pwc-archive/papers-with-abstracts huggingface dataset:
Optimized 3D Point Labeling with Leaders Using the Beams Displacement Method
Authors: Zhiwei Wei, Nai Yang, Wenjia Xu, Su Ding, Li Minmin, Li You, Guo Renzhong
Date: 2024-06-28 00:00:00
Tasks: Position

In three-dimensional geographical scenes, adding labels with leader lines to point features can significantly improve their visibility. Leadered labels have a large degree of freedom in position con-figuration, but existing methods are mostly based on limited position candidate models, which not only fail to effectively utilize the map space but also make it difficult to consider the relative relationships between labels. Therefore, we conceptualize the dynamic configuration process of computing label positions as akin to solving a map displacement problem. We use a triangulated graph to delineate spatial relationships among labels and calculate the forces exerted on labels considering the constraints associated with point feature labels. Then we use the Beams Displacement Method to iteratively calculate new positions for the labels. Our experimental outcomes demonstrate that this method effectively mitigates label overlay issues while maintaining minimal average directional deviation between adjacent labels. Furthermore, this method is adaptable to various types of leader line labels. Meanwhile, we also discuss the block processing strategy to improve the efficiency of label configuration and analyze the impact of different proximity graphs.
Neural Texture Block Compression
Authors: Shin Fujieda, Takahiro Harada
Date: 2024-06-27 00:00:00

Block compression is a widely used technique to compress textures in real-time graphics applications, offering a reduction in storage size. However, their storage efficiency is constrained by the fixed compression ratio, which substantially increases storage size when hundreds of high-quality textures are required. In this paper, we propose a novel block texture compression method with neural networks, Neural Texture Block Compression (NTBC). NTBC learns the mapping from uncompressed textures to block-compressed textures, which allows for significantly reduced storage costs without any change in the shaders.Our experiments show that NTBC can achieve reasonable-quality results with up to about 70% less storage footprint, preserving real-time performance with a modest computational overhead at the texture loading phase in the graphics pipeline.
Controlling the color appearance of objects by optimizing the illumination spectrum
Authors: Mariko Yamaguchi, Masaru Tsuchida, Takahiro Matsumoto, Tetsuro Tokunaga, Takayoshi Mochizuki
Date: 2024-06-17 00:00:00
Tasks: Metamerism

We have developed an innovative lighting system that changes specific target colors while keeping the lights appearing naturally white. By precisely controlling the spectral power distribution (SPD) of illumination and harnessing the unique phenomenon of metamerism, our system achieves unique color variations in ways you've never seen before. Our system calculates the optimal SPDs of illumination for given materials to intensively induce metamerism, and then synthesizes the illumination using various colors of LEDs. We successfully demonstrated the system's implementation at Paris Fashion Week 2024. As models step onto the stage, their dresses initiate a captivating transformation. Our system altering the colors of the dresses, showcasing an impressive transition from one stunning color to another.
1-Lipschitz Neural Distance Fields
Authors: Guillaume Coiffier, Louis Bethune
Date: 2024-06-14 00:00:00

Neural implicit surfaces are a promising tool for geometry processing that represent a solid object as the zero level set of a neural network. Usually trained to approximate a signed distance function of the considered object, these methods exhibit great visual fidelity and quality near the surface, yet their properties tend to degrade with distance, making geometrical queries hard to perform without the help of complex range analysis techniques. Based on recent advancements in Lipschitz neural networks, we introduce a new method for approximating the signed distance function of a given object. As our neural function is made 1- Lipschitz by construction, it cannot overestimate the distance, which guarantees robustness even far from the surface. Moreover, the 1-Lipschitz constraint allows us to use a different loss function, called the hinge-Kantorovitch-Rubinstein loss, which pushes the gradient as close to unit-norm as possible, thus reducing computation costs in iterative queries. As this loss function only needs a rough estimate of occupancy to be optimized, this means that the true distance function need not to be known. We are therefore able to compute neural implicit representations of even bad quality geometry such as noisy point clouds or triangle soups. We demonstrate that our methods is able to approximate the distance function of any closed or open surfaces or curves in the plane or in space, while still allowing sphere tracing or closest point projections to be performed robustly.
Simplicits: Mesh-Free, Geometry-Agnostic, Elastic Simulation
Authors: Vismay Modi, Nicholas Sharp, Or Perel, Shinjiro Sueda, David I. W. Levin
Date: 2024-06-09 00:00:00

