Miracle-dz/UniFAR-FTUs huggingface dataset:
Robustness Analysis of Pedestrian Detectors for Surveillance

To obtain effective pedestrian detection results in surveillance video, there have been many methods proposed to handle the problems from severe occlusion, pose variation, clutter background, and so on. Besides detection accuracy, a robust surveillance video system should be stable to video quality degradation by network transmission, environment variation, and so on. In this paper, we conduct the research on the robustness of pedestrian detection algorithms to video quality degradation. The main contribution of this paper includes the following three aspects. First, a large-scale distorted surveillance video data set (DSurVD) is constructed from high-quality video sequences and their corresponding distorted versions. Second, we design a method to evaluate detection stability and a robustness measure called robustness quadrangle, which can be adopted to the visualize detection accuracy of pedestrian detection algorithms on high-quality video sequences and stability with video quality degradation. Third, the robustness of seven existing pedestrian detection algorithms is evaluated by the built DSurVD. Experimental results show that the robustness can be further improved for existing pedestrian detection algorithms. In addition, we provide much in-depth discussion on how different distortion types influence the performance of pedestrian detection algorithms, which is important to design effective pedestrian detection algorithms for surveillance.
A Detection Method for Anomaly Flow in Software Defined Network

As a new type of network structure, the Software Defined Network (SDN) provides a new solution for network flow management and optimization, which has made the accurate detection of anomaly SDN flows a hot research topic. This paper presents an SDN-based flow detection method, builds structures for detecting anomaly SDN flows and performs classification detection on the flows using the double P-value of transductive confidence machines for K-nearest neighbors algorithm. The experimental results show that the algorithm proposed achieves a lower false positive rate, higher precision, and better adaptation to the SDN environment than do other algorithms of the same type.
Mitigation of residual flux for high-temperature superconductor (HTS) transformer by controlled switching of HTS breaker arc model

AC circuit breaker's controlled switching is a popular method to reduce dielectric and thermal stresses during switching of transformers, transmission lines, reactor and shunt capacitors. The magnitude of inrush current often reaches five to nine times of rated magnetizing current and thus it affects the network stability. Particularly, it affects the superconductivity of HTS transformer. Moreover, a residual flux is developed in HTS transformer due to high inrush current. The amount of flux may increase to a very high value that has direct impact to have a very high transient inrush current. This paper presents inrush current mitigation phenomena in a single-phase HTS transformer by controlled switching of HTS breaker arc model. The inrush current phenomenon is modeled for a single-phase HTS transformer in this paper. The mitigation of inrush current is restrained to a lower level by the reclosing of a new type of arc model named HTS breaker arc model and the residual flux also minimized. The calculating method is established on the investigation of fast switching timing and characteristics of the HTS breaker arc model.
TensorLightning: A Traffic-Efficient Distributed Deep Learning on Commodity Spark Clusters

With the recent success of deep learning, the amount of data and computation continues to grow daily. Hence a distributed deep learning system that shares the training workload has been researched extensively. Although a scale-out distributed environment using commodity servers is widely used, not only is there a limit due to synchronous operation and communication traffic but also combining deep neural network (DNN) training with existing clusters often demands additional hardware and migration between different cluster frameworks or libraries, which is highly inefficient. Therefore, we propose TensorLightning which integrates the widely used data pipeline of Apache Spark with powerful deep learning libraries, Caffe and TensorFlow. TensorLightning embraces a brand-new parameter aggregation algorithm and parallel asynchronous parameter managing schemes to relieve communication discrepancies and overhead. We redesign the elastic averaging stochastic gradient descent algorithm with pruned and sparse form parameters. Our approach provides the fast and flexible DNN training with high accessibility. We evaluated our proposed framework with convolutional neural network and recurrent neural network models; the framework reduces network traffic by 67% with faster convergence.
Two-phase access-backhaul scheduling for TDD two-transceiver sum rate maximization

