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Articles 5281 - 5310 of 302425

Full-Text Articles in Physical Sciences and Mathematics

Seasonal Variability And Predictability Of Monsoon Precipitation In Southern Africa, Matthew F. Horan, Fred Kucharski, Moetasim Ashfaq Mar 2024

Seasonal Variability And Predictability Of Monsoon Precipitation In Southern Africa, Matthew F. Horan, Fred Kucharski, Moetasim Ashfaq

Faculty Publications

Rainfed agriculture is the mainstay of economies across Southern Africa (SA), where most precipitation is received during the austral summer monsoon. This study aims to further our understanding of monsoon precipitation predictability over SA. We use three natural climate forcings, El Niño–Southern Oscillation, Indian Ocean Dipole (IOD), and the Indian Ocean Precipitation Dipole (IOPD)—the dominant precipitation variability mode—to construct an empirical model that exhibits significant skill over SA during monsoon in explaining precipitation variability and in forecasting it with a five-month lead. While most explained precipitation variance (50%–75%) comes from contemporaneous IOD and IOPD, preconditioning all three forcings is key …

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Improving Automatic Transcription Using Natural Language Processing, Anna Kiefer Mar 2024

Improving Automatic Transcription Using Natural Language Processing, Anna Kiefer

Master's Theses

Digital Democracy is a CalMatters and California Polytechnic State University initia-
tive to promote transparency in state government by increasing access to the Califor-
nia legislature. While Digital Democracy is made up of many resources, one founda-
tional step of the project is obtaining accurate, timely transcripts of California Senate
and Assembly hearings. The information extracted from these transcripts provides
crucial data for subsequent steps in the pipeline. In the context of Digital Democracy,
upleveling is when humans verify, correct, and annotate the transcript results after
the legislative hearings have been automatically transcribed. The upleveling process
is done with the …

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Automated Tree Mortality Detection Using Ubiquitously Available Public Data, Michael T. Huggins Mar 2024

Automated Tree Mortality Detection Using Ubiquitously Available Public Data, Michael T. Huggins

Master's Theses

Understanding the dynamic interplay between fire severity, topography, and tree mortality, is crucial for predicting future forest dynamics and enhancing resilience against climate change-induced wildfire regimes. This thesis develops a multi-sensor approach for automated estimation of tree mortality, then applies it to examine trends in tree mortality over a six-year period across a fire affected study site in the Trinity River basin in Northern California. The Random Forest model uses publicly available USGS 3D Elevation Program Lidar (3DEP) and NAIP imagery as inputs and is likely to be easily adaptable to other landscapes. The model had a Receiver Operating Characteristic …

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Thermal, Electrical, And Spin Transport: Encompassing Low-Damping Ferromagnets And Antiferromagnetic/Ferromagnetic Heterostructures, Matthew Ryan Natale Mar 2024

Thermal, Electrical, And Spin Transport: Encompassing Low-Damping Ferromagnets And Antiferromagnetic/Ferromagnetic Heterostructures, Matthew Ryan Natale

Electronic Theses and Dissertations

Continuing technological advancements bring forth escalating challenges in global energy consumption and subsequent power dissipation, posing significant economic and environmental concerns. In response to these difficulties, the fields of thermoelectrics, spintronics, and spincaloritronics emerge as contemporary solutions, each presenting unique advantages. Thermoelectric devices, based on the Seebeck effect, other a passive, carbon-free energy generating solution from waste heat. Although current thermoelectric technology encounters hurdles in achieving optimal efficiencies without intricate designs or complex materials engineering, recently research into low-damping metallic ferromagnetic thin films have provided a new method to enhance spin wave lifetimes, thus contributing to thermoelectric voltage improvements. As …

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Wayne E. Sabbe Arkansas Soil Fertility Studies 2023, Nathan A. Slaton Mar 2024

Wayne E. Sabbe Arkansas Soil Fertility Studies 2023, Nathan A. Slaton

Arkansas Agricultural Experiment Station Research Series

Rapid technological changes in crop management and production require that the research efforts be presented in an expeditious manner. The contributions of soil fertility and fertilizers are major production factors in all Arkansas crops. The studies described within will allow producers to compare their practices with the university’s research efforts. Additionally, soil-test data and fertilizer sales are presented to allow comparisons among years, crops, and other areas within Arkansas.

