pwc-archive/papers-with-abstracts huggingface dataset:
A Survey on Speech Large Language Models
Authors: Jing Peng, Yucheng Wang, Yangui Fang, Yu Xi, Xu Li, Xizhuo Zhang, Kai Yu
Date: 2024-10-24 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Emotion Recognition, Speech Emotion Recognition, speech-recognition, Speech Recognition, Speech-to-Text, Speech-to-Text Translation, Spoken Language Understanding, Survey

Large Language Models (LLMs) exhibit strong contextual understanding and remarkable multitask performance. As a result, researchers have been actively exploring the integration of LLMs into the domain of speech understanding, with a primary focus on a broad range of speech-to-text tasks. These include automatic speech recognition (ASR), speech-to-text translation (ST), speech emotion recognition (SER), and others. We refer to such models as Speech LLMs, which are typically built on a unified architecture that follows the pipeline of Audio Feature Extraction -> Multimodal Information Fusion -> LLM Inference. This approach enables richer audio feature extraction while facilitating end-to-end fusion of audio and text modalities, thereby achieving deeper understanding and reasoning from audio data. This paper elucidates the development of Speech LLMs, offering an in-depth analysis of system architectures. Through extensive research and a series of targeted experiments, the paper assesses the advancements in Speech LLMs and their potential for cross-task integration within the speech understanding field. Furthermore, it highlights key challenges identified through experimentation, such as the dormancy of LLMs under certain conditions. The paper further explores training strategies for Speech LLMs, proposes potential solutions based on these findings, and offers valuable insights and references for future research.
Hyperspectral Spatial Super-Resolution using Keystone Error
Authors: Ankur Garg, Meenakshi Sarkar, S. Manthira Moorthi, Debajyoti Dhar
Date: 2024-10-24 00:00:00
Tasks: Super-Resolution

Hyperspectral images enable precise identification of ground objects by capturing their spectral signatures with fine spectral resolution.While high spatial resolution further enhances this capability, increasing spatial resolution through hardware like larger telescopes is costly and inefficient. A more optimal solution is using ground processing techniques, such as hypersharpening, to merge high spectral and spatial resolution data. However, this method works best when datasets are captured under similar conditions, which is difficult when using data from different times. In this work, we propose a superresolution approach to enhance hyperspectral data's spatial resolution without auxiliary input. Our method estimates the high-resolution point spread function (PSF) using blind deconvolution and corrects for sampling-related blur using a model-based superresolution framework. This differs from previous approaches by not assuming a known highresolution blur. We also introduce an adaptive prior that improves performance compared to existing methods. Applied to the visible and near-infrared (VNIR) spectrometer of HySIS, ISRO hyperspectral sensor, our algorithm removes aliasing and boosts resolution by approximately 1.3 times. It is versatile and can be applied to similar systems.
Advancements in Image Resolution: Super-Resolution Algorithm for Enhanced EOS-06 OCM-3 Data
Authors: Ankur Garg, Tushar Shukla, Purvee Joshi, Debojyoti Ganguly, Ashwin Gujarati, Meenakshi Sarkar, KN Babu, Mehul Pandya, S. Manthira Moorthi, Debajyoti Dhar
Date: 2024-10-24 00:00:00
Tasks: Earth Observation, Image Reconstruction, Super-Resolution

The Ocean Color Monitor-3 (OCM-3) sensor is instrumental in Earth observation, achieving a critical balance between high-resolution imaging and broad coverage. This paper explores innovative imaging methods employed in OCM-3 and the transformative potential of super-resolution techniques to enhance image quality. The super-resolution model for OCM-3 (SOCM-3) addresses the challenges of contemporary satellite imaging by effectively navigating the trade-off between image clarity and swath width. With resolutions below 240 meters in Local Area Coverage (LAC) mode and below 750 meters in Global Area Coverage (GAC) mode, coupled with a wide 1550-kilometer swath and a 2-day revisit time, SOCM-3 emerges as a leading asset in remote sensing. The paper details the intricate interplay of atmospheric, motion, optical, and detector effects that impact image quality, emphasizing the necessity for advanced computational techniques and sophisticated algorithms for effective image reconstruction. Evaluation methods are thoroughly discussed, incorporating visual assessments using the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) metric and computational metrics such as Line Spread Function (LSF), Full Width at Half Maximum (FWHM), and Super-Resolution (SR) ratio. Additionally, statistical analyses, including power spectrum evaluations and target-wise spectral signatures, are employed to gauge the efficacy of super-resolution techniques. By enhancing both spatial resolution and revisit frequency, this study highlights significant advancements in remote sensing capabilities, providing valuable insights for applications across cryospheric, vegetation, oceanic, coastal, and domains. Ultimately, the findings underscore the potential of SOCM-3 to contribute meaningfully to our understanding of finescale oceanic phenomena and environmental monitoring.
Cochlear Implantation of Slim Pre-curved Arrays using Automatic Pre-operative Insertion Plans
Authors: Kareem O. Tawfik, Mohammad M. R. Khan, Ankita Patro, Miriam R. Smetak, David Haynes, Robert F. Labadie, René H. Gifford, Jack H. Noble
Date: 2024-10-24 00:00:00

