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Articles 901 - 930 of 23191
Full-Text Articles in Entire DC Network
Morphology-Aware Hrv Estimation From Wrist Ppg In Sedentary Scenarios, Changshuo Hu, Hung Manh Pham, Dong Ma
Morphology-Aware Hrv Estimation From Wrist Ppg In Sedentary Scenarios, Changshuo Hu, Hung Manh Pham, Dong Ma
Research Collection School Of Computing and Information Systems
Photoplethysmography (PPG) is widely used in wearable devices for non-invasive heart rate variability (HRV) monitoring. While most prior work focuses on mitigating motion artifacts, recent studies highlight that even subtle contact pressure variations can distort waveform morphology and lead to inaccurate HRV estimates. In this work, we propose a morphology-aware deep learning framework that conditions HRV estimation on beat-level waveform types. Our model jointly encodes the raw PPG waveform and a sequence of pressure-induced morphology labels using parallel encoders, integrates them via cross-attention, and predicts normal-to-normal (NN) intervals and beat count to support downstream HRV computation. Evaluated on the public …
Developing A Strong Cps Defender: An Evolutionary Approach, Qingyuan Hu, Christopher M. Poskitt, Jun Sun, Yuqi Chen
Developing A Strong Cps Defender: An Evolutionary Approach, Qingyuan Hu, Christopher M. Poskitt, Jun Sun, Yuqi Chen
Research Collection School Of Computing and Information Systems
Cyber-physical systems (CPSs) are used extensively in critical infrastructure, underscoring the need for anomaly detection systems that are able to catch even the most motivated attackers. Traditional anomaly detection techniques typically do `one-off' training on datasets crafted by experts or generated by fuzzers, potentially limiting their ability to generalize to unseen and more subtle attack strategies. Stopping at this point misses a key opportunity: a defender can actively challenge the attacker to find more nuanced attacks, which in turn can lead to more effective detection capabilities. Building on this concept, we propose Evo-Defender, an evolutionary framework that iteratively strengthens CPS …
A Review Of The 2024/25 International Moots Season, Siyuan Chen
A Review Of The 2024/25 International Moots Season, Siyuan Chen
Research Collection Yong Pung How School Of Law
This is the 11th annual review of Singapore’s performance in international moot court competitions. In a nutshell, 2024/25 has been a tremendous, record-breaking season for Singapore – and a particular duck has finally been broken. In the highest-tier or Grand Slam competitions, Singapore Management University emerged world champions in the Jessup, Vis East, International Criminal Court, and Frankfurt competitions; the previous world record for most Grand Slam wins in a season was 2 (held by various universities), and this new record may well increase to 5, depending on whether SMU can successfully defend its world championship at the Lachs Space …
Product Technology Adoption And Aggregate Innovation, Qiugu He, Xuan Luo, Fan Zheng
Product Technology Adoption And Aggregate Innovation, Qiugu He, Xuan Luo, Fan Zheng
Research Collection School Of Economics
We document that a firm's research and development (R&D) expenditure depends on its product diversity. Combining with the fact that Chinese manufacturers often enter new product markets via technology adoption, we develop a quantitative framework of innovation and technology adoption, allowing firms to expand their product scopes. Firms adopt technologies across multiple fields to expand their knowledge base, which in turn serves as an input for subsequent innovation or adoption. Counterfactual analysis from the 2000s reveals that two-thirds of knowledge privately held by all firms is generated through adoption, accounting for one-third of aggregate innovation.
