pwc-archive/papers-with-abstracts huggingface dataset:
Cross-modal Retrieval with Improved Graph Convolution
Authors: ZHANG Hongtu, HUA Chunjian, JIANG Yi, YU Jianfeng, CHEN Ying
Date: 2023-03-07 00:00:00
Proceeding: Computer Engineering and Applications 2023 3
Tasks: Cross-Modal Retrieval, Representation Learning, Retrieval, Sentence

Aiming at the problem that existing image text cross-modal retrieval is difficult to fully exploit the local  consistency in the mode in the common subspace, a cross-modal retrieval method based on improved graph convolution was proposed. In order to improve the local consistency within each mode, the modal diagram is constructed with  a single sample as a node, fully mining the interactive information between features. In order to solve the problem that  graph convolution network can only do shallow learning, the method of adding initial residual link and weight identity  map in each layer of graph convolution is adopted to alleviate this phenomenon. In order to jointly update the central  node features through higher-order and lower-order neighbor information, an improvement is proposed to reduce  neighbor nodes and increase the number of layers in graph convolution network. In order to learn highly locally consistent and semantically consistent public representation, shared common representation learning layer weight, and  jointly optimize the semantic constraints within the modes and the modal invariant constraints between modes in the  common subspace. The experimental results show that on the two cross-modal data sets of Wikipedia and Pascal  Sentence, the average mAP values of different retrieval tasks are 2.2%-42.1% and 3.0%-54.0% higher than the 11  existing methods.
Fast Partitioning of Vector-valued Images
Authors: Martin Storath, Andreas Weinmann
Date: 2014-09-23 00:00:00
Proceeding: SIAM Journal on Imaging Sciences 2014 9

We propose a fast splitting approach to the classical variational formulation of the image partitioning problem, which is frequently referred to as the Potts or piecewise constant Mumford--Shah model. For vector-valued images, our approach is significantly faster than the methods based on graph cuts and convex relaxations of the Potts model which are presently the state-of-the-art. The computational costs of our algorithm only grow linearly with the dimension of the data space which contrasts the exponential growth of the state-of-the-art methods. This allows us to process images with high-dimensional codomains such as multispectral images. Our approach produces results of a quality comparable with that of graph cuts and the convex relaxation strategies, and we do not need an a priori discretization of the label space. Furthermore, the number of partitions has almost no influence on the computational costs, which makes our algorithm also suitable for the reconstruction of piecewise constant (color or vectorial) images.
Testing the simplicity of strategy-proof mechanisms
Authors: Alexander L. Brown, Daniel G. Stephenson, Rodrigo A. Velez
Date: 2024-04-18 00:00:00

This paper experimentally evaluates four mechanisms intended to achieve the Uniform outcome in rationing problems (Sprumont, 1991). Our benchmark is the dominant-strategy, direct-revelation mechanism of the Uniform rule. A strategically equivalent mechanism that provides non-binding feedback during the reporting period greatly improves performance. A sequential revelation mechanism produces modest improvements despite not possessing dominant strategies. A novel, obviously strategy-proof mechanism, devised by Arribillaga et al. (2023), does not improve performance. We characterize each alternative to the direct mechanism, finding general lessons about the advantages of real-time feedback and sequentiality of play as well as the potential shortcomings of an obviously strategy-proof mechanism.
Secure Semantic Communication for Image Transmission in the Presence of Eavesdroppers
Authors: Shunpu Tang, Chen Liu, Qianqian Yang, Shibo He, Dusit Niyato
Date: 2024-04-18 00:00:00
Tasks: Semantic Communication

