Miracle-dz/UniFAR-FTUs huggingface dataset:
Backseat Teleoperator: Affective Feedback with On-Screen Agents to Influence Teleoperation

We investigate if an on-screen agent that reacts to a teleoperator's driving performance (e.g., by showing fear during poor driving) can influence teleoperation. Serving as a kind of virtual passenger, we explore if and how this agent's reactions may impact teleoperation. Our design concept is to create an emotional response in the operator (e.g., to feel bad for the agent), with the ultimate goal of shaping driving behavior (e.g., to slow down to calm the agent). We designed and implemented two proof-of-concept agent personas that react differently to operator driving. By conducting an initial proof-of-concept study comparing our agents to a base case, we were able to observe the impact of our agent personas on operator experience, perception of the robot, and driving behavior. While our results failed to find compelling evidence of changed teleoperator behavior, we did demonstrate that emotional on-screen agents can alter teleoperator emotion. Our initial results support the plausibility of passenger agents for impacting teleoperation, and highlight potential for more targeted, ongoing work in applying social techniques to teleoperation interfaces.
Wide Bandgap Devices in AC Electric Drives: Opportunities and Challenges

Wide bandgap (WBG) device-based power electronics converters are more efficient and lightweight than silicon-based converters. WBG devices are an enabling technology for many motor drive applications and new classes of compact and efficient motors. This paper reviews the potential applications and advances enabled by WBG devices in ac motor drives. Industrial motor drive products using WBG devices are reviewed, and the benefits are highlighted. This paper also discusses the technical challenges, converter design considerations, and design tradeoffs in realizing the full potential of WBG devices in motor drives. There is a tradeoff between high switching frequency and other issues such as high dv/dt and electromagnetic interference. The problems of high common mode currents and bearing and insulation damage, which are caused by high dv/dt, and the reliability of WBG devices are discussed.
Quality of Experience from Cache Hierarchies: Keep your low-bitrate close, and high-bitrate closer

Recent studies into streaming media delivery suggest that performance gains from cache hierarchies such as Information-Centric Networks (ICNs) may be negated by Dynamic Adaptive Streaming (DAS), the de facto method for retrieving multimedia content. The bitrate adaptation mechanisms that drive video streaming clash with caching hierarchies in ways that affect users' Quality of Experience (QoE). Cache performance also diminishes as consumers dynamically select content encoded at different bitrates. In this paper we use the evidence to draw a novel insight: in a cache hierarchy for adaptive streaming content, bitrates should be prioritized over or alongside popularity and hit rates. We build on this insight to propose RippleCache as a family of cache placement schemes that safeguard high-bitrate content at the edge and push low-bitrate content into the network core. Doing so reduces contention of cache resources, as well as congestion in the network. To validate RippleCache claims we construct two separate implementations. We design RippleClassic as a benchmark solution that optimizes content placement by maximizing a measure for cache hierarchies shown to have high correlation with QoE. In addition, our lighter-weight RippleFinder is then re-designed with distributed execution for application in large-scale systems. RippleCache performance gains are reinforced by evaluations in NS-3 against state-of-the-art baseline approaches, using standard measures of QoE as defined by the DASH Industry Forum. Measurements show that RippleClassic and RippleFinder deliver content that suffers less oscillation and rebuffering, as well as the highest levels of video quality, indicating overall improvements to QoE.
Evaluation of the Use of Streaming Graph Processing Algorithms for Road Congestion Detection

Real-time road congestion detection allows improving traffic safety and route planning. In this work, we propose to use streaming graph processing algorithms for road congestion detection and evaluate their accuracy and performance. We represent road infrastructure sensors in the form of a directed weighted graph and adapt the Connected Components algorithm and some existing graph processing algorithms, originally used for community detection in social network graphs, for the task of road congestion detection. In our approach, we detect Connected Components or communities of sensors with similarly weighted edges that reflect different states in the traffic, e.g., free flow or congested state, in regions covered by detected sensor groups. We have adapted and implemented the Connected Components and community detection algorithms for detecting groups in the weighted sensor graphs in batch and streaming manner. We evaluate our approach by building and processing the road infrastructure sensor graph for Stockholm's highways using real-world data from the Motorway Control System operated by the Swedish traffic authority. Our results indicate that the Connected Components and DenGraph community detection algorithms can detect congestion with accuracy up to ? 94% for Connected Components and up to ? 88% for DenGraph. The Louvain Modularity algorithm for community detection fails to detect congestion regions for sparsely connected graphs, representing roads that we have considered in this study. The Hierarchical Clustering algorithm using speed and density readings is able to detect congestion without details, such as shockwaves.
Review of Automated Systems for Upper Limbs Functional Assessment in Neurorehabilitation