Hypothesis: Pre-operative cochlear implant (CI) electrode array (EL) insertion plans created by automated image analysis methods can improve positioning of slim pre-curved EL. Background: This study represents the first evaluation of a system for patient-customized EL insertion planning for a slim pre-curved EL. Methods: Twenty-one temporal bone specimens were divided into experimental and control groups and underwent cochlear implantation. For the control group, the surgeon performed a traditional insertion without an insertion plan. For the experimental group, customized insertion plans guided entry site, trajectory, curl direction, and base insertion depth. An additional 35 clinical insertions from the same surgeon were analyzed, 7 of which were conducted using the insertion plans. EL positioning was analyzed using post-operative imaging auto-segmentation techniques, allowing measurement of angular insertion depth (AID), mean modiolar distance (MMD), and scalar position. Results: In the cadaveric temporal bones, 3 scalar translocations, including 2 foldovers, occurred in 14 control group insertions. In the clinical insertions, translocations occurred in 2 of 28 control cases. No translocations or folds occurred in the 7 experimental temporal bone and the 7 experimental clinical insertions. Among the non-translocated cases, overall AID and MMD were 401(41) degrees and 0.34(0.13) mm for the control insertions. AID and MMD for the experimental insertions were 424(43) degrees and 0.34(0.09) mm overall and were 432(19) and 0.30(0.07) mm for cases where the planned insertion depth was achieved. Conclusions: Trends toward improved EL positioning within scala tympani were observed when EL insertion plans are used. Variability in MMD was significantly reduced (0.07mm vs 0.13 mm, p=0.039) when the planned depth was achieved.
Position-Aided Semantic Communication for Efficient Image Transmission: Design, Implementation, and Experimental Results
Authors: Peiwen Jiang, Chao-Kai Wen, Shi Jin, Jun Zhang
Date: 2024-10-24 00:00:00
Tasks: Position, Semantic Communication

Semantic communication, augmented by knowledge bases (KBs), offers substantial reductions in transmission overhead and resilience to errors. However, existing methods predominantly rely on end-to-end training to construct KBs, often failing to fully capitalize on the rich information available at communication devices. Motivated by the growing convergence of sensing and communication, we introduce a novel Position-Aided Semantic Communication (PASC) framework, which integrates localization into semantic transmission. This framework is particularly designed for position-based image communication, such as real-time uploading of outdoor camera-view images. By utilizing the position, the framework retrieves corresponding maps, and then an advanced foundation model (FM)-driven view generator is employed to synthesize images closely resembling the target images. The PASC framework further leverages the FM to fuse the synthesized image with deviations from the real one, enhancing semantic reconstruction. Notably, the framework is highly flexible, capable of adapting to dynamic content and fluctuating channel conditions through a novel FM-based parameter optimization strategy. Additionally, the challenges of real-time deployment are addressed, with the development of a hardware testbed to validate the framework. Simulations and real-world tests demonstrate that the proposed PASC approach not only significantly boosts transmission efficiency, but also remains robust in diverse and evolving transmission scenarios.
Sensing Accuracy Optimization for Communication-assisted Dual-baseline UAV-InSAR
Authors: Mohamed-Amine Lahmeri, Víctor Mustieles-Pérez, Martin Vossiek, Gerhard Krieger, Robert Schober
Date: 2024-10-24 00:00:00