Performances In Last-Mile Deliveries Involving Cyclic Services Of Shuttles And Cargo Bikes, Byung Kwon Lee, Young Joo Kim, Joyce M. W. Low
Performances In Last-Mile Deliveries Involving Cyclic Services Of Shuttles And Cargo Bikes, Byung Kwon Lee, Young Joo Kim, Joyce M. W. Low
Research Collection Lee Kong Chian School Of Business
This study introduces a novel approximation model that integrates the ideologies of order statistics and max-plus algebra in representing the collection and distribution service schemes of a last-mile delivery system. Under the respective schemes, electric cargo bikes (e-bikes) perform the last-mile delivery near a microhub with shuttles collecting packages from multiple local logistics hubs (or local hubs, for short) and/or distributing packages to multiple microhubs. The framework is designed to estimate the cyclic capacity under varying cargo carrying capacities of the e-bikes and service network configurations. Real-world data, such as the geographic conditions of the streets around the microhubs in …
Omnivton: Training-Free Universal Virtual Try-On, Zhaotong Yang, Yuhui Li, Shengfeng He, Xinzhe Li, Yangyang Xu, Junyu Dong, Yong Du
Omnivton: Training-Free Universal Virtual Try-On, Zhaotong Yang, Yuhui Li, Shengfeng He, Xinzhe Li, Yangyang Xu, Junyu Dong, Yong Du
Research Collection School Of Computing and Information Systems
Image-based Virtual Try-On (VTON) techniques rely on either supervised in-shop approaches, which ensure high fidelity but struggle with cross-domain generalization, or unsupervised in-the-wild methods, which improve adaptability but remain constrained by data biases and limited universality. A unified, training-free solution that works across both scenarios remains an open challenge. We propose OmniVTON, the first training-free universal VTON framework that decouples garment and pose conditioning to achieve both texture fidelity and pose consistency across diverse settings. To preserve garment details, we introduce a garment prior generation mechanism that aligns clothing with the body, followed by continuous boundary stitching technique to achieve …
Fcad: Feature-Coupled Anisotropic Diffusion For Continuous Graph Learning, Amitoz Azad, Zhiyuan Zhang
Fcad: Feature-Coupled Anisotropic Diffusion For Continuous Graph Learning, Amitoz Azad, Zhiyuan Zhang
Research Collection School Of Computing and Information Systems
In this work, we propose a novel continuous graph neural network called FCAD (Feature-Coupled Anisotropic Diffusion) for the task of node classification on graphs. Our approach is motivated by the success of feature-coupled anisotropic diffusion PDEs in multivalued image restoration. Our method introduces a total variation regularization-inspired anisotropic term to control diffusion between nodes and incorporates a learnable parameterization for feature coupling during the diffusion process. Our model performs competitively against several GNN baselines for both heterophilous and homophilous graphs, demonstrating notable benefits for heterophilous graphs due to the learnable feature coupling.
Lsfdnet: A Single-Stage Fusion And Detection Network For Ships Using Swir And Lwir, Yanyin Guo, Runxuan An, Junwei Li, Zhiyuan Zhang
Lsfdnet: A Single-Stage Fusion And Detection Network For Ships Using Swir And Lwir, Yanyin Guo, Runxuan An, Junwei Li, Zhiyuan Zhang
Research Collection School Of Computing and Information Systems
Traditional ship detection methods primarily rely on single-modal approaches, such as visible or infrared images, which limit their application in complex scenarios involving varying lighting conditions and heavy fog. To address this issue, we explore the advantages of short-wave infrared (SWIR) and long-wave infrared (LWIR) in ship detection and propose a novel single-stage image fusion detection algorithm called LSFDNet. This algorithm leverages feature interaction between the image fusion and object detection subtask networks, achieving remarkable detection performance and generating visually impressive fused images. To further improve the saliency of objects in the fused images and improve the performance of the …
Diffusionmat: Alpha Matting As Deterministic Sequential Refinement Learning, Yangyang Xu, Shengfeng He, Wenqi Shao, Yong Du, Kwan-Yee K. Wong, Yu Qiao, Jun Yu, Ping Luo
Diffusionmat: Alpha Matting As Deterministic Sequential Refinement Learning, Yangyang Xu, Shengfeng He, Wenqi Shao, Yong Du, Kwan-Yee K. Wong, Yu Qiao, Jun Yu, Ping Luo
Research Collection School Of Computing and Information Systems
