Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting effects of borderline personality symptoms and self‐concept and identity disturbances on internet addiction, depression, and suicidality in college students: A prospective study

The aims of this study were to evaluate the predicting effects of borderline personality symptoms and self‐concept and identity disturbances on internet addiction, significant depression, and suicidality among college students at follow‐up assessments conducted 1 year later. A sample of 500 college students aged between 20 and 30 years participated in this study. Their levels of borderline personality symptoms, self‐concept and identity disturbances, internet addiction, depression, and suicidality at baseline and at follow‐up interviews were assessed through the Borderline Symptoms List, Self‐concept and Identity Measure, Chen Internet Addiction Scale, Beck Depression Inventory‐II, and questions related to suicidality from the Epidemiological version of the Kiddie Schedule for Affective Disorders and Schizophrenia, respectively. A total of 324 college students received follow‐up assessments 1 year later. Among them, 15.4%, 27.5%, and 17% had internet addiction, significant depression, and suicidality, respectively. Our result revealed the severity of borderline symptoms, disturbed identity, unconsolidated identity, and lack of identity at initial assessment increased the occurrence of internet addiction, significant depression, and suicidality at follow‐up assessment except for the predictive effect of unconsolidated identity on internet addiction. The results indicated that self‐concept and identity and borderline symptoms may have a significant role in the risk of mental health problems in college students.
A Real-Time Coprime Line Scan Super-Resolution System for Ultra-Fast Microscopy

A fundamental technical challenge for ultra-fast cell microscopy is the tradeoff between imaging throughput and resolution. In addition to throughput, real-time applications such as image-based cell sorting further requires ultra-low imaging latency to facilitate rapid decision making on a single-cell level. Using a novel coprime line scan sampling scheme, a real-time low-latency hardware super-resolution system for ultra-fast time-stretch microscopy is presented. The proposed scheme utilizes analog-to-digital converter with a carefully tuned sampling pattern (shifted sampling grid) to enable super-resolution image reconstruction using line scan input from an optical front-end. A fully pipelined FPGA-based system is built to efficiently handle the real-time high-resolution image reconstruction process with the input subpixel samples while achieving minimal output latency. The proposed super-resolution sampling and reconstruction scheme is parametrizable and is readily applicable to different line scan imaging systems. In our experiments, an imaging latency of ${0.29}\; \mu {\rm s}$ has been achieved based on a pixel-stream throughput of 4.123 giga pixels per second, which translates into imaging throughput of approximately 120000 cells per second.
Effects of psychosocial and physical stress on lactate and anxiety levels

Abstract Both intense physical activity and potent psychosocial stressors increase blood lactate. Raising lactate levels by infusing the chemical can have an anxiogenic effect. Here, we compare the relationship between changes in lactate and anxiety levels resulting from two standardized stressors. We investigated the effects of the Multiple Wingate All-Out Performance Test (WG), a strong physical stressor, and the Trier Social Stress Test (TSST), a well-documented psychosocial stressor, in 32 healthy young men using a crossover design. Before and after each stressor, venous blood was analyzed for lactate levels and the State-Trait-Anxiety Inventory (STAI) was given. Both stressors raised both lactate and anxiety significantly above their baseline levels. While the rise in lactate was much higher for the WG than for the TSST, the rises in anxiety were nearly equal. Individual fitness did not influence the results for lactate or anxiety. No significant relation between lactate and anxiety changes were found. Thus, our results as well as the literature suggest that lactate is an important variable for understanding the impact of sport participation.
NGTS-5b: a highly inflated planet offering insights into the sub-Jovian desert

Context. Planetary population analysis gives us insight into formation and evolution processes. For short-period planets, the sub-Jovian desert has been discussed in recent years with regard to the planet population in the mass/period and radius/period parameter space without taking stellar parameters into account. The Next Generation Transit Survey (NGTS) is optimised for detecting planets in this regime, which allows for further analysis of the sub-Jovian desert. Aims. With high-precision photometric surveys (e.g. with NGTS and TESS), which aim to detect short period planets especially around M/K-type host stars, stellar parameters need to be accounted for when empirical data are compared to model predictions. Presenting a newly discovered planet at the boundary of the sub-Jovian desert, we analyse its bulk properties and use it to show the properties of exoplanets that border the sub-Jovian desert. Methods. Using NGTS light curve and spectroscopic follow-up observations, we confirm the planetary nature of planet NGTS-5b and determine its mass. Using exoplanet archives, we set the planet in context with other discoveries. Results. NGTS-5b is a short-period planet with an orbital period of 3.3569866 ± 0.0000026 days. With a mass of 0.229 ± 0.037 MJup and a radius of 1.136 ± 0.023 RJup, it is highly inflated. Its mass places it at the upper boundary of the sub-Jovian desert. Because the host is a K2 dwarf, we need to account for the stellar parameters when NGTS-5b is analysed with regard to planet populations. Conclusions. With red-sensitive surveys (e.g. with NGTS and TESS), we expect many more planets around late-type stars to be detected. An empirical analysis of the sub-Jovian desert should therefore take stellar parameters into account.
Towards Integrated Mid-Infrared Gas Sensors

