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2023

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Articles 691 - 720 of 12577

Full-Text Articles in Physical Sciences and Mathematics

A Multidisciplinary Approach To Resolving The End-Guadalupian Extinction, Christopher R. Fielding, Scott E. Bryan, James L. Crowley, Tracy D. Frank, Michael T. Hren, Chris Mays, Stephen Mcloughlin, Jun Shen, Peter J. Wagner, Arne Winguth, Cornelia Winguth Dec 2023

A Multidisciplinary Approach To Resolving The End-Guadalupian Extinction, Christopher R. Fielding, Scott E. Bryan, James L. Crowley, Tracy D. Frank, Michael T. Hren, Chris Mays, Stephen Mcloughlin, Jun Shen, Peter J. Wagner, Arne Winguth, Cornelia Winguth

Geosciences Faculty Publications and Presentations

The transition from the middle to late Permian (Guadalupian–Lopingian) is claimed to record one or more extinction events that rival the ‘Big Five’ in terms of depletion of biological diversity and reorganization of ecosystem structure. Yet many questions remain as to whether the events recorded in separate regions were synchronous, causally related, or were of a magnitude rivaling other major crises in Earth's history. In this paper, we survey some major unresolved issues related to the Guadalupian–Lopingian transition and offer a multidisciplinary approach to advance understanding of this under-appreciated biotic crisis by utilizing records in Southern Hemisphere high-palaeolatitude settings. We …

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The Surface Atmosphere Integrated Field Laboratory (Sail) Campaign, D. R. Feldman, A. C. Aiken, W. R. Boos, R. W. H. Carroll, V. Chandrasekar, S. Collis, J. M. Creamean, G. De Boer, J. Deems, P. J. Demott, J. Fan, A. N. Flores, D. Gochis, M. Grover, T. C. J. Hill, A. Hodshire, E. Hulm, C. C. Hume, R. Jackson, F. Junyent, A. Kennedy, M. Kumjian, E. J. T. Levin, J. D. Lundquist, J. O'Brien, M. S. Raleigh, J. Reithel, A. Rhoades, K. Rittger, W. Rudisill, Z. Sherman, E. Siirila-Woodburn, S. M. Skiles, J. N. Smith, R. C. Sullivan, A. Theisen, M. Tuftedal, A. C. Varble, A. Wiedlea, S. Wielandt, K. Williams, Z. Xu Dec 2023

The Surface Atmosphere Integrated Field Laboratory (Sail) Campaign, D. R. Feldman, A. C. Aiken, W. R. Boos, R. W. H. Carroll, V. Chandrasekar, S. Collis, J. M. Creamean, G. De Boer, J. Deems, P. J. Demott, J. Fan, A. N. Flores, D. Gochis, M. Grover, T. C. J. Hill, A. Hodshire, E. Hulm, C. C. Hume, R. Jackson, F. Junyent, A. Kennedy, M. Kumjian, E. J. T. Levin, J. D. Lundquist, J. O'Brien, M. S. Raleigh, J. Reithel, A. Rhoades, K. Rittger, W. Rudisill, Z. Sherman, E. Siirila-Woodburn, S. M. Skiles, J. N. Smith, R. C. Sullivan, A. Theisen, M. Tuftedal, A. C. Varble, A. Wiedlea, S. Wielandt, K. Williams, Z. Xu

Geosciences Faculty Publications and Presentations

The science of mountainous hydrology spans the atmosphere through the bedrock and inherently crosses physical and disciplinary boundaries: land–atmosphere interactions in complex terrain enhance clouds and precipitation, while watersheds retain and release water over a large range of spatial and temporal scales. Limited observations in complex terrain challenge efforts to improve predictive models of the hydrology in the face of rapid changes. The Upper Colorado River exemplifies these challenges, especially with ongoing mismatches between precipitation, snowpack, and discharge. Consequently, the U.S. Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) user facility has deployed an observatory to the East River Watershed …

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Janus: Toward Preventing Counterfeits In Supply Chains Utilizing A Multi-Quorum Blockchain, Vika Crossland, Connor Dellwo, Golam Bashar, Gaby G. Dagher Dec 2023

