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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Mukherjee, Rajdeep Bohra, Abhinav Banerjee, Akash Sharma, Soumya Hegde, Manjunath Shaikh, Afreen Shrivastava, Shivani Dasgupta, Koustuv Ganguly, Niloy Ghosh, Saptarshi Goyal, Pawan Department of Computer Science and Engineering IIT Kharagpur India Goldman Sachs Data Science and Machine Learning Group India Leibniz University of Hannover Germany

Despite tremendous progress in automatic summarization, state-of-the-art methods are predominantly trained to excel in summarizing short newswire articles, or documents with strong layout biases such as scientific articles or government reports. Efficient techniques to summarize financial documents, discussing facts and figures, have largely been unexplored, majorly due to the unavailability of suitable datasets. In this work, we present ECTSum, a new dataset with transcripts of earnings calls (ECTs), hosted by publicly traded companies, as documents, and experts-written short telegram-style bullet point summaries derived from corresponding Reuters articles. ECTs are long unstructured documents without any prescribed length limit or format. We benchmark our dataset with state-of-the-art summarization methods across various metrics evaluating the content quality and factual consistency of the generated summaries. Finally, we present a simple yet effective approach, ECT-BPS, to generate a set of bullet points that precisely capture the important facts discussed in the calls. © 2022 Association for Computational Linguistics.

关键词： Benchmarking

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On Lp - Lp′ estimates for a class of strongly damped wave equations

arXiv

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arXiv 2024年

作者： Mohamad, Haidar Mathematical Institute for Machine Learning and Data Science KU Eichstätt–Ingolstadt Ingolstadt85049 Germany

The purpose of this paper is to obtain a fundamental Lp - Lp0 estimate for a class of a strongly damped wave equations where the damping operator is given by -δ∆ with δ ≥ 0 and the constant in the estimate is independent of the damping parameter δ. The method used here is based on an estimate of the semigroup associated to the linear equation on the Besov spaces and their elementary *** Codes 35L05, 35L71, 35A99, 35B40 Copyright © 2024, The Authors. All rights reserved.

关键词： Damping

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Method for Finding an Investment Strategy in the Case of a Sparse Covariance Matrix 17

Method for Finding an Investment Strategy in the Case of a S...

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17th International Conference on Management of Large-Scale System Development, MLSD 2024

作者： Gorelik, Victor Zolotova, Tatiana Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Department of Simulation Systems and Operations Research Moscow Russia Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia

An optimality principle is proposed for making investment decisions based on efficiency and risk assessments with a sparse covariance matrix. The method is implemented as a program with a graphical interface and demon... 详细信息

ISBN: (纸本)9798350375718

关键词： Marketplaces

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Track C1: Safety Verification of Deep Neural Networks (DNNs) 1st

Track C1: Safety Verification of Deep Neural Networks (DNNs...

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1st International Conference on Bridging the Gap between AI and Reality, AISoLA 2023

作者： Neider, Daniel Johnson, Taylor T. Chair for Verification and Formal Guarantees of Machine Learning TU Dortmund University Dortmund Germany Research Center for Trustworthy Data Science and Security Dortmund Germany Institute for Software Integrated Systems Vanderbilt University NashvilleTN United States

ISBN: (纸本)9783031460012

Formal verification of neural networks and broader machine learning models is an emerging field that has gained significant attention due to the growing use and impact of these data-driven methods. This track explores techniques for formally verifying neural networks and other machine learning models across various application domains. It includes papers and presentations discussing new methodologies, software frameworks, technical approaches, and case studies. Benchmarks play a crucial role in evaluating the effectiveness and scalability of these methods. Currently, available benchmarks mainly focus on computer vision problems, such as local robustness to adversarial perturbations of image classifiers. To address this limitation, this track compiles and publishes benchmarks comprising machine learning models and their specifications across domains such as computer vision, finance, security, and others. These benchmarks will help assess the suitability and applicability of formal verification methods in diverse domains. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Formal Methods Formal Verification Neural Networks Safety of Autonomy

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A Comprehensive Approach To Healthcare Chatbots Using SpaCy NLP And Naive Bayes Algorithm

A Comprehensive Approach To Healthcare Chatbots Using SpaCy ...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Shruthi G Sinchana K C Soumya C P Sugnyan S Abbigeri Department of CSE Center for Data Science and Applied Machine Learning (CDSAML) PES University Bengaluru India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

