pwc-archive/papers-with-abstracts huggingface dataset:
Robust Communication Between Parties with Nearly Independent Preferences
Authors: Alistair Barton
Date: 2024-03-20 00:00:00

We study finite-state communication games in which the sender's preference is perturbed by random private idiosyncrasies. Persuasion is generically impossible within the class of statistically independent sender/receiver preferences -- contrary to prior research establishing persuasive equilibria when the sender's preference is precisely transparent. Nevertheless, robust persuasion may occur when the sender's preference is only slightly state-dependent/idiosyncratic. This requires approximating an `acyclic' equilibrium of the transparent preference game, generically implying that this equilibrium is also `connected' -- a generalization of partial-pooling equilibria. It is then necessary and sufficient that the sender's preference satisfy a monotonicity condition relative to the approximated equilibrium. If the sender's preference further satisfies a `semi-local' version of increasing differences, then this analysis extends to sender preferences that rank pure actions (but not mixed actions) according to a state-independent order. We apply these techniques to study (1) how ethical considerations, such as empathy for the receiver, may improve or impede comm
The Limits of Identification in Discrete Choice
Authors: Christopher P. Chambers, Christopher Turansick
Date: 2024-03-20 00:00:00

This paper uncovers tight bounds on the number of preferences permissible in identified random utility models. We show that as the number of alternatives in a discrete choice model becomes large, the fraction of preferences admissible in an identified model rapidly tends to zero. We propose a novel sufficient condition ensuring identification, which is strictly weaker than some of those existing in the literature. While this sufficient condition reaches our upper bound, an example demonstrates that this condition is not necessary for identification. Using our new condition, we show that the classic ``Latin Square" example from social choice theory is identified from stochastic choice data.
On Equilibrium Determinacy in Overlapping Generations Models with Money
Authors: Tomohiro Hirano, Alexis Akira Toda
Date: 2024-03-20 00:00:00

This paper provides a detailed analysis of the local determinacy of monetary and non-monetary steady states in Tirole (1985)'s classical two-period overlapping generations model with capital and production. We show that the sufficient condition for local determinacy in endowment economies provided by Scheinkman (1980) does not generalize to models with production: there are robust examples with arbitrary utility functions in which the non-monetary steady state is locally determinate or indeterminate. In contrast, the monetary steady state is locally determinate under fairly weak conditions.
BanglaNum -- A Public Dataset for Bengali Digit Recognition from Speech
Authors: Mir Sayeed Mohammad, Azizul Zahid, Md Asif Iqbal
Date: 2024-03-20 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Automatic speech recognition (ASR) converts the human voice into readily understandable and categorized text or words. Although Bengali is one of the most widely spoken languages in the world, there have been very few studies on Bengali ASR, particularly on Bangladeshi-accented Bengali. In this study, audio recordings of spoken digits (0-9) from university students were used to create a Bengali speech digits dataset that may be employed to train artificial neural networks for voice-based digital input systems. This paper also compares the Bengali digit recognition accuracy of several Convolutional Neural Networks (CNNs) using spectrograms and shows that a test accuracy of 98.23% is achievable using parameter-efficient models such as SqueezeNet on our dataset.
S2LIC: Learned Image Compression with the SwinV2 Block, Adaptive Channel-wise and Global-inter Attention Context
Authors: Yongqiang Wang, Haisheng Fu, Qi Cao, Shang Wang, Zhenjiao Chen, Feng Liang
Date: 2024-03-21 00:00:00
Tasks: Image Compression, MS-SSIM, SSIM

