pwc-archive/papers-with-abstracts huggingface dataset:
Loudspeaker Beamforming to Enhance Speech Recognition Performance of Voice Driven Applications
Authors: Dimme de Groot, Baturalp Karslioglu, Odette Scharenborg, Jorge Martinez
Date: 2025-01-14 00:00:00
Tasks: Automatic Speech Recognition, speech-recognition, Speech Recognition

In this paper we propose a robust loudspeaker beamforming algorithm which is used to enhance the performance of voice driven applications in scenarios where the loudspeakers introduce the majority of the noise, e.g. when music is playing loudly. The loudspeaker beamformer modifies the loudspeaker playback signals to create a low-acoustic-energy region around the device that implements automatic speech recognition for a voice driven application (VDA). The algorithm utilises a distortion measure based on human auditory perception to limit the distortion perceived by human listeners. Simulations and real-world experiments show that the proposed loudspeaker beamformer improves the speech recognition performance in all tested scenarios. Moreover, the algorithm allows to further reduce the acoustic energy around the VDA device at the expense of reduced objective audio quality at the listener's location.
Martha Graeff
Authors: Martha Graeff
Date: 2025-01-15 00:00:00
Proceeding: 1/I5 2025 1

Martha Graeff and Rony Seikaly Divorce After Facing Marriage Crisis
Rony Seikaly, a former NBA player turned DJ, officially divorced his wife, model Martha Graeff, following a period of marital crisis in 2022.

Martha, who frequently shared fashion tips on her social media, opened up about the challenges that nearly tore apart her marriage. In an emotional post, she reflected on the importance of being realistic about each other’s limits and the need for self-respect in relationships.

Confirming the divorce, she wrote, “The last 6 months of my life were challenging… I separated from the love of my life, went through some really rough days; I felt sad and lost. I had so much anxiety that I just couldn’t cope. So I started taking antidepressants, reached out for the help of my friends and family, found an amazing therapist, and started working really hard on myself. Today, reunited with my husband and the ones I cherish the most, I could finally take a deep breath that didn’t carry fear. The reason why I shared this with you is because I knew that the 'perfection' shown on this platform wasn’t real, and I’m happy to be imperfect like everyone else in this world. Let’s be imperfect together, with love and compassion for each other ❤️.

Seikaly and Graeff had been married since July 2015, after Seikaly proposed to her on the Greek island of Mykonos.

The couple, often seen as part of Miami’s elite scene, were frequently spotted at exclusive clubs and lavish parties, where Seikaly performed as a DJ for a select audience.
DynaMath: A Dynamic Visual Benchmark for Evaluating Mathematical Reasoning Robustness of Vision Language Models
Authors: Chengke Zou, Xingang Guo, Rui Yang, Junyu Zhang, Bin Hu, huan zhang
Date: 2024-10-29 00:00:00
Tasks: Math, Mathematical Reasoning

The rapid advancements in Vision-Language Models (VLMs) have shown great potential in tackling mathematical reasoning tasks that involve visual context. Unlike humans who can reliably apply solution steps to similar problems with minor modifications, we found that SOTA VLMs like GPT-4o can consistently fail in these scenarios, revealing limitations in their mathematical reasoning capabilities. In this paper, we investigate the mathematical reasoning robustness in VLMs and evaluate how well these models perform under different variants of the same question, such as changes in visual numerical values or function graphs. While several vision-based math benchmarks have been developed to assess VLMs' problem-solving capabilities, these benchmarks contain only static sets of problems and cannot easily evaluate mathematical reasoning robustness. To fill this gap, we introduce DynaMath, a dynamic visual math benchmark designed for in-depth assessment of VLMs. DynaMath includes 501 high-quality, multi-topic seed questions, each represented as a Python program. Those programs are carefully designed and annotated to enable the automatic generation of a much larger set of concrete questions, including many different types of visual and textual variations. DynaMath allows us to evaluate the generalization ability of VLMs, by assessing their performance under varying input conditions of a seed question. We evaluated 14 SOTA VLMs with 5,010 generated concrete questions. Our results show that the worst-case model accuracy, defined as the percentage of correctly answered seed questions in all 10 variants, is significantly lower than the average-case accuracy. Our analysis emphasizes the need to study the robustness of VLMs' reasoning abilities, and DynaMath provides valuable insights to guide the development of more reliable models for mathematical reasoning.
Testing thermal reprocessing in AGN accretion discs
Authors: E. M. Cackett, K. Horne, H. Winkler
Date: 2007-06-11 00:00:00

