Miracle-dz/UniFAR-FTUs huggingface dataset:
Branching characteristics of positive streamers in nitrogen-oxygen gas mixtures

One of the most important feature of streamers is that they frequently branch during the development process. In this paper, the branching characteristics of positive streamers are experimentally investigated in nitrogen-oxygen gas mixtures. Pulsed voltages with the amplitude of 10–20 kV are applied to a point-plane gap. The gas pressure is 100 mbar and the concentrations of the oxygen are 0.01 and 20%. The branch angle, the cross-sectional area of streamer channels before and after branching, as well as the ratio between streamer branching length and width are obtained. It is found that the streamer branching angle for 0.01% oxygen is less than that for 20% oxygen. The average ratio of the streamer cross-section area after branching to that before branching is 0.711 and changes slightly for different oxygen concentrations. The branching ratio decreases significantly from 12.6±2.6 to 6.7±1.5 when lowering the oxygen content from 20% to 0.01%, which indicates that the streamers branch more frequently at a certain distance. Since the nitrogen-oxygen ratio directly decides the electron production rate by photoionization ahead of streamers, the results imply that the possibility of streamer branching may correlate with the photoionization rate in the streamer tip.
A cross-sectional survey on cold chain management of vaccines in Cebu, Philippines

Background: Appropriate cold chain management is the foundation of safety and quality of vaccines. Objectives: This cross-sectional study was conducted to assess the cold chain management of the rural health units of Consolacion and Liloan, Cebu, Philippines on August to September 2017. Methods: Data was collected using a structured questionnaire, which was developed based on previous studies of cold chain survey. The questionnaire was administered to one personnel who is responsible for the storage and maintenance of vaccines in each public health center (PHC). Results: Of 42 targeted PHCs, only 52.4% (n=22) agreed to join in the study. The results of the study indicated that storage units and equipments were available in all 22 PHCs, even though only five of them (22.7%) stored vaccines. The majority of PHCs (90.9%, n=20) did not have access to a generator and only 9% (n=2) had a voltage stabilizer connected to the refrigerator. Refrigerators that were equipped with thermometer were only found in 68.2% (n=15) PHCs. No statistically significant relationship was found (p=0.159) between the statuses of PHCs to store vaccine and the level of knowledge of health professionals assigned to manage the vaccine. Conclusions: Primary health centers that store vaccines have at least one functional refrigerator and freezer and alternative power sources. Contingency plans in the event of mechanical and power failure as well as proper temperature monitoring are needed. Personnel handling vaccines must be updated on proper storage and transport of such like the use of cold boxes and ice packs to maintain cold chain. Improvement of cold-chain management for vaccines in Cebu City’s PHCs was necessary.
Single-Site Localization Based on a New Type of Fingerprint for Massive MIMO-OFDM Systems

Localization for mobile devices is drawing increasing interest from both industry and academia as location-based services spring up in recent years. For rich scattering environments, such as urban areas and indoor corridors, fingerprint-based localization techniques are very promising. In this paper, we propose a fingerprint-based single-site localization method for massive multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. A new angle delay channel power matrix fingerprint is extracted from instantaneous channel state information by taking full advantage of the high resolution in the angle and delay domains for massive MIMO-OFDM systems. A new fingerprint similarity criterion is proposed to facilitate localization. Based on the new criterion, an efficient location estimation method is developed. To reduce storage overhead and matching complexity, we also propose a fingerprint compression method and a two-stage fingerprint clustering algorithm for database preprocessing. Numerical results demonstrate the desirable performance of the proposed localization method.
LeFlow: Enabling Flexible FPGA High-Level Synthesis of Tensorflow Deep Neural Networks

Recent work has shown that Field-Programmable Gate Arrays (FPGAs) play an important role in the acceleration of Machine Learning applications. Initial specification of machine learning applications are often done using a high-level Python-oriented framework such as Tensorflow, followed by a manual translation to either C or RTL for synthesis using vendor tools. This manual translation step is time-consuming and requires expertise that limit the applicability of FPGAs in this important domain. In this paper, we present an open-source tool-flow that maps numerical computation models written in Tensorflow to synthesizable hardware. Unlike other tools, which are often constrained by a small number of inflexible templates, our flow uses Google's XLA compiler which emits LLVM code directly from a Tensorflow specification. This LLVM code can then be used with a high-level synthesis tool to automatically generate hardware. We show that our flow allows users to generate Deep Neural Networks with very few lines of Python code.
Asynchronous Corner Detection and Tracking for Event Cameras in Real Time

