pwc-archive/papers-with-abstracts huggingface dataset:
Autoencoder-based Joint Communication and Sensing of Multiple Targets
Authors: Charlotte Muth, Laurent Schmalen
Date: 2023-01-23 00:00:00

We investigate the potential of autoencoders (AEs) for building a joint communication and sensing (JCAS) system that enables communication with one user while detecting multiple radar targets and estimating their positions. Foremost, we develop a suitable encoding scheme for the training of the AE and for targeting a fixed false alarm rate of the target detection during training. We compare this encoding with the classification approach using one-hot encoding for radar target detection. Furthermore, we propose a new training method that complies with possible ambiguities in the target locations. We consider different options for training the detection of multiple targets. We can show that our proposed approach based on permuting and sorting can enhance the angle estimation performance so that single snapshot estimations with a low standard deviation become possible. We outperform an ESPRIT benchmark for small numbers of measurement samples.
The neural architecture of language: Integrative modeling converges on predictive processing
Authors: Martin Schrimpf, Idan Blank, Greta Tuckute, Carina Kauf, Eghbal Hosseini, Nancy Kanwisher, Joshua Tenenbaum, Evelina Fedorenko
Date: 2021-11-04 00:00:00
Proceeding: Proceedings of the National Academy of Sciences 2021 11
Tasks: Language Modelling, Probing Language Models, Reading Comprehension

The neuroscience of perception has recently been revolutionized with an integrative modeling approach in which computation, brain function, and behavior are linked across many datasets and many computational models. By revealing trends across models, this approach yields novel insights into cognitive and neural mechanisms in the target domain. We here present a systematic study taking this approach to higher-level cognition: human language processing, our species' signature cognitive skill. We find that the most powerful "transformer" models predict nearly 100% of explainable variance in neural responses to sentences and generalize across different datasets and imaging modalities (functional MRI and electrocorticography). Models' neural fits ("brain score") and fits to behavioral responses are both strongly correlated with model accuracy on the next-word prediction task (but not other language tasks). Model architecture appears to substantially contribute to neural fit. These results provide computationally explicit evidence that predictive processing fundamentally shapes the language comprehension mechanisms in the human brain.
Rethinking and Improving Robustness of Convolutional Neural Networks: a Shapley Value-based Approach in Frequency Domain
Authors: Yiting Chen, Qibing Ren, Junchi Yan
Date: 2022-11-01 00:00:00
Proceeding: NIPS 2022 11
Tasks: Adversarial Attack, Adversarial Robustness, Data Augmentation, Fairness, image-classification, Image Classification

The existence of adversarial examples poses concerns for the robustness of convolutional neural networks (CNN), for which a popular hypothesis is about the frequency bias phenomenon: CNNs rely more on high-frequency components (HFC) for classification than humans, which causes the brittleness of CNNs. However, most previous works manually select and roughly divide the image frequency spectrum and conduct qualitative analysis. In this work, we introduce Shapley value, a metric of cooperative game theory, into the frequency domain and propose to quantify the positive (negative) impact of every frequency component of data on CNNs. Based on the Shapley value, we quantify the impact in a fine-grained way and show intriguing instance disparity. Statistically, we investigate adversarial training(AT) and the adversarial attack in the frequency domain. The observations motivate us to perform an in-depth analysis and lead to multiple novel hypotheses about i) the cause of adversarial robustness of the AT model; ii) the fairness problem of AT between different classes in the same dataset; iii) the attack bias on different frequency components. Finally, we propose a Shapley-value guided data augmentation technique for improving the robustness. Experimental results on image classification benchmarks show its effectiveness.
New Challenges for Content Privacy in Speech and Audio
Authors: Jennifer Williams, Karla Pizzi, Shuvayanti Das, Paul-Gauthier Noe
Date: 2023-01-21 00:00:00