Janus: Toward Preventing Counterfeits In Supply Chains Utilizing A Multi-Quorum Blockchain, Vika Crossland, Connor Dellwo, Golam Bashar, Gaby G. Dagher

Computer Science Faculty Publications and Presentations

The modern pharmaceutical supply chain lacks transparency and traceability, resulting in alarming rates of counterfeit products entering the market. These illegitimate products cause harm to end users and wreak havoc on the supply chain itself, costing billions of dollars in profit loss. In this paper, in response to the Drug Supply Chain Security Act (DSCSA), we introduce Janus, a novel pharmaceutical track-and-trace system that utilizes blockchain and cloning-resistant hologram tags to prevent counterfeits from entering the pharmaceutical supply chain. We design a multi-quorum consensus protocol that achieves load balancing across the network. We perform a security analysis to show robustness …

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Experimental Analysis Of Nonlinear Wave Propagation In Bistable Mechanical Metamaterials With A Defect, Samuel R. Harre Dec 2023

Experimental Analysis Of Nonlinear Wave Propagation In Bistable Mechanical Metamaterials With A Defect, Samuel R. Harre

Department of Mechanical and Materials Engineering: Dissertations, Theses, and Student Research

Mechanical metamaterials built up of compliant units can support the propagation of linear and nonlinear waves. A popular architecture consists of a one-dimensional chain of bistable elements connected by linear springs. This type of chain can support nonlinear transition waves that switch each element from one stable state to the other as they propagate along the chain. One way to manipulate the propagation of such waves is via introduction of a local inhomogeneity, i.e., a defect in the otherwise periodic chain. Recent analytical and numerical work has shown that based on its initial velocity, a transition wave may be reflected, …

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2023 December - Tennessee Monthly Climate Report, Tennessee Climate Office, East Tennessee State University Dec 2023

2023 December - Tennessee Monthly Climate Report, Tennessee Climate Office, East Tennessee State University

Tennessee Climate Office Monthly Report

No abstract provided.

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Development, Voice, And Vulnerability: A Rhetorical Analysis Of The Policy-Making Discourse Regarding The Paris Agreement As An Organizational Response To Climate Change, David Almanza-Canas Dec 2023

Development, Voice, And Vulnerability: A Rhetorical Analysis Of The Policy-Making Discourse Regarding The Paris Agreement As An Organizational Response To Climate Change, David Almanza-Canas

UNLV Theses, Dissertations, Professional Papers, and Capstones

On December 12, 2015, the Paris Agreement was officially ratified by 196 sovereign entities. This treaty represents a global call to action to ameliorate the impact of human activities on our environment, and it creates a means of cooperation through financial support and transparent industrial practices with the goal of promoting accountability across the world. This treaty and the discourse surrounding it present fertile ground for the academic understanding of persuasive practices in policy-making. By examining the rhetorical implications of the Paris Agreement as a global policy, scholars can gain new insight about the communities represented in the conversation as …

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Impact Of Weather Factors On Airport Arrival Rates: Application Of Machine Learning In Air Transportation, Robert W. Maxson, Dothang Truong, Woojin Choi Dec 2023

Impact Of Weather Factors On Airport Arrival Rates: Application Of Machine Learning In Air Transportation, Robert W. Maxson, Dothang Truong, Woojin Choi

Publications

Weather is responsible for approximately 70% of air transportation delays in the National Airspace System, and delays resulting from convective weather alone cost airlines and passengers millions of dollars each year due to delays that could be avoided. This research sought to establish relationships between environmental variables and airport efficiency estimates by data mining archived weather and airport performance data at ten geographically and climatologically different airports. Several meaningful relationships were discovered from six out of ten airports using various machine learning methods within an overarching data mining protocol, and the developed models were tested using historical data.

