pwc-archive/papers-with-abstracts huggingface dataset:
How Wash Traders Exploit Market Conditions in Cryptocurrency Markets
Authors: Hunter Ng
Date: 2024-11-08 00:00:00

Wash trading, the practice of simultaneously placing buy and sell orders for the same asset to inflate trading volume, has been prevalent in cryptocurrency markets. This paper investigates whether wash traders in Bitcoin act deliberately to exploit market conditions and identifies the characteristics of such manipulative behavior. Using a unique dataset of 18 million transactions from Mt. Gox, once the largest Bitcoin exchange, I find that wash trading intensifies when legitimate trading volume is low and diminishes when it is high, indicating strategic timing to maximize impact in less liquid markets. The activity also exhibits spillover effects across platforms and decreases when trading volumes in other asset classes like stocks or gold rise, suggesting sensitivity to broader market dynamics. Additionally, wash traders exploit periods of heightened media attention and online rumors to amplify their influence, causing rapid but short-lived spikes in legitimate trading volume. Using an exogenous demand shock associated with illicit online marketplaces, I find that wash trading responds to contemporaneous events affecting Bitcoin demand. These results advance the understanding of manipulative practices in digital currency markets and have significant implications for regulators aiming to detect and prevent wash trading.
A Strategic Topology on Information Structures
Authors: Dirk Bergemann, Stephen Morris, Rafael Veiel
Date: 2024-11-14 00:00:00

Two information structures are said to be close if, with high probability, there is approximate common knowledge that interim beliefs are close under the two information structures. We define an "almost common knowledge topology" reflecting this notion of closeness. We show that it is the coarsest topology generating continuity of equilibrium outcomes. An information structure is said to be simple if each player has a finite set of types and each type has a distinct first-order belief about payoff states. We show that simple information structures are dense in the almost common knowledge topology and thus it is without loss to restrict attention to simple information structures in information design problems.
Industrial symbiosis: How to apply successfully
Authors: Limor Hatsor, Artyom Jelnov
Date: 2024-11-13 00:00:00

The premise of industrial symbiosis IS is that advancing a circular economy that reuses byproducts as inputs in production is valuable for the environment. We challenge this premise in a simple model. Ceteris paribus, IS is an environmentally friendly approach; however, implementing IS may introduce increased pollution into the market equilibrium. The reason for this is that producers' incentives for recycling can be triggered by the income gained from selling recycled waste in the secondary market, and thereby may not align with environmental protection. That is, producers may boost production and subsequent pollution to sell byproducts without internalizing the pollution emitted in the primary industry or the recycling process. We compare the market solution to the social optimum and identify a key technology parameter - the share of reused byproducts that may have mutual benefits for firms, consumers, and the environment.
Equilibrium Cycle: A "Dynamic" Equilibrium
Authors: Tushar Shankar Walunj, Shiksha Singhal, Veeraruna Kavitha, Jayakrishnan Nair
Date: 2024-11-13 00:00:00

In this paper, we introduce a novel equilibrium concept, called the equilibrium cycle, which seeks to capture the outcome of oscillatory game dynamics. Unlike the (pure) Nash equilibrium, which defines a fixed point of mutual best responses, an equilibrium cycle is a set-valued solution concept that can be demonstrated even in games where best responses do not exist (for example, in discontinuous games). The equilibrium cycle identifies a Cartesian product set of action profiles that satisfies three important properties: stability against external deviations, instability against internal deviations, and minimality. This set-valued equilibrium concept generalizes the classical notion of the minimal curb set to discontinuous games. In finite games, the equilibrium cycle is related to strongly connected sink components of the best response graph.
Orchestrating Organizational Politics: Baron and Ferejohn Meet Tullock
Authors: Qiang Fu, Zenan Wu, Yuxuan Zhu
Date: 2024-11-13 00:00:00

