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Articles 40621 - 40650 of 294409

Full-Text Articles in Physical Sciences and Mathematics

Topological Explanation Of Why Complex Numbers Are Needed In Quantum Physics, Julio C. Urenda, Vladik Kreinovich Jul 2023

Topological Explanation Of Why Complex Numbers Are Needed In Quantum Physics, Julio C. Urenda, Vladik Kreinovich

Departmental Technical Reports (CS)

In quantum computing, we only use states in which all amplitudes are real numbers. So why do we need complex numbers with non-zero imaginary part in quantum physics in general? In this paper, we provide a simple topological explanation for this need, explanation based on the Second Law of Thermodynamics.

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The Appropriateness Of Outlier Exclusion Approaches Depends On The Expected Contamination: Commentary On André (2022), Daniel Villanova Jul 2023

The Appropriateness Of Outlier Exclusion Approaches Depends On The Expected Contamination: Commentary On André (2022), Daniel Villanova

Marketing Faculty Publications and Presentations

In a recent article, André (2022) addressed the decision to exclude outliers using a threshold across conditions or within conditions and offered a clear recommendation to avoid within-conditions exclusions because of the possibility for large false-positive inflation. In this commentary, I note that André’s simulations did not include the situation for which within-conditions exclusion has previously been recommended—when across-conditions exclusion would exacerbate selection bias. Examining test performance in this situation confirms the recommendation for within-conditions exclusion in such a circumstance. Critically, the suitability of exclusion criteria must be considered in relationship to assumptions about data-generating mechanisms.

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Numerical Design And Optimization Of Near-Infrared Band- Pass Filter, Hafiza Syeeda Faiza, Ghazi Aman Nowsherwan, Basem A. Abu Izneid, Muhammad Azhar, Saira Riaz, Syed Sajjad Hussain, Saira Ikram, Mohsin Khan, Shahzad Naseem, Mohammad Kanan, Ibrahim M. Mansour Jul 2023

Numerical Design And Optimization Of Near-Infrared Band- Pass Filter, Hafiza Syeeda Faiza, Ghazi Aman Nowsherwan, Basem A. Abu Izneid, Muhammad Azhar, Saira Riaz, Syed Sajjad Hussain, Saira Ikram, Mohsin Khan, Shahzad Naseem, Mohammad Kanan, Ibrahim M. Mansour

Applied Mathematics & Information Sciences

Band-pass filters functioning in the near-infrared (IR) range are desired for laser technology, multi-photon fluorescence, and IR imaging applications. In this study, we have designed four band-pass filters in the near Infrared spectrum (900-1200 nm) by vertically stacking different high and low-index materials. The band-pass filters are modelled by Essential Macleod software with different thicknesses. The layer’s thicknesses were optimized in such a way to provide the negligible reflectance and maximum transmission on the front side. All the simulated band-pass filters exhibit high transmittance, but TiO2/Al2O3 and Ta2O5/Al2O3 outperforms other modelled structure in terms of performance due to the better …

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Nuclear Magnetic Resonance Studies Of Potential Environmental Contaminants Interacting With Dna, Anna Hopkins, Leah B. Casabianca Jul 2023

Nuclear Magnetic Resonance Studies Of Potential Environmental Contaminants Interacting With Dna, Anna Hopkins, Leah B. Casabianca

Chemistry Summer Research Program

No abstract provided.

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Big Data For A Deep Problem: Exploring Natural Deep Eutectic Solvent (Nades) Properties Through Rdkit And Data Analytics, Grayson Weavil, Lucas B. Ayres, Carlos D. Garcia Jul 2023

Big Data For A Deep Problem: Exploring Natural Deep Eutectic Solvent (Nades) Properties Through Rdkit And Data Analytics, Grayson Weavil, Lucas B. Ayres, Carlos D. Garcia

Chemistry Summer Research Program

No abstract provided.

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The Dynamic Shift To Green Chemistry: Investigating The Spectral Behavior Of Natural Deep Eutectic Solvents (Nades) And Their Performance As Maldi-Tof Matrices, Grayson Weavil, Lucas B. Ayres, Miguel Jose-Bueno, Rakesh Sachdeva, Carlos D. Garcia Jul 2023

The Dynamic Shift To Green Chemistry: Investigating The Spectral Behavior Of Natural Deep Eutectic Solvents (Nades) And Their Performance As Maldi-Tof Matrices, Grayson Weavil, Lucas B. Ayres, Miguel Jose-Bueno, Rakesh Sachdeva, Carlos D. Garcia

Chemistry Summer Research Program

No abstract provided.