The recent emergence of bioinspired event cameras has opened up exciting new possibilities in high-frequency tracking, bringing robustness to common problems in traditional vision, such as lighting changes and motion blur. In order to leverage these attractive attributes of the event cameras, research has been focusing on understanding how to process their unusual output: an asynchronous stream of events. With the majority of existing techniques discretizing the event-stream essentially forming frames of events grouped according to their timestamp, we are still to exploit the power of these cameras. In this spirit, this letter proposes a new, purely event-based corner detector, and a novel corner tracker, demonstrating that it is possible to detect corners and track them directly on the event stream in real time. Evaluation on benchmarking datasets reveals a significant boost in the number of detected corners and the repeatability of such detections over the state of the art even in challenging scenarios with the proposed approach while enabling more than a 4$\times$ speed-up when compared to the most efficient algorithm in the literature. The proposed pipeline detects and tracks corners at a rate of more than 7.5 million events per second, promising great impact in high-speed applications.
Getting the Work-Nonwork Interface You Are Looking for: The Relevance of Work-Nonwork Boundary Management Fit

Recently, work-family scholars have empirically demonstrated the importance of congruence between employees' boundary management preferences and boundary management supplies provided by the work environment in relation to employee attitudes and behavior. However, a theoretically grounded construct that captures this congruence is lacking. The present study addresses this gap by developing the construct and measure of work-nonwork boundary management fit, based on the needs-supplies fit framework. We cross-validate the scale in three independent samples (n = 188, diverse group of employees, n = 75, employees from one hospital, and n = 81, employees from one car company) and in a fourth sample (n = 458, working parents), we demonstrated the importance of work-nonwork boundary management fit for employee well-being (i.e., stress and work-life conflict). In particular, we confirmed its unique role in predicting employee well-being, above and beyond workload and work interrupting nonwork behaviors. Hence, we argue for considering work-nonwork boundary management fit when studying how work-family policies and organizational culture affect employees in the workplace.
Pharmacogenetic studies with oral anticoagulants. Genome-wide association studies in vitamin K antagonist and direct oral anticoagulants

Oral anticoagulants (OAs) are the recommended drugs to prevent cardiovascular events and recurrence in patients with atrial fibrillation (AF) and cardioembolic stroke. We conducted a literature search to review the current state of OAs pharmacogenomics, focusing on Genome Wide Association Studies (GWAs) in patients treated with vitamin K antagonists (VKAs) and direct oral anticoagulants (DOACs). VKAs: Warfarin, acenocoumarol, fluindione and phenprocoumon have long been used, but their interindividual variability and narrow therapeutic/safety ratio makes their dosage difficult. GWAs have been useful in finding genetic variants associated with VKAs response. The main genes involved in VKAs pharmacogenetics are: VKORC1, CYP2C19 and CYP4F2. Variants in these genes have been included in pharmacogenetic algorithms to predict the VKAs dose individually in each patient depending on their genotype and clinical variables. DOACs: Dabigatran, apixaban, rivaroxaban and edoxaban have been approved for patients with AF. They have stable pharmacokinetics and do not require routine blood checks, thus avoiding most of the drawbacks of VKAs. Except for a GWAs performed in patients treated with dabigatran, there is no Genome Wide pharmacogenomics data for DOACs. Pharmacogenomics could be useful to predict the better clinical response and avoid adverse events in patients treated with anticoagulants, identifying the most appropriate anticoagulant drug for each patient. Current pharmacogenomics data show that the polymorphisms affecting VKAs or DOACs are different, concluding that personalized medicine based on pharmacogenomics could be possible. However, more studies are required to implement personalized medicine in clinical practice with OA and based on pharmacogenetics of DOACs.
BAM: Bottleneck Attention Module

Recent advances in deep neural networks have been developed via architecture search for stronger representational power. In this work, we focus on the effect of attention in general deep neural networks. We propose a simple and effective attention module, named Bottleneck Attention Module (BAM), that can be integrated with any feed-forward convolutional neural networks. Our module infers an attention map along two separate pathways, channel and spatial. We place our module at each bottleneck of models where the downsampling of feature maps occurs. Our module constructs a hierarchical attention at bottlenecks with a number of parameters and it is trainable in an end-to-end manner jointly with any feed-forward models. We validate our BAM through extensive experiments on CIFAR-100, ImageNet-1K, VOC 2007 and MS COCO benchmarks. Our experiments show consistent improvement in classification and detection performances with various models, demonstrating the wide applicability of BAM. The code and models will be publicly available.
Backscatter Relay Communications Powered by Wireless Energy Beamforming