In this paper, we introduce DiffusionMat, a novel image matting framework that employs a diffusion model for the transition from coarse to refined alpha mattes. Diverging from conventional methods that utilize trimaps merely as loose guidance for alpha matte prediction, our approach treats image matting as a deterministic sequential refinement learning process. This process begins with the addition of noise to trimaps and iteratively denoises them using a pre-trained diffusion model, which incrementally guides the prediction towards a clean alpha matte. The key innovation of our framework is a correction module that adjusts the output at each denoising step, ensuring …
Fine-Grained Abnormality Prompt Learning For Zero-Shot Anomaly Detection, Jiawen Zhu, Yew‑Soon Ong, Chunhua Shen, Guansong Pang
Fine-Grained Abnormality Prompt Learning For Zero-Shot Anomaly Detection, Jiawen Zhu, Yew‑Soon Ong, Chunhua Shen, Guansong Pang
Research Collection School Of Computing and Information Systems
Current zero-shot anomaly detection (ZSAD) methods show remarkable success in prompting large pre-trained visionlanguage models to detect anomalies in a target dataset without using any dataset-specific training or demonstration. However, these methods often focus on crafting/learning prompts that capture only coarse-grained semantics of abnormality, e.g., high-level semantics like ‘damaged’, ‘imperfect’, or ‘defective’ objects. They therefore have limited capability in recognizing diverse abnormality details that deviate from these general abnormal patterns in various ways. To address this limitation, we propose FAPrompt, a novel framework designed to learn Fine-grained Abnormality Prompts for accurate ZSAD. To this end, a novel Compound Abnormality Prompt …
Impact Of Original Versus Reposted Social Endorsements On Content Consumption: The Moderating Role Of Endorsers’ Network Characteristics, Anqi Zhao, Qian Tang
Impact Of Original Versus Reposted Social Endorsements On Content Consumption: The Moderating Role Of Endorsers’ Network Characteristics, Anqi Zhao, Qian Tang
Research Collection School Of Computing and Information Systems
Social endorsements broadcast endorsers’ positive attitudes toward content or products, especially to their social ties. Original endorsements created by endorsers can be propagated further as reposted endorsements. Both are important marketing tools to increase content consumption, yet their differences are unclear. This study compares the impacts of original and reposted endorsements on content consumption and their contingencies on the endorsers’ network characteristics. Using data on social endorsements of YouTube videos on Twitter, we find that original endorsements (i.e., original tweets) significantly boost content consumption, and the effect is positively moderated by the endorsers’ network size but not their tie strength. …
Information-Bottleneck Driven Binary Neural Network For Change Detection, Kaijie Yin, Zhiyuan Zhang, Shu Kong, Tian Gao, Cheng-Zhong Xu, Hui Kong
Information-Bottleneck Driven Binary Neural Network For Change Detection, Kaijie Yin, Zhiyuan Zhang, Shu Kong, Tian Gao, Cheng-Zhong Xu, Hui Kong
Research Collection School Of Computing and Information Systems
In this paper, we propose Binarized Change Detection (BiCD), the first binary neural network (BNN) designed specifically for change detection. Conventional network binarization approaches, which directly quantize both weights and activations in change detection models, severely limit the network's ability to represent input data and distinguish between changed and unchanged regions. This results in significantly lower detection accuracy compared to real-valued networks. To overcome these challenges, BiCD enhances both the representational power and feature separability of BNNs, improving detection performance. Specifically, we introduce an auxiliary objective based on the Information Bottleneck (IB) principle, guiding the encoder to retain essential input …
Southeast Asia Higher Education Changes And Emerging Futures, Muhammad Hali Aprimadya, Lily Kong, Angel Calderon, Hamish Coates
Southeast Asia Higher Education Changes And Emerging Futures, Muhammad Hali Aprimadya, Lily Kong, Angel Calderon, Hamish Coates
Research Collection School of Social Sciences
This paper unpacks recent developments and future directions in Southeast Asian higher education. It takes stock of higher education systems in three selected countries, Australia, Indonesia and Singapore. It sketches these systems, distils several changes around 2024, and discusses emerging directions for education, research and universities. The paper concludes by looking at what is required to catalyse transformations likely to impel future progress.