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Investigation Of Vitamin D Metabolites Using Different Ion Mobility-Mass Spectrometry (Im-Ms) Methods, Selena Kingsley, Christopher D. Chouinard Jul 2023

Investigation Of Vitamin D Metabolites Using Different Ion Mobility-Mass Spectrometry (Im-Ms) Methods, Selena Kingsley, Christopher D. Chouinard

Chemistry Summer Research Program

No abstract provided.

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Prediction Of Dislocation Densities In Pure Metals And Alloys, J. Garey Weaver, Enrique Martinez Jul 2023

Prediction Of Dislocation Densities In Pure Metals And Alloys, J. Garey Weaver, Enrique Martinez

Chemistry Summer Research Program

No abstract provided.

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Supramolecular Π-Donor/Acceptor Arrays Based On Metal-Organic Cage Inclusion Complexes, Sean Hannigan, Evan Thibodeaux, Sourav Saha Jul 2023

Supramolecular Π-Donor/Acceptor Arrays Based On Metal-Organic Cage Inclusion Complexes, Sean Hannigan, Evan Thibodeaux, Sourav Saha

Chemistry Summer Research Program

No abstract provided.

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Measurement Of Protein And Drug Adsorption On Nanoparticles By Competition With Dyes, Ashleigh Carroll, Jason Mcneill Jul 2023

Measurement Of Protein And Drug Adsorption On Nanoparticles By Competition With Dyes, Ashleigh Carroll, Jason Mcneill

Chemistry Summer Research Program

No abstract provided.

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Using Alginate Beads Modified With Graphite As An Effective Electrode, Madeline Mchugh, Barbara G. S. Guinati, Carlos D. Garcia Jul 2023

Using Alginate Beads Modified With Graphite As An Effective Electrode, Madeline Mchugh, Barbara G. S. Guinati, Carlos D. Garcia

Chemistry Summer Research Program

No abstract provided.

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Polymersomes As Chemotherapeutic Agents For Non-Invasive Treatment Of Glioblastoma, Tj Fletcher, Molli Garifo, Jessica Larsen, Jessica Larsen Jul 2023

Polymersomes As Chemotherapeutic Agents For Non-Invasive Treatment Of Glioblastoma, Tj Fletcher, Molli Garifo, Jessica Larsen, Jessica Larsen

Chemistry Summer Research Program

No abstract provided.

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Accessing Rare Heterocycles From Alkynyl Ethers And Nitrogenous Electrophiles & The Development Of Small Molecule Inhibitors Against Naegleria Fowleri Infection, James W. D. Morris, Samuel Kwain, Daniel C. Whitehead Jul 2023

Accessing Rare Heterocycles From Alkynyl Ethers And Nitrogenous Electrophiles & The Development Of Small Molecule Inhibitors Against Naegleria Fowleri Infection, James W. D. Morris, Samuel Kwain, Daniel C. Whitehead

Chemistry Summer Research Program

No abstract provided.

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Combining Alginate Beads With Methylene To Create A Biosensor To Assess The Quality Of Milk, Vivien Zheng, Barbara Guinati, Carlos D. Garcia Jul 2023

Combining Alginate Beads With Methylene To Create A Biosensor To Assess The Quality Of Milk, Vivien Zheng, Barbara Guinati, Carlos D. Garcia

Chemistry Summer Research Program

No abstract provided.

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Multi-Catalysis: Trifluoromethylation Of Amides, Ethan Apsley, Jason Wilt, Giovani Gutierrez, Byoungmoo Kim Jul 2023

Multi-Catalysis: Trifluoromethylation Of Amides, Ethan Apsley, Jason Wilt, Giovani Gutierrez, Byoungmoo Kim

Chemistry Summer Research Program

No abstract provided.

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A Universal Unbiased Method For Classification From Aggregate Observations, Zixi Wei, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Xiaofeng Zhu, Heng Tao Shen Jul 2023

A Universal Unbiased Method For Classification From Aggregate Observations, Zixi Wei, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Xiaofeng Zhu, Heng Tao Shen

Machine Learning Faculty Publications

In conventional supervised classification, true labels are required for individual instances. However, it could be prohibitive to collect the true labels for individual instances, due to privacy concerns or unaffordable annotation costs. This motivates the study on classification from aggregate observations (CFAO), where the supervision is provided to groups of instances, instead of individual instances. CFAO is a generalized learning framework that contains various learning problems, such as multiple-instance learning and learning from label proportions. The goal of this paper is to present a novel universal method of CFAO, which holds an unbiased estimator of the classification risk for arbitrary …

