Miracle-dz/UniFAR-FTUs huggingface dataset:
Development and Validation of Dummies and Human Models Used in Crash Test

The crash test dummy, an important tool for car crash safety tests, is of great significance to explore the injury biomechanics of the occupants and improve the safety performance of the vehicle. The article mainly consists of four parts: brief introduction of injury mechanism, early experiments for obtaining biomechanical response (animal tests, cadaver tests, and volunteer tests), and development and validation of mechanical dummies and computational models. This study finds that the current crash test dummies are generally designed based on European and American, so they have limitations on the damage prediction of other regions. Further research in the crash test dummy needs the participation of various countries in order to develop a crash test dummy that meets the national conditions of each country. Simultaneously, it is necessary to develop dummies of vulnerable groups, such as the elderly dummy and obese people dummy.
On uniform exponential ergodicity of Markovian multiclass many-server queues in the Halfin-Whitt regime

We study ergodic properties of Markovian multiclass many-server queues that are uniform over scheduling policies and the size of the system. The system is heavily loaded in the Halfin–Whitt regime, and the scheduling policies are work conserving and preemptive. We provide a unified approach via a Lyapunov function method that establishes Foster–Lyapunov equations for both the limiting diffusion and the prelimit diffusion-scaled queuing processes simultaneously. We first study the limiting controlled diffusion and show that if the spare capacity (safety staffing) parameter is positive, the diffusion is exponentially ergodic uniformly over all stationary Markov controls, and the invariant probability measures have uniform exponential tails. This result is sharp because when there is no abandonment and the spare capacity parameter is negative, the controlled diffusion is transient under any Markov control. In addition, we show that if all the abandonment rates are positive, the invariant probability measures have sub-Gaussian tails regardless whether the spare capacity parameter is positive or negative. Using these results, we proceed to establish the corresponding ergodic properties for the diffusion-scaled queuing processes. In addition to providing a simpler proof of previous results in Gamarnik and Stolyar [Gamarnik D, Stolyar AL (2012) Multiclass multiserver queueing system in the Halfin-Whitt heavy traffic regime: asymptotics of the stationary distribution. Queueing Systems 71(1–2):25–51], we extend these results to multiclass models with renewal arrival processes, albeit under the assumption that the mean residual life functions are bounded. For the Markovian model with Poisson arrivals, we obtain stronger results and show that the convergence to the stationary distribution is at an exponential rate uniformly over all work-conserving stationary Markov scheduling policies.
From theory to practice. A Political Psychologists's Campaing for Office

El autor, un veterano psicologo politico, volvio de la academia a la practica en noviembre de 2002, cuando se presento a las elecciones como candidato a la Camara de Representantes de Maine. Al igual que su oponente republicano compitio bajo el sistema de Clean Election Candidate. Se analiza la estrategia de campana que consistio fundamentalmente en pedir el voto puerta-a-puerta. Los resultados obtenidos se interpretan haciendo un analisis de la composicion de los votantes del distrito (incluyo a 10 comunidades), y del otro candidato. Se plantea el papel importante que el debate de las armas tuvo en el resultado de la campana. A pesar de la inferioridad inicial, 2:1 (Republicanos sobre Democratas) en los ciudadanos registrados, el autor obtuvo el 46% de votos, mejorando asi los resultados de los Democratas en anteriores campanas legislativas del distrito. Un aumento que se atribuye a la amplia campana puerta-a-puerta.
Real-Time Distributed Taxi Ride Sharing

Taxicabs play an important role in urban public transportation. Analyzing taxi traffic of Shanghai, San Francisco, and New York City, we have found that the short trips within city are mostly of commuters during office hours and span a specific city area. Now, if the large number of commuters are ready to share their rides, that will have a huge impact on the ‘super-commute’ problem faced in various cities of USA and around the world. While ride-sharing can increase taxi occupancy and profit for drivers and savings for passengers, it reduces the overall on-road traffic and thereby the average commute time and carbon foot-print. While centralized ride-sharing services, like car-pooling, can address the problem to some extent, they lack scalability and power to dynamically adapt the taxi schedule for best results. In this paper, we propose a four-way model for the ride-sharing problem and develop a novel distributed taxi ride sharing (TRS) algorithm to address dynamic scheduling of ride sharing requests. Our algorithm shows the overall reduction in total distance travelled by taxis as a result of ride sharing. Empirical results using large scale taxi GPS traces from Shanghai, China show that TRS algorithm can grossly outperform a Taxi Distance Minimization (TDM) algorithm. TRS accommodates 33% higher ride share among passengers while dealing with 44,241 requests handled by 4,000 taxis on a single day in Shanghai.
The Multipurpose Application WeChat: A Review on Recent Research