Optical gas sensors play an increasingly important role in many applications. Sensing techniques based on mid-infrared absorption spectroscopy offer excellent stability, selectivity and sensitivity, for numerous possibilities expected for sensors integrated into mobile and wearable devices. Here we review recent progress towards the miniaturization and integration of optical gas sensors, with a focus on low-cost and low-power consumption devices.
A Minimal Set of Electrodes for Motor Imagery BCI to Control an Assistive Device in Chronic Stroke Subjects: A Multi-Session Study

The brain-computer interface (BCI) system has been developed to assist people with motor disability. To make the system more user-friendly, it is a challenge to reduce the electrode preparation time and have a good reliability. This study aims to find a minimal set of electrodes for an individual stroke subject for motor imagery to control an assistive device using functional electrical stimulation for 20 sessions with accuracy higher than 90%. The characteristics of this minimal electrode set were evaluated with two popular algorithms: Fisher's criterion and support-vector machine recursive feature elimination (SVM-RFE). The number of calibration sessions for channel selection required for robust control of these 20 sessions was also investigated. Five chronic stroke patients were recruited for the study. Our results suggested that the number of calibration sessions for channel selection did not have a significant effect on the classification accuracy. A performance index devised in this study showed that one training day with 12 electrodes using the SVM-RFE method achieved the best balance between the number of electrodes and accuracy in the 20-session data. Generally, 8-36 channels were required to maintain accuracy higher than 90% in 20 BCI training sessions for chronic stroke patients.
Differential physiological sensitivity to child compliance behaviors in abusing, neglectful, and non-maltreating mothers

Abstract We examined time-ordered associations between children's compliance behavior and maternal respiratory sinus arrhythmia (RSA) in a sample of 127 child-maltreating (physical abuse, physical neglect, emotional abuse) and 94 non-maltreating mothers and their preschool-aged children. Child prosocial and aversive compliance behaviors and maternal RSA were continuously collected during a joint challenge task. Child behavior and mother RSA were longitudinally nested within-person and subjected to multilevel modeling (MLM), with between-person child maltreatment subtype and level of inconsistent parenting modeled as moderators. Both child maltreatment type and inconsistent parenting moderated the effects of child compliance on maternal RSA. Increases in children's prosocial compliance behaviors led to decreasing RSA in physically abusive mothers 30s later (i.e., increasing arousal), but predicted increases in non-maltreating mothers’ RSA (i.e., increasing calm). Inconsistent parenting (vacillating between autonomy-support and strict control) also moderated the effects of children's compliance behavior on maternal physiology, weakening the effects of child prosocial compliance on subsequent maternal RSA. These findings highlight variations in mothers’ physiological sensitivity to their children's prosocial behavior that may play a role in the development of coercive cycles, and underscore the need to consider individual differences in parents’ physiological sensitivity to their children to effectively tailor interventions across the spectrum of risk.
Recent Progress on Graphene/Polyaniline Composites for High-performance Supercapacitors

Electrode materials are crucial for the electrochemical performance of supercapacitors. In view of the high specific surface area, high conductivity of graphene nanosheets and the high pseudocapacitance of polyaniline (PANI), the combination of graphene with PANI has become a research hotspot. In this work, we summarize the recent advance on the synthesis of PANI and graphene/PANI composites, and their application in supercapacitors. The synthesis of PANI is the basis of preparing graphene/PANI composites, so we first introduce the synthesis methods of PANI. Then, the advances of two dimensional (2D) and three dimensional (3D) graphene/PANI composites are summarized according to the inherent feature of graphene. The 2D composites of pristine graphene and functionalized graphene with PANI are introduced separately; furthermore, the 3D composites are classified into three sections, including flexible graphene/PANI composites, graphene framework based composites, and printable graphene/PANI composites. At last, aiming at solving the current challenges of graphene/PANI composites, we put forward some strategies for preparing high performance graphene/PANI composite electrodes.
Recovery colleges as a mental health innovation

