pwc-archive/papers-with-abstracts huggingface dataset:
Convergence Analysis of Sequential Federated Learning on Heterogeneous Data
Authors: Yipeng Li, Xinchen Lyu
Date: 2023-11-06 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Federated Learning

There are two categories of methods in Federated Learning (FL) for joint training across multiple clients: i) parallel FL (PFL), where clients train models in a parallel manner; and ii) sequential FL (SFL), where clients train models in a sequential manner. In contrast to that of PFL, the convergence theory of SFL on heterogeneous data is still lacking. In this paper, we establish the convergence guarantees of SFL for strongly/general/non-convex objectives on heterogeneous data. The convergence guarantees of SFL are better than that of PFL on heterogeneous data with both full and partial client participation. Experimental results validate the counterintuitive analysis result that SFL outperforms PFL on extremely heterogeneous data in cross-device settings.
A Hybrid Multi-Scale Attention Convolution and Aging Transformer Network for Alzheimer's Disease Diagnosis
Authors: Xingyu Gao; Hongjie Cai; Manhua Liu
Date: 2023-04-07 00:00:00
Proceeding: journal 2023 4

Deep neural networks have been successfully investigated in the computational analysis of structural magnetic resonance imaging (sMRI) data for the diagnosis of dementia, such as Alzheimer's disease (AD). The disease-related changes in sMRI may be different in local brain regions, which have variant structures but with some correlations. In addition, aging increases the risk of dementia. However, it is still challenging to capture the local variations and long-range correlations of different brain regions and make use of the age information for disease diagnosis. To address these problems, we propose a hybrid network with multi-scale attention convolution and aging transformer for AD diagnosis. First, to capture the local variations, a multi-scale attention convolution is proposed to learn the feature maps with multi-scale kernels, which are adaptively aggregated by an attention module. Then, to model the long-range correlations of brain regions, a pyramid non-local block is used on the high-level features to learn more powerful features. Finally, we propose an aging transformer subnetwork to embed the age information into image features and capture the dependencies between subjects at different ages. The proposed method can learn not only the subject-specific rich features but also the inter-subject age correlations in an end-to-end framework. Our method is evaluated with T1-weighted sMRI scans from a large cohort of subjects on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results demonstrate that our method has achieved promising performance for AD-related diagnosis.
Simultaneous Optimal System and Controller Design for Multibody Systems with Joint Friction using Direct Sensitivities
Authors: Adwait Verulkar, Corina Sandu, Adrian Sandu, Daniel Dopico
Date: 2023-12-25 00:00:00
Tasks: Computational Efficiency, Friction, Sensitivity

Real-world multibody systems are often subject to phenomena like friction, joint clearances, and external events. These phenomena can significantly impact the optimal design of the system and its controller. This work addresses the gradient-based optimization methodology for multibody dynamic systems with joint friction using a direct sensitivity approach for gradient computation. After a thorough review of various friction models developed over the years, the Brown McPhee model has been found to be the most suitable for the study due to its accuracy for dynamic simulation and its compatibility with sensitivity analysis. The methodology supports co-design of the system and its controller, which is especially relevant for applications like robotics and servo-mechanical systems where the actuation and the design are highly dependent on each other. Numerical results are obtained using a new implementation of the MBSVT (Multi-Body Systems at Virginia Tech) software package; MBSVT 2.0 is reprogrammed in Julia for ease of implementation while maintaining high computational efficiency. Three case studies are provided to demonstrate the attractive properties of simultaneous optimal design and control approach for certain applications.
Graph Neural Network-Based Bandwidth Allocation for Secure Wireless Communications
Authors: Xin Hao, Phee Lep Yeoh, Yuhong Liu, Changyang She, Branka Vucetic, Yonghui Li
Date: 2023-12-13 00:00:00
Tasks: Graph Neural Network, Scheduling

