Miracle-dz/UniFAR-FTUs huggingface dataset:
Entropy production in a photovoltaic cell

Entropy is one of the central quantities in thermodynamics, whose flow between two systems determines the statistics of energy transfers. In quantum systems entropy is non-linear in density matrix whose time evolution is cumbersome. Using recent developments in the Keldysh formalism for the evolution of nonlinear quantum information measures (Phys. Rev. B 91, 174307 (2015)), we study the flow of von Neumann and Renyi entropies in a generic four-level quantum system that is weakly coupled to equilibrium heat engines. We show that noise-induced coherence has significant influence on the entropy flow of the quantum heat engine. We determine analytical optimization of couplings for the purpose of designing optimal artificial energy transfer systems.
CAVSim: A Microscope Traffic Simulator for Connected and Automated Vehicles Environment

Connected and automated vehicles (CAVs) are expected to play a vital role in the next-generation intelligent transportation system. In recent years, researchers have proposed various cooperative driving methods for CAVs' decisions and planning, and there is an urgent need for a suitable and unified traffic simulator to evaluate and test these methods. However, existing traffic simulators have three critical deficiencies for CAV simulation needs: (1) most of them are from the perspective of traffic flow simulation and have strong simplification and assumptions for vehicle modeling; (2) CAVs are different from traditional human-driven vehicles (HVs) and have new properties, which require new driving models; (3) the existing traffic simulators are inconvenient to deploy the emerging cooperative driving methods because their modeling of the traffic system architecture is traditional. In this paper, we introduce CAVSim, a novel microscope traffic simulator for the CAV environment, to address these deficiencies. CAVSim is modularly developed according to the emerging architecture for the CAV environment, emphasizes more detailed driving behaviors of CAVs, and highlights the decision and planning components in the CAV environment. With CAVSim, researchers can quickly deploy decision and planning methods at different levels, evaluate and test their performance, and explore their impact on the traffic flow in the CAV environment.
Mixed logical dynamic based path-tracking model predictive control for autonomous vehicles

A novel model predictive control (MPC) strategy for autonomous vehicles path tracking based on mixed logical dynamic (MLD) system is proposed in this article. To simplify the vehicle lateral dynamics model and describe the complex nonlinear characteristics of tire lateral forces with higher accuracy, a piecewise linear affine (PWA) tire lateral force model is proposed by dividing the tire force curve into saturated, nonlinear and linear segments. In addition, to avoid excessive number of partitions caused by using segmented lateral force models for both front and rear tires, the front tire lateral force is used as the control input instead of steering angle in this article. In the PWA model, due to the coupling between the control input and partition, it will be difficult to perform state prediction according to the classical MPC method in this case where the control input and partition are unknown simultaneously. Therefore, an MLD system which is equivalent to the PWA model is constructed by designing new logical variables and auxiliary variables and using the conversion between Boolean algebraic logical expressions and the inequality set. In the optimization problem based on the MLD system, the control input and the logical variables characterizing the partition can be optimized together as independent variables. Then, the MLD-based optimization problem is transformed into a standard quadratic programming problem and the mixed-integer quadratic programming (MIQP) algorithm is employed to calculate its optimal solution. Furthermore, to reduce the online computational burden and improve the real-time performance, an explicit model predictive control (EMPC) strategy for MLD systems is given. Finally, the performance of the controller is evaluated in simulations. Simulation results show that the controller proposed in this article can track the reference path in real time and has a similar lateral error Root Mean Square (RMS) as the NMPC path tracking controller designed based on a complex nonlinear model.
The factors for choosing among NB-IoT, LoRaWAN, and Sigfox radio communication technologies for IoT networking

The designing process of an IoT (Internet of Things) network requires adequate knowledge of various communication technologies that make the connection of the IoT modules possible. Many important factors such as scalability, bandwidth, data rate (speed), coverage, power consumption, and security support need to be considered to answer the needs of an IoT application with regards to the implemented radio communication technology. This paper studies the choices of three major LPWAN (Low-Power Wide-Area Networks) technologies that are currently leading in the market of radio communication technologies. Focusing on Sigfox, LoRaWAN (Low-Range Wide-Area Networks), and NB-IoT (Narrow-Band Internet of Things), this work intends to give the respective pros and cons of the mentioned technologies and a clear view of the recent trends and effective choices of radio communication technologies for major smart IoT applications.
Towards Fast Crash-Consistent Cluster Checkpointing

