pwc-archive/papers-with-abstracts huggingface dataset:
STRUM-LLM: Attributed and Structured Contrastive Summarization
Authors: Beliz Gunel, James B. Wendt, Jing Xie, Yichao Zhou, Nguyen Vo, Zachary Fisher, Sandeep Tata
Date: 2024-03-25 00:00:00
Tasks: Attribute, Decision Making

Users often struggle with decision-making between two options (A vs B), as it usually requires time-consuming research across multiple web pages. We propose STRUM-LLM that addresses this challenge by generating attributed, structured, and helpful contrastive summaries that highlight key differences between the two options. STRUM-LLM identifies helpful contrast: the specific attributes along which the two options differ significantly and which are most likely to influence the user's decision. Our technique is domain-agnostic, and does not require any human-labeled data or fixed attribute list as supervision. STRUM-LLM attributes all extractions back to the input sources along with textual evidence, and it does not have a limit on the length of input sources that it can process. STRUM-LLM Distilled has 100x more throughput than the models with comparable performance while being 10x smaller. In this paper, we provide extensive evaluations for our method and lay out future directions for our currently deployed system.
Improved Training for 3D Point Cloud Classification
Authors: Sneha Paul, Zachary Patterson, Nizar Bouguila
Date: 2023-01-01 00:00:00
Proceeding: Structural, Syntactic, and Statistical Pattern Recognition (S+SSPR) 2023 1
Tasks: 3D Object Classification, 3D Point Cloud Classification, Classification, Point Cloud Classification, Transfer Learning

The point cloud is a 3D geometric data of irregular format. As a result, they are needed to be transformed into 3D voxels or a collection of images before being fed into models. This unnecessarily increases the volume of the data and the complexities of dealing with it. PointNet is a pioneering approach in this direction that feeds the 3D point cloud data directly to a model. This research work is developed on top of the existing PointNet architecture. The ModelNet10 dataset, a collection of 3D images with 10 class labels, has been used for this study. The goal of the study is to improve the accuracy of PointNet. To achieve this, a few variations of encoder models have been proposed along with improved training protocol, and transfer learning from larger datasets in this research work. Also, an extensive hyperparameter study has been done. The experiments in this research work achieve a 6.10% improvement over the baseline model. The code for this work is publicly available at https://github.com/snehaputul/ImprovedPointCloud.
Keeping Up With the Winner! Targeted Advertisement to Communities in Social Networks
Authors: Shailaja Mallick, Vishwaraj Doshi, Do Young Eun
Date: 2024-03-29 00:00:00

When a new product enters a market already dominated by an existing product, will it survive along with this dominant product? Most of the existing works have shown the coexistence of two competing products spreading/being adopted on overlaid graphs with same set of users. However, when it comes to the survival of a weaker product on the same graph, it has been established that the stronger one dominates the market and wipes out the other. This paper makes a step towards narrowing this gap so that a new/weaker product can also survive along with its competitor with a positive market share. Specifically, we identify a locally optimal set of users to induce a community that is targeted with advertisement by the product launching company under a given budget constraint. To this end, we model the system as competing Susceptible-Infected-Susceptible (SIS) epidemics and employ perturbation techniques to quantify and attain a positive market share in a cost-efficient manner. Our extensive simulation results with real-world graph dataset show that with our choice of target users, a new product can establish itself with positive market share, which otherwise would be dominated and eventually wiped out of the competitive market under the same budget constraint.
Development of a full-Scale approach to predict overlay reflective crack
Authors: Zehui Zhu, Imad L. Al-Qadi
Date: 2024-03-29 00:00:00