The proliferation of 3D representations, from explicit meshes to implicit neural fields and more, motivates the need for simulators agnostic to representation. We present a data-, mesh-, and grid-free solution for elastic simulation for any object in any geometric representation undergoing large, nonlinear deformations. We note that every standard geometric representation can be reduced to an occupancy function queried at any point in space, and we define a simulator atop this common interface. For each object, we fit a small implicit neural network encoding spatially varying weights that act as a reduced deformation basis. These weights are trained to learn physically significant motions in the object via random perturbations. Our loss ensures we find a weight-space basis that best minimizes deformation energy by stochastically evaluating elastic energies through Monte Carlo sampling of the deformation volume. At runtime, we simulate in the reduced basis and sample the deformations back to the original domain. Our experiments demonstrate the versatility, accuracy, and speed of this approach on data including signed distance functions, point clouds, neural primitives, tomography scans, radiance fields, Gaussian splats, surface meshes, and volume meshes, as well as showing a variety of material energies, contact models, and time integration schemes.
Analyzing Speech Motor Movement using Surface Electromyography in Minimally Verbal Adults with Autism Spectrum Disorder
Authors: Wazeer Zulfikar, Nishat Protyasha, Camila Canales, Heli Patel, James Williamson, Laura Sarnie, Lisa Nowinski, Nataliya Kosmyna, Paige Townsend, Sophia Yuditskaya, Tanya Talkar, Utkarsh Oggy Sarawgi, Christopher McDougle, Thomas Quatieri, Pattie Maes, Maria Mody
Date: 2024-07-11 00:00:00

Adults who are minimally verbal with autism spectrum disorder (mvASD) have pronounced speech difficulties linked to impaired motor skills. Existing research and clinical assessments primarily use indirect methods such as standardized tests, video-based facial features, and handwriting tasks, which may not directly target speech-related motor skills. In this study, we measure activity from eight facial muscles associated with speech using surface electromyography (sEMG), during carefully designed tasks. The findings reveal a higher power in the sEMG signals and a significantly greater correlation between the sEMG channels in mvASD adults (N=12) compared to age and gender-matched neurotypical controls (N=14). This suggests stronger muscle activation and greater synchrony in the discharge patterns of motor units. Further, eigenvalues derived from correlation matrices indicate lower complexity in muscle coordination in mvASD, implying fewer degrees of freedom in motor control.
CoSTAP: Clutter Suppression in Co-Pulsing FDA-STAP
Authors: Wanghan Lv, Kumar Vijay Mishra
Date: 2024-07-14 00:00:00

Range-dependent clutter suppression poses significant challenges in airborne frequency diverse array (FDA) radar, where resolving range ambiguity is particularly difficult. Traditional space-time adaptive processing (STAP) techniques used for clutter mitigation in FDA radars operate in the physical domain defined by first-order statistics. In this paper, unlike conventional airborne uniform FDA, we introduce a space-time-range adaptive processing (STRAP) method to exploit second-order statistics for clutter suppression in the newly proposed co-pulsing FDA radar. This approach utilizes co-prime frequency offsets (FOs) across the elements of a co-prime array, with each element transmitting at a non-uniform co-prime pulse repetition interval (C-Cube). By incorporating second-order statistics from the co-array domain, the co-pulsing STRAP or CoSTAP benefits from increased degrees of freedom (DoFs) and low computational cost while maintaining strong clutter suppression capabilities. However, this approach also introduces significant computational burdens in the coarray domain. To address this, we propose an approximate method for three-dimensional (3-D) clutter subspace estimation using discrete prolate spheroidal sequences (DPSS) to balance clutter suppression performance and computational cost. We first develop a 3-D clutter rank evaluation criterion to exploit the geometry of 3-D clutter in a general scenario. Following this, we present a clutter subspace rejection method to mitigate the effects of interference such as jammer. Compared to existing FDA-STAP algorithms, our proposed CoSTAP method offers superior clutter suppression performance, lower computational complexity, and enhanced robustness to interference. Numerical experiments validate the effectiveness and advantages of our method.
Near-Field User Localization and Channel Estimation for XL-MIMO Systems: Fundamentals, Recent Advances, and Outlooks
Authors: Hao Lei, Jiayi Zhang, Zhe Wang, Huahua Xiao, Bo Ai, Emil Björnson
Date: 2024-07-14 00:00:00