This paper aims at maximizing the sum rate of a time division duplex two-transceiver system via the joint access and backhaul scheduling operation. The original scheduling problem that simultaneously considers the causal relationship constraint, the access-backhaul link matching constraint and the minimum data rate requirement is formulated. By using the access-backhaul constraints, the original problem is equivalently simplified into a zero-one integer linear programming with non-polynomial calculation complexity. Based on the simplified problem, an upper bound of the optimal scheduling is achieved. To obtain a practical scheduling solution with reduced calculation complexity, a two-phase access-backhaul scheduling (TPABS) scheme is proposed to approach the obtained upper scheduling bound. The causal relationship constraint and the minimum required data rate are guaranteed to be satisfied in TPABS. Simulation results indicate that TPABS approaches the proposed upper bound with less than 10% sum rate loss.
Multimodal Communication in Aphasia: Perception and Production of Co-speech Gestures During Face-to-Face Conversation

The role of nonverbal communication in patients with post-stroke language impairment (aphasia) is not yet fully understood. This study investigated how aphasic patients perceive and produce co-speech gestures during face-to-face interaction, and whether distinct brain lesions would predict the frequency of spontaneous co-speech gesturing. For this purpose, we recorded samples of conversations in patients with aphasia and healthy participants. Gesture perception was assessed by means of a head-mounted eye-tracking system, and the produced co-speech gestures were coded according to a linguistic classification system. The main results are that meaning-laden gestures (e.g., iconic gestures representing object shapes) are more likely to attract visual attention than meaningless hand movements, and that patients with aphasia are more likely to fixate co-speech gestures overall than healthy participants. This implies that patients with aphasia may benefit from the multimodal information provided by co-speech gestures. On the level of co-speech gesture production, we found that patients with damage to the anterior part of the arcuate fasciculus showed a higher frequency of meaning-laden gestures. This area lies in close vicinity to the premotor cortex and is considered to be important for speech production. This may suggest that the use of meaning-laden gestures depends on the integrity of patients’ speech production abilities.
A new event detection technique for residential load monitoring

In the process of non-intrusive load monitoring, the first step is to find the switching on/off events as accurate as possible. However, there are extensive disturbances in the recorded data, due to the voltage fluctuations, electrical noise and etc, resulting in either false detection or event missing. To solve this problem, a new event detection technique is proposed in this paper. This technique uses median filter algorithm and the proposed ripple mitigation algorithm to effectively remove the unexpected disturbances and extract the real signal of switching on/off events. The proposed algorithms are very easy to implement in the low cost devices. The lab data and BLUED data tests show more than 90% accurate rate of event detection.
Profiling the metabolic fitness of a special operations police unit

Fitness is essential to specialist police forces, who have higher occupational demands than general police, and vital to performance and mission success. However, little research has been done profiling the metabolic fitness of these units and how they compare to other populations. The objective of this study was to profile the aerobic fitness of a specialist police unit.
Responses of Intestinal Mucosal Barrier Functions of Rats to Simulated Weightlessness

Exposure to microgravity or weightlessness leads to various adaptive and pathophysiological alterations in digestive structures and physiology. The current study was carried out to investigate responses of intestinal mucosal barrier functions to simulated weightlessness, by using the hindlimb unloading rats model. Compared with normal controls, simulated weightlessness damaged the intestinal villi and structural integrity of tight junctions, up-regulated the expression of pro-apoptotic protein Bax while down-regulated the expression of anti-apoptotic protein Bcl-2, thus improved the intestinal permeability. It could also influence intestinal microbiota composition with the expansion of Bacteroidetes and decrease of Firmicutes. The predicted metagenomic analysis emphasized significant dysbiosis associated differences in genes involved in membrane transport, cofactors and vitamins metabolism, energy metabolism, and genetic information processing. Moreover, simulated weightlessness could modify the intestinal immune status characterized by the increase of proinflammatory cytokines, decrease of secretory immunoglobulin A, and activation of TLR4/MyD88/NF-κB signaling pathway in ileum. These results indicate the simulated weightlessness disrupts intestinal mucosal barrier functions in animal model. The data also emphasize the necessity of monitoring and regulating astronauts’ intestinal health during real space flights to prevent breakdowns in intestinal homeostasis of crewmembers.
Aircraft Fault Accommodation With Consideration of Actuator Control Authority and Gyro Availability

