pwc-archive/papers-with-abstracts huggingface dataset:
AI-Generated Incentive Mechanism and Full-Duplex Semantic Communications for Information Sharing
Authors: Hongyang Du, Jiacheng Wang, Dusit Niyato, Jiawen Kang, Zehui Xiong, Dong In Kim
Date: 2023-03-03 00:00:00
Tasks: Deep Reinforcement Learning, Mixed Reality

The next generation of Internet services, such as Metaverse, rely on mixed reality (MR) technology to provide immersive user experiences. However, the limited computation power of MR headset-mounted devices (HMDs) hinders the deployment of such services. Therefore, we propose an efficient information sharing scheme based on full-duplex device-to-device (D2D) semantic communications to address this issue. Our approach enables users to avoid heavy and repetitive computational tasks, such as artificial intelligence-generated content (AIGC) in the view images of all MR users. Specifically, a user can transmit the generated content and semantic information extracted from their view image to nearby users, who can then use this information to obtain the spatial matching of computation results under their view images. We analyze the performance of full-duplex D2D communications, including the achievable rate and bit error probability, by using generalized small-scale fading models. To facilitate semantic information sharing among users, we design a contract theoretic AI-generated incentive mechanism. The proposed diffusion model generates the optimal contract design, outperforming two deep reinforcement learning algorithms, i.e., proximal policy optimization and soft actor-critic algorithms. Our numerical analysis experiment proves the effectiveness of our proposed methods. The code for this paper is available at https://github.com/HongyangDu/SemSharing
Features Disentangled Semantic Broadcast Communication Networks
Authors: Shuai Ma, Weining Qiao, Youlong Wu, Hang Li, Guangming Shi, Dahua Gao, Yuanming Shi, Shiyin Li, Naofal Al-Dhahir
Date: 2023-03-03 00:00:00
Tasks: feature selection

Single-user semantic communications have attracted extensive research recently, but multi-user semantic broadcast communication (BC) is still in its infancy. In this paper, we propose a practical robust features-disentangled multi-user semantic BC framework, where the transmitter includes a feature selection module and each user has a feature completion module. Instead of broadcasting all extracted features, the semantic encoder extracts the disentangled semantic features, and then only the users' intended semantic features are selected for broadcasting, which can further improve the transmission efficiency. Within this framework, we further investigate two information-theoretic metrics, including the ultimate compression rate under both the distortion and perception constraints, and the achievable rate region of the semantic BC. Furthermore, to realize the proposed semantic BC framework, we design a lightweight robust semantic BC network by exploiting a supervised autoencoder (AE), which can controllably disentangle sematic features. Moreover, we design the first hardware proof-of-concept prototype of the semantic BC network, where the proposed semantic BC network can be implemented in real time. Simulations and experiments demonstrate that the proposed robust semantic BC network can significantly improve transmission efficiency.
Quality of service based radar resource management for synchronisation problems
Authors: Tobias Müller, Sebastian Durst, Pascal Marquardt, Stefan Brüggenwirth
Date: 2023-03-03 00:00:00
Tasks: Decision Making, Management

An intelligent radar resource management is an essential building block of any modern radar system. The quality of service based resource allocation model (Q-RAM) provides a framework for profound and quantifiable decision-making but lacks a representation of inter-task dependencies that can e.g. arise for tracking and synchronisation tasks. As a consequence, synchronisation is usually performed in fixed non-optimal patterns. We present an extension of Q-RAM which enables the resource allocation to consider complex inter-task dependencies and can produce adaptive and intelligent synchronisation schemes. The provided experimental results demonstrate a significant improvement over traditional strategies.
Energy and Bandwidth Efficiency of Event-Based Communication
Authors: Christopher Willuweit, Carsten Bockelmann, Armin Dekorsy
Date: 2023-03-03 00:00:00

