pwc-archive/papers-with-abstracts huggingface dataset:
Rethinking Multi-Interest Learning for Candidate Matching in Recommender Systems
Authors: Yueqi Xie, Jingqi Gao, Peilin Zhou, Qichen Ye, Yining Hua, Jaeboum Kim, Fangzhao Wu, Sunghun Kim
Date: 2023-02-28 00:00:00
Tasks: Recommendation Systems

Existing research efforts for multi-interest candidate matching in recommender systems mainly focus on improving model architecture or incorporating additional information, neglecting the importance of training schemes. This work revisits the training framework and uncovers two major problems hindering the expressiveness of learned multi-interest representations. First, the current training objective (i.e., uniformly sampled softmax) fails to effectively train discriminative representations in a multi-interest learning scenario due to the severe increase in easy negative samples. Second, a routing collapse problem is observed where each learned interest may collapse to express information only from a single item, resulting in information loss. To address these issues, we propose the REMI framework, consisting of an Interest-aware Hard Negative mining strategy (IHN) and a Routing Regularization (RR) method. IHN emphasizes interest-aware hard negatives by proposing an ideal sampling distribution and developing a Monte-Carlo strategy for efficient approximation. RR prevents routing collapse by introducing a novel regularization term on the item-to-interest routing matrices. These two components enhance the learned multi-interest representations from both the optimization objective and the composition information. REMI is a general framework that can be readily applied to various existing multi-interest candidate matching methods. Experiments on three real-world datasets show our method can significantly improve state-of-the-art methods with easy implementation and negligible computational overhead. The source code will be released.
Optimization of a three-phase Induction Motor for Electric Vehicles Based on Hook-Jews Optimization Method
Authors: Arash Mousaei, Sahar Aziz Mohammadabadi
Date: 2023-02-28 00:00:00

In this paper, the Hook-Jews (HJ) optimization method is used to optimize a 3-phase Squirrel-Cage Induction Motor (SCIM) as an Electric Vehicle's (EV) motor. Optimal designs with different numbers of poles, different nominal and maximum speeds, and different numbers of grooves are compared and the best one is selected. The optimization method used has advantages such as simple programming, omission of gradients, short convergence times, and the possibility of changing individual parameters. Design parameter variations for optimal designs with rated speeds for 2-pole and 4-pole motors are shown and explained. The results show that his 2-pole motor with the rectangular stator and rotor slots and a rated speed of 1800 rpm has the highest efficiency.
A fuzzy control scheme for deployment of space tethered system with tension constraint
Authors: Shidong Xu ∗, Hao Wen, Zheng Huang, Dongping Jin
Date: 2020-08-20 00:00:00
Proceeding: Aerospace Science and Technology 2020 8

This paper is concerned with fuzzy control for deployment of space tethered system subject to tension constraint. The space tethered system under consideration consists of two satellites connected through a straight, massless, and inextensible tether. Unlike the existing results, in this study a fuzzy model of the underactuated space tethered system is established to mathematically derive a fuzzy control law in analytical form, which can achieve the tether deployment in a fast and stable manner. In the meantime, the asymmetric constraint on tether tension is handled in controller design and stability analysis such that tether tension can be kept within a prescribed positive range during the deployment. Compared with the knowledge-based fuzzy controller design approaches, the proposed fuzzy controller is derived from the fuzzy model based on a rigorous mathematical foundation. The developed fuzzy control technique provides a new solution to the control design of underactuated dynamic systems with complex constraints. Finally, illustrative simulation results are presented to prove the effectiveness of the developed control scheme.
Vehicular Behavior-Aware Beamforming Design for Integrated Sensing and Communication Systems
Authors: Dingyan Cong, Shuaishuai Guo, Shuping Dang, Haixia Zhang
Date: 2023-02-27 00:00:00
Tasks: Autonomous Vehicles, Integrated sensing and communication, ISAC