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Possible Role Of Correlation Coefficients And Network Analysis Of Multiple Intracellular Proteins In Blood Cells Of Patients With Bipolar Disorder In Studying The Mechanism Of Lithium Responsiveness: A Proof-Concept Study, Keming Gao, Marzieh Ayati, Nicholas M. Kaye, Mehmet Koyutürk, Joseph R. Calabrese, Eric Christian, Hillard M. Lazarus, David Kaplan Mar 2024

Possible Role Of Correlation Coefficients And Network Analysis Of Multiple Intracellular Proteins In Blood Cells Of Patients With Bipolar Disorder In Studying The Mechanism Of Lithium Responsiveness: A Proof-Concept Study, Keming Gao, Marzieh Ayati, Nicholas M. Kaye, Mehmet Koyutürk, Joseph R. Calabrese, Eric Christian, Hillard M. Lazarus, David Kaplan

Computer Science Faculty Publications and Presentations

Background: The mechanism of lithium treatment responsiveness in bipolar disorder (BD) remains unclear. The aim of this study was to explore the utility of correlation coefficients and protein-to-protein interaction (PPI) network analyses of intracellular proteins in monocytes and CD4+ lymphocytes of patients with BD in studying the potential mechanism of lithium treatment responsiveness. Methods: Patients with bipolar I or II disorder who were diagnosed with the MINI for DSM-5 and at any phase of the illness with at least mild symptom severity and received lithium (serum level ≥ 0.6 mEq/L) for 16 weeks were divided into two groups, responders (≥50% …

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Non-Relativistic Limit Of Selected Terms From The Sme Dirac Lagrangian, Quinn Reece Mar 2024

Non-Relativistic Limit Of Selected Terms From The Sme Dirac Lagrangian, Quinn Reece

Physics

We examine a selection of individual CPT/Lorentz violating terms present in the relativistic lagrangian for a free spin- 1 2 Dirac fermion of mass m in the Standard Model Extension. Euler-Lagrange relations will be applied to give Dirac-like equations including these terms and a novel procedure will be used to generate non-relativistic limits of these equations which are Schr¨odinger-Pauli-like equations. These equations will be analyzed using classical quantum mechanics toy problems to gain physical intuition for the effects of the CPT violating terms, and the results will be discussed. We will conclude with discussion on future work will include the …

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New Geochemical And Geochronological Insights On Forearc Magmatism Across The Sanak-Baranof Belt, Southern Alaska: A Tale Of Two Belts, Adrian A. Wackett, Diane R. Smith, Cameron Davidson, John I. Garver Mar 2024

New Geochemical And Geochronological Insights On Forearc Magmatism Across The Sanak-Baranof Belt, Southern Alaska: A Tale Of Two Belts, Adrian A. Wackett, Diane R. Smith, Cameron Davidson, John I. Garver

Geosciences Faculty Research

The Sanak- Baranof belt includes a series of near- trench plutons that intrude the outboard Chugach– Prince William terrane over ~2200 km along the southern Alaskan margin. We present new petrological, geochronological, and geochemical data for comagmatic microgranitoid enclaves and granitoid rocks from the Crawfish Inlet (ca. 53– 47 Ma) and Krestof Island (ca. 52 Ma) plutons on Baranof and Krestof Islands, as well as the Mount Stamy (ca. 51 Ma) and Mount Draper (ca. 54– 53 Ma) plutons and associated mafic rocks that intrude the Boundary block at Nunatak Fiord near Yakutat, Alaska, USA. These data suggest that intrusion …

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Method For Calibrating An Arduino-Based Soil Moisture Sensor Using Van Genuchten’S Equation, Victorino A. Bato, John Osbert Alindogan Mar 2024

Method For Calibrating An Arduino-Based Soil Moisture Sensor Using Van Genuchten’S Equation, Victorino A. Bato, John Osbert Alindogan

The Philippine Agricultural Scientist

Instructions on building a do-it-yourself soil moisture sensor using Arduino microcontrollers abound on the Internet. These do-it-yourself soil moisture sensors are favorite projects by both plant and microcontroller enthusiasts because the parts are affordable and the end product is functional. In the absence of proper calibration, these soil moisture sensors cannot generate usable information on the status of plant-available-water in the soil. This research demonstrates the calibration of an Arduino-based soil moisture sensor using the soil moisture characteristic curve of a clay loam soil. Van Genuchten’s model was used to generate an equation and various parameters specific to the soil. …