The thermal reprocessing hypothesis in AGN, where EUV/X-ray photons are reprocessed by the accretion disc into optical/UV photons, predicts wavelength-dependent time delays between the optical continuum at different wavelengths. Recent photometric monitoring by Sergeev et al. has shown that the time-delay is observed in 14 AGN, and generally seen to increase with increasing wavelength, as predicted in the reprocessing scenario. We fit the observed time delays and optical spectral energy distribution using a disc reprocessing model. The model delivers estimates for the nuclear reddening, the product of black hole mass times accretion rate, and the distance to each object. However, the distances at face value give H_0 = 44 +/- 5 km/s/Mpc - a factor of 1.6 smaller than generally accepted. We discuss the implications of this on the reprocessing model.
Overview of the SDSS-IV MaNGA Survey: Mapping Nearby Galaxies at Apache Point Observatory
Authors: Kevin Bundy, Matthew A. Bershady, David R. Law, Renbin Yan, Niv Drory, Nicholas MacDonald, David A. Wake, Brian Cherinka, José R. Sánchez-Gallego, Anne-Marie Weijmans, Daniel Thomas, Christy Tremonti, Karen Masters, Lodovico Coccato, Aleksandar M. Diamond-Stanic, Alfonso Aragón-Salamanca, Vladimir Avila-Reese, Carles Badenes, Jésus Falcón-Barroso, Francesco Belfiore, Dmitry Bizyaev, Guillermo A. Blanc, Joss Bland-Hawthorn, Michael R. Blanton, Joel R. Brownstein, Nell Byler, Michele Cappellari, Charlie Conroy, Aaron A. Dutton, Eric Emsellem, James Etherington, Peter M. Frinchaboy, Hai Fu, James E. Gunn, Paul Harding, Evelyn J. Johnston, Guinevere Kauffmann, Karen Kinemuchi, Mark A. Klaene, Johan H. Knapen, Alexie Leauthaud, Cheng Li, Lihwai Lin, Roberto Maiolino, Viktor Malanushenko, Elena Malanushenko, Shude Mao, Claudia Maraston, Richard M. McDermid, Michael R. Merrifield, Robert C. Nichol, Daniel Oravetz, Kaike Pan, John K. Parejko, Sebastian F. Sanchez, David Schlegel, Audrey Simmons, Oliver Steele, Matthias Steinmetz, Karun Thanjavur, Benjamin A. Thompson, Jeremy L. Tinker, Remco C. E. van den Bosch, Kyle B. Westfall, David Wilkinson, Shelley Wright, Ting Xiao, Kai Zhang
Date: 2014-12-03 00:00:00
Tasks: Survey

We present an overview of a new integral field spectroscopic survey called MaNGA (Mapping Nearby Galaxies at Apache Point Observatory), one of three core programs in the fourth-generation Sloan Digital Sky Survey (SDSS-IV) that began on 2014 July 1. MaNGA will investigate the internal kinematic structure and composition of gas and stars in an unprecedented sample of 10,000 nearby galaxies. We summarize essential characteristics of the instrument and survey design in the context of MaNGA's key science goals and present prototype observations to demonstrate MaNGA's scientific potential. MaNGA employs dithered observations with 17 fiber-bundle integral field units that vary in diameter from 12" (19 fibers) to 32" (127 fibers). Two dual-channel spectrographs provide simultaneous wavelength coverage over 3600-10300 A at R~2000. With a typical integration time of 3 hr, MaNGA reaches a target r-band signal-to-noise ratio of 4-8 (per A, per 2" fiber) at 23 AB mag per sq. arcsec, which is typical for the outskirts of MaNGA galaxies. Targets are selected with stellar mass greater than 1e9 Msun using SDSS-I redshifts and i-band luminosity to achieve uniform radial coverage in terms of the effective radius, an approximately flat distribution in stellar mass, and a sample spanning a wide range of environments. Analysis of our prototype observations demonstrates MaNGA's ability to probe gas ionization, shed light on recent star formation and quenching, enable dynamical modeling, decompose constituent components, and map the composition of stellar populations. MaNGA's spatially resolved spectra will enable an unprecedented study of the astrophysics of nearby galaxies in the coming 6 yr.
Enabling NAS with Automated Super-Network Generation
Authors: J. Pablo Muñoz, Nikolay Lyalyushkin, Yash Akhauri, Anastasia Senina, Alexander Kozlov, Nilesh Jain
Date: 2021-12-20 00:00:00
Tasks: Neural Architecture Search, valid