Semantic communication (SemCom) has emerged as a key technology for the forthcoming sixth-generation (6G) network, attributed to its enhanced communication efficiency and robustness against channel noise. However, the open nature of wireless channels renders them vulnerable to eavesdropping, posing a serious threat to privacy. To address this issue, we propose a novel secure semantic communication (SemCom) approach for image transmission, which integrates steganography technology to conceal private information within non-private images (host images). Specifically, we propose an invertible neural network (INN)-based signal steganography approach, which embeds channel input signals of a private image into those of a host image before transmission. This ensures that the original private image can be reconstructed from the received signals at the legitimate receiver, while the eavesdropper can only decode the information of the host image. Simulation results demonstrate that the proposed approach maintains comparable reconstruction quality of both host and private images at the legitimate receiver, compared to scenarios without any secure mechanisms. Experiments also show that the eavesdropper is only able to reconstruct host images, showcasing the enhanced security provided by our approach.
Unknown Interference Modeling for Rate Adaptation in Cell-Free Massive MIMO Networks
Authors: Mahmoud Zaher, Emil Björnson, Marina Petrova
Date: 2024-04-18 00:00:00
Tasks: Scheduling

Co-channel interference poses a challenge in any wireless communication network where the time-frequency resources are reused over different geographical areas. The interference is particularly diverse in cell-free massive multiple-input multiple-output (MIMO) networks, where a large number of user equipments (UEs) are multiplexed by a multitude of access points (APs) on the same time-frequency resources. For realistic and scalable network operation, only the interference from UEs belonging to the same serving cluster of APs can be estimated in real-time and suppressed by precoding/combining. As a result, the unknown interference arising from scheduling variations in neighboring clusters makes the rate adaptation hard and can lead to outages. This paper aims to model the unknown interference power in the uplink of a cell-free massive MIMO network. The results show that the proposed method effectively describes the distribution of the unknown interference power and provides a tool for rate adaptation with guaranteed target outage.
Hybrid Navigation Acceptability and Safety
Authors: Benoit Clement, Marie Dubromel, Paulo E. Santos, Karl Sammut, Michelle Oppert, Feras Dayoub
Date: 2024-04-18 00:00:00

Autonomous vessels have emerged as a prominent and accepted solution, particularly in the naval defence sector. However, achieving full autonomy for marine vessels demands the development of robust and reliable control and guidance systems that can handle various encounters with manned and unmanned vessels while operating effectively under diverse weather and sea conditions. A significant challenge in this pursuit is ensuring the autonomous vessels' compliance with the International Regulations for Preventing Collisions at Sea (COLREGs). These regulations present a formidable hurdle for the human-level understanding by autonomous systems as they were originally designed from common navigation practices created since the mid-19th century. Their ambiguous language assumes experienced sailors' interpretation and execution, and therefore demands a high-level (cognitive) understanding of language and agent intentions. These capabilities surpass the current state-of-the-art in intelligent systems. This position paper highlights the critical requirements for a trustworthy control and guidance system, exploring the complexity of adapting COLREGs for safe vessel-on-vessel encounters considering autonomous maritime technology competing and/or cooperating with manned vessels.
Advancing Speech Translation: A Corpus of Mandarin-English Conversational Telephone Speech
Authors: Shannon Wotherspoon, William Hartmann, Matthew Snover
Date: 2024-03-25 00:00:00
Tasks: Translation

This paper introduces a set of English translations for a 123-hour subset of the CallHome Mandarin Chinese data and the HKUST Mandarin Telephone Speech data for the task of speech translation. Paired source-language speech and target-language text is essential for training end-to-end speech translation systems and can provide substantial performance improvements for cascaded systems as well, relative to training on more widely available text data sets. We demonstrate that fine-tuning a general-purpose translation model to our Mandarin-English conversational telephone speech training set improves target-domain BLEU by more than 8 points, highlighting the importance of matched training data.
Practical Considerations for Discrete-Time Implementations of Continuous-Time Control Barrier Function-Based Safety Filters
Authors: Lukas Brunke, SiQi Zhou, Mingxuan Che, Angela P. Schoellig
Date: 2024-04-18 00:00:00

