Miracle-dz/UniFAR-FTUs huggingface dataset:
Growth of III-V Antimonide Heterostructure Nanowires on Silicon Substrate for Esaki Tunnel Diode

Integration of low bandgap antimonide based nanowires on Si substrate has been attracting huge attention for opto-electronic applications. In this work we demonstrated InAs/InSb and InAs/GaSb heterostructure nanowires on Si substrate by metal organic chemical vapor deposition. We grew high quality axial InSb heterostructure segment on InAs stem by self-catalyzed growth technique, which paves a way to tune the crystal structure of InSb. In case of InAs-GaSb core-shell architecture, GaSb crystal quality highly depends on InAs core. We successfully demonstrated basic electrical characteristics of InAs-GaSb core-shell nanowire which exhibits negative differential resistance at 0.8 V and peak-to-valley current ratio of 3.84.
Alcohol Consumption and COVID-19–Related Stress Among Health Care Workers: The Need for Continued Stress-Management Interventions

Objectives: Although a known association exists between stress and alcohol consumption among health care workers (HCWs), it is not known how the COVID-19 pandemic has affected this association. We assessed pandemic work-related stress and alcohol consumption of HCWs. Methods: We emailed a cross-sectional, anonymous survey in June 2020 to approximately 550 HCWs at an academic hospital in New Orleans, Louisiana. HCWs from all departments were eligible to complete the survey. Questions measured work-related stress and emotional reactions to the pandemic (using the Middle East Respiratory Syndrome [MERS-CoV] Staff Questionnaire), depressive symptoms (using the Patient Health Questionnaire–9 [PHQ-9]), coping habits (using the Brief COPE scale), and pre–COVID-19 (March 2020) and current (June 2020) alcohol consumption. We measured alcohol consumption using the Alcohol Use Disorders Identification Test–Consumption (AUDIT–C), with scores >3 considered positive. We asked 4 open-ended questions for in-depth analysis. Results: One-hundred two HCWs participated in the survey. The average AUDIT–C scores for current and pre–COVID-19 alcohol consumption were 3.1 and 2.8, respectively. The level of current alcohol consumption was associated with avoidant coping (r = 0.46, P < .001). Relative increases in alcohol consumption from March to June 2020 were positively associated with PHQ-9 score and greater emotional reactions to the pandemic. Availability of mental health services was ranked second to last among desired supports. Qualitative data demonstrated high levels of work-related stress from potential exposure to COVID-19 and job instability, as well as social isolation and negative effects of the pandemic on their work environment. Conclusions: Ongoing prevention-based interventions that emphasize stress management rather than mental or behavioral health conditions are needed.
Modification of Neurogenic Colonic Motor Behaviours by Chemogenetic Ablation of Calretinin Neurons

How the enteric nervous system determines the pacing and propagation direction of neurogenic contractions along the colon remains largely unknown. We used a chemogenetic strategy to ablate enteric neurons expressing calretinin (CAL). Mice expressing human diphtheria toxin receptor (DTR) in CAL neurons were generated by crossing CAL-ires-Cre mice with Cre-dependent ROSA26-DTR mice. Immunohistochemical analysis revealed treatment with diphtheria toxin incurred a 42% reduction in counts of Hu-expressing colonic myenteric neurons (P = 0.036), and 57% loss of CAL neurons (comprising ∼25% of all Hu neurons; P = 0.004) compared to control. As proportions of Hu-expressing neurons, CAL neurons that contained nitric oxide synthase (NOS) were relatively spared (control: 15 ± 2%, CAL-DTR: 13 ± 1%; P = 0.145), while calretinin neurons lacking NOS were significantly reduced (control: 26 ± 2%, CAL-DTR: 18 ± 5%; P = 0.010). Colonic length and pellet sizes were significantly reduced without overt inflammation or changes in ganglionic density. Interestingly, colonic motor complexes (CMCs) persisted with increased frequency (mid-colon interval 111 ± 19 vs. 189 ± 24 s, CAL-DTR vs. control, respectively, P < 0.001), decreased contraction size (mid-colon AUC 26 ± 24 vs. 59 ± 13 gram/seconds, CAL-DTR vs. control, respectively, P < 0.001), and lacked preferential anterograde migration (P < 0.001). The functional effects of modest calretinin neuron ablation, particularly increased neurogenic motor activity frequencies, differ from models that incur general enteric neuron loss, and suggest calretinin neurons may contribute to pacing, force, and polarity of CMCs in the large bowel.
Parental Participation in the Environment: Scale Validation Across Parental Role, Income, and Region