Recently, deep learning technology has been successfully applied in the field of image compression, leading to superior rate-distortion performance. It is crucial to design an effective and efficient entropy model to estimate the probability distribution of the latent representation. However, the majority of entropy models primarily focus on one-dimensional correlation processing between channel and spatial information. In this paper, we propose an Adaptive Channel-wise and Global-inter attention Context (ACGC) entropy model, which can efficiently achieve dual feature aggregation in both inter-slice and intraslice contexts. Specifically, we divide the latent representation into different slices and then apply the ACGC model in a parallel checkerboard context to achieve faster decoding speed and higher rate-distortion performance. In order to capture redundant global features across different slices, we utilize deformable attention in adaptive global-inter attention to dynamically refine the attention weights based on the actual spatial relationships and context. Furthermore, in the main transformation structure, we propose a high-performance S2LIC model. We introduce the residual SwinV2 Transformer model to capture global feature information and utilize a dense block network as the feature enhancement module to improve the nonlinear representation of the image within the transformation structure. Experimental results demonstrate that our method achieves faster encoding and decoding speeds and outperforms VTM-17.1 and some recent learned image compression methods in both PSNR and MS-SSIM metrics.
Assimilation of SWOT Altimetry and Sentinel-1 Flood Extent Observations for Flood Reanalysis -- A Proof-of-Concept
Authors: Thanh Huy Nguyen, Sophie Ricci, Andrea Piacentini, Charlotte Emery, Raquel Rodriguez Suquet, Santiago Peña Luque
Date: 2024-03-21 00:00:00
Tasks: Earth Observation

In spite of astonishing advances and developments in remote sensing technologies, meeting the spatio-temporal requirements for flood hydrodynamic modeling remains a great challenge for Earth Observation. The assimilation of multi-source remote sensing data in 2D hydrodynamic models participates to overcome such a challenge. The recently launched Surface Water and Ocean Topography (SWOT) wide-swath altimetry satellite provides a global coverage of water surface elevation at a high resolution. SWOT provides complementary observation to radar and optical images, increasing the opportunity to observe and monitor flood events. This research work focuses on the assimilation of 2D flood extent maps derived from Sentinel-1 C-SAR imagery data, and water surface elevation from SWOT as well as in-situ water level measurements. An Ensemble Kalman Filter (EnKF) with a joint state-parameter analysis is implemented on top of a 2D hydrodynamic TELEMAC-2D model to account for errors in roughness, input forcing and water depth in floodplain subdomains. The proposed strategy is carried out in an Observing System Simulation Experiment based on the 2021 flood event over the Garonne Marmandaise catchment. This work makes the most of the large volume of heterogeneous data from space for flood prediction in hindcast mode paves the way for nowcasting.
Impact of regularization on achieved resolution in 3D tunable structured illumination microscopy (TSIM)
Authors: Arash Atibi, Abdulaziz Alqahtani, Mohammed Younis, Chrysanthe Preza
Date: 2024-03-20 00:00:00

We present a study that evaluates the impact of regularization on the achieved resolution in restorations from a novel three-dimensional (3D) Structured Illumination Microscopy (3D-SIM) system with desirable tunability properties. This contribution is the first performance evaluation of the Tunable SIM (TSIM) system through the restoration process. The study quantifies the achieved resolution in restorations, from simulated TSIM data of a 3D star-like object, at various expected resolution limits controlled by system parameters, and at different noise levels mitigated by the Generalized Wiener filter, a computationally efficient method, successfully applied to other conventional 3D-SIM systems. We show that theoretical TSIM resolution limits are attained in the absence of noise, while with increasing noise levels, the necessary increase in regularization and residual restoration artifacts contributed to a $\sim$ 5%-10% and a 20% reduction in the axial achieved resolution, in 20-dB and 15-dB data, respectively, which is within the pixel size (20 nm) limitation.
Modeling the Label Distributions for Weakly-Supervised Semantic Segmentation
Authors: Linshan Wu, Zhun Zhong, Jiayi Ma, Yunchao Wei, Hao Chen, Leyuan Fang, Shutao Li
Date: 2024-03-20 00:00:00
Tasks: Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

