pwc-archive/papers-with-abstracts huggingface dataset:
Constrained Exploration in Reinforcement Learning with Optimality Preservation
Authors: Peter C. Y. Chen
Date: 2023-04-05 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

We consider a class of reinforcement-learning systems in which the agent follows a behavior policy to explore a discrete state-action space to find an optimal policy while adhering to some restriction on its behavior. Such restriction may prevent the agent from visiting some state-action pairs, possibly leading to the agent finding only a sub-optimal policy. To address this problem we introduce the concept of constrained exploration with optimality preservation, whereby the exploration behavior of the agent is constrained to meet a specification while the optimality of the (original) unconstrained learning process is preserved. We first establish a feedback-control structure that models the dynamics of the unconstrained learning process. We then extend this structure by adding a supervisor to ensure that the behavior of the agent meets the specification, and establish (for a class of reinforcement-learning problems with a known deterministic environment) a necessary and sufficient condition under which optimality is preserved. This work demonstrates the utility and the prospect of studying reinforcement-learning problems in the context of the theories of discrete-event systems, automata and formal languages.
Technology-Circuit-Algorithm Tri-Design for Processing-in-Pixel-in-Memory (P2M)
Authors: Md Abdullah-Al Kaiser, Gourav Datta, Sreetama Sarkar, Souvik Kundu, Zihan Yin, Manas Garg, Ajey P. Jacob, Peter A. Beerel, Akhilesh R. Jaiswal
Date: 2023-04-06 00:00:00

The massive amounts of data generated by camera sensors motivate data processing inside pixel arrays, i.e., at the extreme-edge. Several critical developments have fueled recent interest in the processing-in-pixel-in-memory paradigm for a wide range of visual machine intelligence tasks, including (1) advances in 3D integration technology to enable complex processing inside each pixel in a 3D integrated manner while maintaining pixel density, (2) analog processing circuit techniques for massively parallel low-energy in-pixel computations, and (3) algorithmic techniques to mitigate non-idealities associated with analog processing through hardware-aware training schemes. This article presents a comprehensive technology-circuit-algorithm landscape that connects technology capabilities, circuit design strategies, and algorithmic optimizations to power, performance, area, bandwidth reduction, and application-level accuracy metrics. We present our results using a comprehensive co-design framework incorporating hardware and algorithmic optimizations for various complex real-life visual intelligence tasks mapped onto our P2M paradigm.
A Context-Switching/Dual-Context ROM Augmented RAM using Standard 8T SRAM
Authors: Md Abdullah-Al Kaiser, Edwin Tieu, Ajey P. Jacob, Akhilesh R. Jaiswal
Date: 2023-04-06 00:00:00

The landscape of emerging applications has been continually widening, encompassing various data-intensive applications like artificial intelligence, machine learning, secure encryption, Internet-of-Things, etc. A sustainable approach toward creating dedicated hardware platforms that can cater to multiple applications often requires the underlying hardware to context-switch or support more than one context simultaneously. This paper presents a context-switching and dual-context memory based on the standard 8T SRAM bit-cell. Specifically, we exploit the availability of multi-VT transistors by selectively choosing the read-port transistors of the 8T SRAM cell to be either high-VT or low-VT. The 8T SRAM cell is thus augmented to store ROM data (represented as the VT of the transistors constituting the read-port) while simultaneously storing RAM data. Further, we propose specific sensing methodologies such that the memory array can support RAM-only or ROM-only mode (context-switching (CS) mode) or RAM and ROM mode simultaneously (dual-context (DC) mode). Extensive Monte-Carlo simulations have verified the robustness of our proposed ROM-augmented CS/DC memory on the Globalfoundries 22nm-FDX technology node.
Coarse Grained FLS-based Processor with Prognostic Malfunction Feature for UAM Drones using FPGA
Authors: Hossam O. Ahmed
Date: 2023-04-04 00:00:00