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Attachment A Demonstration Of Need For Butte Priority Soils Operable Unit (Bpsou) Temporary Construction Surface Water Performance Standards Variance Request For Grove Gulch Sedimentation Bay Remedial Action Site, Josh Bryson Mar 2024

Attachment A Demonstration Of Need For Butte Priority Soils Operable Unit (Bpsou) Temporary Construction Surface Water Performance Standards Variance Request For Grove Gulch Sedimentation Bay Remedial Action Site, Josh Bryson

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

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Xfuzz: Machine Learning Guided Cross-Contract Fuzzing, Yinxing Xue, Jiaming Ye, Wei Zhang, Jun Sun, Lei Ma, Haijun Wang, Jianjun Zhao Mar 2024

Xfuzz: Machine Learning Guided Cross-Contract Fuzzing, Yinxing Xue, Jiaming Ye, Wei Zhang, Jun Sun, Lei Ma, Haijun Wang, Jianjun Zhao

Research Collection School Of Computing and Information Systems

Smart contract transactions are increasingly interleaved by cross-contract calls. While many tools have been developed to identify a common set of vulnerabilities, the cross-contract vulnerability is overlooked by existing tools. Cross-contract vulnerabilities are exploitable bugs that manifest in the presence of more than two interacting contracts. Existing methods are however limited to analyze a maximum of two contracts at the same time. Detecting cross-contract vulnerabilities is highly non-trivial. With multiple interacting contracts, the search space is much larger than that of a single contract. To address this problem, we present xFuzz , a machine learning guided smart contract fuzzing framework. …

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Conditional Neural Heuristic For Multiobjective Vehicle Routing Problems, Mingfeng Fan, Yaoxin Wu, Zhiguang Cao, Wen Song, Guillaume Sartoretti, Huan Liu, Guohua Wu Mar 2024

Conditional Neural Heuristic For Multiobjective Vehicle Routing Problems, Mingfeng Fan, Yaoxin Wu, Zhiguang Cao, Wen Song, Guillaume Sartoretti, Huan Liu, Guohua Wu

Research Collection School Of Computing and Information Systems

Existing neural heuristics for multiobjective vehicle routing problems (MOVRPs) are primarily conditioned on instance context, which failed to appropriately exploit preference and problem size, thus holding back the performance. To thoroughly unleash the potential, we propose a novel conditional neural heuristic (CNH) that fully leverages the instance context, preference, and size with an encoder–decoder structured policy network. Particularly, in our CNH, we design a dual-attention-based encoder to relate preferences and instance contexts, so as to better capture their joint effect on approximating the exact Pareto front (PF). We also design a size-aware decoder based on the sinusoidal encoding to explicitly …

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Strongly Magnetized Accretion In Two Ultracompact Binary Systems, Thomas J. Maccarone, Thomas Kupfer, Edgar Najera Casarrubias, Liliana E. Rivera Sandoval, Aarran W. Shaw, Christoper T. Britt, Jan Van Roestel, David R. Zurek Mar 2024

Strongly Magnetized Accretion In Two Ultracompact Binary Systems, Thomas J. Maccarone, Thomas Kupfer, Edgar Najera Casarrubias, Liliana E. Rivera Sandoval, Aarran W. Shaw, Christoper T. Britt, Jan Van Roestel, David R. Zurek

Physics and Astronomy Faculty Publications and Presentations

We present the discoveries of two of AM CVn systems, Gaia14aae and SDSS J080449.49+161624.8, which show X-ray pulsations at their orbital periods, indicative of magnetically collimated accretion. Both also show indications of higher rates of mass transfer relative to the expectations from binary evolution driven purely by gravitational radiation, based on existing optical data for Gaia14aae, which show a hotter white dwarf temperature than expected from standard evolutionary models, and X-ray data for SDSS J080449.49+161624.8 which show a luminosity 10−100 times higher than those for other AM CVn at similar orbital periods. The higher mass transfer rates could be driven …