Privacy in speech and audio has many facets. A particularly under-developed area of privacy in this domain involves consideration for information related to content and context. Speech content can include words and their meaning or even stylistic markers, pathological speech, intonation patterns, or emotion. More generally, audio captured in-the-wild may contain background speech or reveal contextual information such as markers of location, room characteristics, paralinguistic sounds, or other audible events. Audio recording devices and speech technologies are becoming increasingly commonplace in everyday life. At the same time, commercialised speech and audio technologies do not provide consumers with a range of privacy choices. Even where privacy is regulated or protected by law, technical solutions to privacy assurance and enforcement fall short. This position paper introduces three important and timely research challenges for content privacy in speech and audio. We highlight current gaps and opportunities, and identify focus areas, that could have significant implications for developing ethical and safer speech technologies.
Rig Inversion by Training a Differentiable Rig Function
Authors: Mathieu Marquis Bolduc, Hau Nghiep Phan
Date: 2023-01-11 00:00:00

Rig inversion is the problem of creating a method that can find the rig parameter vector that best approximates a given input mesh. In this paper we propose to solve this problem by first obtaining a differentiable rig function by training a multi layer perceptron to approximate the rig function. This differentiable rig function can then be used to train a deep learning model of rig inversion.
Cutting a Cake Fairly for Groups Revisited
Authors: Erel Segal-haLevi, Warut Suksompong
Date: 2023-01-22 00:00:00

Cake cutting is a classic fair division problem, with the cake serving as a metaphor for a heterogeneous divisible resource. Recently, it was shown that for any number of players with arbitrary preferences over a cake, it is possible to partition the players into groups of any desired size and divide the cake among the groups so that each group receives a single contiguous piece and every player is envy-free. For two groups, we characterize the group sizes for which such an assignment can be computed by a finite algorithm, showing that the task is possible exactly when one of the groups is a singleton. We also establish an analogous existence result for chore division, and show that the result does not hold for a mixed cake.
Location Division Multiple Access for Near-Field Communications
Authors: Zidong Wu, Linglong Dai
Date: 2023-01-22 00:00:00

Spatial division multiple access (SDMA) is essential to improve the spectrum efficiency for multi-user multiple-input multiple-output (MIMO) communications. The classical SDMA for massive MIMO with hybrid precoding heavily relies on the angular orthogonality in the far field to distinguish multiple users at different angles, which fails to fully exploit spatial resources in the distance domain. With dramatically increasing number of antennas, extremely large-scale antenna array (ELAA) introduces additional resolution in the distance domain in the near field. In this paper, we propose the concept of location division multiple access (LDMA) to provide a new possibility to enhance spectrum efficiency. The key idea is to exploit extra spatial resources in the distance domain to serve different users at different locations (determined by angles and distances) in the near field. Specifically, the asymptotic orthogonality of beam focusing vectors in the distance domain is proved, which reveals that near-field beam focusing is able to focus signals on specific locations to mitigate inter-user interferences. Simulation results verify the superiority of the proposed LDMA over classical SDMA in different scenarios.
Resource Allocation with Stability Constraints of an Edge-cloud controlled AGV
Authors: Shreya Tayade, Peter Rost, Andreas Maeder, Hans Schotten
Date: 2023-01-23 00:00:00
Tasks: Scheduling

The paper proposes Resource Allocation (RA) schemes for a closed loop feedback control system by analysing the control-communication dependencies. We consider an Automated Guided Vehicle (AGV) that communicates with a controller located in an edge-cloud over a wireless fading channel. The control commands are transmitted to an AGV and the position state is feedback to the controller at every time-instant. A control stability based scheduling metric 'Probability of Instability' is evaluated for the resource allocation. The performance of stability based RA scheme is compared with the maximum SNR based RA scheme and control error first approach in an overloaded and non-overloaded scenario. The RA scheme with the stability constraints significantly reduces the resource utilization and is able to schedule more number of AGVs while maintaining its stability. Moreover, the proposed RA scheme is independent of control state and depends upon consecutive packet errors, the control parameters like sampling time and AGV velocity. Furthermore, we also analyse the impact of RA schemes on the AGV's stability and error performance, and evaluated the number of unstable AGVs.
Estimation of Source and Receiver Positions, Room Geometry and Reflection Coefficients From a Single Room Impulse Response
Authors: Wangyang Yu, W. Bastiaan Kleijn
Date: 2023-01-22 00:00:00
Tasks: Room Impulse Response (RIR)

