Digital Commons Network^™

Determinants Of Bangladeshi Banking Inefficiency: Do Non-Performing Loans And Basel Iii Affect Banking Inefficiency?, Mohammad Abdul Matin Chowdhury, S. M. Shamsul Alam, Wan Rohaida Wan Husain, Rafikul Islam, Anwar Hossain

DLSU Business & Economics Review

The efficiency of commercial banking is a crucial determinant of the longevity of the financial system. High credit risk is a significant feebleness that leads to high non-performing loans (NPLs), which reduce banking efficiency in any economy. In this context, this study aims to identify the determinants of banking inefficiency in Bangladesh. The Data Envelopment Analysis (DEA) technique was employed to measure banking efficiency, whereas TOBIT regression was performed to identify the determinants of inefficiency of 38 commercial banks from 2016–2022. Findings demonstrated size, ownership structure and orientation, capital structure regulations (BASEL III), GDP growth, and inflation as significant determinants …

Budget Deficit Spending Causes Inflation, Roberto B. Raymundo, Paulynne J. Castillo

DLSU Business & Economics Review

Using the Cochrane-Orcutt iterative procedure, the paper provides strong statistical evidence that inflation is a monetary phenomenon caused by budget deficit spending when the central bank buys government debt. Regression results validate that increasing budget deficits lead to the issuance of more debt securities the central bank uses to back the creation of new money. The central bank purchases government securities from commercial banks to implement expansionary monetary policy. The increase in money supply is not possible without the issuance of government debt securities, which, in turn, is only undertaken by the Bureau of Treasury when it finances budget deficits. …

High Frequency Factor Analysis With Partially Observable Factors, Dachuan Chen, Wenqi Lu, Siyu Xie

Research Collection School Of Economics

This paper considers a novel factor structure – Partially Observable Factor Model – where both observable factors and latent factors exist in the model simultaneously. Such factor structure can make sure both interpretability and goodness-of-fit at the same time. Necessary estimation methodologies for this partially observable factor model are developed in this paper for the high frequency data. The proposed estimation methodology is robust to jumps, microstructure noise and asynchronous observation times simultaneously.When the observable factors are exogenous, we provide the estimation theory for the integrated eigenvalues of the residual covariance matrix, which including the bias-corrected estimator, central limit theorem …

Information Production By Institutions And Information Extraction By Underwriters In Hybrid Ipo Auctions, Thomas J. Chemmanur, Pengfei Ma, Qianqian Yu

Research Collection Lee Kong Chian School Of Business

We analyze the informational properties of hybrid IPO auctions using a large and unique database of institutional bids from Chinese IPO auctions. We find strong evidence of information production by institutions about the intrinsic values of IPO firms and of underwriters extracting and using this information in IPO pricing. The IPO offer price is more sensitive to bids from institutions able to produce more precise information. In particular, the offer price is more sensitive to bids from domestic institutions, compared to bids from foreign institutions who likely have less knowledge or experience about the Chinese firms and financial market due …

Efficient Prompt Tuning For Hierarchical Ingredient Recognition, Yinxuan Gui, Bin Zhu, Jingjing Chen, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Fine-grained ingredient recognition presents a significant challenge due to the diverse appearances of ingredients, resulting from different cutting and cooking methods. While existing approaches have shown promising results, they still require extensive training costs and focus solely on fine-grained ingredient recognition. In this paper, we address these limitations by introducing an efficient prompt-tuning framework that adapts pretrained visual-language models (VLMs), such as CLIP, to the ingredient recognition task without requiring full model finetuning. Additionally, we introduce three-level ingredient hierarchies to enhance both training performance and evaluation robustness. Specifically, we propose a hierarchical ingredient recognition task, designed to evaluate model performance …

Reaction Control Mechanism In Deoxyuridine 5'-Triphosphate Nucleotidohydrolase, Aaron Delay

School of Biological Sciences: Dissertations, Theses, and Student Research

Deoxyuridine 5'-triphosphate nucleotidohydrolase (dUTPase) is an enzyme involved in the pyrimidine biosynthesis pathway, a key component of cellular DNA metabolism. It regulates intracellular uracil carrying triphosphate levels by hydrolyzing dUTP, thereby providing a substrate for thymidylate synthase (TS). The effective inhibition of dUTPase is expected to enhance TS-targeted chemotherapy by promoting thymine-less apoptosis.

