Public commemorations and remembrance

Public commemorative artefacts (including public monuments) typically mark out some historical subject – typically, a person or an event – as important for a community to remember. This chapter surveys the budding literature on the historical character of public commemorative artefacts. First, it details three typical aims of public commemorative artefacts as they pertain to public remembrance. They declare the importance of some historical subject, impart ethical or political lessons, and foster community identity that is grounded in shared remembrance of the past. Next, it outlines two common problems with public commemorative artefacts. They can present incomplete or distorted accounts of history, and lead people to abdicate responsibility for the past. The chapter proposes an account of democratic public historiography that addresses the problems with public commemorative artefacts.Submitted/Accepted versio

Backdoor in deep learning: new threats and opportunities

Deep learning has become increasingly popular due to its remarkable ability to learn high-dimensional feature representations. Numerous algorithms and models have been developed to enhance the application of deep learning across various real-world tasks, including image classification, natural language processing, and autonomous driving. However, deep learning models are susceptible to backdoor threats, where an attacker manipulates the training process or data to cause incorrect predictions on malicious samples containing specific triggers, while maintaining normal performance on benign samples. With the advancement of deep learning, including evolving training schemes and the need for large-scale training data, new threats in the backdoor domain continue to emerge. Conversely, backdoors can also be leveraged to protect deep learning models, such as through watermarking techniques. In this thesis, we conduct an in-depth investigation into backdoor techniques from three novel perspectives. In the first part of this thesis, we demonstrate that emerging deep learning training schemes can introduce new backdoor risks. Specifically, pre-trained Natural Language Processing (NLP) models can be easily adapted to a variety of downstream language tasks, significantly accelerating the development of language models. However, the pre-trained model becomes a single point of failure for these downstream models. We propose a novel task-agnostic backdoor attack against pre-trained NLP models, wherein the adversary does not need prior information about the downstream tasks when implanting the backdoor into the pre-trained model. Any downstream models transferred from this malicious model will inherit the backdoor, even after extensive transfer learning, revealing the severe vulnerability of pre-trained foundation models to backdoor attacks. In the second part of this thesis, we develop novel backdoor attack methods suited to new threat scenarios. The rapid expansion of deep learning models necessitates large-scale training data, much of which is unlabeled and outsourced to third parties for annotation. To ensure data security, most datasets are read-only for training samples, preventing the addition of input triggers. Consequently, attackers can only achieve data poisoning by uploading malicious annotations. In this practical scenario, all existing data poisoning methods that add triggers to the input are infeasible. Therefore, we propose new backdoor attack methods that involve poisoning only the labels without modifying any input samples. In the third part of this thesis, we utilize the backdoor technique to proactively protect our deep learning models, specifically for intellectual property protection. Considering the complexity of deep learning tasks, generating a well-trained deep learning model requires substantial computational resources, training data, and expertise. Therefore, it is essential to protect these assets and prevent copyright infringement. Inspired by backdoor attacks that can induce specific behaviors in target models through carefully designed samples, several watermarking methods have been proposed to protect the intellectual property of deep learning models. Model owners can train their models to produce unique outputs for certain crafted samples and use these samples for ownership verification. While various extraction techniques have been designed for supervised deep learning models, challenges arise when applying them to deep reinforcement learning models due to differences in model features and scenarios. Therefore, we propose a novel watermarking scheme to protect deep reinforcement learning models from unauthorized distribution. Instead of using spatial watermarks as in conventional deep learning models, we design temporal watermarks that minimize potential impact and damage to the protected deep reinforcement learning model while achieving high-fidelity ownership verification. In summary, this thesis investigates the evolving landscape of backdoor threats during the development of deep learning techniques and the use of backdoors for beneficial purposes in intellectual property protection.Doctor of Philosoph

Microfluidic SERS biosensor based on Au-semicoated photonic crystals for melanoma diagnosis

Surface-enhanced Raman scattering (SERS) shows great promise for early diagnosis due to its high specificity and rapid detection capabilities. However, its application is often hindered by substrate instability and insufficient interaction between the substrate and incident light. To address these challenges, a photonic-plasmonic strategy is often employed to enhance sensing performance but it is generally limited by the low efficiency of plasmonic metal and optical cavity resonances. In this study, we significantly improved resonance efficiency by optimizing the photonic crystal configuration and designing Au-semicoated polystyrene nanospheres. These modifications maximized light capture and resonance efficiency, resulting in a 790-fold enhancement of the Raman signal with a relative standard deviation of only 4.58%. This approach was further developed into microfluidic biosensors for melanoma diagnosis, achieving a 2-3 order-of-magnitude improvement over comparable SERS biosensors. We believe this technology has the potential to significantly improve the efficiency of early diagnosis and clinical medical analysis.Published versionThis work was supported by the National Natural Science Foundation of China NSFC (61821002, 62375049, 62335003, 62075041), the Basic Research Program of Jiangsu Province (BK20222007). W. Wang also acknowledges the support of the China Scholarship Council program (Project ID: 202306090123)

