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Ecumenical Modal Logic 1

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3rd International Workshop on Dynamic Logic, DaLí 2020

作者： Marin, Sonia Pereira, Luiz Carlos Pimentel, Elaine Sales, Emerson Department of Computer Science University College London London United Kingdom Philosophy Department PUC-Rio Rio de Janeiro Brazil Department of Mathematics UFRN Natal Brazil Graduate Program in Applied Mathematics and Statistics UFRN Natal Brazil

ISBN: (数字)9783030658403

ISBN: (纸本)9783030658397

The discussion about how to put together Gentzen’s systems for classical and intuitionistic logic in a single unified system is back in fashion. Indeed, recently Prawitz and others have been discussing the so called ecumenical Systems, where connectives from these logics can co-exist in peace. In Prawitz’ system, the classical logician and the intuitionistic logician would share the universal quantifier, conjunction, negation, and the constant for the absurd, but they would each have their own existential quantifier, disjunction, and implication, with different meanings. Prawitz’ main idea is that these different meanings are given by a semantical framework that can be accepted by both parties. In this work we extend Prawitz’ ecumenical idea to alethic K -modalities. © 2020, Springer Nature Switzerland AG.

关键词： computer circuits

Improved LSTM hyperparameters alongside sentiment walk-forward validation for time series prediction

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Journal of Open Innovation: Technology, Market, and Complexity 2025年第1期11卷

作者： Wahyuddin, Eko Putra Caraka, Rezzy Eko Kurniawan, Robert Caesarendra, Wahyu Gio, Prana Ugiana Pardamean, Bens Department of Statistical Computing Politeknik Statistika STIS Jakarta 13330 Indonesia Statistics Indonesia (BPS) Jl. Dr Sutomo 6-8 Jakarta Indonesia School of Economics and Business Telkom University Bandung 40257 Indonesia Research Center for Data and Information Sciences Research Organization for Electronics and Informatics National Research and Innovation Agency (BRIN) Bandung 40135 Indonesia Faculty of Integrated Technologies Universiti Brunei Darussalam Gadong BE1410 Brunei Darussalam Department of Mathematics Universitas Sumatera Utara Medan 20155 Indonesia Bioinformatics and Data Science Research Centre Bina Nusantara University DKI Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University DKI Jakarta 11480 Indonesia

This study aims to address the common issue of biased estimation errors in time series modeling by analyzing the error in locating ideal hyperparameters and defining appropriate validation methods. Specifically, it focuses on predicting the stock price of Bank Rakyat Indonesia using a combination of historical stock prices, technical indicators, exchange rates, and news sentiment data, while determining the optimal variables for deep learning models. Employing a deep learning-based Long-Short Term Memory (LSTM) model, the study optimizes hyperparameters alongside walk-forward validation for time series prediction. It explores different combinations of variables and adapts the sliding window approach to the context of the data. The results highlight the importance of optimizing hyperparameters and utilizing walk-forward validation for accurate time series prediction. The model incorporating historical stock prices and sentiment scores outperforms others, achieving an RMSE of 96.61 and MAE of 86.97. Incorporating sentiment scores reduces RMSE by 39.55 % compared to models using only historical stock prices, while adding technical indicators does not yield improvement. This study contributes to the field by addressing the issue of biased estimation errors in time series modeling, offering insights into the optimization of hyperparameters and validation methods for accurate predictions. It also underscores the significance of incorporating sentiment analysis alongside historical stock prices for improved forecasting accuracy. © 2024 The Authors

关键词： LSTM Sentiment analysis Stock price prediction Time series modeling Validation methods

What we should learn from pandemic publishing

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arXiv 2024年

作者： Sikdar, Satyaki Venturini, Sara Charpignon, Marie-Laure Kumar, Sagar Rinaldi, Francesco Tudisco, Francesco Fortunato, Santo Majumder, Maimuna S. Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Department of Computer Science Loyola University Chicago ChicagoIL United States Senseable City Laboratory Massachusetts Institute of Technology CambridgeMA United States Department of Mathematics "Tullio Levi-Civita" University of Padova Padova Italy Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA United States Network Science Institute Northeastern University BostonMA United States School of Mathematics The University of Edinburgh Edinburgh United Kingdom School of Mathematics Gran Sasso Science Institute L’Aquila Italy Department of Pediatrics Harvard Medical School BostonMA United States Computational Health Informatics Program Boston Children’s Hospital BostonMA United States