With currently over one billion monthly active users, the Chinese social media and multipurpose application WeChat (, Wēixìn, micro-message) has become one of the world’s most popular social media platforms. Despite its enormous number of users in Asia, WeChat is still not well known in Western countries. Against this background, the present review aims to provide the reader with a comprehensive overview on the functionality of this application, comparison with other popular applications such as Facebook/WhatsApp and previous research. Although WeChat has become an integral part of everyday life for many users, research has only recently begun to examine the impact of this development on the societal and individual levels. The present review summarizes the literature on this topic with a focus on the motives to engage in using the app and potential detrimental effects of excessive use. In the context of the growing popularity and increasing usage times of the app – in particular in Asian countries – future research seems warranted to examine systematically how social media platforms such as WeChat will affect interpersonal communication behavior, well-being, and mental health. The direct comparison of WeChat’s influence on the mentioned variables compared with its competitors Facebook and WhatsApp often used in Western countries will also be of high importance.
Coverage and Rate Analysis of Millimeter Wave NOMA Networks With Beam Misalignment

Non-orthogonal multiple access (NOMA) and millimeter wave (mm-wave) are two key enabling technologies for fifth generation (5G) wireless networks. In this paper, we develop a general performance analysis framework for downlink NOMA transmission in mm-wave networks with spatially random users taking into account link blockages and directional beamforming. To facilitate NOMA transmission in mm-wave networks, we propose an angle-based user pairing strategy, where the base station first randomly selects one user and then pairs it with the line-of-sight user that has the minimum relative angle difference. The proposed strategy increases the probability that both NOMA users are covered by the main lobe created by directional beamforming. To account for the randomness of link blockages and user locations, we consider dynamic user ordering among the paired NOMA users. Tools from stochastic geometry are utilized to derive the coverage probability, outage sum rate, and ergodic sum rate of the proposed NOMA scheme, where beam misalignment at both the base station and users is taken into account. Simulations validate the performance analysis and show that the proposed NOMA scheme achieves a larger coverage probability and higher outage and ergodic sum rates than conventional NOMA with distance-based user pairing and orthogonal multiple access.
A Broadband Multi-Mode Compressive Sensing Current Sensor SoC in 0.16 $\mu$ m CMOS

Broadband current sensors are key components in numerous applications, including power conversion, motor control, and smart-metering. We present a compressive sensing (CS) current sensor system-on-chip (SoC) designed and fabricated in STM 0.16 $\mu \text{m}$ Bipolar-CMOS-DMOS technology. The SoC is capable of measuring currents with amplitudes of up to 10 A peak with a sensing bandwidth of 1 MHz. Two broadband current sensing cores, each consisting of a Hall-effect probe, an AFE, and a 2 MS/s 9-bit ADC, are monolithically integrated together with a digital multi-mode compressive sensing encoder (DCSE) for data-rate reduction. We focus on the evaluation of CS as data compression codec for current sensing applications and on the details of the DCSE architecture designed for general purpose use. We introduce multi-block decoding that is a decoding modality to improve the reconstruction quality of “off-grid sparse” signals commonly occurring in practice. Moreover, we provide measurements of both the compression performance and power consumption of the SoC employed in two exemplary real-world applications; namely, sparse current sensing, and electrical appliance detection for non-intrusive load monitoring based on compressive measurements.
SoftEdgeNet: SDN Based Energy-Efficient Distributed Network Architecture for Edge Computing

The volume of data traffic has increased exponentially due to the explosive growth of IoT devices and the arrival of many new IoT applications. Due to the large volume of data generated from IoT devices, limited bandwidth, high latency, and realtime analysis requirements, the conventional centralized network architecture cannot meet users requirements. Intensive real-time data analysis is one of the major challenges in current state-ofthe- art centralized architectures due to the ubiquitous deployment of different types of sensors. To address the current challenges and adhere to the principles of architectural design, we are proposing a SoftEdgeNet model, which is a novel SDN-based distributed layered network architecture with a blockchain technique for a sustainable edge computing network. At the fog layer, we introduce an SDN-based secure fog node architecture to mitigate security attacks and provide real-time analytics services. We are also proposing a flow rule partition, and allocation algorithm at the edge of the network. The evaluation result shows our proposed model allows for a significant improvement of the interactions in real time data transmission. In terms of the ability to mitigate flooding attacks, the bandwidth is maintained above 9 Mb/s until the attack rates exceed 2000 PPS in the hardware environment and the bandwidth remained almost unchanged in the software environment. In the case of scalability of the proposed model, our proposed algorithm has performed better and proceeded linearly with the increase in traffic volume.
Deep Convolutional Traffic Light Recognition for Automated Driving