Traditionally, the assessment of upper limb (UL) motor function in neurorehabilitation is carried out by clinicians using standard clinical tests for objective evaluation, but which could be influenced by the clinician’s subjectivity or expertise. The automation of such traditional outcome measures (tests) is an interesting and emerging field in neurorehabilitation. In this paper, a systematic review of systems focused on automation of traditional tests for assessment of UL motor function used in neurological rehabilitation is presented. A systematic search and review of related articles in the literature were conducted. The chosen works were analyzed according to the automation level, the data acquisition systems, the outcome generation method, and the focus of assessment. Finally, a series of technical requirements, guidelines, and challenges that must be considered when designing and implementing fully-automated systems for upper extremity functional assessment are summarized. This paper advocates the use of automated assessment systems (AAS) to build a rehabilitation framework that is more autonomous and objective.
Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems

Social recommendation leverages social information to solve data sparsity and cold-start problems in traditional collaborative filtering methods. However, most existing models assume that social effects from friend users are static and under the forms of constant weights or fixed constraints. To relax this strong assumption, in this paper, we propose dual graph attention networks to collaboratively learn representations for two-fold social effects, where one is modeled by a user-specific attention weight and the other is modeled by a dynamic and context-aware attention weight. We also extend the social effects in user domain to item domain, so that information from related items can be leveraged to further alleviate the data sparsity problem. Furthermore, considering that different social effects in two domains could interact with each other and jointly influence users' preferences for items, we propose a new policy-based fusion strategy based on contextual multi-armed bandit to weigh interactions of various social effects. Experiments on one benchmark dataset and a commercial dataset verify the efficacy of the key components in our model. The results show that our model achieves great improvement for recommendation accuracy compared with other state-of-the-art social recommendation methods.
Elimination of Failures in Railway Gearboxes by Regenerative Coasting

Railway gearboxes are subjected to vibrations arising from gear meshing, roller bearings forces and wheel-rail contact. This paper shows how peculiar service conditions made the application of a well known and perfectly working gearbox installed on a metro vehicle rather critical. After a long investigation, the root cause of the problem was found in running conditions at top speed, which happened for long distances in coasting, being a rather long section of the line made of quite distant stations on a mild slope line, a situation that that kind of vehicle never encountered in other lines. Remedies are discussed with reference to mechanical and electric features of the vehicle. The adoption of a new procedure, called “regenerative coasting” allowed to potentially solve the problem of premature failures in service.
PINE: Optimizing Performance Isolation in Container Environments

With the development of virtualization technologies, containers are widely used to provide a light-weight isolated runtime environment. Compared with virtual machines, containers can achieve high-resource utilization and provide a more convenient way of sharing, but there are significant security challenges due to potential resource contention among services. When the regular services that share the storage system with the key services are over-used, a lot of resources may be preempted, which breakdowns the whole system and delays other services. This paper addressed the performance isolation in fierce resource competition situations, that is, when different services (latency-sensitive services and throughput-first services) are deployed on a host machine using container technology. We proposed PINE, a performance isolation optimization solution in container environments, which can adaptively allocate the storage resource for each service according to their performance behaviors through dynamical resource management and I/O concurrency configuration. The experimental results show that PINE can effectively optimize the performance of the running services and achieve the optimal result in a relatively short time.
Long-range interaction induced collective dynamical behaviors

Long-range interacting systems are omnipresent in nature. We investigate here the collective dynamical behavior in a long-range interacting system consisting of coupled Stuart–Landau limit cycle oscillators. In particular, we analyze the impact of a repulsive coupling along with a symmetry breaking coupling. We report that the addition of repulsive coupling of sufficient strength can induce a swing of the synchronized state which will start disappearing with increasing disorder as a function of the repulsive coupling. We also deduce analytical stability conditions for the oscillatory states including synchronized state, solitary state, two-cluster state as well as oscillation death state. Finally, we have also analyzed the effect of power-law exponent on the observed dynamical states.
Residual Pyramid Learning for Single-Shot Semantic Segmentation

Pixel-level semantic segmentation is a challenging task with a huge amount of computation, especially if the input sizes are large. In the segmentation network, apart from the pyramid backbone network, an extra decoder network is often employed to recover the spatial detail information. In this paper, we put forward a method for single-shot segmentation in a feature residual pyramid network (RPNet), which learns the coarse results and residuals of segmentations by decomposing the label at different levels of residual blocks. Specifically speaking, we use the residual features to learn the edges and details, and we also use the top-level feature to learn the coarse segmentation result. At the testing phase, the predicted residuals are used to enhance the details of the coarse segmentation result. Residual learning blocks split the network into several shallow sub-networks by level-wise training, which facilitates the gradient propagation in the RPNet. We then evaluate the proposed method and compare it with the recent state-of-the-art methods on CamVid and Cityscapes datasets. The proposed single-shot segmentation based on the RPNet achieves impressive results with high efficiency on the pixel-level segmentation task.
Fisher: An Efficient Container Load Prediction Model with Deep Neural Network in Clouds

