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Far far away
No full textFar Far Away is an interactive installation that explores the themes of alienation, memory, and
the search for identity through the lens of parallel worlds and quantum theory. Inspired by the concept
of Superimposition (Caltech, n.d.), the project imagines spaces where fragments of the self-retreat
beyond the boundaries of perception.
Grounded in a fictional narrative, the work follows a protagonist who experiences emotional
separation, withdrawing into a parallel world shaped by memory and longing. This personal story
mirrors broader societal experiences of disconnection, questioning what it means to belong in a
contemporary world marked by fragmentation and escapism.
This work exists in physical and digital forms. The physicality of the work exists as extensions
of self, where interactions with tangible elements gradually reveal the protagonist’s state of mind
through the spatial design of the installation. As the viewer engages more deeply, a parallel world
emerges—unlocked through focused interaction with an Arduino-enhanced object. This interaction
unveils the protagonist’s personal story, conveyed through sound and visuals in the digital realm.
Developed using Unreal Engine, the work creates a hand-built, immersive environment that resists the
logic of automation and emphasizes the role of human imagination. The project exists in both digital
and physical forms, highlighting the duality between the self that navigates society and the self that
retreats inward.
Through this experience, Far Far Away invites audiences to step into liminal spaces, reflect
on personal experiences of disconnection, and consider the psychological impact of contemporary life.Bachelor's degre
Nacre‐like ceramic–metal composites: State‐of‐the‐art, challenges, and opportunities
No full textNacre's unique brick-and-mortar microstructure, composed of aragonite platelets bonded by biopolymers, serves as a source of inspiration for designing composites with combined strength and toughness. By replicating nacre's microstructure, researchers have developed nacre-like (NL) ceramic–metal (CM) composites with superior mechanical performance and functionalities, enabling diverse applications. This review critically examines the recent advancements in NL CM composites, emphasizing their mechanical and functional properties, including strength, toughness, hardness, wear resistance, lightweight design, and electrical and thermal conductivity. The fabrication processes are categorized into two primary routes, detailing the specific microstructures that result and assessing the advantages, limitations, and alternatives associated with each process. Comparative analyses of flexural strength and fracture toughness across various NL CM composites, based on ceramic–metal combinations and ceramic volume fractions, are presented, along with discussions of specific strength and specific fracture toughness. Deviations in mechanical properties relative to the rule of mixtures are highlighted, with explanations for observed enhancements or reductions. This review highlights that good interfacial bonding between the ceramics and the metals can significantly enhance the strength and toughness of NL CM composites even at lower ceramic volume fractions. This review also explores the multifunctional attributes of some NL CM composites, concluding with an outlook on the challenges, future opportunities, and potential applications of NL CM composites in advanced material engineering.National Research Foundation (NRF)Submitted/Accepted versionThe authors acknowledge funding from the National Research Foundation of Singapore, Singapore (award NRFF 12-2020-0002)
MeshAnything: artist-created mesh generation with autoregressive transformers
No full textRecently, 3D assets created via reconstruction and generation have matched the
quality of manually crafted assets, highlighting their potential for replacement.
However, this potential is largely unrealized because these assets always need to be converted to meshes for 3D industry applications, and the meshes produced
by current mesh extraction methods are significantly inferior to Artist-Created
Meshes (AMs), i.e., meshes created by human artists. Specifically, current mesh
extraction methods rely on dense faces and ignore geometric features, leading to
inefficiencies, complicated post-processing, and lower representation quality. To
address these issues, we introduce MeshAnything, a model that treats mesh extraction as a generation problem, producing AMs aligned with specified shapes.