Everyday Norms Have Become More Permissive Over Time And Vary Across Cultures, K. Eriksson, P. Strimling, I. Vartanova, Andree Hartanto
Everyday Norms Have Become More Permissive Over Time And Vary Across Cultures, K. Eriksson, P. Strimling, I. Vartanova, Andree Hartanto
Research Collection School of Social Sciences
Every social situation that people encounter in their daily lives comes with a set of unwritten rules about what behavior is considered appropriate or inappropriate. These everyday norms can vary across societies: some societies may have more permissive norms in general or for certain behaviors, or for certain behaviors in specific situations. In a preregistered survey of 25,422 participants across 90 societies, we map societal differences in 150 everyday norms and show that they can be explained by how societies prioritize individualizing moral foundations such as care and liberty versus binding moral foundations such as purity. Specifically, societies with more …
The Future Of Higher Education Is Under Siege: Reinvention May Be Its Salvation, Lily Kong
The Future Of Higher Education Is Under Siege: Reinvention May Be Its Salvation, Lily Kong
Research Collection School of Social Sciences
Trust in universities, once a bedrock institution of modern society, is fraying. Affordability, political scrutiny, and cultural divides weigh heavily. Readers are all too aware of the wrenching developments: funding cuts, visa restrictions, pauses in student enrolment processes. These concerns are not confined to US universities only. From Australia to the United Kingdom to South Korea to the Global South, university leaders face parallel strains — affordability, governance, and relevance — and feel the weight of a future under siege.
Can Singapore Be A City That Bounces Back And Gives Back?, Lily Kong
Can Singapore Be A City That Bounces Back And Gives Back?, Lily Kong
Research Collection School of Social Sciences
In a commentary, SMU President Professor Lily Kong called for a widened definition of sustainability in city-making. She proposed that cities must be resilient, regenerative and restorative (3Rs) — able to withstand shocks, replenish ecosystems, and restore human well-being. Prof Kong cited Singapore’s Draft Master Plan 2025 as an illustration of balancing technical innovation with inclusive engagement, stressing that trust is the “invisible infrastructure” of resilience. She also emphasised the role of universities as conveners of dialogue and innovation, spotlighting SMU’s co-convening of the City Dialogues in Vienna and its hosting of the Lee Kuan Yew Global Business Plan Competition, …
A Debt To Whom? The Nature Questions In Sino‑Sri Lankan Development Narratives, Orlando Woods, Kanchana Ruwanpura, Loritta Chan, Barnabas Mah
A Debt To Whom? The Nature Questions In Sino‑Sri Lankan Development Narratives, Orlando Woods, Kanchana Ruwanpura, Loritta Chan, Barnabas Mah
Research Collection College of Integrative Studies
This chapter critically examines dominant narratives around Sri Lanka’s debt and development in the context of Sino‑Sri Lankan relations and the Belt and Road Initiative, questioning assumptions about “debt‑trap” explanations and highlighting how narratives about development, nature and agency shape understanding of the country’s economic and ecological futures.
On Modeling Of Multiplicative Bias Factor For Multivariate Degradation Data, Bingxin Yan, Qiuzhuang Sun, Zhisheng Ye
On Modeling Of Multiplicative Bias Factor For Multivariate Degradation Data, Bingxin Yan, Qiuzhuang Sun, Zhisheng Ye
Research Collection College of Integrative Studies
Multivariate degradation data are increasingly common due to advances in sensor technology. They provide rich information for reliability analysis and remaining useful life (RUL) prediction. The multivariate measurement typically contains actual degradation of multiple performance characteristics (PCs) and measurement errors. The multiple PCs often have different physical meanings with different orders of magnitude, making the associated measurement errors vary in the orders of magnitude as well. It is highly desirable to have a parsimonious model accounting for the possible correlation in degradation measurements, so that more flexibility can be reserved for modeling the actual multivariate degradation. This study proposes a …
Early Signs The Eu Carbon Border Adjustment Mechanism Is Reshaping Eu-India Steel Trade, Gian Luca Vriz, Theodor Florian Cojoianu, Carolyn Fischer, Luca Taschini
Early Signs The Eu Carbon Border Adjustment Mechanism Is Reshaping Eu-India Steel Trade, Gian Luca Vriz, Theodor Florian Cojoianu, Carolyn Fischer, Luca Taschini
Research Collection College of Integrative Studies
This study examines early signals of the EU CBAM's impact on iron and steel exporters from India by integrating firm-level export data with third-party estimates of manufacturers' emissions and production. Changes in trade flows and pricing during the CBAM reporting phase indicate that high-emission-intensity firms have decreased both their average unit prices and shipment sizes to the EU, while low-emission-intensity firms have not changed shipment behavior and may have benefited from increasing prices. Such supply chain adjustments are in line with EU policy intentions, creating opportunities for low-emission producers while reducing reliance on high-emission suppliers to the EU.
