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Aquarius, Hannah Fuller

Gandy Dancer Archives

No abstract provided.

Extraction Of Anthocyanins From Purple Sweet Potato Using Supercritical Carbon Dioxide And Conventional Approaches, Gabriel Laquete De Barros, Arda Tuhanioglu, Sumanjot Kaur, Leonardo Lora, Ali Ubeyitogullari

Food Science Faculty Publications and Presentations

This research investigates the potential of supercritical carbon dioxide (SC-CO₂) technology as a green and sustainable approach to improve anthocyanin extraction from purple sweet potatoes (PSP). The study explored different extraction parameters (i.e., temperatures of 35–55 °C, cosolvent concentrations of 10–30 % (w/w), ethanol ratios of 30–70 % (v/v), pressures of 30–40 MPa, extraction times of 120–180 min, and solvent: sample ratios of 25:1–45:1(v/w)), to optimize the total phenolic content (TPC), anthocyanin content (ANC), and antioxidant activity (AA). Additionally, conventional extraction methods using different solvent (ethanol and methanol) mixtures, solvent: sample ratios (15:1; 45:1; 6:1 (v/w)), temperatures (35–50 …

The Association Between Childhood Trauma And Adulthood Depression And Posttraumatic Symptoms. Mediated Through Emotion Regulation And Perceived Social Support., Josue David Luna Jr.

Theses and Dissertations

Depression and posttraumatic stress are two psychological consequences commonly reported by adults exposed to adverse childhood experiences. High prevalence of childhood adversities in the United States is a significant concern. Despite this, the specific causal mechanisms connecting childhood adversity to adult psychopathology remain undiscovered and require further research. This research aimed to determine whether perceived social support, cognitive reappraisal, and expressive suppression, two alternate forms of emotion regulation, would independently mediate the link between adverse childhood experiences and psychopathology in adulthood among a sample of U.S. Hispanic students. A total of 814 undergraduate students completed an anonymous online battery of …

Relative Uptake Of Tomato Carotenoids By In Vitro Intestinal And Prostate Cancer Cells, Nancy E Moran, Brianna Alexander, Shivi Garg, Nathan Marchant, Noor A Hason

Children’s Nutrition Research Center Staff Publications

Background: Consumption of tomatoes and tomato carotenoids is associated with a reduced risk of prostate cancer. Prostate tissue accumulates tomato carotenoids, including lycopene, β-carotene, and phytoene. Phytoene accumulation is relatively greater in the prostate than that of lycopene, but the metabolic determinants of tissue carotenoid profiles are poorly understood.

Objectives: The purpose of this study was to determine if differences in stability, cellular uptake, and clearance of phytoene compared with lycopene or β-carotene by prostate and intestinal cells may explain differences in observed tissue carotenoid profiles.

Methods: Gene and protein expression for carotenoid metabolism in prostate cell lines were analyzed …

Are Prediction Models Moving Closer To Clinical Application In The Diagnosis Of Type Of Diabetes In Youth?, Fida Bacha

Children’s Nutrition Research Center Staff Publications

No abstract provided.

Single-Shot Mev-Resolution Hard X-Ray Spectrograph For Cavity-Based X-Ray Free Electron Laser, Keshab Kauchha

Dissertations

The Cavity-Based X-ray Free Electron Laser (CBXFEL) is a possible future direction in the development of fully coherent hard X-ray sources of high spectral brilliance, a narrow spectral bandwidth of ≃ 1 − 100 meV, and a high repetition rate of ≃ 1 MHz. A diagnostic tool is required to measure CBXFEL spectra with a meV resolution on a shot-to-shot basis.

The CBXFEL hard X-ray spectrograph is designed to image 9.831 keV X-rays in a ≃ 200 meV spectral window and with a spectral resolution of a few meV using an LCLS XFEL (Linac Coherent Light Source X-ray Free Electron …

The Constitutional Limits To The Taxing Power, Ari Glogower

Fordham Law Review

The modern U.S. Supreme Court has elevated the apportionment requirement for direct taxes into the most important constitutional limitation to Congress’s taxing power. The U.S. Constitution requires that any “direct tax” must be apportioned among the states by population, which is impracticable or impossible for a tax today. The modern interpretative approach focuses on the formal categorization of the tax base, as either a “direct tax” or not. This approach could bar Congress from enacting certain taxes—such as a federal wealth tax or possibly even capital income tax reforms—simply through their formal labeling as direct taxes.