Resurfacing a moderately deteriorated Portland cement concrete (PCC) pavement with asphalt concrete (AC) layers is considered an efficient rehabilitation practice. However, reflective cracks may develop shortly after resurfacing because of discontinuities (e.g. joints and cracks) in existing PCC pavement. In this paper, a new accelerated full-scale testing approach was developed to study reflective crack growth in AC overlays. Two hydraulic actuators were used to simulate a moving dual-tire assembly with a loading rate of more than five-thousand-wheel passes per hour. A load cycle consists of three steps, simulating a tire approaching, moving across, and leaving a PCC discontinuity. Experiments were conducted to compare the reflective crack behaviour of two overlay configurations. Both test sections were fully cracked in less than an hour. The initiation and propagation of reflective cracks were explicitly documented using crack detectors in conjunction with a camera. The proposed full-scale testing protocol offers a repeatable and efficient approach to systematically investigate the effects of various overlay configurations, thus enabling the identification of optimal design against reflective cracking.
Analysis of Signal Distortion in Molecular Communication Channels Using Frequency Response
Authors: Shoichiro Kitada, Taishi Kotsuka, Yutaka Hori
Date: 2024-03-29 00:00:00

Molecular communication (MC) is a concept in communication engineering, where diffusive molecules are used to transmit information between nano or micro-scale chemical reaction systems. Engineering MC to control the reaction systems in cells is expected for many applications such as targeted drug delivery and biocomputing. Toward control of the reaction systems as desired via MC, it is important to transmit signals without distortion by MC since the reaction systems are often triggered depending on the concentration of signaling molecules arriving at the cells. In this paper, we propose a method to analyze signal distortion caused by diffusion-based MC channels using frequency response of channels. The proposed method provides indices that quantitatively evaluate the magnitude of distortion and shows parameter conditions of MC channels that suppress signal distortion. Using the proposed method, we demonstrate the design procedure of specific MC channels that satisfy given specifications. Finally, the roles of MC channels in nature are discussed from the perspective of signal distortion.
Low-cost adaptive obstacle avoidance trajectory control for express delivery drone
Authors: Yanhui Zhang, Caisheng Wei, Yifan Zhang, Congcong Tian, Weifang Chen
Date: 2024-03-29 00:00:00

This paper studies quadcopters obstacle avoidance trajectory control (OATC) problem for express delivery. A new nonlinear adaptive learning controller that is low-cost and portable to different wheelbase sizes is proposed to adapt to large-angle maneuvers and load changes in UAV delivery missions. The controller consists of a nonlinear variable gain (NLVG) function and an extreme value search (ES) algorithm to reduce overshoot and settling time. Finally, simulations were conducted on a quadcopter to verify the effectiveness of the proposed control scheme under two typical collision-free trajectories.
Nonlinear Voltage Regulation of an Auxiliary Energy Storage of a Multiport Interconnection
Authors: Felipe Morales, Rafael Cisneros, Romeo Ortega, Antonio Sanchez-Squella
Date: 2024-03-29 00:00:00

In this article, we propose a nonlinear voltage control to ensure power exchange in a multiport interconnected system, which consists of a bidirectional DC-DC converter and generating-storing devices. The converter topology under consideration is two-stage, composed of an interconnection of a buck with a boost converter. The motivation for this work is the explosive increase in the use of DC-DC converters due to the massification of renewable energies, electric vehicles powertrains, and energy storage systems, where fuel cells or batteries can be used as power backup or high-power support during transient phenomena. The converter's voltage step-up and step-down capabilities allow the use of supercapacitors with voltage limits that exceed those required by the load, thus enabling its use in a broader range of applications. The control design for this system does not correspond to that in standard applications involving power converters. As it is known, the latter consists of finding a control law such that the closed-loop system has an asymptotically stable equilibrium point fulfilling the voltage regulation objectives. Instead, in this application, the state does not tend to an equilibrium value in order for the system to be regulated. The converter voltage is regulated at desired some setpoint whereas the other variables are only required to be bounded. To achieve a dynamic response that best adapts to changes in system demand and ensure stability over the defined wide operating range we propose a novel control strategy that exploits the partially cascaded structure of the system. Numerical and experimental results validate our approach.
Mechanochemical bistability of intestinal organoids enables robust morphogenesis
Authors: Shi-Lei Xue, Qiutan Yang, Prisca Liberali, Edouard Hannezo
Date: 2024-03-29 00:00:00

