Miracle-dz/UniFAR-FTUs huggingface dataset:
Online Body Schema Adaptation Based on Internal Mental Simulation and Multisensory Feedback

In this paper, we describe a novel approach to obtain automatic adaptation of the robot body schema and to improve the robot perceptual and motor skills based on this body knowledge. Predictions obtained through a mental simulation of the body are combined with the real sensory feedback to achieve two objectives simultaneously: body schema adaptation and markerless 6D hand pose estimation. The body schema consists of a computer graphics simulation of the robot, which includes the arm and head kinematics (adapted online during the movements) and an appearance model of the hand shape and texture. The mental simulation process generates predictions on how the hand will appear in the robot camera images, based on the body schema and the proprioceptive information (i.e. motor encoders). These predictions are compared to the actual images using Sequential Monte Carlo techniques to feed a particle-based Bayesian estimation method to estimate the parameters of the body schema. The updated body schema will improve the estimates of the 6D hand pose, which is then used in a closed-loop control scheme (i.e. visual servoing), enabling precise reaching. We report experiments with the iCub humanoid robot that support the validity of our approach. A number of simulations with precise ground-truth were performed to evaluate the estimation capabilities of the proposed framework. Then, we show how the use of high-performance GPU programming and an edge-based algorithm for visual perception allow for real-time implementation in real world scenarios.
SLEEP ENHANCES LEARNING IN CHILDREN 1 Sleep enhances memory consolidation in children

Sleep is an active state that plays an important role in the consolidation of memory. It has been found to enhance explicit memories in both adults and children. However, in contrast to adults, children do not always show a sleep-related improvement in implicit learning. The majority of research on sleep-dependent learning focuses on adults; hence the current study examined sleep-related effects on two tasks in children. Thirty-three typically developing children aged 6 to 12 years took part in the study. Actigraphy was used to monitor sleep. Sleep-dependent learning was assessed using a novel non-word learning task, and the Tower of Hanoi cognitive puzzle, which involves discovering an underlying rule to aid completion of the task. Children were trained on the two tasks and retested following approximately equal retention intervals of both wake and sleep. After sleep, children showed significant improvements in performance of 14% on the non-word learning task and 25% on the Tower of Hanoi task, but no significant change in score following the wake retention interval. Improved performance on the Tower of Hanoi may have been due to children consolidating explicit aspects of the task, for example rule-learning or memory of previous sequences; thus we propose that sleep is necessary for consolidation of explicit memory in children. Sleep quality and duration were not related to children’s task performance. If such experimental sleep-related learning enhancement is generalizable to everyday life, then it is clear that sleep plays a vital role in children’s educational attainment. Abbreviations: REM rapid eye movement, Non-REM non-rapid eye movement
Autocorrelation performance improvement of CML Hyperchaos sequence based on APAS theorem

Hyperchaos sequences have been widely used as a kind of complex pseudorandom sequences. But some well known Hyperchaos sequences, such as CML sequence, have poor autocorrelation performance, and their applications are limited. Based on recently presented APAS theorem, we modified CML map to S-CML map, and S-CML sequence has excellent autocorrelation performance, whereas its other performances are remained invariant or become better, and can be used more widely.
A Qualitative Investigation of Health Care Professionals’, Patients’ and Partners’ Views on Psychosocial Issues and Related Interventions for Couples Coping with Cancer

Introduction There is growing evidence that cancer affects couples as an interdependent system and that couple-based psychosocial interventions are efficacious in reducing distress and improving coping skills. However, adoption of a couples-focused approach into cancer care is limited. Previous research has shown that patients and partners hold differing views from health care professionals (HCPs) regarding their psychosocial needs, and HCPs from different disciplines also hold divergent views regarding couples’ psychosocial needs. This study aimed to explore the perspectives of HCPs and couples on the provision of couple-focused psychosocial care in routine cancer services. Methods A qualitative study using semi-structured interviews was undertaken with 20 HCPs (medical oncologists, nurses, psycho-oncology professionals) and 20 couples where one member had been diagnosed with cancer (breast, prostate, head/neck, bowel, multiple myeloma). Interviews were analysed using the framework approach. Results Three core themes were identified: “How Do Couples Cope with Cancer? ” emphasised the positive and negative coping strategies used by couples, and highlighted that partners perceived a lack of engagement by HCPs. “What Is Couple-focused Psychosocial Care for People with Cancer? ” described varying perspectives regarding the value of couple-focused psychosocial care and variation in the types of support couples need among HCPs and couples. Whereas most couples did not perceive a need for specialist couple-focused support and interventions, most HCPs felt couple-focused psychosocial care was necessary. “How Can Couple-Focused Psychosocial Care be Improved? ” described couples’ view of a need for better provision of information, and the importance of their relationship with oncology clinicians. HCPs identified a lack of confidence in responding to the emotional needs of couples, and barriers to providing psychosocial care, including challenges identifying distress (through screening) and referring distressed individuals/couples for specialist assessment. Conclusions The three core themes revealed discrepancies about couple-focused psychosocial care between HCPs and couples, and HCPs from different professional backgrounds, and several barriers to the provision of psychosocial care for couples. Despite HCPs and couples acknowledging that a couple-focused approach to psycho-social support was potentially beneficial, the majority of couples did not feel they needed specific couple-focused interventions. These issues and recommendations for future research are discussed.
ImNet: An Imperative Network Programming Language