In this paper, we study the optimization of the sensing accuracy of unmanned aerial vehicle (UAV)-based dual-baseline interferometric synthetic aperture radar (InSAR) systems. A swarm of three UAV-synthetic aperture radar (SAR) systems is deployed to image an area of interest from different angles, enabling the creation of two independent digital elevation models (DEMs). To reduce the InSAR sensing error, i.e., the height estimation error, the two DEMs are fused based on weighted averaging techniques into one final DEM. The heavy computations required for this process are performed on the ground. To this end, the radar data is offloaded in real time via a frequency division multiple access (FDMA) air-to-ground backhaul link. In this work, we focus on improving the sensing accuracy by minimizing the worst-case height estimation error of the final DEM. To this end, the UAV formation and the power allocated for offloading are jointly optimized based on alternating optimization (AO), while meeting practical InSAR sensing and communication constraints. Our simulation results demonstrate that the proposed solution can significantly improve the sensing accuracy compared to classical single-baseline UAV-InSAR systems and other benchmark schemes.
STAR-RIS-Enabled Full-Duplex Integrated Sensing and Communication System
Authors: Yu Liu, Gaojie Chen, Yun Wen, Qu Luo, Chiya Zhang, Dusit Niyato
Date: 2024-10-24 00:00:00
Tasks: Integrated sensing and communication, ISAC

Traditional self-interference cancellation (SIC) methods are common in full-duplex (FD) integrated sensing and communication (ISAC) systems. However, exploring new SIC schemes is important due to the limitations of traditional approaches. With the challenging limitations of traditional SIC approaches, this paper proposes a novel simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-enabled FD ISAC system, where STAR-RIS enhances simultaneous communication and target sensing and reduces self-interference (SI) to a level comparable to traditional SIC approaches. The optimization of maximizing the sensing signal-to-interference-plus-noise ratio (SINR) and the communication sum rate, both crucial for improving sensing accuracy and overall communication performance, presents significant challenges due to the non-convex nature of these problems. Therefore, we develop alternating optimization algorithms to iteratively tackle these problems. Specifically, we devise the semi-definite relaxation (SDR)-based algorithm for transmit beamformer design. For the reflecting and refracting coefficients design, we adopt the successive convex approximation (SCA) method and implement the SDR-based algorithm to tackle the quartic and quadratic constraints. Simulation results validate the effectiveness of the proposed algorithms and show that the proposed deployment can achieve better performance than that of the benchmark using the traditional SIC approach without STAR-RIS deployment.
Uplink Cell-Free Massive MIMO OFDM with Phase Noise-Aware Channel Estimation: Separate and Shared Local Oscillators
Authors: Yibo Wu, Luca Sanguinetti, Musa Furkan Keskin, Ulf Gustavsson, Alexandre Graell i Amat, Henk Wymeersch
Date: 2024-10-24 00:00:00

Cell-free massive multiple-input multiple-output (mMIMO) networks enhance coverage and spectral efficiency (SE) by distributing antennas across access points (APs) with phase coherence between APs. However, the use of cost-efficient local oscillators (LOs) introduces phase noise (PN) that compromises phase coherence, even with centralized processing. Sharing an LO across APs can reduce costs in specific configurations but cause correlated PN between APs, leading to correlated interference that affects centralized combining. This can be improved by exploiting the PN correlation in channel estimation. This paper presents an uplink orthogonal frequency division multiplexing (OFDM) signal model for PN-impaired cell-free mMIMO, addressing gaps in single-carrier signal models. We evaluate mismatches from applying single-carrier methods to OFDM systems, showing how they underestimate the impact of PN and produce over-optimistic achievable SE predictions. Based on our OFDM signal model, we propose two PN-aware channel and common phase error estimators: a distributed estimator for uncorrelated PN with separate LOs and a centralized estimator with shared LOs. We introduce a deep learning-based channel estimator to enhance the performance and reduce the number of iterations of the centralized estimator. The simulation results show that the distributed estimator outperforms mismatched estimators with separate LOs, whereas the centralized estimator enhances distributed estimators with shared LOs.
Reconstruction with prior support information and non-Gaussian constraints
Authors: Xiaotong Liu, Yiyu Liang
Date: 2024-10-09 00:00:00

