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Path-Gradient Estimators for Continuous Normalizing Flows

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arXiv 2022年

Recent work has established a path-gradient estimator for simple variational Gaussian distributions and has argued that the path-gradient is particularly beneficial in the regime in which the variational distribution approaches the exact target distribution. In many applications, this regime can however not be reached by a simple Gaussian variational distribution. In this work, we overcome this crucial limitation by proposing a path-gradient estimator for the considerably more expressive variational family of continuous normalizing flows. We outline an efficient algorithm to calculate this estimator and establish its superior performance empirically. © 2022, CC BY.

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T-Cell Receptor Optimization with Reinforcement learning and Mutation Polices for Precision Immunotherapy 27th

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T-Cell Receptor Optimization with Reinforcement Learning an...

27th International Conference on Research in Computational Molecular Biology, RECOMB 2023

作者： Chen, Ziqi Min, Martin Renqiang Guo, Hongyu Cheng, Chao Clancy, Trevor Ning, Xia Computer Science and Engineering The Ohio State University ColumbusOH43210 United States Machine Learning Department NEC Labs PrincetonNJ08540 United States Digital Technologies Research Centre National Research Council Canada Ontario Canada Department of Medicine Baylor College of Medicine HoustonTX77030 United States NEC Oncolmmunity AS Oslo Cancer Cluster Innovation Park Ullernchausséen 64 Oslo0379 Norway Biomedical Informatics The Ohio State University ColumbusOH43210 United States Translational Data Analytics Institute The Ohio State University ColumbusOH43210 United States

ISBN: (纸本)9783031291180

T cells monitor the health status of cells by identifying foreign peptides displayed on their surface. T-cell receptors (TCRs), which are protein complexes found on the surface of T cells, are able to bind to these peptides. This process is known as TCR recognition and constitutes a key step for immune response. Optimizing TCR sequences for TCR recognition represents a fundamental step towards the development of personalized treatments to trigger immune responses killing cancerous or virus-infected cells. In this paper, we formulated the search for these optimized TCRs as a reinforcement learning (Formula Presented) problem, and presented a framework (Formula Presented) with a mutation policy using proximal policy optimization. (Formula Presented) mutates TCRs into effective ones that can recognize given peptides. (Formula Presented) leverages a reward function that combines the likelihoods of mutated sequences being valid TCRs measured by a new scoring function based on deep autoencoders, with the probabilities of mutated sequences recognizing peptides from a peptide-TCR interaction predictor. We compared (Formula Presented) with multiple baseline methods and demonstrated that (Formula Presented) significantly outperforms all the baseline methods to generate positive binding and valid TCRs. These results demonstrate the potential of(Formula Presented) for both precision immunotherapy and peptide-recognizing TCR motif discovery. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Reinforcement learning

Facilitated machine learning for image-based fruit quality assessment

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arXiv 2022年

作者： Knott, Manuel Perez-Cruz, Fernando Defraeye, Thijs Empa Swiss Federal Laboratories for Materials Science Technology Laboratory for Biomimetic Membranes and Textiles St. Gallen Switzerland Swiss Data Science Center ETH Zurich and EPFL Zurich Switzerland Institute for Machine Learning Department of Computer Science ETH Zurich Switzerland

Image-based machine learning models can be used to make the sorting and grading of agricultural products more efficient. In many regions, implementing such systems can be difficult due to the lack of centralization and automation of postharvest supply chains. Stakeholders are often too small to specialize in machine learning, and large training data sets are unavailable. We propose a machine learning procedure for images based on pre-trained Vision Transformers. It is easier to implement than the current standard approach of training Convolutional Neural Networks (CNNs) as we do not (re-)train deep neural networks. We evaluate our approach based on two data sets for apple defect detection and banana ripeness estimation. Our model achieves a competitive classification accuracy equal to or less than one percent below the best-performing CNN. At the same time, it requires three times fewer training samples to achieve a 90% accuracy. © 2022, CC BY.

关键词： Computer vision

Exploring the Transcriptional and Translational Features Using Deep Neural Networks for mRNAs Classification

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Exploring the Transcriptional and Translational Features Usi...