Communication and sensing are two important features of connected and autonomous vehicles (CAVs). In traditional vehicle-mounted devices, communication and sensing modules exist but in an isolated way, resulting in a waste of hardware resources and wireless spectrum. In this paper, to cope with the above inefficiency, we propose a vehicular behavior-aware integrated sensing and communication (VBA-ISAC) beamforming design for the vehicle-mounted transmitter with multiple antennas. In this work, beams are steered based on vehicular behaviors to assist driving and meanwhile provide spectral-efficient uplink data services with the help of a roadside unit (RSU). Specifically, we first predict the area of interest (AoI) to be sensed based on the vehicles' trajectories. Then, we formulate a VBA-ISAC beamforming design problem to sense the AoI while maximizing the spectral efficiency of uplink communications, where a trade-off factor is introduced to balance the communication and sensing performance. A semi-definite relaxation-based beampattern mismatch minimization (SDR-BMM) algorithm is proposed to solve the formulated problem. To reduce the hardware cost and power consumption, we further improve the proposed VBA-ISAC beamforming design by introducing the hybrid analog-digital (HAD) structure. Numerical results verify the effectiveness of VBA-ISAC scheme and show that the proposed beamforming design outperforms the benchmarks in both spectral efficiency and radar beampattern.
Randomized Kaczmarz in Adversarial Distributed Setting
Authors: Longxiu Huang, Xia Li, Deanna Needell
Date: 2023-02-24 00:00:00

Developing large-scale distributed methods that are robust to the presence of adversarial or corrupted workers is an important part of making such methods practical for real-world problems. In this paper, we propose an iterative approach that is adversary-tolerant for convex optimization problems. By leveraging simple statistics, our method ensures convergence and is capable of adapting to adversarial distributions. Additionally, the efficiency of the proposed methods for solving convex problems is shown in simulations with the presence of adversaries. Through simulations, we demonstrate the efficiency of our approach in the presence of adversaries and its ability to identify adversarial workers with high accuracy and tolerate varying levels of adversary rates.
Robust one-shot estimation over shared networks in the presence of denial-of-service attacks
Authors: Xu Zhang, Marcos M. Vasconcelos
Date: 2023-02-28 00:00:00
Tasks: Blocking

Multi-agent systems often communicate over low-power shared wireless networks in unlicensed spectrum, prone to denial-of-service attacks. We consider the following scenario: multiple pairs of agents communicating strategically over shared communication networks in the presence of a jammer who may launch a denial-of-service. We cast this problem as a game between a coordinator who optimizes the transmission and estimation policies jointly and a jammer who optimizes its probability of performing an attack. We consider two cases: point-to-point channels and large-scale networks with a countably infinite number of sensor-receiver pairs. When the jammer proactively attacks the channel, the game is nonconvex from the coordinator's perspective. However, despite the lack of convexity, we construct a saddle point equilibrium solution for any multi-variate Gaussian distribution for the observations. When the jammer is reactive, we obtain an algorithm based on sequential convex optimization, which converges swiftly to first-order Nash-equilibria. Interestingly, blocking the channel is often optimal when the jammer is reactive, even when it is idle, to create ambiguity at the receiver.
Line-of-Sight Probability for Outdoor-to-Indoor UAV-Assisted Emergency Networks
Authors: Gaurav Duggal, R. Michael Buehrer, Nishith Tripathi, Jeffrey H. Reed
Date: 2023-02-27 00:00:00

For emergency response scenarios like firefighting in urban environments, there is a need to both localize emergency responders inside the building and also support a high bandwidth communication link between the responders and a command-and-control center. The emergency networks for such scenarios can be established with the quick deployment of Unmanned Aerial Vehicles (UAVs). Further, the 3D mobility of UAVs can be leveraged to improve the quality of the wireless link by maneuvering them into advantageous locations. This has motivated recent propagation measurement campaigns to study low-altitude air-to-ground channels in both 5G-sub6 GHz and 5G-mmWave bands. In this paper, we develop a model for the link in a UAV-assisted emergency location and/or communication system. Specifically, given the importance of Line-of-Sight (LoS) links in localization as well as mmWave communication, we derive a closed-form expression for the LoS probability. This probability is parameterized by the UAV base station location, the size of the building, and the size of the window that offers the best propagation path. An expression for coverage probability is also derived. The LoS probability and coverage probabilities derived in this paper can be used to analyze the outdoor UAV-to-indoor propagation environment to determine optimal UAV positioning and the number of UAVs needed to achieve the desired performance of the emergency network.
Joint Spectrum and Power Allocation for V2X Communications with Imperfect CSI
Authors: Peng Wang, Weihua Wu, Jiayi Liu, Guanhua Chai, Li Feng
Date: 2023-02-21 00:00:00
Tasks: Self-Learning