Parental participation has gained significant attention in environmental psychology, which has revealed a need for an instrument that can measure parental participation with children regarding environmental issues. The present study met this need by validating the parental participation in the environment (PPE) scale. This process began with 45 Chinese parents participating in an individual interview and group discussions, which helped generate a list of eighteen parent-child environmental activities. The activities were then modified and validated in the current study with a diverse group of 969 parents recruited from six major Chinese cities. Both score structure evidence and generalizability evidence were obtained within this sample, and psychometric tests suggested a single factor construct with nine items. Once the PPE scale was revised, it showed measurement invariance across the parent who responded to the items (mother vs. father), across the child’s primary caregiver (mother vs. father vs. grandparent), across the family’s living region (North China vs. South China), as well as across the family’s income group. Finally, evidence based on relations to other variables showed a relationship among parents’ PPE, pro-environmental behavior, and connectedness with nature. As a result, the study provided a novel measure to assess pro-environmental socialization via parental participation.
Critical Transmission Paths of Aggregate Embodied Carbon Emission Influencing Factors in China

Carbon emissions, being embedded in sectorial production chains, need to be reduced through targeted carbon emission reduction strategies. For such a reason, it is urgent to assess the contributions of different influencing factors among different sectors for different supply chains. Focused on China, being the world’s largest carbon emitter, this paper uses the latest 2018 China’s input-output table to assess different factors, at sectoral scale, related to embodied carbon emissions. The analysis proved that the total final use factor prompted the largest emission growth, while the input-output structure factor inhibited the emission increase significantly. The gross fixed capital formation category was the largest contributor to aggregate embodied carbon emission growth, followed by urban consumption and export categories. The construction was identified as a key sector for its embodied carbon emission, the most relevant intermediate inputs to construction sector are the manufacture of non-metallic mineral products, the smelting and rolling of metals, and the production and supply of electric power and heat power sectors. Results indicate that, in the case of China, low-carbon building materials should be favored in the future. Meanwhile, energy-saving should be promoted among urban residents to increase the effectiveness of carbon emissions reduction strategies.
Preservice Primary-School Educators’ Perceptions of the Usefulness of and Problems With Art Education: A Case Study

Art education has traditionally been considered a discipline related to leisure. A case study of primary preservice generalist educators at a Spanish university was conducted to determine whether or not this perspective has evolved. It was of interest to examine whether preservice educators understand the way in which art education develops comprehensive and analytical skills. This study integrated both qualitative and quantitative methodologies to examine future teachers’ perceptions of the usefulness of this subject and the problems it presents. Outcomes indicate that there is still a long way to go to transform preservice primary generalist teachers’ false perceptions related to art teaching.
Rhizomatic Encounters With Inter/Transmedia Art: A Pedagogy for Learning and Teaching Experiential Contemporary Art

Teaching and learning through contemporary art has gained validity as a powerful and effective pedagogy in contemporary art education practice. In this article, we highlight characteristics of contemporary art and rhizomatic learning theory, and then we propose a pedagogical model that combines them for teaching and learning through experiential contemporary art. We also explain how to implement this model by showing examples of students’ rhizomatic encounters with inter/transmedia art created by Taiwanese artist teams. As the artworks offered present moments as sites of learning or places in process, students were triggered by the intersection of sounds and visuals and recalled their memories along with relevant themes; they then re/created personal meanings and/or new knowledge. Thus, the proposed pedagogy model with the implementation guidelines (autonomy, documentation, and group discussion of collective rhizomatic learning artifacts) is effective. Strategies and challenges for implementing this model in art education are recommended and discussed.
Deep Learning Algorithm Based on CT Images in the Diagnosis of Urinary System

In recent years, the radiation dose of CT has attracted more and more attention, which has become one of the main obstacles in the application of CT, and also promoted the application of low-dose technology. This paper mainly studies the application of deep learning algorithm based on CT image in the diagnosis of urinary system. IVU was performed by routine examination. After intravenous injection of 100ml contrast medium, urinary tract films during abdominal compression and after decompression were taken. CT scan was performed immediately after IVU (usually within 2 hours), and no additional contrast medium was injected. According to the original MR images of 53 fetuses, the signal intensity of the right lower lung field and liver of fetal Fiesta coronal sequence were measured on AW4.0 workstation (avoid the adjacent heart and large blood vessel structures as far as possible, and the left lung was measured in 3 fetuses with cystadenomatous malformation of the right lung), and then the lung / liver signal intensity ratio was calculated. The P values of bilateral urinary system malformation group, unilateral urinary system malformation group, lung malformation group and other malformation groups were less than 0.01, the difference was statistically significant. The results show that the deep learning algorithm of CT image plays a positive role in the diagnosis of urinary diseases.
Handwriting digits recognition in real scenario based on deep network and recurrent network

