pwc-archive/papers-with-abstracts huggingface dataset:
Agent Performing Autonomous Stock Trading under Good and Bad Situations
Authors: Yunfei Luo, Zhangqi Duan
Date: 2023-06-06 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning

Stock trading is one of the popular ways for financial management. However, the market and the environment of economy is unstable and usually not predictable. Furthermore, engaging in stock trading requires time and effort to analyze, create strategies, and make decisions. It would be convenient and effective if an agent could assist or even do the task of analyzing and modeling the past data and then generate a strategy for autonomous trading. Recently, reinforcement learning has been shown to be robust in various tasks that involve achieving a goal with a decision making strategy based on time-series data. In this project, we have developed a pipeline that simulates the stock trading environment and have trained an agent to automate the stock trading process with deep reinforcement learning methods, including deep Q-learning, deep SARSA, and the policy gradient method. We evaluate our platform during relatively good (before 2021) and bad (2021 - 2022) situations. The stocks we've evaluated on including Google, Apple, Tesla, Meta, Microsoft, and IBM. These stocks are among the popular ones, and the changes in trends are representative in terms of having good and bad situations. We showed that before 2021, the three reinforcement methods we have tried always provide promising profit returns with total annual rates around $70\%$ to $90\%$, while maintain a positive profit return after 2021 with total annual rates around 2% to 7%.
Reinforcement Learning-Based Control of CrazyFlie 2.X Quadrotor
Authors: Arshad Javeed, Valentín López Jiménez
Date: 2023-06-06 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning

The objective of the project is to explore synergies between classical control algorithms such as PID and contemporary reinforcement learning algorithms to come up with a pragmatic control mechanism to control the CrazyFlie 2.X quadrotor. The primary objective would be performing PID tuning using reinforcement learning strategies. The secondary objective is to leverage the learnings from the first task to implement control for navigation by integrating with the lighthouse positioning system. Two approaches are considered for navigation, a discrete navigation problem using Deep Q-Learning with finite predefined motion primitives, and deep reinforcement learning for a continuous navigation approach. Simulations for RL training will be performed on gym-pybullet-drones, an open-source gym-based environment for reinforcement learning, and the RL implementations are provided by stable-baselines3
Designing Decision Support Systems Using Counterfactual Prediction Sets
Authors: Eleni Straitouri, Manuel Gomez Rodriguez
Date: 2023-06-06 00:00:00
Tasks: counterfactual, Prediction

Decision support systems for classification tasks are predominantly designed to predict the value of the ground truth labels. However, since their predictions are not perfect, these systems also need to make human experts understand when and how to use these predictions to update their own predictions. Unfortunately, this has been proven challenging. In this context, it has been recently argued that an alternative type of decision support systems may circumvent this challenge. Rather than providing a single label prediction, these systems provide a set of label prediction values constructed using a conformal predictor, namely a prediction set, and forcefully ask experts to predict a label value from the prediction set. However, the design and evaluation of these systems have so far relied on stylized expert models, questioning their promise. In this paper, we revisit the design of this type of systems from the perspective of online learning and develop a methodology that does not require, nor assumes, an expert model. Our methodology leverages the nested structure of the prediction sets provided by any conformal predictor and a natural counterfactual monotonicity assumption to achieve an exponential improvement in regret in comparison to vanilla bandit algorithms. We conduct a large-scale human subject study ($n = 2{,}751$) to compare our methodology to several competitive baselines. The results show that, for decision support systems based on prediction sets, limiting experts' level of agency leads to greater performance than allowing experts to always exercise their own agency. We have made available the data gathered in our human subject study as well as an open source implementation of our system at https://github.com/Networks-Learning/counterfactual-prediction-sets.
Multi-constrained Symmetric Nonnegative Latent Factor Analysis for Accurately Representing Large-scale Undirected Weighted Networks
Authors: Yurong Zhong, Zhe Xie, Weiling Li, Xin Luo
Date: 2023-06-06 00:00:00
Tasks: Representation Learning