Safety filters based on control barrier functions (CBFs) have become a popular method to guarantee safety for uncertified control policies, e.g., as resulting from reinforcement learning. Here, safety is defined as staying in a pre-defined set, the safe set, that adheres to the system's state constraints, e.g., as given by lane boundaries for a self-driving vehicle. In this paper, we examine one commonly overlooked problem that arises in practical implementations of continuous-time CBF-based safety filters. In particular, we look at the issues caused by discrete-time implementations of the continuous-time CBF-based safety filter, especially for cases where the magnitude of the Lie derivative of the CBF with respect to the control input is zero or close to zero. When overlooked, this filter can result in undesirable chattering effects or constraint violations. In this work, we propose three mitigation strategies that allow us to use a continuous-time safety filter in a discrete-time implementation with a local relative degree. Using these strategies in augmented CBF-based safety filters, we achieve safety for all states in the safe set by either using an additional penalty term in the safety filtering objective or modifying the CBF such that those undesired states are not encountered during closed-loop operation. We demonstrate the presented issue and validate our three proposed mitigation strategies in simulation and on a real-world quadrotor.
Long Duration Battery Sizing, Siting, and Operation Under Wildfire Risk Using Progressive Hedging
Authors: Ryan Piansky, Georgia Stinchfield, Alyssa Kody, Daniel K. Molzahn, Jean-Paul Watson
Date: 2024-04-18 00:00:00

Battery sizing and siting problems are computationally challenging due to the need to make long-term planning decisions that are cognizant of short-term operational decisions. This paper considers sizing, siting, and operating batteries in a power grid to maximize their benefits, including price arbitrage and load shed mitigation, during both normal operations and periods with high wildfire ignition risk. We formulate a multi-scenario optimization problem for long duration battery storage while considering the possibility of load shedding during Public Safety Power Shutoff (PSPS) events that de-energize lines to mitigate severe wildfire ignition risk. To enable a computationally scalable solution of this problem with many scenarios of wildfire risk and power injection variability, we develop a customized temporal decomposition method based on a progressive hedging framework. Extending traditional progressive hedging techniques, we consider coupling in both placement variables across all scenarios and state-of-charge variables at temporal boundaries. This enforces consistency across scenarios while enabling parallel computations despite both spatial and temporal coupling. The proposed decomposition facilitates efficient and scalable modeling of a full year of hourly operational decisions to inform the sizing and siting of batteries. With this decomposition, we model a year of hourly operational decisions to inform optimal battery placement for a 240-bus WECC model in under 70 minutes of wall-clock time.
Stability Certificates for Receding Horizon Games
Authors: Sophie Hall, Dominic Liao-McPherson, Giuseppe Belgioioso, Florian Dörfler
Date: 2024-04-18 00:00:00
Tasks: Autonomous Driving, Decision Making, valid

Game-theoretic MPC (or Receding Horizon Games) is an emerging control methodology for multi-agent systems that generates control actions by solving a dynamic game with coupling constraints in a receding-horizon fashion. This control paradigm has recently received an increasing attention in various application fields, including robotics, autonomous driving, traffic networks, and energy grids, due to its ability to model the competitive nature of self-interested agents with shared resources while incorporating future predictions, dynamic models, and constraints into the decision-making process. In this work, we present the first formal stability analysis based on dissipativity and monotone operator theory that is valid also for non-potential games. Specifically, we derive LMI-based certificates that ensure asymptotic stability and are numerically verifiable. Moreover, we show that, if the agents have decoupled dynamics, the numerical verification can be performed in a scalable manner. Finally, we present tuning guidelines for the agents' cost function weights to fulfill the certificates and, thus, ensure stability.
Cost and CO2 emissions co-optimisation of green hydrogen production in a grid-connected renewable energy system
Authors: Sleiman Farah, Neeraj Bokde, Gorm Bruun Andresen
Date: 2024-04-18 00:00:00