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An Evaluation Of Lead Concentration In Commercially Available Tiles, Daidre N. Gamboa Dec 2023

An Evaluation Of Lead Concentration In Commercially Available Tiles, Daidre N. Gamboa

UNLV Theses, Dissertations, Professional Papers, and Capstones

Workers in the manufacturing and construction industries are at risk of lead exposure in projects that involve removal or installation of tiles, and other individuals exposed to lead, including children may also be at risk. The concern with lead in tiles is thought to be related to the glazing process or the country where the tile was produced. Multiple regulations are in place in the U.S. to protect people from lead exposure, including through surface coatings or painted surfaces. But these regulations do not cover glazed tiles. This study examined whether there are differences in lead concentration in (1) tiles …

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Beyond Corporate Greenwashing: Discourse Of A 'Just' Electric Energy Transition Materialized At The Thacker Pass Lithium Mine, Laekyn Kelley Dec 2023

Beyond Corporate Greenwashing: Discourse Of A 'Just' Electric Energy Transition Materialized At The Thacker Pass Lithium Mine, Laekyn Kelley

UNLV Theses, Dissertations, Professional Papers, and Capstones

Thacker Pass in Northern Nevada is a rich desert ecosystem with spiritual significance to local Indigenous peoples, and it is also the site for what will be, for now, the United States’ largest open-pit lithium mine. Lithium is one mineral constituent of electric batteries which are essential to current U.S. electric energy transition policy, a transition which policymakers and other public groups have called on to be done in a way which is just. However, what exactly a just electric energy transition looks like in places like Thacker Pass is under continued negotiation in theoretical and practical senses. Existing research …

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An Efficient And Chemoselective Method To Generate Arynes, Bryan Metze, Riley A. Roberts, Aleksandra Nilova, David R. Stuart Dec 2023

An Efficient And Chemoselective Method To Generate Arynes, Bryan Metze, Riley A. Roberts, Aleksandra Nilova, David R. Stuart

Chemistry Faculty Publications and Presentations

Arynes hold immense potential as reactive intermediates in organic synthesis as they engage in a diverse range of mechanistically distinct chemical reactions. However, the poor functional group compatibility of generating arynes or their precursors has stymied their widespread use. Here, we show that generating arynes by deprotonation of an arene and elimination of an “onium” leaving group is mild, efficient and broad in scope. This is achieved by using aryl(TMP)iodonium salts (TMP = 2,4,6-trimethoxyphenyl) as the aryne precursor and potassium phosphate as the base, and a range of arynophiles are compatible. Additionally, we have performed the first quantitative analysis of …

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Character Amenability Of Vector-Valued Algebras, Terje Hill, David Robbins Dec 2023

Character Amenability Of Vector-Valued Algebras, Terje Hill, David Robbins

Faculty Scholarship

Let {Ax:x∈X} be a collection of complex Banach algebras indexed by the compact Hausdorff space X. We investigate the character amenability of certain algebras A of Ax-valued functions in relation to the character amenability of the Ax.

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Deep Isolation Forest For Anomaly Detection, Hongzuo Xu, Guansong Pang, Yijie Wang, Yongjun Wang Dec 2023

Deep Isolation Forest For Anomaly Detection, Hongzuo Xu, Guansong Pang, Yijie Wang, Yongjun Wang

Research Collection School Of Computing and Information Systems

Isolation forest (iForest) has been emerging as arguably the most popular anomaly detector in recent years due to its general effectiveness across different benchmarks and strong scalability. Nevertheless, its linear axis-parallel isolation method often leads to (i) failure in detecting hard anomalies that are difficult to isolate in high-dimensional/non-linear-separable data space, and (ii) notorious algorithmic bias that assigns unexpectedly lower anomaly scores to artefact regions. These issues contribute to high false negative errors. Several iForest extensions are introduced, but they essentially still employ shallow, linear data partition, restricting their power in isolating true anomalies. Therefore, this paper proposes deep isolation …

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Neural Airport Ground Handling, Yaoxin Wu, Jianan Zhou, Yunwen Xia, Xianli Zhang, Zhiguang Cao, Jie Zhang Dec 2023

Neural Airport Ground Handling, Yaoxin Wu, Jianan Zhou, Yunwen Xia, Xianli Zhang, Zhiguang Cao, Jie Zhang