Wireless sensor nodes need a drastically reduced technical complexity to fit constraints of future applications. Reducing complexity often results in a degradation of energy and bandwidth efficiency. An interesting new approach that promises to reduce both technical complexity and energy consumption is event-based communication (EBC). While practical low-complexity implementations of such systems have already been proposed, the general question of energy and bandwidth efficiency remains open. In this paper, we compare these between EBC and a system relying on classical uniform sampling. We show that EBC is indeed much more energy efficient, and this comes at the cost of bandwidth efficiency. Therefore EBC is particularly suitable in combination with ultra-wideband communication.
Joint Beamforming for RIS-Assisted Integrated Sensing and Communication Systems
Authors: Yongqing Xu, Yong Li, J. Andrew Zhang, Marco Di Renzo, Tony Q. S. Quek
Date: 2023-03-03 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communications (ISAC) is an emerging critical technique for the next generation of communication systems. However, due to multiple performance metrics used for communication and sensing, the limited degrees-of-freedom (DoF) in optimizing ISAC systems poses a challenge. Reconfigurable intelligent surfaces (RIS) can introduce new DoF for beamforming in ISAC systems, thereby enhancing the performance of communication and sensing simultaneously. In this paper, we propose two optimization techniques for beamforming in RIS-assisted ISAC systems. The first technique is an alternating optimization (AO) algorithm based on the semidefinite relaxation (SDR) method and a one-dimension iterative (ODI) algorithm, which can maximize the radar mutual information (MI) while imposing constraints on the communication rates. The second technique is an AO algorithm based on the Riemannian gradient (RG) method, which can maximize the weighted ISAC performance metrics. Simulation results verify the effectiveness of the proposed schemes. The AO-SDR-ODI method is shown to achieve better communication and sensing performance, than the AO-RG method, at a higher complexity. It is also shown that the mean-squared-error (MSE) of the estimates of the sensing parameters decreases as the radar MI increases.
Reconfigurable Radar Signal Processing Accelerator for Integrated Sensing and Communication System
Authors: Aakanksha Tewari, Shragvi Sidharth Jha, Akanksha Sneh, Sumit J Darak, Shobha Sundar Ram
Date: 2023-03-03 00:00:00
Tasks: Integrated sensing and communication, ISAC, Joint Radar-Communication

IEEE 802.11ad-based integrated sensing and communications (ISAC) have been identified as a potential solution for enabling next-generation intelligent transportation systems in the millimeter wave (mmW) spectrum. The radar functionality within the ISAC enables accurate detection and localization of mobile targets, which can significantly speed up the selection of the optimal high-directional narrow beam required for mmW communications between the base station and mobile target. To bring ISAC to reality, a radar signal processing (RSP) accelerator, co-located with the wireless communication physical layer (PHY), on edge platforms, is desired. In this work, we discuss the three-dimensional digital hardware RSP framework for 802.11ad-based ISAC to detect the range, azimuth, and Doppler velocity of multiple targets. We present an efficient reconfigurable architecture for RSP on multi-processor system-on-chip (MPSoC) via hardware-software co-design, word-length optimization, and serial-parallel configurations. We demonstrate the functional correctness of the proposed fixed-point architecture and significant savings in resource utilization (40-70%), execution time (1.5x improvement), and power consumption (50%) over floating-point architecture. The acceleration on hardware offers a 120-factor improvement in execution time over the benchmark Quad-core processor. The proposed architecture enables on-the-fly reconfigurability to support different azimuth precision and Doppler velocity resolution, offering a real-time trade-off between functional accuracy and detection time. We implement end-to-end ISAC comprising RSP and PHY on MPSoC and demonstrate significant improvement in throughput over IEEE 802.11ad.
SDR-Based 5G NR C-Band I/Q Monitoring and Surveillance in Urban Area Using a Helikite
Authors: Sung Joon Maeng, Ozgur Ozdemir, İsmail Güvenç, Mihail L. Sichitiu, Magreth Mushi, Rudra Dutta, Monisha Ghosh
Date: 2023-03-03 00:00:00