One of the most recent architectures of networks is Software-Defined Networks (SDNs) using a con- troller appliance to control the set of switches on the network. The controlling process includes installing or uninstalling packet-processing rules on flow tables of switches. This paper presents a high-level imperative network programming language, called ImNet, to facilitate writing efficient, yet simple, programs executed by controller to manage switches. ImNet is simply-structured, expressive, compositional, and imperative. This paper also introduces an operational semantics to ImNet. Detailed examples of programs (with their operational semantics) constructed in ImNet are illustrated in the paper as well.
Myostatin inhibitors as therapies for muscle wasting associated with cancer and other disorders

Purpose of reviewThis review summarizes recent progress in the development of myostatin inhibitors for the treatment of muscle wasting disorders. It also focuses on findings in myostatin biology that may have implications for the development of antimyostatin therapies. Recent findingsThere has been progress in evaluating antimyostatin therapies in animal models of muscle wasting disorders. Some programs have progressed into clinical development with initial results showing positive impact on muscle volume.In normal mice myostatin deficiency results in enlarged muscles with increased total force but decreased specific force (total force/total mass). An increase in myofibrillar protein synthesis without concomitant satellite cell proliferation and fusion leads to muscle hypertrophy with unchanged myonuclear number. A specific force reduction is not observed when atrophied muscle, the predominant therapeutic target of myostatin inhibitor therapy, is made myostatindeficient.Myostatin has been shown to be expressed by a number of tumor cell lines in mice and man. SummaryMyostatin inhibition remains a promising therapeutic strategy for a range of muscle wasting disorders.
Remaining Energy-Aware Power Management Mechanism in the 802.16e MAC

The sleep-mode operation in power management helps to increase the life of a mobile station by saving energy consumption while it increases the MAC service data unit (SDU) response delay. Due to the trade-off relationship between these two performance metrics, it is important to determine the suitable values for two operating parameters, the initial sleep interval (Tmin) and the final sleep interval (Tmax), which manipulate the sleep-mode operation in the IEEE 802.16e MAC. To reach a fuller understanding of this problem, this paper first presents the sleep-mode operation. A new power management mechanism based on the knowledge on remaining energy in a mobile subscriber station (MSS) is then proposed. Benefits of this mechanism are to reduce the MAC SDU response delay under sufficient energy and to increase the life of a station under insufficient remaining energy. An analytical model for sleep-mode operation is developed, and the proposed mechanism is validated by computer simulation.
Power analysis and minimization techniques for embedded DSP software

Power is becoming a critical constraint for designing embedded applications. Current power analysis techniques based on circuit-level or architectural-level simulation are either impractical or inaccurate to estimate the power cost for a given piece of application software. In this paper, an instruction-level power analysis model is developed for an embedded digital signal processor (DSP) based on physical current measurements. Significant points of difference have been observed between the software power model for this custom DSP processor and the power models that have been developed earlier for some general purpose commercial microprocessors. In particular, the effect of circuit state on the power cost of an instruction stream is more marked in the case of this DSP processor. In addition, the processor has special architectural features that allow dual memory accesses and packing of instructions into pairs. The energy reduction possible through the use of these features is studied. The on-chip Booth multiplier on the processor is a major source of energy consumption for DSP programs. A microarchitectural power model for the multiplier is developed and analyzed for further power minimization. In order to exploit all of the above effects, a scheduling technique based on the new instruction-level power model is proposed. Several example programs are provided to illustrate the effectiveness of this approach. Energy reductions varying from 26% to 73% have been observed. These energy savings are real and have been verified through physical measurement. It should be noted that the energy reduction essentially comes for free. It is obtained through software modification, and thus, entails no hardware overhead. In addition, there is no loss of performance since the running times of the modified programs either improve or remain unchanged.
Zoning for hierarchical network optimization in software defined networks