Many overall safety factors need to be considered in the next generation of Urban Air Mobility (UAM) systems and addressing these can become the anchor point for such technology to reach consent for worldwide application. On the other hand, fulfilling the safety requirements from an exponential increase of prolific UAM systems, is extremely complicated, and requires careful consideration of a variety of issues. One of the key goals of these Unmanned Air Systems (UAS) is the requirement to support the launch and control of hundreds of thousands of these advanced drones in the air simultaneously. Given the impracticalities of training the corresponding number of expert pilots, achieving this goal can only be realized through safe operation in either fullautonomous or semi-autonomous modes. According to many recent studies, the majority of flight accidents are concentrated on the last three stages of a flight trip, which include the Initial Approach, Final Approach, and Landing Phases of an airplane trip. Therefore, this paper proposes a novel decentralized processing system for enhancing the safety factors during the critical phases of Vertical and/or Short Take-Off and Landing (V/STOL) drones. This has been achieved by adopting several processing and control algorithms such as an Open Fuzzy Logic System (FLS) integrated with a Flight Rules Unit (FRU), FIR filters, and a novel Prognostic Malfunction processing unit. After applying several optimization techniques, this novel coarse-grained Autonomous Landing Guidance Assistance System (ALGAS3) processing architecture has been optimized to achieve a maximum computational processing performance of 70.82 Giga Operations per Second (GOPS). Also, the proposed ALGAS3 system shows an ultra-low dynamic thermal power dissipation (I/O and core) of 145.4 mW which is ideal for mobile avionic systems using INTEL 5CGXFC9D6F27C7 FPGA chip.
Impact Sensitivity Analysis of Cooperative Adaptive Cruise Control Against Resource-Limited Adversaries
Authors: Mischa Huisman, Carlos Murguia, Erjen Lefeber, Nathan van de Wouw
Date: 2023-04-05 00:00:00
Tasks: Sensitivity

Cooperative Adaptive Cruise Control (CACC) is a technology that allows groups of vehicles to form in automated, tightly-coupled platoons. CACC schemes exploit Vehicle-to-Vehicle (V2V) wireless communications to exchange information between vehicles. However, the use of communication networks brings security concerns as it exposes network access points that the adversary can exploit to disrupt the vehicles' operation and even cause crashes. In this manuscript, we present a sensitivity analysis of CACC schemes against a class of resource-limited attacks. We present a modelling framework that allows us to systematically compute outer ellipsoidal approximations of reachable sets induced by attacks. We use the size of these sets as a security metric to quantify the potential damage of attacks affecting different signals in a CACC-controlled vehicle and study how two key system parameters change this metric. We carry out a sensitivity analysis for two different controller implementations (as given the available sensors there is an infinite number of realizations of the same controller) and show how different controller realizations can significantly affect the impact of attacks. We present extensive simulation experiments to illustrate the results.
Causal Discovery and Optimal Experimental Design for Genome-Scale Biological Network Recovery
Authors: Ashka Shah, Arvind Ramanathan, Valerie Hayot-Sasson, Rick Stevens
Date: 2023-04-06 00:00:00
Tasks: Causal Discovery, Experimental Design

Causal discovery of genome-scale networks is important for identifying pathways from genes to observable traits - e.g. differences in cell function, disease, drug resistance and others. Causal learners based on graphical models rely on interventional samples to orient edges in the network. However, these models have not been shown to scale up the size of the genome, which are on the order of 1e3-1e4 genes. We introduce a new learner, SP-GIES, that jointly learns from interventional and observational datasets and achieves almost 4x speedup against an existing learner for 1,000 node networks. SP-GIES achieves an AUC-PR score of 0.91 on 1,000 node networks, and scales up to 2,000 node networks - this is 4x larger than existing works. We also show how SP-GIES improves downstream optimal experimental design strategies for selecting interventional experiments to perform on the system. This is an important step forward in realizing causal discovery at scale via autonomous experimental design.
Wardrop Equilibrium Can Be Boundedly Rational: A New Behavioral Theory of Route Choice
Authors: Jiayang Li, Zhaoran Wang, Yu Marco Nie
Date: 2023-04-05 00:00:00
Tasks: Decision Making

