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Encrypted machine learning of molecular quantum properties

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arXiv 2022年

作者： Weinreich, Jan von Rudorff, Guido Falk von Lilienfeld, O. Anatole University of Vienna Faculty of Physics Kolingasse 14-16 WienAT-1090 Austria University of Vienna Vienna Doctoral School in Physics Boltzmanngasse 5 Vienna1090 Austria University Kassel Department of Chemistry Heinrich-Plett-Str.40 Kassel34132 Germany Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Departments of Chemistry Materials Science and Engineering and Physics University of Toronto St. George Campus TorontoON Canada Machine Learning Group Technische Universität Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany

Large machine learning models with improved predictions have become widely available in the chemical sciences. Unfortunately, these models do not protect the privacy necessary within commercial settings, prohibiting the use of potentially extremely valuable data by others. Encrypting the prediction process can solve this problem by double-blind model evaluation and prohibits the extraction of training or query data. Unfortunately, contemporary ML models based on fully homomorphic encryption or federated learning are either too expensive for practical use or have to trade higher speed for weaker security. We have implemented secure and computationally feasible encrypted machine learning models using oblivious transfer enabling and secure predictions of molecular quantum properties across chemical compound space. However, we find that encrypted predictions using kernel ridge regression models are a million times more expensive than without encryption. This demonstrates a dire need for a compact machine learning model architecture, including molecular representation and kernel matrix size, that minimizes model evaluation costs. © 2022, CC BY.

关键词： Forecasting

Traffic4cast at NeurIPS 2022 – Predict Dynamics along Graph Edges from Sparse Node data: Whole City Traffic and ETA from Stationary Vehicle Detectors

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arXiv 2023年

作者： Neun, Moritz Eichenberger, Christian Martin, Henry Spanring, Markus Siripurapu, Rahul Springer, Daniel Deng, Leyan Wu, Chenwang Lian, Defu Zhou, Min Lumiste, Martin Ilie, Andrei Wu, Xinhua Lyu, Cheng Lu, Qing-Long Mahajan, Vishal Lu, Yichao Li, Jiezhang Li, Junjun Gong, Yue-Jiao Grötschla, Florian Mathys, Joël Wei, Ye Haitao, He Fang, Hui Malm, Kevin Tang, Fei Kopp, Michael Kreil, David Hochreiter, Sepp Vienna Austria Institute of Cartography and Geoinformation ETH Zurich Switzerland School of Data Science University of Science and Technology of China China Huawei Noah’s Ark Lab Bolt Technology Tallinn Estonia University of Bucharest Bucharest Romania Department of Civil and Environmental Engineering Northeastern University BostonMA United States Transportation Systems Engineering Technical University of Munich Germany Layer 6 AI Toronto Canada School of Coumpute Science and Engineering South China University of Technology Guangzhou China ETH Zurich Switzerland Department of Computer Science Loughborough University Loughborough United Kingdom School of Architecture Building and Civil Engineering Loughborough University Loughborough United Kingdom HERE Technologies ChicagoIL United States Kaiko Zurich Switzerland Machine Learning Institute Johannes Kepler University Linz Austria

The global trends of urbanization and increased personal mobility force us to rethink the way we live and use urban space. The Traffic4cast competition series tackles this problem in a data-driven way, advancing the latest methods in machine learning for modeling complex spatial systems over time. In this edition, our dynamic road graph data combine information from road maps, 1012 probe data points, and stationary vehicle detectors in three cities over the span of two years. While stationary vehicle detectors are the most accurate way to capture traffic volume, they are only available in few locations. Traffic4cast 2022 explores models that have the ability to generalize loosely related temporal vertex data on just a few nodes to predict dynamic future traffic states on the edges of the entire road graph. In the core challenge, participants are invited to predict the likelihoods of three congestion classes derived from the speed levels in the GPS data for the entire road graph in three cities 15 min into the future. We only provide vehicle count data from spatially sparse stationary vehicle detectors in these three cities as model input for this task. The data are aggregated in 15 min time bins for one hour prior to the prediction time. For the extended challenge, participants are tasked to predict the average travel times on super-segments 15 min into the future – super-segments are longer sequences of road segments in the graph. The competition results provide an important advance in the prediction of complex city-wide traffic states just from publicly available sparse vehicle data and without the need for large amounts of real-time floating vehicle data. © 2023, CC BY-NC-SA.