Software defined networking (SDN) decouples control plane functionality from the data plane and features the presence of programmable dumb network devices, which have no or little intelligence and take control commands from a central controller at the control plane. The central controller is responsible for controlling data plane hardware and optimizing network operation. Centralized network optimization and control is impractical or infeasible when the network becomes too large in size or loading. Distributed network optimization comes into play under this circumstance. Fully distributed network optimization requires local intelligence at individual network elements, against the basic concept of SDN. In this paper we consider SDN-friendly zone-based distributed network optimization and studies the integral network zoning problem, that is, how to group network elements into zones such as to minimize the overhead of distributed network optimization. We give a mathematical formulation of the problem and show that it is NP complete. We then present three heuristic solutions and evaluate their performance through simulation.
Patient selection and assessment recommendations for deep brain stimulation in Tourette syndrome

In response to recent publicity regarding the potential use of deep brain stimulation (DBS) for reducing tic severity in Tourette's syndrome (TS), the Tourette Syndrome Association convened a group of TS and DBS experts to develop recommendations to guide the early use and potential clinical trials of DBS for TS and other tic disorders. The goals of these recommendations are to ensure that all surgical candidates are (1) fully informed about the risks, benefits, and alternative treatments available; (2) receive a comprehensive evaluation before surgery to ensure that DBS is clearly the appropriate clinical treatment choice; and (3) that early clinical experience will be documented publicly to facilitate rational decision‐making for both clinical care and future clinical trials. © 2006 Movement Disorder Society
SwiftLink: Serendipitous Navigation Strategy for Large-Scale Document Collections

The multiplication of large-scale document collections has created the need for robust and adaptive access strategies in many applicative areas. In this paper, we depart from the traditional document search paradigm to move onto the construction of a collection navigation strategy. We thus detail a model where user clicks are taken as expression of interest rather than positive search feedback. In terms of the interpretation of user interaction, our model is close to the recommendation paradigm. However, it exploits user feedback in a navigational procedure to perform an approximation of interactive metric learning procedures. Our model is directly compatible with distributed data indexing and is therefore inherently built to cope with scalability issues. The evaluation of such models is discussed and implemented over the context of an image collection. Experiments are presented where the user browses a collection of images with changing center of focus in the course of the browsing operations. Results show that our model is effectively able to recommend the user a relevant path through the collection and to adapt according to variable expressed interests.
Evaluation of antemortem sampling to estimate chronic wasting disease prevalence in free-ranging mule deer

We conducted a field study to evaluate tonsillar biopsy immunohistochemistry (IHC) as a tool for diagnosing chronic wasting disease (CWD) in live, free-ranging mule deer (Odocoileus hemionus) and estimating CWD prevalence. Initially, we evaluated and refined techniques for collecting tonsillar biopsies from mule deer. Using a simple mouth gag and a 6-mm biopsy forceps, and taking the biopsy starting at the rostral rim of the tonsillar sinus, we obtained 155/161 (96%) samples that yielded ≥1 lymphoid follicle. To compare antemortem and postmortem survey techniques and assure biopsy-based estimates would not substantially underestimate true prevalence, we examined tonsillar biopsies from 161 free-ranging mule deer from 2 populations where CWD is endemic. We then calculated prevalence (p b ) and compared this to prevalence (p h ) estimated from tonsil samples from 161 deer harvested or culled in spatial and temporal proximity to our study areas; we considered the latter a close approximation of true prevalence. Biopsy-based prevalence estimates exceeded prevalence estimated by tonsillar IHC of samples from harvested or culled deer. Although 95% CIs for p h - p b included 0 for area-specific estimates, biopsy-based estimates were ≥3 times higher than harvest-based estimates in both study areas. Moreover, when data from both study areas were combined, p h (= 0.025) was lower than p b (= 0.081) and the 95% CI for p h - p b (-0.104 to -0.007) did not include 0. Observed differences in prevalence most likely reflected spatial or temporal variation in populations (or subpopulations) of deer sampled. Tonsillar biopsy IHC appears to be reliable for detecting CWD infections in live mule deer and estimating prevalence in affected populations, thereby representing a new tool with potential utility in CWD management, particularly in areas where harvest-based sampling is infeasible.
Whole Genome Complete Resequencing of Bacillus subtilis Natto by Combining Long Reads with High-Quality Short Reads