How pattern and form are generated in a reproducible manner during embryogenesis remains poorly understood. Intestinal organoid morphogenesis involves a number of mechanochemical regulators, including cell-type specific cytoskeletal forces and osmotically-driven lumen volume changes. However, whether and how these forces are coordinated in time and space via feedbacks to ensure robust morphogenesis remains unclear. Here, we propose a minimal physical model of organoid morphogenesis with local cellular mechano-sensation, where lumen volume changes can impact epithelial shape via both direct mechanical (passive) and indirect mechanosensitive (active) mechanisms. We show how mechano-sensitive feedbacks on cytoskeletal tension generically give rise to morphological bistability, where both bulged (open) and budded (closed) crypt states are possible and dependent on the history of volume changes. Such bistability can explain several paradoxical experimental observations, such as the importance of the timing of lumen shrinkage and robustness of the final morphogenetic state to mechanical perturbations. More quantitatively, we performed mechanical and pharmacological experiments to validate the key modelling assumptions and make quantitative predictions on organoid morphogenesis. This suggests that bistability arising from feedbacks between cellular tensions and fluid pressure could be a general mechanism to allow for the coordination of multicellular shape changes in developing systems.
Predicting the impact of e-commerce indices on international trade in Iran and other selected members of the Organization for Economic Co-operation and Development (OECD) by using the artificial intelligence and P-VAR model
Authors: Soheila Khajoui, Saeid Dehyadegari, Sayyed Abdolmajid Jalaee
Date: 2024-03-29 00:00:00

This study aims at predicting the impact of e-commerce indicators on international trade of the selected OECD countries and Iran, by using the artificial intelligence approach and P-VAR. According to the nature of export, import, GDP, and ICT functions, and the characteristics of nonlinearity, this analysis is performed by using the MPL neural network. The export, import, GDP, and ICT findings were examined with 99 percent accuracy. Using the P-VAR model in the Eviews software, the initial database and predicted data were applied to estimate the impact of e-commerce on international trade. The findings from analyzing the data show that there is a bilateral correlation between e-commerce which means that ICT and international trade affect each other and the Goodness of fit of the studied model is confirmed.
Variability in Aggregate Personal Income Across Industrial Sectors During COVID-19 Shock: A Time-Series Exploration
Authors: Didarul Islam, Mohammad Abdullah Al Faisal
Date: 2024-03-29 00:00:00
Tasks: Time Series

This study explored the variability in Aggregate Personal Income (PI) across 13 major industrial sectors in the US during the COVID-19 pandemic. Utilizing time-series data from 2010 Q1 to 2019 Q4, we employed Autoregressive Integrated Moving Average (ARIMA) models to establish baseline trends in Personal Income (PI) before the pandemic. We then extended these models to forecast PI values for the subsequent 14 quarters, from 2020 Q1 to 2023 Q2, as if the pandemic had never happened. This forecasted data was compared with the actual PI data collected during the pandemic to quantify its impacts. This approach allowed for the assessment of both immediate and extended effects of COVID-19 on sector-specific PI. Our study highlighted the resilience of PI in sectors like Utilities, Retail, Finance, Real Estate, and Healthcare, with Farming showing an early recovery in PI, despite significant initial setbacks. In contrast, PI in Accommodation and Food Services experienced delayed recovery, contributing significantly to the overall impact variance alongside Farming (53.26\% and 33.26\% respectively). Finance and Utilities demonstrated positive deviations, suggesting a lesser impact or potential benefit in early pandemic stages. Meanwhile, sectoral PI in Manufacturing, Wholesale and Education showed moderate recovery, whereas Construction and Government lagged in resilience. The aggregate economic impact, initially negative at -0.027 in 2020 Q1, drastically worsened to -1.42 in Q2, but improved by Q4, reflecting a broader trend of adaptation and resilience across all the sectors during the pandemic.
Optimal Auction Design with Contingent Payments and Costly Verification
Authors: Ian Ball, Teemu Pekkarinen
Date: 2024-03-29 00:00:00