Green hydrogen is essential for producing renewable fuels that are needed in sectors that are hard to electrify directly. Hydrogen production in a grid-connected hybrid renewable energy plant necessitates smart planning to meet long-term hydrogen trading agreements while minimising costs and emissions. Previous research analysed economic and environmental impact of hydrogen production based on full foresight of renewable energy availabilty, electricity price, and CO2 intensity in the electricity grid. However, the full foresight assumption is impractical in day-to-day operation, often leading to underestimations of both the cost and CO2 emissions associated with hydrogen production. Therefore, this research introduces a novel long-term planner that uses historical data and short-term forecasts to plan hydrogen production in the day-to-day operation of a grid-connected hybrid renewable energy plant. The long-term planner co-minimises cost and CO2 emissions to determine the hydrogen production for the next day taking into account the remaining hydrogen production and the time remaining until the end of the delivery period, which can be a week, a month, or a year. Extended delivery periods provide operation flexibility, enabling cost and CO2 emissions reductions. Significant reductions in CO2 emissions can be achieved with relatively small increases in the levelised cost. Under day-to-day operation, the levelised cost of hydrogen is marginally higher than that of the full foresight; the CO2 emissions can be up to 60% higher. Despite a significant portion of the produced hydrogen not meeting the criteria for green hydrogen designation under current rules, CO2 emissions are lower than those from existing alternative hydrogen production methods. These results underscore the importance of balancing cost considerations with environmental impacts in operational decision-making.
Segmented Model-Based Hydrogen Delivery Control for PEM Fuel Cells: a Port-Hamiltonian Approach
Authors: Lalitesh Kumar, Jian Chen, Chengshuai Wu, Yuzhu Chen, Arjan van der Schaft
Date: 2024-04-18 00:00:00

This paper proposes an extended interconnection and damping assignment passivity-based control technique (IDA-PBC) to control the pressure dynamics in the fuel delivery subsystem (FDS) of proton exchange membrane fuel cells. The fuel cell stack is a distributed parameter model which can be modeled by partial differential equations PDEs). In this paper, the segmentation concept is used to approximate the PDEs model by ordinary differential equations (ODEs) model. Therefore, each segments are having multiple ODEs to obtain the lump-sum model of the segments. Subsequently, a generalized multi-input multi-output lumped parameters model is developed in port-Hamiltonian framework based on mass balance to minimize the modeling error. The modeling errors arises due to the difference between spatially distributed pressures in FDS segments, and also due to the difference between the actual stack pressure and the measured output pressure of the anode. The segments interconnection feasibilities are ensured by maintaining passivity of each segment. With consideration of re-circulation and bleeding of the anode in the modeling, an extended energy-shaping and output tracking IDA-PBC based state-feedback controller is proposed to control the spatially distributed pressure dynamics in the anode. Furthermore, a sliding mode observer of high order is designed to estimate the unmeasurable pressures in FDS with known disturbances. Performance recovery of output feedback control is accomplished with explicit stability analysis. The effectiveness of the proposed IDA-PBC approach is validated by the simulation results.
A New Hybrid Automaton Framework with Partial Differential Equation Dynamics
Authors: Tianshu Bao, Hengrong Du, Weiming Xiang, Taylor T. Johnson
Date: 2024-04-18 00:00:00

This paper presents the syntax and semantics of a novel type of hybrid automaton (HA) with partial differential equation (PDE) dynamic, partial differential hybrid automata (PDHA). In PDHA, we add a spatial domain $X$ and harness a mathematic conception, partition, to help us formally define the spatial relations. While classically the dynamics of HA are described by ordinary differential equations (ODEs) and differential inclusions, PDHA is capable of describing the behavior of cyber-physical systems (CPS) with continuous dynamics that cannot be modelled using the canonical hybrid systems' framework. For the purposes of analyzing PDHA, we propose another model called the discrete space partial differential hybrid automata (DSPDHA) which handles discrete spatial domains using finite difference methods (FDM) and this simple and intuitive approach reduces the PDHA into HA with ODE systems. We conclude with two illustrative examples in order to exhibit the nature of PDHA and DSPDHA.
IoT-Driven Cloud-based Energy and Environment Monitoring System for Manufacturing Industry
Authors: Nitol Saha, Md Masruk Aulia, Md. Mostafizur Rahman, Mohammed Shafiul Alam Khan
Date: 2024-04-17 00:00:00

