Miracle-dz/UniFAR-FTUs huggingface dataset:
Downlink Multi-User MIMO Transmission for Radiation Pattern Reconfigurable Antenna Systems

MU-MIMO transmission relies on exploiting multi-user diversity among a multitude of users. In this paper, to enhance MU-MIMO transmission, techniques for designing and exploiting reconfigurable antennas (RAs) are developed. A single-element RA is capable of generating modes with different radiation pattern and polarization states resulting in multiple different channel states for each user. This capability expands the search space for user-pairing optimization. To reduce complexity, an iterative user and antenna mode selection algorithm is proposed. Based on parasitic tuning, an RA optimized for MU-MIMO transmission and capable of creating four pattern types is designed using a genetic algorithm and full-wave electromagnetic analyses. Pattern- and channel-aware mode-set generation methods are developed, where mode groups are determined to reduce channel estimation overhead and mode selection complexity while achieving superior performance. Simulation results indicate that the proposed schemes need a lower number of users and/or relax user selection requirements to benefit from MU-MIMO transmissions. It is also seen that more legacy antennas are needed to achieve similar MU-MIMO performance provided by the RA system. Simulations show up to 16-dB signal-to-noise ratio gains for a single cell with 12 users, where the base station has four RAs and four single-antenna users are simultaneously served via MU-MIMO transmission.
Trauma-Related Shame, Sexual Assault Severity, Thwarted Belongingness, and Perceived Burdensomeness Among Female Undergraduate Survivors of Sexual Assault

Background and Aim: This study evaluated trauma-related shame as a mediator of the association between sexual assault severity and perceived burdensomeness and thwarted belongingness. Method: A total of 164 female undergraduates who reported attempted or completed sexual assault completed self-report measures of sexual assault, trauma-related shame, perceived burdensomeness, and thwarted belongingness. Results: Using path analysis, trauma-related shame mediated the association between sexual assault severity and perceived burdensomeness, and between sexual assault severity and thwarted belongingness. Limitations: The findings of this study are limited by the retrospective, self-report, and cross-sectional nature of these data, and do not allow for causal inference. Conclusion: Trauma-related shame warrants additional investigation as a mechanism that explains the association between sexual assault and psychosocial risk factors for suicidal ideation and behavior.
High Quality Depth Estimation from Monocular Images Based on Depth Prediction and Enhancement Sub-Networks

This paper addresses the problem of depth estimation from a single RGB image. Previous methods mainly focus on the problems of depth prediction accuracy and output depth resolution, but seldom of them can tackle these two problems well. Here, we present a novel depth estimation framework based on deep convolutional neural network (CNN) to learn the mapping between monocular images and depth maps. The proposed architecture can be divided into two components, i.e., depth prediction and depth enhancement sub-networks. We first design a depth prediction network based on the ResNet architecture to infer the scene depth from color image. Then, a depth enhancement network is concatenated to the end of the depth prediction network to obtain a high resolution depth map. Experimental results show that the proposed method outperforms other methods on benchmark RGB-D datasets and achieves state-of-the-art performance.
Development of a Soft Exosuit for Industriale Applications

Wearable robotic devices and exoskeletons, that assist human beings in physically-demanding tasks have the potential to both increase productivity and reduce the risk of musculoskeletal disorders. Soft exoskeletons, known as exosuit, provide improved portability and fit. While many exosuits are populating the market to support light weights, very few are powerful enough to reinforce workers in lifting heavy loads. Adopting the advantages of novel soft-robotic principles, we propose a voice-controlled upper limb exosuit designed to aid its user in lifting up to 10kg per arm. The exosuit uses a wire-driven mechanism to transmit power from a proximally-located actuation stage to the shoulder and elbow. Forces are transmitted to the human body via soft, textile-based components. We evaluate the impact of the device on the muscular effort of a wearer in both a lifting and a holding task. Holding a weight of 14kg with the exosuit results in an average reduction in muscular effort of the biceps brachii and anterior deltoid of 50% and 68%, respectively. Similarly, lifting a weight of 7kg with the exosuit reduces the muscular activity of the same two muscles by 23.4% and 41.2%, respectively.
Contact-Resistive Sensing of Touch and Airflow Using A Rat Whisker

Rats rely heavily on tactile information from their whiskers to acquire information about their surroundings. A whisker has no sensors along its length. Instead, mechanical deformation of the whisker is sensed via receptors at its base. The present study introduces a micro-sensor developed specifically to imitate the sensing of biological rat whiskers. The sensor responds to bending moments resulting from touch and/or airflow in two axes. The sensor was designed based on analytical models from cantilever beam theory, and the models were validated with finite-element analysis. Sensors were then fabricated using micro-milled molds and integrated into an Arduino-based circuit for simple signal acquisition. The present work begins to develop the technology to allow investigation of important engineering aspects of the rat vibrissal system at Ix scale. In addition to its potential use in novel engineering applications, the sensor could aid neuroscientists in their understanding of the rat vibrissal-trigeminal pathway.
D2D-Based Vehicular Communication With Delayed CSI Feedback