Robust traffic light detection and state recognition is of crucial importance on the path to automated vehicles. However, the mere classification of the signaled states does not suffice at complex multi-lane intersections. Rather, a complete understanding of the intersection, but at least the recognition of additional information (like arrows displayed on the traffic lights) is necessary. In this work, we developed a unified deep convolutional traffic light recognition system on the basis of the Faster R-CNN architecture, which is able to not only detect traffic lights and classify their state, but also distinguish their type (circle, straight, left, and right). An in-depth analysis of its performance on the large and diverse DriveU Traffic Light Dataset shows an overall detection performance of 0.92 Average Precision for traffic lights of width greater than 8 px. Additionally, other kinds of traffic lights, e.g. pedestrian lights, have been identified as main cause of false positives. Moreover, we evaluated the usefulness of the developed system to assess the traffic light states for all present driving directions revealing inconsistencies among multiple detections in single images.
Multicasting in Next-Generation Software-Defined Heterogeneous Wireless Networks

Multicasting is an efficient mechanism to disseminate popular multimedia content to multiple interested third generation partnership project (3GPP) cellular users. To further enhance the multicast cellular capacity, non-3GPP WLAN networks can be leveraged to relieve the load of the 3GPP cellular networks. In this paper, we propose the distributed procedure of de-touring the multicast traffic to WLAN in the current LTE and WLAN heterogeneous network architecture. We also propose promising software-defined networking (SDN)-based centralized approach that facilitates multicast content dissemination, bringing efficiency and flexibility to the network. We analyze both the distributed and centralized approaches based on the multicast content popularity. Our analysis reveals that the SDN-based approach in heterogeneous cellular communication and multicasting brings significant radio resource savings. We also conduct packet-level simulation with a real SDN controller and smartphones to confirm the feasibility of the proposed SDN-based approach.
Counterfactuals uncover the modular structure of deep generative models

Deep generative models can emulate the perceptual properties of complex image datasets, providing a latent representation of the data. However, manipulating such representation to perform meaningful and controllable transformations in the data space remains challenging without some form of supervision. While previous work has focused on exploiting statistical independence to disentangle latent factors, we argue that such requirement is too restrictive and propose instead a non-statistical framework that relies on counterfactual manipulations to uncover a modular structure of the network composed of disentangled groups of internal variables. Experiments with a variety of generative models trained on complex image datasets show the obtained modules can be used to design targeted interventions. This opens the way to applications such as computationally efficient style transfer and the automated assessment of robustness to contextual changes in pattern recognition systems.
Criteria and Method for Assessing the Functional State of a Human Operator in a Complex Organizational and Technical System

The most responsible processes in complex automated production are implemented under the control of a human operator. In such conditions, the activity of the human operator is characterized by high psychoemotional stress, which can negatively affect the quality of solvable tasks. Therefore, continuous monitoring of a functional state of the people operating complex devices and equipment is necessary. The traditional methods of assessing the functional state of a person allow one to estimate current state. However, in a complex production environment, this is not enough. A proactive assessment is needed to timely implement alternative management without serious consequences. The article presents the criterion and method for assessing and predicting the functional state of a human operator. The method is based on the concept of a functional state as a measure of adaptive capabilities, introduced by R. Baevsky. But unlike the Baevsky's method, which uses the heart rate variability as an initial information, it is suggested to use the parameters of interrelations in simplified model equations of the cardiovascular system. At the same time, the problem of identification and construction of a model of the cardiovascular system is solved, using two synchronous biosignals: electrocardiograms and photoplethysmograms.
A New Strategy for Neural Emulator Learning Rate Tuning

In the present paper, a real time recurrent learning-based emulator is presented for nonlinear system. The neural emulator is developed with fully connected recurrent neural networks. The instantaneous neural emulator parameters adapt themselves to estimate dynamical behaviors using a starting term. An intuitive and bad choice of this term can affect the emulation performances. To overcome this problem, an adaptation of the emulator learning rate, independently of the starting term, is proposed in this work for nonlinear system. The effectiveness of the offered strategy is illustrated through numerical simulations in comparison with the method based on the starting term. The obtained results using the proposed strategy are far better than those obtained with the last one.
Occupancy Networks: Learning 3D Reconstruction in Function Space

With the advent of deep neural networks, learning-based approaches for 3D reconstruction have gained popularity. However, unlike for images, in 3D there is no canonical representation which is both computationally and memory efficient yet allows for representing high-resolution geometry of arbitrary topology. Many of the state-of-the-art learning-based 3D reconstruction approaches can hence only represent very coarse 3D geometry or are limited to a restricted domain. In this paper, we propose Occupancy Networks, a new representation for learning-based 3D reconstruction methods. Occupancy networks implicitly represent the 3D surface as the continuous decision boundary of a deep neural network classifier. In contrast to existing approaches, our representation encodes a description of the 3D output at infinite resolution without excessive memory footprint. We validate that our representation can efficiently encode 3D structure and can be inferred from various kinds of input. Our experiments demonstrate competitive results, both qualitatively and quantitatively, for the challenging tasks of 3D reconstruction from single images, noisy point clouds and coarse discrete voxel grids. We believe that occupancy networks will become a useful tool in a wide variety of learning-based 3D tasks.
Construction and characterization of a synthetic Baculovirus-inducible 39K promoter

