Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Objective Optimization Control of Distributed Electric Drive Vehicles Based on Optimal Torque Distribution

To improve the total efficiency of the drive system and the driving safety of distributed electric drive vehicles, this paper proposes a multi-objective optimization method based on torque allocation optimization. First, in the vehicle nonlinear dynamics model, the response surface method is used to perform regression analysis on the test data of the drive motor to obtain the drive motor efficiency function. Second, based on the demand torque value of the distributed electric drive system, the objective functions that characterize the optimization of the drive system efficiency and the optimization of the vehicle driving safety are established. Moreover, the linear weighting method with adaptive weight coefficients is used to transform the solution of the above two objective functions into a multi-objective optimization problem under constraint conditions. Furthermore, the second-generation nondominated sorting genetic algorithm (NSGA-II) and the hybrid genetic Tabu search algorithm (HGTSA) are used to solve the above multi-objective optimization problem to obtain the optimal torque distribution of the distributed electric drive system. Finally, the NEDC operating conditions were selected to verify NSGA-II, the HGTSA and the commonly used average distribution method. The simulation test results show that NSGA-II and the HGTSA can improve the driving efficiency and vehicle driving safety of distributed electric drive systems relative to the average distribution method. In particular, the optimization effect of the HGTSA is more prominent, and stability is more quickly achieved.
P4Bricks: Enabling multiprocessing using Linker-based network data plane architecture

Packet-level programming languages such as P4 usually require to describe all packet processing functionalities for a given programmable network device within a single program. However, this approach monopolizes the device by a single large network application program, which prevents possible addition of new functionalities by other independently written network applications. We propose P4Bricks, a system which aims to deploy and execute multiple independently developed and compiled P4 programs on the same reconfigurable hardware device. P4Bricks is based on a Linker component that merges the pro-grammable parsers/deparsers and restructures the logical pipeline of P4 programs by refactoring, decomposing and scheduling the pipelines' tables. It merges P4 programs according to packet processing semantics (parallel or sequential) specified by the network operator and runs the programs on the stages of the same hardware pipeline, thereby enabling multiprocessing. This paper presents the initial design of our system with an ongoing implementation and studies P4 language's fundamental constructs facilitating merging of independently written programs.
An Algorithm for Tracking Multiple Fish Based on Biological Water Quality Monitoring

Abnormal water quality will increase the occlusion rate among fish schools, which causes difficulties in fish detection and tracking. In order to solve this problem, a multiple fish tracking algorithm for red snapper is proposed in this paper. In the detection stage, we use the Otsu adaptive segmentation algorithm to extract fish targets based on the background subtraction method, following which the fish tracking feature parameters can be obtained based on the fish geometric features. In the tracking stage, the Kalman filter is employed to first estimate the motion state, and then the cost function is constructed from the position of the fish body, target area, and the direction information. Finally, fish school tracking is realized by the interframe relationship matrix. We applied several tracking methods with various schemes to experimental videos of swimming fish schools in different environments. The experimental results show that the proposed tracking algorithm exhibits improved performance and robustness.
Wideband Load Pull Measurement Techniques: Architecture, Accuracy, and Applications

This paper concentrates on load pull measurements for high-frequency devices and circuits. We will discuss the architecture and the design aspects of a state-of-the-art load pull measurement system which enables wideband impedance control with complex modulated signals both at RF and baseband frequencies. We will present several application examples, ranging from high speed load pull for technology evaluations and power amplifier design to modulated testing for 5G applications. Finally, we will describe a procedure for evaluating traceable uncertainty of power measurements as a function of the load impedance.
Dynamically Reconstructing Minimum Spanning Trees After Swapping Pairwise Vertices

The minimum spanning tree (MST) problem is a fundamental problem in computer science and operations research, which has many real-life network design applications. Given a graph <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula> with <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> vertices and <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> edges, starting from an MST (denoted by <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula>) covering a subgraph of <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula>, it is usually needed to reconstruct a new MST after swapping two vertices <inline-formula> <tex-math notation="LaTeX">$v\in T$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$v' \notin T$ </tex-math></inline-formula>. For this purpose, the most popular choice is to reconstruct an MST from scratch, for which the current fastest algorithm (Kruskal’s algorithm based on Fibonacci heap) requires a time complexity of <inline-formula> <tex-math notation="LaTeX">$O(m+n\cdot \log n)$ </tex-math></inline-formula>, implying that a high time complexity of <inline-formula> <tex-math notation="LaTeX">$O(n^{2})\cdot O(m+n\cdot \log n)$ </tex-math></inline-formula> is needed to evaluate all the <inline-formula> <tex-math notation="LaTeX">$O(n^{2})$ </tex-math></inline-formula> possible swapping-based moves. In order to evaluate these moves more efficiently, we integrate a series of dynamic techniques to develop a fast dynamic swap-vertex move operator, which significantly reduces the overall time complexity from <inline-formula> <tex-math notation="LaTeX">$O(n^{2})\cdot O(m+n\cdot \log n)$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$O(n)\cdot O(m\cdot \log n)$ </tex-math></inline-formula>. We also strictly prove the correctness of the introduced method. Finally, we choose three well-studied Steiner/spanning tree problems as our test bed and carry out extensive experiments on 140 representative instances to show the effectiveness and efficiency of the proposed method. More importantly, as a general-purpose method, the dynamic swap-vertex move operator could be easily adapted to many other tree-related problems.
Local Computation with Adaptive Spatial Clustering for Multi-Size Motion Patch Proposals in WAMI