Imagine a world where medical assistance is just a conversation away, where artificial intelligence helps navigate your health concerns with precision, by the intersection of machine learning and healthcare, our chatbot emerges as a powerful ally for patients everywhere. This research work focuses on developing a healthcare chatbot which provides diagnosis of patients by taking user-input symptoms, suggesting medications and also hospitals in a particular region. Both speech and text options are introduced to interact with the user. Firstly, the user is prompted to enter the symptoms faced by them, the symptoms are extracted from user input sentences using SpaCy NLP. These extracted symptoms are fed to a Multinomial Naïve Bayes Classifier for disease prediction. Additionally, chatbot also provides the required medications and the hospitals in the region from the data stored in the CSV and PDF files. The user interface of the chatbot is built using Flask. SQL is leveraged for user account management and to store the diagnosis history of the user. The dataset used for training is a CSV file consisting of 132 unique symptoms, 42 unique diseases and a total of 4,920 combinations of symptoms and associated diseases. The dataset is pre-processed in the form of 1s and 0s to make it suitable for our Multinomial Naive Bayes model which achieved a test accuracy of 95% which is better than the existing models having an accuracy of around 93%.

关键词： Training Structured Query Language Accuracy Hospitals Navigation machine learning User interfaces Chatbots Medical diagnostic imaging Diseases

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Enhancing Agricultural Yield Predictions with Real-Time IoT Sensor data and machine learning Integration 5

Enhancing Agricultural Yield Predictions with Real-Time IoT ...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Chandiraprakash, N. Chinnasamy, A. Ashok, M. Malla Reddy College of Engineering Department of Artificial Intelligence and Machine Learning Hyderabad India School of Computing Department of Data Science and Business Systems SRMIST kattankulathur campus Chennai India Malla Reddy College of Engineering Hyderabad India

ISBN: (纸本)9798331518097

This research extends previous studies on the effects of climate change on agricultural yield predictions by integrating advanced machine learning (ML) methods with real time environmental sensing data. It builds on the basic usage of Long Short-Term Memory (LSTM) and Random Forest (RF) methods by employing real time data streams from IoT based field sensors that measure soil moisture, nutrient levels and pest activity. The aim is to enhance the prediction model's adaption to changing climatic circumstances, therefore increasing their utility in agricultural management. This study proposes a dynamic adaptation feature that uses real time data to make timely adjustments to crop management predictions. Moreover, the integration of Convolutional Neural Network (CNN) with LSTM is being investigated to increase the spatial and temporal accuracy of these predictions. By integrating conventional climatic data with detailed micro environmental elements from IoT sensors, this method obtains a aspects which affects the crop growth. This study also focuses on developing a userfriendly interface that allows farmers to easily access predictive developments and recommendations, allowing for more informed decision making. This technique is predicted to make substantial advances in precision agriculture, giving more effective methods for regulating agricultural productivity in dynamic climatic variability. These results may improve food security and sustainability by optimizing resource utilization and reducing the environmental impact of farming operations. © 2024 IEEE.

关键词： Smart agriculture

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Identifying General Mechanism Shifts in Linear Causal Representations 38

Identifying General Mechanism Shifts in Linear Causal Repres...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Tianyu Bello, Kevin Locatello, Francesco Aragam, Bryon Ravikumar, Pradeep Department of Statistics and Data Sciences University of Texas Austin United States Booth School of Business University of Chicago United States Machine Learning Department Carnegie Mellon University United States Institute of Science and Technology Austria Austria

We consider the linear causal representation learning setting where we observe a linear mixing of d unknown latent factors, which follow a linear structural causal model. Recent work has shown that it is possible to recover the latent factors as well as the underlying structural causal model over them, up to permutation and scaling, provided that we have at least d environments, each of which corresponds to perfect interventions on a single latent node (factor). After this powerful result, a key open problem faced by the community has been to relax these conditions: allow for coarser than perfect single-node interventions, and allow for fewer than d of them, since the number of latent factors d could be very large. In this work, we consider precisely such a setting, where we allow a smaller than d number of environments, and also allow for very coarse interventions that can very coarsely change the entire causal graph over the latent factors. On the flip side, we relax what we wish to extract to simply the list of nodes that have shifted between one or more environments. We provide a surprising identifiability result that it is indeed possible, under some very mild standard assumptions, to identify the set of shifted nodes. Our identifiability proof moreover is a constructive one: we explicitly provide necessary and sufficient conditions for a node to be a shifted node, and show that we can check these conditions given observed data. Our algorithm lends itself very naturally to the sample setting where instead of just interventional distributions, we are provided datasets of samples from each of these distributions. We corroborate our results on both synthetic experiments as well as an interesting psychometric dataset. The code can be found at https://***/TianyuCodings/iLCS. © 2024 Neural information processing systems foundation. All rights reserved.