The integration of wireless power transfer (WPT) with the low-power backscatter communications provides a promising way to sustain battery-less wireless networks. In this paper, we consider a backscatter communication network wirelessly powered by a power beacon station (PBS). Each backscatter radio uses the harvested energy to power its data transmissions, in which some other radios can help as the wireless relays with an aim to improve throughput performance by cooperative transmission. Under this setting, we formulate a throughput maximization problem to jointly optimize WPT and the relay strategy of the backscatter radios. An iterative algorithm with reduced complexity and communication overhead is proposed to decompose the original problem into two sub-problems distributed at the PBS and the backscatter receiver. Moreover, we take uncertain channel information into consideration and formulate robust counter-parts of the throughput maximization problem when either the backscatter or relay channel is subject to estimation errors. The difficulty of the robust counter-part lies in the coupling of the PBS’ power allocation and relay strategy in matrix inequalities, which is addressed by alternating optimization with guaranteed convergence. Numerical results reveal that the cooperative relay strategy of the backscatter radios significantly improves the throughput performance.
Learning to Listen, Read, and Follow: Score Following as a Reinforcement Learning Game

Score following is the process of tracking a musical performance (audio) with respect to a known symbolic representation (a score). We start this paper by formulating score following as a multimodal Markov Decision Process, the mathematical foundation for sequential decision making. Given this formal definition, we address the score following task with state-of-the-art deep reinforcement learning (RL) algorithms such as synchronous advantage actor critic (A2C). In particular, we design multimodal RL agents that simultaneously learn to listen to music, read the scores from images of sheet music, and follow the audio along in the sheet, in an end-to-end fashion. All this behavior is learned entirely from scratch, based on a weak and potentially delayed reward signal that indicates to the agent how close it is to the correct position in the score. Besides discussing the theoretical advantages of this learning paradigm, we show in experiments that it is in fact superior compared to previously proposed methods for score following in raw sheet music images.
An Efficient FPGA Implementation of ECC Modular Inversion over F256

Elliptic Curve Cryptography (ECC) provides high security levels with shorter keys than other public-key cryptosystems such as RSA. Usually modular inversion operation is a choke point in realizing the public-key cryptosystem. Based on the Extended Euclidean Algorithm, this work proposes an efficient FPGA implementation of ECC modular inversion over F256. According to this proposed algorithm, one modular inversion requires 320 clock cycles with a maximum clock frequency of 144.011MHz on a Xilinx Virtex-7 FPGA device which gives a computation time of 2. 22μs. On the other words, our scenario can perform 450 thousand times division operations in one second approximately. Compared to other available literature, our scheme presented in this paper provides a high performance FPGA implementation of 256-bit modular inversion over F256. This makes the elliptic curve cryptography have important practical value in hardware implementation.
Supragranular Pyramidal Cells Exhibit Early Metabolic Alterations in the 3xTg-AD Mouse Model of Alzheimer’s Disease

The impairment of cerebral glucose utilization is an early and predictive biomarker of Alzheimer’s disease (AD) that is likely to contribute to memory and cognition disorders during the progression of the pathology. Yet, the cellular and molecular mechanisms underlying these metabolic alterations remain poorly understood. Here we studied the glucose metabolism of supragranular pyramidal cells at an early presymptomatic developmental stage in non-transgenic (non-Tg) and 3xTg-AD mice, a mouse model of AD replicating numerous hallmarks of the disease. We performed both intracellular glucose imaging with a genetically encoded fluorescence resonance energy transfer (FRET)-based glucose biosensor and transcriptomic profiling of key molecular elements of glucose metabolism with single-cell multiplex RT-PCR (scRT-mPCR). We found that juvenile pyramidal cells exhibit active glycolysis and pentose phosphate pathway at rest that are respectively enhanced and impaired in 3xTg-AD mice without alteration of neuronal glucose uptake or transcriptional modification. Given the importance of glucose metabolism for neuronal survival, these early alterations could initiate or at least contribute to the later neuronal dysfunction of pyramidal cells in AD.
DLA: Compiler and FPGA Overlay for Neural Network Inference Acceleration

Overlays have shown significant promise for field-programmable gate-arrays (FPGAs) as they allow for fast development cycles and remove many of the challenges of the traditional FPGA hardware design flow. However, this often comes with a significant performance burden resulting in very little adoption of overlays for practical applications. In this paper, we tailor an overlay to a specific application domain, and we show how we maintain its full programmability without paying for the performance overhead traditionally associated with overlays. Specifically, we introduce an overlay targeted for deep neural network inference with only ~1% overhead to support the control and reprogramming logic using a lightweight very-long instruction word (VLIW) network. Additionally, we implement a sophisticated domain specific graph compiler that compiles deep learning languages such as Caffe or Tensorflow to easily target our overlay. We show how our graph compiler performs architecture-driven software optimizations to significantly boost performance of both convolutional and recurrent neural networks (CNNs/RNNs) – we demonstrate a 3x improvement on ResNet-101 and a 12x improvement for long short-term memory (LSTM) cells, compared to naive implementations. Finally, we describe how we can tailor our hardware overlay, and use our graph compiler to achieve ~900 fps on GoogLeNet on an Intel Arria 10 1150 – the fastest ever reported on comparable FPGAs.
New wearable sensor in the shape of a braided cord (Kumihimo)