Machine Learning models are expensive to train: they require expensive high-compute hardware and have long training times. Therefore, models are extra sensitive to program faults or unexpected system crashes, which can erase hours if not days worth of work. While there are plenty of strategies designed to mitigate the risk of unexpected system downtime, the most popular strategy in machine learning is called checkpointing: periodically saving the state of the model to persistent storage. Checkpointing is an effective strategy, however, it requires carefully balancing two operations: how often a checkpoint is made (the checkpointing schedule), and the cost of creating a checkpoint itself. In this paper, we leverage Python Memory Manager (PyMM), which provides Python support for Persistent Memory and emerging Persistent Memory technology (Optane DC) to accelerate the checkpointing operation while maintaining crash consistency. We first show that when checkpointing models, PyMM with persistent memory can save from minutes to days of checkpointing runtime. We then further optimize the checkpointing operation with PyMM and demonstrate our approach with the KMeans and Gaussian Mixture Model algorithms on two real-world datasets, MNIST and MusicNet. Through evaluation, we show that these two algorithms achieve a checkpointing speedup of a factor between 10 and 75x for KMeans and over 3x for GMM against the current state-of-the-art checkpointing approaches. We also verify that our solution recovers from crashes, while traditional approaches cannot.
Design of an adaptive lighting system able to consider the evolution of pavement reflection properties according to their moisture state

The optimization of road lighting installations is nowadays mainly considered from the point of view of reducing energy costs by using the advantages of LED technology when designing or renovating lighting installations. Knowing the actual reflection properties of the road to be illuminated enables further optimization but these are too often not measured. Moreover, the evolution of these properties according to the surface state of the road (dry, moist, wet, soaked) is never considered. This paper proposes a methodology to design an adaptive lighting system that takes into account the optical properties of the pavement according to the weather conditions in order to optimize the visibility offered to users. This methodology is based on numerous simulations of potential pavement surface states. By comparing the developed adaptive lighting system with a conventional lighting system, it is shown that the overall optimization of the lighting operation is achieved both on photometric and on energy aspects.
Scavenging PyPi for VLSI Packages

Application Specific Integrated Circuits and Field Programmable Gate Arrays as well as their respective design flows have been the focus of many studies. As such the use of EDA tools becomes a necessity while researchers develop and test their proposals. Most of these tools are proprietary, rendering them cost inefficient and sometimes redundant due to their commercial nature, especially for academic use. In our paper we present and briefly describe a few open-source alternatives hosted in PyPI, a software repository for Python related projects, and we evaluate them in terms of their application field, development stage as well as their community evaluation.
Two resonators with negative and positive reactive components to achieve an exceptional point of degeneracy

We present a scheme supporting an exceptional point of degeneracy (EPD) using connected Foster and non-Foster resonators. One resonator contains positive components, whereas the second resonator contains negative components. We show a second-order EPD where two eigenvalues and eigenvectors coalesce. This circuit can be used to make ultra-sensitive sensors.
A CNN Model with Discretized Mobile Features for Depression Detection

Depression has been a serious mental illness for a long time, which significantly influences people’s life quality. Meanwhile, as the smartphone becomes an integral part of people’s lives, it creates the opportunity to analyze users’ feelings through their phone usage and sensor data. However, previous studies mainly adopt machine-learning methods for depression detection, ignoring the sequential patterns hidden in them. In this study, we aim to monitor the symptoms of depression through sequential mobile data collected from phones and their sensors. First, we establish a deep-learning model called Dep-caser to fully utilize the sequential information in mobile data. Next, we introduce a discretization method based on Information Value to deal with data sparsity and outliers. In total, we recruited 257 people to join the study and extracted five-day longitudinal data from their smartphones and electronic bands. We conduct two experiments to examine the effectiveness of the Dep-caser and discretization method respectively. The results demonstrate that Dep-caser outperforms most of the machine learning methods and the discretization further improves the performance of the deep-learning model to achieve an overall accuracy of 0.83. Our study shows the promising future to adopt deep-learning models with sequential phone usage and sensing data to detect depression.
Interaction-aware Prediction of Occupancy Regions based on a POMDP Framework

Extensive research has been done in the field of automated driving systems to reliably and accurately predict the motion of surrounding vehicles. Predicted occupancy regions are a suitable starting point for trajectory planning, criticality estimation, etc. However, interactions between traffic participants are difficult to predict due to their high complexity and mutual dependencies. This work proposes a novel approach for modeling these traffic interactions with the help of a Partially Observable Markov Decision Process (POMDP). A key component of the presented approach is that the resulting policy is not used for the control of the EGO vehicle. Instead, it is utilized for the generation of continuous predicted occupancy regions. The approach applies a particle ensemble approximation in order to solve the POMDP optimization. Hereby, the reward function is a central aspect to incorporate expert knowledge in the statistical framework. The potential of the proposed method is demonstrated using typical urban junction scenarios.
Short-Term Prediction of City Traffic Flow via Convolutional Deep Learning