IEEE International Conference on Bioinformatics and Computational Biology (ICBCB)

作者： Amira Kefi Morris Chukhman Vinayakumar Karintha Sadok Bouamama Jie Yang Chunyu Liu Department of BioMedical Engineering College of Engineering University of Illinois at Chicago Chicago IL USA Department of Bioengineering College of Engineering University of Illinois at Chicago Chicago IL USA Machine Learning Practice UST Aliso Viejo CA National Engineering School of Manouba (ENSI) Manouba University LaManouba Tunisia Department of Mathematics Statistics and Computer Science University of Illinois at Chicago Chicago USA Department of Psychiatry Department of Neuroscience and physiology SUNY Upstate Medical University Syracuse NY USA

Recent advent of the second and third generation of sequencing has uncovered many novel transcripts. These novel transcripts could have crucial functions in different biological processes and might be related to challenging diseases and pathogenesis. However, whether these genes should be classified as protein coding RNAs (pcRNAs) or long non-coding RNAs (lncRNAs) is still debated and unclear. In this study we propose a coding potential classification framework based on deep neural networks and novel features from RNA-seq and Ribo-seq data to classify RNAs transcripts into protein coding and long non coding. As far as we know, this is the first method that uses RNA-seq and Ribo-seq as predictors to classify RNAs using a deep neural network model. Compared to other methods, the prediction of our method reached 97.4% accuracy.

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ElasticLaneNet: An Efficient Geometry-Flexible Approach for Lane Detection

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arXiv 2023年

作者： Feng, Yaxin Lan, Yuan Zhang, Luchan Xiang, Yang Department of Mathematics Hong Kong University of Science and Technology Clear Water Bay Hong Kong Algorithms of Machine Learning and Autonomous Driving Research Lab HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian Shenzhen China College of Mathematics and Statistics Shenzhen University Shenzhen518060 China

The task of lane detection involves identifying the boundaries of driving areas in real-time. Recognizing lanes with variable and complex geometric structures remains a challenge. In this paper, we explore a novel and flexible way of implicit lanes representation named Elastic Lane map (ELM), and introduce an efficient physics-informed end-to-end lane detection framework, namely, ElasticLaneNet (Elastic interaction energy-informed Lane detection Network). The approach considers predicted lanes as moving zero-contours on the flexibly shaped ELM that are attracted to the ground truth guided by an elastic interaction energy-loss function (EIE loss). Our framework well integrates the global information and low-level features. The method performs well in complex lane scenarios, including those with large curvature, weak geometry features at intersections, complicated cross lanes, Y-shapes lanes, dense lanes, etc. We apply our approach on three datasets: SDLane, CULane, and TuSimple. The results demonstrate exceptional performance of our method, with the state-of-the-art results on the structurally diverse SDLane, achieving F1-score of 89.51, Recall rate of 87.50, and Precision of 91.61 with fast inference speed. Copyright © 2023, The Authors. All rights reserved.

关键词： Geometry

Predicting the Initial Conditions of the Universe using a Deterministic Neural Network

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arXiv 2023年

作者： Jindal, Vaibhav Liang, Albert Singh, Aarti Ho, Shirley Jamieson, Drew Machine Learning Department Carnegie Mellon University United States Cohesity Two Sigma Investments United States Center for Computational Astrophysics Flatiron Institute United States Department of Physics Center for Data Science New York University United States Department of Astrophysical Sciences Princeton University United States Max Planck Institute for Astrophysics Germany Carnegie Mellon University United States

Finding the initial conditions that led to the current state of the universe is challenging because it involves searching over an intractable input space of initial conditions, along with modeling their evolution via tools such as N-body simulations which are computationally expensive. Recently, deep learning has emerged as a surrogate for N-body simulations by directly learning the mapping between the linear input of an N-body simulation and the final nonlinear output from the simulation, significantly accelerating the forward modeling. However, this still does not reduce the search space for initial conditions. In this work, we pioneer the use of a deterministic convolutional neural network for learning the reverse mapping and show that it accurately recovers the initial linear displacement field over a wide range of scales (−1h), despite the one-to-many mapping of the inverse problem (due to the divergent backward trajectories at smaller scales). Specifically, we train a V-Net architecture, which outputs the linear displacement of an N-body simulation, given the nonlinear displacement at redshift z = 0 and the cosmological parameters. The results of our method suggest that a simple deterministic neural network is sufficient for accurately approximating the initial linear states, potentially obviating the need for the more complex and computationally demanding backward modeling methods that were recently proposed. © 2023, CC BY.

