Miracle-dz/UniFAR-FTUs huggingface dataset:
Cyber-physical production systems' design challenges

Recent advances in information technologies and artificial intelligence are enabling the creation of intelligent and highly reconfigurable factories which will lead to unprecedented production development. The notion of Cyber-Physical Production System (CPPS), denoting a system where mechatronic components are coupled to a smart logical entity that enables these factory units to interact in an adaptive way, has been presented as one of the cornerstones of what is perceived to be the 4th Industrial Revolution. However, more than two decades of research have shown that such a vision is not as new as recent programmes suggest and that certain developments are only now reaching a maturity stage that renders them usable with limitations. These are due to a set of persistent design challenges that undermine their acceptance along with the fact that the preconditions for operating such systems globally are far from being satisfied. This paper provides an integrated, clear and critical view on pending design issues that still need to be satisfied. In this context, the paper concentrates on the conceptual and technical design barriers that relate to the development of cyber-physical production components and their operation in the context of a CPPS.
Individual and aggregate scattering matrices and cross-sections : conservation laws and reciprocity

For systems of multiple spheres, we investigate in detail the `individual’ and aggregate electromagnetic scattering matrices, and their relations with conservation laws, reciprocity and the optical theorem. In order for these relations to adopt their simplest form, care is taken to completely extract both incoming and outgoing phase factors in the de®nitions. We illustrate that the `individual’ cross-sections in an aggregate are de®ned only in terms of part of the total ®eld, and consequently do not individually obey conservation laws or reciprocity; these relations should be satis®ed for the scattering by the entire aggregate. We demonstrate that for scatterer centred transfer matrices, the conservation laws and reciprocity are automatically satis®ed regardless of whether or not su cient multipolarities were retained in the description of individual scatterers. Derivations and results are worked out in a particularly compact and transparent formalism, including magnetic permeability contrast, and the possibility of complex polarizations.
Multiband tangent space mapping and feature selection for classification of EEG during motor imagery

Objective. When designing multiclass motor imagery-based brain–computer interface (MI-BCI), a so-called tangent space mapping (TSM) method utilizing the geometric structure of covariance matrices is an effective technique. This paper aims to introduce a method using TSM for finding accurate operational frequency bands related brain activities associated with MI tasks. Approach. A multichannel electroencephalogram (EEG) signal is decomposed into multiple subbands, and tangent features are then estimated on each subband. A mutual information analysis-based effective algorithm is implemented to select subbands containing features capable of improving motor imagery classification accuracy. Thus obtained features of selected subbands are combined to get feature space. A principal component analysis-based approach is employed to reduce the features dimension and then the classification is accomplished by a support vector machine (SVM). Main results. Offline analysis demonstrates the proposed multiband tangent space mapping with subband selection (MTSMS) approach outperforms state-of-the-art methods. It acheives the highest average classification accuracy for all datasets (BCI competition dataset 2a, IIIa, IIIb, and dataset JK-HH1). Significance. The increased classification accuracy of MI tasks with the proposed MTSMS approach can yield effective implementation of BCI. The mutual information-based subband selection method is implemented to tune operation frequency bands to represent actual motor imagery tasks.
TransPath: Representation Learning for Heterogeneous Information Networks via Translation Mechanism

In this paper, we propose a novel network representation learning model TransPath to encode heterogeneous information networks (HINs). Traditional network representation learning models aim to learn the embeddings of a homogeneous network. TransPath is able to capture the rich semantic and structure information of a HIN via meta-paths. We take advantage of the concept of translation mechanism in knowledge graph which regards a meta-path, instead of an edge, as a translating operation from the first node to the last node. Moreover, we propose a user-guided meta-path sampling strategy which takes users’ preference as a guidance, which could explore the semantics of a path more precisely, and meanwhile improve model efficiency via the avoidance of other noisy and meaningless meta-paths. We evaluate our model on two large-scale real-world data sets database systems and logic programming (DBLP) and YELP, and two benchmark tasks similarity search and node classification. We observe that TransPath outperforms other state-of-the-art baselines consistently and significantly.
Resilience in Wives of persons with Alcoholism: An Indian exploration