Research Collection School Of Computing and Information Systems

Airport ground handling (AGH) offers necessary operations to flights during their turnarounds and is of great importance to the efficiency of airport management and the economics of aviation. Such a problem involves the interplay among the operations that leads to NP-hard problems with complex constraints. Hence, existing methods for AGH are usually designed with massive domain knowledge but still fail to yield high-quality solutions efficiently. In this paper, we aim to enhance the solution quality and computation efficiency for solving AGH. Particularly, we first model AGH as a multiple-fleet vehicle routing problem (VRP) with miscellaneous constraints including precedence, time windows, …

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The Value Of Official Website Information In The Credit Risk Evaluation Of Smes, Cuiqing Jiang, Chang Yin, Qian Tang, Zhao Wang Dec 2023

The Value Of Official Website Information In The Credit Risk Evaluation Of Smes, Cuiqing Jiang, Chang Yin, Qian Tang, Zhao Wang

Research Collection School Of Computing and Information Systems

The official websites of small and medium-sized enterprises (SMEs) not only reflect the willingness of an enterprise to disclose information voluntarily, but also can provide information related to the enterprises’ historical operations and performance. This research investigates the value of official website information in the credit risk evaluation of SMEs. To study the effect of different kinds of website information on credit risk evaluation, we propose a framework to mine effective features from two kinds of information disclosed on the official website of a SME—design-based information and content-based information—in predicting its credit risk. We select the SMEs in the software …

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Estimating Propensity For Causality-Based Recommendation Without Exposure Data, Zhongzhou Liu, Yuan Fang, Min Wu Dec 2023

Estimating Propensity For Causality-Based Recommendation Without Exposure Data, Zhongzhou Liu, Yuan Fang, Min Wu

Research Collection School Of Computing and Information Systems

Causality-based recommendation systems focus on the causal effects of user-item interactions resulting from item exposure (i.e., which items are recommended or exposed to the user), as opposed to conventional correlation-based recommendation. They are gaining popularity due to their multi-sided benefits to users, sellers and platforms alike. However, existing causality-based recommendation methods require additional input in the form of exposure data and/or propensity scores (i.e., the probability of exposure) for training. Such data, crucial for modeling causality in recommendation, are often not available in real-world situations due to technical or privacy constraints. In this paper, we bridge the gap by proposing …

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Meet The Staff Dec 2023

Meet The Staff

The Synapse: Intercollegiate science magazine

No abstract provided.

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Graph Contrastive Learning With Stable And Scalable Spectral Encoding, Deyu Bo, Yuan Fang, Yang Liu, Chuan Shi Dec 2023

Graph Contrastive Learning With Stable And Scalable Spectral Encoding, Deyu Bo, Yuan Fang, Yang Liu, Chuan Shi

Research Collection School Of Computing and Information Systems

Graph contrastive learning (GCL) aims to learn representations by capturing the agreements between different graph views. Traditional GCL methods generate views in the spatial domain, but it has been recently discovered that the spectral domain also plays a vital role in complementing spatial views. However, existing spectral-based graph views either ignore the eigenvectors that encode valuable positional information, or suffer from high complexity when trying to address the instability of spectral features. To tackle these challenges, we first design an informative, stable, and scalable spectral encoder, termed EigenMLP, to learn effective representations from the spectral features. Theoretically, EigenMLP is invariant …

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Memory Network-Based Interpreter Of User Preferences In Content-Aware Recommender Systems, Nhu Thuat Tran, Hady W. Lauw Dec 2023

Memory Network-Based Interpreter Of User Preferences In Content-Aware Recommender Systems, Nhu Thuat Tran, Hady W. Lauw

Research Collection School Of Computing and Information Systems

This article introduces a novel architecture for two objectives recommendation and interpretability in a unified model. We leverage textual content as a source of interpretability in content-aware recommender systems. The goal is to characterize user preferences with a set of human-understandable attributes, each is described by a single word, enabling comprehension of user interests behind item adoptions. This is achieved via a dedicated architecture, which is interpretable by design, involving two components for recommendation and interpretation. In particular, we seek an interpreter, which accepts holistic user’s representation from a recommender to output a set of activated attributes describing user preferences. …