Although the reaction mechanism of dUTPase has been studied, the human nuclear homotrimeric form remains complex, as all three subunits cooperatively form a single active site. As a result, the detailed molecular dynamics of its catalysis are still not fully understood. In this study, I investigated the …

Perencanaan Investasi Hijau Pada Usaha Menengah Besar Di Provinsi Jawa Tengah, Avi Budi Setiawan, Maulida Dewi Pangestika, Putri Patria Kusuma, Mochammad Yusuf

Jurnal Ekonomi dan Pembangunan Indonesia

This study aims to analyze green investment planning in medium and large enterprises in Central Java Province. This study employs several analytical tools, including regression with quadratic variables, Klassen Typology Analysis, and Analytic Network Process (ANP). The research locus is 35 districts/cities in Central Java Province. The results of the study show that industries in Central Java contribute significantly to economic transformation, but also harm environmental quality, the regression results show the Pollution Haven Hypothesis in several main industries in Central Java. The sectors that have potential to reduce the quality of the environment are oil and gas mining, textiles, …

Pengaruh Demokrasi Terhadap Pertumbuhan Ekonomi Daerah Di Indonesia, Jayanti Kusumaningrum Utomo, Widyono Soetjipto

Jurnal Ekonomi dan Pembangunan Indonesia

This study observes that previous research on the relationship between democracy and economic growth has produced ambiguous outcomes. The aim of this paper is to estimate the relationship between democracy and regional economic growth in Indonesia. Using a fixed effects model with 374 provincial-level observations, the results indicate a statistically significant and positive relationship between democracy and regional economic growth in Indonesia.

O-Mapl: Offline Multi-Agent Preference Learning, The Viet Bui, Tien Mai, Hong Thanh Nguyen

Research Collection School Of Computing and Information Systems

Inferring reward functions from demonstrations is a key challenge in reinforcement learning (RL), particularly in multi-agent RL (MARL). The large joint state-action spaces and intricate inter-agent interactions in MARL make inferring the joint reward function especially challenging. While prior studies in single-agent settings have explored ways to recover reward functions and expert policies from human preference feedback, such studies in MARL remain limited. Existing methods typically combine two separate stages, supervised reward learning, and standard MARL algorithms, leading to unstable training processes. In this work, we exploit the inherent connection between reward functions and Q functions in cooperative MARL to …

Unified Neural Backdoor Removal With Only Few Clean Samples Through Unlearning And Relearning, Nay Myat Min, Long H. Pham, Jun Sun

Research Collection School Of Computing and Information Systems

Deep neural networks have achieved remarkable success across various applications; however, their vulnerability to backdoor attacks poses severe security risks—especially in situations where only a limited set of clean samples is available for defense. In this work, we address this critical challenge by proposing ULRL (UnLearn and ReLearn for backdoor removal), a novel two-phase approach for comprehensive backdoor removal. Our method first employs an unlearning phase, in which the network’s loss is intentionally maximized on a small clean dataset to expose neurons that are excessively sensitive to backdoor triggers. Subsequently, in the relearning phase, these suspicious neurons are recalibrated using …

Jme 4110: Automatic Umbrella Opener, Sam Nieder, Adam Freeman

Washington University / UMSL Mechanical Engineering Design Project JME 4110

Manually opening and closing a patio umbrella can be time consuming and laborious. While there are remote controlled umbrellas for sale, they are expensive and may not be a reasonable option for most people. Our task was to create a device that can be retrofitted onto any patio umbrella, that allows it to be opened and closed remotely.