Recently, more and more applications have been deployed in container, and prediction of container load is essential in Clouds for improving resource utilization and achieving service-level agreements. However, accurate prediction of container load in Clouds remains an extremely challenge because the container load fluctuates drastically at small timescales. Furthermore, with many metrics of container in Clouds, it is hard to find which metrics are going to be useful. To address these challenges, we design an efficient container load prediction model named Fisher to improve the accuracy and efficiency of prediction. It mainly includes two modules: a metrics selection module and a neural network training module. We first selects relevant metrics by metrics selection module which is a novel algorithm for shape-based time-series clustering. Afterwards, we apply a powerful deep neural network model built with bidirectional long short-term memory to predict actual load one-step-ahead. We evaluate Fisher using a 30-day load trace from a data center with 500 containers. Experiments show that Fisher can reduce training metrics while maintaining prediction accuracy. More importantly, our model significantly improves prediction accuracy over 50\% compared to other state-of-the-art methods based on auto regressive integrated moving-average and long short-term memory.
Examining the Extent of Self-Regulatory Strategy Use and Writing Competence of Iranian EFL Learners

In recent years, many L2 writing scholars have extensively explored the writers’ composing processes and the particular strategies they use for this purpose. In the same regard, the present study intended to examine the relationship between self-regulatory strategy use and writing competence of Iranian EFL learners. A total of 125 BA level students of English Language and Literature and English Language Teaching from three State Universities in Iran participated in the study and the necessary data were collected by administering an argumentative essay prompt, as a measure of the students’ writing competence, and a self-regulatory strategy use in writing. The results of correlation and multiple regression analyses indicated that there was a low level of relationship between these two constructs and none of the sub-scales in self-regulatory scale could best predict students’ writing competence, respectively. The selfregulatory strategy uses of more-and less-skilled student writers were also compared, but the results of independent samples t-test indicated that there was no significant difference between these learners in this regard. On the whole, the findings of present study revealed that Iranian EFL learners did not have an adequate command of writing strategies and were not able to use these mental resources while composing their texts, which is mostly the result of conducting writing classes in traditional modes and focusing upon product-oriented approaches. Therefore, due to the significance of strategy use in learning, EFL teachers must engage in effective efforts to develop their students’ self-regulated strategies with strategy training embedded in their writing courses.
BitSplit-Net: Multi-bit Deep Neural Network with Bitwise Activation Function

Significant computational cost and memory requirements for deep neural networks (DNNs) make it difficult to utilize DNNs in resource-constrained environments. Binary neural network (BNN), which uses binary weights and binary activations, has been gaining interests for its hardware-friendly characteristics and minimal resource requirement. However, BNN usually suffers from accuracy degradation. In this paper, we introduce "BitSplit-Net", a neural network which maintains the hardware-friendly characteristics of BNN while improving accuracy by using multi-bit precision. In BitSplit-Net, each bit of multi-bit activations propagates independently throughout the network before being merged at the end of the network. Thus, each bit path of the BitSplit-Net resembles BNN and hardware friendly features of BNN, such as bitwise binary activation function, are preserved in our scheme. We demonstrate that the BitSplit version of LeNet-5, VGG-9, AlexNet, and ResNet-18 can be trained to have similar classification accuracy at a lower computational cost compared to conventional multi-bit networks with low bit precision (<= 4-bit). We further evaluate BitSplit-Net on GPU with custom CUDA kernel, showing that BitSplit-Net can achieve better hardware performance in comparison to conventional multi-bit networks.
The Interconnection of Sustainability and Collaborative Consumption

Collaborative consumption is a recently emerged consumption pattern based on the practice of sharing, swapping, lending, bartering and other similar modes of exchange, practiced instead of the conventional way of buying products and services. The unique character of this consumption practice suggests a shift in the idea of private ownership, where accessing products becomes more important than owning them. The scope of this thesis examines the factors influencing the emergence of these initiatives in relation to sustainability and attempts to recognize the possible changes occurring towards the perception of private ownership. For this purpose a qualitative case study approach examines the concept of clothing libraries in five cities across Sweden.
Healthy or Not: A Way to Predict Ecosystem Health in GitHub