This interpretation inflates apportionment’s …

Distorted Narratives In The Treatment Program Complex, Evelyn Lia Malavé

Fordham Law Review

Problem-solving courts and alternatives to incarceration have been both celebrated as successful attempts to address the factors that lead to defendants’ involvement in the criminal legal system and critiqued as ineffective reforms that worsen mass incarceration. Specifically, critiques of the “treatment program complex” have tended to focus on how it harms defendants by exposing them to higher levels of incarceration if they fail to complete court mandates. But these critiques have failed to account for another way the treatment program complex harms defendants: by suppressing their voices regarding what kind of help they need and how they are affected by …

Plausibly Illiberal: Sua Sponte Dismissals Of Pro Se Complaints Under The Prison Litigation Reform Act, Pierce Edlich

Fordham Law Review

Over a quarter of civil litigants file suit in federal court without an attorney. Most unrepresented litigants are in prison, and many of their cases raise complex, delicate constitutional questions. The number of unrepresented litigants in federal courts rose steadily until the 1990s, when Congress passed the Prison Litigation Reform Act (PLRA) of 1995 to limit the burden of frivolous inmate litigation on federal courts. Since 1996, the PLRA has defined courts’ procedural obligations for inmate and in forma pauperis (IFP) suits, often filed pro se. The PLRA requires that courts screen and dismiss sua sponte cases that are frivolous, …

Lost At The South China Sea: A Legal Rationale For Joining Unclos, Dean Feinman

Fordham Law Review

Rising tensions in the South China Sea are a tired front of great power competition. Since President Barack Obama’s “pivot to Asia,” there has been renewed attention on growing Chinese naval power and influence, which stands to challenge the U.S. status as the world’s preeminent naval force. Despite a clear and obvious interest in maintaining a favorable global oceans regime, the United States has not ratified the United Nations Convention on the Law of the Sea (UNCLOS), the principal international instrument governing the use of the oceans. Instead, the United States argues that ratification is unnecessary because UNCLOS “embodies” customary …

Compelling Compliance: An Analysis Of Methods For Enforcing New York’S Compulsory Education Law As Applied To Haredi Yeshivas, Kaitlyn Sivertsen

Fordham Law Review

This Note explains recent findings that many of New York’s Haredi yeshivas are failing to comply with the compulsory education laws and advocacy for the state to heighten enforcement. It examines other community members’ opposition to this belief and numerous legal challenges. This Note advances legal scholarship by pointing out that although these debates regarding education and religious freedom are important, in the context of New York’s Haredi yeshivas, they are futile without also recognizing that New York’s statute does not provide for efficient means of enforcement. There is little to no existing literature on this specific issue. Thus, this …

Malicious Compliance: Examining Revenge Behaviors In Organizations, Patricia Jewel Emily Morrison

Theses and Dissertations

Revenge is a multifaceted behavior that manifests in various forms. The purpose of this dissertation was to examine a particular form of revenge known as malicious compliance, which has yet to be sufficiently researched. A two-study approach was used to examine malicious compliance. In the first study, qualitative data was collected from an online forum for sharing malicious compliance experiences. In the second study, 352 participants completed a mixed-methods survey online. Data from both studies was analyzed in RStudio and MAXQDA. Study 1 utilized unsupervised machine learning techniques, non-machine learning techniques, and thematic coding techniques to analyze the forum dataset. …

Exploring Keebet Von Benda-Beckmann's Legal Pluralism In Slum Transformation: An Ethnographic Study Of Urban Revitalization In Indonesia, Imam Koeswahyono, Diah Pawestri Maharani, Nadyah Maylika Rahmawati, Ika Ismudyahwati

The Indonesian Journal of Socio-Legal Studies

Slum areas in Indonesia present a significant challenge, with government programs such as the National Slum Upgrading Program (NSUP) aiming to address urban poverty and improve living conditions. Despite the implementation of formal policies, the interplay between legal frameworks and local practices often determines the success of such initiatives. Drawing on Keebet von Benda-Beckmann’s theory of legal pluralism, this research examines the transformation of slums into productive and sustainable zones, focusing on Kampung Jodipan, Kampung Heritage Kajoetangan, and Kampung Kreatif Dago Pojok. These case studies reveal how formal legal systems interact with informal norms and community-driven practices in achieving urban …