Jme 4110: Design And Evaluation Of A Remote-Operated Tool For High-Ceiling Light Bulb Maintenance, Faiq Kureshi, Austin Carr, Phuc Truong, Klevis Malaj

Washington University / UMSL Mechanical Engineering Design Project JME 4110

Changing light bulbs in high ceilings is a common but hazardous task. Existing tools are awkward, unstable, and often lead to dropped bulbs, damaged fixtures, or the need for ladders, increasing both risk and effort. There is a clear need for a safe, accurate, and efficient solution that allows bulb replacement from the ground without compromising speed or precision

Exploring Covid-19 Vaccination Behavior: A Cross-Country Study Among Pregnant And Postpartum Women In Brazil, Ghana, Kenya, And Pakistan, Rupali Limaye, Berhaun Fesshaye, Emilly Miller, Prachi Singh, Saleem Jessani, Muhammad Asim, Ferdinand Okwaro, Caroline Badzi, Emefa Amoah, Marleen Temmerman

Centre of Excellence in Women and Child Health

Pregnant women infected with SARS-CoV2 are more likely to be hospitalized and require ventilation, compared to non-pregnant women. Although the development of the COVID-19 vaccine was regarded as a scientific breakthrough among many, the pace of development in combination with delayed and unclear recommendations for maternal vaccination led to slower vaccine uptake among this population. We explored the decision-making process for COVID-19 vaccination among pregnant and postpartum women in four countries: Brazil, Ghana, Kenya, and Pakistan through 201 in-depth interviews. A grounded theory approach was used for analysis, and a socio-ecological framework was used to synthesize emerging themes. Four levels …

Covid-19 Vaccine Attitudes And Behaviors Among Pregnant Women In Nairobi, Kenya With Diverse Socio-Economic And Educational Backgrounds, Jessica Schue, Ferdinand Okwaro, Ingrid Gichere, Daizy Cherono, Mandeep Sura, Emily Miller, Berhaun Fesshaye, Prachi Singh, Grace Belayneh Belayneh, Rupali Limaye, Marleen Temmerman

Centre of Excellence in Women and Child Health

Introduction

Methods

Placental Transfer Of Sars-Cov-2 Antibodies In Mother-Neonate Pairs: A Prospective Nested Cohort Study, Alex Mugo, Angela Koech, Liberty Cantrell, Moses Mukhanya, Isaac Mwaniki, Joseph Mutunga, Merryn Voysey, Rachel Craik, Marleen Temmerman, Geoffrey Omuse

Centre of Excellence in Women and Child Health

Background: Newborns depend on the transfer of IgG across the placenta to acquire protection against pathogens. We assessed the placental transfer of SARS-CoV-2 antibodies, primarily derived from infection, from seropositive pregnant women enrolled in a pregnancy cohort in Kilifi, Kenya.

Methods :The study was nested within a prospective observational multi-country cohort study. All available paired maternal delivery and cord blood samples were selected. Maternal sera were tested for SARS-CoV-2 receptor binding domain (RBD) IgM/IgG total antibodies using the Wantai assay. For positive samples, maternal and corresponding cord blood samples were tested for SARS-CoV-2 IgG antibodies against the spike (anti-spike) and …

Contribution Of Maternal Adherence To The Effect Of Multiple Micronutrient Supplementation During Pregnancy: A Systematic Review And Individual Participant Data Meta-Analysis, Emily R. Smith, Filomena Gomes, Seth Adu-Afarwuah, Victor M. Aguayo, Shams El Arifeen, Zulfiqar Ahmed Bhutta, Ellen C. Caniglia, Parul Christian, Arjumand Rizvi, Sajid Bashir Soofi

Centre of Excellence in Women and Child Health

Multiple micronutrient supplements (MMS) in pregnancy reduces risk of infant low birthweight (LBW) and improves other maternal and infant outcomes compared with iron and folic acid (IFA) supplements alone. However, the impact of timing of initiation and adherence on the MMS effectiveness in real-world programs remains unclear. To address this, we conducted a 2-stage individual participant data meta-analysis that included 15 randomized trials (61,204 pregnant women) and assessed whether the relative effect of MMS differed by the following: adherence alone; adherence in combination with gestational age at initiation; and the total number of tablets taken. We also evaluated the observational …