In this study, we introduce a novel model, termed the Weighted Basis Pursuit Dequantization ($\omega$-BPDQ$_p$), which incorporates prior support information by assigning weights on the $\ell_1$ norm in the $\ell_1$ minimization process and replaces the $\ell_2$ norm with the $\ell_p$ norm in the constraint. This adjustment addresses cases where noise deviates from a Gaussian distribution, such as quantized errors, which are common in practice. We demonstrate that Restricted Isometry Property (RIP$_{p,q}$) and Weighted Robust Null Space Property ($\omega$-RNSP$_{p,q}$) ensure stable and robust reconstruction within $\omega$-BPDQ$_p$, with the added observation that standard Gaussian random matrices satisfy these properties with high probability. Moreover, we establish a relationship between RIP$_{p,q}$ and $\omega$-RNSP$_{p,q}$ that RIP$_{p,q}$ implies $\omega$-RNSP$_{p,q}$. Additionally, numerical experiments confirm that the incorporation of weights and the non-Gaussian constraint results in improved reconstruction quality.
Simultaneously Infer Cell Pseudotime,Velocity Field and Gene Interaction from Multi-Branch scRNA-seq Data with scPN
Authors: Zhen Zhou, Jiachen Li, Hongyi Xin, Xiaoyong Pan, Hong-Bin Shen
Date: 2024-10-24 00:00:00

Modeling cellular dynamics from single-cell RNA sequencing (scRNA-seq) data is critical for understanding cell development and underlying gene regulatory relationships. Many current methods rely on single-cell velocity to obtain pseudotime, which can lead to inconsistencies between pseudotime and velocity. It is challenging to simultaneously infer cell pseudotime and gene interaction networks, especially in multi-branch differentiation scenarios. We present single-cell Piecewise Network (scPN), a novel high-dimensional dynamical modeling approach that iteratively extracts temporal patterns and inter-gene relationships from scRNA-seq data. To tackle multi-branch differentiation challenges, scPN models gene regulatory dynamics using piecewise gene-gene interaction networks, offering an interpretable framework for deciphering complex gene regulation patterns over time. Results on synthetic data and multiple scRNA-seq datasets demonstrate the superior performance of scPN in reconstructing cellular dynamics and identifying key transcription factors involved in development compared to existing methods. To the best of our knowledge, scPN is the first attempt at modeling that can recover pseudotime, velocity fields, and gene interactions all at once on multi-branch datasets.
Confidence is detection-like in high-dimensional spaces
Authors: Wiktoria Łuczak, Kevin O'Neill, Stephen M. Fleming
Date: 2024-10-24 00:00:00
Tasks: Sensitivity

Confidence estimates are often "detection-like" - driven by positive evidence in favour of a decision. This empirical observation has been interpreted as showing human metacognition is limited by biases or heuristics. Here we show that Bayesian confidence estimates also exhibit heightened sensitivity to decision-congruent evidence in higher-dimensional signal detection theoretic spaces, leading to detection-like confidence criteria. This effect is due to a nonlinearity induced by normalisation of confidence by a large number of unchosen alternatives. Our analysis suggests that detection-like confidence is rational when computing confidence in a higher-dimensional evidence space than that assumed by the experimenter.
Learning Image Derived PDE-Phenotypes from fMRI Data
Authors: Ion Bica, Ryan Trang, Rui Hu, Wanhua Su, Zhichun Zhai, Qingrun Zhang
Date: 2024-10-08 00:00:00
Tasks: Dimensionality Reduction

Partial Differential Equations (PDEs) model various physical phenomena, such as electromagnetic fields and fluid mechanics. Methods like Sparse Identification of Nonlinear Dynamics (SINDy) and PDE-Net 2.0 have been developed to identify and model PDEs based on data using sparse optimization and deep neural networks, respectively. While PDE models are less commonly applied to fMRI data, they hold the potential for uncovering hidden connections and essential components in brain activity. Using the ADHD200 dataset, we applied Canonical Independent Component Analysis (CanICA) and Uniform Manifold Approximation (UMAP) for dimensionality reduction of fMRI data. We then used Sparse Ridge Regression to identify PDEs from the reduced data, achieving high accuracy in classifying attention deficit hyperactivity disorder (ADHD). The study demonstrates a novel approach to extracting meaningful features from fMRI data for neurological disorder analysis to understand the role of oxygen transport (delivery $\&$ consumption) in the brain during neural activity relevant for studying intracranial pathologies.
Bioenergetic trophic trade-offs determine mass-dependent extinction thresholds across the Cenozoic
Authors: Justin D. Yeakel, Matthew C. Hutchinson, Christopher P. Kempes, Paul L. Koch, Jacquelyn L. Gill, Mathias M. Pires
Date: 2024-10-24 00:00:00