关键词： Travel time

Development of Modern Forecasting Models

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Development of Modern Forecasting Models

International Conference on Management of Large-Scale System Development (MLSD)

作者： Vera Ivanyuk Konstantin Shuvalov Gurami Akhobadze Victoria Malekova Alexey Mikhailov Kiril Levchenko Department of Data Analysis and Machine Learning Financial University Under the Government of the Russian Federation Moscow Russia Department of Higher Mathematics Bauman Moscow State Technical University Moscow Russia Laboratory of Management of Large-Scale System Development V.A.Trapeznikov Institute of Control Sciences of RAS Moscow Russia Laboratory of Network Systems Control V. A.Trapeznikov Institute of Control Sciences of RAS Moscow Russia Department of Financial Markets and Financial Engineering Financial University Under the Government of the Russian Federation Moscow Russia Department of Mathematics Financial University Under the Government of the Russian Federation Moscow Russia

The paper analyzed ensemble forecasting models. The results showed that ensemble models can improve forecasting performance by compensating for the shortcomings of weak models.

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T-Cell Receptor Optimization with Reinforcement learning and Mutation Policies for Precision Immunotherapy

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arXiv 2023年

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 ON 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

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 (RL) problem, and presented a framework TCRPPO with a mutation policy using proximal policy optimization. TCRPPO mutates TCRs into effective ones that can recognize given peptides. TCRPPO 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 TCRPPO with multiple baseline methods and demonstrated that TCRPPO significantly outperforms all the baseline methods to generate positive binding and valid TCRs. These results demonstrate the potential of TCRPPO for both precision immunotherapy and peptide-recognizing TCR motif discovery. © 2023, CC BY.

关键词： Reinforcement learning

Ndmfcs: An Automatic Fruit Counting System in Modern Apple Orchard Using Abatement of Abnormal Fruit Detection

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SSRN

SSRN 2023年

作者： Wu, Zhenchao Sun, Xiaoming Jiang, Hanhui Mao, Wulan Li, Rui Andriyanov, Nikita Soloviev, Vladimir Fu, Longsheng College of Mechanical and Electronic Engineering Northwest A&F University Shaanxi Yangling712100 China Key Laboratory of Agricultural Internet of Things Ministry of Agriculture and Rural Affairs Shaanxi Yangling712100 China Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service Shaanxi Yangling712100 China Northwest A&F University Shenzhen Research Institute Guangdong Shenzhen518000 China Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation Moscow125167 Russia Institute of Agricultural Mechanization Xinjiang Academy of Agricultural Sciences Urumqi830000 China

Automatic fruit counting is an important task for growers to estimate yield and manage orchards. Although many deep-learning-based fruit detection algorithms have been developed to improve performance of automatic fruit counting systems, abnormal fruit detection has often been caused by these algorithms detecting non-target fruits that have similar growth characteristics to target fruits. Abnormal fruit detection in modern apple orchard, i.e. detected fruits in the back row of trees (DFBRT) or on the ground (DFG), would result in a higher number of fruits counting than the ground truth. This study proposes an automatic fruit counting system called NDMFCS (Normal Detection Matched Fruit Counting System) to solve this problem for improving fruit counting accuracy in modern apple orchard. NDMFCS consists of three sub-systems, i.e. object detection based on You Only Look Once Version 4-tiny (YOLOv4-tiny), abatement of abnormal fruit detection based on threshold, and fruit counting based on trunk tracking and ID assignment. YOLOv4-tiny was selected to implement detection of fruits and trunks, whose output is confidence and pixel coordinates of detected object. The DFBRT and the DFG were defined as abnormal fruit detection and abated by thresholds to improve detection performance of fruit. Detected fruits were removed when their distance from camera is further than a distance threshold (defined as the DFBRT) or the confidence of fruit detection is less than a confidence threshold (defined as the DFG). Finally, fruit counting was implemented by trunk tracking and ID assignment, where each fruit was assigned a unique tracking ID. Results on 10 sets of original videos indicated that average fruit detection precision was improved from 89.1% to 93.3% after abatement of abnormal fruit detection. Also, Multiple Object Tracking Accuracy and Multiple Object Tracking Precision were improved on average by 4.2% and 3.3%, respectively, while average ID Switch Rate was decreased on ave