Llmscan: Causal Scan For Llm Misbehavior Detection, Mengdi Zhang, Kai Kiat Goh, Peixin Zhang, Jun Sun, Lin Xin Rose, Hongyu Zhang

Research Collection School Of Computing and Information Systems

Despite the success of Large Language Models (LLMs) across various fields, their potential to generate untruthful and harmful responses poses significant risks, particularly in critical applications. This highlights the urgent need for systematic methods to detect and prevent such misbehavior. While existing approaches target specific issues such as harmful responses, this work introduces LLMSCAN, an innovative LLM monitoring technique based on causality analysis, offering a comprehensive solution. LLMSCAN systematically monitors the inner workings of an LLM through the lens of causal inference, operating on the premise that the LLM’s ‘brain’ behaves differently when generating harmful or untruthful responses. By analyzing …

Advancing Food Nutrition Estimation Via Visual-Ingredient Feature Fusion, Huiyan Qi, Bin Zhu, Chong-Wah Ngo, Jingjing Chen, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Nutrition estimation is an important component of promoting healthy eating and mitigating diet-related health risks. Despite advances in tasks such as food classification and ingredient recognition, progress in nutrition estimation is limited due to the lack of datasets with nutritional annotations. To address this issue, we introduce FastFood, a dataset with 84,446 images across 908 fast food categories, featuring ingredient and nutritional annotations. In addition, we propose a new model-agnostic Visual-Ingredient Feature Fusion (VIF2 ) method to enhance nutrition estimation by integrating visual and ingredient features. Ingredient robustness is improved through synonym replacement and resampling strategies during training. The ingredient-aware …

Unveiling Knowledge Boundary Of Large Language Models For Trustworthy Information Access, Yang Deng, Moxin Li, Liang Pang, Wenxuan Zhang, Wai Lam

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have emerged as powerful tools for generating content and facilitating information seeking across diverse domains. While their integration into conversational systems opens new avenues for interactive information-seeking experiences, their effectiveness is constrained by their knowledge boundaries—the limits of what they know and their ability to provide reliable, truthful, and contextually appropriate information. Understanding these boundaries is essential for maximizing the utility of LLMs for real-time information seeking while ensuring their reliability and trustworthiness. In this tutorial, we will explore the taxonomy of knowledge boundary in LLMs, addressing their handling of uncertainty, response calibration, and mitigation of …

Repairing Adversarial Texts Through Perturbation, Guoliang Dong, Jingyi Wang, Jun Sun, Sudipta Chattopadhyay, Xinyu Wang, Ting Dai, Jie Shi, Jin Song Dong

Research Collection School Of Computing and Information Systems

It is known that neural networks are subject to attacks through adversarial perturbations. Worse yet, such attacks are impossible to eliminate, i.e., the adversarial perturbation is still possible after applying mitigation methods such as adversarial training. Multiple approaches have been developed to detect and reject such adversarial inputs. Rejecting suspicious inputs however may not be always feasible or ideal. First, normal inputs may be rejected due to false alarms generated by the detection algorithm. Second, denial-of-service attacks may be conducted by feeding such systems with adversarial inputs. To address this, in this work, we focus on the text domain and …

Leakage-Resilient Easily Deployable And Efficiently Searchable Encryption (Edese), Jiaming Yuan, Yingjiu Li, Jun Li, Daoyuan Wu, Jianting Ning, Yangguang Tian, Robert H. Deng