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Diversity-Enhancing Generative Network For Few-Shot Hypothesis Adaptation, Ruijiang Dong, Feng Liu, Haoang Chi, Tongliang Liu, Mingming Gong, Gang Niu, Masashi Sugiyama, Bo Han Jul 2023

Diversity-Enhancing Generative Network For Few-Shot Hypothesis Adaptation, Ruijiang Dong, Feng Liu, Haoang Chi, Tongliang Liu, Mingming Gong, Gang Niu, Masashi Sugiyama, Bo Han

Machine Learning Faculty Publications

Generating unlabeled data has been recently shown to help address the few-shot hypothesis adaptation (FHA) problem, where we aim to train a classifier for the target domain with a few labeled target-domain data and a well-trained source-domain classifier (i.e., a source hypothesis), for the additional information of the highly-compatible unlabeled data. However, the generated data of the existing methods are extremely similar or even the same. The strong dependency among the generated data will lead the learning to fail. In this paper, we propose a diversity-enhancing generative network (DEG-Net) for the FHA problem, which can generate diverse unlabeled data with …

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Identifiability Of Label Noise Transition Matrix, Yang Liu, Hao Cheng, Kun Zhang Jul 2023

Identifiability Of Label Noise Transition Matrix, Yang Liu, Hao Cheng, Kun Zhang

Machine Learning Faculty Publications

The noise transition matrix plays a central role in the problem of learning with noisy labels. Among many other reasons, a large number of existing solutions rely on the knowledge of it. Identifying and estimating the transition matrix without ground truth labels is a critical and challenging task. When label noise transition depends on each instance, the problem of identifying the instance-dependent noise transition matrix becomes substantially more challenging. Despite recently proposed solutions for learning from instance-dependent noisy labels, the literature lacks a unified understanding of when such a problem remains identifiable. The goal of this paper is to characterize …

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Adaptive Compositional Continual Meta-Learning, Bin Wu, Jinyuan Fang, Xiangxiang Zeng, Shangsong Liang, Qiang Zhang Jul 2023

Adaptive Compositional Continual Meta-Learning, Bin Wu, Jinyuan Fang, Xiangxiang Zeng, Shangsong Liang, Qiang Zhang

Machine Learning Faculty Publications

This paper focuses on continual meta-learning, where few-shot tasks are heterogeneous and sequentially available. Recent works use a mixture model for meta-knowledge to deal with the heterogeneity. However, these methods suffer from parameter inefficiency caused by two reasons: (1) the underlying assumption of mutual exclusiveness among mixture components hinders sharing meta-knowledge across heterogeneous tasks. (2) they only allow increasing mixture components and cannot adaptively filter out redundant components. In this paper, we propose an Adaptive Compositional Continual Meta-Learning (ACML) algorithm, which employs a compositional premise to associate a task with a subset of mixture components, allowing meta-knowledge sharing among heterogeneous …

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Fast Rates For Maximum Entropy Exploration, Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Éric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard Jul 2023

Fast Rates For Maximum Entropy Exploration, Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Éric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard

Machine Learning Faculty Publications

We address the challenge of exploration in reinforcement learning (RL) when the agent operates in an unknown environment with sparse or no rewards. In this work, we study the maximum entropy exploration problem of two different types. The first type is visitation entropy maximization previously considered by Hazan et al. (2019) in the discounted setting. For this type of exploration, we propose a game-theoretic algorithm that has Oe(H3S2A/ε2) sample complexity thus improving the ε-dependence upon existing results, where S is a number of states, A is a number of actions, H is an episode length, and ε is a desired …

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Evolving Semantic Prototype Improves Generative Zero-Shot Learning, Shiming Chen, Wenjin Hou, Ziming Hong, Xiaohan Ding, Yibing Song, Xinge You, Tongliang Liu, Kun Zhang Jul 2023

Evolving Semantic Prototype Improves Generative Zero-Shot Learning, Shiming Chen, Wenjin Hou, Ziming Hong, Xiaohan Ding, Yibing Song, Xinge You, Tongliang Liu, Kun Zhang

Machine Learning Faculty Publications

In zero-shot learning (ZSL), generative methods synthesize class-related sample features based on predefined semantic prototypes. They advance the ZSL performance by synthesizing unseen class sample features for better training the classifier. We observe that each class's predefined semantic prototype (also referred to as semantic embedding or condition) does not accurately match its real semantic prototype. So the synthesized visual sample features do not faithfully represent the real sample features, limiting the classifier training and existing ZSL performance. In this paper, we formulate this mismatch phenomenon as the visual-semantic domain shift problem. We propose a dynamic semantic prototype evolving (DSP) method …