As one of the most fundamental concepts in transportation science, Wardrop equilibrium (WE) has always had a relatively weak behavioral underpinning. To strengthen this foundation, one must reckon with bounded rationality in human decision-making processes, such as the lack of accurate information, limited computing power, and sub-optimal choices. This retreat from behavioral perfectionism in the literature, however, was typically accompanied by a conceptual modification of WE. Here, we show that giving up perfect rationality need not force a departure from WE. On the contrary, WE can be reached with global stability in a routing game played by boundedly rational travelers. We achieve this result by developing a day-to-day (DTD) dynamical model that mimics how travelers gradually adjust their route valuations, hence choice probabilities, based on past experiences. Our model, called cumulative logit (CumLog), resembles the classical DTD models but makes a crucial change: whereas the classical models assume routes are valued based on the cost averaged over historical data, ours values the routes based on the cost accumulated. To describe route choice behaviors, the CumLog model only uses two parameters, one accounting for the rate at which the future route cost is discounted in the valuation relative to the past ones and the other describing the sensitivity of route choice probabilities to valuation differences. We prove tha CumLog always converges to WE, regardless of the initial point, as long as the behavioral parameters satisfy certain mild conditions. Our theory thus upholds WE's role as a benchmark in transportation systems analysis. It also resolves the theoretical challenge posed by Harsanyi's instability problem by explaining why equally good routes at WE are selected with different probabilities.
Mutual Interference Mitigation for MIMO-FMCW Automotive Radar
Authors: Sian Jin, Pu Perry Wang, Petros Boufounos, Philip V. Orlik, Ryuhei Takahashi, Sumit Roy
Date: 2023-04-06 00:00:00
Tasks: object-detection, Object Detection

This paper considers mutual interference mitigation among automotive radars using frequency-modulated continuous wave (FMCW) signal and multiple-input multiple-output (MIMO) virtual arrays. For the first time, we derive a general interference signal model that fully accounts for not only the time-frequency incoherence, e.g., different FMCW configuration parameters and time offsets, but also the slow-time code MIMO incoherence and array configuration differences between the victim and interfering radars. Along with a standard MIMO-FMCW object signal model, we turn the interference mitigation into a spatial-domain object detection under incoherent MIMO-FMCW interference described by the explicit interference signal model, and propose a constant false alarm rate (CFAR) detector. More specifically, the proposed detector exploits the structural property of the derived interference model at both \emph{transmit} and \emph{receive} steering vector space. We also derive analytical closed-form expressions for probabilities of detection and false alarm. Performance evaluation using both synthetic-level and phased array system-level simulation confirms the effectiveness of our proposed detector over selected baseline methods.
25 Years of Signal Processing Advances for Multiantenna Communications
Authors: Emil Björnson, Yonina C. Eldar, Erik G. Larsson, Angel Lozano, H. Vincent Poor
Date: 2023-04-05 00:00:00

Wireless communication technology has progressed dramatically over the past 25 years, in terms of societal adoption as well as technical sophistication. In 1998, mobile phones were still in the process of becoming compact and affordable devices that could be widely utilized in both developed and developing countries. There were "only" 300 million mobile subscribers in the world [1]. Cellular networks were among the first privatized telecommunication markets, and competition turned the devices into fashion accessories with attractive designs that could be individualized. The service was circumscribed to telephony and text messaging, but it was groundbreaking in that, for the first time, telecommunication was between people rather than locations. Wireless networks have changed dramatically over the past few decades, enabling this revolution in service provisioning and making it possible to accommodate the ensuing dramatic growth in traffic. There are many contributing components, including new air interfaces for faster transmission, channel coding for enhanced reliability, improved source compression to remove redundancies, and leaner protocols to reduce overheads. Signal processing is at the core of these improvements, but nowhere has it played a bigger role than in the development of multiantenna communication. This article tells the story of how major signal processing advances have transformed the early multiantenna concepts into mainstream technology over the past 25 years. The story therefore begins somewhat arbitrarily in 1998. A broad account of the state-of-the-art signal processing techniques for wireless systems by 1998 can be found in [2], and its contrast with recent textbooks such as [3]-[5] reveals the dramatic leap forward that has taken place in the interim.
Can a Laplace PDE Define Air Corridors through Low-Altitude Airspace?
Authors: Aeris El Asslouj, Ella Atkins, Hossein Rastgoftar
Date: 2023-04-05 00:00:00