Nowadays, traffic management and sustainable mobility are central topics for intelligent transportation systems (ITS). Thanks to new technologies, it is possible to collect real-time data to monitor the traffic situation and contextual information by sensors. An important challenge in ITS is the ability to predict road traffic flow data. The short-term predictions (10-60 minutes) of traffic flow data is a complex nonlinear task that has been the subject of many research efforts in past few decades. Accessing traffic flow data is mandatory for a large number of applications that have to guarantee a high level of services such as traffic flow analysis, traffic flow reconstruction, which in their turn are used to compute predictions needed to perform what-if analysis, forecast routing, conditioned routing, predictions of pollutant, etc. This paper proposes a solution for short-term prediction of traffic flow data by using a architecture capable to exploit Convolutional Bidirectional Deep Long Short Term Memory neural networks (CONV-BI-LSTM). The solution adopts a different architecture and features, so as to overcome the state-of-the-art solutions and provides precise predictions addressing traffic flow data in cities, which are tendentially very noisy with respect to the ones measured in high-speed roads, the latter being the validation context for the majority of state-of-the-art solutions. The proposed solution has been developed and validated in the city context and data via Sii-Mobility, a smart city mobility and transport national project and it is currently in use in other contexts such as in Snap4City PCP EC, TRAFAIR CEF, and REPLICATE H2020 SCC1, and it is operative in those areas.
A Generalized Noncontact Vital-Sign Sensing System Based on MIMO FMCW Radar Sensors

A novel generalized non-contact vital-sign system configuration is proposed based on the MIMO (Multiple Input Multiple Output) FMCW (Frequency Modulated Continuous Wave) radar sensors. The proposed generalized radar system configuration can track the location of individuals and extract their respiration and heart rates with the multiple signal classification algorithm (MUSIC). In the phase tracking process, with the proposed joint double phase unwrap and linear fitting process, the tiny vital sign information can be extracted from the random motion interference. In this work, two TX antennas transmit signals simultaneously in binary phase modulation model and the digital beamforming process is performed to steer the main beam of the antenna to the desired direction. The proposed generalized radar configuration has been verified experimentally to demonstrate the versatility of the system for life tracking and monitoring.
CommonRoad-Reach: A Toolbox for Reachability Analysis of Automated Vehicles

In recent years, reachability analysis has gained considerable popularity in motion planning and safeguarding of automated vehicles (AVs). While existing tools for reachability analysis mainly focus on general-purpose algorithms for formal verification of dynamical systems, a toolbox tailored to AV-specific applications is not yet available. In this study, we present CommonRoad-Reach, which is a toolbox that integrates different methods for computing reachable sets and extracting driving corridors for AVs in dynamic traffic scenarios. Our toolbox provides a Python interface and an efficient C++implementation for real-time applications. The toolbox is integrated within the CommonRoad benchmark suite and is available at commonroad.in. tum.de.
Surrogate ML/AI Model Benchmarking for FAIR Principles' Conformance

We present benchmarking platform for surrogate ML/AI models that enables the essential properties for open science and allow them to be findable, accessible, interoperable, and reusable. We also present a use case of cloud cover modeling, analysis, and experimental testing based on a large dataset of multi-spectral satellite sensor data. We use this particular evaluation to highlight the plethora of choices that need resolution for the life cycle of supporting the scientific workflows with data-driven models that need to be first trained to satisfactory accuracy and later monitored during field usage for proper feedback into both computational results and future data model improvements. Unlike traditional testing, performance, or analysis efforts, we focus exclusively on science-oriented metrics as the relevant figures of merit.
A Late Asymmetric Fusion Approach To Eliminate False Positives

This study presents an asymmetric late fusion approach to fuse camera and lidar outputs from different neural networks. By fusing two different modalities, each network's false positive results are eliminated and a superior vector space compared to single modality methods is created. Proposed 3D bounding boxes from lidar networks are supported with 2D segmentation networks that work with a camera. Ground plane provided by the lidar input is also detected to eliminate possible 2D camera drivable area false positive results, namely, roadblocks, grass, and side of tunnel walls. 3 camera networks and 6 different 3D lidar networks tested with KITTI dataset illustrate that the presented fusion approach exceeds the performance of Lidar-only methods up to 9.87% category wise. Comparison with two different fusion approaches also show our superior performance.
The Rock Physics Handbook