In this paper, we report experimental results in collectting and processing 5G NR I/Q samples in the 3.7~GHz C-band by using software-defined radio (SDR)-mounted helikite. We use MATLAB's 5G toolbox to post-process the collected data, to obtain the synchronization signal block (SSB) from the I/Q samples and then go through the cell search, synchronization procedures, and reference signal received power (RSRP) and reference signal received quality (RSRQ) calculation. We plot these performance metrics for various physical cell identities as a function of the helikite's altitude. Furthermore, building on our experience with the collected and post-processed data, we discuss potential vulnerabilities of 5G NR systems to surveillance, jamming attacks, and post quantum era attacks.
LTE I/Q Data Set for UAV Propagation Modeling, Communication, and Navigation Research
Authors: Sung Joon Maeng, Ozgur Ozdemir, İsmail Güvenç, Mihail L. Sichitiu, Magreth Mushi, Rudra Dutta
Date: 2023-03-03 00:00:00

Recently, unmanned aerial vehicles (UAVs) have been receiving significant attention due to their wide range of potential application areas. To support UAV use cases with beyond visual line of sight (BVLOS) and autonomous flights, cellular networks can serve as ground connectivity points, and they can provide remote control and payload communication for UAV links. However, there are limited data sets to study the coverage of cellular technologies for UAV flights at different altitudes and develop machine learning (ML) techniques for improving UAV communication and navigation. In this article, we present raw LTE I/Q sample data sets from physical field experiments in the Lake Wheeler farm area of the NSF AERPAW experimentation platform. We fly a UAV that carries a software-defined radio (SDR) at altitudes ranging from 30~m to 110~m and collect raw I/Q samples from an SDR-based LTE base station on the ground operating at 3.51 GHz. We adopt a standard metadata format for reproducing the results from the collected data sets. The post-processing of raw I/Q samples using MATLAB's 4G LTE toolbox is described and various representative results are provided. In the end, we discuss the possible ways that our provided data set, post-processing sample code, and sample experiment code for collecting I/Q measurements and vehicle control can be used by other ML researchers in the future.
LSTM-ACB-Based Random Access for Mixed Traffic IoT Networks
Authors: Herman L. dos Santos, João H. I. Souza, José Carlos Marinello, Taufik Abrão
Date: 2023-03-02 00:00:00
Tasks: Traffic Prediction

We propose a novel random access (RA) protocol that accounts for the network traffic in mixed URLLC-mMTC scenarios. By considering an IoT environment under high mMTC traffic demand, we model the traffic of each service using realistic statistical models, with the mMTC and URLLC use modes presenting a long-term traffic regularity. A long-short term memory (LSTM) neural network (NN) is used as a network traffic predictor, enabling a traffic-aware resource slicing (RS) scheme, aided by a contention access control barring (ACB)-based procedure. The proposed method combines a grant-based RA scheme, where it is introduced an intermediate step in grant-free RA, to deal with collisions. The protocol presents a small overhead, supporting a higher number of packets in a frame thanks to the congestion alleviation enabled by the ACB procedure. Numerical results show the effectiveness in combining the three procedures in terms of accuracy for traffic prediction, resource utilization and channel loading for RS, and increased throughput.% for the proposed LSTM-ACB-based RA protocol. The comparison with a grant-free benchmark reveals substantial improvement in system performance.
A Geometrical Structure for Predator-Avoidance Fish Schooling
Authors: Aditya Dewanto Hartono, Ton Viet Ta, Linh Thi Hoai Nguyen
Date: 2023-03-03 00:00:00

This paper conducts a numerical study of a geometrical structure called $\epsilon$-school for predator-avoidance fish schools, based on our previous mathematical model. Our results show that during a predator attack, the number of $\epsilon$-school increases from one to a certain value. After the attack, the number of $\epsilon$-school decreases in the first two predator-avoidance patterns, but continues to increase in the third pattern. A constant value for the number of the $\epsilon$-school is observed in the last pattern. These suggests that when the predator is approaching, each individual in the school focuses more on avoiding the predator, rather than on interacting with its schoolmates. Such a trait is in agreement with real-life behavior in the natural ecosystem.
A Concise and Formal Definition of RAF Sets and the RAF Algorithm
Authors: Wim Hordijk
Date: 2023-03-03 00:00:00