We propose an algorithm to estimate source and receiver positions, room geometry and reflection coefficients from a single room impulse response simultaneously. It is based on a symmetry analysis of the room impulse response. The proposed method utilizes the times of arrivals of the direct path, first order reflections and second order reflections. The proposed method is robust to erroneous pulses and non-specular reflections. It can be applied to any room with parallel walls as long as the required arrival times of reflections are available. In contrast to the state-of-art method, we do not restrict the location of source and receiver.
Digital Twins for Marine Operations: A Brief Review on Their Implementation
Authors: Federico Zocco, Hsueh-Cheng Wang, Mien Van
Date: 2023-01-16 00:00:00

While the concept of a digital twin to support maritime operations is gaining attention for predictive maintenance, real-time monitoring, control, and overall process optimization, clarity on its implementation is missing in the literature. Therefore, in this review we show how different authors implemented their digital twins, discuss our findings, and finally give insights on future research directions.
Optimal Inter-area Oscillation Damping Control: A Transfer Deep Reinforcement Learning Approach with Switching Control Strategy
Authors: Siyuan Liang, Long Huo, Xin Chen, Peiyuan Sun
Date: 2023-01-23 00:00:00
Tasks: Deep Reinforcement Learning

Wide-area damping control for inter-area oscillation (IAO) is critical to modern power systems. The recent breakthroughs in deep learning and the broad deployment of phasor measurement units (PMU) promote the development of datadriven IAO damping controllers. In this paper, the damping control of IAOs is modeled as a Markov Decision Process (MDP) and solved by the proposed Deep Deterministic Policy Gradient (DDPG) based deep reinforcement learning (DRL) approach. The proposed approach optimizes the eigenvalue distribution of the system, which determines the IAO modes in nature. The eigenvalues are evaluated by the data-driven method called dynamic mode decomposition. For a given power system, only a subset of generators selected by participation factors needs to be controlled, alleviating the control and computing burdens. A Switching Control Strategy (SCS) is introduced to improve the transient response of IAOs. Numerical simulations of the IEEE-39 New England power grid model validate the effectiveness and advanced performance of the proposed approach as well as its robustness against communication delays. In addition, we demonstrate the transfer ability of the DRL model trained on the linearized power grid model to provide effective IAO damping control in the non-linear power grid model environment.
Forecaster-aided User Association and Load Balancing in Multi-band Mobile Networks
Authors: Manan Gupta, Sandeep Chinchali, Paul Varkey, Jeffrey G. Andrews
Date: 2023-01-23 00:00:00
Tasks: Model Predictive Control, Reinforcement Learning (RL)

Cellular networks are becoming increasingly heterogeneous with higher base station (BS) densities and ever more frequency bands, making BS selection and band assignment key decisions in terms of rate and coverage. In this paper, we decompose the mobility-aware user association task into (i) forecasting of user rate and then (ii) convex utility maximization for user association accounting for the effects of BS load and handover overheads. Using a linear combination of normalized mean-squared error and normalized discounted cumulative gain as a novel loss function, a recurrent deep neural network is trained to reliably forecast the mobile users' future rates. Based on the forecast, the controller optimizes the association decisions to maximize the service rate-based network utility using our computationally efficient (speed up of 100x versus generic convex solver) algorithm based on the Frank-Wolfe method. Using an industry-grade network simulator developed by Meta, we show that the proposed model predictive control (MPC) approach improves the 5th percentile service rate by 3.5x compared to the traditional signal strength-based association, reduces the median number of handovers by 7x compared to a handover agnostic strategy, and achieves service rates close to a genie-aided scheme. Furthermore, our model-based approach is significantly more sample-efficient (needs 100x less training data) compared to model-free reinforcement learning (RL), and generalizes well across different user drop scenarios.
Early Warning Software for Emergency Department Crowding
Authors: Jalmari Tuominen, Teemu Koivistoinen, Juho Kanniainen, Niku Oksala, Ari Palomäki, Antti Roine
Date: 2023-01-22 00:00:00
Tasks: Articles, Management