This paper develops a high-density air corridor traffic flow model for Uncrewed Aircraft System (UAS) operation in urban low altitude airspace. To maximize throughput with safe separation guarantees, we define an airspace spatiotemporal planning problem. For the spatial planning, we propose a multi-floor UAS coordination structure divided into a finite number of air corridors safely wrapping buildings and obstacles. We use the USGS Lidar data to map buildings and in turn generate air corridors by modeling UAS coordination as ideal fluid flow with the streamlines obtained by solving the Laplace partial differential equation (PDE). Proper boundary conditions for the differential equations are imposed to direct air corridors along the floors desired motion direction. For temporal planning, we use 4-dimensional path-finding through the corridor network with A* search to maximize airspace usability given each UAS initial and destination waypoint pair.
Fast QTMT Partition for VVC Intra Coding Using U-Net Framework
Authors: Zhao Zan, Leilei Huang, Shushi Chen, Xiantao Zhang, Zhenghui Zhao, Haibing Yin, Yibo Fan
Date: 2023-04-06 00:00:00

Versatile Video Coding (VVC) has significantly increased encoding efficiency at the expense of numerous complex coding tools, particularly the flexible Quad-Tree plus Multi-type Tree (QTMT) block partition. This paper proposes a deep learning-based algorithm applied in fast QTMT partition for VVC intra coding. Our solution greatly reduces encoding time by early termination of less-likely intra prediction and partitions with negligible BD-BR increase. Firstly, a redesigned U-Net is recommended as the network's fundamental framework. Next, we design a Quality Parameter (QP) fusion network to regulate the effect of QPs on the partition results. Finally, we adopt a refined post-processing strategy to better balance encoding performance and complexity. Experimental results demonstrate that our solution outperforms the state-of-the-art works with a complexity reduction of 44.74% to 68.76% and a BD-BR increase of 0.60% to 2.33%.
A Unified Taxonomy for Automated Vehicles: Individual, Cooperative, Collaborative, On-Road, and Off-Road
Authors: Fredrik Warg, Anders Thorsén, Victoria Vu, Carl Bergenhem
Date: 2023-04-05 00:00:00

Various types of vehicle automation is increasingly used in a variety of environments including road vehicles such as cars or automated shuttles, confined areas such as mines or harbours, or in agriculture and forestry. In many use cases, the benefits are greater if several automated vehicles (AVs) cooperate to aid each other reach their goals more efficiently, or collaborate to complete a common task. Taxonomies and definitions create a common framework that helps researchers and practitioners advance the field. However, most existing work focus on road vehicles. In this paper, we review and extend taxonomies and definitions to encompass individually acting as well as cooperative and collaborative AVs for both on-road and off-road use cases. In particular, we introduce classes of collaborative vehicles not defined in existing literature, and define levels of automation suitable for vehicles where automation applies to additional functions in addition to the driving task.
Emergent Coordination through Game-Induced Nonlinear Opinion Dynamics
Authors: Haimin Hu, Kensuke Nakamura, Kai-Chieh Hsu, Naomi Ehrich Leonard, Jaime Fernández Fisac
Date: 2023-04-05 00:00:00
Tasks: Decision Making

We present a multi-agent decision-making framework for the emergent coordination of autonomous agents whose intents are initially undecided. Dynamic non-cooperative games have been used to encode multi-agent interaction, but ambiguity arising from factors such as goal preference or the presence of multiple equilibria may lead to coordination issues, ranging from the "freezing robot" problem to unsafe behavior in safety-critical events. The recently developed nonlinear opinion dynamics (NOD) provide guarantees for breaking deadlocks. However, choosing the appropriate model parameters automatically in general multi-agent settings remains a challenge. In this paper, we first propose a novel and principled procedure for synthesizing NOD based on the value functions of dynamic games conditioned on agents' intents. In particular, we provide for the two-player two-option case precise stability conditions for equilibria of the game-induced NOD based on the mismatch between agents' opinions and their game values. We then propose an optimization-based trajectory optimization algorithm that computes agents' policies guided by the evolution of opinions. The efficacy of our method is illustrated with a simulated toll station coordination example.
Selective Data Augmentation for Robust Speech Translation
Authors: Rajul Acharya, Ashish Panda, Sunil Kumar Kopparapu
Date: 2023-03-22 00:00:00
Tasks: Data Augmentation, Machine Translation, Translation