When faults of some flight actuators have occurred, an unexpected need to use larger magnitudes and higher actuation rates of fault-free actuators is often caused if fault-tolerance design is not considered. This paper presents a fault-tolerant flight control (FTFC) capable of accommodating aircraft actuator failures while respecting to the physical control authority of healthy actuators, on the basis of sliding mode (SM) and adaptive techniques. Furthermore, this paper accounts for the availability of the measured angular rate. In the presence of reliable gyro onboard, the sensed pitch rate can be applied to the developed FTFC loop. In the absence of pitch rate measurement, a SM observer (SMO) is exploited to recover the pitch rate information, while the integrated design of the SMO and FTFC is investigated. Simulation studies of a nonlinear Boeing 747 longitudinal model are conducted to manifest the effectiveness of the proposed schemes.
Improving Reliability and Scalability of LoRaWANs Through Lightweight Scheduling

Providing low power and long range (LoRa) connectivity is the goal of most Internet of Things networks, e.g., LoRa, but keeping communication reliable is challenging. LoRa networks are vulnerable to the capture effect. Cell-edge nodes have a high chance of losing packets due to collisions, especially when high spreading factors (SFs) are used that increase time on air. Moreover, LoRa networks face the problem of scalability when they connect thousands of nodes that access the shared channels randomly. In this paper, we propose a new MAC layer—RS-LoRa—to improve reliability and scalability of LoRa wide-area networks (LoRaWANs). The key innovation is a two-step lightweight scheduling: 1) a gateway schedules nodes in a coarse-grained manner through dynamically specifying the allowed transmission powers and SFs on each channel and 2) based on the coarse-grained scheduling information, a node determines its own transmission power, SF, and when and on which channel to transmit. Through the proposed lightweight scheduling, nodes are divided into different groups, and within each group, nodes use similar transmission power to alleviate the capture effect. The nodes are also guided to select different SFs to increase the network reliability and scalability. We have implemented RS-LoRa in NS-3 and evaluated its performance through extensive simulations. Our results demonstrate the benefit of RS-LoRa over the legacy LoRaWAN, in terms of packet error ratio, throughput, and fairness. For instance, in a single-cell scenario with 1000 nodes, RS-LoRa can reduce the packet error ratio of the legacy LoRaWAN by nearly 20%.
Isolation and molecular analysis of circulating tumor cells from lung cancer patients using a microfluidic chip type cell sorter

Circulating tumor cells (CTCs) are a tumor‐derived material utilized for liquid‐based biopsy; however, capturing rare CTCs for further molecular analysis remains technically challenging, especially in non‐small‐cell lung cancer. Here, we report the results of a clinical evaluation of On‐chip Sort, a disposable microfluidic chip‐based cell sorter, for capture and molecular analysis of CTCs from patients with lung adenocarcinoma. Peripheral blood was collected from 30 metastatic lung adenocarcinoma patients to enumerate CTCs using both On‐chip Sort and CellSearch in a blind manner. Captured cells by On‐chip Sort were subjected to further molecular analysis. Peripheral blood samples were also used for detection of EGFR mutations in plasma using droplet digital PCR. Significantly more CTCs were detected by On‐chip Sort (22/30; median 5; range, 0–18 cells/5 mL blood) than by CellSearch (9/30; median, 0; range, 0–12 cells/7.5 mL) (P < 0.01). Thirteen of 30 patients who had a negative CTC count by CellSearch had a positive CTC count by On‐chip Sort. EGFR mutations in CTCs captured by On‐chip Sort were observed in 40.0% (8/20) of patients with EGFR‐mutated primary tumor. EGFR mutations were often observed in 53.3% (8/15) of patients detected in plasma DNA. Expressions of EGFR and vimentin protein on CTCs were also successfully assessed using On‐chip Sort. These results suggest that On‐chip Sort is an efficient method to detect and capture rare CTCs from patients with lung adenocarcinoma that are undetectable with CellSearch. Mutation detection using isolated CTCs remains to be further tackled (UMIN000012488).
Andreev Spectroscopy of Molecular States in Resonant and Charge Accumulation Regime