Body size drives the energetic demands of organisms, constraining trophic interactions between species and playing a significant role in shaping the feasibility of species' populations in a community. On macroevolutionary timescales, these demands feed back to shape the selective landscape driving the evolution of body size and diet. We develop a theoretical framework for a three-level trophic food chain -- typical for terrestrial mammalian ecosystems -- premised on bioenergetic trade-offs to explore mammalian population dynamics. Our results show that interactions between predators, prey, and external subsidies generate instabilities linked to body size extrema, corresponding to observed limits of predator size and diet. These instabilities generate size-dependent constraints on coexistence and highlight a feasibility range for carnivore size between 40 to 110 kg, encompassing the mean body size of terrestrial Cenozoic hypercarnivores. Finally, we show that predator dietary generalization confers a selective advantage to larger carnivores, which then declines at megapredator body sizes, aligning with diet breadth estimates for contemporary and Pleistocene species. Our framework underscores the importance of understanding macroevolutionary constraints through the lens of ecological pressures, where the selective forces shaping and reshaping the dynamics of communities can be explored.
Natural selection at multiple scales
Authors: Steven A. Frank
Date: 2024-10-24 00:00:00

Natural selection acts on traits at different scales, often with opposing consequences. This article identifies the particular forces that act at each scale and how those forces combine to determine the overall evolutionary outcome. A series of extended models derive from the tragedy of the commons, illustrating opposing forces at different scales. Examples include the primary tension between conflict and cooperation and the evolution of virulence, sex ratios, dispersal, and evolvability. The unified analysis subsumes interactions within and between species by generalizing multitrait interactions. Expanded notions of recombination and cotransmission arise. The core theoretical approach isolates the fundamental forces of selection, including marginal valuation, correlation between interacting entities, and reproductive value. Those fundamental forces act as partial causes that combine at different temporal and spatial scales. Modeling focuses on statics, in the sense of how different forces at various scales tend to oppose each other, ultimately combining to shape traits. That type of static analysis emphasizes explanation rather than the calculation of dynamics. The article builds on the duality between explanation versus calculation in terms of statics versus dynamics. The literature often poses that duality as a controversy, whereas this article develops the pair as complementary tools that together provide deeper understanding. Along the way, the unified approach clarifies the subtle distinctions between kin selection, multilevel selection, and inclusive fitness, subsuming these topics into the broader perspectives of fundamental forces and multiple scales.
Self-supervised inter-intra period-aware ECG representation learning for detecting atrial fibrillation
Authors: Xiangqian Zhu, Mengnan Shi, Xuexin Yu, Chang Liu, Xiaocong Lian, Jintao Fei, Jiangying Luo, Xin Jin, Ping Zhang, Xiangyang Ji
Date: 2024-10-08 00:00:00
Tasks: Atrial Fibrillation Detection, Representation Learning, Self-Supervised Learning

Atrial fibrillation is a commonly encountered clinical arrhythmia associated with stroke and increased mortality. Since professional medical knowledge is required for annotation, exploiting a large corpus of ECGs to develop accurate supervised learning-based atrial fibrillation algorithms remains challenging. Self-supervised learning (SSL) is a promising recipe for generalized ECG representation learning, eliminating the dependence on expensive labeling. However, without well-designed incorporations of knowledge related to atrial fibrillation, existing SSL approaches typically suffer from unsatisfactory capture of robust ECG representations. In this paper, we propose an inter-intra period-aware ECG representation learning approach. Considering ECGs of atrial fibrillation patients exhibit the irregularity in RR intervals and the absence of P-waves, we develop specific pre-training tasks for interperiod and intraperiod representations, aiming to learn the single-period stable morphology representation while retaining crucial interperiod features. After further fine-tuning, our approach demonstrates remarkable AUC performances on the BTCH dataset, \textit{i.e.}, 0.953/0.996 for paroxysmal/persistent atrial fibrillation detection. On commonly used benchmarks of CinC2017 and CPSC2021, the generalization capability and effectiveness of our methodology are substantiated with competitive results.