A new piezoelectric sensor was developed in the shape of a traditional Japanese braided cord known as a Kumihimo by weaving piezoelectric poly-l-lactic acid (PLLA) fibers (piezoelectric PLLA braided cord). We found that the piezoelectric PLLA braided cord tied in various decorative knots, and worn as an accessory such as a choker or necklace, can detect pulse waves, swallowing, and coughing in a systematic way in one's daily life. To demonstrate this, we developed a system for measuring pulse waves with a Wi-Fi communication system using a piezoelectric PLLA braided cord with decorative knots.
Two-Stream Part-Based Deep Representation for Human Attribute Recognition

Recognizing human attributes in unconstrained environments is a challenging computer vision problem. State-of-the-art approaches to human attribute recognition are based on convolutional neural networks (CNNs). The de facto practice when training these CNNs on a large labeled image dataset is to take RGB pixel values of an image as input to the network. In this work, we propose a two-stream part-based deep representation for human attribute classification. Besides the standard RGB stream, we train a deep network by using mapped coded images with explicit texture information, that complements the standard RGB deep model. To integrate human body parts knowledge, we employ the deformable part-based models together with our two-stream deep model. Experiments are performed on the challenging Human Attributes (HAT-27) Dataset consisting of 27 different human attributes. Our results clearly show that (a) the two-stream deep network provides consistent gain in performance over the standard RGB model and (b) that the attribute classification results are further improved with our two-stream part-based deep representations, leading to state-of-the-art results.
Seroprevalence of Toxocara in Children from Urban and Rural Areas of Ilam Province, West Iran

Objectives The present study was performed to determine the seroprevalence of Toxocara in children ≤ 10 years old, from rural and urban areas of Ilam. Methods Serum samples from 383 children ≤ 10 years old, were selected randomly from rural and urban areas of Ilam province and surveyed using enzyme-linked immunosorbent assays. Results The total rate of infection with Toxocara was 22% (31% with a history of contact with dogs and cats, and 14% without a history of contact). Of those infected, 23% were male and 18% were female, 36% lived a rural life and 20% had an urban life. A significant correlation was found between the incidence of disease, and urban and rural life, as well as exposure to dogs and cats. There was no correlation between prevalence and gender or age. Conclusion Given the high prevalence of infection with Toxocara amongst children in Ilam province, preventive work in the community such as education in risk management and periodic treatment with anti-parasitic drugs and elimination of stray dogs and cats is an appropriate measure.
Detecting Autism Based on Eye-Tracking Data from Web Searching Tasks

The ASD diagnosis requires a long, elaborate, and expensive procedure, which is subjective and is currently restricted to behavioural, historical, and parent-report information. In this paper, we present an alternative way for detecting the condition based on the atypical visual-attention patterns of people with autism. We collect gaze data from two different kinds of tasks related to processing of information from web pages: Browsing and Searching. The gaze data is then used to train a machine learning classifier whose aim is to distinguish between participants with autism and a control group of participants without autism. In addition, we explore the effects of the type of the task performed, different approaches to defining the areas of interest, gender, visual complexity of the web pages and whether or not an area of interest contained the correct answer to a searching task. Our best-performing classifier achieved 0.75 classification accuracy for a combination of selected web pages using all gaze features. These preliminary results show that the differences in the way people with autism process web content could be used for the future development of serious games for autism screening. The gaze data, R code, visual stimuli and task descriptions are made freely available for replication purposes.
TAVI: Simplification Is the Ultimate Sophistication

Since its introduction in 2002, TAVI has evolved dramatically and is now standard of care for intermediate risk patients when the femoral approach can be implemented safely. The development of innovative transcatheter heart valves (THVs) and refinement of technical skills have contributed to the decrease in complication rates associated with TAVI4. Increased experience, smaller sheaths, rigorous pre-procedural planning and improved vascular closing techniques have resulted in markedly lower rates of vascular complications. The next step is the simplification of the procedure, which should contribute to a further decrease in complications, and also reduce procedural time, hospital stay as well as staff workload and costs. Moving to conscious sedation, no predilatation, no temporary pace maker and use of the radial approach as the contralateral approach are all instrumental in achieving this ultimate refinement.