By converting 3D assets in any 3D representation into AMs, MeshAnything can
be integrated with various 3D asset production methods, thereby enhancing their
application across the 3D industry. The architecture of MeshAnything comprises
a VQ-VAE and a shape-conditioned decoder-only transformer. We first learn a
mesh vocabulary using the VQ-VAE, then train the shape-conditioned decoderonly transformer on this vocabulary for shape-conditioned autoregressive mesh
generation. Our extensive experiments show that our method generates AMs with
hundreds of times fewer faces, significantly improving storage, rendering, and
simulation efficiencies, while achieving precision comparable to previous methods.Agency for Science, Technology and Research (A*STAR)Ministry of Education (MOE)Published versionThis study is supported under the RIE2020 Industry Alignment Fund – Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner(s). This research is also supported by the MoE AcRF Tier 2 grant (MOE-T2EP20223- 0001)
Dynamic initialization for LiDAR-inertial SLAM
No full textThe accuracy of the initial state, including initial velocity, gravity direction, and inertial measurement unit (IMU) biases, is critical for the initialization of LiDAR-inertial simultaneous location and mapping (SLAM) systems. Inaccurate initial values can reduce initialization speed or lead to failure. When the system faces urgent tasks, robust and fast initialization is required while the robot is moving, such as during the swift assessment of rescue environments after natural disasters, bomb disposal, and restarting LiDAR-inertial SLAM in rescue missions. However, existing initialization methods usually require the platform to remain stationary, which is ineffective when the robot is in motion. To address this issue, this article introduces a robust and fast dynamic initialization method for LiDAR-inertial systems (D-LI-Init). This method iteratively aligns LiDAR-based odometry with IMU measurements to achieve system initialization. To enhance the reliability of the LiDAR odometry module, the LiDAR and gyroscope are tightly integrated within the error state iterated Kalman filter framework. The gyroscope compensates for rotational distortion in the point cloud. Translational distortion compensation occurs during the iterative update phase, resulting in the output of LiDAR-gyroscope odometry. The proposed method can initialize the system no matter the robot is moving or stationary. Experiments on public datasets and real-world environments demonstrate that the D-LI-Init algorithm can effectively serve various platforms, including vehicles, handheld devices, and UAVs. D-LI-Init completes dynamic initialization regardless of specific motion patterns. To benefit the research community, we have open-sourced our code and test datasets on GitHub.Submitted/Accepted versionThis work was supported in part by the Shandong Key R&D Program of China under Grant 2024CXGC010213
Data watermarking for sequential recommender systems
No full textIn the era of large foundation models, data has become a crucial component in building high-performance AI systems. As the demand for high-quality and large-scale data continues to rise, data copyright protection is attracting increasing attention. In this work, we explore the problem of data watermarking for sequential recommender systems, where a watermark is embedded into the target dataset and can be detected in models trained on that dataset. We focus on two settings: dataset watermarking, which protects the ownership of the entire dataset, and user watermarking, which safeguards the data of individual users. We present a method named Dataset Watermarking for Recommender Systems (DWRS) to address them. We define the watermark as a sequence of consecutive items inserted into normal users' interaction sequences. We define a Receptive Field (RF) to guide the inserting process to facilitate the memorization of the watermark. Extensive experiments on five representative sequential recommendation models and three benchmark datasets demonstrate the effectiveness of DWRS in protecting data copyright while preserving model utility.Ministry of Education (MOE)Published versionThis research is supported by the Ministry of Education, Singapore, under its Academic Research Fund (Tier 2 Award MOE-T2EP20221-0013 and Tier 1 Award (RG20/24)). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the Ministry of Education, Singapore. The Australian Research Council partially supports this work under the streams of Future Fellowship (Grant No. FT210100624), the Discovery Project (Grant No. DP240101108), and the Linkage Projects (Grant No. LP230200892 and LP240200546)
Low-valent main group 14 complexes for catalysis
No full textChapter 2 – NHC-Silyliumylidene Cation-Catalyzed Hydroboration of Isocyanates with
Pinacolborane.
Chapter 3 – XD2-Supported Low-Valent Group 14 Complexes.
Chapter 4 – XD2Sn-Catalyzed Hydroboration of Aldehydes and Ketones using HBpin.