Catalytic reforming of biomass pyrolysis gas over Ni catalysts: alumina, spent fluid catalytic cracking catalyst and char as supports

The potential replaceability of a Ni catalyst supported on commercial α-Al2O3 (Ni/Al2O3) by Ni on biomass-derived char (Ni/Char), and Ni on spent fluid catalytic cracking catalyst (Ni/FCC) for steam reforming of biomass pyrolysis gas was investigated (14 h at 850°C, steam/carbon ratio = 5). The catalysts reformed 60–80 % of C2-C5 hydrocarbons, producing 2.7–4.1 mg min−1 of H2. The reforming activity of Ni/Al2O3 and Ni/FCC was higher compared to Ni/Char, indicating the beneficial role of metal oxide supports. The use of Al2O3 and FCC resulted in a lower thermo-oxidative stability of coke formed on Ni/Al2O3 and Ni/FCC compared to Ni/Char. Furthermore, the deposited Ni showed higher stability towards oxidation by steam into NiO in case of Al2O3 and FCC compared to char. According to reforming activity, H2 production rate, coking, and Ni oxidation of the catalysts, FCC has better prospects as an alternative support in a reforming catalyst than char.Agency for Science, Technology and Research (A*STAR)National Research Foundation (NRF)Public Utilities Board (PUB)This research is supported by A*STAR under its RIE2025 Industry Alignment Fund- Industry Collaboration Projects (IAF-ICP) Programme (Award I2101E0006). This research is also supported by the National Research Foundation, Singapore, and PUB, Singapore’s National Water Agency under its RIE2025 Urban Solutions and Sustainability (USS) (Water) Centre of Excellence (CoE) Programme which provides funding to the Nanyang Environment & Water Research Institute (NEWRI) of the Nanyang Technological University, Singapore (NTU). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore and PUB, Singapore’s National Water Agency

Unravelling the role of filler surface wettability in long-term mechanical and dielectric properties of epoxy resin composites under hygrothermal aging

Epoxy resin (EP) incorporating inorganic fillers has garnered significant attention in the electrical and electronic industries due to its enhanced dielectric and mechanical properties, but its long-term performance under harsh conditions remains a critical concern. This study investigates the effects of filler surface wettability on the durability of EP-SiO2 composites. Micro-sized SiO2 with hydrophilic (HP) and hydrophobic (HB) surfaces are prepared via surface treatment, before they are incorporated into epoxy resin and subjected to hygrothermal aging at 95 °C and 95 % relative humidity for up to 1200 h. Comprehensive characterizations of wettability, microstructure, mechanical properties, and dielectric performance are conducted. Results show that the composite with hydrophilic fillers, HP-SiO2-EP, exhibits superior dispersion and interfacial adhesion compared to its hydrophobic counterpart, HB-SiO2-EP. Consequently, HP-SiO2-EP demonstrates higher initial tensile strength, Young's modulus, and dielectric breakdown strength. Finite element simulations reveal the breakdown mechanism, highlighting that the hydrophobic SiO2 filler with interfacial defects results in earlier mechanical and dielectric failure. Furthermore, HP-SiO2-EP shows better resistance to hygrothermal aging compared to HB-SiO2-EP, with smaller increases in dielectric constant (+13 % vs. +28 %) and dielectric loss (+234 % vs. +311 %), as well as lower decrease in volume resistivity (-89 % vs. -93 %). This study provides valuable insights into the relationship between filler surface wettability and long-term composite performance, contributing to the design of more reliable materials for advanced dielectric applications.Energy Market Authority (EMA)Ministry of Education (MOE)National Research Foundation (NRF)Submitted/Accepted versionThis research is supported by SP Group, the National Research Foundation, Singapore, the Energy Market Authority, under its Energy Programme (EMA-EP010-SNJL-002) and Ministry of Education, Singapore (RG7/24)

Identifying the knowledge and capacity gaps in Southeast Asian insect conservation

Insects represent most of terrestrial animal biodiversity, and multiple reports suggest that their populations are declining globally due to anthropogenic impacts. Yet, a high proportion of insect species remain undescribed and limited data on their population dynamics hamper insect conservation efforts. This is particularly critical in tropical biodiversity hotspots such as Southeast Asia. To identify knowledge and capacity gaps in Southeast Asian insect conservation, we performed a quantitative review of insect occurrence records, studies for the region and global '#conservation' posts from Twitter. We found that occurrence records increased over time, and were dominated by butterflies. Overall, studies were largely focused on pest and vector groups, and insect conservation and ecology studies were lacking in many countries. Despite an increase in local authorships and funding sources over time, the majority of these were still located outside of Southeast Asia. In '#conservation' posts, insects were highly under-represented and insect-related content was biased towards popular groups such as bees and butterflies. We suggest potential solutions to address these gaps, such as integrative taxonomic approaches, and increasing regional collaborations and public engagements. Crucially, we stress the need for political will and funding to overcome the impediments towards insect conservation efforts in Southeast Asia.Ministry of Education (MOE)Nanyang Technological UniversitySubmitted/Accepted versionXRO was supported by the Nanyang President’s Graduate Scholarship from Nanyang Technological University. BT was supported by the Nanyang Scholarship from Nanyang Technological University. CHC acknowledges support from Pomona College and the Bezos Earth Fund. EMS and NP were supported by the Singapore Ministry of Education (MOE) Academic Research Fund (AcRF) Tier 2 grant (Grant number: MOE- T2EP30221–0020)