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Coco: A Coupled Contrastive Framework For Unsupervised Domain Adaptive Graph Classification, Nan Yin, Li Shen, Mengzhu Wang, Long Lan, Zeyu Ma, Chong Chen, Xian Sheng Hua, Xiao Luo Jul 2023

Coco: A Coupled Contrastive Framework For Unsupervised Domain Adaptive Graph Classification, Nan Yin, Li Shen, Mengzhu Wang, Long Lan, Zeyu Ma, Chong Chen, Xian Sheng Hua, Xiao Luo

Machine Learning Faculty Publications

Although graph neural networks (GNNs) have achieved impressive achievements in graph classification, they often need abundant task-specific labels, which could be extensively costly to acquire. A credible solution is to explore additional labeled graphs to enhance unsupervised learning on the target domain. However, how to apply GNNs to domain adaptation remains unsolved owing to the insufficient exploration of graph topology and the significant domain discrepancy. In this paper, we propose Coupled Contrastive Graph Representation Learning (CoCo), which extracts the topological information from coupled learning branches and reduces the domain discrepancy with coupled contrastive learning. CoCo contains a graph convolutional network …

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Convergence Of Proximal Point And Extragradient-Based Methods Beyond Monotonicity: The Case Of Negative Comonotonicity, Eduard Gorbunov, Adrien Taylor, Samuel Horváth, Gauthier Gidel Jul 2023

Convergence Of Proximal Point And Extragradient-Based Methods Beyond Monotonicity: The Case Of Negative Comonotonicity, Eduard Gorbunov, Adrien Taylor, Samuel Horváth, Gauthier Gidel

Machine Learning Faculty Publications

Algorithms for min-max optimization and variational inequalities are often studied under monotonicity assumptions. Motivated by non-monotone machine learning applications, we follow the line of works (Diakonikolas et al., 2021; Lee & Kim, 2021; Pethick et al., 2022; Böhm, 2022) aiming at going beyond monotonicity by considering the weaker negative comonotonicity assumption. In this work, we provide tight complexity analyses for the Proximal Point (PP), Extragradient (EG), and Optimistic Gradient (OG) methods in this setup, closing several questions on their working guarantees beyond monotonicity. In particular, we derive the first non-asymptotic convergence rates for PP under negative comonotonicity and star-negative comonotonicity …

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Unleashing Mask: Explore The Intrinsic Out-Of-Distribution Detection Capability, Jianing Zhu, Hengzhuang Li, Jiangchao Yao, Tongliang Liu, Jianliang Xu, Bo Han Jul 2023

Unleashing Mask: Explore The Intrinsic Out-Of-Distribution Detection Capability, Jianing Zhu, Hengzhuang Li, Jiangchao Yao, Tongliang Liu, Jianliang Xu, Bo Han

Machine Learning Faculty Publications

Out-of-distribution (OOD) detection is an indispensable aspect of secure AI when deploying machine learning models in real-world applications. Previous paradigms either explore better scoring functions or utilize the knowledge of outliers to equip the models with the ability of OOD detection. However, few of them pay attention to the intrinsic OOD detection capability of the given model. In this work, we generally discover the existence of an intermediate stage of a model trained on in-distribution (ID) data having higher OOD detection performance than that of its final stage across different settings, and further identify one critical data-level attribution to be …

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A Unified Optimization Framework Of Ann-Snn Conversion: Towards Optimal Mapping From Activation Values To Firing Rates, Haiyan Jiang, Srinivas Anumasa, Giulia De Masi, Huan Xiong, Bin Gu Jul 2023

A Unified Optimization Framework Of Ann-Snn Conversion: Towards Optimal Mapping From Activation Values To Firing Rates, Haiyan Jiang, Srinivas Anumasa, Giulia De Masi, Huan Xiong, Bin Gu

Machine Learning Faculty Publications

Spiking Neural Networks (SNNs) have gained significant attention for their energy-efficient and fast-inference capabilities, but training SNNs from scratch can be challenging due to the discrete nature of spikes. One alternative method is to convert an Artificial Neural Network (ANN) into an SNN, known as ANN-SNN conversion. Currently, existing ANN-SNN conversion methods often involve redesigning the ANN with a new activation function, rather than utilizing the traditional ReLU, and converting it to an SNN. However, these methods do not take into account the potential performance loss between the regular ANN with ReLU and the tailored ANN. In this work, we …