Since the emergence of COVID-19, discussions of ongoing pandemic-related research have accounted for an unprecedented share of media coverage and debate in the public sphere1. The urgency of the pandemic forced researchers to operate on an accelerated timeline, as both policymakers and the public relied on the most current evidence to guide their decisions and behaviors. With high demand for rapid pandemic-related insights and lower barriers to entry via preprint servers, the volume of COVID-19 articles skyrocketed2. The pressing need for research triggered the participation of many researchers with expertise in the science of infectious disease outbreaks (‘outbreak scientists’), who were joined by researchers from other disciplines (‘bellwethers’) and more junior researchers still in training (‘newcomers’) with the common goal of advancing the frontiers of pandemic science and informing policy decisions3. Please see Box 1 for details of this taxonomy. © 2024, CC BY-NC-ND.

关键词： COVID-19

Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI

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arXiv 2024年

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. © 2024, CC BY-NC-ND.

关键词： Large datasets

Experimental implementation of an efficient test of quantumness

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Physical Review A 2024年第1期109卷 012610-012610页

作者： Laura Lewis Daiwei Zhu Alexandru Gheorghiu Crystal Noel Or Katz Bahaa Harraz Qingfeng Wang Andrew Risinger Lei Feng Debopriyo Biswas Laird Egan Thomas Vidick Marko Cetina Christopher Monroe Institute for Quantum Information and Matter and Department of Computing and Mathematical Sciences California Institute of Technology Pasadena California 91125 USA Division of Physics Mathematics and Astronomy California Institute of Technology Pasadena California 91125 USA Joint Quantum Institute Departments of Physics and Electrical and Computer Engineering University of Maryland College Park Maryland 20742 USA Joint Center for Quantum Information and Computer Science NIST/University of Maryland College Park Maryland 20742 USA IonQ Inc. College Park Maryland 20740 USA Departments of Electrical and Computer Engineering University of Maryland College Park Maryland 20742 USA Institute for Theoretical Studies ETH Zürich Zürich CH 8001 Switzerland Duke Quantum Center and Department of Physics Duke University Durham North Carolina 27708 USA Department of Electrical and Computer Engineering Duke University Durham North Carolina 27708 USA Chemical Physics Program and Institute for Physical Science and Technology University of Maryland College Park Maryland 20742 USA

A test of quantumness is a protocol where a classical user issues challenges to a quantum device to determine if it exhibits nonclassical behavior, under certain cryptographic assumptions. Recent attempts to implement such tests on current quantum computers rely on either interactive challenges with efficient verification or noninteractive challenges with inefficient (exponential time) verification. In this paper, we execute an efficient noninteractive test of quantumness on an ion-trap quantum computer. Our results significantly exceed the bound for a classical device's success.

关键词： Quantum cryptography Quantum information with trapped ions Quantum protocols Quantum verification

A Lattice Boltzmann Method to Quantify the Rare Event Driven Far-Field Impact of Ocean Oil Spills

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SSRN

SSRN 2023年

作者： Zhang, Zhanyang Schäfer, Tobias Kress, Michael E. Computer Science Department College of Staten Island City University of New York 2800 Victory Boulevard Staten IslandNY10314 United States Doctoral Program in Computer Science The Graduate Center City University of New York 365 5th Avenue New YorkNY10016 United States Mathematics Department College of Staten Island City University of New York 2800 Victory Boulevard Staten IslandNY10314 United States Doctoral Program in Physics The Graduate Center City University of New York 365 5th Avenue New YorkNY10016 United States

