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Robust Low-rank Tensor Train Recovery

IEEE Transactions on Signal Processing 2025年 73卷 2022-2038页

作者： Qin, Zhen Zhu, Zhihui Ohio State University Department of Computer Science and Engineering ColumbusOH43201 United States

Tensor train (TT) decomposition represents an N-order tensor using O(N) matrices (i.e., factors) of small dimensions, achieved through products among these factors. Due to its compact representation, TT decomposition has found wide applications, including various tensor recovery problems in signal processing and quantum information. In this paper, we study the problem of reconstructing a TT format tensor from measurements that are contaminated by outliers with arbitrary values. Given the vulnerability of smooth formulations to corruptions, we use an l1 loss function to enhance robustness against outliers. We first establish the l1/l2-restricted isometry property (RIP) for Gaussian measurement operators, demonstrating that the information in the TT format tensor can be preserved using a number of measurements that grows linearly with N. We also prove the sharpness property for the l1 loss function optimized over TT format tensors. Building on the l1/l2-RIP and sharpness property, we then propose two complementary methods to recover the TT format tensor from the corrupted measurements: the projected subgradient method (PSubGM), which optimizes over the entire tensor, and the factorized Riemannian subgradient method (FRSubGM), which optimizes directly over the factors. Compared to PSubGM, the factorized approach FRSubGM significantly reduces the memory cost at the expense of a slightly slower convergence rate. Nevertheless, we show that both methods, with diminishing step sizes, converge linearly to the ground-truth tensor given an appropriate initialization, which can be obtained by a truncated spectral method. To the best of our knowledge, this is the first work to provide a theoretical analysis of the robust TT recovery problem and to demonstrate that TT-format tensors can be robustly recovered even when a certain fraction of measurements are arbitrarily corrupted. We conduct various numerical experiments to demonstrate the effectiveness of the two methods in robust

关键词： Tensors

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High Performance MPI over the Slingshot Interconnect

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Journal of computer science & Technology 2023年第1期38卷 128-145页

作者： Kawthar Shafie Khorassani Chen-Chun Chen Bharath Ramesh Aamir Shafi Hari Subramoni Dhabaleswar K.Panda Department of Computer Science and Engineering The Ohio State UniversityColumbusOH 43210U.S.A.

The Slingshot interconnect designed by HPE/Cray is becoming more relevant in high-performance computing with its deployment on the upcoming exascale *** particular,it is the interconnect empowering the first exascale and highest-ranked supercomputer in the world,*** offers various features such as adaptive routing,congestion control,and isolated *** deployment of newer interconnects sparks interest related to performance,scalability,and any potential bottlenecks as they are critical elements contributing to the scalability across nodes on these *** this paper,we delve into the challenges the Slingshot interconnect poses with current state-of-the-art MPI(message passing interface)*** particular,we look at the scalability performance when using Slingshot across *** present a comprehensive evaluation using various MPI and communication libraries including Cray MPICH,Open-MPI+UCX,RCCL,and MVAPICH2 on CPUs and GPUs on the Spock system,an early access cluster deployed with Slingshot-10,AMD MI100 GPUs and AMD Epyc Rome CPUs to emulate the Frontier *** also evaluate preliminary CPU-based support of MPI libraries on the Slingshot-11 interconnect.

关键词： AMD GPU interconnect technology MPI(message passing interface) Slingshot

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Hybridization of Attention UNet with Repeated Atrous Spatial Pyramid Pooling for Improved Brain Tumor Segmentation 22

Hybridization of Attention UNet with Repeated Atrous Spatial...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Chowdhury, Satyaki Roy Mirzaei, Golrokh The Ohio State University Department of Electrical and Computer Engineering Columbus United States The Ohio State University Department of Computer Science and Engineering Columbus United States

ISBN: (纸本)9798331520526

Brain tumors are highly heterogeneous in both spatial distribution and scale, making their segmentation in medical images a challenging task that can lead to diagnostic and therapeutic errors. Automating tumor segmentation has the potential to enhance objectivity and repeatability while significantly reducing turnaround time. Conventional convolutional neural networks (CNNs) often struggle to accurately capture the wide range of tumor sizes and shapes, leading to suboptimal performance. To address these limitations, UNet has emerged as a widely adopted solution for semantic segmentation, leveraging a downsampling-upsampling architecture to segment tumors. Building on this foundation, we propose a novel architecture that combines Attention-UNet with repeated Atrous Spatial Pyramid Pooling (ASPP). ASPP captures multi-scale contextual information effectively through parallel atrous convolutions with varying dilation rates. Our approach demonstrates substantial improvements over UNet, Attention UNet, and Attention UNet with Spatial Pyramid Pooling, establishing a new benchmark for tumor segmentation tasks. © 2025 IEEE.