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Linear Classifier: An Often-Forgotten Baseline For Text Classification, Yu Chen Lin, Si An Chen, Jie Jyun Liu, Chih Jen Lin Jul 2023

Linear Classifier: An Often-Forgotten Baseline For Text Classification, Yu Chen Lin, Si An Chen, Jie Jyun Liu, Chih Jen Lin

Machine Learning Faculty Publications

Large-scale pre-trained language models such as BERT are popular solutions for text classification. Due to the superior performance of these advanced methods, nowadays, people often directly train them for a few epochs and deploy the obtained model. In this opinion paper, we point out that this way may only sometimes get satisfactory results. We argue the importance of running a simple baseline like linear classifiers on bag-of-words features along with advanced methods. First, for many text data, linear methods show competitive performance, high efficiency, and robustness. Second, advanced models such as BERT may only achieve the best results if properly …

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Discovery Of Extraordinary X-Ray Emission From Magnetospheric Interaction In The Unique Binary Stellar System Ε Lupi, B. Das, V. Petit, Y. Nazé, M. F. Corcoran, David H. Cohen, A. Biswas, P. Chandra, A. David-Uraz, M. A. Leutenegger, C. Neiner, H. Pablo, E. Paunzen, M. E. Shultz, A. Ud-Doula, G. A. Wade Jul 2023

Discovery Of Extraordinary X-Ray Emission From Magnetospheric Interaction In The Unique Binary Stellar System Ε Lupi, B. Das, V. Petit, Y. Nazé, M. F. Corcoran, David H. Cohen, A. Biswas, P. Chandra, A. David-Uraz, M. A. Leutenegger, C. Neiner, H. Pablo, E. Paunzen, M. E. Shultz, A. Ud-Doula, G. A. Wade

Physics & Astronomy Faculty Works

We report detailed X-ray observations of the unique binary system ϵ Lupi, the only known short-period binary consisting of two magnetic early-type stars. The components have comparably strong, but anti-aligned magnetic fields. The orbital and magnetic properties of the system imply that the magnetospheres overlap at all orbital phases, suggesting the possibility of variable inter-star magnetospheric interaction due to the non-negligible eccentricity of the orbit. To investigate this effect, we observed the X-ray emission from ϵ Lupi, both near and away from periastron passage, using the Neutron Star Interior Composition Explorer mission (NICER) X-ray Telescope. We find that the system …

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Cryogrinding And Physical Analysis Of A Thermoplastic Polyurethane Elastomer, Timothy E. Whitesell, Marcia Reeves, David L. Helm, Joseph S. Thrasher Jul 2023

Cryogrinding And Physical Analysis Of A Thermoplastic Polyurethane Elastomer, Timothy E. Whitesell, Marcia Reeves, David L. Helm, Joseph S. Thrasher

Chemistry Summer Research Program

No abstract provided.

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Stimuli-Responsive Self-Folding Bilayer Polymer Thin Films, Astrid Garcia, Dimuthu Edirisinghe, Bogdan Oliy, George Chumanov Jul 2023

Stimuli-Responsive Self-Folding Bilayer Polymer Thin Films, Astrid Garcia, Dimuthu Edirisinghe, Bogdan Oliy, George Chumanov

Chemistry Summer Research Program

No abstract provided.

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Exploring Model Dynamics For Accumulative Poisoning Discovery, Jianing Zhu, Xiawei Guo, Jiangchao Yao, Chao Du, Li He, Shuo Yuan, Tongliang Liu, Liang Wang, Bo Han Jul 2023

Exploring Model Dynamics For Accumulative Poisoning Discovery, Jianing Zhu, Xiawei Guo, Jiangchao Yao, Chao Du, Li He, Shuo Yuan, Tongliang Liu, Liang Wang, Bo Han

Machine Learning Faculty Publications

Adversarial poisoning attacks pose huge threats to various machine learning applications. Especially, the recent accumulative poisoning attacks show that it is possible to achieve irreparable harm on models via a sequence of imperceptible attacks followed by a trigger batch. Due to the limited data-level discrepancy in real-time data streaming, current defensive methods are indiscriminate in handling the poison and clean samples. In this paper, we dive into the perspective of model dynamics and propose a novel information measure, namely, Memorization Discrepancy, to explore the defense via the model-level information. By implicitly transferring the changes in the data manipulation to that …

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