141 justified on cost-effectiveness alone, with many more required for full national coverage. Each region of Australia needs a cluster of headspace entrylevel portals seamlessly linked to transdiagnostic specialized care integrating mental and physical health with alcohol and other drugs expertise, vocational interventions and online/ digital health platforms. Assertive and intensive home-based care, and clinicians with expertise in complex syndromes (such as borderline, eating, mood and psychotic disorders) are missing elements, and interface with hospital-based services is therefore needed. Strong national oversight to assure integrative commissioning, stronger financial models, additional funding streams, longer tenure and greater depth of expertise will strengthen the capacity of the model. The youth mental health paradigm is in its infancy and will be driven by a dynamic blend of grassroots and professional leadership. Early adopters, inspiring leaders, philanthropic visionaries and patrons have emerged in progressive regions of the world, notably Ireland, Canada, Denmark, Israel, the Netherlands, France, Singapore, and parts of England and California. Child and adolescent psychiatry, still a seriously undersized speciality, has begun to recognize the need and opportunity for a paradigm shift, which it has labelled “transitional psychiatry”. Momentum within and beyond the mental health field is building and could be decisive in paving the way for a wider revolution in mental health care.
Advance in predicting subcellular localization of multi-label proteins and its implication for developing multi-target drugs.

The smallest unit of life is a cell, which contains numerous protein molecules. Most of the functions critical to the cell's survival are performed by these proteins located in its different organelles, usually called ''subcellular locations". Information of subcellular localization for a protein can provide useful clues about its function. To reveal the intricate pathways at the cellular level, knowledge of the subcellular localization of proteins in a cell is prerequisite. Therefore, one of the fundamental goals in molecular cell biology and proteomics is to determine the subcellular locations of proteins in an entire cell. It is also indispensable for prioritizing and selecting the right targets for drug development. Unfortunately, it is both time-consuming and costly to determine the subcellular locations of proteins purely based on experiments. With the avalanche of protein sequences generated in the post-genomic age, it is highly desired to develop computational methods for rapidly and effectively identifying the subcellular locations of uncharacterized proteins based on their sequences information alone. Actually, considerable progresses have been achieved in this regard. This review is focused on those methods, which have the capacity to deal with multi-label proteins that may simultaneously exist in two or more subcellular location sites. Protein molecules with this kind of characteristic are vitally important for finding multi-target drugs, a current hot trend in drug development. Focused in this review are also those methods that have use-friendly web-servers established so that the majority of experimental scientists can use them to get the desired results without the need to go through the detailed mathematics involved.
Efficient Similarity-aware Compression to Reduce Bit-writes in Non-Volatile Main Memory for Image-based Applications

Image bitmaps have been widely used in in-memory applications, which consume lots of storage space and energy. Compared with legacy DRAM, non-volatile memories (NVMs) are suitable for bitmap storage due to the salient features in capacity and power savings. However, NVMs suffer from higher latency and energy consumption in writes compared with reads. Although compressing data in write accesses to NVMs on-the-fly reduces the bit-writes in NVMs, existing precise or approximate compression schemes show limited performance improvements for data of bitmaps, due to the irregular data patterns and variance in data. We observe that the data containing bitmaps show the pixel-level similarity due to the analogous contents in adjacent pixels. By exploiting the pixel-level similarity, we propose SimCom, an efficient similarity-aware compression scheme in hardware layer, to compress data for each write access on-the-fly. The idea behind SimCom is to compress continuous similar words into the pairs of base words with runs. With the aid of domain knowledge of images, SimCom adaptively selects an appropriate compression mode to achieve an efficient trade-off between image quality and memory performance. We implement SimCom on GEM5 with NVMain and evaluate the performance with real-world workloads. Our results demonstrate that SimCom reduces 33.0%, 34.8% write latency and saves 28.3%, 29.0% energy than state-of-the-art FPC and BDI with minor quality loss of 3%.
Data-Driven Based Method for Power System Time-Varying Composite Load Modeling

Fast and accurate load parameters identification has great impact on the power systems operation and stability analysis. This paper proposes a novel transfer reinforcement learning based method to identify composite ZIP and induction motor (IM) load models. An imitation learning process is firstly introduced to improve the exploitation and exploration process. The transfer learning process is then employed to overcome the challenge of time consuming optimization when dealing with new tasks. An Associative memory is designed to realize demension reduction and knowledge learning and transfer between different optimization tasks. Agents can exploit the optimal knowledge from source tasks to accelerate search rate and improve solution accuracy. The greedy rule is adopted to balance global search and local search. Convergency analysis shows that the proposed method can converge to the global optimal solution with probability 1. The performance of the proposed ITQ appraoch have been validated on 68-bus system. Simulation results in multi-test cases verify that the proposed method has superior convergence rate and stability.
Transparent pronunciation scoring using articulatorily weighted phoneme edit distance

