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29th International Conference on Research in Computational Molecular Biology, RECOMB 2025

作者： Raeisi Dehkordi, Siavash Jia, Zhaoyang Estabrook, Joey Hauenstein, Jen Miller, Neil Güleray-Lafci, Naz Neesen, Jürgen Hastie, Alex Pang, Andy Wing Chun Dremsek, Paul Bafna, Vineet Department of Computer Science and Engineering UC San Diego La JollaCA United States Bionano Genomics Inc. 9540 Towne Centre Drive Suite 100 San DiegoCA92121 United States Halicioğlu Data Science Institute UC San Diego La JollaCA United States Institute of Medical Genetics Center for Pathobiochemistry and Genetics Medical University of Vienna Vienna1090 Austria

ISBN: (纸本)9783031902512

The whole genome karyotype represents the sequence of large chromosomal segments that define an individual’s genotype, encompassing variants such as aneuploidies, balanced, and unbalanced translocations. Karyotype analysis is essential for identifying genetic risk factors, guiding diagnoses, and informing treatment and genetic counseling for constitutional disorders. Optical Genome Mapping (OGM) offers a high-resolution alternative to traditional microscopic karyotyping. We present OMKar, an automated tool that leverages OGM data to generate virtual karyotypes by processing structural variants (SVs) and copy number variants (CNVs) into a breakpoint graph. OMKar employs Integer Linear Programming to ensure chromosomal balance and identifies Eulerian paths to reconstruct chromosomes. In simulation tests, OMKar reconstruction achieved 88% precision and 95% recall for SV concordance and a 95% Jaccard score for CNV concordance. It was further validated on 154 clinical samples from ten different sites, where it successfully reconstructed 144 of the 154 karyotypes. OMKar identified genetic mechanisms for five disorders missed by traditional karyotyping, demonstrating its robustness in detecting constitutional disorders. The tool is publicly available at https://***/siavashre/OMKar. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Gene therapy

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Robust online joint state/input/parameter estimation of linear systems

arXiv

引用

arXiv 2022年

作者： Brouillon, Jean-Sébastien Moffat, Keith Dörfler, Florian Ferrari-Trecate, Giancarlo The Institute of Mechanical Engineering École Polytechnique Fédérale de Lausanne Switzerland The Electrical Engineering and Computer Science Department Uc Berkeley United States Switzerland

This paper presents a method for jointly estimating the state, input, and parameters of linear systems in an online fashion. The method is specially designed for measurements that are corrupted with non-Gaussian noise or outliers, which are commonly found in engineering applications. In particular, it combines recursive, alternating, and iteratively-reweighted least squares into a single, one-step algorithm, which solves the estimation problem online and benefits from the robustness of leastdeviation regression methods. The convergence of the iterative method is formally guaranteed. Numerical experiments show the good performance of the estimation algorithm in presence of outliers and in comparison to state-of-the-art methods. © 2022, CC BY.

关键词： Linear systems

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ConceptEVA: Concept-Based Interactive Exploration and Customization of Document Summaries

arXiv

引用

arXiv 2023年

作者： Zhang, Xiaoyu Li, Jianping Kelvin Chi, Po-Wei Chandrasegaran, Senthil Ma, Kwan-Liu Department of Computer Science University of California Davis DavisCA United States Databricks Work Done at UC Davis San JoseCA United States Work Done at UC Davis AustinTX United States Faculty of Industrial Design Engineering TU Delft Delft Netherlands

With the most advanced natural language processing and artificial intelligence approaches, effective summarization of long and multi-topic documents—such as academic papers—for readers from different domains still remains a challenge. To address this, we introduce ConceptEVA, a mixed-initiative approach to generate, evaluate, and customize summaries for long and multi-topic documents. ConceptEVA incorporates a custom multi-task longformer encoder decoder to summarize longer documents. Interactive visualizations of document concepts as a network reflecting both semantic relatedness and co-occurrence help users focus on concepts of interest. The user can select these concepts and automatically update the summary to emphasize them. We present two iterations of ConceptEVA evaluated through an expert review and a within-subjects study. We find that participants’ satisfaction with customized summaries through ConceptEVA is higher than their own manually-generated summary, while incorporating critique into the summaries proved challenging. Based on our findings, we make recommendations for designing summarization systems incorporating mixed-initiative interactions. © 2023, CC BY-NC-ND.