As a standard baseline and bench-mark for learning, classification and computer vision systems, the MNIST database of handwritten digits have witnessed early stages of applying numerous machine learning methods to image processing systems. Some "near-human performance" have been achieved on the MNIST database, using a committee of neural networks, state-of-the-art performance of 0.18% error achieved. In this paper, a novel extension of MNIST database with multi-labels is present, marked as ML_MNIST, hand-writing digits written in Chinese way. In addition to the conventional labels referring to category of handwriting digits, ML_MNIST featured the distinct styles of them. Upon this ML_MNIST, several neural network models are given, in track of numerous deep learning methods, including convolutional neural network and recurrent neural network.
Practical application of computer network security data encryption technology based on Cloud Computing

Computers have become an indispensable application tool for people’s work and life. With the continuous development of the Internet, the application scope and application groups of computers have been expanding, and many security problems have also arisen, such as the theft of personal information, which leads to the inability to guarantee personal property. Information theft leads to business damage, which seriously affects people’s work and life. The main reasons for these network security problems are that the popularity of data encryption technology is not high, people lack the awareness of data security management, and cannot guarantee the safe operation of computer networks. Therefore, it is of great practical significance to study the application of data encryption technology in computer network security.
Deep Hierarchical Attention Active Learning for Mental Disorder Unlabeled Data in AIoMT

In the Artificial Intelligence of Medical Things (AIoMT), Internet-Delivered Psychological Treatment (IDPT) effectively improves the quality of mental health treatments. With the advent of COVID-19, psychological tasks have become overloaded and complicated for medical professionals due to the overlap of sentimental values. The development of an AIoMT tool requires labeling of data to achieve clinical-level performance. Text data requires an appropriate set of linguistic features for vector latent representation and segmentation. Emotional biases could lead to incorrect segmentation of patient-authorized texts, and labeling emotional data is time-consuming. In this article, we propose an assistant tool for psychologists to assist them in mental health treatment and note-taking. We first extend the word and emotion lexicon and then apply a hierarchical attention method to support data labeling. The learned latent representation uses word position prediction and sentence-level attention to create a semantic framework. The augmented vector representation helps in highlighting words and classifying nine different symptoms from the text written by the patient. Our experimental results show that the emotion lexicon helps to increase the accuracy by 5% without affecting the overall results, and that the hierarchical attention method achieves an F1 score of 0.89.
A Secure and Anonymous Communicate Scheme over the Internet of Things

Anonymous exchange of data has a strong demand in many scenarios. With the development of IoT and wireless networks, plenty of smart devices are interconnected through wireless technologies such as 5G and Wi-Fi, making it possible to use them for information exchanging. The authors find a P2P network model for secure and anonymous communication, which is a typical Crowds system and the operating mechanism meets the characteristics of limited-resources of IoT devices. Based on this network model, the authors design a lightweight communication scheme for the remote-control system in this work, using two kinds of Virtual-Spaces to achieve the purpose of identity announced and data exchanged. The authors implemented a prototype system of the scheme and tested it over the Freenet, proving that the scheme can effectively resist the impact of flow analysis on the anonymity of communication while ensuring communication data security. By analyzing the scheme’s performance, the author believes that the scheme is practical and is suitable for scenarios that are not time-sensitive but require high anonymity.
Logic-based intelligence for batteryless sensors

The emergence of embedded machine learning has enabled the migration of intelligence from the cloud to the edge and to the sensors. To explore the practicalities of wide-spread deployments of these intelligent sensors, we look beyond traditional arithmetic-based neural networks (NNs) to the logic-based learning algorithm called the Tsetlin Machine (TM). TMs have not yet been implemented and explored on general purpose microcontrollers especially that are intermittently powered. In this paper, we argue that their simple architecture makes them a promising candidate for batteryless ML systems. However, in their current form, they are not suitable to be deployed on resource-constrained sensors because of the substantial memory footprint of trained models. To tackle this issue, we propose a lossless compression scheme based on run-length encoding and evaluate against standard TMs for vision and acoustic workloads. We show that our encoding can compress the model by up to 99% without accuracy loss. This translates into lower memory footprint and better energy efficiency (up to 4.9x) compared to the original Tsetlin Machine algorithm, and provides promising trade offs when compared against binary neural networks.
Using Field Programmable Gate Array Hardware for the Performance Improvement of Ultrasonic Wave Propagation Imaging System

Recently, wave propagation imaging based on laser scanning-generated elastic waves has been intensively used for nondestructive inspection. However, the proficiency of the conventional software based system reduces when the scan area is large since the processing time increases significantly due to unavoidable processor multitasking, where computing resources are shared with multiple processes. Hence, the field programmable gate array (FPGA) was introduced for a wave propagation imaging method in order to obtain extreme processing time reduction. An FPGA board was used for the design, implementing post-processing ultrasonic wave propagation imaging (UWPI). The results were compared with the conventional system and considerable improvement was observed, with at least 78% (scanning of 100 × 100 ㎟ with 0.5 ㎜ interval) to 87.5% (scanning of 200 × 200 ㎟ with 0.5 ㎜ interval) less processing time, strengthening the claim for the research. This new concept to implement FPGA technology into the UPI system will act as a break-through technology for full-scale automatic inspection.
Causal Graphs for Missing Data: A Gentle Introduction