Near real-time moving object detection in Wide Area Motion Imagery (WAMI) can support applications in many fields. However, since the targets’ search space are extremely large, current state-of-the-art methods usually suffers high computational cost. It’s crucial to recommend candidate regions for detection first. Different from most research that extracts candidate blobs as proposals, this paper attempts to offer high-quality patches, which can provide background info and be taken as many algorithms’ input (convolutional neural network, optical flow, block matching, etc.). First by the idea that local errors are tolerable, neighborhood frame differencing with local computation is applied to roughly obtain irregular blobs. After that, an adaptive spatial clustering algorithm which utilizes grid and density reachable, is proposed to generate multi-size motion patches quickly. Compared with traditional clustering, advantages of this algorithm include parameter-free, saving of time and widely applications. Experimental results show that the proposed method is competitive especially in dense traffic regions.
Online and Real-Time Tracking with the GM-PHD Filter using Group Management and Relative Motion Analysis

In this paper, we propose an online and real-time multi-target tracking method exploiting the tracking-by-detection approach. The proposed method includes a two-stage data association strategy with the Gaussian mixture probability density filter and an occlusion handling method using group management and motion analysis. Also, we devise a new measure namely sum-of-intersection-over-area to determine the targets’ merge, occlusion, and split used in the group management scheme. To verify that proposed framework works efficiently at multi-target tracking tasks, we evaluate our tracker on the UA-DETRAC dataset which contains about 140,000 of images with the vehicle detection responses. The experiment results show that our tracker not only runs faster than 400 fps but also achieves the competitive tracking performance with the second PR-MOTA score against the state-of-the-art trackers.
Symbol-Level Precoding for Low Complexity Transmitter Architectures in Large-Scale Antenna Array Systems

In this paper, we consider three transmitter designs for symbol-level-precoding (SLP), a technique that mitigates multiuser interference (MUI) in multiuser systems by designing the transmitted signals using the channel state information and the information-bearing symbols. The considered systems tackle the high hardware complexity and power consumption of existing SLP techniques by reducing or completely eliminating fully digital radio frequency (RF) chains. The first proposed architecture referred to as, Antenna Selection SLP, minimizes the MUI by activating a subset of the available antennas and thus, reducing the number of required RF chains to the number of active antennas. In the other two architectures, which we refer to as RF domain SLP, the processing happens entirely in the RF domain, thus eliminating the need for multiple fully digital RF chains altogether. Instead, the analog phase shifters directly modulate the signals on the transmit antennas. The precoding design for all the considered cases is formulated as a constrained least squares problem and efficient algorithmic solutions are developed via the Coordinate Descent method. Simulations provide insights into the power efficiency of the proposed schemes and the improvements over the fully digital counterparts.
Migraine and Mood Disorders: Prevalence, Clinical Correlations and Disability