Speech translation (ST) systems translate speech in one language to text in another language. End-to-end ST systems (e2e-ST) have gained popularity over cascade systems because of their enhanced performance due to reduced latency and computational cost. Though resource intensive, e2e-ST systems have the inherent ability to retain para and non-linguistic characteristics of the speech unlike cascade systems. In this paper, we propose to use an e2e architecture for English-Hindi (en-hi) ST. We use two imperfect machine translation (MT) services to translate Libri-trans en text into hi text. While each service gives MT data individually to generate parallel ST data, we propose a data augmentation strategy of noisy MT data to aid robust ST. The main contribution of this paper is the proposal of a data augmentation strategy. We show that this results in better ST (BLEU score) compared to brute force augmentation of MT data. We observed an absolute improvement of 1.59 BLEU score with our approach.
Parsimonious Identification of Continuous-Time Systems: A Block-Coordinate Descent Approach
Authors: Rodrigo A. González, Cristian R. Rojas, Siqi Pan, James S. Welsh
Date: 2023-04-06 00:00:00

The identification of electrical, mechanical, and biological systems using data can benefit greatly from prior knowledge extracted from physical modeling. Parametric continuous-time identification methods can naturally incorporate this knowledge, which leads to interpretable and parsimonious models. However, some applications lead to model structures that lack parsimonious descriptions using unfactored transfer functions, which are commonly used in standard direct approaches for continuous-time system identification. In this paper we characterize this parsimony problem, and develop a block-coordinate descent algorithm that delivers parsimonious models by sequentially estimating an additive decomposition of the transfer function of interest. Numerical simulations show the efficacy of the proposed approach.
Cascaded Calibration of Mechatronic Systems via Bayesian Inference
Authors: Max van Meer, Emre Deniz, Gert Witvoet, Tom Oomen
Date: 2023-04-06 00:00:00
Tasks: Bayesian Inference, Position, regression

Sensors in high-precision mechatronic systems require accurate calibration, which is achieved using test beds that, in turn, require even more accurate calibration. The aim of this paper is to develop a cascaded calibration method for position sensors of mechatronic systems while taking into account the variance of the calibration model of the test bed. The developed calibration method employs Gaussian Process regression to obtain a model of the position-dependent sensor inaccuracies by combining prior knowledge of the sensor with data using Bayesian inference. Monte Carlo simulations show that the developed calibration approach leads to significantly higher calibration accuracy when compared to alternative regression techniques, especially when the number of available calibration points is limited. The results indicate that more accurate calibration of position sensors is possible with fewer resources.
FeFET-based MirrorBit cell for High-density NVM storage
Authors: Paritosh Meihar, Rowtu Srinu, Vivek Saraswat, Sandip Lashkare, Halid Mulaosmanovic, Ajay Kumar Singh, Stefan Dünkel, Sven Beyer, Udayan Ganguly
Date: 2023-04-06 00:00:00
Tasks: Vocal Bursts Intensity Prediction

HfO2-based Ferroelectric field-effect transistor (FeFET) has become a center of attraction for non-volatile memory applications because of their low power, fast switching speed, high scalability, and CMOS compatibility. In this work, we show an n-channel FeFET-based Multibit memory, termed MirrorBit, which effectively doubles the chip density via programming the gradient ferroelectric polarizations in the gate using an appropriate biasing scheme. We have experimentally demonstrated MirrorBit on GlobalFoundries HfO2-based FeFET devices fabricated at 28 nm bulk HKMG CMOS technology. Retention of MirrorBit states has been shown up to $10^5$ s at different temperatures. Also, the endurance is found to be more than $10^3$ cycles. A TCAD simulation is also presented to explain the origin and working of MirrorBit states based on the FeFET model calibrated using the GlobalFoundries FeFET device. We have also proposed the array-level implementation and sensing methodology of the MirrorBit memory. Thus, we have converted 1-bit FeFET into 2-bit FeFET using a particular programming scheme in existing FeFET, without needing any notable fabrication process alteration, to double the chip density for high-density non-volatile memory storage.