Extremely large-scale multiple-input multipleoutput (XL-MIMO) is believed to be a cornerstone of sixth-generation (6G) wireless networks. XL-MIMO uses more antennas to both achieve unprecedented spatial degrees of freedom (DoFs) and exploit new electromagnetic (EM) phenomena occurring in the radiative near-field. The near-field effects provide the XL-MIMO array with depth perception, enabling precise localization and spatially multiplexing jointly in the angle and distance domains. This article delineates the distinctions between near-field and far-field propagation, highlighting the unique EM characteristics introduced by having large antenna arrays. It thoroughly examines the challenges these new near-field characteristics pose for user localization and channel estimation and provides a comprehensive review of new algorithms developed to address them. The article concludes by identifying critical future research directions.
Perturbation-based Sequence Selection for Probabilistic Amplitude Shaping
Authors: Mohammad Taha Askari, Lutz Lampe
Date: 2024-07-12 00:00:00

We introduce a practical sign-dependent sequence selection metric for probabilistic amplitude shaping and propose a simple method to predict the gains in signal-to-noise ratio (SNR) for sequence selection. The proposed metric provides a $0.5$ dB SNR gain for single-polarized 256-QAM transmission over a long-haul fiber link.
Where are the bits in atoms? A perspective on the physical origin and evolutionary nature of information
Authors: Wouter van der Wijngaart
Date: 2024-07-06 00:00:00

Information is a structural pattern that represents another structural pattern. This perspective hypothesizes that modelling of structure creation through causal sets can elucidate the natural origin, evolution and ontology of information.
Aligning Models with Their Realization through Model-based Systems Engineering
Authors: Lovis Justin Immanuel Zenz, Erik Heiland, Peter Hillmann, Andreas Karcher
Date: 2024-06-18 00:00:00
Tasks: object-detection, Object Detection

In this paper, we propose a method for aligning models with their realization through the application of model-based systems engineering. Our approach is divided into three steps. (1) Firstly, we leverage domain expertise and the Unified Architecture Framework to establish a reference model that fundamentally describes some domain. (2) Subsequently, we instantiate the reference model as specific models tailored to different scenarios within the domain. (3) Finally, we incorporate corresponding run logic directly into both the reference model and the specific models. In total, we thus provide a practical means to ensure that every implementation result is justified by business demand. We demonstrate our approach using the example of maritime object detection as a specific application (specific model / implementation element) of automatic target recognition as a service reoccurring in various forms (reference model element). Our approach facilitates a more seamless integration of models and implementation, fostering enhanced Business-IT alignment.
Data Analysis of Decision Support for Sustainable Welfare in The Presence of GDP Threshold Effects: A Case Study of Interactive Data Exploration
Authors: Fahimeh Asgari, Seyedeh Gol Ara Ghoreishi, Matin Khajavi, Ali Foozoni, Ali Ala, Ahmad Gholizadeh Lonbar
Date: 2024-07-12 00:00:00