ABSTRACT Introduction: Both migraine and mood disorders are prevalent disorders with many studies demonstrating that they are comorbid with each other with increased migraine-related disability in such patients. Aim: The aim of the study is to test the hypothesis that mood disorders are comorbid with migraine with increased disability and to identify any clinical features in migraineurs which may be associated with mood disorders. Materials and Methods: Patients presenting with complaints of headache to the Neurology Outpatient Department of a Tertiary CARE Hospital from August 01, 2016 to February 28, 2017, were subjected to International Classification of Headache Disorder 3 beta criteria to satisfy a diagnosis of migraine and were assessed in detail as to headache characteristics. Mood disorders were assessed by Hospital Anxiety and Depression Scale and migraine-related disability was assessed by Migraine Disability Assessment Questionnaire. Patients with serious medical complaints, known previous psychiatric disease, other types of headaches and recent prophylactic drug intake were carefully excluded. Results: A total of 133 patients were studied. The duration and frequency of migraine headaches were found to correlate with the presence of mood disorders and the migraine-related disability in patients with comorbid mood disorders was significantly higher. Factors such as total duration of migraine, aura, vomiting, phono, and photophobia were not found to be statistically correlated with mood disorders. Conclusions: Rates of depression and anxiety in migraine vary widely in various studies due to variations in study criteria, population characteristics and various scales used. We found a prevalence of 16.54% of anxiety and 9.02% of depression in migraineurs, a rate comparable to or less than many studies in international literature and a significantly increased disability in individuals with comorbid mood disorders and migraine. Routinely including questionnaires such as HAD in screening patients with migraine to rule out comorbid mood disorders may be warranted. Because we have carefully excluded all other primary (especially tension and medication overuse headaches) and secondary headaches and selected prophylactic drug naïve patients, we contend that this study provides a clear clinical profile of migraineurs with mood disorders.
A Secure Service-Specific OverlayComposition Model for Cloud Networks

Mobile cloud service subscribers acquire and produce both simple and complex services resulting in tremendous amounts of stored data. Increasing demand for complex services coupled with limitations in current mobile networks has led to the generation of cloud service composition solutions. In this paper, we introduce a cloud mobile subscriber data caching method that allows cloud subscribers to retrieve data in a faster and more efficient way. The solution also allows the retrieval of user-specific composed data through a service-specific overlay (SSO) composition technique.Additionally, a Workflow-net based mathematical framework called Secure Workflownet is proposed to enhance security attributes of SSOs. Simulation results show increased successful file hit ratio and demonstrate how task coverage is achieved in an adequate timely manner when constructing service composition workflows.
Novel Role of Dehydrated Human Amnion/Chorion Membrane in Healing of Denuded Ureter in Robot-Assisted Excision of Endometriosis

Ureteral injury is an uncommon but potentially morbid complication following any open or endoscopic pelvic procedure. Gynecologic surgeries alone make up 50 percent of nonurologic ureteral injuries leading to prolonged hospital stays, secondary interventions, and potential loss of renal function. The use of AmnioFix ® a processed dehydrated, immunologically privileged cellular amniotic membrane allograft has been well established in urologic and gynecologic procedures. These allografts contain human extracellular matrix components, growth factors, and cytokines that mediate inflammation and facilitate would healing. We report the first application of AmnioFix on a denuded ureter during a case of robotic-assisted excision of endometriosis. We include a literature review and discussion on the management outcomes of iatrogenic injury to the ureters.
Clustering Approach for Multidimensional Recommender Systems

Side information has been incorporated into traditional recommender systems to further enhance their performance, especially to alleviate the data sparsity and cold start issues. Side information in recommendations are the user-item related contents like user demographic data, movie genre, contextual or multi-criteria ratings. Incorporation of side information into classical recommender system often leads to multidimensionality problem, which imposes new challenges for the researchers. Therefore, the main objective of this work is to develop a side information based recommender system and handle multidimensionality issue to produce improved recommendations. The proposed approach is divided into three phases. In the first phase, user clusters are created using a side information clustering. In the second phase, top-K neighborhood set formed through intra-cluster distance computation using Mahalanobis distance measure. In the third phase, prediction and recommendations are generated for the users. Experimental results show the superiority of clustering based approach over non-clustering approach.
Linear Precoder Design for SWIPT With Finite-Alphabet Inputs and Statistical CSI

This paper considers the transmit linear precoding problem for a multiple-input-multiple-output system with simultaneous wireless information and power transfer (SWIPT), where we assume that the transmitter only has statistical channel state information. The optimal precoder is designed based on maximizing the lower bound of average mutual information under the constraints of transmitting power and harvested energy level. Different from previous works on SWIPT, this paper formulates the design from the standpoint of realistic communication systems with finite-alphabet input signals instead of Gaussian input signals. The formulated problem is NP-hard, so a globally optimal solution cannot be found with polynomial-time complexity. However, by exploiting the structure of the problem, we develop two algorithms to obtain a near optimal solution, among which one is based on a semidefinite relaxation (SDR) technique and the other is based on power allocation. The SDR-based algorithm has more extensive applicability, while the power-allocation-based algorithm offers higher efficiency. The simulation results show the efficacy of the proposed algorithms.
Adaptive Repetition Scheme with Machine Learning for 3GPP NB-IoT