关键词： Natural language processing systems

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Meta-learning Spiking Neural Networks with Surrogate Gradient Descent

arXiv

引用

arXiv 2022年

作者： Stewart, Kenneth M. Neftci, Emre O. Department of Computer Science UC Irvine IrvineCA United States Department of Computer Science Department of Cognitive Sciences UC Irvine Peter Grünberg Institute – Neuromorphic Software Ecosystems Forschungszentrum Jülich Germany Faculty of Electrical Engineering and Information Technology RWTH Aachen Germany

Adaptive "life-long" learning at the edge and during online task performance is an aspirational goal of AI research. Neuromorphic hardware implementing Spiking Neural Networks (SNNs) are particularly attractive in this regard, as their real-time, event-based, local computing paradigm makes them suitable for edge implementations and fast learning. However, the long and iterative learning that characterizes state-of-the-art SNN training is incompatible with the physical nature and real-time operation of neuromorphic hardware. Bi-level learning, such as meta-learning is increasingly used in deep learning to overcome these limitations. In this work, we demonstrate gradient-based meta-learning in SNNs using the surrogate gradient method that approximates the spiking threshold function for gradient estimations. Because surrogate gradients can be made twice differentiable, well-established, and effective second-order gradient meta-learning methods such as Model Agnostic Meta Learning (MAML) can be used. We show that SNNs meta-trained using MAML match or exceed the performance of conventional Artificial Neural Networks (ANNs) meta-trained with MAML on event-based meta-datasets. Furthermore, we demonstrate the specific advantages that accrue from meta-learning: fast learning without the requirement of high precision weights or gradients. Our results emphasize how meta-learning techniques can become instrumental for deploying neuromorphic learning technologies on real-world problems. Copyright © 2022, The Authors. All rights reserved.

关键词： Gradient methods

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Generative AI in Medicine

arXiv

引用

arXiv 2024年

作者： Shanmugam, Divya Agrawal, Monica Movva, Rajiv Chen, Irene Y. Ghassemi, Marzyeh Jacobs, Maia Pierson, Emma Department of Computer Science Cornell Tech New York10044 United States Department of Biostatistics and Bioinformatics Duke University Durham27705 United States Department of Computer Science Duke University Durham27708 United States Department of Computational Precision Health UC Berkeley UCSF Berkeley94709 United States Department of Electrical Engineering and Computer Science Berkeley AI Research Berkeley94709 United States Department of Electrical Engineering and Computer Science Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge02139 United States Department of Computer Science Northwestern University Evanston60208 United States Department of Preventive Medicine Northwestern University Evanston60208 United States Department of Population Health Sciences Weill Cornell Medical College New York10044 United States

The increased capabilities of generative AI have dramatically expanded its possible use cases in medicine. We provide a comprehensive overview of generative AI use cases for clinicians, patients, clinical trial organizers, researchers, and trainees. We then discuss the many challenges – including maintaining privacy and security, improving transparency and interpretability, upholding equity, and rigorously evaluating models – which must be overcome to realize this potential, and the open research directions they give rise to. © 2024, CC BY.

关键词： Generative adversarial networks

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Machine learning based autism spectrum disorder detection from videos 22

Machine learning based autism spectrum disorder detection fr...

引用

22nd IEEE International Conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： Wu, Chongruo Liaqat, Sidrah Helvaci, Halil Chcung, Sen-Ching Samson Chuah, Chen-Nee Ozonoff, Sally Young, Gregory University of California Department of Computer Science DavisCA United States University of Kentucky Department of Electrical and Computer Engineering LexingtonKY United States University of California Department of Electrical and Computer Engineering DavisCA United States UC Davis MIND Institute University of California DavisCA United States

ISBN: (纸本)9781728162676

Early diagnosis of Autism Spectrum Disorder (ASD) is crucial for best outcomes to interventions. In this paper, we present a machine learning (ML) approach to ASD diagnosis based on identifying specific behaviors from videos of infants of ages 6 through 36 months. The behaviors of interest include directed gaze towards faces or objects of interest, positive affect, and vocalization. The dataset consists of 2000 videos of 3-minute duration with these behaviors manually coded by expert raters. Moreover, the dataset has statistical features including duration and frequency of the above mentioned behaviors in the video collection as well as independent ASD diagnosis by clinicians. We tackle the ML problem in a two-stage approach. Firstly, we develop deep learning models for automatic identification of clinically relevant behaviors exhibited by infants in a one-on-one interaction setting with parents or expert clinicians. We report baseline results of behavior classification using two methods: (1) image based model (2) facial behavior features based model. We achieve 70% accuracy for smile, 68% accuracy for look face, 67% for look object and 53% accuracy for vocalization. Secondly, we focus on ASD diagnosis prediction by applying a feature selection process to identify the most significant statistical behavioral features and a over and under sampling process to mitigate the class imbalance, followed by developing a baseline ML classifier to achieve an accuracy of 82% for ASD diagnosis. © 2021 IEEE.