Autocatalytic sets are self-catalyzing and self-sustaining chemical reaction networks that are believed to have played an important role in the origin of life. They have been studied extensively both theoretically as well as experimentally. This short note provides (1) a complete and formal definition of autocatalytic sets (or RAF sets), and (2) an efficient algorithm to detect such sets in arbitrary reaction networks. Although both have been presented in various forms in earlier publications, this note serves as a concise and convenient reference.
An adaptive volatility method for probabilistic forecasting and its application to the M6 financial forecasting competition
Authors: Joseph de Vilmarest, Nicklas Werge
Date: 2023-03-03 00:00:00
Tasks: Decision Making, Stochastic Optimization

In this paper, we address the problem of probabilistic forecasting using an adaptive volatility method rooted in classical time-varying volatility models and leveraging online stochastic optimization algorithms. These principles were successfully applied in the M6 forecasting competition under the team named AdaGaussMC. Our approach takes a unique path by embracing the Efficient Market Hypothesis (EMH) instead of trying to beat the market directly. We focus on evaluating the efficient market, emphasizing the importance of online forecasting in adapting to the dynamic nature of financial markets. The three key points of our approach are: (a) apply the univariate time-varying volatility model AdaVol, (b) obtain probabilistic forecasts of future returns, and (c) optimize the competition metrics using stochastic gradient-based algorithms. We contend that the simplicity of our approach contributes to its robustness and consistency. Remarkably, our performance in the M6 competition resulted in an overall 7th ranking, with a noteworthy 5th position in the forecasting task. This achievement, considering the perceived simplicity of our approach, underscores the efficacy of our adaptive volatility method in the realm of probabilistic forecasting.
Optimal probabilistic forecasts for risk management
Authors: Yuru Sun, Worapree Maneesoonthorn, Ruben Loaiza-Maya, Gael M. Martin
Date: 2023-03-03 00:00:00
Tasks: Management, Prediction

This paper explores the implications of producing forecast distributions that are optimized according to scoring rules that are relevant to financial risk management. We assess the predictive performance of optimal forecasts from potentially misspecified models for i) value-at-risk and expected shortfall predictions; and ii) prediction of the VIX volatility index for use in hedging strategies involving VIX futures. Our empirical results show that calibrating the predictive distribution using a score that rewards the accurate prediction of extreme returns improves the VaR and ES predictions. Tail-focused predictive distributions are also shown to yield better outcomes in hedging strategies using VIX futures.
The self-exciting nature of the bid-ask spread dynamics
Authors: Ruihua Ruan, Emmanuel Bacry, Jean-François Muzy
Date: 2023-03-03 00:00:00

The bid-ask spread, which is defined by the difference between the best selling price and the best buying price in a Limit Order Book at a given time, is a crucial factor in the analysis of financial securities. In this study, we propose a "State-dependent Spread Hawkes model" (SDSH) that accounts for various spread jump sizes and incorporates the impact of the current spread state on its intensity functions. We apply this model to the high-frequency data from the Cac40 Euronext market and capture several statistical properties, such as the spread distributions, inter-event time distributions, and spread autocorrelation functions. We illustrate the ability of the SDSH model to forecast spread values at short-term horizons.
Auto-weighted Multi-view Clustering for Large-scale Data
Authors: Xinhang Wan, Xinwang Liu, Jiyuan Liu, Siwei Wang, Yi Wen, Weixuan Liang, En Zhu, Zhe Liu, Lu Zhou
Date: 2023-01-21 00:00:00
Tasks: Clustering

