DR-NTU (Data) (Nanyang Technological University)
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1955 research outputs found
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Dual-Conductive and Stiffness-Morphing Microneedle Patch Enables Continuous in Planta Monitoring of Electrophysiological Signal and Ion Fluctuation
No full textThe use of conductive microneedles presents a promising solution for achieving high-fidelity electrophysiological recordings with minimal impact on interfaced tissue. However, conventional metal-based microneedle suffers from high electrochemical impedance and mechanical mismatch. In this paper, we report a dual-conductive (i.e., both ionic and electronic conductive) and stiffness-morphing microneedle patch (DSMNP) for high-fidelity electrophysiological recordings with reduced tissue damage. The polymeric network of the DSMNP facilitates electrolyte absorption, and therefore allows the transition of stiffness from 6.82 N m-1 to 0.5139 N m-1. Furthermore, the nanoporous conductive polymer increased the specific electrochemical surface area after tissue penetration, resulting in ultralow specific impedance of 840 kΩ mm2 at 10 Hz. DSMNPs detected variation potential and action potential in real time and cation fluctuations in plants in response to environmental stimuli. After swelling, DSMNPs mechanically “lock” into biological tissues and prevent motion artifact by providing stable interface. These results demonstrate the potential of DSMNPs for various applications in the field of plant physiology research and smart agriculture
One-Way Reflection-Free Exciton-Polariton Spin-Filtering Channel
No full textWe consider theoretically exciton-polaritons in a strip of honeycomb lattice with zigzag edges and it is shown that the interplay among the spin-orbit coupling, Zeeman splitting, and an on-site detuning between sublattices can give rise to a band structure where all the edge states of the system split in energy. Within an energy interval, one of the spin-polarized edge states resides with the gapless bulk that has opposite spin. Being surrounded by opposite spin, and the absence of the backward-propagating edge state, ensures both reflection-free and feedback-suppressed one-way flow of polaritons with one particular spin in the system. The edge states in this system are more localized than those in the standard topological-polariton systems and are fully spin polarized. This paves the way for feedback-free spin-selective polariton channels for transferring information in polariton networks
Research Data for MOE Tier 1 Grant
No full textResearch Data for MOE Tier 1 Grant, "Domestic and Transnational Legal Risks and Opportunities for Foreign Businesses in China.
Replication Data for: Designing hybrid-guidance large mode area fiber for high-power lasers
No full textThis dataset contains the related data for journal article titled "Designing hybrid-guidance large mode area fiber for high-power lasers", published in Results in Physics journal (https://doi.org/10.1016/j.rinp.2023.106491
Development and Evaluation data for Multilingual Everyday Recordings - Language Identification on Code-Switched Child-Directed Speech (MERLIon CCS) Challenge
No full textThe inaugural Multilingual Everyday Recordings - Language Identification on Code-Switched Child-Directed Speech (MERLIon CCS) Challenge focuses on developing robust language identification and language diarization systems that are reliable for non-standard, accented, spontaneous code-switched, child-directed speech collected via Zoom. This dataset contains the development set and evaluation sets for two Tasks in the 2023 MERLIon CCS Challenge, a special session at INTERSPEECH 2023 (Theme: 'Inclusive Spoken Language Science and Technology – Breaking Down Barriers').
As videocalls become increasingly ubiquitous, we present a unique first-of-its-kind Zoom videocall dataset. The MERLIon CCS Challenge tackles automatic language identification and language diarization in a subset of audio recordings from the Talk Together Study, where parents narrated an onscreen wordless picturebook to their child. The main objectives of this inaugural challenge are:
• to benchmark the current and novel language identification and language diarization systems in a code-switching scenario including extremely short utterances;
• to test the robustness of such systems under accented speech;
• to challenge the research community to propose novel solutions in terms of adaptation, training, and novel embedding extraction for this particular set of tasks.
The challenge features language identification (Task 1) and language diarization (Task 2). Two tracks, open and closed, are available. The tracks differ by the data used during system training. More information can be found in the MERLIon CCS Challenge Evaluation Plan and the MERLIon CCS Challenge GitHub.
The public release of the Challenge audio data includes minor revisions following the conclusion of the challenge, constituting no more than .0001% of the labeled data.
Due to the nature of the audio and the data release agreement with the participants, all downloads from this repository will require an agreement to the terms of use.
To preview the metadata associated with the datasets contained here, you can access the documentation without downloading any files here.
This collection contains two versions of the data, a legacy archive (LEGACY_ARCHIVE) containing all original files in their original file structure and a set of download files (DOWNLOAD_FILES), formatted for efficient download. In the section below, please click Tree view to see the file structure
Related Data for: Tier 1 grant RG221
No full textSelective On-Site Hydrogen Peroxide Synthesis via Electrochemical Oxygen Reduction Reactio
Efficacy and feasibility of a Human-AI sleep coaching model
No full textThis repository contains data and resources related to the study conducted to evaluate the efficacy and feasibility of a human-AI sleep coaching model, test the performance of the QA system, and build an extensive training dataset and perform text mining of recording conversations between human health coaches and students. The dataset is organized in the following structure to facilitate understanding and reuse. This study was approved by the Institutional Review Board of Nanyang Technological University (IRB-2021-739) and is funded by the Accelerating Creativity & Excellence award (grant number 020373-00001)
Synthetic noise dataset
No full textThe synthetic noise dataset is divided into 3 subsets: 80,000 noise tracks for training, 2,000 noise tracks for validation, and remaining 2,000 noise tracks for testing. The synthetic noise tracks are generated by filtering white noise through various band-pass filters with randomly chosen center frequencies and bandwidths. Each noise track in the dataset has a 1-second duration with a sample rate of 16 kHz
Replication Data for: Fingerprint-Enhanced Graph Attention Network (FinGAT) Model for Antibiotic Discovery
No full textArtificial Intelligence (AI) techniques are of great potential to fundamentally change antibiotic discovery industries. Efficient and effective molecular featurization is key to all highly accurate learning models for antibiotic discovery. In this paper, we propose a fingerprint-enhanced graph attention network (FinGAT) model by the combination of sequence-based 2D fingerprints and structure-based graph representation. In our feature learning process, sequence information is transformed into a fingerprint vector, and structural information is encoded through a GAT module into another vector. These two vectors are concatenated and input into a multilayer perceptron (MLP) for antibiotic activity classification. Our model is extensively tested and compared with existing models. It has been found that our FinGAT can outperform various state-of-the-art GNN models in antibiotic discovery