关键词： Diagnosis

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Block-Wise Mixed-Precision Quantization: Enabling High Efficiency for Practical ReRAM-based DNN Accelerators

arXiv

引用

arXiv 2023年

作者： Wu, Xueying Hanson, Edward Wang, Nansu Zheng, Qilin Yang, Xiaoxuan Yang, Huanrui Li, Shiyu Cheng, Feng Pande, Partha Pratim Doppa, Janardhan Rao Chakrabarty, Krishnendu Li, Hai The Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States The EECS Department UC Berkeley BerkeleyCA94720 United States The School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85281 United States The School of Electrical Engineering and Computer Science Washington State University PullmanWA99163 United States

Resistive random access memory (ReRAM)-based processing-in-memory (PIM) architectures have demonstrated great potential to accelerate Deep Neural Network (DNN) training/inference. However, the computational accuracy of analog PIM is compromised due to the non-idealities, such as the conductance variation of ReRAM cells. The impact of these non-idealities worsens as the number of concurrently activated wordlines and bitlines increases. To guarantee computational accuracy, only a limited number of wordlines and bitlines of the crossbar array can be turned on concurrently, significantly reducing the achievable parallelism of the architecture. While the constraints on parallelism limit the efficiency of the accelerators, they also provide a new opportunity for fine-grained mixed-precision quantization. To enable efficient DNN inference on practical ReRAM-based accelerators, we propose an algorithm-architecture co-design framework called Block-Wise mixed-precision Quantization (BWQ). At the algorithm level, BWQ-A introduces a mixed-precision quantization scheme at the block level, which achieves a high weight and activation compression ratio with negligible accuracy degradation. We also present the hardware architecture design BWQ-H, which leverages the low-bit-width models achieved by BWQ-A to perform high-efficiency DNN inference on ReRAM devices. BWQ-H also adopts a novel precision-aware weight mapping method to increase the ReRAM crossbar’s throughput. Our evaluation demonstrates the effectiveness of BWQ, which achieves a 6.08× speedup and a 17.47× energy saving on average compared to existing ReRAM-based architectures. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Time series analysis and anomaly detection for trustworthy smart homes

引用

computers and Electrical engineering 2022年 102卷

作者： Priyadarshini, Ishaani Alkhayyat, Ahmed Gehlot, Anita Kumar, Raghvendra School of Information UC Berkeley United States College of technical engineering The Islamic University Najaf Iraq UIT Uttaranchal University Uttarakhand Dehradun India Department of Computer Science and Engineering GIET University India

The IoT network is expected to harbor several zettabytes of information in the future. Since trust and integrity are critical to IoT, it is essential to imbibe trust into the IoT environment for ensuring dependability and reliability. We propose a machine learning-based trustworthy system for the IoT-based smart home environment. Multiple appliances connected through the internet are susceptible to privacy issues, hence utmost care must be taken to ensure trust in the network. We consider the energy data and weather information with respect to smart homes, for comprehending the relationship between energy consumption by appliances and time period for detecting anomalous usage of appliances using the SDAR-based Change Finder algorithm. Time series analysis is performed using ARIMA, SARIMA, LSTM, Prophet, Light GBM, and VAR. The evaluation has been performed using RMSE, MSE, and MAE, and the study establishes that the ARIMA model outperforms the other models. © 2022

关键词： Time series analysis

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Semi-parametric inference based on adaptively collected data

arXiv

引用

arXiv 2023年

作者： Lin, Licong Khamaru, Koulik Wainwright, Martin J. Department of Electrical Engineering and Computer Sciences UC Berkeley United States Department of Statistics UC Berkeley United States Department of Statistics Rutgers University United States Laboratory for Information and Decision Systems Statistics and Data Science Center EECS and Mathematics Massachusetts Institute of Technology United States

Many standard estimators, when applied to adaptively collected data, fail to be asymptotically normal, thereby complicating the construction of confidence intervals. We address this challenge in a semi-parametric context: estimating the parameter vector of a generalized linear regression model contaminated by a non-parametric nuisance component. We construct suitably weighted estimating equations that account for adaptivity in data collection, and provide conditions under which the associated estimates are asymptotically normal. Our results characterize the degree of "explorability" required for asymptotic normality to hold. For the simpler problem of estimating a linear functional, we provide similar guarantees under much weaker assumptions. We illustrate our general theory with concrete consequences for various problems, including standard linear bandits and sparse generalized bandits, and compare with other methods via simulation studies. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Hardware-efficient residual networks for FPGAs

arXiv

引用

arXiv 2021年

作者： Weng, Olivia Khodamoradi, Alireza Kastner, Ryan Dept. of Computer Science and Engineering UC San Diego United States

Residual networks (ResNets) employ skip connections in their networks-reusing activations from previous layers-to improve training convergence, but these skip connections create challenges for hardware implementations of ResNets. The hardware must either wait for skip connections to be processed before processing more incoming data or buffer them elsewhere. Without skip connections, ResNets would be more hardware-efficient. Thus, we present the teacher-student learning method to gradually prune away all of a ResNet's skip connections, constructing a network we call NonResNet. We show that when implemented for FPGAs, NonResNet decreases ResNet's BRAM utilization by 9% and LUT utilization by 3% and increases throughput by 5%. Copyright © 2021, The Authors. All rights reserved.

关键词： Field programmable gate arrays (FPGA)

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