Adan: Adaptive Nesterov Momentum Algorithm For Faster Optimizing Deep Models, Xingyu Xie, Pan Zhou, Huan Li, Zhouchen Lin, Shuicheng Yan

Research Collection School Of Computing and Information Systems

In deep learning, different kinds of deep networks typically need different optimizers, which have to be chosen after multiple trials, making the training process inefficient. To relieve this issue and consistently improve the model training speed across deep networks, we propose the ADAptive Nesterov momentum algorithm, Adan for short. Adan first reformulates the vanilla Nesterov acceleration to develop a new Nesterov momentum estimation (NME) method, which avoids the extra overhead of computing gradient at the extrapolation point. Then Adan adopts NME to estimate the gradient's first- and second-order moments in adaptive gradient algorithms for convergence acceleration. Besides, we prove that …

3d Snapshot: Invertible Embedding Of 3d Neural Representations In A Single Image, Yuqin Lu, Bailin Deng, Zhixuan Zhong, Tianle Zhang, Yuhui Quan, Hongmin Cai, Shengfeng He

Research Collection School Of Computing and Information Systems

3D neural rendering enables photo-realistic reconstruction of a specific scene by encoding discontinuous inputs into a neural representation. Despite the remarkable rendering results, the storage of network parameters is not transmission-friendly and not extendable to metaverse applications. In this paper, we propose an invertible neural rendering approach that enables generating an interactive 3D model from a single image (i.e., 3D Snapshot). Our idea is to distill a pre-trained neural rendering model (e.g., NeRF) into a visualizable image form that can then be easily inverted back to a neural network. To this end, we first present a neural image distillation method …

Generative Semi-Supervised Graph Anomaly Detection, Hezhe Qiao, Qingsong Wen, Xiaoli Li, Ee-Peng Lim, Guansong Pang

Research Collection School Of Computing and Information Systems

This work considers a practical semi-supervised graph anomaly detection (GAD) scenario, where part of the nodes in a graph are known to be normal, contrasting to the extensively explored unsupervised setting with a fully unlabeled graph. We reveal that having access to the normal nodes, even just a small percentage of normal nodes, helps enhance the detection performance of existing unsupervised GAD methods when they are adapted to the semi-supervised setting. However, their utilization of these normal nodes is limited. In this paper we propose a novel Generative GAD approach (namely GGAD) for the semi-supervised scenario to better exploit the …

Learning To Handle Complex Constraints For Vehicle Routing Problems, Jieyi Bi, Yining Ma, Jianan Zhou, Wen Song, Zhiguang Cao, Yaoxin Wu, Jie Zhang

Research Collection School Of Computing and Information Systems

Vehicle Routing Problems (VRPs) can model many real-world scenarios and often involve complex constraints. While recent neural methods excel in constructing solutions based on feasibility masking, they struggle with handling complex constraints, especially when obtaining the masking itself is NP-hard. In this paper, we propose a novel Proactive Infeasibility Prevention (PIP) framework to advance the capabilities of neural methods towards more complex VRPs. Our PIP integrates the Lagrangian multiplier as a basis to enhance constraint awareness and introduces preventative infeasibility masking to proactively steer the solution construction process. Moreover, we present PIP-D, which employs an auxiliary decoder and two adaptive …

Inverse Factorized Soft Q-Learning For Cooperative Multi-Agent Imitation Learning, The Viet Bui, Tien Mai, Thanh Nguyen

Research Collection School Of Computing and Information Systems

This paper concerns imitation learning (IL) in cooperative multi-agent systems.The learning problem under consideration poses several challenges, characterized by high-dimensional state and action spaces and intricate inter-agent dependencies. In a single-agent setting, IL was shown to be done efficiently via an inverse soft-Q learning process. However, extending this framework to a multi-agent context introduces the need to simultaneously learn both local value functions to capture local observations and individual actions, and a joint value function for exploiting centralized learning.In this work, we introduce a new multi-agent IL algorithm designed to address these challenges. Our approach enables thecentralized learning by leveraging …

Trustworthy Web3 Domains: A Framework For Digital Identity Verification, Yi Meng Lau, Ping Fan Ke

Research Collection School Of Computing and Information Systems

As decentralized applications evolve, digital identities represented through Web3 domain names gained prominence. This study addresses the challenges of establishing trust in Web3 domain names. The decentralized nature of Web3 introduces complexities in verifying domain name authenticity, making them targets for malicious activities such as cybersquatting and phishing. We propose a comprehensive framework that enhances traditional identification, authentication, and authorization processes by incorporating technological and social trust elements. This framework enables organizations and users to systematically assess the trustworthiness of Web3 domain names, offering a structured approach to managing digital identities in decentralized environments.