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Structural Breakpoint Detection in Noisy Time Series Using an Augmented Matrix Profile Index

Structural Breakpoint Detection in Noisy Time Series Using a...

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IEEE International Conference on data Mining Workshops (ICDM Workshops)

作者： Marco Fuchs Mathematical Institute for Machine Learning and Data Science Catholic University of Eichstätt-Ingolstadt Ingolstadt Germany

ISBN: (数字)9798331530631

ISBN: (纸本)9798331530648

We introduce a method for partitioning a time series into segments. The method extends the recently introduced Fast Low-Cost Semantic Segmentation (FLUSS) algorithm to increase its robustness against noise and to automatically learn two required hyperparameters. FLUSS is part of the Matrix Profile framework and conceptualises the relationship between every subsequence and its nearest-neighbor as an arc spanning over the time series. It then determines segment boundaries by selecting time indexes which are spanned over by significantly fewer arcs than an average index, such that most subsequences on either side of the time index find their closest neighbor at the same side of the index. We extend FLUSS in two directions. First, we consider the Z nearest neighbors of each subsequence to compute its neighbors’ concentration along the time series as a measure for how characteristic a sequence itself is for a particular segment. This measure is then used to filter out non-characteristic sequences, aimed at increasing the robustness of the segmentation against noise. Second, FLUSS requires the user to set two important yet hard to determine hyperparameters, namely the number of segments and the length of exclusion zones around the index of an already found segment boundary. We introduce a procedure to learn both hyperparameters implicitly by setting the tolerance parameter of an iterative end-point fit algorithm, which extracts (local) minima from a meta time series denoting the number of arcs that cross a time series index. We test the method on a set of 32 diverse and publicly available univariate time series, where it demonstrates promising results.

关键词： Sensitivity Semantic segmentation Time series analysis Noise Robustness Time measurement Partitioning algorithms Indexes Noise measurement Unsupervised learning

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High-pressure phases of liquid silane studied using first-principles molecular dynamics simulations

引用

Physical Review B 2024年第2期110卷 024106-024106页

作者： Y. H. Badjo B. Boates B. C. Saha S. A. Bonev Department of Physics Physical and Life Sciences Directorate Physics Division Machine Learning and Data Science Cash App Block San Francisco California 94110 USA

The structural and electronic transformations of liquid silane as a function of pressure are studied using first-principles molecular dynamics simulations. We present results for two isotherms, 1000 K and 2000 K, in the pressure range from ambient up to 265 GPa. The phases of dense liquid silane bear some similarities with its crystalline phases but there are significant differences as well. Notably, we find the emergence of a mixed polymeric-molecular H2 phase with strong temperature dependence and metallization in the liquid occurring at significantly lower pressure than in the solid. We report dc conductivity, which reaches higher values, and has a different pressure dependence and physical origin than recent analysis of experimental measurements has suggested.

关键词： Electronic structure High-pressure studies Liquid-liquid phase transition Structural properties Liquids Density functional calculations Molecular dynamics

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Joint Stream: Malignant Region learning for Breast Cancer Diagnosis

arXiv

引用

arXiv 2024年

作者： Rehman, Abdul Hussein, Sarfaraz Sultani, Waqas Intelligent Machine Lab Information Technology University Lahore54000 Pakistan Machine Learning and Data Science @ The Home Depot United States

Early diagnosis of breast cancer (BC) significantly contributes to reducing the mortality rate worldwide. The detection of different factors and biomarkers such as Estrogen receptor (ER), Progesterone receptor (PR), Human epidermal growth factor receptor 2 (HER2) gene, Histological grade (HG), Auxiliary lymph node (ALN) status, and Molecular subtype (MS) can play a significant role in improved BC diagnosis. However, the existing methods predict only a single factor which makes them less suitable to use in diagnosis and designing a strategy for treatment. In this paper, we propose to classify the six essential indicating factors (ER, PR, HER2, ALN, HG, MS) for early BC diagnosis using H&E stained WSI's. To precisely capture local neighboring relationships, we use spatial and frequency domain information from the large patch size of WSI's malignant regions. Furthermore, to cater the variable number of regions of interest sizes and give due attention to each region, we propose a malignant region learning attention network. Our experimental results demonstrate that combining spatial and frequency information using the malignant region learning module significantly improves multi-factor and single-factor classification performance on publicly available datasets. © 2024, CC BY-NC-SA.

关键词： Diseases

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