An Undirected Weighted Network (UWN) is frequently encountered in a big-data-related application concerning the complex interactions among numerous nodes, e.g., a protein interaction network from a bioinformatics application. A Symmetric High-Dimensional and Incomplete (SHDI) matrix can smoothly illustrate such an UWN, which contains rich knowledge like node interaction behaviors and local complexes. To extract desired knowledge from an SHDI matrix, an analysis model should carefully consider its symmetric-topology for describing an UWN's intrinsic symmetry. Representation learning to an UWN borrows the success of a pyramid of symmetry-aware models like a Symmetric Nonnegative Matrix Factorization (SNMF) model whose objective function utilizes a sole Latent Factor (LF) matrix for representing SHDI's symmetry rigorously. However, they suffer from the following drawbacks: 1) their computational complexity is high; and 2) their modeling strategy narrows their representation features, making them suffer from low learning ability. Aiming at addressing above critical issues, this paper proposes a Multi-constrained Symmetric Nonnegative Latent-factor-analysis (MSNL) model with two-fold ideas: 1) introducing multi-constraints composed of multiple LF matrices, i.e., inequality and equality ones into a data-density-oriented objective function for precisely representing the intrinsic symmetry of an SHDI matrix with broadened feature space; and 2) implementing an Alternating Direction Method of Multipliers (ADMM)-incorporated learning scheme for precisely solving such a multi-constrained model. Empirical studies on three SHDI matrices from a real bioinformatics or industrial application demonstrate that the proposed MSNL model achieves stronger representation learning ability to an SHDI matrix than state-of-the-art models do.
Energy-based Assessment and Driving Behavior of ACC Systems and Humans Inside Platoons
Authors: Theocharis Apostolakis, Michail A. Makridis, Anastasios Kouvelas, Konstantinos Ampountolas
Date: 2023-06-07 00:00:00

Evidence in the literature shows that automated and human driving modes demonstrate different driving characteristics, i.e., headway policy, spacing policy, reaction time, comfortable acceleration, and others. These differences alter observed traffic dynamics and have an impact on energy consumption. This paper assesses the energy footprint of commercially implemented adaptive cruise control (ACC) systems and human drivers in car-following formation via different models using empirical observations on very similar driving cycles and/or routes. Most importantly, it initiates a critical discussion of the findings under the behavioral properties of each mode. Findings show that: ACC systems propagate an increasing energy consumption upstream, while human drivers do not; they succeed in maintaining a constant time-headway policy, operating very reliably; they develop a strong bond with their leader compared to their human counterparts; the two modes (humans and ACCs) are operating in different phase-space areas with room for improvement. Overall, findings show that ACC systems must be optimized to achieve a trade-off between functional requirements and eco-driving instructions.
Introducing a new benchmarked dataset for activity monitoring
Authors: Attila Reiss, Didier Stricker
Date: 2012-06-18 00:00:00
Proceeding: International Symposium on Wearable Computers 2012 6
Tasks: Benchmarking, Classification

This paper addresses the lack of a commonly used, standard dataset and established benchmarking problems for physical activity monitoring. A new dataset - recorded from 18 activities performed by 9 subjects, wearing 3 IMUs and a HR-monitor - is created and made publicly available. Moreover, 4 classification problems are benchmarked on the dataset, using a standard data processing chain and 5 different classifiers. The benchmark shows the difficulty of the classification tasks and exposes new challenges for physical activity monitoring.
Dynamic Programming on a Quantum Annealer: Solving the RBC Model
Authors: Jesús Fernández-Villaverde, Isaiah Hull
Date: 2023-06-07 00:00:00
Tasks: Combinatorial Optimization

We introduce a novel approach to solving dynamic programming problems, such as those in many economic models, on a quantum annealer, a specialized device that performs combinatorial optimization. Quantum annealers attempt to solve an NP-hard problem by starting in a quantum superposition of all states and generating candidate global solutions in milliseconds, irrespective of problem size. Using existing quantum hardware, we achieve an order-of-magnitude speed-up in solving the real business cycle model over benchmarks in the literature. We also provide a detailed introduction to quantum annealing and discuss its potential use for more challenging economic problems.
A scientometric analysis of the effect of COVID-19 on the spread of research outputs
Authors: Gianpaolo Zammarchi, Andrea Carta, Silvia Columbu, Luca Frigau, Monica Musio
Date: 2023-06-01 00:00:00
Tasks: quantile regression

The spread of the Sars-COV-2 pandemic in 2020 had a huge impact on the life course of all of us. This rapid spread has also caused an increase in the research production in topics related to COVID-19 with regard to different aspects. Italy has, unfortunately, been one of the first countries to be massively involved in the outbreak of the disease. In this paper we present an extensive scientometric analysis of the research production both at global (entire literature produced in the first 2 years after the beginning of the pandemic) and local level (COVID-19 literature produced by authors with an Italian affiliation). Our results showed that US and China are the most active countries in terms of number of publications and that the number of collaborations between institutions varies according to geographical distance. Moreover, we identified the medical-biological as the fields with the greatest growth in terms of literature production. Furthermore, we also better explored the relationship between the number of citations and variables obtained from the data set (e.g. number of authors per article). Using multiple correspondence analysis and quantile regression we shed light on the role of journal topics and impact factor, the type of article, the field of study and how these elements affect citations.
Generative Adversarial Shaders for Real-Time Realism Enhancement
Authors: Arturo Salmi, Szabolcs Cséfalvay, James Imber
Date: 2023-05-24 00:00:00