De novo microbial genome sequencing reached a turning point with third-generation sequencing (TGS) platforms, and several microbial genomes have been improved by TGS long reads. Bacillus subtilis natto is closely related to the laboratory standard strain B. subtilis Marburg 168, and it has a function in the production of the traditional Japanese fermented food “natto. ” The B. subtilis natto BEST195 genome was previously sequenced with short reads, but it included some incomplete regions. We resequenced the BEST195 genome using a PacBio RS sequencer, and we successfully obtained a complete genome sequence from one scaffold without any gaps, and we also applied Illumina MiSeq short reads to enhance quality. Compared with the previous BEST195 draft genome and Marburg 168 genome, we found that incomplete regions in the previous genome sequence were attributed to GC-bias and repetitive sequences, and we also identified some novel genes that are found only in the new genome.
STAT3 Regulates Proliferation and Immunogenicity of the Ewing Family of Tumors In Vitro

The Ewing sarcoma family of tumors (ESFT) represents an aggressive spectrum of malignant tumour types with common defining histological and cytogenetic features. To evaluate the functional activation of signal transducer and activator of transcription 3 (STAT3) in ESFT, we evaluated its activation in primary tissue sections and observed the functional consequences of its inhibition in ESFT cell lines. STAT3 was activated (tyrosine 705-phosphorylated) in 18 out of 31 primary tumours (58%), either diffusely (35%) or focally (23%). STAT3 was constitutively activated in 3 out of 3 ESFT cell lines tested, and its specific chemical inhibition resulted in complete loss of cell viability. STAT3 inhibition in ESFT cell lines was associated with several consistent changes in chemokine profile suggesting a role of STAT3 in ESFT in both cell survival and modification of the cellular immune environment. Together these data support the investigation of STAT3 inhibitors for the Ewing family of tumors.
Measurement Invariance of the UTAUT Constructs in the Caribbean.

This article employs confirmatory factor analysis to evaluate the factorial validity and the cross-national comparability of the UTAUT constructs with respect to mobile learning in higher education in four Caribbean countries. Except for the measurement of one factor, the UTAUT constructs exhibit adequate reliability and validity. Though full metric invariance is not achieved, cross-national comparisons of the regression relationships among the factors are still possible. In addition, non-invariant item intercepts also affect the comparisons of the factor means. Partial scalar invariance is required.
Empirical Estimation of Concept Compressibility and Generalization Error

In this paper we focus on the relationship between concept complexity and generalization error of learned concept descriptions. After showing a simple way to estimate concept complexity from learning data, we empirically link this estimation to the generalization error of different learning algorithms. While it is expected that the error increases with the complexity of the concept, quite interesting is the way it increases. Experiments, in fact, show a linear relationship that improves upon the super-linear behavior predicted by known theoretical bounds. Furthermore, the results suggest that the linear relationship between concept complexity and generalization error is a property independent from specific learning algorithms.
Sensibility estimation method for youth slang by using sensibility co-occurrence feature vector obtained from microblog

Social networking sites such as Twitter provide more opportunities to express what people think or intend in short text. In short text, abbreviations such as "ASAP" or "joinus" and emoticons are often used. Because these expressions are not registered into the existing dictionaries, these are analyzed as unknown expressions. That can be a bottleneck for improving accuracy of reputation analysis in text mining. To use context for unknown word clustering is a major method, however, it usually requires word segmentation process and it has weakness for split errors of unknown expressions such as youth slang. In this paper, we proposed a method to obtain the appropriate context even though unknown expressions cause split errors and estimate sensibility expressed in the text. Because the dimensions of the obtained context vector were enormous, we also proposed a method to create a feature vector based on the co-occurrence of the sensibility words as simple expression with low dimension. As an evaluation experiment, the proposed method showed certain accuracy even with the small training data.
Neural Networks for Color Image Segmentation: Application to Sapwood Assessment

This paper presents a method for detecting sapwood in hard wood such as cherry and maple. In the wood industry most applications need aesthetical boards. Thus the sapwood area on the board has to be detected as a defect region and be removed. To achieve this process, we classify the regions of the wood into two groups, by using neural networks techniques: sapwood is classified as a defect region while heartwood is considered as a good region. The use of neural networks by properly tuning the input vector provides a high defect detection rate with a low false positive rate.
Dynamic software architecture oriented service composition and evolution

Service-oriented computing (SOC) is believed to be prominent for the efficient and effective businesses development. However, current SOC technologies and standards are challenged by the changeful environment and user requirements. Concerns for runtime evolution are evoked to realize extensible and adaptive service-oriented applications. In this paper we argue that software architecture, esp. dynamic software architecture (DSA), should be used as a complement view for the commonly adopted workflow views in service composition. Furthermore, a novel reification mechanism for DSA is proposed to enable the runtime evolution of the architecture. A corresponding system named Artemis-ARC is implemented to support the development, execution and dynamic reconfiguration of service-oriented applications.