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Rome: Evaluating Pre-Trained Vision-Language Models On Reasoning Beyond Visual Common Sense, Kankan Zhou, Eason Lai, Au Wei Bin Yeong, Kyriakos Mouratidis, Jing Jiang Dec 2023

Rome: Evaluating Pre-Trained Vision-Language Models On Reasoning Beyond Visual Common Sense, Kankan Zhou, Eason Lai, Au Wei Bin Yeong, Kyriakos Mouratidis, Jing Jiang

Research Collection School Of Computing and Information Systems

Humans possess a strong capability for reasoning beyond common sense. For example, given an unconventional image of a goldfish laying on the table next to an empty fishbowl, a human would effortlessly determine that the fish is not inside the fishbowl. The case, however, may be different for a vision-language model, whose reasoning could gravitate towards the common scenario that the fish is inside the bowl, despite the visual input. In this paper, we introduce a novel probing dataset named ROME (reasoning beyond commonsense knowledge) to evaluate whether the state-of-the-art pre-trained vision-language models have the reasoning capability to correctly interpret …

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Monocular Depth Estimation For Glass Walls With Context: A New Dataset And Method, Yuan Liang, Bailin Deng, Wenxi Liu, Jing Qin, Shengfeng He Dec 2023

Monocular Depth Estimation For Glass Walls With Context: A New Dataset And Method, Yuan Liang, Bailin Deng, Wenxi Liu, Jing Qin, Shengfeng He

Research Collection School Of Computing and Information Systems

Traditional monocular depth estimation assumes that all objects are reliably visible in the RGB color domain. However, this is not always the case as more and more buildings are decorated with transparent glass walls. This problem has not been explored due to the difficulties in annotating the depth levels of glass walls, as commercial depth sensors cannot provide correct feedbacks on transparent objects. Furthermore, estimating depths from transparent glass walls requires the aids of surrounding context, which has not been considered in prior works. To cope with this problem, we introduce the first Glass Walls Depth Dataset (GW-Depth dataset). We …

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Examining The Inter-Consistency Of Large Language Models: An In-Depth Analysis Via Debate, Kai Xiong, Xiao Ding, Yixin Cao, Ting Liu, Bing Qin Dec 2023

Examining The Inter-Consistency Of Large Language Models: An In-Depth Analysis Via Debate, Kai Xiong, Xiao Ding, Yixin Cao, Ting Liu, Bing Qin

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have shown impressive capabilities in various applications, but they still face various inconsistency issues. Existing works primarily focus on the inconsistency issues within a single LLM, while we complementarily explore the inter-consistency among multiple LLMs for collaboration. To examine whether LLMs can collaborate effectively to achieve a consensus for a shared goal, we focus on commonsense reasoning, and introduce a formal debate framework (FORD) to conduct a three-stage debate among LLMs with real-world scenarios alignment: fair debate, mismatched debate, and roundtable debate. Through extensive experiments on various datasets, LLMs can effectively collaborate to reach a consensus …

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Robust Prompt Optimization For Large Language Models Against Distribution Shifts, Moxin Li, Wenjie Wang, Fuli Feng, Yixin Cao, Jizhi Zhang, Tat-Seng Chua Dec 2023

Robust Prompt Optimization For Large Language Models Against Distribution Shifts, Moxin Li, Wenjie Wang, Fuli Feng, Yixin Cao, Jizhi Zhang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Large Language Model (LLM) has demonstrated significant ability in various Natural Language Processing tasks. However, their effectiveness is highly dependent on the phrasing of the task prompt, leading to research on automatic prompt optimization using labeled task data. We reveal that these prompt optimization techniques are vulnerable to distribution shifts such as subpopulation shifts, which are common for LLMs in real-world scenarios such as customer reviews analysis. In this light, we propose a new problem of robust prompt optimization for LLMs against distribution shifts, which requires the prompt optimized over the labeled source group can simultaneously generalize to an unlabeled …