Energy usage and GDP have been the subject of numerous studies over the past decades. It has been overlooked by previous studies that energy consumption correlates with economic growth in relation to GDP. This study uses threshold regression and Granger causality testing to identify GDP-dependent causality in ten OECD countries over the last 10 years. There is a significant correlation between economic growth and energy consumption. Energy consumption and short-term economic growth are statistically significantly correlated. There is a significant positive effect of energy consumption (EC) on GDP in the short run below the threshold of $10,936 USD since the coefficient is 3.34 and the p-value is 0.0252. There is a -0.0127 correlation coefficient and a 0.0327 p-value associated with GDP Granger-cause EC over the long run. EC and GDP are not causally related for GDP per capita above $10,936 USD. In the long run, GDP Granger causes EC with a coefficient of -0.1638 and a p-value of 0.0675. According to the study, sustainable development requires sustainable use of natural resources, technological investment, foreign direct investment, and gross fixed capital formation. Economic growth can be boosted while adhering to sustainability goals by implementing these recommendations.
CommRad: Context-Aware Sensing-Driven Millimeter-Wave Networks
Authors: Ish Kumar Jain, Suriyaa MM, Dinesh Bharadia
Date: 2024-07-11 00:00:00
Tasks: Autonomous Vehicles, Object Tracking

Millimeter-wave (mmWave) technology is pivotal for next-generation wireless networks, enabling high-data-rate and low-latency applications such as autonomous vehicles and XR streaming. However, maintaining directional mmWave links in dynamic mobile environments is challenging due to mobility-induced disruptions and blockage. While effective, the current 5G NR beam training methods incur significant overhead and scalability issues in multi-user scenarios. To address this, we introduce CommRad, a sensing-driven solution incorporating a radar sensor at the base station to track mobile users and maintain directional beams even under blockages. While radar provides high-resolution object tracking, it suffers from a fundamental challenge of lack of context, i.e., it cannot discern which objects in the environment represent active users, reflectors, or blockers. To obtain this contextual awareness, CommRad unites wireless sensing capabilities of bi-static radio communication with the mono-static radar sensor, allowing radios to provide initial context to radar sensors. Subsequently, the radar aids in user tracking and sustains mobile links even in obstructed scenarios, resulting in robust and high-throughput directional connections for all mobile users at all times. We evaluate this collaborative radar-radio framework using a 28 GHz mmWave testbed integrated with a radar sensor in various indoor and outdoor scenarios, demonstrating a 2.5x improvement in median throughput compared to a non-collaborative baseline.
UAV Networks Surveillance Implementing an Effective Load-Aware Multipath Routing Protocol (ELAMRP)
Authors: Raja Vavekanand, Kira Sam, Vijay Singh
Date: 2024-06-25 00:00:00

In this work uses innovative multi-channel load-sensing techniques to deploy unmanned aerial vehicles (UAVs) for surveillance. The research aims to improve the quality of data transmission methods and improve the efficiency and reliability of surveillance systems by exploiting the mobility and adaptability of UAVs does the proposed protocol intelligently distribute network traffic across multiple channels, considering the load of each channel, While addressing challenges such as load balancing, this study investigates the effectiveness of the protocol by simulations or practical tests on The expected results have improved UAV-based surveillance systems, more flexible and efficient networks for applications such as security, emergency response and the environment alignment of monitoring -Offering infrastructures, which contribute to efficient and reliable monitoring solutions.
Trajectory Tracking for Unmanned Aerial Vehicles in 3D Spaces under Motion Constraints
Authors: Saurabh Kumar, Shashi Ranjan Kumar, Abhinav Sinha
Date: 2024-07-15 00:00:00
Tasks: Position

This article presents a three-dimensional nonlinear trajectory tracking control strategy for unmanned aerial vehicles (UAVs) in the presence of spatial constraints. As opposed to many existing control strategies, which do not consider spatial constraints, the proposed strategy considers spatial constraints on each degree of freedom movement of the UAV. Such consideration makes the design appealing for many practical applications, such as pipeline inspection, boundary tracking, etc. The proposed design accounts for the limited information about the inertia matrix, thereby affirming its inherent robustness against unmodeled dynamics and other imperfections. We rigorously show that the UAV will converge to its desired path by maintaining bounded position, orientation, and linear and angular speeds. Finally, we demonstrate the effectiveness of the proposed strategy through various numerical simulations.
Advancing Continual Learning for Robust Deepfake Audio Classification
Authors: Feiyi Dong, Qingchen Tang, Yichen Bai, Zihan Wang
Date: 2024-07-14 00:00:00
Tasks: Audio Classification, Classification, Continual Learning, Face Swapping