Application of realism enhancement methods, particularly in real-time and resource-constrained settings, has been frustrated by the expense of existing methods. These achieve high quality results only at the cost of long runtimes and high bandwidth, memory, and power requirements. We present an efficient alternative: a high-performance, generative shader-based approach that adapts machine learning techniques to real-time applications, even in resource-constrained settings such as embedded and mobile GPUs. The proposed learnable shader pipeline comprises differentiable functions that can be trained in an end-to-end manner using an adversarial objective, allowing for faithful reproduction of the appearance of a target image set without manual tuning. The shader pipeline is optimized for highly efficient execution on the target device, providing temporally stable, faster-than-real time results with quality competitive with many neural network-based methods.
Evolution of 3GPP Standards Towards True Extended Reality (XR) Support in 6G Networks
Authors: Ali A. Esswie, Morris Repeta
Date: 2023-06-06 00:00:00
Tasks: Mixed Reality

Extended reality (XR) is a key innovation of 5G-advanced and beyond networks. The diverse XR use-cases, including virtual reality, augmented reality, and mixed reality, transform the way humans interact with surrounding environments. Thus, XR technology enables true immersive experiences of novel services spanning, e.g., e-commerce, healthcare, and education, respectively. However, the efficient support of XR services over existing and future cellular systems is highly challenging and requires multiple radio design improvements, due to the unique XR traffic and performance characteristics. Thus, this article surveys the state-of-art 3GPP standardization activities (release-18) for integrating the XR service class into the 5G-advanced specifications, highlighting the major XR performance challenges. Furthermore, the paper introduces valuable insights and research directions for supporting true XR services over the next-generation 6G networks, where multiple novel radio design mindsets and protocol enhancements are proposed and evaluated using extensive system level simulations, including solutions for application-native dynamic performance reporting, traffic-dependent control channel design, collaborative device aggregation for XR capacity boosting and offload, respectively.
4D Millimeter-Wave Radar in Autonomous Driving: A Survey
Authors: Zeyu Han, Jiahao Wang, Zikun Xu, Shuocheng Yang, Lei He, Shaobing Xu, Jianqiang Wang, Keqiang Li
Date: 2023-06-07 00:00:00
Tasks: Autonomous Driving, Point Cloud Generation, Survey

The 4D millimeter-wave (mmWave) radar, proficient in measuring the range, azimuth, elevation, and velocity of targets, has attracted considerable interest within the autonomous driving community. This is attributed to its robustness in extreme environments and the velocity and elevation measurement capabilities. However, despite the rapid advancement in research related to its sensing theory and application, there is a conspicuous absence of comprehensive surveys on the subject of 4D mmWave radar. In an effort to bridge this gap and stimulate future research, this paper presents an exhaustive survey on the utilization of 4D mmWave radar in autonomous driving. Initially, the paper provides reviews on the theoretical background and progress of 4D mmWave radars, encompassing aspects such as the signal processing workflow, resolution improvement approaches, and extrinsic calibration process. Learning-based radar data quality improvement methods are present following. Then, this paper introduces relevant datasets and application algorithms in autonomous driving perception, localization and mapping tasks. Finally, this paper concludes by forecasting future trends in the realm of 4D mmWave radar in autonomous driving. To the best of our knowledge, this is the first survey specifically dedicated to the 4D mmWave radar in autonomous driving.
Dynamic Geometry-Based Stochastic Channel Modeling for Polarized MIMO Systems with Moving Scatterers
Authors: Hamed Radpour, Laxmikant Minz, Seong-Ook Park, Duck-Yong Kim, Young-Chan Moon
Date: 2023-06-07 00:00:00

This paper introduces a four-dimensional (4D) geometry-based stochastic model (GBSM) for polarized multiple-input multiple-output (MIMO) systems with moving scatterers. We propose a novel motion path model with high degrees of freedom based on the Brownian Motion (BM) random process for randomly moving scatterers. This model is capable of analyzing the effect of both deterministically and randomly moving scatterers on channel properties. The mixture of Von Mises Fisher (VMF) distribution is considered for scatterers resulting in a more general and practical model. The proposed motion path model is applied to the clusters of scatterers with the mixture of VMF distribution, and a closed form formula for calculating space time correlation function (STCF) is achieved, allowing the study of the behavior of channel correlation and channel capacity in the time domain with the presence of stationary and moving scatterers. To obtain numerical results for channel capacity, we employed Monte Carlo simulation method for channel realization purpose. The impact of moving scatterers on the performance of polarized MIMO systems is evaluated using 2 by 2 MIMO configurations with various dual polarizations, i.e. V/V, V/H, and slanted 45{\deg} polarizations for different signal-to-noise (SNR) regimes. The proposed motion path model can be applied to study various dynamic systems with moving objects. The presented process and achieved formula are general and can be applied to polarized MIMO systems with any arbitrary number of antennas and polarizations.
Active Reconfigurable Intelligent Surface for the Millimeter-Wave Frequency Band: Design and Measurement Results
Authors: Hamed Radpour, Markus Hofer, Lukas Walter Mayer, Andreas Hofmann, Martin Schiefer, Thomas Zemen
Date: 2023-06-07 00:00:00