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Powerful Radio Sources In The Southern Sky. Iii. First Results Of The Optical Spectroscopic Campaign, A. García-Pérez, H. A. Peña-Herazo, A. Jimenez-Gallardo, V. Chavushyan, F. Massaro, S. V. White, A. Capetti, B. Balmaverde, W. R. Forman, Juan P. Madrid Mar 2024

Powerful Radio Sources In The Southern Sky. Iii. First Results Of The Optical Spectroscopic Campaign, A. García-Pérez, H. A. Peña-Herazo, A. Jimenez-Gallardo, V. Chavushyan, F. Massaro, S. V. White, A. Capetti, B. Balmaverde, W. R. Forman, Juan P. Madrid

Physics and Astronomy Faculty Publications and Presentations

We recently built the G4Jy-3CRE catalog of extragalactic radio sources. This catalog lists 264 powerful radio sources selected with similar criteria to those of the revised Third Cambridge Catalog, but visible from the Southern Hemisphere. A literature search revealed that 119 sources in the G4Jy-3CRE catalog (i.e., 45%) lack a firm spectroscopic redshift measurement. Here, we present a campaign aimed at acquiring optical spectra of G4Jy-3CRE sources and measuring their redshifts. We used single-slit observations obtained with the Víctor Blanco Telescope, the New Technology Telescope, the Southern Astrophysical Research Telescope, and the 2.1 m telescope of the Observatorio Astronómico Nacional …

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Numerical Simulations For Fractional Differential Equations Of Higher Order And A Wright-Type Transformation, Mariana Nacianceno, Tamer Oraby, Hansapani Rodrigo, Y. Sepulveda, Josef A. Sifuentes, Erwin Suazo, T. Stuck, J. Williams Mar 2024

Numerical Simulations For Fractional Differential Equations Of Higher Order And A Wright-Type Transformation, Mariana Nacianceno, Tamer Oraby, Hansapani Rodrigo, Y. Sepulveda, Josef A. Sifuentes, Erwin Suazo, T. Stuck, J. Williams

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

In this work, a new relationship is established between the solutions of higher fractional differential equations and a Wright-type transformation. Solutions could be interpreted as expected values of functions in a random time process. As applications, we solve the fractional beam equation, fractional electric circuits with special functions as external sources, and derive d’Alembert’s formula for the fractional wave equation. Due to this relationship, we present two methods for simulating solutions of fractional differential equations. The two approaches use the interpretation of the Caputo derivative of a function as a Wright-type transformation of the higher derivative of the function. In …

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Supply Is Not Limulus: Research Review Of Horseshoe Crab Conservation In The Face Of Intense Pharmaceutical Demand, Zoya Galeev Mar 2024

Supply Is Not Limulus: Research Review Of Horseshoe Crab Conservation In The Face Of Intense Pharmaceutical Demand, Zoya Galeev

University Honors Theses

Horseshoe crabs are being used by the pharmaceutical industry to conduct endotoxin tests using LAL derived from the organism’s blood to ensure safe medical practice. Their annual collection and bleeding, while not always leading to mortality, affects horseshoe crab behavior and health. This research seeks to understand how the American horseshoe crab, L. polyphemus, is being used by pharmaceutical agencies and the implications that their harvesting has on the industry and the conservation of the species. Studies were collected from the past decade across two databases, Web of Science (WOS) and PubMed, to assess present conservation techniques to reduce …

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Mask2former With Improved Query For Semantic Segmentation In Remote-Sensing Images, Shichen Guo, Qi Wang, Shiming Xiang, Shuwen Wang, Xuezhi Wang Mar 2024

Mask2former With Improved Query For Semantic Segmentation In Remote-Sensing Images, Shichen Guo, Qi Wang, Shiming Xiang, Shuwen Wang, Xuezhi Wang

Computer Science Faculty Publications and Presentations

Semantic segmentation of remote sensing (RS) images is vital in various practical applications, including urban construction planning, natural disaster monitoring, and land resources investigation. However, RS images are captured by airplanes or satellites at high altitudes and long distances, resulting in ground objects of the same category being scattered in various corners of the image. Moreover, objects of different sizes appear simultaneously in RS images. For example, some objects occupy a large area in urban scenes, while others only have small regions. Technically, the above two universal situations pose significant challenges to the segmentation with a high quality for RS …