In NB-IoT systems, UEs with poor signal quality employ more repetitions to compensate for additional signal attenuation. Excessively high CE levels and repetitions of UEs lead to wastage of valuable wireless resources, whereas inadequate CE levels and repetitions result in data retrieval failure at the receiving end. Therefore, a machine learning-based adaptive repetition scheme for a 3GPP NB-IoT system is proposed in this work to effectively improve overall network transmission efficiency. The results of simulation show the effect of the discount factor? on the convergence behavior of the proposed scheme, with a lower discount factor value denoting the myopic behavior of the proposed scheme, which results from the fact that it places more emphasis on immediate rewards. And the propose scheme is capable of effectively improving the average spectral efficiency.
Solving View Selection Problem to Query Plan Using Meta Heuristic Methods

Selecting views to materialize is one of the most important decisions in designing a data warehouse. Decision support systems usually use complex queries on large databases. Because responding time should be small, therefore query plan is very important. In this paper, we submit four advances in view materialization. First, a more robust optimization function, Minimum View Selection Problem, and data cube lattices as well as multi-view processing plan (MVPP) are formalized. The decreased View Selection Problem allows for multiple querying nodes, partial and full materialization, and data constriction. The contribution of the research is to select an appropriate set of views that minimize total query response time and the cost of selected views. The traditional method ineffective, using Polynomial Greedy Algorithm (PGA) and genetic algorithm (GA), views of the Data Warehouse can be optimized . :
A Game-Theoretic Learning Approach for Anti-Jamming Dynamic Spectrum Access in Dense Wireless Networks

In this paper, we investigate the anti-jamming channel selection problem for interference mitigation (IM) based dense wireless networks in dynamic environment, in which the active user set is variable due to their specific traffic demands. We jointly consider the mutual interference among users and external jamming in IM-based dense wireless networks, and propose a generalized maximum protocol interference and jamming model to accurately capture the mutual interference and external jamming. Then, the anti-jamming channel selection problem is formulated as an anti-jamming dynamic game, and subsequently it is proved to be an exact potential game, which has at least one pure strategy Nash equilibrium (NE). Based on the stochastic learning theory, a distributed anti-jamming channel selection algorithm (DACSA) is proposed to find the NE solution. Moreover, the simulation results are presented to demonstrate the effectiveness of the proposed DACSA algorithm.
Why You Should Listen to This Song: Reason Generation for Explainable Recommendation

Explainable recommendation, which makes a user aware of why such items are recommended has received a lot of attention as a highly practical research topic. The goal of our research is to make the users feel as if they are receiving recommendations from their friends. To this end, we formulate a new challenging problem called reason generation for explainable recommendation in conversation applications, and propose a solution that generates a natural language explanation of the reason for recommending an item to that particular user. Evaluation with manual assessments indicates that our generated reasons are relevant to songs and personalized to users. They are also fluent and easy to understand. A large-scale online experiments show that our method outperforms manually selected reasons by 8.2% in terms of click-through rate.
Serum Interleukin-6 is Not Linked with Sleep-Quality, Restless Legs Syndrome, and Depression, But with 6-Month Survival in Hematological Malignancies

ABSTRACT Background: Serum interleukin (IL)-6 has been found to be associated with sleep quality, mood, and survival in patients with solid tumors. Results in these studies were confounded by knowledge of diagnosis to study subjects. Moreover, such data among subjects with hematological malignancies and data regarding restless legs syndrome is limited. The present study was, therefore, conducted to assess the sleep quality, depression, and restless leg syndrome in hematological malignancies and to study if there is any role of IL6 associated with it. Methods: Sixty-six subjects having hematological malignancy were included in this study after excluding the potential confounders. Sleep quality was examined using Pittsburg Sleep Quality Index, depression by the Patient Health Questionnaire-9. Diagnosis of RLS was made through clinical examination. Serum for measurement of IL-6 was collected at baseline and after 1 month of initiation of chemotherapy. Patients were followed up for 6 months. Results: Average age of study subjects was 50.16 years with male predominance. Nearly 22.7% had clinical depression, 28.8% had poor quality sleep, and restless legs syndrome (RLS) was reported in 6.1% cases. Nearly 22.7% patients died at 6 months. Disturbed sleep at baseline was associated with depression (odds ratio [OR] =7.89) and poor 6 months survival. Serum IL-6 did not show any association with sleep quality, restless-legs-syndrome, and depression. However, baseline high level of serum IL-6 (OR = 26.06) and low level after chemotherapy (OR = 0.03) were associated with poor survival at 6 months. Conclusion: Poor quality sleep, depression, and RLS are prevalent among adult subjects with hematological malignancies. Sleep disturbance, high pretreatment inflammatory and lowering of inflammatory load after chemotherapy increase likelihood for poor prognosis. Serum IL-6 did not show any association with sleep quality, restless legs syndrome and depression.