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Molca: Molecular Graph-Language Modeling With Cross-Modal Projector And Uni-Modal Adapter, Zhiyuan Liu, Sihang Li, Yanchen Luo, Hao Fei, Yixin Cao, Kenji Kawaguchi, Xiang Wang, Tat-Seng Chua Dec 2023

Molca: Molecular Graph-Language Modeling With Cross-Modal Projector And Uni-Modal Adapter, Zhiyuan Liu, Sihang Li, Yanchen Luo, Hao Fei, Yixin Cao, Kenji Kawaguchi, Xiang Wang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Language Models (LMs) have demonstrated impressive molecule understanding ability on various 1D text-related tasks. However, they inherently lack 2D graph perception — a critical ability of human professionals in comprehending molecules’ topological structures. To bridge this gap, we propose MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter. MolCA enables an LM (i.e., Galactica) to understand both text- and graph-based molecular contents via the cross-modal projector. Specifically, the cross-modal projector is implemented as a QFormer to connect a graph encoder’s representation space and an LM’s text space. Further, MolCA employs a uni-modal adapter (i.e., LoRA) for the LM’s efficient …

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Covariance-Based Causal Debiasing For Entity And Relation Extraction, Lin Ren, Yongbin Liu, Yixin Cao, Chunping Ouyang Dec 2023

Covariance-Based Causal Debiasing For Entity And Relation Extraction, Lin Ren, Yongbin Liu, Yixin Cao, Chunping Ouyang

Research Collection School Of Computing and Information Systems

Joint entity and relation extraction tasks aim to recognize named entities and extract relations simultaneously. Suffering from a variety of data biases, such as data selection bias, and distribution bias (out of distribution, long-tail distribution), serious concerns can be witnessed to threaten the model’s transferability, robustness, and generalization. In this work, we address the above problems from a causality perspective. We propose a novel causal framework called covariance and variance optimization framework (OVO) to optimize feature representations and conduct general debiasing. In particular, the proposed covariance optimizing (COP) minimizes characterizing features’ covariance for alleviating the selection and distribution bias and …

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Ensemble-Based Deep Reinforcement Learning For Vehicle Routing Problems Under Distribution Shift, Yuan Jiang, Zhiguang Cao, Yaoxin Wu, Wen Song, Jie Zhang Dec 2023

Ensemble-Based Deep Reinforcement Learning For Vehicle Routing Problems Under Distribution Shift, Yuan Jiang, Zhiguang Cao, Yaoxin Wu, Wen Song, Jie Zhang

Research Collection School Of Computing and Information Systems

While performing favourably on the independent and identically distributed (i.i.d.) instances, most of the existing neural methods for vehicle routing problems (VRPs) struggle to generalize in the presence of a distribution shift. To tackle this issue, we propose an ensemble-based deep reinforcement learning method for VRPs, which learns a group of diverse sub-policies to cope with various instance distributions. In particular, to prevent convergence of the parameters to the same one, we enforce diversity across sub-policies by leveraging Bootstrap with random initialization. Moreover, we also explicitly pursue inequality between sub-policies by exploiting regularization terms during training to further enhance diversity. …

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Knowledge Graph Enhanced Aspect-Based Sentiment Analysis Incorporating External Knowledge, Autumn Teo, Zhaoxia Wang, Haibo Pen, Budhitama Subagdja, Seng-Beng Ho, Boon Kiat Quek Dec 2023

Knowledge Graph Enhanced Aspect-Based Sentiment Analysis Incorporating External Knowledge, Autumn Teo, Zhaoxia Wang, Haibo Pen, Budhitama Subagdja, Seng-Beng Ho, Boon Kiat Quek

Research Collection School Of Computing and Information Systems

Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. To better comprehend long complicated sentences and obtain accurate aspect-specific information, linguistic and commonsense knowledge are generally required in this task. However, most current methods employ complicated and inefficient approaches to incorporate external knowledge, e.g., directly searching the graph nodes. Additionally, the complementarity between external knowledge and linguistic information has not been thoroughly studied. To this end, we propose a knowledge graph augmented network (KGAN), which aims to effectively incorporate external knowledge with explicitly syntactic and contextual information. In particular, KGAN captures the sentiment feature representations from multiple different …