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Transiam: Aggregating Multi-Modal Visual Features With Locality For Medical Image Segmentation, Xuejian Li, Shiqiang Ma, Junhai Xu, Jijun Tang, Shengfeng He, Fei Guo Mar 2024

Transiam: Aggregating Multi-Modal Visual Features With Locality For Medical Image Segmentation, Xuejian Li, Shiqiang Ma, Junhai Xu, Jijun Tang, Shengfeng He, Fei Guo

Research Collection School Of Computing and Information Systems

Automatic segmentation of medical images plays an important role in the diagnosis of diseases. On single-modal data, convolutional neural networks have demonstrated satisfactory performance. However, multi-modal data encompasses a greater amount of information rather than single-modal data. Multi-modal data can be effectively used to improve the segmentation accuracy of regions of interest by analyzing both spatial and temporal information. In this study, we propose a dual-path segmentation model for multi-modal medical images, named TranSiam. Taking into account that there is a significant diversity between the different modalities, TranSiam employs two parallel CNNs to extract the features which are specific to …

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Iterative Graph Self-Distillation, Hanlin Zhang, Shuai Lin, Weiyang Liu, Pan Zhou, Jian Tang, Xiaodan Liang, Eric Xing Mar 2024

Iterative Graph Self-Distillation, Hanlin Zhang, Shuai Lin, Weiyang Liu, Pan Zhou, Jian Tang, Xiaodan Liang, Eric Xing

Research Collection School Of Computing and Information Systems

Recently, there has been increasing interest in the challenge of how to discriminatively vectorize graphs. To address this, we propose a method called Iterative Graph Self-Distillation (IGSD) which learns graph-level representation in an unsupervised manner through instance discrimination using a self-supervised contrastive learning approach. IGSD involves a teacher-student distillation process that uses graph diffusion augmentations and constructs the teacher model using an exponential moving average of the student model. The intuition behind IGSD is to predict the teacher network representation of the graph pairs under different augmented views. As a natural extension, we also apply IGSD to semi-supervised scenarios by …

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Ditmos: Delving Into Diverse Tiny-Model Selection On Microcontrollers, Xiao Ma, Shengfeng He, Hezhe Qiao, Dong Ma Mar 2024

Ditmos: Delving Into Diverse Tiny-Model Selection On Microcontrollers, Xiao Ma, Shengfeng He, Hezhe Qiao, Dong Ma

Research Collection School Of Computing and Information Systems

Enabling efficient and accurate deep neural network (DNN) inference on microcontrollers is non-trivial due to the constrained on-chip resources. Current methodologies primarily focus on compressing larger models yet at the expense of model accuracy. In this paper, we rethink the problem from the inverse perspective by constructing small/weak models directly and improving their accuracy. Thus, we introduce DiTMoS, a novel DNN training and inference framework with a selectorclassifiers architecture, where the selector routes each input sample to the appropriate classifier for classification. DiTMoS is grounded on a key insight: a composition of weak models can exhibit high diversity and the …

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Community Similarity Based On User Profile Joins, Konstantinos Theocharidis, Hady Wirawan Lauw Mar 2024

Community Similarity Based On User Profile Joins, Konstantinos Theocharidis, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Similarity joins on multidimensional data are crucial operators for recommendation purposes. The classic ��-join problem finds all pairs of points within �� distance to each other among two ��-dimensional datasets. In this paper, we consider a novel and alternative version of ��-join named community similarity based on user profile joins (CSJ). The aim of CSJ problem is, given two communities having a set of ��-dimensional users, to find how similar are the communities by matching every single pair of users (a user can be matched with at most one other user) having an absolute difference of at most �� per …

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Revisiting The Markov Property For Machine Translation, Cunxiao Du, Hao Zhou, Zhaopeng Tu, Jing Jiang Mar 2024

Revisiting The Markov Property For Machine Translation, Cunxiao Du, Hao Zhou, Zhaopeng Tu, Jing Jiang

Research Collection School Of Computing and Information Systems

In this paper, we re-examine the Markov property in the context of neural machine translation. We design a Markov Autoregressive Transformer (MAT) and undertake a comprehensive assessment of its performance across four WMT benchmarks. Our findings indicate that MAT with an order larger than 4 can generate translations with quality on par with that of conventional autoregressive transformers. In addition, counter-intuitively, we also find that the advantages of utilizing a higher-order MAT do not specifically contribute to the translation of longer sentences.