With the development of open source community, through the interaction of developers, the collaborative development of software, and the sharing of software tools, the formation of open source software ecosystem has matured. Natural ecosystems provide ecological services on which human beings depend. Maintaining a healthy natural ecosystem is a necessity for the sustainable development of mankind. Similarly, maintaining a healthy ecosystem of open source software is also a prerequisite for the sustainable development of open source communities, such as GitHub. This paper takes GitHub as an example to analyze the health condition of open source ecosystem and, also, it is a research area in Symmetry. Firstly, the paper presents the healthy definition of GitHub open source ecosystem health and, then, according to the main components of natural ecosystem health, the paper proposes the health indicators and health indicators evaluation method. Based on the above, the GitHub ecosystem health prediction method is proposed. By analyzing the projects and data collected in GitHub, it is found that, using the proposed evaluation indicators and method, we can analyze the healthy development trend of the GitHub ecosystem and contribute to the stability of ecosystem development.
StartNet: Online Detection of Action Start in Untrimmed Videos

We propose StartNet to address Online Detection of Action Start (ODAS) where action starts and their associated categories are detected in untrimmed, streaming videos. Previous methods aim to localize action starts by learning feature representations that can directly separate the start point from its preceding background. It is challenging due to the subtle appearance difference near the action starts and the lack of training data. Instead, StartNet decomposes ODAS into two stages: action classification (using ClsNet) and start point localization (using LocNet). ClsNet focuses on per-frame labeling and predicts action score distributions online. Based on the predicted action scores of the past and current frames, LocNet conducts class-agnostic start detection by optimizing long-term localization rewards using policy gradient methods. The proposed framework is validated on two large-scale datasets, THUMOS’14 and ActivityNet. The experimental results show that StartNet significantly outperforms the state-of-the-art by 15%-30% p-mAP under the offset tolerance of 1-10 seconds on THUMOS’14, and achieves comparable performance on ActivityNet with 10 times smaller time offset.
Artificial gauge fields and optical flux lattices

The area of quantum simulation with ultracold atoms is a constantly evolving new area of research. Advancements are happening at rapid rate and many research lines are being currently explored. The behavior of charged particles in a magnetic field leads to many interesting physical phenomena. The limited tunability of the environment where such phenomena occur has lead to an interest in simulating gauge fields through the manipulation an ultracold atomic gas in different ways. One particular method that has successfully been implemented is the accumulation of a geometrical phase. The following literature thesis will give a brief introduction into how charged particles behave in the presence of a homogeneous magnetic field, and then continue on to discuss one of the ways this can be simulated in an ultracold quantum gas experiment through optically dressed states. Finally, this thesis will conclude with a discussion of a recent theoretical proposal for simulating a magnetic flux in ultra cold neutral atoms known as an optical flux lattice.
Potential Roles of Purkinje Fibers in Ischemia-Induced Arrhythmias

The fast conduction system, in particular the His-PurkinJe-System (HPS), is a key element for coordinated electrical activation of the heart. However, it is often omitted in computational studies. We hypothesized that the inclusion of the HPS is necessary when investigating arrhythmia maintenance and termination in an ischemic heart. We used a computational model of regionally-ischemic human ventricles reconstructed from magnetic resonance imaging data, and combined this with a rule-based HPS that produced a realistic activation pattern. Simulations using a high-frequency pacing protocol showed that re-entrant waves through the ischemic region may retrogradely activate the HPS, leading to self-terminating ventricular tachycardia (VT). Simulations without the HPS maintained the ischemia-induced VT, highlighting the role of the HPS in arrhythmia termination. Optical mapping recordings from isolated Langendorf-perfused rabbit hearts during regional ischemia and ischemia-reperfusion are compatible with the conclusions from the in-silico model, showing patterns of re-entry and termination that may be generated from retrograde HPS conduction.
Gender Differences in Hemodynamic Regulation and Cardiovascular Adaptations to Dynamic Exercise

Exercise is a major challenge for cardiovascular apparatus since it recruits chronotropic, inotropic, pre-load, and afterload reserves. Regular physical training induces several physiological adaptations leading to an increase in both cardiac volume and mass. It appears that several gender-related physiological and morphological differences exist in the cardiovascular adjustments and adaptations to dynamic exercise in humans. In this respect, gender may be important in determining these adjustments and adaptations to dynamic exercise due to genetic, endocrine, and body composition differences between sexes. Females seem to have a reduced vasoconstriction and a lower vascular resistance in comparison to males, especially after exercise. Significant differences exist also in the cardiovascular adaptations to physical training, with trained women showing smaller cardiac volume and wall thickness compared with male athletes. In this review, we summarize these differences.