关键词： Semantic Segmentation

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Attention-Based Speech Enhancement Using Human Quality Perception Modeling

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IEEE/ACM Transactions on Audio Speech and Language Processing 2024年 32卷 250-260页

作者： Nayem, Khandokar Md. Williamson, Donald S. Indiana University Department of Computer Science BloomingtonIN47408 United States Ohio State University Department of Computer Science and Engineering ColumbusOH43210 United States

Perceptually-inspired objective functions such as the perceptual evaluation of speech quality (PESQ), signal-to-distortion ratio (SDR), and short-time objective intelligibility (STOI), have recently been used to optimize performance of deep-learning-based speech enhancement algorithms. These objective functions, however, do not always strongly correlate with a listener's assessment of perceptual quality, so optimizing with these measures often results in poorer performance in real-world scenarios. In this work, we propose an attention-based enhancement approach that uses learned speech embedding vectors from a mean-opinion score (MOS) prediction model and a speech enhancement module to jointly enhance noisy speech. The MOS prediction model estimates the perceptual MOS of speech quality, as assessed by human listeners, directly from the audio signal. The enhancement module also employs a quantized language model that enforces spectral constraints for better speech realism and performance. We train the model using real-world noisy speech data that has been captured in everyday environments and test it using unseen corpora. The results show that our proposed approach significantly outperforms other approaches that are optimized with objective measures, where the predicted quality scores strongly correlate with human judgments. © 2014 IEEE.

关键词： Speech enhancement

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Novel Use of Software engineering Concepts for STEM Education 1

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3rd International Conference on Advances in Computing Research, ACR 2025

作者： Soundarajan, Neelam Department of Computer Science and Engineering Ohio State University Columbus United States

ISBN: (数字)9783031876479

ISBN: (纸本)9783031876462

It is almost universally recognized that STEM education is in crisis. At the same time, many careers require employees to possess well developed knowledge of STEM concepts. Numerous attempts have been made at revising curricula of STEM courses but with little success. The genesis of the problem lies in the Algebra course(s) that students take in their middle school and/or high school years. Many children who perform relatively well in courses on arithmetic in earlier grades have difficulties with the algebra course. Since algebra is foundational for both advanced math courses as well many other STEM topics such as statistics and data analysis, poor performance in algebra and lack of conceptual understanding leads to increasingly poor performance in later STEM courses. In this paper, we show that a set of key concepts of Software engineering, such as encapsulation, specifications of the operations provided by the encapsulation without reference to their implementations and closures provide a promising approach to helping students develop conceptual understanding. Moreover, this observation also allows us to build systems that include appropriate scaffolding to provide less capable students appropriate support, guidance and additional exercises to improve both their skills and dept. of understanding. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Specifications

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V2V: Efficiently Synthesizing Video Results for Video Queries 40

V2V: Efficiently Synthesizing Video Results for Video Querie...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Winecki, Dominik Nandi, Arnab The Ohio State University Computer Science & Engineering Columbus United States

ISBN: (纸本)9798350317152

Querying video data has become increasingly popular and useful. Video queries can be complex, ranging from retrieval tasks ("find me the top videos that have... "), to analytics ("how many videos contained object X per day?"), to excerpting tasks ("highlight and zoom into scenes with object X near object Y"), or combinations thereof. Results for video queries are still typically shown as either relational data or a primitive collection of clickable thumbnails on a web page. Presenting query results in this form is an impedance mismatch with the video medium: they are cumbersome to skim through and are in a different modality and information density compared to the source data. We describe V2V, a system to efficiently synthesize video results for video queries. V2V returns a fully-edited video, allowing the user to consume results in the same manner as the source videos. A key challenge is that synthesizing video results from a collection of videos is computationally intensive, especially within interactive query response times. To address this, V2V features a grammar to express video transformations in a declarative manner and a heuristic optimizer that improves the efficiency of V2V processing in a manner similar to how databases execute relational queries. Experiments show that our V2V optimizer enables video synthesis to run 3× faster. © 2024 IEEE.

关键词： Websites

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Monitoring the impacts of rainfall characteristics on sediment loss from road construction sites

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Environmental science and Pollution Research 2024年第22期31卷 32428-32440页

作者： Grimm, Alec G. Tirpak, Richard A. Winston, Ryan J. Dept. of Food Agricultural and Biological Engineering The Ohio State University 590 Woody Hayes Dr ColumbusOH43210 United States Dept. of Civil Environmental and Geodetic Engineering The Ohio State University 2070 Neil Ave ColumbusOH43210 United States

Exposed soils associated with active construction sites provide opportunities for erosion and sediment transport during storm events, introducing risks associated with excess sediment to downstream infrastructure and aquatic biota. A better understanding of the drivers of sediment transport in construction site runoff is needed to improve the design and performance of erosion and sediment control measures (ESCMs). Eleven monitoring locations on 3 active road construction sites in central ohio were established to characterize runoff quality from points of concentrated flow during storm events. Grab samples were analyzed for total suspended solids (TSS), turbidity, and particle size distribution (PSD). Median TSS concentrations and turbidity levels across all samples were 626 mg/L (range 25–28,600 mg/L) and 759 NTU (range 22–33,000 NTU), respectively. The median PSD corresponded to a silty clay loam, mirroring the soil texture of much of ohio’s subsoils. TSS concentrations and turbidity were significantly positively correlated with the rainfall intensity 10 min prior to sample collection, suggesting that higher flow rates created greater shear stress on bare soil which resulted in more erosion. Conversely, rainfall duration was negatively correlated with particle size, indicating that prolonged moisture from rainfall promoted the dispersion of soil aggregates which mobilized smaller particles. Multivariable linear regression models revealed that higher rainfall intensities corresponded to higher turbidity values, while higher TSS concentrations were associated with higher rainfall intensities, dept.s, and durations. Results from this study highlight the importance of reducing raindrop impact and subsequent shear stress applied by concentrated flows through the use of ESCMs to limit sediment export from construction sites. © The Author(s) 2024.