Weakly-Supervised Semantic Segmentation (WSSS) aims to train segmentation models by weak labels, which is receiving significant attention due to its low annotation cost. Existing approaches focus on generating pseudo labels for supervision while largely ignoring to leverage the inherent semantic correlation among different pseudo labels. We observe that pseudo-labeled pixels that are close to each other in the feature space are more likely to share the same class, and those closer to the distribution centers tend to have higher confidence. Motivated by this, we propose to model the underlying label distributions and employ cross-label constraints to generate more accurate pseudo labels. In this paper, we develop a unified WSSS framework named Adaptive Gaussian Mixtures Model, which leverages a GMM to model the label distributions. Specifically, we calculate the feature distribution centers of pseudo-labeled pixels and build the GMM by measuring the distance between the centers and each pseudo-labeled pixel. Then, we introduce an Online Expectation-Maximization (OEM) algorithm and a novel maximization loss to optimize the GMM adaptively, aiming to learn more discriminative decision boundaries between different class-wise Gaussian mixtures. Based on the label distributions, we leverage the GMM to generate high-quality pseudo labels for more reliable supervision. Our framework is capable of solving different forms of weak labels: image-level labels, points, scribbles, blocks, and bounding-boxes. Extensive experiments on PASCAL, COCO, Cityscapes, and ADE20K datasets demonstrate that our framework can effectively provide more reliable supervision and outperform the state-of-the-art methods under all settings. Code will be available at https://github.com/Luffy03/AGMM-SASS.
HyperVQ: MLR-based Vector Quantization in Hyperbolic Space
Authors: Nabarun Goswami, Yusuke Mukuta, Tatsuya Harada
Date: 2024-03-18 00:00:00
Tasks: Quantization, Representation Learning

The success of models operating on tokenized data has heightened the need for effective tokenization methods, particularly in vision and auditory tasks where inputs are naturally continuous. A common solution is to employ Vector Quantization (VQ) within VQ Variational Autoencoders (VQVAEs), transforming inputs into discrete tokens by clustering embeddings in Euclidean space. However, Euclidean embeddings not only suffer from inefficient packing and limited separation - due to their polynomial volume growth - but are also prone to codebook collapse, where only a small subset of codebook vectors are effectively utilized. To address these limitations, we introduce HyperVQ, a novel approach that formulates VQ as a hyperbolic Multinomial Logistic Regression (MLR) problem, leveraging the exponential volume growth in hyperbolic space to mitigate collapse and improve cluster separability. Additionally, HyperVQ represents codebook vectors as geometric representatives of hyperbolic decision hyperplanes, encouraging disentangled and robust latent representations. Our experiments demonstrate that HyperVQ matches traditional VQ in generative and reconstruction tasks, while surpassing it in discriminative performance and yielding a more efficient and disentangled codebook.
Machine Learning and Transformers for Thyroid Carcinoma Diagnosis: A Review
Authors: Yassine Habchi, Hamza Kheddar, Yassine Himeur, Mohamed Chahine Ghanem
Date: 2024-03-17 00:00:00
Tasks: Diagnostic, Management, Prognosis

The growing interest in developing smart diagnostic systems to help medical experts process extensive data for treating incurable diseases has been notable. In particular, the challenge of identifying thyroid cancer (TC) has seen progress with the use of machine learning (ML) and big data analysis, incorporating Transformers to evaluate TC prognosis and determine the risk of malignancy in individuals. This review article presents a summary of various studies on AI-based approaches, especially those employing Transformers, for diagnosing TC. It introduces a new categorization system for these methods based on artificial intelligence (AI) algorithms, the goals of the framework, and the computing environments used. Additionally, it scrutinizes and contrasts the available TC datasets by their features. The paper highlights the importance of AI instruments in aiding the diagnosis and treatment of TC through supervised, unsupervised, or mixed approaches, with a special focus on the ongoing importance of Transformers and large language models (LLMs) in medical diagnostics and disease management. It further discusses the progress made and the continuing obstacles in this area. Lastly, it explores future directions and focuses within this research field.
Optimization for MIMO Integrated Sensing and Communications
Authors: Lie-Liang Yang
Date: 2024-03-21 00:00:00
Tasks: ISAC

The fundamentals of MIMO communications and MIMO sensing are firstly analyzed with regard to channel and sensing capacities. It is shown that the different objectives of communications and sensing lead to different signaling waveforms required for achieving their capacities. Hence, the optimization of integrated sensing and communications (ISAC) is relied on a trade-off expected between the performance of communications and that of sensing. Following this observation, the design and resource optimization in general MIMO ISAC systems are discussed along with the analysis of some existing ISAC schemes. Furthermore, the design of ISAC in mmWave communications is addressed. Specifically, the principle of sensing in mmWave systems is established, and a range of optimization alternatives for ISAC design in mmWave systems are reviewed.
Bistatic Doppler Frequency Estimation with Asynchronous Moving Devices for Integrated Sensing and Communications
Authors: Gianmaria Ventura, Zaman Bhalli, Michele Rossi, Jacopo Pegoraro
Date: 2024-03-21 00:00:00
Tasks: Integrated sensing and communication, ISAC