This paper designs a graph neural network (GNN) to improve bandwidth allocations for multiple legitimate wireless users transmitting to a base station in the presence of an eavesdropper. To improve the privacy and prevent eavesdropping attacks, we propose a user scheduling algorithm to schedule users satisfying an instantaneous minimum secrecy rate constraint. Based on this, we optimize the bandwidth allocations with three algorithms namely iterative search (IvS), GNN-based supervised learning (GNN-SL), and GNN-based unsupervised learning (GNN-USL). We present a computational complexity analysis which shows that GNN-SL and GNN-USL can be more efficient compared to IvS which is limited by the bandwidth block size. Numerical simulation results highlight that our proposed GNN-based resource allocations can achieve a comparable sum secrecy rate compared to IvS with significantly lower computational complexity. Furthermore, we observe that the GNN approach is more robust to uncertainties in the eavesdropper's channel state information, especially compared with the best channel allocation scheme.
SC-GS: Sparse-Controlled Gaussian Splatting for Editable Dynamic Scenes
Authors: Yi-Hua Huang, Yang-tian Sun, ZiYi Yang, Xiaoyang Lyu, Yan-Pei Cao, Xiaojuan Qi
Date: 2023-12-04 00:00:00
Proceeding: CVPR 2024 1
Tasks: Novel View Synthesis

Novel view synthesis for dynamic scenes is still a challenging problem in computer vision and graphics. Recently, Gaussian splatting has emerged as a robust technique to represent static scenes and enable high-quality and real-time novel view synthesis. Building upon this technique, we propose a new representation that explicitly decomposes the motion and appearance of dynamic scenes into sparse control points and dense Gaussians, respectively. Our key idea is to use sparse control points, significantly fewer in number than the Gaussians, to learn compact 6 DoF transformation bases, which can be locally interpolated through learned interpolation weights to yield the motion field of 3D Gaussians. We employ a deformation MLP to predict time-varying 6 DoF transformations for each control point, which reduces learning complexities, enhances learning abilities, and facilitates obtaining temporal and spatial coherent motion patterns. Then, we jointly learn the 3D Gaussians, the canonical space locations of control points, and the deformation MLP to reconstruct the appearance, geometry, and dynamics of 3D scenes. During learning, the location and number of control points are adaptively adjusted to accommodate varying motion complexities in different regions, and an ARAP loss following the principle of as rigid as possible is developed to enforce spatial continuity and local rigidity of learned motions. Finally, thanks to the explicit sparse motion representation and its decomposition from appearance, our method can enable user-controlled motion editing while retaining high-fidelity appearances. Extensive experiments demonstrate that our approach outperforms existing approaches on novel view synthesis with a high rendering speed and enables novel appearance-preserved motion editing applications. Project page: https://yihua7.github.io/SC-GS-web/
An extension of May's Theorem to three alternatives: axiomatizing Minimax voting
Authors: Wesley H. Holliday, Eric Pacuit
Date: 2023-12-21 00:00:00

May's Theorem [K. O. May, Econometrica 20 (1952) 680-684] characterizes majority voting on two alternatives as the unique preferential voting method satisfying several simple axioms. Here we show that by adding some desirable axioms to May's axioms, we can uniquely determine how to vote on three alternatives (setting aside tiebreaking). In particular, we add two axioms stating that the voting method should mitigate spoiler effects and avoid the so-called strong no show paradox. We prove a theorem stating that any preferential voting method satisfying our enlarged set of axioms, which includes some weak homogeneity and preservation axioms, must choose from among the Minimax winners in all three-alternative elections. When applied to more than three alternatives, our axioms also distinguish Minimax from other known voting methods that coincide with or refine Minimax for three alternatives.
Multi-Armed Bandit Learning for Content Provisioning in Network of UAVs
Authors: Amit Kumar Bhuyan, Hrishikesh Dutta, Subir Biswas
Date: 2023-12-18 00:00:00
Tasks: Attribute, Decision Making