This paper examines the optimal organizational rules that govern the process of dividing a fixed surplus. The process is modeled as a sequential multilateral bargaining game with costly recognition. The designer sets the voting rule -- i.e., the minimum number of votes required to approve a proposal -- and the mechanism for proposer recognition, which is modeled as a biased generalized lottery contest. We show that for diverse design objectives, the optimum can be achieved by a dictatorial voting rule, which simplifies the game into a standard biased contest model.
On the Welfare (Ir)Relevance of Two-Stage Models
Authors: Mikhail Freer, Hassan Nosratabadi
Date: 2024-11-13 00:00:00

In a two-stage model of choice a decision maker first shortlists a given menu and then applies her preferences. We show that a sizeable class of these models run into significant issues in terms of identification of preferences (welfare-relevance) and thus cannot be used for welfare analysis. We classify these models by their revealed preference principles and expose the principle that we deem to be the root of their identification issue. Taking our analysis to an experimental data, we observe that half of the alternatives that are revealed preferred to another under rational choice are left revealed preferred to nothing for any member of this class of models. Furthermore, the welfare-relevance of the specific models established in the literature are much worse. The model with the highest welfare-relevance produces a revealed preference relation with the average density of 2% (1 out of 45 possible comparisons revealed), while rational choice does 63% (28 out of 45 possible comparisons). We argue that the issue is not an inherent feature of two-stage models, and rather lies in the approach with which the first stage is modelled in the literature.
An End-To-End Stuttering Detection Method Based On Conformer And BILSTM
Authors: Xiaokang Liu, Changqing Xu, Yudong Yang, Lan Wang, Nan Yan
Date: 2024-11-14 00:00:00
Tasks: Event Detection, Multi-Task Learning

Stuttering is a neurodevelopmental speech disorder characterized by common speech symptoms such as pauses, exclamations, repetition, and prolongation. Speech-language pathologists typically assess the type and severity of stuttering by observing these symptoms. Many effective end-to-end methods exist for stuttering detection, but a commonly overlooked challenge is the uncertain relationship between tasks involved in this process. Using a suitable multi-task strategy could improve stuttering detection performance. This paper presents a novel stuttering event detection model designed to help speech-language pathologists assess both the type and severity of stuttering. First, the Conformer model extracts acoustic features from stuttered speech, followed by a Long Short-Term Memory (LSTM) network to capture contextual information. Finally, we explore multi-task learning for stuttering and propose an effective multi-task strategy. Experimental results show that our model outperforms current state-of-the-art methods for stuttering detection. In the SLT 2024 Stuttering Speech Challenge based on the AS-70 dataset [1], our model improved the mean F1 score by 24.8% compared to the baseline method and achieved first place. On this basis, we conducted relevant extensive experiments on LSTM and multi-task learning strategies respectively. The results show that our proposed method improved the mean F1 score by 39.8% compared to the baseline method.
State-Space Estimation of Spatially Dynamic Room Impulse Responses using a Room Acoustic Model-based Prior
Authors: Kathleen MacWilliam, Thomas Dietzen, Randall Ali, Toon van Waterschoot
Date: 2024-11-13 00:00:00

The estimation of room impulse responses (RIRs) between static loudspeaker and microphone locations can be done using a number of well-established measurement and inference procedures. While these procedures assume a time-invariant acoustic system, time variations need to be considered for the case of spatially dynamic scenarios where loudspeakers and microphones are subject to movement. If the RIR is modeled using image sources, then movement implies that the distance to each image source varies over time, making the estimation of the spatially dynamic RIR particularly challenging. In this paper, we propose a procedure to estimate the early part of the spatially dynamic RIR between a stationary source and a microphone moving on a linear trajectory at constant velocity. The procedure is built upon a state-space model, where the state to be estimated represents the early RIR, the observation corresponds to a microphone recording in a spatially dynamic scenario, and time-varying distances to the image sources are incorporated into the state transition matrix obtained from static RIRs at the start and end point of the trajectory. The performance of the proposed approach is evaluated against state-of-the-art RIR interpolation and state-space estimation methods using simulations, demonstrating the potential of the proposed state-space model.
When Mamba Meets xLSTM: An Efficient and Precise Method with the xLSTM-VMUNet Model for Skin lesion Segmentation
Authors: Zhuoyi Fang, Jiajia Liu, Kexuan Shi, Qiang Han
Date: 2024-11-14 00:00:00
Tasks: Computational Efficiency, Lesion Segmentation, Mamba, Segmentation, Skin Lesion Segmentation