Molecular electronics represents the ultimate step of the miniaturization process of the integrated circuits. Including molecules in manmade devices may introduce novel functionalities in nanodevices, such as the possibility to interact with biological environments with tremendous implications in several fields. With these motivations, we present a Bogoliubov-de Gennes description of the transport properties of a Normal-metal/molecule/Superconductor (N/m/S) junction formed by contacting an annular molecule (m) (e.g., a benzene molecule) with a normal (N) and a superconducting electrode (S). Differently from N/m/N junctions, the superconducting correlations in S play a crucial role in determining the transparency of the system. We demonstrate that the zero-bias differential conductance of the device is strongly suppressed in presence of charge accumulation on the molecule, while a resonant transmission with maximum conductance is observed when the charge stored on the molecule is negligible. We explain these findings observing that the space distribution of molecular quantum states is strongly affected by the Andreev mechanism, which represents the dominant scattering process at the m/S interface when the energy of the incident quasi-particle is below the superconducting energy gap. The relevance of these findings is briefly discussed.
Correlation of FT-IR Fingerprint and α-Glucosidase Inhibitory Activity of Salak (Salacca zalacca) Fruit Extracts Utilizing Orthogonal Partial Least Square

Salak fruit (Salacca zalacca), commonly known as snake fruit, is used indigenously as food and for medicinal applications in Southeast Asia. This study was conducted to evaluate the α-glucosidase inhibitory activity of salak fruit extracts in correlation to its Fourier transform infrared spectroscopy (FT-IR) fingerprint, utilizing orthogonal partial least square. This calibration model was applied to develop a rapid analytical method tool for quality control of this fruit. A total of 36 extracts prepared with different solvent ratios of ethanol–water (100, 80, 60, 40.20, 0% v/v) and their α-glucosidase inhibitory activities determined. The FT-IR spectra of ethanol–water extracts measured in the region of 400 and 4000 cm−1 at a resolution of 4 cm−1. Multivariate analysis with a combination of orthogonal partial least-squares (OPLS) algorithm was used to correlate the bioactivity of the samples with the FT-IR spectral data. The OPLS biplot model identified several functional groups (C–H, C=O, C–N, N–H, C–O, and C=C) which actively induced α-glucosidase inhibitory activity.
Implementation and validation of random exponential marking (REM) in ns-3

The problem of bufferbloat has reignited interest in studying Active Queue Management (AQM) algorithms. Significant efforts have been taken by AQM and Packet Scheduling Working Group at IETF to bring more awareness about the performance benefits of deploying AQM algorithms in the Internet. However, experimental analysis of these algorithms is necessary prior to real time deployment. Network simulators like ns-3 are useful tools to perform such preliminary studies. Random Exponential Marking (REM) is one of the popular AQM algorithms. It decouples congestion measure from performance measure, and aims to stabilize the performance measure around the target queue length regardless of the number of users. This paper presents the implementation of a new model for REM in ns-3. The correctness of the proposed model has been validated by comparing the results obtained from it, to those obtained from the ns-2 model developed by authors of REM.
Epidemiology of the Hill Tribe HIV/AIDS Populations, Thailand.