This paper proposes an unmanned aerial vehicle (UAV) aided content management system in communication-challenged disaster scenarios. Without cellular infrastructure in such scenarios, community of stranded users can be provided access to situation-critical contents using a hybrid network of static and traveling UAVs. A set of relatively static anchor UAVs can download content from central servers and provide content access to its local users. A set of ferrying UAVs with wider mobility can provision content to users by shuffling them across different anchor UAVs while visiting different communities of users. The objective is to design a content dissemination system that on-the-fly learns content caching policies for maximizing content availability to the stranded users. This paper proposes a decentralized Top-k Multi-Armed Bandit Learning model for UAV-caching decision-making that takes geo-temporal differences in content popularity and heterogeneity in content demands into consideration. The proposed paradigm is able to combine the expected reward maximization attribute and a proposed multi-dimensional reward structure of Top-k Multi-Armed Bandit, for caching decision at the UAVs. This study is done for different user-specified tolerable access delay, heterogeneous popularity distributions, and inter-community geographical characteristics. Functional verification and performance evaluation of the proposed caching framework is done for a wide range of network size, UAV distribution, and content popularity.
aoip.ai: An Open-Source P2P SDK
Authors: Joseph Konan, Shikhar Agnihotri, Chia-Chun Hsieh
Date: 2023-12-01 00:00:00

This white paper introduces aoip.ai, a groundbreaking open-source SDK incorporating peer-to-peer technology and advanced AI integration to transform VoIP and IoT applications. It addresses key market challenges by enhancing data security, elevating communication quality, and providing greater flexibility for developers and users. Developed in collaboration with Carnegie Mellon University, aoip.ai sets a new standard for decentralized and democratized communication solutions.
LLM Interactive Optimization of Open Source Python Libraries -- Case Studies and Generalization
Authors: Andreas Florath
Date: 2023-12-08 00:00:00

With the advent of large language models (LLMs) like GPT-3, a natural question is the extent to which these models can be utilized for source code optimization. This paper presents methodologically stringent case studies applied to well-known open source python libraries pillow and numpy. We find that contemporary LLM ChatGPT-4 (state September and October 2023) is surprisingly adept at optimizing energy and compute efficiency. However, this is only the case in interactive use, with a human expert in the loop. Aware of experimenter bias, we document our qualitative approach in detail, and provide transcript and source code. We start by providing a detailed description of our approach in conversing with the LLM to optimize the _getextrema function in the pillow library, and a quantitative evaluation of the performance improvement. To demonstrate qualitative replicability, we report further attempts on another locus in the pillow library, and one code locus in the numpy library, to demonstrate generalization within and beyond a library. In all attempts, the performance improvement is significant (factor up to 38). We have also not omitted reporting of failed attempts (there were none). We conclude that LLMs are a promising tool for code optimization in open source libraries, but that the human expert in the loop is essential for success. Nonetheless, we were surprised by how few iterations were required to achieve substantial performance improvements that were not obvious to the expert in the loop. We would like bring attention to the qualitative nature of this study, more robust quantitative studies would need to introduce a layer of selecting experts in a representative sample -- we invite the community to collaborate.
Consistent and Relevant: Rethink the Query Embedding in General Sound Separation
Authors: Yuanyuan Wang, Hangting Chen, Dongchao Yang, Jianwei Yu, Chao Weng, Zhiyong Wu, Helen Meng
Date: 2023-12-24 00:00:00