In Vehicle-to-Everything (V2X) communication, the high mobility of vehicles generates the Doppler shift which leads to channel uncertainties. Moreover, the reasons for channel uncertainties also include the finite channel feedback, channels state information (CSI) loss and latency. With this concern, we formulate a joint spectrum and power allocation problem for V2X communication with imperfect CSI. Specifically, the sum capacity of cellular user equipments (CUEs) is maximized subject to the minimum Signal-to-Interference-and-Noise Ratio (SINR) requirements of CUEs and the outage probability constraints of vehicular user equipments (VUEs). Then, two different robust resource allocation approaches are designed to solve the problem. One is Bernstein Approximation-based Robust Resource Allocation approach. More specifically, Bernstein approximations are employed to convert the chance constraint into a calculable constraint, and Bisection search method is proposed to obtain the optimal allocation solution with low complexity. Then, for further reducing the computational complexity, Self-learning Robust Resource Allocation approach, which includes a learning method and an analytical mapping method, is proposed as the second approach. The learning method is devised to learn the uncertainty set which transforms the chance constraint into calculable constraints, and the analytical mapping method is proposed to obtain closed-form solutions of the resource allocation problem. Finally, the simulation results prove that the proposed approaches can improve the capacity of all CUEs effectively whilst ensuring the reliability of the channel.
Performance Limits of a Deep Learning-Enabled Text Semantic Communication under Interference
Authors: Tilahun M. Getu, Walid Saad, Georges Kaddoum, Mehdi Bennis
Date: 2023-02-15 00:00:00
Tasks: Semantic Communication

Although deep learning (DL)-enabled semantic communication (SemCom) has emerged as a 6G enabler by minimizing irrelevant information transmission -- minimizing power usage, bandwidth consumption, and transmission delay, its benefits can be limited by radio frequency interference (RFI) that causes substantial semantic noise. Such semantic noise's impact can be alleviated using an interference-resistant and robust (IR$^2$) SemCom design, though no such design exists yet. To stimulate fundamental research on IR2 SemCom, the performance limits of a popular text SemCom system named DeepSC are studied in the presence of (multi-interferer) RFI. By introducing a principled probabilistic framework for SemCom, we show that DeepSC produces semantically irrelevant sentences as the power of (multi-interferer) RFI gets very large. We also derive DeepSC's practical limits and a lower bound on its outage probability under multi-interferer RFI, and propose a (generic) lifelong DL-based IR$^2$ SemCom system. We corroborate the derived limits with simulations and computer experiments, which also affirm the vulnerability of DeepSC to a wireless attack using RFI.
Digital Over-the-Air Federated Learning in Multi-Antenna Systems
Authors: Sihua Wang, Mingzhe Chen, Cong Shen, Changchuan Yin, Christopher G. Brinton
Date: 2023-02-04 00:00:00
Tasks: Federated Learning

In this paper, the performance optimization of federated learning (FL), when deployed over a realistic wireless multiple-input multiple-output (MIMO) communication system with digital modulation and over-the-air computation (AirComp) is studied. In particular, a MIMO system is considered in which edge devices transmit their local FL models (trained using their locally collected data) to a parameter server (PS) using beamforming to maximize the number of devices scheduled for transmission. The PS, acting as a central controller, generates a global FL model using the received local FL models and broadcasts it back to all devices. Due to the limited bandwidth in a wireless network, AirComp is adopted to enable efficient wireless data aggregation. However, fading of wireless channels can produce aggregate distortions in an AirComp-based FL scheme. To tackle this challenge, we propose a modified federated averaging (FedAvg) algorithm that combines digital modulation with AirComp to mitigate wireless fading while ensuring the communication efficiency. This is achieved by a joint transmit and receive beamforming design, which is formulated as an optimization problem to dynamically adjust the beamforming matrices based on current FL model parameters so as to minimize the transmitting error and ensure the FL performance. To achieve this goal, we first analytically characterize how the beamforming matrices affect the performance of the FedAvg in different iterations. Based on this relationship, an artificial neural network (ANN) is used to estimate the local FL models of all devices and adjust the beamforming matrices at the PS for future model transmission. The algorithmic advantages and improved performance of the proposed methodologies are demonstrated through extensive numerical experiments.
Audio Retrieval for Multimodal Design Documents: A New Dataset and Algorithms
Authors: Prachi Singh, Srikrishna Karanam, Sumit Shekhar
Date: 2023-02-28 00:00:00
Tasks: Retrieval