We study the design of an auction for an income-generating asset such as an intellectual property license. Each bidder has a signal about his future income from acquiring the asset. After the asset is allocated, the winner's income from the asset is realized privately. The principal can audit the winner, at a cost, and then charge a payment contingent on the winner's realized income. We solve for an auction that maximizes revenue, net auditing costs. The winning bidder is charged linear royalties up to a cap. A higher bidder pays more in cash and faces a lower royalty cap.
Strategic complementarities as stochastic control under sticky price
Authors: Lambert Dong
Date: 2024-03-28 00:00:00

We examine how monetary shocks spread throughout an economic model characterized by sticky prices and general equilibrium, where the pricing strategies of firms are interlinked, fostering a mutually beneficial relationship. In this dynamic equilibrium, pricing choices of firms are influenced by overall economic factors, which are themselves affected by these decisions. We approach this situation using a path integral control method, yielding several important insights. We confirm the presence and uniqueness of the equilibrium and scrutinize the impulse response function (IRF) of output subsequent to a shock affecting the entire economy.
Non-Exponential Reverberation Modeling Using Dark Velvet Noise
Authors: Jon Fagerström, Sebastian J. Schelcht, Vesa Välimäki
Date: 2024-03-29 00:00:00

Previous research on late-reverberation modeling has mainly focused on exponentially decaying room impulse responses, whereas methods for accurately modeling non-exponential reverberation remain challenging. This paper extends the previously proposed basic dark-velvet-noise reverberation algorithm and proposes a parametrization scheme for modeling late reverberation with arbitrary temporal energy decay. Each pulse in the velvet-noise sequence is routed to a single dictionary filter that is selected from a set of filters based on weighted probabilities. The probabilities control the spectral evolution of the late-reverberation model and are optimized to fit a target impulse response via non-negative least-squares optimization. In this way, the frequency-dependent energy decay of a target late-reverberation impulse response can be fitted with mean and maximum T60 errors of 4% and 8%, respectively, requiring about 50% less coloration filters than a previously proposed filtered velvet-noise algorithm. Furthermore, the extended dark-velvet-noise reverberation algorithm allows the modeled impulse response to be gated, the frequency-dependent reverberation time to be modified, and the model's spectral evolution and broadband decay to be decoupled. The proposed method is suitable for the parametric late-reverberation synthesis of various acoustic environments, especially spaces that exhibit a non-exponential energy decay, motivating its use in musical audio and virtual reality.
3D-Speaker-Toolkit: An Open-Source Toolkit for Multimodal Speaker Verification and Diarization
Authors: Yafeng Chen, Siqi Zheng, Hui Wang, Luyao Cheng, Tinglong Zhu, Rongjie Huang, Chong Deng, Qian Chen, Shiliang Zhang, Wen Wang, Xihao Li
Date: 2024-03-29 00:00:00
Tasks: Self-Supervised Learning, speaker-diarization, Speaker Diarization, Speaker Recognition, Speaker Verification

We introduce 3D-Speaker-Toolkit, an open-source toolkit for multimodal speaker verification and diarization, designed for meeting the needs of academic researchers and industrial practitioners. The 3D-Speaker-Toolkit adeptly leverages the combined strengths of acoustic, semantic, and visual data, seamlessly fusing these modalities to offer robust speaker recognition capabilities. The acoustic module extracts speaker embeddings from acoustic features, employing both fully-supervised and self-supervised learning approaches. The semantic module leverages advanced language models to comprehend the substance and context of spoken language, thereby augmenting the system's proficiency in distinguishing speakers through linguistic patterns. The visual module applies image processing technologies to scrutinize facial features, which bolsters the precision of speaker diarization in multi-speaker environments. Collectively, these modules empower the 3D-Speaker-Toolkit to achieve substantially improved accuracy and reliability in speaker-related tasks. With 3D-Speaker-Toolkit, we establish a new benchmark for multimodal speaker analysis. The toolkit also includes a handful of open-source state-of-the-art models and a large-scale dataset containing over 10,000 speakers. The toolkit is publicly available at https://github.com/modelscope/3D-Speaker.
Evolving Semantic Communication with Generative Model
Authors: Shunpu Tang, Qianqian Yang, Deniz Gündüz, Zhaoyang Zhang
Date: 2024-03-29 00:00:00
Tasks: model, Semantic Communication