Make Yourself Comfortable: Nudging Urban Heat And Noise Mitigation With Smartwatch-Based Just-In-Time Adaptive Interventions (Jitai), Clayton Miller, Xuan Yun Chua, Matias Quintana, Binyu Lei, Filip Biljecki, Mario Frei
Make Yourself Comfortable: Nudging Urban Heat And Noise Mitigation With Smartwatch-Based Just-In-Time Adaptive Interventions (Jitai), Clayton Miller, Xuan Yun Chua, Matias Quintana, Binyu Lei, Filip Biljecki, Mario Frei
Research Collection College of Integrative Studies
Humans can play a more active role in improving their comfort in the built environment if given the right information at the right place and time. This paper outlines the use of Just-in-Time Adaptive Interventions (JITAI) implemented in the context of the built environment to provide information that helps humans minimize the impact of heat and noise on their daily lives. This framework is based on the open-source Cozie iOS smartwatch platform. It includes data collection through micro-surveys and intervention messages triggered by environmental, contextual, and personal history conditions. An eight-month deployment of the method was completed in Singapore with …
Hlcg: A Hierarchical Lane-Changing Gaming Decision Model For Heterogeneous Traffic Flow On Two-Lane Highways, Tianyi Wang, Chong He, Hao Li, Yixuan Li, Yiming Xu, Yangyang Wang, Junfeng Jiao
Hlcg: A Hierarchical Lane-Changing Gaming Decision Model For Heterogeneous Traffic Flow On Two-Lane Highways, Tianyi Wang, Chong He, Hao Li, Yixuan Li, Yiming Xu, Yangyang Wang, Junfeng Jiao
Research Collection College of Integrative Studies
Discretionary lane-changing behavior is one of the most common highway operations, which seriously affects traffic efficiency and safety. Nowadays, connected and automated vehicles (CAVs) are advancing rapidly, though not yet fully widespread. As a result, a mixed traffic environment with traditional human-driven vehicles (HDVs) and CAVs will persist for the foreseeable future. To achieve effective automatic lane-changing maneuvers, it’s necessary to propose a lane-changing decision model for heterogeneous traffic flow on two-lane highways. This paper firstly extends longitudinal car-following models based on the intelligent driver model and lateral lane-changing models using quintic polynomial curves to accommodate heterogeneous traffic flow, and …
Towards A Digital Twin For Smart Resilient Cities: Real-Time Fire And Smoke Tracking And Prediction Platform For Community Awareness (Firecom), Kijin Seong, Junfeng Jiao, Ryan Lewis Hardesty, Arya Farahi, Paul Navratil, Nate Casebeer, Braniff Davis, Justice Jones, Dev Niyogi
Towards A Digital Twin For Smart Resilient Cities: Real-Time Fire And Smoke Tracking And Prediction Platform For Community Awareness (Firecom), Kijin Seong, Junfeng Jiao, Ryan Lewis Hardesty, Arya Farahi, Paul Navratil, Nate Casebeer, Braniff Davis, Justice Jones, Dev Niyogi
Research Collection College of Integrative Studies
This paper discusses the development and application of a digital twin (DT) for urban resilience, focusing on an integrated platform for real-time fire and smoke. The proposed platform, FireCom, adapts DT concepts for the unique challenges of urban fire management, which differ significantly from regional wildfire systems. Through an exploratory case study in Austin, Texas, in the United States, this research bridges the theoretical foundations of 3D DT with their practical application in fire and smoke management. By fusing diverse data sources, ranging from air quality sensors and meteorological data to 3D urban infrastructure, FireCom supports both emergency response and …
Teaching Diffusion Models To Ground Alpha Matte, Tianyi Xiang, Weiying Zheng, Yutao Jiang, Tingrui Shen, Hewei Yu, Yangyang Xu, Shengfeng He
Teaching Diffusion Models To Ground Alpha Matte, Tianyi Xiang, Weiying Zheng, Yutao Jiang, Tingrui Shen, Hewei Yu, Yangyang Xu, Shengfeng He
Research Collection School Of Computing and Information Systems