Context: Mental health has currently shifted focus from "deficit" to strength-based approaches such as Resilience. Coping styles and personality factors have been well studied in Wives of persons with Alcoholism (WopA) but not Resilience. Alcohol dependence in spouse is seen as an adversity. Aims: To evaluate Resilience in the WopA and explore its relationship with marital quality and clinical variables of Alcohol Dependence in their husbands. Settings and Design: A cross-sectional study in a tertiary care hospital in Bangalore, Karnataka. Subjects and Methods: WopA (n=34) between 25–55 years, were assessed for Resilience using Resilience Scale for Adults (RSA), while marital quality was assessed using Marital Quality Scale (MQS). The Severity of Alcohol Dependence, Age of onset of Initiation, Age of onset of Problem Drinking, and Age of onset of Dependence were evaluated in their husbands. Statistical Analysis: Independent sample t-test, Chi-square test, and Pearson's correlation were used. Results: Majority of the WopA (82%) scored low on the RSA. Low Resilience (LR) WopA scored significantly lower on all factors of RSA except the perception of future; in comparison to High Resilience (HR) WopA. Additionally, the LR WopA reported significantly poorer marital quality. Conclusions: Most WopA had low Resilience. LR WopA also had significantly poor marital quality. These findings need to be studied further in a larger population with culturally appropriate scales. The low scoring Resilience factors amongst WopA may be utilized in strength-based psychotherapeutic approaches. There is a need to improve the understanding of Resilience and its assessment in this population.
Rotation Adaptive Visual Object Tracking with Motion Consistency

Visual Object tracking research has undergone significant improvement in the past few years. The emergence of tracking by detection approach in tracking paradigm has been quite successful in many ways. Recently, deep convolutional neural networks have been extensively used in most successful trackers. Yet, the standard approach has been based on correlation or feature selection with minimal consideration given to motion consistency. Thus, there is still a need to capture various physical constraints through motion consistency which will improve accuracy, robustness and more importantly rotation adaptiveness. Therefore, one of the major aspects of this paper is to investigate the outcome of rotation adaptiveness in visual object tracking. Among other key contributions, the paper also includes various consistencies that turn out to be extremely effective in numerous challenging sequences than the current state-of-the-art.
An mHealth App for Decision-Making Support in Wound Dressing Selection (WounDS): Protocol for a User-Centered Feasibility Study

Background Primary care health professionals, especially family physicians, see a variety of wounds, and yet—despite the frequency of providing wound care—many family physicians do not feel confident in wound care management. This is partly due to a lack of formal wound education in Family Medicine programs. While there are numerous electronic wound care resources available in the UK and North America, none were identified that address the specific need in supporting clinical decision-making in wound dressing selection. At the same time, healthcare providers are increasingly using technology in personal and professional contexts, and a logical extension is to use technology for knowledge translation strategies. Objective This work developed a prototype mobile health software application named WounDS, designed to support clinical decision-making in selecting wound dressings. This article presents the development and evaluation plan for the WounDS app. Methods WounDS has been developed on the iOS platform. The primary specification included ease of use, in that one of the primary influences in user adoption would be the ability to receive a wound dressing recommendation in under 30 seconds and under 5 taps on the screen. The WounDS app guides users through a series of binary decisions for assessing the wound and provides a wound dressing recommendation. The selection algorithm is based in best practices using the Wound Bed Preparation Paradigm. Results Current work is underway to examine the implementation needs for WounDS to be most effectively utilized and to pilot test its feasibility and use in clinical care. Data will be collected through user trials, focus groups, and user metadata will be collected within the app. Optimizing these preconditions will enable a subsequent phase of study to determine effects on clinical decision-making and clinical outcomes. Conclusions WounDS is designed for knowledge translation, use of technology in clinical decision-making, and continuity of care. The benefits of WounDS include the potential to improve healthcare providers’ competency in wound management and to improve wound healing through better alignment with evidence-based best practices in wound dressing selection, consistency in care from primary to community care, and subsequent downstream impacts in quality of life for patients.
Towards Accurate and High-Speed Spiking Neuromorphic Systems with Data Quantization-Aware Deep Networks

Deep Neural Networks (DNNs) have gained immense success in cognitive applications and greatly pushed today’s artificial intelligence forward. The biggest challenge in executing DNNs is their extremely data-extensive computations. The computing efficiency in speed and energy is constrained when traditional computing platforms are employed in such computational hungry executions. Spiking neuromorphic computing (SNC) has been widely investigated in deep networks implementation own to their high efficiency in computation and communication. However, weights and signals of DNNs are required to be quantized when deploying the DNNs on the SNC, which results in unacceptable accuracy loss. Previous works mainly focus on weights discretize while inter-layer signals are mainly neglected. In this work, we propose to represent DNNs with fixed integer inter-layer signals and fixed-point weights while holding good accuracy. We implement the proposed DNNs on the memristor-based SNC system as a deployment example. With 4-bit data representation, our results show that the accuracy loss can be controlled within 0.02% (2.3%) on MNIST (CIFAR-10). Compared with the 8-bit dynamic fixed-point DNNs, our system can achieve more than 9.8× speedup, 89.1% energy saving, and 30% area saving.
Traumatic Events and Symptoms Among Mexican Deportees in a Border Community