Recent Neural Architecture Search (NAS) solutions have produced impressive results training super-networks and then deriving subnetworks, a.k.a. child models that outperform expert-crafted models from a pre-defined search space. Efficient and robust subnetworks can be selected for resource-constrained edge devices, allowing them to perform well in the wild. However, constructing super-networks for arbitrary architectures is still a challenge that often prevents the adoption of these approaches. To address this challenge, we present BootstrapNAS, a software framework for automatic generation of super-networks for NAS. BootstrapNAS takes a pre-trained model from a popular architecture, e.g., ResNet- 50, or from a valid custom design, and automatically creates a super-network out of it, then uses state-of-the-art NAS techniques to train the super-network, resulting in subnetworks that significantly outperform the given pre-trained model. We demonstrate the solution by generating super-networks from arbitrary model repositories and make available the resulting super-networks for reproducibility of the results.
Scalable Cosmic AI Inference using Cloud Serverless Computing with FMI
Authors: Mills Staylor, Amirreza Dolatpour Fathkouhi, Md Khairul Islam, Kaleigh O'Hara, Ryan Ghiles Goudjil, Geoffrey Fox, Judy Fox
Date: 2025-01-08 00:00:00
Tasks: Astronomy

Large-scale astronomical image data processing and prediction is essential for astronomers, providing crucial insights into celestial objects, the universe's history, and its evolution. While modern deep learning models offer high predictive accuracy, they often demand substantial computational resources, making them resource-intensive and limiting accessibility. We introduce the Cloud-based Astronomy Inference (CAI) framework to address these challenges. This scalable solution integrates pre-trained foundation models with serverless cloud infrastructure through a Function-as-a-Service (FaaS) Message Interface (FMI). CAI enables efficient and scalable inference on astronomical images without extensive hardware. Using a foundation model for redshift prediction as a case study, our extensive experiments cover user devices, HPC (High-Performance Computing) servers, and Cloud. CAI's significant scalability improvement on large data sizes provides an accessible and effective tool for the astronomy community. The code is accessible at https://github.com/UVA-MLSys/AI-for-Astronomy.
RTLSquad: Multi-Agent Based Interpretable RTL Design
Authors: Bowei Wang, Qi Xiong, Zeqing Xiang, Lei Wang, Renzhi Chen
Date: 2025-01-06 00:00:00
Tasks: Code Generation

Optimizing Register-Transfer Level (RTL) code is crucial for improving hardware PPA performance. Large Language Models (LLMs) offer new approaches for automatic RTL code generation and optimization. However, existing methods often lack decision interpretability (sufficient, understandable justification for decisions), making it difficult for hardware engineers to trust the generated results, thus preventing these methods from being integrated into the design process. To address this, we propose RTLSquad, a novel LLM-Based Multi-Agent system for interpretable RTL code generation. RTLSquad divides the design process into exploration, implementation, and verification & evaluation stages managed by specialized agent squads, generating optimized RTL code through inter-agent collaboration, and providing decision interpretability through the communication process. Experiments show that RTLSquad excels in generating functionally correct RTL code and optimizing PPA performance, while also having the capability to provide decision paths, demonstrating the practical value of our system.
Completing Sets of Prototype Transfer Functions for Subspace-based Direction of Arrival Estimation of Multiple Speakers
Authors: Daniel Fejgin, Simon Doclo
Date: 2025-01-13 00:00:00
Tasks: Direction of Arrival Estimation