Responding to the latest developments in rock physics research, this popular reference book has been thoroughly updated while retaining its comprehensive coverage of the fundamental theory, concepts, and laboratory results. It brings together the vast literature from the field to address the relationships between geophysical observations and the underlying physical properties of Earth materials - including water, hydrocarbons, gases, minerals, rocks, ice, magma and methane hydrates. This third edition includes expanded coverage of topics such as effective medium models, viscoelasticity, attenuation, anisotropy, electrical-elastic cross relations, and highlights applications in unconventional reservoirs. Appendices have been enhanced with new materials and properties, while worked examples (supplemented by online datasets and MATLAB® codes) enable readers to implement the workflows and models in practice. This significantly revised edition will continue to be the go-to reference for students and researchers interested in rock physics, near-surface geophysics, seismology, and professionals in the oil and gas industries.
Sit-to-stand and Stand-to-sit Activities Recognition by Visible Light Sensing

Monitoring and analyzing basic human daily life activities will help in enhancing the quality of life for both healthy and physically handicapped people. The recognition of sit-to-stand and stand-to-sit transitions in activities of daily living (ADL) is complex task. This is due to the intricate body’s movements during such postural transition. This work proposes a novel method for detecting sit-to-stand and stand-to-sit postural transitions in addition to other human physical activities such as walk and no-walk. In contrast to previous methods for such transitions determination, our solution does not require complex time- or frequency-domain based algorithms. Our solution relies on fusing motion data collected from an inertial measurement unit device with light data generated from visible light sensing technology utilizing an RGB photodiode. By utilizing a low-complex decision tree algorithm, the activity can be precisely recognized in resource-efficient way. The applicability of our approach was tested through two scenarios representing various ADL in smart environment.
Gap feasibility with and without adaption for autonomous vehicle's lane changing: a forward-backward reachability approach

Lane changing (LC) is a typical driving task of connected and autonomous vehicles (CAV). In a typical LC event, CAV need to follow certain trajectory and enters the gap at target lane. While there exists many LC planning models, gap feasibility, which should be a preposition stage of LC planning, is rarely investigated. Furthermore, if the neighboring gap is not feasible, CAV needs to accelerate/decelerate to accommodate downstream/upstream gaps. This LC process with adaption then include an accelerate/decelerate part and a LC part. The terminal state of the first part is called the lane changing initiation state (LCIS), i.e. the initiation state of the second part. The downstream/upstream gap feasibility analysis needs to determine the LCIS, a state set rather than a single state. The above gap feasibility problem is more complex and current methods cannot solve this problem. This research proposes a forward-backward reachability model to address this issue. A forward-backward vehicle dynamics model is constructed for this purpose. The Pontryagin Maximal Principle (PMP) is employed to determine the speed curve of the acceleration/deceleration part. The proposed method is tested using a numerical example, which proves the usefulness of the model.
An experimental protocol for exploration of stress in an immersive VR scenario with EEG

Stress is a subject always relevant to scientific research due to the numerous implications in human life. Typical biomarkers used in the physiological evaluation of stress include Electrocardiography, cortisol levels, galvanic skin response and other. Recently, one less widely used instrument for the assessment of stress that has been re-emerged due to advancements in computational power and machine learning techniques, is Electroencephalography. Moreover, as Virtual Reality HMDs are being rapidly adopted by the research community it becomes apparent that leveraging the offered advantages of VR for the exploration of stress can lead to novel controlable and reproducable experimental procedures. In this paper we combine EEG, ECG and the Perceived Stress Scale with a Virtual Reality phobia induction setting, to propose a protocol for assessing stress. The suggested protocol can be used for functional brain connectivity investigation and thus the evaluation of stress while it and can be expanded via the incorporation of machine learning algorithms for automatic stress level classification.
Large scale LTE-V2X Test Attempt via Real Deployment

With the development of C-V2X technology, the Internet of Vehicles has entered the commercialization stage. However, it is yet difficult to evaluate the performance of C-V2X under large scale deployment. This paper proposes a large-scale C-V2X (e.g., LTE-V2X) communication test scheme in real traffic scenarios. Based on the test scheme, 198 On Board Units (OBUs) were deployed on site and a simulation platform was built on OMNeT++. Then the performance of OBUs with LTE-V2X capability simultaneous communication under different parameter configurations is tested in real scenarios, including two typical traffic environments, intersection and straight road. More evaluation metrics are analyzed than in previous studies from the mass of data, such Channel Busy Ratio (CBR), Inter-Transmit Time (ITT), Packet Error Ratio (PER) and delay, to explore the communication law in terms of congestion. Specifically, we try to add a congestion algorithm to optimize performance. The test results show that vehicle density significantly impacts the communication performance between devices due to resource contention, and congestion control can reduce PER and CBR to some extent. Finally, we compare the real test results with simulation results, the effectiveness of the actual deployment of large-scale testing is verified.