关键词： Object detection

CROSS-POPULATION AMPLITUDE COUPLING IN HIGH-DIMENSIONAL OSCILLATORY NEURAL TIME SERIES

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arXiv 2021年

作者： Bong, Heejong Ventura, Valérie Yttri, Eric Smith, Matthew A. Kass, Robert E. Department of Statistics and Data Sciences Carnegie Mellon University United States Machine Learning Department Carnegie Mellon University United States Department of Biological Sciences Carnegie Mellon University United States Department of Biomedical Engineering Carnegie Mellon University United States Neuroscience Institute Carnegie Mellon University United States

An important outstanding problem in analysis of neural data is to characterize interactions across brain regions from high-dimensional multiple-electrode recordings during a behavioral experiment. A leading theory, based on a considerable body of research, is that oscillations represent coordinated activity across populations of neurons. We sought to quantify time-varying covariation of oscillatory amplitudes across two brain regions, during a memory task, based on neural potentials recorded from 96 electrodes in each region. We extended probabilistic Canonical Correlation Analysis (CCA) to the time series setting, which provides a new interpretation of multiset CCA based on cross-correlation of latent time series. Because the latent time series covariance matrix is high-dimensional, we assumed sparsity of partial correlations within a range of possible interesting time series lead-lag effects to derive procedures for estimation and inference. We found the resulting methodology to perform well in realistic settings, and we applied it to data recorded from prefrontal cortex and visual area V4 to produce results that are highly plausible based on existing *** Codes 62P10 (Primary) 62H22, 62H25, 92B20 (Secondary) © 2021, CC BY.

关键词： Time series

Correction: Multivariate clustering for maximizing the small cell users’ performance based on the dynamic interference alignment

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Wireless Networks 2023年第1期30卷 593-593页

作者： Dakshinamoorthy, Prabakar Vaitilingam, Saminadan Sundar, Ramesh Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India Department of Electronics and Communication Engineering Puducherry Technological University Puducherry India Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

Acoustic Resonance Recognition of Coins

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Acoustic Resonance Recognition of Coins

IEEE Instrumentation and Measurement Technology Conference

作者： Ivan Kraljevski Frank Duckhorn Yong Chul Ju Constanze Tschoepe Christian Richter Matthias Wolff Cognitive Material Diagnostics Fraunhofer IKTS Cottbus Germany Machine Learning and Data Analysis Fraunhofer IKTS Dresden Germany Chair of Communications Engineering BTU Cottbus-Senftenberg Cottbus Germany

ISBN: (数字)9781728144603

ISBN: (纸本)9781728144610

In this study, we compare different machine learning approaches applied to acoustic resonance recognition of coins. Euro-cents and Euro-coins were classified by the sound emerging when throwing the coins onto a hard *** used dataset is a representative example of a small data which was collected in carefully prepared *** to the small number of coin specimens and the count of the collected observations, it was interesting to see whether deep learning methods can achieve similarly or maybe even better classification performances compared with more traditional *** results of the multi-class prediction of coin denominations are presented and compared in terms of balanced accuracy and Matthews Correlation Coefficient metrics. The feature analysis methods combined with the employed classifiers achieved acceptable results, despite the relatively small dataset.

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Convolutional Autoencoders for Health Indicators Extraction in Piezoelectric Sensors

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Convolutional Autoencoders for Health Indicators Extraction ...

IEEE Sensors

作者： Ivan Kraljevski Frank Duckhorn Constanze Tschoepe Matthias Wolff Cognitive Material Diagnostics Fraunhofer IKTS Cottbus Germany Machine Learning and Data Analysis Fraunhofer IKTS Dresden Germany Chair of Communications Engineering BTU Cottbus-Senftenberg Cottbus Germany

ISBN: (数字)9781728168012

ISBN: (纸本)9781728168029

We present a method for extracting health indicators from piezoelectric sensors applied in the case of microfluidic valves. Convolutional autoencoders were used to train a model on the normal operating conditions and tested on signals of different valves. The results of the model performance evaluation, as well as, the qualitative presentation of the indicator plots for each tested component, showed that the used approach is capable of detecting features that correspond to increasing component degradation. The extracted health indicators are the prerequisite and input for reliable remaining useful life prediction.

关键词： Valves Sensors Hidden Markov models Training Feature extraction Degradation Convolution