Research Collection School Of Computing and Information Systems

Easily Deployable and Efficiently Searchable Encryption (EDESE) is a cryptographic primitive designed for practical searchable applications, offering efficient search and easy deployment. However, it remains vulnerable to Leakage-Abuse attacks, allowing adversaries to exploit keyword-matching processes to extract sensitive information. To address these vulnerabilities, we introduce Leakage-Resilient EDESE (LR-EDESE) with k-indistinguishability and controlled leakage functions. We then propose Volume Leakage-Resilient EDESE (VLR-EDESE), a new scheme to protect against both query and document volume leakage. Our experimental results demonstrate that at k = 5000 (maximum security setting), VLR-EDESE incurs an overhead of 63× compared to the baseline EDESE without leakage protection, outperforming …

Runtime Anomaly Detection For Drones: An Integrated Rule-Mining And Unsupervised Learning Approach, Ivan Wei Han Tan, Wei Minn, Christopher M. Poskitt, Lwin Khin Shar, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Unmanned Aerial Vehicles (UAVs), commonly referred to as drones, have witnessed a remarkable surge in popularity due to their versatile applications. These cyber-physical systems depend on multiple sensor inputs, such as cameras, GPS receivers, accelerometers, and gyroscopes, with faults potentially leading to physical instability and serious safety concerns. To mitigate such risks, anomaly detection has emerged as a crucial safeguarding mechanism, capable of identifying the physical manifestations of emerging issues and allowing operators to take preemptive action at runtime. Recent anomaly detection methods based on LSTM neural networks have shown promising results, but three challenges persist: the need for models …

Milpbench: A Large-Scale Benchmark Test Suite For Mixed Integer Linear Programming Problems, Huigen Ye, Yaoyang Cheng, Hua Xu, Zhiguang Cao, Hanzhang Qin

Research Collection School Of Computing and Information Systems

Mixed-integer linear programming (MILP) is a cornerstone of optimization with applications across numerous domains. However, the development and evaluation of MILP-solving algorithms are hindered by existing benchmark datasets, which are often limited in scale, lack diversity, and are poorly structured, making them inadequate for systematic testing across different solving approaches, especially for machine learning (ML)-based methods. To address these issues, we introduce MILPBench, a large-scale benchmark suite comprising 100,000 MILP instances organized into 60 well-categorized classes. Using structural properties and embedding similarity metrics, we developed a novel classification framework to ensure both intra-class homogeneity and inter-class diversity. In addition to …

Collaboration With Dynamic Open Ad Hoc Team Via Team State Modelling, Jing Sun, Cong Zhang, Zhiguang Cao

Research Collection School Of Computing and Information Systems

Open ad hoc teamwork presents the challenging problem of designing an autonomous agent that can rapidly adapt to collaborate with teammates without prior coordination in an open environment. Existing methods primarily rely on fixed, predefined teammate types, overlooking the fact that teammates may change dynamically. To address this limitation, we propose a novel reinforcement learning approach, the Open Online Teammate Adaptation Framework (Open-OTAF), which enables a controlled agent to collaborate with dynamic teammates in open ad hoc environments. To achieve this, the controlled agent employs a dual teamwork situation inference model to capture the current teamwork state, facilitating decision-making under …

Meta-Black-Box-Optimization Through Offline Q-Function Learning, Zeyuan Ma, Zhiguang Cao, Zhou Jiang, Hongshu Guo, Yue-Jiao Gong

Research Collection School Of Computing and Information Systems

Recent progress in Meta-Black-Box-Optimization (MetaBBO) has demonstrated that using RL to learn a meta-level policy for dynamic algorithm configuration (DAC) over an optimization task distribution could significantly enhance the performance of the low-level BBO algorithm. However, the online learning paradigms in existing works makes the efficiency of MetaBBO problematic. To address this, we propose an offline learning-based MetaBBO framework in this paper, termed Q-Mamba, to attain both effectiveness and efficiency in MetaBBO. Specifically, we first transform DAC task into long-sequence decision process. This allows us further introduce an effective Q-function decomposition mechanism to reduce the learning difficulty within the intricate …