In this letter, we present for the first time a method to estimate the bistatic Doppler frequency of a target with clock asynchronous and mobile Integrated Sensing And Communication (ISAC) devices. Existing approaches have separately tackled the presence of phase offsets due to clock asynchrony or the additional Doppler shift due to device movement. However, in real ISAC scenarios, these two sources of phase nuisance are concurrently present, making the estimation of the target's Doppler frequency particularly challenging. Our method solves the problem using the sole wireless signal at the receiver, exploiting the invariance of phase offsets across multipath components and the bistatic geometry in an original way. The proposed method is validated via simulation, exploring the impact of different system parameters. Numerical results show that our approach is a viable way of estimating Doppler frequency in bistatic asynchronous ISAC scenarios with mobile devices.
An Efficient Rate Splitting Precoding Approach in Multi-User MISO FDD Systems
Authors: Donia Ben Amor, Michael Joham, Wolfgang Utschick
Date: 2024-03-21 00:00:00

In this work, we develop an efficient precoding strategy for a multi-user multiple-input-single output (MU MISO) system operating in frequency-division-duplex (FDD) mode, where rate splitting multiple access (RSMA) is implemented. To this end, we consider one-layer RS and show its significant impact on the system performance, specifically in the case where the channel state information (CSI) is incomplete at the transmitter. Based on a lower bound on the achievable rate that takes into account the CSI errors, we establish an augmented weighted average mean squared error (AWAMSE) algorithm for the RS setup denoted by AWAMSE-RS, where even the updates for the common and the private precoders are computed via analytical expressions, hence circumventing the need for interior-point methods. Simulation results validate the efficiency of our approach in terms of computational time and its competitiveness in terms of the achievable system throughput compared to state-of-the-art methods and non-RS setups.
Modem Optimization of High-Mobility Scenarios: A Deep-Learning-Inspired Approach
Authors: Hengyu Zhang, Xuehan Wang, Jingbo Tan, Jintao Wang
Date: 2024-03-21 00:00:00

The next generation wireless communication networks are required to support high-mobility scenarios, such as reliable data transmission for high-speed railways. Nevertheless, widely utilized multi-carrier modulation, the orthogonal frequency division multiplex (OFDM), cannot deal with the severe Doppler spread brought by high mobility. To address this problem, some new modulation schemes, e.g. orthogonal time frequency space and affine frequency division multiplexing, have been proposed with different design criteria from OFDM, which promote reliability with the cost of extremely high implementation complexity. On the other hand, end-to-end systems achieve excellent gains by exploiting neural networks to replace traditional transmitters and receivers, but have to retrain and update continually with channel varying. In this paper, we propose the Modem Network (ModNet) to design a novel modem scheme. Compared with end-to-end systems, channels are directly fed into the network and we can directly get a modem scheme through ModNet. Then, the Tri-Phase training strategy is proposed, which mainly utilizes the siamese structure to unify the learned modem scheme without retraining frequently faced up with time-varying channels. Simulation results show the proposed modem scheme outperforms OFDM systems under different highmobility channel statistics.
Learning-to-Learn the Wave Angle Estimation
Authors: Eray Guven, Gunes Karabulut Kurt
Date: 2024-03-21 00:00:00
Tasks: Few-Shot Learning, Meta-Learning