In this chapter we describe how causal graphs can be used for processing miss­ ing data. In particular, we model the missingness process using causal graphs and present graph-based definitions of various missingness mechanisms. Given a graph and a target quantity to be estimated, we present various methods for determining if and how a consistent estimate of the quantity can be computed. We further present techniques for detecting misspecifications in the model. We demonstrate all of the above using toy examples and small graphs, thus making it easy to understand the various intricacies and nuances of graph-based missing data analysis
5G in the sky: the future of high-speed internet via unmanned aerial vehicles

millimeter-wave (mmWave) technology provides new opportunities to support high throughput wireless applications by utilizing the high-frequency spectrum. However, due to the short wavelength and narrow beamwidth of mmWave signal, mmWave networks are highly susceptible to blockage. Hence, they enable high throughput links only when there is a clear line-of-sight (LOS) path between users and terrestrial base stations. Unfortunately, this requirement has limited the applications of mmWave technology in outdoor scenarios. In this paper we propose GigSky, a mmWave network which utilizes UAVs as aerial relays to solve the coverage problem of mmWave networks. In contrast to past work which mostly focuses on the idea and the theoretical aspect of using UAVs in wireless networks, this paper targets the practical issues in building an end-to-end system. In particular, it introduces a new design which enables UAVs to passively beamform to users in different areas on the ground without requiring complex and costly phased arrays which are used in traditional systems to create and steer mmWave beams.
Preventing accidental residential fires: the role of human involvement in non‐injury house fires

Research to date has focused on causal descriptions of residential fire fatalities and injuries, with an emphasis on ignition factor. This paper examines if, how and why human actions or inactions lead to accidental residential fires and identifies the risk factors most closely associated with those fires caused by human unsafe behaviours. Interviews of 182 people who had survived accidental residential fires without serious injury were conducted. Each fire was classified as either of the following: (1) directly caused by human unsafe behaviours; (2) directly caused by human long‐term inactions; or (3) involved no human actions. Frequencies and odds ratio values were presented. Slightly less than half (46%) of the non‐injury fires were directly caused by human unsafe behaviours, 40% involved no human actions and 14% were associated with long‐term human inactions. Examination of the fires caused by human unsafe behaviours compared with no human involvement using odds ratios showed that individuals in the former group were significantly more likely to be mentally ill, physically disabled, asleep, not in full‐time paid employment and more intimate with ignition. Many non‐injury fires do not directly arise from unsafe activity, and most of them were caused by electrical or ignition system failure. New design innovations and/or improvements in appliances may reduce those fires due to appliance maintenance negligence or electrical faults. Identification of the most high‐risk individuals for unsafe behaviours can help inform fire safety educational programmes. Copyright © 2016 John Wiley & Sons, Ltd.
The Case for an Interwoven Parallel Hardware/Software Stack

The layered structure of the system software stacks we use today allows for separation of concerns and increases portability. However, the confluence of widely available virtualization and hardware partitioning technology, new OS techniques, rapidly changing hardware, and significant advances in compiler technology together present a ripe opportunity for restructuring the stack, particularly to support effective parallel execution. We argue that there are cases where layers, particularly the compiler, run-time, kernel, and hardware, should be interwoven, enabling new optimizations and abstractions. We present four examples where we have successfully applied this interweaving model of system design, and we outline several lines of promising ongoing work.
On Coding Techniques for Unsourced Multiple-Access

In this paper, we attempt to gain insights on designing codes for unsourced multiple access by investigating an important special case of a two-user unsourced binary adder channel (2-UBAC). In 2-UBAC the receiver observes a noiseless real sum of two binary vectors. We show several results. First, for a linear code the per-user probability of error (PUPE) equals the fraction of nonminimal codewords, implying that such codes can at most achieve rate-1/2 while capacity of 2-UBAC is 3/4. Second, for sparse-graph codes to jump start an iterative peeling decoder we need to reveal ("pivot") one of the ambiguous symbols. If the pivot is selected randomly then any irregular LDPC code ensemble has a non-vanishing error probability. If the pivot is selected optimally then we show that three regular LDPC code ensembles attain vanishing PUPE: (3, 4), (4, 5) and (5, 6). Our proof does not apply to any other regular LDPC code ensembles, but we believe that they should all have a non-vanishing error probability. Finally, we discuss ideas about (nonlinear) coding to break through the rate-1/2 bottleneck.