The power of visual language models is showcased in visual understanding tasks, where language-guided models achieve impressive flexibility and precision. In this paper, we ex tend this capability to the challenging domain of image matting by framing it as a soft grounding problem, enabling a single diffusion model to handle diverse objects, textures, and transparencies, all directed by descriptive text prompts. Our method teaches the diffusion model to ground alpha mattes by guiding it through a process of instance-level localization and transparency estimation. First, we introduce an intermediate objective that trains the model to accurately localize semantic components of the …
Filterfl: Knowledge Filtering-Based Data-Free Backdoor Defense For Federated Learning, Yanxin Yang, Ming Hu, Xiaofei Xie, Yue Cao, Pengyu Zhang, Yihao Huang, Mingsong Chen
Filterfl: Knowledge Filtering-Based Data-Free Backdoor Defense For Federated Learning, Yanxin Yang, Ming Hu, Xiaofei Xie, Yue Cao, Pengyu Zhang, Yihao Huang, Mingsong Chen
Research Collection School Of Computing and Information Systems
As a distributed machine learning paradigm, Federated Learning (FL) enables large-scale clients to collaboratively train a model without sharing their raw data. However, due to the lack of data auditing for untrusted clients, FL is vulnerable to poisoning attacks, especially backdoor attacks. By using poisoned data for local training or directly changing the model parameters, attackers can easily inject backdoors into the model, which can trigger the model to make misclassification of targeted patterns in images. To address these issues, we propose a novel data-free trigger-generation-based defense approach based on the two characteristics of backdoor attacks: i) triggers are learned …
Cross-Subject Mind Decoding From Inaccurate Representations, Yangyang Xu, Bangzhen Liu, Wenqi Shao, Yong Du, Shengfeng He, Tingting Zhu
Cross-Subject Mind Decoding From Inaccurate Representations, Yangyang Xu, Bangzhen Liu, Wenqi Shao, Yong Du, Shengfeng He, Tingting Zhu
Research Collection School Of Computing and Information Systems
Decoding stimulus images from fMRI signals has advanced with pre-trained generative models. However, existing methods struggle with cross-subject mappings due to cognitive variability and subject-specific differences. This challenge arises from sequential errors, where unidirectional mappings generate partially inaccurate representations that, when fed into diffusion models, accumulate errors and degrade reconstruction fidelity. To address this, we propose the Bidirectional Autoencoder Intertwining framework for accurate decoded representation prediction. Our approach unifies multiple subjects through a Subject Bias Modulation Module while leveraging bidirectional mapping to better capture data distributions for precise representation prediction. To further enhance fidelity when decoding representations into stimulus images, …
Search Trajectory Network-Enhanced Multi-Objective Dynamic Algorithm Configuration, Robbert Reijnen, Zaharah Bukhsh, Hoong Chuin Lau, Yaoxin Wu, Yingqian Zhang
Search Trajectory Network-Enhanced Multi-Objective Dynamic Algorithm Configuration, Robbert Reijnen, Zaharah Bukhsh, Hoong Chuin Lau, Yaoxin Wu, Yingqian Zhang
Research Collection School Of Computing and Information Systems
Deep reinforcement learning (DRL) has emerged as an effective technique for dynamic algorithm configuration, particularly in evolutionary computation, enabling adaptive parameter updates during algorithmic execution. DRL-based methods have shown broad applicability across different problem domains and are designed to configure algorithms without problem-specific information, making them highly transferable across problem variants and scalable to different problem sizes. This paper proposes a novel graph neural network-based approach that learns representations of Search Trajectory Networks (STNs) to track the convergence behavior of multiple objectives and dynamically reconfigures multiobjective evolutionary algorithms during execution. By capturing how solutions evolve and interact over time, the …