Breaking The Reasoning Barrier: A Survey On Llm Complex Reasoning Through The Lens Of Self-Evolution, Tao He, Hao Li, Jingchang Chen, Runxuan Liu, Yixin Cao, Lizi Liao, Zihao Zheng, Zheng Chu, Jiafeng Liang, Ming Liu, Bing Qin

Research Collection School Of Computing and Information Systems

The release of OpenAI’s O1 and subsequent projects like DeepSeek R1 has significantly advanced research on complex reasoning in LLMs. This paper systematically analyzes existing reasoning studies from the perspective of self-evolution, structured into three components: data evolution, model evolution, and self-evolution. Data evolution explores methods to generate higher-quality reasoning training data. Model evolution focuses on training strategies to boost reasoning capabilities. Self-evolution research autonomous system evolution via iterating cycles of data and model evolution. We further discuss the scaling law of self-evolution and analyze representative O1-like works through this lens. By summarizing advanced methods and outlining future directions, this …

Finir: The 2nd Workshop On Financial Information Retrieval In The Era Of Generative Ai, Fengbin Zhu, Yunshan Ma, Fuli Feng, Chao Wang, Huanbo Luan, Guangnan Ye, Shuo Zhang, Dhagash Mehta, Pingping Chen, Bing Xiang, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

Recent advancements in Generative AI, such as Large Language Models (LLMs), have demonstrated remarkable success across various general tasks. Extensive studies have explored leveraging generative models in finance, but significant challenges persist. This half-day workshop explores potential approaches and research directions to address these challenges by equipping generative models with advanced Information Retrieval (IR) models. Specifically, this workshop seeks to provide a platform for discussing innovative ideas that facilitate the advancement of IR technology to enrich generative models in finance from four key perspectives: (i) financial IR techniques (ii) financial IR benchmarking and evaluation (iii) financial systems and agents/assistants (iv) …

Mitigating Regression Faults Induced By Feature Evolution In Deep Learning Systems, Hanmo Yu, Zan Wang, Xuyang Chen, Junjie Chen, Jun Sun, Shuang Liu, Zishuo Dong

Research Collection School Of Computing and Information Systems

Deep learning (DL) systems have been widely utilized across various domains. However, the evolution of DL systems can result in regression faults. In addition to the evolution of DL systems through the incorporation of new data, feature evolution, such as the addition of new features, is also common and can introduce regression faults. In this work, we first investigate the underlying factors that are correlated with regression faults in feature evolution scenarios, i.e., redundancy and contribution shift. Based on our investigation, we propose a novel mitigation approach called FeaProtect, which aims to minimize the impact of these two factors. To …

Crow: Eliminating Backdoors From Large Language Models Via Internal Consistency Regularization, Nay Myat Min, Long H. Pham, Yige Li, Jun Sun

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) are vulnerable to backdoor attacks that manipulate outputs via hidden triggers. Existing defense methods—designed for vision/text classification tasks—fail for text generation. We propose Internal Consistency Regularization (CROW), a defense leveraging the observation that backdoored models exhibit unstable layer-wise hidden representations when triggered, while clean models show smooth transitions. CROW enforces consistency across layers via adversarial perturbations and regularization during finetuning, neutralizing backdoors without requiring clean reference models or trigger knowledge—only a small clean dataset. Experiments across Llama-2 (7B, 13B), CodeLlama (7B, 13B), and Mistral-7B demonstrate CROW’s effectiveness: it achieves significant reductions in attack success rates across …

Technifying Ventures, Yoshiki Ando, Emin Dinlersoz, Jeremy Greenwood, Ruben Piazzesi

Research Collection School Of Economics

How do advanced technology adoption and venture capital (VC) funding impact employment and growth? An analysis of data from the US Census Bureau suggests that while both advanced technology use and VC funding matter on their own for firm outcomes, their joint presence is most strongly correlated with higher employment levels. VC presence is linked with a high increase in employment, though primarily among a limited subset of firms. In contrast, technology adoption is associated with a smaller rise in employment, yet it influences a considerably larger number of firms. A model of startups is created, focusing on decisions to …