Device-to-device (D2D) communications can combine the superiorities of both the cellular network and the Ad hoc network, and it is an efficient way to improve the capacity of short distance communication. D2D communications underlaying a cellular network can support vehicle-to-vehicle (V2V) communications. Channel state information (CSI) is crucial to the performance optimization of network. However, it is difficult to obtain the accurate CSI in the highly mobile vehicular environment. CSI of vehicular links is reported to the BS periodically. The delayed CSI feedback exists in the links of V2V and vehicle-to-CUE. The delayed message and the dynamic environment in the V2V network result in the uncertainty of channel gain. In order to improve the reliability of transmission, the vehicular delay time and the speed information are considered in the channel model. The sum-rate maximization problem under uncertainty constrains is formulated, and successive convex approximation is adopted to make the non-convex problem solvable. Two different forms of uncertain channel model are analyzed, and they are converted to the same form of optimization problem. The robust power allocation algorithm is given. The simulation results show the feasibility of the proposed scheme.
Spirituality and Social Media: The Search for Support Among Black Men Who Have Sex With Men in Tennessee

ABSTRACT Young Black Men Who Have Sex With Men (BMSM) have been the subject of much research focused on health disparities in HIV occurrence, stigma, and mental health. Although such research is important, fewer studies focus on other equally salient areas of their lives such as spirituality, religious practices, and social support. Informed by literature on social support, this research endeavors to better understand these dynamics for a group of young BMSM who reside in a metropolitan city in Tennessee. Focus group results and content analysis uncover themes related to religion and resilience; queering Christianity; and virtual spirituality. Participants expressed an overarching need for support, safe spaces, genuine relationships, and godly instruction, conveyed through traditional Black Church involvement and nontraditional Internet usage. Findings are important for strategic, proactive, cross-generational collaboration with young BMSM to holistically meet their varied needs.
Learning Tensor Latent Features

We study the problem of learning latent feature models (LFMs) for tensor data commonly observed in science and engineering such as hyperspectral imagery. However, the problem is challenging not only due to the non-convex formulation, the combinatorial nature of the constraints in LFMs, but also the high-order correlations in the data. In this work, we formulate a tensor latent feature learning problem by representing the data as a mixture of high-order latent features and binary codes, which are memory efficient and easy to interpret. To make the learning tractable, we propose a novel optimization procedure, Binary matching pursuit (BMP), that iteratively searches for binary bases via a MAXCUT-like boolean quadratic solver. Such a procedure is guaranteed to achieve an? suboptimal solution in O($1/\epsilon$) greedy steps, resulting in a trade-off between accuracy and sparsity. When evaluated on both synthetic and real datasets, our experiments show superior performance over baseline methods.
Maximizing system utilization via parallelism management for co-located parallel applications

With an increasing number of cores and memory controllers in multiprocessor platforms, co-location of parallel applications is gaining on importance. Key to achieve good performance is allocating the proper number of threads to co-located applications. This paper presents NuPoCo, a framework for automatically managing parallelism of co-located parallel applications on NUMA multi-socket multi-core systems. NuPoCo maximizes the utilization of CPU cores and memory controllers by dynamically adjusting the number of threads for co-located parallel applications. Evaluated with various scenarios of co-located OpenMP applications on a 64-core AMD and a 72-core Intel machine, NuPoCo achieves a reduction of the total turnaround time by 10-20% compared to the default Linux scheduler and an existing parallelism management policy focusing on CPU utilization only.
SDN Controller Placement at the Edge: Optimizing Delay and Overheads

Fog architectures at the network edge are becoming a popular research trend to provide elastic resources and services to end-users, where the processing capacity resides at the network periphery as opposed to traditional data-centers. Despite their momentum, the control plane of these architectures remains complex and challenging to implement. To enhance control capability, in this work, we propose to use Software Defined Networking. SDN moves the control logic off data plane devices and onto external network entities, the controllers. We provide a proof-of-concept implementation of a multi-controller edge system and measure traffic delay and overheads. The results reveal the sensitivity of delay to the location of controllers and the magnitude of inter-controller and controller-node overheads. Guided by the above, we model the problem of determining the placement of controllers in the edge network. Using linearization and supermodular function techniques, we present approximation solutions which perform close to optimal and better than state-of-the-art methods.
Wave Excitation Force Estimation for Wave Energy Converters of the Point-Absorber Type