Reconfigurable intelligent surfaces (RISs) will play a key role to establish reliable low-latency millimeter wave (mmWave) communication links for indoor automation and control applications. In case of a blocked line-of-sight between the base station (BS) and the user equipment (UE), a RIS mounted on a wall or on a ceiling enables a bypass for the radio communication link. In this work, we present an active RIS for the mmWave frequency band. Each RIS element uses a field effect transistor (FET) to amplify the reflected signal and an orthogonal polarization transformation to increase the isolation between impinging and reflected radio wave. By switching the bias voltage at gate and drain of the FET we can establish four states for each RIS element: two reflection states with different phase shifts, an active amplification and an off state. We present results of the active RIS with 37 patch antenna elements arranged in a hexagonal grid for a center frequency of 25.8 GHz. The RIS field patterns obtained by numerical simulations and by empirical measurements in an anechoic chamber are compared. They show a good match and the received power is improved by 12 dB in the active mode of the RIS compared to the reflective mode.
Constraints on OPF Surrogates for Learning Stable Local Volt/Var Controllers
Authors: Zhenyi Yuan, Guido Cavraro, Jorge Cortés
Date: 2023-06-07 00:00:00

We consider the problem of learning local Volt/Var controllers in distribution grids (DGs). Our approach starts from learning separable surrogates that take both local voltages and reactive powers as arguments and predict the reactive power setpoints that approximate optimal power flow (OPF) solutions. We propose an incremental control algorithm and identify two different sets of slope conditions on the local surrogates such that the network is collectively steered toward desired configurations asymptotically. Our results reveal the trade-offs between each set of conditions, with coupled voltage-power slope constraints allowing an arbitrary shape of surrogate functions but risking limitations on exploiting generation capabilities, and reactive power slope constraints taking full advantage of generation capabilities but constraining the shape of surrogate functions. AC power flow simulations on the IEEE 37-bus feeder illustrate their guaranteed stability properties and respective advantages in two DG scenarios.
Microcontroller Based Portable Measurement System for GaN and SiC Devices Characterization
Authors: Alberto Vella, Giuseppe Galioto, Giuseppe Costantino Giaconia
Date: 2023-06-06 00:00:00

The aim of this work is to design and implement an embedded system capable to characterize some relevant figures of merit of Gallium Nitride and Silicon Carbide transistors in a wide range of frequencies. In particular, the designed system is focused on measuring the parameters involved in both the power loss phenomena and the reliability of the device during switching operations. Both the employment of a low-cost microcontroller unit and the equivalent-time sampling technique contributed to make the measurement system flexible, affordable and capable of enhanced sampling performance. As a result, different GaN and SiC devices were compared, in order to characterize the behavior of the measured quantities with respect to the switching frequency.
Differences in boundary behavior in the 3D vertex and Voronoi models
Authors: Elizabeth Lawson-Keister, Tao Zhang, M. Lisa Manning
Date: 2023-06-06 00:00:00
Tasks: Open-Ended Question Answering

An important open question in the modeling of biological tissues is how to identify the right scale for coarse-graining, or equivalently, the right number of degrees of freedom. For confluent biological tissues, both vertex and Voronoi models, which differ only in their representation of the degrees of freedom, have effectively been used to predict behavior, including fluid-solid transitions and cell tissue compartmentalization, which are important for biological function. However, recent work in 2D has hinted that there may be differences between the two models in systems with heterotypic interfaces between two tissue types, and there is a burgeoning interest in 3D tissue models. Therefore, we compare the geometric structure and dynamic sorting behavior in mixtures of two cell types in both 3D vertex and Voronoi models. We find that while the cell shape indices exhibit similar trends in both models, the registration between cell centers and cell orientation at the boundary are significantly different between the two models. We demonstrate that these macroscopic differences are caused by changes to the cusp-like restoring forces introduced by the different representations of the degrees of freedom at the boundary, and that the Voronoi model is more strongly constrained by forces that are an artifact of the way the degrees of freedom are represented. This suggests that vertex models may be more appropriate for 3D simulations of tissues with heterotypic contacts.