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Monocular Bev Perception Of Road Scenes Via Front-To-Top View Projection, Wenxi Liu, Qi Li, Weixiang Yang, Jiaxin Cai, Yuanhong Yu, Yuexin Ma, Shengfeng He, Jia Pan Mar 2024

Monocular Bev Perception Of Road Scenes Via Front-To-Top View Projection, Wenxi Liu, Qi Li, Weixiang Yang, Jiaxin Cai, Yuanhong Yu, Yuexin Ma, Shengfeng He, Jia Pan

Research Collection School Of Computing and Information Systems

HD map reconstruction is crucial for autonomous driving. LiDAR-based methods are limited due to expensive sensors and time-consuming computation. Camera-based methods usually need to perform road segmentation and view transformation separately, which often causes distortion and missing content. To push the limits of the technology, we present a novel framework that reconstructs a local map formed by road layout and vehicle occupancy in the bird's-eye view given a front-view monocular image only. We propose a front-to-top view projection (FTVP) module, which takes the constraint of cycle consistency between views into account and makes full use of their correlation to strengthen …

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On The Effects Of Information Asymmetry In Digital Currency Trading, Kwansoo Kim, Robert John Kauffman Mar 2024

On The Effects Of Information Asymmetry In Digital Currency Trading, Kwansoo Kim, Robert John Kauffman

Research Collection School Of Computing and Information Systems

We report on two studies that examine how social sentiment influences information asymmetry in digital currency markets. We also assess whether cryptocurrency can be an investment vehicle, as opposed to only an instrument for asset speculation. Using a dataset on transactions from an exchange in South Korea and sentiment from Korean social media in 2018, we conducted a study of different trading behavior under two cryptocurrency trading market microstructures: a bid-ask spread dealer's market and a continuous trading buy-sell, immediate trade execution market. Our results highlight the impacts of positive and negative trader social sentiment valences on the effects of …

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T-Sciq: Teaching Multimodal Chain-Of-Thought Reasoning Via Large Language Model Signals For Science Question Answering, Lei Wang, Yi Hu, Jiabang He, Xing Xu, Ning Liu, Hui Liu, Heng Tao Shen Mar 2024

T-Sciq: Teaching Multimodal Chain-Of-Thought Reasoning Via Large Language Model Signals For Science Question Answering, Lei Wang, Yi Hu, Jiabang He, Xing Xu, Ning Liu, Hui Liu, Heng Tao Shen

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have recently demonstrated exceptional performance in various Natural Language Processing (NLP) tasks. They have also shown the ability to perform chain-of-thought (CoT) reasoning to solve complex problems. Recent studies have explored CoT reasoning in complex multimodal scenarios, such as the science question answering task, by fine-tuning multimodal models with high-quality human-annotated CoT rationales. However, collecting high-quality COT rationales is usually time-consuming and costly. Besides, the annotated rationales are hardly accurate due to the external essential information missed. To address these issues, we propose a novel method termed T-SciQ that aims at teaching science question answering with …

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Test-Time Augmentation For 3d Point Cloud Classification And Segmentation, Tuan-Anh Vu, Srinjay Sarkar, Zhiyuan Zhang, Binh-Son Hua, Sai-Kit Yeung Mar 2024

Test-Time Augmentation For 3d Point Cloud Classification And Segmentation, Tuan-Anh Vu, Srinjay Sarkar, Zhiyuan Zhang, Binh-Son Hua, Sai-Kit Yeung

Research Collection School Of Computing and Information Systems

Data augmentation is a powerful technique to enhance the performance of a deep learning task but has received less attention in 3D deep learning. It is well known that when 3D shapes are sparsely represented with low point density, the performance of the downstream tasks drops significantly. This work explores test-time augmentation (TTA) for 3D point clouds. We are inspired by the recent revolution of learning implicit representation and point cloud upsampling, which can produce high-quality 3D surface reconstruction and proximity-to-surface, respectively. Our idea is to leverage the implicit field reconstruction or point cloud upsampling techniques as a systematic way …

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Towards Understanding Convergence And Generalization Of Adamw, Pan Zhou, Xingyu Xie, Zhouchen Lin, Shuicheng Yan Mar 2024