Towards General Conceptual Model Editing Via Adversarial Representation Engineering, Yihao Zhang, Zeming Wei, Jun Sun, Meng Sun

Research Collection School Of Computing and Information Systems

Since the rapid development of Large Language Models (LLMs) has achieved remarkable success, understanding and rectifying their internal complex mechanisms has become an urgent issue. Recent research has attempted to interpret their behaviors through the lens of inner representation. However, developing practical and efficient methods for applying these representations for general and flexible model editing remains challenging. In this work, we explore how to leverage insights from representation engineering to guide the editing of LLMs by deploying a representation sensor as an editing oracle. We first identify the importance of a robust and reliable sensor during editing, then propose an …

Question-Attentive Review-Level Explanation For Neural Rating Regression, Trung Hoang Le, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Recommendation explanations help to improve their acceptance by end users. Explanations come in many different forms. One that is of interest here is presenting an existing review of the recommended item as the explanation. The challenge is in selecting a suitable review, which is customarily addressed by assessing the relative importance or “attention” of each review to the recommendation objective. Our focus is improving review-level explanation by leveraging additional information in the form of questions and answers (QA). The proposed framework employs QA in an attention mechanism that aligns reviews to various QAs of an item and assesses their contribution …

Custom Permission Misconfigurations In Android: A Large-Scale Security Analysis, Rui Li, Wenrui Diao, Debin Gao

Research Collection School Of Computing and Information Systems

Android’s popularity is due to its openness and vast app ecosystem. Global developers can use Android Studio and rich Android APIs to create their apps. Within this ecosystem, Android permissions play a crucial role in managing access to resources, with system permissions controlled by system apps and custom permissions declared by third-party apps. However, the security of custom permissions has not received enough attention from the mobile security community, resulting in a lack of thorough evaluation of security practices for app developers using custom permissions. This study systematically evaluated the misconfiguration of custom permissions by Android app developers. It is …

Long-Tailed Out-Of-Distribution Detection Via Normalized Outlier Distribution Adaptation, Wenjun Miao, Guansong Pang, Jin Zheng, Xiao Bai

Research Collection School Of Computing and Information Systems

Onekeychallenge in Out-of-Distribution (OOD) detection is the absence of groundtruth OOD samples during training. One principled approach to address this issue is to use samples from external datasets as outliers (i.e., pseudo OOD samples) to train OOD detectors. However, we find empirically that the outlier samples often present a distribution shift compared to the true OOD samples, especially in LongTailed Recognition (LTR) scenarios, where ID classes are heavily imbalanced, i.e., the true OOD samples exhibit very different probability distribution to the head and tailed ID classes from the outliers. In this work, we propose a novel approach, namely normalized outlier …

Chain Of Preference Optimization: Improving Chain-Of-Thought Reasoning In Llms, Xuan Zhang, Chao Du, Tianyu Pang, Qian Liu, Wei Gao, Min Lin

Research Collection School Of Computing and Information Systems

The recent development of chain-of-thought (CoT) decoding has enabled large language models (LLMs) to generate explicit logical reasoning paths for complex problem-solving. However, research indicates that these paths are not always deliberate and optimal. The tree-of-thought (ToT) method employs tree-searching to extensively explore the reasoning space and find better reasoning paths that CoT decoding might overlook. This deliberation, however, comes at the cost of significantly increased inference complexity. In this work, we demonstrate that fine-tuning LLMs leveraging the search tree constructed by ToT allows CoT to achieve similar or better performance, thereby avoiding the substantial inference burden. This is achieved …

Gotcha ! This Model Uses My Code ! Evaluating Membership Leakage Risks In Code Models, Zhou Yang, Zhipeng Zhao, Chenyu Wang, Jieke Shi, Dongsum Kim, Donggyun Han, David Lo