For researching effects of gamification in foreign language learning for children in the "Say It Again, Kid! " project we developed a feedback paradigm that can drive gameplay in pronunciation learning games. We describe our scoring system based on the difference between a reference phone sequence and the output of a multilingual CTC phoneme recogniser. We present a white-box scoring model of mapped weighted Levenshtein edit distance between reference and error with error weights for articulatory differences computed from a training set of scored utterances. The system can produce a human-readable list of each detected mispronunciation's contribution to the utterance score. We compare our scoring method to established black box methods.
Inferring player engagement in a pervasive experience

We investigate the prediction of player engagement to address temporal issues arising from the long-term character of pervasive experiences such as interruptibility, mutual player state awareness, disengagement and synchronization on re-engagement. We introduce a model that operationalizes engagement in terms of the elapsed and response time in game messages. We designed and conducted an experiment based on the experience-sampling method to evaluate our model on the basis of a long-term SMS-based game called Day of the Figurines. Statistical analysis supports the hypothesis that player engagement can be predicted by the continuous data properties elapsed time and response time. Our findings point towards further research towards the adaptation of pervasive experiences to the player's temporal context.
Memristive Quantized Neural Networks: A Novel Approach to Accelerate Deep Learning On-Chip

Existing deep neural networks (DNNs) are computationally expensive and memory intensive, which hinder their further deployment in novel nanoscale devices and applications with lower memory resources or strict latency requirements. In this paper, a novel approach to accelerate on-chip learning systems using memristive quantized neural networks (M-QNNs) is presented. A real problem of multilevel memristive synaptic weights due to device-to-device (D2D) and cycle-to-cycle (C2C) variations is considered. Different levels of Gaussian noise are added to the memristive model during each adjustment. Another method of using memristors with binary states to build M-QNNs is presented, which suffers from fewer D2D and C2C variations compared with using multilevel memristors. Furthermore, methods of solving the sneak path issues in the memristive crossbar arrays are proposed. The M-QNN approach is evaluated on two image classification datasets, that is, ten-digit number and handwritten images of mixed National Institute of Standards and Technology (MNIST). In addition, input images with different levels of zero-mean Gaussian noise are tested to verify the robustness of the proposed method. Another highlight of the proposed method is that it can significantly reduce computational time and memory during the process of image recognition.
Revisiting total recognition discriminability in Huntington’s and Alzheimer’s disease: New insights from the CVLT-3

Abstract The original and second editions of the California Verbal Learning Test (CVLT) used nonparametric and parametric methods, respectively, to assess Total Recognition Discriminability (RD). In a previous study, we found evidence that the nonparametric formula may be more sensitive than the parametric formula to high false positive (FP) rates and provide more accurate assessments of yes/no recognition in neurodegenerative populations prone to high FP rates, including Alzheimer’s disease (AD). In the present study, we extended our investigation to examine the utility of CVLT-3 nonparametric and parametric Total RD indices in the assessment and comparison of yes/no recognition in individuals with Huntington’s disease (HD) and AD in mild and moderate stages of dementia. Findings suggested that the CVLT-3 nonparametric Total RD index was more sensitive than the parametric index to HD and AD differences in yes/no recognition across mild and moderate stages of dementia. Additionally, group differences on total FP errors were more closely mirrored by group differences on the nonparametric Total RD index. The present results bolster our previous findings and highlight the utility of examining nonparametric (in addition to parametric) Total RD on the CVLT-3 in assessments of yes/no recognition involving clinical populations prone to high FP rates.
Molecular random tilings as glasses

We have recently shown that p-terphenyl-3,5,3′,5′-tetracarboxylic acid adsorbed on graphite self-assembles into a two-dimensional rhombus random tiling. This tiling is close to ideal, displaying long-range correlations punctuated by sparse localized tiling defects. In this article we explore the analogy between dynamic arrest in this type of random tilings and that of structural glasses. We show that the structural relaxation of these systems is via the propagation–reaction of tiling defects, giving rise to dynamic heterogeneity. We study the scaling properties of the dynamics and discuss connections with kinetically constrained models of glasses.
Metabolic syndrome: Association between prevalence and risk at worksites

Abstract This study, conducted at major Japanese companies, aimed to determine if asymptomatic workers in workplaces with a high prevalence of metabolic syndrome have a greater risk of developing metabolic syndrome. Data were obtained from the health records of 298,145 people, from 2011 to 2015. We collected data on the participants’ age, sex, physical examinations, laboratory tests, and lifestyle behaviors. To test whether the risk of metabolic syndrome in asymptomatic workers differed between groups with a higher and lower prevalence in 2011, Cox proportional hazards regression model was performed, with the covariates being controlled for. The analysis showed that the risk of metabolic syndrome among asymptomatic workers in the high-prevalence group was about 1.1-fold elevated compared to those within the low-prevalence group. As a follow-up to these results, interventions aimed at asymptomatic workers should be provided in workplaces with a high prevalence of metabolic syndrome.