This research focused on the development of a cost-effective IoT solution for energy and environment monitoring geared towards manufacturing industries. The proposed system is developed using open-source software that can be easily deployed in any manufacturing environment. The system collects real-time temperature, humidity, and energy data from different devices running on different communication such as TCP/IP, Modbus, etc., and the data is transferred wirelessly using an MQTT client to a database working as a cloud storage solution. The collected data is then visualized and analyzed using a website running on a host machine working as a web client.
Perspectives on Contractivity in Control, Optimization, and Learning
Authors: Alexander Davydov, Francesco Bullo
Date: 2024-04-17 00:00:00

Contraction theory is a mathematical framework for studying the convergence, robustness, and modularity properties of dynamical systems and algorithms. In this opinion paper, we provide five main opinions on the virtues of contraction theory. These opinions are (i) contraction theory is a unifying framework emerging from classical and modern works, (ii) contractivity is computationally-friendly, robust, and modular stability, (iii) numerous dynamical systems are contracting, (iv) contraction theory is relevant to modern applications, and (v) contraction theory can be vastly extended in numerous directions. We survey recent theoretical and applied research in each of these five directions.
An Overview of Electromagnetic Illusions: Empowering Smart Environments with Reconfigurable Metasurfaces
Authors: Hamidreza Taghvaee, Mohsen Khalily, Gabriele Gradoni, Rahim Tafazolli
Date: 2024-04-18 00:00:00

This study delves into the innovative landscape of metasurfaces, with a particular focus on their role in achieving EM illusion (EMI) a facet of paramount significance. The control of EM waves assumes a pivotal role in mitigating issues such as signal degradation, interference, and reduced communication range. Furthermore, the engineering of waves serves as a foundational element in achieving invisibility or minimized detectability. This survey unravels the theoretical underpinnings and practical designs of EMI coatings, which have been harnessed to develop functional metasurfaces. EMI, practically achieved through engineered coatings, confers a strategic advantage by either reducing the radar cross-section of objects or creating misleading footprints. In addition to illustrating the outstanding achievements in reconfigurable cloaking, this study culminates in the proposal of a novel approach, suggesting the emergence of EMI without the need for physically coating the device to conceal and thus proposing the concept of a smart EMI environment. This groundbreaking work opens a new way for engineers and researchers to unlock exotic and versatile designs that build on reconfigurable intelligent surfaces (RIS). Crucially the designs enabled by the proposed approach, present a wide array of applications, encompassing camouflaging, deceptive sensing, radar cognition control, and defence security, among others. In essence, this research stands as a beacon guiding the exploration of uncharted territories in wave control through smart EMI environments, with profound implications spanning basic academic research in RIS through advanced security technologies and communication systems.
KnotResolver: Tracking self-intersecting filaments in microscopy using directed graphs
Authors: Dhruv Khatri, Shivani A. Yadav, Chaitanya A. Athale
Date: 2024-04-18 00:00:00
Tasks: Contour Detection, Object Tracking, Time Series

Quantification of microscopy time-series of in vitro reconstituted motor driven microtubule (MT) transport in 'gliding assays' is typically performed using computational object tracking tools. However, these are limited to non-intersecting and rod-like filaments. Here, we describe a novel computational image-analysis pipeline, KnotResolver, to track image time-series of highly curved self-intersecting looped filaments (knots) by resolving cross-overs. The code integrates filament segmentation and cross-over or 'knot' identification based on directed graph representation, where nodes represent cross-overs and edges represent the path connecting them. The graphs are mapped back to contours and the distance to a reference minimized. We demonstrate the utility of the tool by segmentation and tracking MTs from experiments with dynein-driven wave like filament looping. The accuracy of contour detection is sub-pixel accuracy, and Dice scores indicate a robustness to noise, better than currently used tools. Thus KnotResolver overcomes multiple limitations of widely used tools in microscopy of cytoskeletal filament-like structures.