Towards Understanding Convergence And Generalization Of Adamw, Pan Zhou, Xingyu Xie, Zhouchen Lin, Shuicheng Yan

Research Collection School Of Computing and Information Systems

AdamW modifies Adam by adding a decoupled weight decay to decay network weights per training iteration. For adaptive algorithms, this decoupled weight decay does not affect specific optimization steps, and differs from the widely used ℓ2-regularizer which changes optimization steps via changing the first- and second-order gradient moments. Despite its great practical success, for AdamW, its convergence behavior and generalization improvement over Adam and ℓ2-regularized Adam (ℓ2-Adam) remain absent yet. To solve this issue, we prove the convergence of AdamW and justify its generalization advantages over Adam and ℓ2-Adam. Specifically, AdamW provably converges but minimizes a dynamically regularized loss that …

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Attack As Detection: Using Adversarial Attack Methods To Detect Abnormal Examples, Zhe Zhao, Guangke Chen, Tong Liu, Taishan Li, Fu Song, Jingyi Wang, Jun Sun Mar 2024

Attack As Detection: Using Adversarial Attack Methods To Detect Abnormal Examples, Zhe Zhao, Guangke Chen, Tong Liu, Taishan Li, Fu Song, Jingyi Wang, Jun Sun

Research Collection School Of Computing and Information Systems

As a new programming paradigm, deep learning (DL) has achieved impressive performance in areas such as image processing and speech recognition, and has expanded its application to solve many real-world problems. However, neural networks and DL are normally black-box systems; even worse, DL-based software are vulnerable to threats from abnormal examples, such as adversarial and backdoored examples constructed by attackers with malicious intentions as well as unintentionally mislabeled samples. Therefore, it is important and urgent to detect such abnormal examples. Although various detection approaches have been proposed respectively addressing some specific types of abnormal examples, they suffer from some limitations; …

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Ur2m: Uncertainty And Resource-Aware Event Detection On Microcontrollers, Hong Jia, Young D. Kwon, Dong Ma, Nhat Pham, Lorena Qendro, Tam Vu, Cecilia Mascolo Mar 2024

Ur2m: Uncertainty And Resource-Aware Event Detection On Microcontrollers, Hong Jia, Young D. Kwon, Dong Ma, Nhat Pham, Lorena Qendro, Tam Vu, Cecilia Mascolo

Research Collection School Of Computing and Information Systems

Traditional machine learning techniques are prone to generating inaccurate predictions when confronted with shifts in the distribution of data between the training and testing phases. This vulnerability can lead to severe consequences, especially in applications such as mobile healthcare. Uncertainty estimation has the potential to mitigate this issue by assessing the reliability of a model's output. However, existing uncertainty estimation techniques often require substantial computational resources and memory, making them impractical for implementation on microcontrollers (MCUs). This limitation hinders the feasibility of many important on-device wearable event detection (WED) applications, such as heart attack detection. In this paper, we present …

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High-Precision U-Pb Geochronology Links Magmatism In The Southwestern Laurentia Large Igneous Province And Midcontinent Rift, M. T. Mohr, M. D. Schmitz, N. L. Swanson-Hysell, K. E. Karlstrom, F. A. Macdonald, M. E. Holland, Y. Zhang, N. S. Anderson Mar 2024

High-Precision U-Pb Geochronology Links Magmatism In The Southwestern Laurentia Large Igneous Province And Midcontinent Rift, M. T. Mohr, M. D. Schmitz, N. L. Swanson-Hysell, K. E. Karlstrom, F. A. Macdonald, M. E. Holland, Y. Zhang, N. S. Anderson

Geosciences Faculty Publications and Presentations

The Southwestern Laurentia large igneous province (SWLLIP) comprises voluminous, widespread ca 1.1 Ga magmatism in the southwestern United States and northern Mexico. The timing and tempo of SWLLIP magmatism and its relationship to other late Mesoproterozoic igneous provinces have been unclear due to difficulties in dating mafic rocks at high precision. New precise U-Pb zircon dates for comagmatic felsic segregations within mafic rocks reveal distinct magmatic episodes at ca. 1098 Ma (represented by massive sills in Death Valley, California, the Grand Canyon, and central Arizona) and ca. 1083 Ma (represented by the Cardenas Basalts in the Grand Canyon and a …

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