To estimate the direction of arrival (DOA) of multiple speakers, subspace-based prototype transfer function matching methods such as multiple signal classification (MUSIC) or relative transfer function (RTF) vector matching are commonly employed. In general, these methods require calibrated microphone arrays, which are characterized by a known array geometry or a set of known prototype transfer functions for several directions. In this paper, we consider a partially calibrated microphone array, composed of a calibrated binaural hearing aid and a (non-calibrated) external microphone at an unknown location with no available set of prototype transfer functions. We propose a procedure for completing sets of prototype transfer functions by exploiting the orthogonality of subspaces, allowing to apply matching-based DOA estimation methods with partially calibrated microphone arrays. For the MUSIC and RTF vector matching methods, experimental results for two speakers in noisy and reverberant environments clearly demonstrate that for all locations of the external microphone DOAs can be estimated more accurately with completed sets of prototype transfer functions than with incomplete sets. \c{opyright}20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
SynthSoM: A synthetic intelligent multi-modal sensing-communication dataset for Synesthesia of Machines (SoM)
Authors: Xiang Cheng, Ziwei Huang, Yong Yu, Lu Bai, Mingran Sun, Zengrui Han, Ruide Zhang, Sijiang Li
Date: 2025-01-13 00:00:00

Given the importance of datasets for sensing-communication integration research, a novel simulation platform for constructing communication and multi-modal sensory dataset is developed. The developed platform integrates three high-precision software, i.e., AirSim, WaveFarer, and Wireless InSite, and further achieves in-depth integration and precise alignment of them. Based on the developed platform, a new synthetic intelligent multi-modal sensing-communication dataset for Synesthesia of Machines (SoM), named SynthSoM, is proposed. The SynthSoM dataset contains various air-ground multi-link cooperative scenarios with comprehensive conditions, including multiple weather conditions, times of the day, intelligent agent densities, frequency bands, and antenna types. The SynthSoM dataset encompasses multiple data modalities, including radio-frequency (RF) channel large-scale and small-scale fading data, RF millimeter wave (mmWave) radar sensory data, and non-RF sensory data, e.g., RGB images, depth maps, and light detection and ranging (LiDAR) point clouds. The quality of SynthSoM dataset is validated via statistics-based qualitative inspection and evaluation metrics through machine learning (ML) via real-world measurements. The SynthSoM dataset is open-sourced and provides consistent data for cross-comparing SoM-related algorithms.
An Extended Survey and a Comparison Framework for Dataflow Models of Computation and Communication
Authors: Guillaume Roumage, Selma Azaiez, Cyril Faure, Stéphane Louise
Date: 2025-01-13 00:00:00

Dataflow Model of Computation and Communications (DF MoCCs) is a formalism used to specify the behavior of Cyber-Physical Systems (CPSs). DF MoCCs are widely used in the design of CPSs, as they provide a high-level of abstraction to specify the system's behavior. DF MoCCs rules give semantics to a dataflow specification of a CPS, and static analysis algorithms rely on these semantics to guarantee safety properties of the dataflow specification, such as bounded memory usage and deadlock freeness. A wide range of DF MoCCs exists, each with its own characteristics and static analyses. This paper presents a survey of those DF MoCCs and a classification in eight categories. In addition, DF MoCCs are characterized by a comprehensive list of features and static analyses, which reflect their expressiveness and analyzability. Based on this characterization, a framework is proposed to compare the expressiveness and the analyzability of DF MoCCs quantitatively.
Bigger Isn't Always Better: Towards a General Prior for Medical Image Reconstruction
Authors: Lukas Glaszner, Martin Zach
Date: 2025-01-13 00:00:00
Tasks: CT Reconstruction, Image Reconstruction

Diffusion model have been successfully applied to many inverse problems, including MRI and CT reconstruction. Researchers typically re-purpose models originally designed for unconditional sampling without modifications. Using two different posterior sampling algorithms, we show empirically that such large networks are not necessary. Our smallest model, effectively a ResNet, performs almost as good as an attention U-Net on in-distribution reconstruction, while being significantly more robust towards distribution shifts. Furthermore, we introduce models trained on natural images and demonstrate that they can be used in both MRI and CT reconstruction, out-performing model trained on medical images in out-of-distribution cases. As a result of our findings, we strongly caution against simply re-using very large networks and encourage researchers to adapt the model complexity to the respective task. Moreover, we argue that a key step towards a general diffusion-based prior is training on natural images.
Determining Disturbance Recovery Conditions by Inverse Sensitivity Minimization
Authors: Michael W. Fisher, Ian A. Hiskens
Date: 2025-01-13 00:00:00
Tasks: Sensitivity