Automatic melanoma segmentation is essential for early skin cancer detection, yet challenges arise from the heterogeneity of melanoma, as well as interfering factors like blurred boundaries, low contrast, and imaging artifacts. While numerous algorithms have been developed to address these issues, previous approaches have often overlooked the need to jointly capture spatial and sequential features within dermatological images. This limitation hampers segmentation accuracy, especially in cases with indistinct borders or structurally similar lesions. Additionally, previous models lacked both a global receptive field and high computational efficiency. In this work, we present the xLSTM-VMUNet Model, which jointly capture spatial and sequential features within dermatological images successfully. xLSTM-VMUNet can not only specialize in extracting spatial features from images, focusing on the structural characteristics of skin lesions, but also enhance contextual understanding, allowing more effective handling of complex medical image structures. Experiment results on the ISIC2018 dataset demonstrate that xLSTM-VMUNet outperforms VMUNet by 4.85% on DSC and 6.41% on IoU on the ISIC2017 dataset, by 1.25% on DSC and 2.07% on IoU on the ISIC2018 dataset, with faster convergence and consistently high segmentation performance. Our code is available at https://github.com/FangZhuoyi/XLSTM-VMUNet.
Enabling Efficient Wearables: An Analysis of Low-Power Microcontrollers for Biomedical Applications
Authors: Dimitrios Samakovlis, Stefano Albini, Rubén Rodríguez Álvarez, Denisa-Andreea Constantinescu, Pasquale Davide Schiavone, Miguel Peón-Quirós, David Atienza
Date: 2024-11-14 00:00:00

Breakthroughs in ultra-low-power chip technology are transforming biomedical wearables, making it possible to monitor patients in real time with devices operating on mere {\mu}W. Although many studies have examined the power performance of commercial microcontrollers, it remains unclear which ones perform best across diverse application profiles and which hardware features are most crucial for minimizing energy consumption under varying computational loads. Identifying these features for typical wearable applications and understanding their effects on performance and energy efficiency are essential for optimizing deployment strategies and informing future hardware designs. In this work, we conduct an in-depth study of state-of-the-art (SoA) micro-controller units(MCUs) in terms of processing capability and energy efficiency using representative end-to-end SoA wearable applications. We systematically benchmark each platform across three primary application phases: idle, data acquisition, and processing, allowing a holistic assessment of the platform processing capability and overall energy efficiency across varying patient-monitoring application profiles. Our detailed analysis of performance and energy discrepancies across different platforms reveals key strengths and limitations of the current low-power hardware design and pinpoints the strengths and weaknesses of SoA MCUs. We conclude with actionable insights for wearable application designers and hardware engineers, aiming to inform future hardware design improvements and support optimal platform selection for energy-constrained biomedical applications.
Movable Antenna Enhanced Networked Full-Duplex Integrated Sensing and Communication System
Authors: Yuan Guo, Wen Chen, Qingqing Wu, Yang Liu, Qiong Wu, Kunlun Wang, Jun Li, Lexi Xu
Date: 2024-11-14 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) is envisioned as a key technology for future sixth-generation (6G) networks. Classical ISAC system considering monostatic and/or bistatic settings will inevitably degrade both communication and sensing performance due to the limited service coverage and easily blocked transmission paths. Besides, existing ISAC studies usually focus on downlink (DL) or uplink (UL) communication demands and unable to achieve the systematic DL and UL communication tasks. These challenges can be overcome by networked FD ISAC framework. Moreover, ISAC generally considers the trade-off between communication and sensing, unavoidably leading to a loss in communication performance. This shortcoming can be solved by the emerging movable antenna (MA) technology. In this paper, we utilize the MA to promote communication capability with guaranteed sensing performance via jointly designing beamforming, power allocation, receiving filters and MA configuration towards maximizing sum rate. The optimization problem is highly difficult due to the unique channel model deriving from the MA. To resolve this challenge, via leveraging the cutting-the-edge majorization-minimization (MM) method, we develop an efficient solution that optimizes all variables via convex optimization techniques. Extensive simulation results verify the effectiveness of our proposed algorithms and demonstrate the substantial performance promotion by deploying MA in the networked FD ISAC system.
Circuit-Compliant Optimization of Dynamic Metasurface Antennas for Near-Field Localization
Authors: Ioannis Gavras, George C. Alexandropoulos
Date: 2024-11-14 00:00:00