A precise incident wave angle estimation in aerial communication is a key enabler in sixth-generation wireless communication network. With this goal, a generic 3-dimensional (3D) channel model is analyzed for air-to-air (A2A) networks under antenna misalignment, radio frequency impairments and polarization loss. The unique aspects of each aerial node are highlighted and the few-shot learning as a model agnostic meta-learning (MAML) classifier is proposed for learning-to-learn (L2L) incident wave angle estimation by utilizing the received signal strength (RSS). Additionally, a more computationally efficient technique, first order model agnostic meta-learning (FOMAML) is implemented. It has been observed that the proposed approach reaches up to 85% training accuracy and 75.4% evaluation accuracy with MAML. Regarding this, a convergence rate and accuracy trade-off have been established for several cases of MAML and FOMAML. For different L2L models trained with limited data, heuristic accuracy performance is determined by an upper bound of the probability of confidence.
A LiDAR-Aided Channel Model for Vehicular Intelligent Sensing-Communication Integration
Authors: Ziwei Huang, Lu Bai, Mingran Sun, Xiang Cheng
Date: 2024-03-21 00:00:00

In this paper, a novel channel modeling approach, named light detection and ranging (LiDAR)-aided geometry-based stochastic modeling (LA-GBSM), is developed. Based on the developed LA-GBSM approach, a new millimeter wave (mmWave) channel model for sixth-generation (6G) vehicular intelligent sensing-communication integration is proposed, which can support the design of intelligent transportation systems (ITSs). The proposed LA-GBSM is accurately parameterized under high, medium, and low vehicular traffic density (VTD) conditions via a sensing-communication simulation dataset with LiDAR point clouds and scatterer information for the first time. Specifically, by detecting dynamic vehicles and static building/tress through LiDAR point clouds via machine learning, scatterers are divided into static and dynamic scatterers. Furthermore, statistical distributions of parameters, e.g., distance, angle, number, and power, related to static and dynamic scatterers are quantified under high, medium, and low VTD conditions. To mimic channel non-stationarity and consistency, based on the quantified statistical distributions, a new visibility region (VR)-based algorithm in consideration of newly generated static/dynamic scatterers is developed. Key channel statistics are derived and simulated. By comparing simulation results and ray-tracing (RT)-based results, the utility of the proposed LA-GBSM is verified.
Adaptive Target Detection for FDA-MIMO Radar with Training Data in Gaussian noise
Authors: Ping Li, Bang Huang, Wen-Qin Wang
Date: 2024-03-21 00:00:00

This paper addresses the problem of detecting a moving target embedded in Gaussian noise with an unknown covariance matrix for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar. To end it, assume that obtaining a set of training data is available. Moreover, we propose three adaptive detectors in accordance with the one-step generalized likelihood ratio test (GLRT), two-step GLRT, and Rao criteria, namely OGLRT, TGLRT, and Rao. The LH adaptive matched filter (LHAMF) detector is also introduced when decomposing the Rao test. Next, all provided detectors have constant false alarm rate (CFAR) properties against the covariance matrix. Besides, the closed-form expressions for false alarm probability (PFA) and detection probability (PD) are derived. Finally, this paper substantiates the correctness of the aforementioned algorithms through numerical simulations.
Sub-Nyquist Sampling OFDM Radar With a Time-Frequency Phase-Coded Waveform
Authors: SeongHyeon Kang, Kawon Han, Songcheol Hong
Date: 2024-03-21 00:00:00

This paper presents a time-frequency phase-coded sub-Nyquist sampling orthogonal frequency division multiplexing (PC-SNS-OFDM) radar system to reduce the analog-to-digital converter (ADC) sampling rate without any additional hardware or signal processing. The proposed radar divides the transmitted OFDM signal into multiple sub-bands along the frequency axis and provides orthogonality to these sub-bands by multiplying phase codes in both the time and frequency domains. Although the sampling rate is reduced by the factor of the number of sub-bands, the sub-bands above the sampling rate are folded into the lowest one due to aliasing. In the process of restoring the signals in folded sub-bands to those in full signal bands, the proposed PC-SNS-OFDM radar effectively eliminates symbol-mismatch noise while introducing trade-offs in the range and Doppler ambiguities. The utilization of phase codes in both the frequency and time domains provides flexible control of the range and Doppler ambiguities. It also improves the signal-to-noise ratio (SNR) of detected targets compared to an earlier sub-Nyquist sampling OFDM radar system. This is validated with simulations and experiments under various sub-Nyquist sampling rates.