We consider and propose a new problem of retrieving audio files relevant to multimodal design document inputs comprising both textual elements and visual imagery, e.g., birthday/greeting cards. In addition to enhancing user experience, integrating audio that matches the theme/style of these inputs also helps improve the accessibility of these documents (e.g., visually impaired people can listen to the audio instead). While recent work in audio retrieval exists, these methods and datasets are targeted explicitly towards natural images. However, our problem considers multimodal design documents (created by users using creative software) substantially different from a naturally clicked photograph. To this end, our first contribution is collecting and curating a new large-scale dataset called Melodic-Design (or MELON), comprising design documents representing various styles, themes, templates, illustrations, etc., paired with music audio. Given our paired image-text-audio dataset, our next contribution is a novel multimodal cross-attention audio retrieval (MMCAR) algorithm that enables training neural networks to learn a common shared feature space across image, text, and audio dimensions. We use these learned features to demonstrate that our method outperforms existing state-of-the-art methods and produce a new reference benchmark for the research community on our new dataset.
Robust Secrecy via Aerial Reflection and Jamming: Joint Optimization of Deployment and Transmission
Authors: Xiao Tang, Hongliang He, Limeng Dong, Lixin Li, Qinghe Du, Zhu Han
Date: 2023-02-28 00:00:00
Tasks: Deep Reinforcement Learning

Reconfigurable intelligent surfaces (RISs) are recognized with great potential to strengthen wireless security, yet the performance gain largely depends on the deployment location of RISs in the network topology. In this paper, we consider the anti-eavesdropping communication established through a RIS at a fixed location, as well as an aerial platform mounting another RIS and a friendly jammer to further improve the secrecy. The aerial RIS helps enhance the legitimate signal and the aerial cooperative jamming is strengthened through the fixed RIS. The security gain with aerial reflection and jamming is further improved with the optimized deployment of the aerial platform. We particularly consider the imperfect channel state information issue and address the worst-case secrecy for robust performance. The formulated robust secrecy rate maximization problem is decomposed into two layers, where the inner layer solves for reflection and jamming with robust optimization, and the outer layer tackles the aerial deployment through deep reinforcement learning. Simulation results show the deployment under different network topologies and demonstrate the performance superiority of our proposal in terms of the worst-case security provisioning as compared with the baselines.
Overview and Performance Analysis of Various Waveforms in High Mobility Scenarios
Authors: Yu Zhou, Haoran Yin, Jiaojiao Xiong, Shiyu Song, Jiajun Zhu, Jinming Du, Haibo Chen, Yanqun Tang
Date: 2023-02-28 00:00:00
Tasks: Vocal Bursts Intensity Prediction

In the high-mobility scenarios of next-generation wireless communication systems (beyond 5G/6G), the performance of orthogonal frequency division multiplexing (OFDM) deteriorates drastically due to the loss of orthogonality between the subcarriers caused by large Doppler frequency shifts. Various emerging waveforms have been proposed for fast time-varying channels with excellent results. In this paper, we classify these waveforms from the perspective of their modulation domain and establish a unified framework to provide a comprehensive system structure comparison. Then we analyze bit error rate (BER) performance of each waveform in doubly selective channels. Through the discussions on their complexity and compatibility with OFDM systems, we finally give the candidate waveform suggestions.
i2LQR: Iterative LQR for Iterative Tasks in Dynamic Environments
Authors: Yifan Zeng, Suiyi He, Han Hoang Nguyen, Yihan Li, Zhongyu Li, Koushil Sreenath, Jun Zeng
Date: 2023-02-28 00:00:00