This paper presents an optimization framework for near-field localization with Dynamic Metasurface Antenna (DMA) receivers. This metasurface technology offers enhanced angular and range resolution realizing efficient hybrid Analog and Digital (A/D) BeamForming (BF) with sub-wavelength-spaced metamaterials of tunable responses. However, the vast majority of the state-of-the-art DMA designs is based on an idealized model for their reception operation, which neglects several practical aspects, such as the inevitable mutual coupling among the densely deployed metamaterials within a given aperture. Capitalizing on a recent circuit-compliant active metasurface model, we present a novel mutual-coupling-aware framework for localization-optimized hybrid A/D BF weights at the reception DMA. To deal with the intrinsic complexity of the deployed model, we introduce first- and second-order approximations for the DMA analog BF matrix that enable efficient optimization, while maintaining accuracy. We derive the Cramer-Rao Bound for the user position estimation which serves as our design objective for the hybrid A/D BF matrices. Closed-form solutions for these matrices for both approximations are presented, whose validity is confirmed via numerical investigations. It is also demonstrated that the proposed DMA design outperforms state-of-the-art multi-antenna reception architectures optimized for the same localization objective.
A Flexible Framework for Grant-Free Random Access in Cell-Free Massive MIMO Systems
Authors: Sai Subramanyam Thoota, Erik G. Larsson
Date: 2024-11-14 00:00:00
Tasks: Action Detection, Activity Detection

We propose a novel generalized framework for grant-free random-access (GFRA) in cell-free massive multiple input multiple-output systems where multiple geographically separated access points (APs) or base stations (BSs) aim to detect sporadically active user-equipment (UEs). Unlike a conventional architecture in which all the active UEs transmit their signature or pilot sequences of equal length, we admit a flexible pilot length for each UE, which also enables a seamless integration into conventional grant-based wireless systems. We formulate the joint UE activity detection and the distributed channel estimation as a sparse support and signal recovery problem, and describe a Bayesian learning procedure to solve it. We develop a scheme to fuse the posterior statistics of the latent variables inferred by each AP to jointly detect the UEs' activities, and utilize them to further refine the channel estimates. In addition, we allude to an interesting point which enables this flexible GFRA framework to encode the information bits from the active UEs. We numerically evaluate the normalized mean square error and the probability of miss-detection performances obtained by the Bayesian algorithm and show that the latent-variable fusion enhances the detection and the channel estimation performances by a large margin. We also benchmark against a genie-aided algorithm which has a prior knowledge of the UEs' activities.
Iterative tomographic reconstruction with TV prior for low-dose CBCT dental imaging
Authors: Louise Friot-Giroux, Françoise Peyrin, Voichita Maxim
Date: 2024-11-14 00:00:00
Tasks: Denoising