Advanced control strategies play a crucial role in increasing the energy extraction capacity of wave energy converters (WECs). So far, the most promising control schemes have predominantly been studied in simulation, based on the idealized assumption of the wave excitation force availability in real time. In practical WEC implementations, this is not the case, since this force cannot be measured directly when the WECs are running. Hence, the force has to be estimated via the measurement of other quantities. Two approaches are presented in this brief to fulfill this objective. The first approach is based on a Kalman filter coupled with a random-walk model of the wave excitation force, while a receding horizon—unknown input estimation approach—is employed for the second one. The proposed estimation methods are evaluated by using real measurements from a laboratory scale WEC.
A portable, automatic data qantizer for deep neural networks

With the proliferation of AI-based applications and services, there are strong demands for efficient processing of deep neural networks (DNNs). DNNs are known to be both compute-and memory-intensive as they require a tremendous amount of computation and large memory space. Quantization is a popular technique to boost efficiency of DNNs by representing a number with fewer bits, hence reducing both computational strength and memory footprint. However, it is a difficult task to find an optimal number representation for a DNN due to a combinatorial explosion in feasible number representations with varying bit widths, which is only exacerbated by layer-wise optimization. Besides, existing quantization techniques often target a specific DNN framework and/or hardware platform, lacking portability across various execution environments. To address this, we propose libnumber, a portable, automatic quantization framework for DNNs. By introducing Number abstract data type (ADT), libnumber encapsulates the internal representation of a number from the user. Then the auto-tuner of libnumber finds a compact representation (type, bit width, and bias) for the number that minimizes the user-supplied objective function, while satisfying the accuracy constraint. Thus, libnumber effectively separates the concern of developing an effective DNN model from low-level optimization of number representation. Our evaluation using eleven DNN models on two DNN frameworks targeting an FPGA platform demonstrates over 8× (7×) reduction in the parameter size on average when up to 7% (1%) loss of relative accuracy is tolerable, with a maximum reduction of 16×, compared to the baseline using 32-bit floating-point numbers. This leads to an geomean speedup of 3.79× with a maximum speedup of 12.77× over the baseline, while requiring only minimal programmer effort.
Social inclusion of people with intellectual disabilities: the impact of place of residence

Abstract Objectives: People with intellectual disabilities are increasingly living in more domestic style accommodation, either in housing provided within a specialised campus setting or in ordinary houses in community settings. The main objective of the study was to determine if the extent of residents' involvement with their families and with the local community varied when they resided in campus settings (n = 55) or community housing (n = 51) and to investigate the main predictors of this involvement. Method: With the resident's permission, their key-workers – mainly nurses – completed standard questionnaires that covered resident characteristics, contact with families and a range of life experiences. Results: Although the type of accommodation did have a significant effect on residents' social inclusion in families and communities, the best predictor of this was the individual's level of dependency in personal self-care. Those who were more dependent tended to be more excluded. Conclusions: Staff working with more dependent residents need to proactively promote their social inclusion although this could be harder to achieve for those living in campus style settings.
Factors Affecting Corporate Happiness within Technology-Based Firms in Andalucia

Studies on corporate happiness have emerged as a new approach to management and productivity. The theory around this leadership focus could change the way firms arrange their own business objectives and goals. In this paper, happiness, as a construct, is analyzed from an economical and entrepreneurial perspective. The goal is to determine if happiness influences productivity and work environments in technology-based firms (TBF) in Andalucia, Spain. A survey, completed by 41 employees, was elaborated on and applied to a group of employees in TBF, based in Sevilla. The results confirm that a happier environment is present in TBF than in non-technological firms. This is due to the strategies applied in TBF to improve happiness and wellness perception in their employees and the influence in productivity and, therefore, in organizational competitiveness.
On Context-Aware DDoS Attacks Using Deep Generative Networks

Distributed Denial of Service (DDoS) attacks continue to be one of the most severe threats in the Internet. The intrinsic challenge in preventing DDoS attacks is to distinguish them from legitimate flash crowds since two have many traffic characteristics in common. Today most DDoS detection techniques focus on finding parametric differences between the patterns in attack and legitimate traffic. However, such techniques are very sensitive to the threshold values set on the parameters and more importantly legitimate traffic features might be mimicked by smart attackers to generate requests that look like flash crowds. In this paper, we propose a framework for training networks for such smart attacks. Our framework is based on Deep Generative Network models and our contributions are two-fold.We first show that legitimate traffic features can be mimicked without explicitly modeling their distributions. Second, we introduce the concept of context-aware DDoS attacks. We show that an attacker can generate traffic that looks similar to flash crowds to be undetected for long periods of time. However, the ability of generating such attacks is constrained by the budget of the attacker. A context-aware attacker is the one that can intelligently use its budget to maximize the damage in the victim network. Our study provides a framework for training networks for such DDoS attack scenarios.
Scene-Aware Soccer Video QoE Assessment - A Compressed-Domain Approach