ABSTRACT Research on traumatic events experienced among Mexicans deported from the United States is scant. Using clinical interviews, this study assessed the frequency of traumatic events and symptoms of post-traumatic stress disorder (PTSD) among 47 Mexican deportees in a U.S.-Mexico border community. The majority of participants (98%) reported having experienced one or more traumatic events (M = 9, SD = 5) and nearly half met DSM-IV-TR criteria for PTSD. Meeting criteria for PTSD was associated with a higher number of traumatic events. Results suggest that prevention, intervention, and policy efforts are necessary to improve the well-being of this understudied immigrant subgroup.
A Systematic Review of Commercial Cognitive Training Devices: Implications for Use in Sport

Background: Cognitive training (CT) aims to develop a range of skills, like attention and decision-making, through targeted training of core cognitive functions. While CT can target context specific skills, like movement anticipation, much CT is domain general, focusing on core abilities (e.g., selective attention) for transfer to a range of real-world tasks, such as spotting opponents. Commercial CT (CCT) devices are highly appealing for athletes and coaches due to their ease of use and eye-catching marketing claims. The extent to which this training transfers to performance in the sporting arena is, however, unclear. Therefore, this paper sought to provide a systematic review of evidence for beneficial training effects of CCT devices and evaluate their application to sport. Methods: An extensive search of electronic databases (PubMed, PsychInfo, GoogleScholar, and SportDiscus) was conducted to identify peer-reviewed evidence of training interventions with commercially available CT devices. Forty-three studies met the inclusion criteria and were retained for quality assessment and synthesis of results. Seventeen studies assessed transfer effects beyond laboratory cognitive tests, but only 1 directly assessed transfer to a sporting task. Results: The review of evidence showed limited support for far transfer benefits from CCT devices to sporting tasks, mainly because studies did not target the sporting environment. Additionally, a number of methodological issues with the CCT literature were identified, including small sample sizes, lack of retention tests, and limited replication of findings by researchers independent of the commercial product. Therefore, evidence for sporting benefits is currently limited by the paucity of representative transfer tests and a focus on populations with health conditions. Conclusions: Currently there is little direct evidence that the use of CCT devices can transfer to benefits for sporting performance. This conclusion, however, stems more from a lack of experimental studies in the sporting field and a lack of experimental rigor, rather than convincing null effects. Subsequently, there is an opportunity for researchers to develop more reliable findings in this area through systematic assessment in athletic populations and major methodological improvements.
Detecting Harmful Hand Behaviors with Machine Learning from Wearable Motion Sensor Data

We propose a machine learning approach for detecting harmful hand behaviors in autistic children. Our approach extracts features from a wearable motion sensor, applies the Random Forest algorithm, and uses sequence postprocessing to correct potentially incorrect classifications. Our experimental results indicate that our proposed system can get a 96% accuracy rate in detecting harmful behavior, 95% accuracy in distinguishing harmful behaviors from normal but similar behaviors.
Using Simulation to Improve Sample-Efficiency of Bayesian Optimization for Bipedal Robots

Learning for control can acquire controllers for novel robotic tasks, paving the path for autonomous agents. Such controllers can be expert-designed policies, which typically require tuning of parameters for each task scenario. In this context, Bayesian optimization (BO) has emerged as a promising approach for automatically tuning controllers. However, when performing BO on hardware for high-dimensional policies, sample-efficiency can be an issue. Here, we develop an approach that utilizes simulation to map the original parameter space into a domain-informed space. During BO, similarity between controllers is now calculated in this transformed space. Experiments on the ATRIAS robot hardware and another bipedal robot simulation show that our approach succeeds at sample-efficiently learning controllers for multiple robots. Another question arises: What if the simulation significantly differs from hardware? To answer this, we create increasingly approximate simulators and study the effect of increasing simulation-hardware mismatch on the performance of Bayesian optimization. We also compare our approach to other approaches from literature, and find it to be more reliable, especially in cases of high mismatch. Our experiments show that our approach succeeds across different controller types, bipedal robot models and simulator fidelity levels, making it applicable to a wide range of bipedal locomotion problems.
Structural basis for the interaction between the cell polarity proteins Par3 and Par6