Objective A retrospective cohort study was conducted to determine the situation and factors related to the death among the HIV/AIDS Hill tribe in Northern Thailand. Material and Method A systematic data-reviewing approach was used to identify the information from the rosters of ARV clinics, OPD cards, and laboratory reports from 16 hospitals in Chiang Rai Province, Thailand. The data were collected from the first reported HIV/AIDS case of the Hill tribe to the end of 2010. A logistic regression models were used to identify associations. A p-value <0.05 was considered as a statistical significance in multiple logistic regression models Results Three thousand one hundred thirty cases were included in the present study. The majority of patients were Akha (46.0%) followed by Lahu (19.7%), 54.6% were males, 44.6% were 26 to 35 years old. The major risk factor of HIV infection was sexual intercourse (91.7%); 33.3% were still alive at the date of data collection, 30.7% were diagnosed with pulmonary TB. Regarding survival status, females had a better surviving rate than males with adjusted OR = 1.41, 95% CI = 1.19-1.66. Those that had the route of transmission as “mother-to-child” and “IDU” had greater chance of survival compared to those who contracted HIV from “sexual intercourse”, with adjusted OR = 2.05, 95% CI = 1.56-2.18, and adjusted OR = 8.45, 95% CI = 1.55-46.13, respectively. Conclusion Thailand needs to develop an appropriate model for providing care at the earlier stage of HIV/AIDS infection to prevent early death for the Hill tribe population.
A MOEA/D approach to exploit the crucial structure of convolution kernels

Deep convolutional neural networks achieved remarkably performance on computer vision tasks. However, they are always over-parameterized and computationally intensive to deploy on computing platforms with limited hardware resources. In this paper, a MOEA/D based pruning approach is proposed. We exploit the crucial structure of convolution kernels using MOEA/D based pruning approach and find 88% computational redundancy in convolutional layers of LeNet-5 without any accuracy loss. Networks are encoded as chromosomes in continues or discrete spaces. A multi-objective evolutionary algorithm, MOEA/D, is employed to evolve multiple convolutional neural networks simultaneously subject to accuracy and computational cost as two conflicting objectives. Several experiments are conducted on three classical image classification tasks. Statistical results demonstrate that our proposed method is both effective and superior to other methods.
Hydroxyl Group and Vasorelaxant Effects of Perillyl Alcohol, Carveol, Limonene on Aorta Smooth Muscle of Rats

The present study used isometric tension recording to investigate the vasorelaxant effect of limonene (LM), carveol (CV), and perillyl alcohol (POH) on contractility parameters of the rat aorta, focusing in particular on the structure-activity relationship. LM, CV, and POH showed a reversible inhibitory effect on the contraction induced by electromechanical and pharmacomechanical coupling. In the case of LM, but not CV and POH, this effect was influenced by preservation of the endothelium. POH and CV but not LM exhibited greater pharmacological potency on BayK-8644-induced contraction and on electromechanical coupling than on pharmacomechanical coupling. In endothelium-denuded preparations, the order of pharmacological potency on electrochemical coupling was LM < CV < POH. These compounds inhibited also, with grossly similar pharmacological potency, the contraction induced by phorbol ester dibutyrate. The present results suggest that LM, CV and POH induced relaxant effect on vascular smooth muscle by means of different mechanisms likely to include inhibition of PKC and IP3 pathway. For CV and POH, hydroxylated compounds, it was in electromechanical coupling that the greater pharmacological potency was observed, thus suggesting a relative specificity for a mechanism likely to be important in electromechanical coupling, for example, blockade of voltage-dependent calcium channel.
Energy-Efficient User Scheduling and Power Allocation for NOMA-Based Wireless Networks With Massive IoT Devices

Nonorthogonal multiple access (NOMA) exhibits superiority in spectrum efficiency and device connections in comparison with the traditional orthogonal multiple access technologies. However, the nonorthogonality of NOMA also introduces intracell interference that has become the bottleneck limiting the performance to be further improved. To coordinate the intracell interference, we investigate the dynamic user scheduling and power allocation problem in this paper. Specifically, we formulate this problem as a stochastic optimization problem with the objective to minimize the total power consumption of the whole network under the constraint of all users’ long-term rate requirements. To tackle this challenging problem, we first transform it into a series of static optimization problems based on the stochastic optimization theory. Afterward, we exploit the special structure of the reformulated problem and adopt the branch-and-bound technique to devise an efficient algorithm, which can obtain the optimal control policies with a low complexity. As a good feature, the proposed algorithm can make decisions only according to the instantaneous system state and can guarantee the long-term network performance. Simulation results demonstrate that the proposed algorithm has good performance in convergence and outperforms other schemes in terms of power consumption and user satisfaction.