关键词： Particle size analysis

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Smart and collaborative industrial IoT: A federated learning and data space approach

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Digital Communications and Networks 2023年第2期9卷 436-447页

作者： Bahar Farahani Amin Karimi Monsefi Cyberspace Research Institute Shahid Beheshti UniversityTehran***Iran Department of Computer Science and Engineering Ohio State UniversityColumbusOH43210USA

Industry 4.0 has become a reality by fusing the Industrial Internet of Things(IIoT)and Artificial Intelligence(AI),providing huge opportunities in the way manufacturing companies ***,the adoption of this paradigm shift,particularly in the field of smart factories and production,is still in its infancy,suffering from various issues,such as the lack of high-quality data,data with high-class imbalance,or poor diversity leading to inaccurate AI ***,data is severely fragmented across different silos owned by several parties for a range of reasons,such as compliance and legal concerns,preventing discovery and insight-driven IIoT ***,valuable and even vital information often remains unutilized as the rise and adoption of AI and IoT in parallel with the concerns and challenges associated with privacy and *** adversely influences interand intra-organization collaborative use of IIoT *** tackle these challenges,this article leverages emerging multi-party technologies,privacy-enhancing techniques(e.g.,Federated Learning),and AI approaches to present a holistic,decentralized architecture to form a foundation and cradle for a cross-company collaboration platform and a federated data space to tackle the creeping fragmented data ***,to evaluate the efficiency of the proposed reference model,a collaborative predictive diagnostics and maintenance case study is mapped to an edge-enabled IIoT *** results show the potential advantages of using the proposed approach for multi-party applications accelerating sovereign data sharing through Findable,Accessible,Interoperable,and Reusable(FAIR)principles.

关键词： Industry 4.0 Industrial internet of things(IIoT) Artificial intelligence(AI) Predictive maintenance(PdM) Condition monitoring(CM) Federated learning(FL) Privacy preservinig machine learning(PPML) Edge computing Fog computing Cloud computing

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Towards Informing Joint Contingency Diagnosis for Autonomous Low-Altitude Flight 43

Towards Informing Joint Contingency Diagnosis for Autonomous...

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43rd AIAA DATC/IEEE Digital Avionics Systems Conference, DASC 2024

作者： Kannally, Connor Furl, Isabel McSherry, Luke Paladugu, Abhinay The Ohio State University Dept. of Integrated Systems Engr. ColumbusOH United States The Ohio State University Dept. of Computer Science & Engr. ColumbusOH United States

ISBN: (纸本)9798350349610

Conceptualized low-altitude flight operations specify highly autonomous vehicles and communication systems with humans responsible for monitoring and supervising such technologies. Parallel efforts suggest that the overall performance of such operations may be improved by supporting the interactions between technologies and humans, which is particularly useful in diagnosing off-nominal behavior. To work towards integrating human and machine decision-making, this paper details the information representation designs and scenarios challenging automation monitoring tasks specified to projected operations. The designs have been implemented as a plugin to the popular BlueSky Air Traffic Management simulation software, with future work planned to test the usefulness of such designs via Human-in-the-Loop investigations. Additionally, a discussion is provided detailing how the developed plugin fits into broader human-automation teaming design and evaluation methodologies (e.g., computational simulation and cognitive walkthroughs), informing the foundation of overall system resilience and supporting the integration of human and machine decision-making in safety-critical systems. © 2024 IEEE.

关键词： Decision making

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A Pre-training Framework that Encodes Noise Information for Speech Quality Assessment

A Pre-training Framework that Encodes Noise Information for ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Sultana, Subrina Williamson, Donald S. Department of Computer Science and Engineering The Ohio State University ColumbusOH United States

ISBN: (纸本)9798350368741

Self-supervised learning (SSL) has grown in interest within the speech processing community, since it produces representations that are useful for many downstream tasks. SSL uses global and contextual methods to produce robust representations, where SSL even outperforms supervised models. Many self-supervised approaches, however, are limited to embedding information about, i.e., the phonemes, speaker identity, and emotion, into the extracted representations, where they become invariant to background sounds due to contrastive learning. This is limiting because many downstream tasks leverage noise information to function accurately. Therefore, we propose a pre-training framework that learns information pertaining to background noise in a supervised manner, while jointly embedding speech information using a self-supervised strategy. We experiment with multiple encoders and show that our framework is useful for perceptual speech quality estimation, which relies on background cues. Our results show that the proposed approach improves performance with fewer parameters, in comparison to multiple baselines. © 2025 IEEE.

关键词： background noise representation learning self-supervised learning speech quality

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