This work introduces a novel control strategy called Iterative Linear Quadratic Regulator for Iterative Tasks (i2LQR), which aims to improve closed-loop performance with local trajectory optimization for iterative tasks in a dynamic environment. The proposed algorithm is reference-free and utilizes historical data from previous iterations to enhance the performance of the autonomous system. Unlike existing algorithms, the i2LQR computes the optimal solution in an iterative manner at each timestamp, rendering it well-suited for iterative tasks with changing constraints at different iterations. To evaluate the performance of the proposed algorithm, we conduct numerical simulations for an iterative task aimed at minimizing completion time. The results show that i2LQR achieves an optimized performance with respect to learning-based MPC (LMPC) as the benchmark in static environments, and outperforms LMPC in dynamic environments with both static and dynamics obstacles.
Incremental Learning of Acoustic Scenes and Sound Events
Authors: Manjunath Mulimani, Annamaria Mesaros
Date: 2023-02-28 00:00:00
Tasks: Acoustic Scene Classification, Audio Tagging, Incremental Learning, Knowledge Distillation, Scene Classification

In this paper, we propose a method for incremental learning of two distinct tasks over time: acoustic scene classification (ASC) and audio tagging (AT). We use a simple convolutional neural network (CNN) model as an incremental learner to solve the tasks. Generally, incremental learning methods catastrophically forget the previous task when sequentially trained on a new task. To alleviate this problem, we propose independent learning and knowledge distillation (KD) between the timesteps in learning. Experiments are performed on TUT 2016/2017 dataset, containing 4 acoustic scene classes and 25 sound event classes. The proposed incremental learner first solves the ASC task with an accuracy of 94.0%. Next, it learns to solve the AT task with an F1 score of 54.4%. At the same time, its performance on the previous ASC task decreases only by 5.1 percentage points due to the additional learning of the AT task.
Training sound event detection with soft labels from crowdsourced annotations
Authors: Irene Martín-Morató, Manu Harju, Paul Ahokas, Annamaria Mesaros
Date: 2023-02-28 00:00:00
Tasks: Event Detection, Sound Event Detection

In this paper, we study the use of soft labels to train a system for sound event detection (SED). Soft labels can result from annotations which account for human uncertainty about categories, or emerge as a natural representation of multiple opinions in annotation. Converting annotations to hard labels results in unambiguous categories for training, at the cost of losing the details about the labels distribution. This work investigates how soft labels can be used, and what benefits they bring in training a SED system. The results show that the system is capable of learning information about the activity of the sounds which is reflected in the soft labels and is able to detect sounds that are missed in the typical binary target training setup. We also release a new dataset produced through crowdsourcing, containing temporally strong labels for sound events in real-life recordings, with both soft and hard labels.
Diagonal State Space Augmented Transformers for Speech Recognition
Authors: George Saon, Ankit Gupta, Xiaodong Cui
Date: 2023-02-27 00:00:00
Tasks: speech-recognition, Speech Recognition

We improve on the popular conformer architecture by replacing the depthwise temporal convolutions with diagonal state space (DSS) models. DSS is a recently introduced variant of linear RNNs obtained by discretizing a linear dynamical system with a diagonal state transition matrix. DSS layers project the input sequence onto a space of orthogonal polynomials where the choice of basis functions, metric and support is controlled by the eigenvalues of the transition matrix. We compare neural transducers with either conformer or our proposed DSS-augmented transformer (DSSformer) encoders on three public corpora: Switchboard English conversational telephone speech 300 hours, Switchboard+Fisher 2000 hours, and a spoken archive of holocaust survivor testimonials called MALACH 176 hours. On Switchboard 300/2000 hours, we reach a single model performance of 8.9%/6.7% WER on the combined test set of the Hub5 2000 evaluation, respectively, and on MALACH we improve the WER by 7% relative over the previous best published result. In addition, we present empirical evidence suggesting that DSS layers learn damped Fourier basis functions where the attenuation coefficients are layer specific whereas the frequency coefficients converge to almost identical linearly-spaced values across all layers.