The small screen of mobile devices and bandwidth limitations of communication networks greatly affect users' quality of experience (QoE), especially for soccer video, which is characterized by rapid movement and small objects. In this paper, a Compressed-domain Soccer Video Quality assessment Model (CSVQM) is proposed based on the fact that soccer video includes three distinct scene types, which cause different concerns for viewers. To reduce complexity and operate in real-time, all model parameters are derived from the compressed video stream without resorting to complete video decoding. The validation shows that CSVQM significantly outperforms conventional models in terms of accuracy, consistency, and complexity.
CCT: A Cross-Concat and Temporal Neural Network for Multi-Label Action Unit Detection

Action Unit (AU) detection is essential for facial expression analysis. However, most existing AU detection algorithms only focus on physical features, e.g., temporal feature and AU correlations, without considering various distributions of AUs, i.e., some AUs are quite less than others. In this work, we propose a novel cross-concat and temporal (CCT) neural network, which simultaneously consider physical features and the distribution differences. First, we design a cross-concat block (CCB) to adapt to the various distributions of AUs. CCB is based on the idea of skip connections since skip connections can reuse features from different layers and capture abundant features of AUs even with relatively small-size training samples. Second, LSTM layers are utilized to capture the temporal dependencies and multi-label learning is utilized for capturing AU correlations. Experimental results on three popular AU detection datasets, BP4D, DISFA, and GFT, show that the proposed algorithm outperforms the state-of-the-art ones.
Optimal Placement and Sizing of Distributed Generators to ensure Protection Coordination

Thanks to the mammoth benefits, integration of Distributed Generators(DGs) is becoming attractive in distribution systems. Penetration of DGs changes the unidirectional structure of radial systems, leading to reversal of power flow. However, the existing relays are non-directional and hence operate based on the magnitude of the short circuit current. This leads to mal-operation of the relays if left un-attented to, even though for low penetration levels, the effects are negligible. This work addresses this issue by optimally sizing and siting the DGs in the non-autonomous micro-grids. Hence a huge investment cost needed to replace the existing relays is saved. The optimal sizing is done considering the installation and fuel cost of DGs using the particle swarm optimization algorithm and the optimal siting is done analytically using ETAP software. MATLAB is used to solve the PSO algorithm. Standard 33 bus test system is used to validate the proposed algorithm.
Design and Realization of Forceps With 3-D Force Sensing Capability for Robot-Assisted Surgical System

Force sensing plays an important role in minimally invasive surgery, which enables surgeons to differentiate tissue stiffness and apply appropriate manipulation forces to avoid additional damages. However, due to size constraints, the implementation of force sensing on the surgical instruments is very difficult. In this paper, a novel type of surgical forceps consisting of double grippers is proposed for directly measuring the 3-D pulling force and grasp force at the tips of surgical instrument for the robot-assisted surgical system. For the design of forceps, a new structural decoupling sensor with 2-D force sensing capability is first developed. And then, it is integrated with the common gripper to be a single part. Mathematical models for the force-strain curves are obtained by a surface piecewise fitting method. After a notion produced that 3-D force can be characterized by double low-dimensional force sensing elements, a new 3-D force decoupling algorithm is presented based on the double grippers of the designed forceps. Furthermore, the validity of the design is evaluated by the finite-element analysis. Finally, for the machined prototype, several static calibration experiments and verifications are conducted using weights. Experimental results have shown that the proposed forceps possess higher performance of direct force sensing, and for the range of 2.5 N, the accuracies of X, Y, and Z axes are approximately 0.023 N, 2.2 mN, and 0.093 N, respectively, satisfying the actual requirements.
On the Semidefinite Programming Algorithm for Energy-Based Acoustic Source Localization in Sensor Networks

The received energy has been becoming an efficient and attractive measure for acoustic source localization due to its cost saving in both energy and computation capability. We investigated the acoustic source localization problem based on received energy measurements in sensor networks. Focusing on the non-logarithmic energy attenuation model, we developed and compared a suite of semidefinite programming (SDP)-based source localization methods due to computational efficiency and numerical reliability. First, we proposed a general SDP-based estimator by jointly estimating the source location and the source radiation power. It yields an efficient estimate for both the scenario where the source is located inside the convex hull formed by sensors and the scenario where the source is located outside the convex hull. Next, a min–max approximation is given to cope with the applicable application of the existing energy-based source localization algorithms relying on the Gaussian energy noise assumption. Furthermore, a novel norm approximation method is proposed according to norm equivalence, which can provide a comparable performance with lower computational complexity. Simulations show that our proposed methods exhibit a superior performance than the existing energy-based source localization estimators.