Power systems naturally experience disturbances, some of which can damage equipment and disrupt consumers. It is important to quickly assess the likely consequences of credible disturbances and take preventive action, if necessary. However, assessing the impact of potential disturbances is challenging because many of the influential factors, such as loading patterns, controller settings and load dynamics, are not precisely known. To address this issue, the paper introduces the concept of parameter-space recovery regions. For each disturbance, the corresponding recovery region is the region of parameter space for which the system will recover to the desired operating point. The boundary of the recovery region establishes the separation between parameter values that result in trouble-free recovery and those that incur undesirable non-recovery. The safety margin for a given set of parameter values is defined as the smallest distance (in parameter space) between the given values and the recovery boundary. Novel numerical algorithms with theoretical guarantees are presented for efficiently computing recovery boundaries and safety margins. Unlike prior methods, which tend to be overly conservative and restricted to low dimensional parameter space, these methods compute safety margins to arbitrary user-specified accuracy and do so efficiently in high dimensional parameter space. The efficacy of the methods is demonstrated using the IEEE 39-bus benchmark power system, where safety margins are computed for cases that consider up to 86 parameters, and reveal unexpected safety implications that would not have been observed otherwise.
Computing Safety Margins of Parameterized Nonlinear Systems for Vulnerability Assessment via Trajectory Sensitivities
Authors: Michael W. Fisher
Date: 2025-01-13 00:00:00

Physical systems experience nonlinear disturbances which have the potential to disrupt desired behavior. For a particular disturbance, whether or not the system recovers from the disturbance to a desired stable equilibrium point depends on system parameter values, which are typically uncertain and time-varying. Therefore, to quantify proximity to vulnerability we define the safety margin to be the smallest change in parameter values from a nominal value such that the system will no longer be able to recover from the disturbance. Safety margins are valuable but challenging to compute as related methods, such as those for robust region of attraction estimation, are often either overly conservative or computationally intractable for high dimensional systems. Recently, we developed algorithms to compute safety margins efficiently and non-conservatively by exploiting the large sensitivity of the system trajectory near the region of attraction boundary to small perturbations. Although these algorithms have enjoyed empirical success, they lack theoretical guarantees that would ensure their generalizability. This work develops a novel characterization of safety margins in terms of trajectory sensitivities, and uses this to derive well-posedness and convergence guarantees for these algorithms, enabling their generalizability and successful application to a large class of nonlinear systems.
Synesthesia of Machines Based Multi-Modal Intelligent V2V Channel Model
Authors: Zengrui Han, Lu Bai, Ziwei Huang, Xiang Cheng
Date: 2025-01-13 00:00:00

This paper proposes a novel sixth-generation (6G) multi-modal intelligent vehicle-to-vehicle (V2V) channel model from light detection and ranging (LiDAR) point clouds based on Synesthesia of Machines (SoM). To explore the mapping relationship between physical environment and electromagnetic space, a new V2V high-fidelity mixed sensing-communication integration simulation dataset with different vehicular traffic densities (VTDs) is constructed. Based on the constructed dataset, a novel scatterer recognition (ScaR) algorithm utilizing neural network SegNet is developed to recognize scatterer spatial attributes from LiDAR point clouds via SoM. In the developed ScaR algorithm, the mapping relationship between LiDAR point clouds and scatterers is explored, where the distribution of scatterers is obtained in the form of grid maps. Furthermore, scatterers are distinguished into dynamic and static scatterers based on LiDAR point cloud features, where parameters, e.g., distance, angle, and number, related to scatterers are determined. Through ScaR, dynamic and static scatterers change with the variation of LiDAR point clouds over time, which precisely models channel non-stationarity and consistency under different VTDs. Some important channel statistical properties, such as time-frequency correlation function (TF-CF) and Doppler power spectral density (DPSD), are obtained. Simulation results match well with ray-tracing (RT)-based results, thus demonstrating the necessity of exploring the mapping relationship and the utility of the proposed model.