The query-based audio separation usually employs specific queries to extract target sources from a mixture of audio signals. Currently, most query-based separation models need additional networks to obtain query embedding. In this way, separation model is optimized to be adapted to the distribution of query embedding. However, query embedding may exhibit mismatches with separation models due to inconsistent structures and independent information. In this paper, we present CaRE-SEP, a consistent and relevant embedding network for general sound separation to encourage a comprehensive reconsideration of query usage in audio separation. CaRE-SEP alleviates the potential mismatch between queries and separation in two aspects, including sharing network structure and sharing feature information. First, a Swin-Unet model with a shared encoder is conducted to unify query encoding and sound separation into one model, eliminating the network architecture difference and generating consistent distribution of query and separation features. Second, by initializing CaRE-SEP with a pretrained classification network and allowing gradient backpropagation, the query embedding is optimized to be relevant to the separation feature, further alleviating the feature mismatch problem. Experimental results indicate the proposed CaRE-SEP model substantially improves the performance of separation tasks. Moreover, visualizations validate the potential mismatch and how CaRE-SEP solves it.
An Implantable Piezofilm Middle Ear Microphone: Performance in Human Cadaveric Temporal Bones
Authors: John Z. Zhang, Lukas Graf, Annesya Banerjee, Aaron Yeiser, Christopher I. McHugh, Ioannis Kymissis, Jeffrey H. Lang, Elizabeth S. Olson, Hideko Heidi Nakajima
Date: 2023-12-22 00:00:00
Tasks: Anatomy

Purpose: One of the major reasons that totally implantable cochlear microphones are not readily available is the lack of good implantable microphones. An implantable microphone has the potential to provide a range of benefits over external microphones for cochlear implant users including the filtering ability of the outer ear, cosmetics, and usability in all situations. This paper presents results from experiments in human cadaveric ears of a piezofilm microphone concept under development as a possible component of a future implantable microphone system for use with cochlear implants. This microphone is referred to here as a drum microphone (DrumMic) that senses the robust and predictable motion of the umbo, the tip of the malleus. Methods: The performance was measured of five DrumMics inserted in four different human cadaveric temporal bones. Sensitivity, linearity, bandwidth, and equivalent input noise were measured during these experiments using a sound stimulus and measurement setup. Results: The sensitivity of the DrumMics was found to be tightly clustered across different microphones and ears despite differences in umbo and middle ear anatomy. The DrumMics were shown to behave linearly across a large dynamic range (46 dB SPL to 100 dB SPL) across a wide bandwidth (100 Hz to 8 kHz). The equivalent input noise (0.1-10 kHz) of the DrumMic and amplifier referenced to the ear canal was measured to be 54 dB SPL and estimated to be 46 dB SPL after accounting for the pressure gain of the outer ear. Conclusion: The results demonstrate that the DrumMic behaves robustly across ears and fabrication. The equivalent input noise performance was shown to approach that of commercial hearing aid microphones. To advance this demonstration of the DrumMic concept to a future prototype implantable in humans, work on encapsulation, biocompatibility, connectorization will be required.
Noise Morphing for Audio Time Stretching
Authors: Eloi Moliner, Leonardo Fierro, Alec Wright, Matti Hämäläinen, Vesa Välimäki
Date: 2023-12-22 00:00:00
Tasks: Resynthesis

This letter introduces an innovative method to enhance the quality of audio time stretching by precisely decomposing a sound into sines, transients, and noise and by improving the processing of the latter component. While there are established methods for time-stretching sines and transients with high quality, the manipulation of noise or residual components has lacked robust solutions in prior research. The proposed method combines sound decomposition with previous techniques for audio spectral resynthesis. The time-stretched noise component is achieved by morphing its time-interpolated spectral magnitude with a white-noise excitation signal. This method stands out for its simplicity, efficiency, and audio quality. The results of a subjective experiment affirm the superiority of this approach over current state-of-the-art methods across all evaluated stretch factors. The proposed technique notably excels in extreme stretching scenarios, signifying a substantial elevation in performance. The proposed method holds promise for a wide range of applications in slow-motion media content, such as music or sports video production.
The UmboMic: A PVDF Cantilever Microphone
Authors: Aaron J. Yeiser, Emma F. Wawrzynek, John Z. Zhang, Lukas Graf, Christopher I. McHugh, Ioannis Kymissis, Elizabeth S. Olson, Jeffrey H. Lang, Hideko Heidi Nakajima
Date: 2023-12-22 00:00:00