Boosting Chart-To-Code Generation In Mllm Via Dual Preference-Guided Refinement, Zhihan Zhang, Yixin Cao, Lizi Liao
Boosting Chart-To-Code Generation In Mllm Via Dual Preference-Guided Refinement, Zhihan Zhang, Yixin Cao, Lizi Liao
Research Collection School Of Computing and Information Systems
Translating chart images into executable plotting scripts-referred to as the chart-to-code generation task-requires Multimodal Large Language Models (MLLMs) to perform fine-grained visual parsing, precise code synthesis, and robust cross-modal reasoning. However, this task is inherently under-constrained: multiple valid code implementations can produce the same visual chart, and evaluation must consider both code correctness and visual fidelity across diverse dimensions. This makes it difficult to learn accurate and generalizable mappings through standard supervised fine-tuning. To address these challenges, we propose a dual preference-guided refinement framework that combines a feedback-driven, dual-modality reward mechanism with iterative preference learning. Our approach introduces a structured …
Auxiliary Prompt Tuning Of Vision‑Language Models For Few‑Shot Out‑Of‑Distribution Detection, Wenjun Miao, Guansong Pang, Zihan Wang, Jin Zheng, Xiao Bai
Auxiliary Prompt Tuning Of Vision‑Language Models For Few‑Shot Out‑Of‑Distribution Detection, Wenjun Miao, Guansong Pang, Zihan Wang, Jin Zheng, Xiao Bai
Research Collection School Of Computing and Information Systems
Recent advancements in CLIP-based out-of-distribution (OOD) detection have shown promising results via regularization on prompt tuning, leveraging background features extracted from a few in-distribution (ID) samples as proxies for OOD features.However, these methods suffer from an inherent limitation: a lack of diversity in the extracted OOD features from the few-shot ID data.To address this issue, we propose to leverage external datasets as auxiliary outlier data (i.e., pseudo OOD samples) to extract rich, diverse OOD features, with the features from not only background regions but also foreground object regions, thereby supporting more discriminative prompt tuning for OOD detection. We further introduce …
Weak Identification Of Long Memory With Implications For Volatility Modeling;, Jia Li, Peter C. B. Phillips, Shuping Shi, Jun Yu
Weak Identification Of Long Memory With Implications For Volatility Modeling;, Jia Li, Peter C. B. Phillips, Shuping Shi, Jun Yu
Research Collection School Of Economics
This paper explores implications of weak identification in common ‘long memory’ and recent ‘rough’ approaches to modeling volatility dynamics of financial assets. We unveil an asymptotic near-observational equivalence between a long memory model with weak autoregressive dynamics and a rough model with a near-unit autoregressive root. Standard methods struggle to distinguish them, and conventional asymptotics are invalid. We propose an identification-robust approach to construct confidence sets that reveal the uncertainty and aid inference. Empirical studies based on realized volatility and trading volume often fail to statistically reject either model, thereby providing evidence of their potential coexistence.
Unpacking Singapore's Leasehold Relativity Table: An Empirical And Legal Analysis, Koon Shing Kwong, Jing Rong Goh, Seng Wei, Edward Ti
Unpacking Singapore's Leasehold Relativity Table: An Empirical And Legal Analysis, Koon Shing Kwong, Jing Rong Goh, Seng Wei, Edward Ti
Research Collection School Of Economics
In Singapore, most land is state-owned, with the state generally issuing leasehold estates via state leases of not more than 99 years1, depending on the intended land use. Naturally, the value of a leasehold estate, which erodes over time as the lease approaches the end of its term, is a key component of the premium charged for lease renewals, or the tax imposed for permission given in relation to a development that would increase the value of the land. By law, the state valuation of leasehold land is prescribed by a leasehold relativity table colloquially known as ‘Bala’s Curve’ or …