Emergency department (ED) crowding is a well-recognized threat to patient safety and it has been repeatedly associated with increased mortality. Accurate forecasts of future service demand could lead to better resource management and has the potential to improve treatment outcomes. This logic has motivated an increasing number of research articles but there has been little to no effort to move these findings from theory to practice. In this article, we present first results of a prospective crowding early warning software, that was integrated to hospital databases to create real-time predictions every hour over the course of 5 months in a Nordic combined ED using Holt-Winters' seasonal methods. We showed that the software could predict next hour crowding with a nominal AUC of 0.98 and 24 hour crowding with an AUC of 0.79 using simple statistical models. Moreover, we suggest that afternoon crowding can be predicted at 1 p.m. with an AUC of 0.84.
Power System Stability Analysis using Neural Network
Authors: Md. Rayid Hasan Mojumder
Date: 2023-01-22 00:00:00

This work focuses on the design of modern power system controllers for automatic voltage regulators (AVR) and the applications of machine learning (ML) algorithms to correctly classify the stability of the IEEE 14 bus system. The LQG controller performs the best time domain characteristics compared to PID and LQG, while the sensor and amplifier gain is changed in a dynamic passion. After that, the IEEE 14 bus system is modeled, and contingency scenarios are simulated in the System Modelica Dymola environment. Application of the Monte Carlo principle with modified Poissons probability distribution principle is reviewed from the literature that reduces the total contingency from 1000k to 20k. The damping ratio of the contingency is then extracted, pre-processed, and fed to ML algorithms, such as logistic regression, support vector machine, decision trees, random forests, Naive Bayes, and k-nearest neighbor. A neural network (NN) of one, two, three, five, seven, and ten hidden layers with 25%, 50%, 75%, and 100% data size is considered to observe and compare the prediction time, accuracy, precision, and recall value. At lower data size, 25%, in the neural network with two-hidden layers and a single hidden layer, the accuracy becomes 95.70% and 97.38%, respectively. Increasing the hidden layer of NN beyond a second does not increase the overall score and takes a much longer prediction time; thus could be discarded for similar analysis. Moreover, when five, seven, and ten hidden layers are used, the F1 score reduces. However, in practical scenarios, where the data set contains more features and a variety of classes, higher data size is required for NN for proper training. This research will provide more insight into the damping ratio-based system stability prediction with traditional ML algorithms and neural networks.
Graph Convolutional Neural Networks for Optimal Power Flow Locational Marginal Price
Authors: Adrian-Petru Surani, Rahul Sahetiya
Date: 2023-01-22 00:00:00

The real-time electricity market with the integration of renewable energies and electric vehicles have been receiving significant attention recently. So far most of the literature addresses the optimal power flow (OPF) problem in the real-time electricity market context by iterative methods. However, solving OPF problems in real-time is challenging due to the high computational complexity by the iterative methods. Motivated by this fact, in this paper, we propose a Chebyshev Graph Convolutional Neural Networks (ChebGCN) to improve the efficiency of integrating low-carbon energy sources into power grids and to address scalability and adaptivity of end-to-end existing OPF solutions. The proposed GCN method is capable to predict the optimal energy market marginal prices in real time. Numerical analysis is used to benchmark the results and validate the improvement.
Second-Order Coverage Control for Multi-Agent UAV Photogrammetry
Authors: Samuel Mallick, Airlie Chapman, Eric Schoof
Date: 2023-01-21 00:00:00