The emergence of new spoofing attacks poses an increasing challenge to audio security. Current detection methods often falter when faced with unseen spoofing attacks. Traditional strategies, such as retraining with new data, are not always feasible due to extensive storage. This paper introduces a novel continual learning method Continual Audio Defense Enhancer (CADE). First, by utilizing a fixed memory size to store randomly selected samples from previous datasets, our approach conserves resources and adheres to privacy constraints. Additionally, we also apply two distillation losses in CADE. By distillation in classifiers, CADE ensures that the student model closely resembles that of the teacher model. This resemblance helps the model retain old information while facing unseen data. We further refine our model's performance with a novel embedding similarity loss that extends across multiple depth layers, facilitating superior positive sample alignment. Experiments conducted on the ASVspoof2019 dataset show that our proposed method outperforms the baseline methods.
Informed FastICA: Semi-Blind Minimum Variance Distortionless Beamformer
Authors: Zbyněk Koldovský, Jiří Málek, Jaroslav Čmejla, Stephen O'Regan
Date: 2024-07-12 00:00:00

Non-Gaussianity-based Independent Vector Extraction leads to the famous one-unit FastICA/FastIVA algorithm when the likelihood function is optimized using an approximate Newton-Raphson algorithm under the orthogonality constraint. In this paper, we replace the constraint with the analytic form of the minimum variance distortionless beamformer (MVDR), by which a semi-blind variant of FastICA/FastIVA is obtained. The side information here is provided by a weighted covariance matrix replacing the noise covariance matrix, the estimation of which is a frequent goal of neural beamformers. The algorithm thus provides an intuitive connection between model-based blind extraction and learning-based extraction. The algorithm is tested in simulations and speaker ID-guided speaker extraction, showing fast convergence and promising performance.
Diff-MST: Differentiable Mixing Style Transfer
Authors: Soumya Sai Vanka, Christian Steinmetz, Jean-Baptiste Rolland, Joshua Reiss, George Fazekas
Date: 2024-07-11 00:00:00
Tasks: Style Transfer

Mixing style transfer automates the generation of a multitrack mix for a given set of tracks by inferring production attributes from a reference song. However, existing systems for mixing style transfer are limited in that they often operate only on a fixed number of tracks, introduce artifacts, and produce mixes in an end-to-end fashion, without grounding in traditional audio effects, prohibiting interpretability and controllability. To overcome these challenges, we introduce Diff-MST, a framework comprising a differentiable mixing console, a transformer controller, and an audio production style loss function. By inputting raw tracks and a reference song, our model estimates control parameters for audio effects within a differentiable mixing console, producing high-quality mixes and enabling post-hoc adjustments. Moreover, our architecture supports an arbitrary number of input tracks without source labelling, enabling real-world applications. We evaluate our model's performance against robust baselines and showcase the effectiveness of our approach, architectural design, tailored audio production style loss, and innovative training methodology for the given task.
Phonetic Richness for Improved Automatic Speaker Verification
Authors: Nicholas Klein, Ganesh Sivaraman, Elie Khoury
Date: 2024-07-10 00:00:00
Tasks: Speaker Recognition, Speaker Verification

When it comes to authentication in speaker verification systems, not all utterances are created equal. It is essential to estimate the quality of test utterances in order to account for varying acoustic conditions. In addition to the net-speech duration of an utterance, it is observed in this paper that phonetic richness is also a key indicator of utterance quality, playing a significant role in accurate speaker verification. Several phonetic histogram based formulations of phonetic richness are explored using transcripts obtained from an automatic speaker recognition system. The proposed phonetic richness measure is found to be positively correlated with voice authentication scores across evaluation benchmarks. Additionally, the proposed measure in combination with net speech helps in calibrating the speaker verification scores, obtaining a relative EER improvement of 5.8% on the Voxceleb1 evaluation protocol. The proposed phonetic richness based calibration provides higher benefit for short utterances with repeated words.