Multi-view clustering has gained broad attention owing to its capacity to exploit complementary information across multiple data views. Although existing methods demonstrate delightful clustering performance, most of them are of high time complexity and cannot handle large-scale data. Matrix factorization-based models are a representative of solving this problem. However, they assume that the views share a dimension-fixed consensus coefficient matrix and view-specific base matrices, limiting their representability. Moreover, a series of large-scale algorithms that bear one or more hyperparameters are impractical in real-world applications. To address the two issues, we propose an auto-weighted multi-view clustering (AWMVC) algorithm. Specifically, AWMVC first learns coefficient matrices from corresponding base matrices of different dimensions, then fuses them to obtain an optimal consensus matrix. By mapping original features into distinctive low-dimensional spaces, we can attain more comprehensive knowledge, thus obtaining better clustering results. Moreover, we design a six-step alternative optimization algorithm proven to be convergent theoretically. Also, AWMVC shows excellent performance on various benchmark datasets compared with existing ones. The code of AWMVC is publicly available at https://github.com/wanxinhang/AAAI-2023-AWMVC.
Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks
Authors: Louis Bethune, Paul Novello, Thibaut Boissin, Guillaume Coiffier, Mathieu Serrurier, Quentin Vincenot, Andres Troya-Galvis
Date: 2023-01-26 00:00:00
Tasks: Image Generation, One-Class Classification

We propose a new method, dubbed One Class Signed Distance Function (OCSDF), to perform One Class Classification (OCC) by provably learning the Signed Distance Function (SDF) to the boundary of the support of any distribution. The distance to the support can be interpreted as a normality score, and its approximation using 1-Lipschitz neural networks provides robustness bounds against $l2$ adversarial attacks, an under-explored weakness of deep learning-based OCC algorithms. As a result, OCSDF comes with a new metric, certified AUROC, that can be computed at the same cost as any classical AUROC. We show that OCSDF is competitive against concurrent methods on tabular and image data while being way more robust to adversarial attacks, illustrating its theoretical properties. Finally, as exploratory research perspectives, we theoretically and empirically show how OCSDF connects OCC with image generation and implicit neural surface parametrization. Our code is available at https://github.com/Algue-Rythme/OneClassMetricLearning
A One-Class Classification method based on Expanded Non-Convex Hulls
Authors: David Novoa-Paradela, Oscar Fontenla-Romero, Bertha Guijarro-Berdiñas
Date: 2022-08-01 00:00:00
Proceeding: Information Fusion 2022 8
Tasks: Anomaly Detection, One-Class Classification, Supervised Anomaly Detection

This paper presents an intuitive, robust and efficient One-Class Classification algorithm. The method developed is called OCENCH (One-class Classification via Expanded Non-Convex Hulls) and bases its operation on the construction of subdivisible and expandable non-convex hulls to represent the target class. The method begins by reducing the dimensionality of the data to two-dimensional spaces using random projections. After that, an iterative process based on Delaunay triangulations is applied to these spaces to obtain simple polygons that characterizes the non-convex shape of the normal class data. In addition, the method subdivides the non- convex hulls to represent separate regions in space if necessary. The method has been evaluated and compared to several main algorithms of the field using real data sets. In contrast to other methods, OCENCH can deal with non-convex and disjointed shapes. Finally, its execution can be carried out in a parallel way, which is interesting to reduce the execution time.
MoveFormer: a Transformer-based model for step-selection animal movement modelling
Authors: Ondřej Cífka, Simon Chamaillé-Jammes, Antoine Liutkus
Date: 2023-03-06 00:00:00
Proceeding: bioRxiv 2023 3

The movement of animals is a central component of their behavioural strategies. Statistical tools for movement data analysis, however, have long been limited, and in particular, unable to account for past movement information except in a very simplified way. In this work, we propose MoveFormer, a new step-based model of movement capable of learning directly from full animal trajectories. While inspired by the classical step-selection framework and previous work on the quantification of uncertainty in movement predictions, MoveFormer also builds upon recent developments in deep learning, such as the Transformer architecture, allowing it to incorporate long temporal contexts. The model predicts an animal's next movement step given its past movement history, including not only purely positional and temporal information, but also any available environmental covariates such as land cover or temperature. We apply our model to a diverse dataset made up of over 1550 trajectories from over 100 studies, and show how it can be used to gain insights about the importance of the provided context features, including the extent of past movement history. Our software, along with the trained model weights, is released as open source.