 in the tamoxifen group and 430 in the placebo group) at high risk for breast cancer, from the International Breast Cancer Intervention Study I, a trial of tamoxifen for breast cancer prevention. Breast density measurements, at baseline and during treatment, were obtained at 12- to 18-month intervals. Multivariable analysis was used to assess associations with breast density. All statistical tests were two-sided. RESULTS Breast density at baseline was similar in placebo (42.6%, 95% confidence interval [CI] = 39.6% to 45.6%) and tamoxifen (41.9%, 95% CI = 38.8% to 45.0%) groups. The main determinants of breast density at baseline were age, menopausal status, body mass index, and previous atypical hyperplasia. A greater density reduction in the tamoxifen group (7.9%, 95% CI = 6.9% to 8.9%) than in the placebo group (3.5%, 95% CI = 2.7% to 4.3%) was apparent within 18 months of treatment (P<.001); the reduction in density continued until 54 months of treatment. After 54 months of tamoxifen treatment, breast density was 28.2% (decrease from baseline = 13.7%, 95% CI = 12.3% to 15.1%; P<.001) in the tamoxifen group and 35.3% (decrease from baseline = 7.3%, 95% CI = 6.1% to 8.4%; P<.001) in the placebo group. The tamoxifen-associated density reduction was apparent in all subgroups, but there was a statistically significant interaction with age. In women aged 45 years or younger at entry, the net reduction with tamoxifen was 13.4% (95% CI = 8.6% to 18.1%), whereas in women older than 55 years, it was 1.1% (95% CI = -3.0% to 5.1%). CONCLUSION Tamoxifen treatment was associated with reduction in breast density, most of which occurred during the first 18 months of treatment.
Recursive Neural Networks for Learning Logical Semantics

Supervised recursive neural network models (RNNs) for sentence meaning have been successful in an array of sophisticated language tasks, but it remains an open question whether they can learn compositional semantic grammars that support logical deduction. We address this question directly by for the first time evaluating whether each of two classes of neural model — plain RNNs and recursive neural tensor networks (RNTNs) — can correctly learn relationships such as entailment and contradiction between pairs of sentences, where we have generated controlled data sets of sentences from a logical grammar. Our first experiment evaluates whether these models can learn the basic algebra of logical relations involved. Our second and third experiments extend this evaluation to complex recursive structures and sentences involving quantification. We find that the plain RNN achieves only mixed results on all three experiments, whereas the stronger RNTN model generalizes well in every setting and appears capable of learning suitable representations for natural language logical inference.
Antenna diversity combining and finite-tap decision feedback equalization for high-speed data transmission

The next-generation wireless communication systems are expected to support high-speed data transmission. Associated with high transmission rates, however, is the problem of multipath intersymbol interference (ISI) due to frequency-selective fading. Decision feedback equalization (DFE) and antenna diversity combining are two practical techniques for combating multipath ISI. Through simulations we investigate the performance of diversity combining, together with DFE, under various numbers of antenna branches and equalization taps, in a quasistationary frequency-selective fading environment with additive white Gaussian noise (AWGN) and cochannel interference (CCI). We consider joint optimization combining and power selection diversity combining. We simulate the combiner, using quaternary phase shift keying (QPSK) modulation with up to four antenna branches. Our results show that using antenna diversity and DFE with joint optimization combining provides performance improvement with lower computational complexity, as compared to that of using either DFE or diversity combining alone for combating ISI.