Recently, learning-based semantic communication (SemCom) has emerged as a promising approach in the upcoming 6G network and researchers have made remarkable efforts in this field. However, existing works have yet to fully explore the advantages of the evolving nature of learning-based systems, where knowledge accumulates during transmission have the potential to enhance system performance. In this paper, we explore an evolving semantic communication system for image transmission, referred to as ESemCom, with the capability to continuously enhance transmission efficiency. The system features a novel channel-aware semantic encoder that utilizes a pre-trained Semantic StyleGAN to extract the channel-correlated latent variables consisting of serval semantic vectors from the input images, which can be directly transmitted over a noisy channel without further channel coding. Moreover, we introduce a semantic caching mechanism that dynamically stores the transmitted semantic vectors in the local caching memory of both the transmitter and receiver. The cached semantic vectors are then exploited to eliminate the need to transmit similar codes in subsequent transmission, thus further reducing communication overhead. Simulation results highlight the evolving performance of the proposed system in terms of transmission efficiency, achieving superior perceptual quality with an average bandwidth compression ratio (BCR) of 1/192 for a sequence of 100 testing images compared to DeepJSCC and Inverse JSCC with the same BCR. Code of this paper is available at \url{https://github.com/recusant7/GAN_SeCom}.
A new framework of sensor selection for developing a fault detection system based on data-envelopment analysis
Authors: Amir Eshaghi Chaleshtori, Abdollah aghaie
Date: 2024-03-29 00:00:00
Tasks: Fault Detection, Fault Diagnosis, Management

Several methods have been proposed to identify which sensor sets are optimal for finding and localizing faults under different conditions for mechanical equipment. In order to preserve acceptable performance while minimizing costs, it is crucial to identify the most effective set of sensors available. Nevertheless, some sensor sets can behave differently in fault detection because of uncertainty in the measurement data contaminated by noise interference. This paper develops new sensor selection models using Data Envelopment Analysis (DEA), which has proven helpful as a management approach for determining an optimal number of sensors, associated costs, and sensor performance in the fault diagnosis. We propose four linear optimization models for sensor selection to design the fault detection system. The validity of the presented models is demonstrated using a vibration dataset collected from a gearbox. Based on the case study results, the proposed methods are effectively superior to the other comparison sensor selection methods in fault detection accuracy.
Fractional Delay Alignment Modulation for Spatially Sparse Wireless Communications
Authors: Zhiwen Zhou, Zhiqiang Xiao, Yong Zeng
Date: 2024-03-29 00:00:00

Delay alignment modulation (DAM) is a novel transmission technique for wireless systems with high spatial resolution by leveraging delay compensation and path-based beamforming, to mitigate the inter-symbol interference (ISI) without resorting to complex channel equalization or multi-carrier transmission. However, most existing studies on DAM consider a simplified scenario by assuming that the channel multi-path delays are integer multiples of the signal sampling interval. This paper investigates DAM for the more general and practical scenarios with fractional multi-path delays. We first analyze the impact of fractional multi-path delays on the existing DAM design, termed integer DAM (iDAM), which can only achieve delay compensations that are integer multiples of the sampling interval. It is revealed that the existence of fractional multi-path delays renders iDAM no longer possible to achieve perfect delay alignment. To address this issue, we propose a more generic DAM design called fractional DAM (fDAM), which achieves fractional delay pre-compensation via upsampling and fractional delay filtering. By leveraging the Farrow filter structure, the proposed approach can eliminate ISI without real-time computation of filter coefficients, as typically required in traditional channel equalization techniques. Simulation results demonstrate that the proposed fDAM outperforms the existing iDAM and orthogonal frequency division multiplexing (OFDM) in terms of symbol error rate (SER) and spectral efficiency, while maintaining a comparable peak-to-average power ratio (PAPR) as iDAM, which is considerably lower than OFDM.