Seeds We Sow by Lai, Gerald Sze Hang

Get full text

Coexistence by Han, HuanHuan

Get full text

You think therefore I am. by Woo, Junhao Samuel.

Get full text

Friendly foe - in-depth exploration in character animation. by Mohd. Fawzir, Firdiana.

Get full text

There's something in nothing. by Nuraidah Abdul Rauf.

Get full text

ComShell. by Ng, Yin Seng.

Get full text

(Dot) Type. by Godfrey, Vanessa Dora.

Get full text

Vectors of power and specters of control. by Tan, Jonathan Wei Peng.

Get full text

Venue : the garden celebrating Singapore’s greenery planning by Zhao, Wenwen

Get full text

Buaian chair by Lee, Ivy Jia Xin

Get full text

Bloom : engagement kit designed to encourage social interaction between people with young-onset Alzheimer's by Komari, Amira

Get full text

2400 by Ang, Kai Lin

Get full text

Lotte Reiniger re-imagined by Kang, Fabian Yong Sheng

Get full text

Morphology‐Directed Light Emission from Fluorescent Janus Colloids for Programmable Chemical‐To‐Optical Signal Transduction by Frank, Bradley D., Nagelberg, Sara, Baryzewska, Agata W., Simón Marqués, Pablo, Antonietti, Markus, Kolle, Mathias, Zeininger, Lukas

“…Informed by experimental observations of morphology‐dependent optical confinement of internally emitted light within the higher refractive index phases, ray‐tracing is used to predict and fine‐tune the droplets’ optical properties and their ability to concentrate light. …”
Get full text

Tango 2: Aligning Diffusion-based Text-to-Audio Generations through Direct Preference Optimization by Majumder, Navonil, Hung, Chia-Yu, Ghosal, Deepanway, Hsu, Wei-Ning, Mihalcea, Rada, Poria, Soujanya

“…The loser outputs, in theory, have some concepts from the prompt missing or in an incorrect order. We fine-tune the publicly available Tango text-to-audio model using diffusion-DPO (direct preference optimization) loss on our preference dataset and show that it leads to improved audio output over Tango and AudioLDM2, in terms of both automatic- and manual-evaluation metrics.…”
Get full text

Defects in β-cell Ca2+ dynamics in age-induced diabetes by Li, Luosheng, Trifunovic, Aleksandra, Köhler, Martin, Wang, Yixin, Petrovic Berglund, Jelena, Illies, Christopher, Juntti-Berggren, Lisa, Larsson, Nils-Göran, Berggren, Per-Olof

“…Our data suggest that aging is associated with a progressive decline in β-cell mitochondrial function that negatively impacts on the fine tuning of Ca2+ dynamics. This is conceptually important since it emphasizes that even relatively modest changes in β-cell signal transduction over time lead to compromised insulin release and a diabetic phenotype.…”
Get full text
Get full text

A two-stage outlier filtering framework for city-scale localization using 3D SfM point clouds by Cheng, Wentao, Chen, Kan, Lin, Weisi, Goesele, Michael, Zhang, Xinfeng, Zhang, Yabin

“…Second, we apply a geometry-based outlier filter to generate a set of fine-grained matches with a novel data-driven geometrical constraint for efficient inlier evaluation. …”
Get full text

Soil liquefaction assessment using soft computing approaches based on capacity energy concept by Chen, Zhixiong, Li, Hongrui, Goh, Anthony Teck Chee, Wu, Chongzhi, Zhang, Wengang

“…Several liquefaction evaluation procedures and approaches have been developed relating the capacity energy to the initial soil parameters, such as the relative density, initial effective confining pressure, fine contents, and soil textural properties. In this study, based on the capacity energy database by Baziar et al. (2011), analyses have been carried out on a total of 405 previously published tests using soft computing approaches, including Ridge, Lasso & LassoCV, Random Forest, eXtreme Gradient Boost (XGBoost), and Multivariate Adaptive Regression Splines (MARS) approaches, to assess the capacity energy required to trigger liquefaction in sand and silty sands. …”
Get full text

Generating domain-specific paraphrases of questions from FAQ by Ng, Jing Rui

“…Firstly, T5 is used to fine-tune on the paraphrase dataset for the task of paraphrase generation. …”
Get full text

Hedonic and non-hedonic bias toward the future by Greene, Preston, Latham, Andrew J., Miller, Kristie, Norton, James

“…Instead, we develop a more fine-grained approach, according to which three factors—positive/negative valence, first/third-person, and hedonic/non-hedonic—each independently influence, but do not determine, whether an event is treated in a future-biased or a time-neutral way. …”
Get full text