Modeling and vulnerability analysis of UAV swarm based on two-layer multi-edge complex network

Swarm systems of unmanned aerial vehicles (UAVs) have emerged as a popular subject of research on the Internet of Things owing to their higher flexibility, efficiency, and reliability than single UAVs. However, little research has been devoted to investigating the vulnerability of UAV swarms, and the traditional network model cannot fully and synchronously characterize their communication-based and mission-based relationships. This study proposes a two-layer multi-edge complex network model to characterize the UAV swarm by considering its status of communication and collaboration for the given mission. The model contains a communication layer and a function layer. In addition, we consider the area of coverage of the UAV swarm, provide the definitions and methods of calculation of three factors influencing its performance, and use them to develop a method to assess its performance for the three typical missions of attack, reconnaissance, and jamming. Furthermore, we propose a framework for the vulnerability analysis of the UAV swarm that can analyze its process of failure and measure its vulnerability. Finally, we use a swarm consisting of 10 UAVs as a case to verify the effectiveness and accuracy of the proposed model

Atomistic and finite element modeling of mechanical properties and energy dissipation mechanisms in 3D aerosolization-based Voronoi graphene foams

Three-dimensional (3D) graphene materials exhibit significant potential for application due to their multifunctional properties, which merge the intrinsic characteristics of 2D graphene with added porosity and unique 3D structural morphologies. In particular, 3D closed-cellular network graphene demonstrates remarkable stiffness while maintaining super-elasticity, outperforming most previously reported carbon-based foams. However, the mechanical properties and energy dissipation mechanisms of these 3D closed-cellular network structures remain poorly understood. To address this, we propose an innovative approach using computational synthesis to construct 3D Voronoi graphene models. Molecular dynamics (MD) and finite element (FE) simulations were then employed to investigate the mechanical properties and microstructure evolution of these 3D Voronoi structures. The results show that the power indices for Young’s modulus, tensile strength, and compressive plateau stress as functions of relative density align closely with the theoretical values for ideal closed-cell foams (1, 1, and 2), indicating that the Voronoi structure exhibits a stretching-dominated deformation behavior. Young’s modulus of the experimental 3D closed-cell graphene precisely follows the fitting function of the continuum model, validating the accuracy of our 3D Voronoi structural morphologies and the significance of our simulation work. Cyclic loading simulations were also conducted to assess the energy absorption and recovery capabilities of 3D graphene. The findings suggest that lower relative densities result in reduced energy dissipation due to less damage at cell boundaries and effective stress relief through bending and folding. In contrast, higher relative densities lead to increased energy dissipation due to higher stress concentrations and associated damage. Overall, this study offers insights into the deformation mechanisms and energy absorption characteristics of 3D Voronoi graphene, enhancing our understanding of the performance and potential applications of 3D graphene.National Research Foundation (NRF)Submitted/Accepted versionThe authors acknowledge funding from the National Research Foundation of Singapore (award NRF-NRFF12-2020-0002)

Recovering Reed-Solomon codes privately

We investigate the problems of privately repairing erasures and evaluating their linear combinations for Reed-Solomon codes with low communication bandwidths. We propose two approaches: one based on hiding subspaces used to form parity-check equations, and another based on multiplying parity-check equations with random polynomials. We also derive a lower bound on the repair bandwidth for the single erasure case under reasonable assumptions about the schemes being used and demonstrate the optimality of the proposed schemes for codes of specific lengths.Ministry of Education (MOE)National Research Foundation (NRF)Submitted/Accepted versionThis research is supported by the National Research Foundation, Singapore under its Strategic Capability Research Centres Funding Initiative, Ministry of Education, Singapore, under its MOE Academic Research Fund Tier 2 Grants MOE-T2EP20121-0007 and MOE-000623-00, Tier 1 Grant RG19/23, Australia Research Council (ARC) DECRA Grant DE180100768, Israel Science Foundation (ISF) under Grant 2462/24. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore

Trump's Gaza plan: a dangerous provocation

Donald Trump’s proposal to turn Gaza into a new “Riviera of the Middle East” by forcibly displacing indigenous Palestinians violates international law and mirrors failed Western tactics. The enduring trauma of the 1948 Nakba (Arabic for “catastrophe”) when the State of Israel was proclaimed by David Ben-Gurion, remains undiminished. The current ceasefire must lead to recognition and implementation of practical and realistic measures to achieve justice for Palestinians.Published versio