Silkworm genetic engineering is widely used in gene function, silk engineering and disease-resistant engineering in most of Asia. Some of the earliest promoter elements are used to control the development of silkworm transgenic expression and gene therapy. However, the low expression and specificity of natural promoters limit the applications of genetic engineering. To construct a highly efficient synthetic inducible promoter in the Bombyx mori (Lepidoptera), we analyzed the regulatory elements and functional regions of the B. mori nucleopolyhedrovirus 39 K promoter. Truncated mutation analysis of the 39 K promoter showed that the transcriptional regulatory region spanning positions − 573 to − 274 and + 1 to + 62 are essential for virus-inducible promoter activity. Further investigations using the electrophoretic mobility shift assay revealed that the baculovirus IE-1 protein binds to the 39 K promoter at the − 310 to − 355 region, and transcription activates the expression of 39 K promoter assay. Finally, we successfully constructed a synthetic inducible promoter that increased the virus-inducing activity of other promoters using the baculovirus-inducible transcriptional activation region that binds to specific core elements of 39 K (i.e., spanning the region − 310 to − 355). In summary, we constructed a novel, synthetic, and highly efficient biological tool, namely, a virus-inducible 39 K promoter, which provides endless possibilities for future research on gene function, gene therapy, and pest control in genetic engineering.
Achieving Energy Efficiency Through Dynamic Computing Offloading in Mobile Edge-Clouds

There is a fundamental and critical problem in modern mobile applications, in which the battery life of mobile devices is usually limited. Recently, some researchers prolong the life of batteries by offloading computation tasks to edge-servers which are deployed near the mobile devices. However, computing offloading causes extra delay, which may severely downgrade the user experience especially for the delay-sensitive applications. Moreover, the dynamic nature of mobile devices and the limited computation capacity of edge-servers also bring another challenges for tradeoff optimization between energy consumption and task completion latency. In this paper, we propose a dynamic computing offloading (DCL) problem, which aims to minimize the maximum energy consumption of the mobile devices with constraints on computation tasks latency in a Mobile Edge-Computing (MEC) network. To solve the problem, we consider two complementary cases: offline case (we sacrifice response time to achieve better service results) and online case (where we have to make immediate offloading decision for each computation task arrived online). For the offline case, we propose an efficient RMCL algorithm, and prove that our RMCL method achieves at least O((log m)/α + 1) of the optimum with high probability, where m is the number of computation tasks in a time slot, and α is a value depending on the minimum edge-server capacity and the maximum computation task demand, with α ≥ 1 under most practical situations. For the online case, we propose an algorithm, named OMCL, which considers a trade off between the latency and energy consumption. The performance of our proposed algorithms is evaluated by formal analysis and simulation on a small-scale system. The simulation results show that the algorithm can reduce the maximum energy consumption in a set of mobile devices by 40% compared with executing computation tasks locally.
Modeling Metropolitan-Area Ambulance Mobility Under Blue Light Conditions

Actions taken immediately following a life-threatening personal health incident are critical for the survival of the sufferer. The timely arrival of specialist ambulance crew in particular often makes the difference between life and death. As a consequence, it is critical that emergency ambulance services achieve short response times. This objective sets a considerable challenge to ambulance services worldwide, especially in metropolitan areas, where the density of incident occurrence and traffic congestion are high. Using London as a case study, in this paper, we consider the advantages and limitations of data-driven methods for ambulance routing and navigation. Our long-term aim is to enable considerable improvements to their operational efficiency through the automated generation of more effective response strategies and tactics. A key ingredient of our approach is to use a large historical dataset of incidents and ambulance location traces to model route selection and arrival times. Working on the London road network graph modified to reflect the differences between emergency and civilian vehicle traffic, we develop a methodology for the precise estimation of expected ambulance speed at the individual road segment level. We demonstrate how a model that exploits this information achieves best predictive performance by implicitly capturing route-specific persistent patterns in changing traffic conditions. We then present a predictive method that achieves a high route similarity score while minimising journey duration error. This is achieved through the combination of a technique that correctly predicts routes selected by the current LAS navigation system and our best performing speed estimation model. This hybrid approach outperforms alternative mobility models. To the best of our knowledge, this paper represents the first attempt to apply a data-driven methodology for route selection and the estimation of arrival times of ambulances travelling with blue lights and sirens on.