Research Collection School Of Computing and Information Systems

Leveraging large-scale datasets from open-source projects and advances in large language models, recent progress has led to sophisticated code models for key software engineering tasks, such as program repair and code completion. These models are trained on data from various sources, including public open-source projects like GitHub and private, confidential code from companies, raising significant privacy concerns. This paper investigates a crucial but unexplored question: What is the risk of membership information leakage in code models? Membership leakage refers to the vulnerability where an attacker can infer whether a specific data point was part of the training dataset. We present …

User Acceptance Of Advice By Ai Agents: Expectation-System Fit Perspective, Jingyuan Cai, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

Algorithms have increasing influence on our daily decisions, especially when the recommendations are presented by human-like AI agents. This study applies the Theory of Effective Use to investigate how the fit between the user’s role expectation for an AI agent and the agent’s interaction style impacts AI advice adoption. We proposed a new concept termed Perceived Expectation-System Fit (PESF) and empirically examined its impact on user perceptions and advice acceptance. We found that low PESF reduces advice acceptance by diminishing cognitive and affective trust in the AI agent. Furthermore, increased algorithm transparency increases PESF's impact on decision-making. Our findings provide …

Delidar: Decoupling Lidars For Pervasive Spatial Computing, Kanatta Gamage Ramesh Darshana Rathnayake, Razat Sutradhar, Abbaas A. M. Nishar, Weerakoon Dulaj S., Ashwin Ashok, Archan Misra

Research Collection School Of Computing and Information Systems

Unbounded proliferation of LiDAR-equipped pervasive devices generates two challenges: (a) mutual interference among emitters and (b) significantly higher sensing energy overhead. We propose a fundamentally different approach for LiDAR sensing, in indoor spaces, that decouples the sensor’s emitter and receiver components. Our proposed approach, called DeLiDAR, centralizes the emitter functionality in one or more stationary nodes that continually emit pulses; this decoupling allows each mobile LiDAR sensor to be an ultra-low power, pure receiver unit consisting solely of passive multiple photodiodes. We explain how the emitter can utilize VLC-based encoding of its pulses to convey parameter settings that allow a …

A Full-History Network Dataset For Btc Asset Decentralization Profiling, Ling Cheng, Qian Shao, Fengzhu Zeng, Feida Zhu

Research Collection School Of Computing and Information Systems

Since its advent in 2009, Bitcoin (BTC) has garnered increasing attention from both academia and industry. However, due to the massive transaction volume, no systematic study has quantitatively measured the asset decentralization degree specifically from a network perspective.In this paper, by conducting a thorough analysis of the BTC transaction network, we first address the significant gap in the availability of full-history BTC graph and network property dataset, which spans over 15 years from the genesis block (1st March, 2009) to the 845651-th block (29, May 2024). We then present the first systematic investigation to profile BTC's asset decentralization and design …

Shield-U: Safeguarding Traffic Sign Recognition Against Perturbation Attacks, Shengmin Xu, Jianfei Sun, Hangcheng Cao, Yulan Gao, Ziyang He, Cong Wu

Research Collection School Of Computing and Information Systems

Traffic sign recognition systems are crucial for the navigation and situation awareness of autonomous vehicles. They leverage deep learning technologies to swiftly and accurately identify traffic signs, even in the most challenging traffic environments. However, security researchers have uncovered a critical vulnerability in these systems: learning-based TSRs are particularly susceptible to physical-world perturbation attacks. Through subtle modifications (i.e., attaching well-designed patches on traffic signs), attackers can deceive the recognition system into making erroneous judgments, which can further lead to serious traffic accidents. Although several defense mechanisms have been proposed to enhance the security of sign recognition systems, these solutions generally …

Ohss: Optimizing Homomorphic Secret Sharing To Support Fast Matrix Multiplication, Shuguang Zhang, Jianli Bai, Kun Tu, Ziyue Yin, Chan Liu

Research Collection School Of Computing and Information Systems

Homomorphic Secret Sharing (HSS) has evolved as a state-of-the-art methodology for achieving secure two-party computation, synthesizing the advantages of secret sharing and homomorphic encryption. This amalgamation ensures minimal computational and communicational overhead, making it particularly adept at arithmetic operations. However, HSS faces challenges in scalability and efficiency when confronted with extensive matrix operations, including both matrix-vector and matrix-matrix multiplications, which are fundamental in numerous privacy-preserving computations, notably within the realm of privacy-preserving machine learning. In this research, we introduce Optimized Homomorphic Secret Sharing (OHSS), a refined version of HSS, crafted to address these limitations. Our contributions include enhancements to the …