Abstract Objective. Cone-beam computed tomography is becoming more and more popular in applications such as 3D dental imaging. Iterative methods compared to the standard Feldkamp algorithm have shown improvements in image quality of reconstruction of low-dose acquired data despite their long computing time. An interesting aspect of iterative methods is their ability to include prior information such as sparsity-constraint. While a large panel of optimization algorithms along with their adaptation to tomographic problems are available, they are mainly studied on 2D parallel or fan-beam data. The issues raised by 3D CBCT and moreover by truncated projections are still poorly understood. Approach. We compare different carefully designed optimization schemes in the context of realistic 3D dental imaging. Besides some known algorithms, SIRT-TV and MLEM, we investigate the primal-dual hybrid gradient (PDHG) approach and a newly proposed MLEM-TV optimizer. The last one is alternating EM steps and TV-denoising, combination not yet investigated for CBCT. Experiments are performed on both simulated data from a 3D jaw phantom and data acquired with a dental clinical scanner. Main results. With some adaptations to the specificities of CBCT operators, PDHG and MLEM-TV algorithms provide the best reconstruction quality. These results were obtained by comparing the full-dose image with a low-dose image and an ultra low-dose image. Significance. The convergence speed of the original iterative methods is hampered by the conical geometry and significantly reduced compared to parallel geometries. We promote the pre-conditioned version of PDHG and we propose a pre-conditioned version of the MLEM-TV algorithm. To the best of our knowledge, this is the first time PDHG and convergent MLEM-TV algorithms are evaluated on experimental dental CBCT data, where constraints such as projection truncation and presence of metal have to be jointly overcome.
FAS for Secure and Covert Communications
Authors: Junteng Yao, Liangxiao Xin, Tuo Wu, Ming Jin, Kai-Kit Wong, Chau Yuen, Hyundong Shin
Date: 2024-11-14 00:00:00
Tasks: Position

This letter considers a fluid antenna system (FAS)-aided secure and covert communication system, where the transmitter adjusts multiple fluid antennas' positions to achieve secure and covert transmission under the threat of an eavesdropper and the detection of a warden. This letter aims to maximize the secrecy rate while satisfying the covertness constraint. Unfortunately, the optimization problem is non-convex due to the coupled variables. To tackle this, we propose an alternating optimization (AO) algorithm to alternatively optimize the optimization variables in an iterative manner. In particular, we use a penalty-based method and the majorization-minimization (MM) algorithm to optimize the transmit beamforming and fluid antennas' positions, respectively. Simulation results show that FAS can significantly improve the performance of secrecy and covertness compared to the fixed-position antenna (FPA)-based schemes.
Orthogonal Linear Array based Product Beamforming for Real Time Underwater 3D Acoustical Imaging
Authors: Mimisha M Menakath, Mahesh Raveendranatha Panicker, Hareesh G
Date: 2024-11-14 00:00:00
Tasks: 3D geometry, Image Reconstruction

Ocean exploration using acoustical 3D imaging is gaining popularity as it provides information about the 3D geometry of the targets even under mild turbid conditions. A major challenge in underwater 3D imaging is the high cost of the planar arrays and the computational complexity of the image reconstruction algorithms. In this work, we introduce two novel aspects, an L shaped array and a quadrant based time domain receive beamforming with a focus on achieving relatively low computational complexity for real-time 3D imaging in underwater environments. An orthogonal combination of two linear arrays to form an L shape is used to perform two independent and parallel 2D delay and sum beamforming and the 3D image of the target is reconstructed using the product of the resulting beams. In the proposed quadrant-based beamforming, each quadrant in the imaging slice is reconstructed in parallel using the orthogonal L-shaped linear arrays which reside at the edges of the planar array for the quadrant to be reconstructed. The proposed L-array solves the multiple-target ambiguity issue of the cross-array and an L-array placed at the center of the uniform planar array. The proposed method has reduced the main lobe width by 1 degree with 11.8 dB increase in the peak side lobe level when compared to the conventional delay and sum beamforming using a uniform planar array. Although there is an increase in the side lobe level, the asymmetric beam pattern of the proposed L array and the quadrant-based beamforming restrict the side lobes within a quadrant. The proposed method achieves a reduction in computation time by a factor of 97 for 3D imaging compared to the conventional method, while maintaining acceptable image quality. For qualitative analysis, the 3D images of different underwater targets have been reconstructed and compared in simulation and experiment.