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Wsdms: Debunk Fake News Via Weakly Supervised Detection Of Misinforming Sentences With Contextualized Social Wisdom, Ruichao Yang, Wei Gao, Jing Ma, Hongzhan Lin, Zhiwei Yang Dec 2023

Wsdms: Debunk Fake News Via Weakly Supervised Detection Of Misinforming Sentences With Contextualized Social Wisdom, Ruichao Yang, Wei Gao, Jing Ma, Hongzhan Lin, Zhiwei Yang

Research Collection School Of Computing and Information Systems

In recent years, we witness the explosion of false and unconfirmed information (i.e., rumors) that went viral on social media and shocked the public. Rumors can trigger versatile, mostly controversial stance expressions among social media users. Rumor verification and stance detection are different yet relevant tasks. Fake news debunking primarily focuses on determining the truthfulness of news articles, which oversimplifies the issue as fake news often combines elements of both truth and falsehood. Thus, it becomes crucial to identify specific instances of misinformation within the articles. In this research, we investigate a novel task in the field of fake news …

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Disentangling Transformer Language Models As Superposed Topic Models, Jia Peng Lim, Hady Wirawan Lauw Dec 2023

Disentangling Transformer Language Models As Superposed Topic Models, Jia Peng Lim, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Topic Modelling is an established research area where the quality of a given topic is measured using coherence metrics. Often, we infer topics from Neural Topic Models (NTM) by interpreting their decoder weights, consisting of top-activated words projected from individual neurons. Transformer-based Language Models (TLM) similarly consist of decoder weights. However, due to its hypothesised superposition properties, the final logits originating from the residual path are considered uninterpretable. Therefore, we posit that we can interpret TLM as superposed NTM by proposing a novel weight-based, model-agnostic and corpus-agnostic approach to search and disentangle decoder-only TLM, potentially mapping individual neurons to multiple …

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Generalized Logit Adjustment: Calibrating Fine-Tuned Models By Removing Label Bias In Foundation Models, Beier Zhu, Kaihua Tang, Qianru Sun, Hanwang Zhang Dec 2023

Generalized Logit Adjustment: Calibrating Fine-Tuned Models By Removing Label Bias In Foundation Models, Beier Zhu, Kaihua Tang, Qianru Sun, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Foundation models like CLIP allow zero-shot transfer on various tasks without additional training data. Yet, the zero-shot performance is less competitive than a fully supervised one. Thus, to enhance the performance, fine-tuning and ensembling are also commonly adopted to better fit the downstream tasks. However, we argue that such prior work has overlooked the inherent biases in foundation models. Due to the highly imbalanced Web-scale training set, these foundation models are inevitably skewed toward frequent semantics, and thus the subsequent fine-tuning or ensembling is still biased. In this study, we systematically examine the biases in foundation models and demonstrate the …

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Refinement-Based Specification And Analysis Of Multi-Core Arinc 653 Using Event-B, Feng Zhang, Leping Zhang, Yongwang Zhao, Yang Liu, Jun Sun Dec 2023

Refinement-Based Specification And Analysis Of Multi-Core Arinc 653 Using Event-B, Feng Zhang, Leping Zhang, Yongwang Zhao, Yang Liu, Jun Sun

Research Collection School Of Computing and Information Systems

ARINC 653 as the de facto standard of partitioning operating systems has been applied in many safety-critical domains. The multi-core version of ARINC 653, ARINC 653 Part 1-4 (Version 4), provides support for services to be utilized with a module that contains multiple processor cores. Formal specification and analysis of this standard document could provide a rigorous specification and uncover concealed errors in the textual description of service requirements. This article proposes a specification method for concurrency on a multi-core platform using Event-B, and a refinement structure for the complicated ARINC 653 Part 1-4 provides a comprehensive, stepwise refinement-based Event-B …

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