When oil is released in ocean waters, some regions, e.g. shrimp farms, are susceptible to oil contamination, even at large distances from the spilling source. In this work, we focus on situations where the majority of the oil is expected to not reach these areas. Due to the interplay of advection and diffusion, still small amounts of oil endanger the region. Then, the effect of the pollution is governed by rare events of oil particles reaching the area under consideration posing a modeling *** compare predictions obtained (1) by GNOME (General NOAA Operational Modeling Environment), a tool developed and used by NOAA, and (2) simulation results from our adaptation of the Lattice Boltzmann Method (LBM) focusing on such areas at larger distances from the oil release. We illustrate the performance of both methods by considering a subarea of the Gulf of Mexico and obtaining the surface velocity field from the Unified Wave Interface-Coupled Model (UWIN-CM). Our initial findings are that the Lattice-Boltzmann approach, due to its algorithmic efficiency, shows advantages over GNOME and should be considered as a possible choice when using simulations in order to predict the likelihood of oil reaching regions far from the main oil spill. © 2023, The Authors. All rights reserved.

关键词： Oil spills

Nonseparable Space-Time Stationary Covariance Functions on Networks cross Time

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arXiv 2022年

作者： Porcu, Emilio White, Philip Genton, Marc G. Department of Mathematics Khalifa University Abu Dhabi United Arab Emirates School of Computer Science and Statistics Trinity College Dublin Dublin Ireland Department of Statistics Brigham Young University ProvoUT United States Statistics Program King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

The advent of data science has provided an increasing number of challenges with high data complexity. This paper addresses the challenge of space-time data where the spatial domain is not a planar surface, a sphere, or a linear network, but a generalized network (termed a graph with Euclidean edges). Additionally, data are repeatedly measured over different temporal instants. We provide new classes of nonseparable space-time stationary covariance functions where space can be a generalized network, a Euclidean tree, or a linear network, and where time can be linear or circular (seasonal). Because the construction principles are technical, we focus on illustrations that guide the reader through the construction of statistically interpretable examples. A simulation study demonstrates that we can recover the correct model when compared to misspecified models. In addition, our simulation studies show that we effectively recover simulation parameters. In our data analysis, we consider a traffic accident dataset that shows improved model performance based on covariance specifications and network-based metrics. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear networks

Manifold Filter-Combine Networks

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arXiv 2023年

作者： Johnson, David R. Chew, Joyce A. Viswanath, Siddharth De Brouwer, Edward Needell, Deanna Krishnaswamy, Smita Perlmutter, Michael Program in Computing Boise State University BoiseID83725 United States Department of Mathematics UCLA Los AngelesCA90095 United States Department of Computer Science Yale University New HavenCT06511 United States Genentech San FranciscoCA94080 United States Department of Genetics Yale University New HavenCT06511 United States Department of Mathematics Boise State University BoiseID83725 United States

In order to better understand manifold neural networks (MNNs), we introduce Manifold Filter-Combine Networks (MFCNs). Our filter-combine framework parallels the popular aggregate-combine paradigm for graph neural networks (GNNs) and naturally suggests many interesting families of MNNs which can be interpreted as manifold analogues of various popular GNNs. We propose a method for implementing MFCNs on high-dimensional point clouds that relies on approximating an underlying manifold by a sparse graph. We then prove that our method is consistent in the sense that it converges to a continuum limit as the number of data points tends to infinity, and we numerically demonstrate its effectiveness on real-world and synthetic data sets. Copyright © 2023, The Authors. All rights reserved.

关键词： Wiener filtering

Faster GröBner Bases Via Domain-Specific Ordering in Parameter Identifiability of Ode Models

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SSRN

SSRN 2023年

作者： Bessonov, Mariya Ilmer, Ilia Konstantinova, Tatiana Ovchinnikov, Alexey Pogudin, Gleb Soto, Pedro Brookhaven National Laboratory United States Department of Mathematics CUNY NYC College of Technology New YorkNY United States Ph.D. Program in Computer Science CUNY Graduate Center New YorkNY United States Department of Mathematics CUNY Queens College New YorkNY United States LIX CNRS École Polytechnique Institute Polytechnique de Paris Paris France Mathematical Institute University of Oxford Oxford United Kingdom

We consider a specific class of polynomial systems that arise in parameter identifiability problems of models of ordinary differential equations (ODE) and discover a method for speeding up the Gr¨obner basis computation by using specific variable ordering and weights coming from the structure of the ODE model. We provide empirical results that show improvement across different symbolic computing frameworks. © 2023, The Authors. All rights reserved.

关键词： Ordinary differential equations