Chapter 5 – XD2Sn-Catalyzed Hydrogenation of Alkynes using HBpin and NH3BH3.Doctor of Philosoph
Layerpano3D: layered 3D panorama for hyper-immersive scene generation
No full text3D immersive scene generation is a challenging yet critical task in computer vision and graphics. A desired virtual 3D scene should 1) exhibit omnidirectional view consistency, and 2) allow for large-range exploration in complex scene hierarchies. Existing methods either rely on successive scene expansion via inpainting or employ panorama representation to represent large FOV scene environments. However, the generated scene suffers from semantic drift during expansion and is unable to handle occlusion among scene hierarchies. To tackle these challenges, we introduce LayerPano3D, a novel framework for full-view, explorable panoramic 3D scene generation from a single text prompt. Our key insight is to decompose a reference 2D panorama into multiple layers at different depth levels, where each layer reveals the unseen space from the reference views via diffusion prior. LayerPano3D comprises multiple dedicated designs: 1) We introduce a new panorama dataset Upright360 , comprising 9k high-quality and upright panorama images, and finetune the advanced Flux model on Upright360 for high-quality, upright and consistent panorama generation related tasks. 2) We pioneer the Layered 3D Panorama as underlying representation to manage complex scene hierarchies and lift it into 3D Gaussians to splat detailed 360-degree omnidirectional scenes with unconstrained viewing paths. Extensive experiments demonstrate that our framework generates state-of-the-art 3D panoramic scene in both full view consistency and immersive exploratory experience. We believe that LayerPano3D holds promise for advancing 3D panoramic scene creation with numerous applications. More examples please visit our webpage: ys-imtech.github.io/projects/LayerPano3D/.Agency for Science, Technology and Research (A*STAR)Ministry of Education (MOE)Published versionThis study is supported by Shanghai AI Laboratory and the Ministry of Education, Singapore, under its MOE AcRF Tier 2 (MOET2EP20221-0012, MOE-T2EP20223-0002), and under the RIE2020 Industry Alignment Fund – Industry Collaboration Projects (IAFICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner(s)
Being Jewish, a good neighbour, and abhorring war
No full textAntisemitism is a rising problem in many parts of the world, including in Southeast Asia. Israel’s latest conflicts in Gaza and with its neighbours have exacerbated the problem. Distinctions must be preserved between people of the Jewish faith and those who support a particular Israeli political agenda.Published versio
Echoes of Rome: Elon Musk's governmental reforms and the lessons from history
No full textThis article analyses Elon Musk’s governmental reforms through the historical lens of the Gracchi brothers’ reforms in ancient Rome. It explores how ambitious attempts to streamline governance and disrupt entrenched interests can lead to elite resistance, political instability, and unintended consequences. It draws parallels between Musk’s bureaucratic overhaul and Rome’s slide into factional conflict.Published versio
Body movement mimic - video-based human body motion transfer
No full textAnimating a still image to reflect realistic human motion is a complex yet transformative task with
wide-ranging applications in entertainment, virtual reality, and digital media. While recent
advances in human motion transfer models, such as MagicAnimate and AnimateAnyone, have
made significant strides, they still face significant challenges. MagicAnimate for one fails to
preserve the natural appearance of the reference subject due to the misalignment of body shape
between the driving video’s DensePose representation of the driving video and the reference
image. This often results in unnatural modifications and distorted outputs. To tackle this challenge,
this project proposes training a shape-aware model that generates DensePose representations
aligned with the reference image’s body shape. The model is a dual-branched U-Net architecture
with cross-attention that effectively transfers textures and skeletal structure. Various experimental
approaches were explored and refined to optimise the model, ultimately resulting in the best
performance that fits memory and time constraints of this project. This enhanced representation is
then used as the driving video within the MagicAnimate pipeline, enabling more accurate motion
transfer by leveraging its ability to preserve fine details and minimise artifacts. The findings from
this study contribute to advancing the robustness of human motion transfer techniques and
enhancing their applicability in diverse real-world scenarios.Bachelor's degre