关键词： Inverse problems

Customer Segmentation Using Clustering Analysis

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Customer Segmentation Using Clustering Analysis

Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), IEEE International Conference on

作者： Moses Makuei Jiet Aahash Kamble Chetan Puri Prajyot Yesankar Prateek Verma Rajendra Rewatkar Department of Computer Science & Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India Department of Artificial Intelligence & Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India Department of Artificial Intelligence & Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India Department of Biomedical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

This research focuses on the crucial role of the clustering technique in data mining, specifically in market forecasting and planning. The study presents a comprehensive report on utilizing the k-means clustering technique and data visualization tools to develop a real- time system for a supermarket, predicting sales across various annual cycles. An open-source dataset of a mall with n = 200 customers divided into k = 3 groups, demonstrated high accuracy in predicting day-to-day sales statistics when compared with actual results. The research highlights the effectiveness of the clustering technique in real-time sales prediction for supermarkets.

关键词： Sociology Refining Decision making data visualization Prediction algorithms Real-time systems Planning

Combined compression and encryption of linear wireless sensor network data using autoencoders

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Scientific Reports 2025年第1期15卷 1-16页

作者： Shylashree, N. Kumar, Sachin Min, Hong Department of Electronics and Communication RV College of Engineering Affiliated to VTU Belagavi Bengaluru 560059 India Big Data and Machine Learning Lab South Ural State University Chelyabinsk Russian Federation Akian College of Science and Engineering American University of Armenia Yerevan 0019 Armenia School of Computing Gachon University Seongnam South Korea

In a linear wireless sensor network (LWSN), sensor nodes are deployed in a linear fashion to monitor and gather data along a linear path or route. Generally, the base station collects the data from the contiguously placed nodes in the same order. When the sensors are deployed closely and with a gradual variation of sensor data along the route, a high degree of correlation exists among the sensed data. The sensed data sequence can be compressed with very low loss in such a situation. In this paper, a joint compression and encryption method for LWSN data is presented. The method is based on the dimension reduction property of an autoencoder at the bottleneck section. The Encoder part of the trained Autoencoder, housed at the Base Station (BS), reduces the number of data samples at the encoded output. Hence, the data gets compressed at the output of the Encoder. The output of the Encoder is encrypted using an asymmetric encryption that provides immunity to the Chosen Plaintext Attack. Thus, both data compression and encryption are achieved together at the BS. Therefore, the procedure at the BS is denoted as joint compression and encryption. The encrypted data is sent to the Cloud Server for secured storage and further distribution to the End User, where it is decrypted and subsequently decompressed by the Decoder part of the trained Autoencoder. The decompressed data sequence is very nearly equal to the original data sequence. The proposed lossy compression has a mean square reconstruction error of less than 0.5 for compression ratios in the range of 5 to 10. The compression time taken is short even though the Autoencoder training process, which occurs once in a while, takes a relatively long time. © The Author(s) 2025.

关键词： Autoencoder with supervised learning Chosen plaintext attack Integer matrix keys Joint compression and encryption Linear wireless sensor network Secured cloud storage

EPR-Net: Constructing non-equilibrium potential landscape via a variational force projection formulation

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arXiv 2023年

作者： Zhao, Yue Zhang, Wei Li, Tiejun Center for Data Science Peking University Beijing100871 China Zuse Institute Berlin Berlin14195 Germany Department of Mathematics and Computer Science Freie Universität Berlin Berlin14195 Germany LMAM and School of Mathematical Sciences Peking University Beijing100871 China Center for Machine Learning Research Peking University Beijing100871 China

We present EPR-Net, a novel and effective deep learning approach that tackles a crucial challenge in biophysics: constructing potential landscapes for high-dimensional non-equilibrium steady-state (NESS) systems. EPR-Net leverages a nice mathematical fact that the desired negative potential gradient is simply the orthogonal projection of the driving force of the underlying dynamics in a weighted inner-product space. Remarkably, our loss function has an intimate connection with the steady entropy production rate (EPR), enabling simultaneous landscape construction and EPR estimation. We introduce an enhanced learning strategy for systems with small noise, and extend our framework to include dimensionality reduction and state-dependent diffusion coefficient case in a unified fashion. Comparative evaluations on benchmark problems demonstrate the superior accuracy, effectiveness, and robustness of EPR-Net compared to existing methods. We apply our approach to challenging biophysical problems, such as an 8D limit cycle and a 52D multi-stability problem, which provide accurate solutions and interesting insights on constructed landscapes. With its versatility and power, EPR-Net offers a promising solution for diverse landscape construction problems in biophysics. Copyright © 2023, The Authors. All rights reserved.

关键词： Entropy