The association between the polarity proteins Par6 and Par3 requires a motif in Par6 that binds to two of the PDZ domains in Par3. Building a polarity scaffold Cell types often exhibit asymmetric distributions of proteins, lipids, and organelles that are linked to specialized functions, and loss of polarization precedes pathological conditions such as cancer. The cell polarity proteins Par3 and Par6 and the kinase aPKC are critical for generating asymmetry in cells. Par3 and Par6 contain multiple PDZ domains, which bind to short motifs to enable protein-protein interactions. Using biochemical analyses, cultured cells, and fly embryos, Renschler et al. identified a PDZ domain–binding motif in Par6 that bound to two of the three PDZ domains in Par3. These results suggest that one Par3 protein could bind to two Par6 proteins, which could serve as the basis for polarity-generating protein scaffolds in cells. Polarity is a fundamental property of most cell types. The Par protein complex is a major driving force in generating asymmetrically localized protein networks and consists of atypical protein kinase C (aPKC), Par3, and Par6. Dysfunction of this complex causes developmental abnormalities and diseases such as cancer. We identified a PDZ domain–binding motif in Par6 that was essential for its interaction with Par3 in vitro and for Par3-mediated membrane localization of Par6 in cultured cells. In fly embryos, we observed that the PDZ domain–binding motif was functionally redundant with the PDZ domain in targeting Par6 to the cortex of epithelial cells. Our structural analyses by x-ray crystallography and NMR spectroscopy showed that both the PDZ1 and PDZ3 domains but not the PDZ2 domain in Par3 engaged in a canonical interaction with the PDZ domain–binding motif in Par6. Par3 thus has the potential to recruit two Par6 proteins simultaneously, which may facilitate the assembly of polarity protein networks through multivalent PDZ domain interactions.
A new norisoprenoid from the leaves of Ficus pumila

Abstract One new norisoprenoid 3,9-dihydroxy dihydro actinidiolide (1), together with eleven known compounds (2–12), were isolated from ethanol extract of the leaves of Ficus pumila collected in Hainan Province, China. Their structures were elucidated by the detailed analysis of comprehensive spectroscopic data. Compounds 1, 2, 4, 5, 8 and 10–12 were isolated from the F. pumila for the first time. All compounds were evaluated for their cytotoxic activity. Compounds 3 and 9 showed weak cytotoxic activity against Hela, MCF-7 and A549 cell lines.
High resolution 3D content creation using unconstrained and uncalibrated cameras

An increasing number of applications require 3D content. However, its creation from real-world data either necessitates expensive equipment, artistic skills, or is constrained, for example, by the range of the utilized sensors. Image-based modeling is rapidly increasing in popularity since cameras are very affordable, widely available, and have a wide image acquisition range suitable for objects of vastly different size. The technique is especially suitable for mobile robotics involving low cost equipment and robots with a light payload, for example, small UAVs. In this paper we describe a novel image-based modeling system, which produces high-quality 3D content automatically from a collection of unconstrained and uncalibrated 2D images. The system estimates camera parameters and a 3D scene geometry using Structure-from-Motion (SfM) and Bundle Adjustment techniques. The point cloud density of 3D scene components is enhanced by exploiting silhouette information of the scene. This hybrid approach dramatically improves the reconstruction of objects with few visual features, for example, unicolored objects, and improves surface smoothness. A high quality texture is created by parameterizing the reconstructed objects using a segmentation and charting approach which also works for objects which are not homeomorphic to a sphere. The resulting parameter space contains one chart for each surface segment. A texture map is created by back projecting the best fitting input images onto each surface segment, and smoothly fusing them together over the corresponding chart by using graph-cut techniques.
Social Psychology and Noninvasive Electrical Stimulation A Promising Marriage

Abstract. Social neuroscience and psychology have made substantial advances in the last few decades. Nonetheless, the field has relied mostly on behavioral, imaging, and other correlational research methods. Here we argue that transcranial direct current stimulation (tDCS) is an effective and relevant technique to be used in this field of research, allowing for the establishment of more causal brain-behavior relationships than can be achieved with most of the techniques used in this field. We review relevant brain stimulation-aided research in the fields of social pain, social interaction, prejudice, and social decision-making, with a special focus on tDCS. Despite the fact that the use of tDCS in Social Neuroscience and Psychology studies is still in its early days, results are promising. As better understanding of the processes behind social cognition becomes increasingly necessary due to political, clinical, and even philosophical demands, the fact that tDCS is arguably rare in Social Neuroscience rese.
Identification and characterization of a novel single‐chain variable fragment (scFv) antibody against Neisseria meningitidis factor H‐binding protein (fHbp)

Purpose. Neisseria meningitidis is the leading global cause of meningitis and sepsis. Detection, followed by identification, of bacterial pathogens is important in medicine and public health. In the present study, we used the ribosome display technique to select single‐chain variable fragments (scFv) that are specific to the surface‐exposed fHbp antigen of N. meningitidis. Methodology. The recombinant fHbp protein was used as the antigen for the immunization of BALB/c mice. Anti‐fHbp VH/k chain ribosome display libraries were assembled by joining VH and k into the VH/k chain with a specially constructed linker by PCR overlap extension. The scFv library was panned against the recombinant fHbp protein by using a single round of the ribosome display method via a rabbit reticulocyte lysate system. Results/Key findings. The selected anti‐fHbp antibody exhibited high affinity and specificity in the enzyme‐linked immunosorbent assay (ELISA) and the whole bacterial cell enzyme‐linked immunosorbent assay (Bact‐ELISA). Conclusion. The affinity of the selected scFv was ˜8.65×109 M−1. The isolated scFv can provide the basis for developing a diagnostic kit.