Unmanned Aerial Vehicles equipped with cameras can be used to automate image capture for generating 3D models via photogrammetry. Current methods rely on a single vehicle to capture images sequentially, or use pre-planned and heuristic imaging configurations. We seek to provide a multi-agent control approach to capturing the images required to 3D map a region. A photogrammetry cost function is formulated that captures the importance of sharing feature-dense areas across multiple images for successful photogrammetry reconstruction. A distributed second-order coverage controller is used to minimise this cost and move agents to an imaging configuration. This approach prioritises high quality images that are simultaneously captured, leading to efficient and scalable 3D mapping of a region. We demonstrate our approach with a hardware experiment, generating and comparing 3D reconstructions from image sets captured using our approach to those captured using traditional methods.
Data-Driven Distributionally Robust Scheduling of Community Integrated Energy Systems with Uncertain Renewable Generations Considering Integrated Demand Response
Authors: Yang Li, Meng Han, Mohammad Shahidehpour, Jiazheng Li, Chao Long
Date: 2023-01-21 00:00:00
Tasks: Computational Efficiency, Generative Adversarial Network, Scheduling

A community integrated energy system (CIES) is an important carrier of the energy internet and smart city in geographical and functional terms. Its emergence provides a new solution to the problems of energy utilization and environmental pollution. To coordinate the integrated demand response and uncertainty of renewable energy generation (RGs), a data-driven two-stage distributionally robust optimization (DRO) model is constructed. A comprehensive norm consisting of the 1-norm and infinity-norm is used as the uncertainty probability distribution information set, thereby avoiding complex probability density information. To address multiple uncertainties of RGs, a generative adversarial network based on the Wasserstein distance with gradient penalty is proposed to generate RG scenarios, which has wide applicability. To further tap the potential of the demand response, we take into account the ambiguity of human thermal comfort and the thermal inertia of buildings. Thus, an integrated demand response mechanism is developed that effectively promotes the consumption of renewable energy. The proposed method is simulated in an actual CIES in North China. In comparison with traditional stochastic programming and robust optimization, it is verified that the proposed DRO model properly balances the relationship between economical operation and robustness while exhibiting stronger adaptability. Furthermore, our approach outperforms other commonly used DRO methods with better operational economy, lower renewable power curtailment rate, and higher computational efficiency.
Integration of Data Driven Technologies in Smart Grids for Resilient and Sustainable Smart Cities: A Comprehensive Review
Authors: Mansoor Ali, Faisal Naeem, Nadir Adam, Georges Kaddoum, Noor Ul Huda, Muhammad Adnan, Muhammad Tariq
Date: 2023-01-20 00:00:00

A modern-day society demands resilient, reliable, and smart urban infrastructure for effective and in telligent operations and deployment. However, unexpected, high-impact, and low-probability events such as earthquakes, tsunamis, tornadoes, and hurricanes make the design of such robust infrastructure more complex. As a result of such events, a power system infrastructure can be severely affected, leading to unprecedented events, such as blackouts. Nevertheless, the integration of smart grids into the existing framework of smart cities adds to their resilience. Therefore, designing a resilient and reliable power system network is an inevitable requirement of modern smart city infras tructure. With the deployment of the Internet of Things (IoT), smart cities infrastructures have taken a transformational turn towards introducing technologies that do not only provide ease and comfort to the citizens but are also feasible in terms of sustainability and dependability. This paper presents a holistic view of a resilient and sustainable smart city architecture that utilizes IoT, big data analytics, unmanned aerial vehicles, and smart grids through intelligent integration of renew able energy resources. In addition, the impact of disasters on the power system infrastructure is investigated and different types of optimization techniques that can be used to sustain the power flow in the network during disturbances are compared and analyzed. Furthermore, a comparative review analysis of different data-driven machine learning techniques for sustainable smart cities is performed along with the discussion on open research issues and challenges.