Objective: We present the "UmboMic," a prototype piezoelectric cantilever microphone designed for future use with totally-implantable cochlear implants. Methods: The UmboMic sensor is made from polyvinylidene difluoride (PVDF) because of its low Young's modulus and biocompatibility. The sensor is designed to fit in the middle ear and measure the motion of the underside of the eardrum at the umbo. To maximize its performance, we developed a low noise charge amplifier in tandem with the UmboMic sensor. This paper presents the performance of the UmboMic sensor and amplifier in fresh cadaveric human temporal bones. Results: When tested in human temporal bones, the UmboMic apparatus achieves an equivalent input noise of 32.3 dB SPL over the frequency range 100 Hz to 7 kHz, good linearity, and a flat frequency response to within 10 dB from about 100 Hz to 6 kHz. Conclusion: These results demonstrate the feasibility of a PVDF-based microphone when paired with a low-noise amplifier. The reported UmboMic apparatus is comparable in performance to a conventional hearing aid microphone. Significance: The proof-of-concept UmboMic apparatus is a promising step towards creating a totally-implantable cochlear implant. A completely internal system would enhance the quality of life of cochlear implant users.
Scaling Down to Scale Up: A Cost-Benefit Analysis of Replacing OpenAI's LLM with Open Source SLMs in Production
Authors: Chandra Irugalbandara, Ashish Mahendra, Roland Daynauth, Tharuka Kasthuri Arachchige, Jayanaka Dantanarayana, Krisztian Flautner, Lingjia Tang, Yiping Kang, Jason Mars
Date: 2023-12-20 00:00:00

Many companies use large language models (LLMs) offered as a service, like OpenAI's GPT-4, to create AI-enabled product experiences. Along with the benefits of ease-of-use and shortened time-to-solution, this reliance on proprietary services has downsides in model control, performance reliability, uptime predictability, and cost. At the same time, a flurry of open-source small language models (SLMs) has been made available for commercial use. However, their readiness to replace existing capabilities remains unclear, and a systematic approach to holistically evaluate these SLMs is not readily available. This paper presents a systematic evaluation methodology and a characterization of modern open-source SLMs and their trade-offs when replacing proprietary LLMs for a real-world product feature. We have designed SLaM, an open-source automated analysis tool that enables the quantitative and qualitative testing of product features utilizing arbitrary SLMs. Using SLaM, we examine the quality and performance characteristics of modern SLMs relative to an existing customer-facing implementation using the OpenAI GPT-4 API. Across 9 SLMs and their 29 variants, we observe that SLMs provide competitive results, significant performance consistency improvements, and a cost reduction of 5x~29x when compared to GPT-4.
Self-Admitted Technical Debt Detection Approaches: A Decade Systematic Review
Authors: Edi Sutoyo, Andrea Capiluppi
Date: 2023-12-19 00:00:00
Tasks: Systematic Literature Review

Technical debt (TD) represents the long-term costs associated with suboptimal design or code decisions in software development, often made to meet short-term delivery goals. Self-Admitted Technical Debt (SATD) occurs when developers explicitly acknowledge these trade-offs in the codebase, typically through comments or annotations. Automated detection of SATD has become an increasingly important research area, particularly with the rise of natural language processing (NLP), machine learning (ML), and deep learning (DL) techniques that aim to streamline SATD detection. This systematic literature review provides a comprehensive analysis of SATD detection approaches published between 2014 and 2024, focusing on the evolution of techniques from NLP-based models to more advanced ML, DL, and Transformers-based models such as BERT. The review identifies key trends in SATD detection methodologies and tools, evaluates the effectiveness of different approaches using metrics like precision, recall, and F1-score, and highlights the primary challenges in this domain, including dataset heterogeneity, model generalizability, and the explainability of models. The findings suggest that while early NLP methods laid the foundation for SATD detection, more recent advancements in DL and Transformers models have significantly improved detection accuracy. However, challenges remain in scaling these models for broader industrial use. This SLR offers insights into current research gaps and provides directions for future work, aiming to improve the robustness and practicality of SATD detection tools.