arXiv subjects

Yi-Cheng Chen

Publications and source records attributed to Yi-Cheng Chen.

7 recordsLinked to original sources

arXiv2022

Homoepitaxy of rhombohedral-stacked MoS2 with room temperature switchable ferroelectricity

The discovery of interfacial ferroelectricity in two-dimensional rhombohedral (3R)-stacked semiconductors opens up a new pathway for achieving ultrathin computing-in-memory devices. However, exploring ferroelectricity switching in natural 3R crystals is difficult due to lack of co-existing 3R stacking domains. Here, we present that MoS2 homoepitaxial patterns with 3R polytypic domains can manifest switchable ferroelectricity at room-temperature. Based on the diffusion limited aggregation theory, such MoS2 patterns are formed under the low Mo chemical potential and low temperature with respect to common chemical vapor deposition synthesis. The alternation of 3R polytypes in the MoS2 homoepitaxial patterns, observed by scanning transmission electron microscopy, accounts for ferroelectricity switching. The MoS2 field-effect transistors with 3R polytypic domains exhibit a repeatable counterclockwise hysteresis with gate voltage sweeping, an indication of ferroelectricity switching, and the memory window exceeds those measured for compact-shaped 3R bilayer devices. This work provides a direct growth concept for layered 3R-based ferroelectric memory.

cond-mat.mtrl-sci↗

arXiv2022

Miutsu: NTU's TaskBot for the Alexa Prize

This paper introduces Miutsu, National Taiwan University's Alexa Prize TaskBot, which is designed to assist users in completing tasks requiring multiple steps and decisions in two different domains -- home improvement and cooking. We overview our system design and architectural goals, and detail the proposed core elements, including question answering, task retrieval, social chatting, and various conversational modules. A dialogue flow is proposed to provide a robust and engaging conversation when handling complex tasks. We discuss the faced challenges during the competition and potential future work.

cs.CL↗

arXiv2021

Accurate and Generalizable Quantitative Scoring of Liver Steatosis from Ultrasound Images via Scalable Deep Learning

Background & Aims: Hepatic steatosis is a major cause of chronic liver disease. 2D ultrasound is the most widely used non-invasive tool for screening and monitoring, but associated diagnoses are highly subjective. We developed a scalable deep learning (DL) algorithm for quantitative scoring of liver steatosis from 2D ultrasound images. Approach & Results: Using retrospectively collected multi-view ultrasound data from 3,310 patients, 19,513 studies, and 228,075 images, we trained a DL algorithm to diagnose steatosis stages (healthy, mild, moderate, or severe) from ultrasound diagnoses. Performance was validated on two multi-scanner unblinded and blinded (initially to DL developer) histology-proven cohorts (147 and 112 patients) with histopathology fatty cell percentage diagnoses, and a subset with FibroScan diagnoses. We also quantified reliability across scanners and viewpoints. Results were evaluated using Bland-Altman and receiver operating characteristic (ROC) analysis. The DL algorithm demonstrates repeatable measurements with a moderate number of images (3 for each viewpoint) and high agreement across 3 premium ultrasound scanners. High diagnostic performance was observed across all viewpoints: area under the curves of the ROC to classify >=mild, >=moderate, =severe steatosis grades were 0.85, 0.90, and 0.93, respectively. The DL algorithm outperformed or performed at least comparably to FibroScan with statistically significant improvements for all levels on the unblinded histology-proven cohort, and for =severe steatosis on the blinded histology-proven cohort. Conclusions: The DL algorithm provides a reliable quantitative steatosis assessment across view and scanners on two multi-scanner cohorts. Diagnostic performance was high with comparable or better performance than FibroScan.

eess.IV↗

arXiv2020

A Time-dependent SIR model for COVID-19 with Undetectable Infected Persons

In this paper, we conduct mathematical and numerical analyses to address the following crucial questions for COVID-19: (Q1) Is it possible to contain COVID-19? (Q2) When will be the peak and the end of the epidemic? (Q3) How do the asymptomatic infections affect the spread of disease? (Q4) What is the ratio of the population that needs to be infected to achieve herd immunity? (Q5) How effective are the social distancing approaches? (Q6) What is the ratio of the population infected in the long run? For (Q1) and (Q2), we propose a time-dependent susceptible-infected-recovered (SIR) model that tracks 2 time series: (i) the transmission rate at time t and (ii) the recovering rate at time t. Such an approach is more adaptive than traditional static SIR models and more robust than direct estimation methods. Using the data provided by China, we show that the one-day prediction errors for the numbers of confirmed cases are almost in 3%, and the total number of confirmed cases is precisely predicted. Also, the turning point, defined as the day that the transmission rate is less than the recovering rate can be accurately predicted. After that day, the basic reproduction number $R_0$ is less than 1. For (Q3), we extend our SIR model by considering 2 types of infected persons: detectable and undetectable infected persons. Whether there is an outbreak in such a model is characterized by the spectral radius of a 2 by 2 matrix that is closely related to $R_0$. For (Q4), we show that herd immunity can be achieved after at least 1-1/$R_0$ fraction of individuals being infected. For (Q5) and (Q6), we analyze the independent cascade (IC) model for disease propagation in a configuration random graph. By relating the propagation probabilities in the IC model to the transmission rates and recovering rates in the SIR model, we show 2 approaches of social distancing that can lead to a reduction of $R_0$.

q-bio.PE↗

arXiv2019

Revival of Quantum Interference by Modulating the Biphotons

The possibility to manipulate the wavepackets of single photons or biphotons has enriched quantum optics and quantum information science, with examples ranging from faithful quantum-state mapping and high-efficiency quantum memory to the purification of single photons. Here we demonstrate another fascinating use of wavepacket manipulation on restoring quantum interference. By modulating the photons' temporal wavepacket, we observe the revival of post-selected entanglement that would otherwise be degraded or lost due to poor quantum interference. Our study shows that the amount of the restored entanglement is only limited by the forms of modulation and can achieve full recovery if the modulation function is properly designed. Our work has potential applications in long-distance quantum communication and linear optical quantum computation, particularly for quantum repeaters and large cluster states.

quant-ph↗

arXiv2019

Thickness dependence of the anomalous Hall effect in thin films of the topological semimetal Co$_2$MnGa

Topological magnetic semimetals promise large Berry curvature through the distribution of the topological Weyl nodes or nodal lines and further novel physics with exotic transport phenomena. We present a systematic study of the structural and magnetotransport properties of Co$_2$MnGa films from thin (20 nm) to bulk like behavior (80 nm), in order to understand the underlying mechanisms and the role on the topology. The magnetron sputtered Co$_2$MnGa films are $L$$2_{\mathrm {1}}$-ordered showing very good heteroepitaxy and a strain-induced tetragonal distortion. The anomalous Hall conductivity was found to be maximum at a value of 1138 S/cm, with a corresponding anomalous Hall angle of 13 %, which is comparatively larger than topologically trivial metals. There is a good agreement between the theoretical calculations and the Hall conductivity observed for the 80 nm film, which suggest that the effect is intrinsic. Thus, the Co$_2$MnGa compound manifests as a promising material towards topologically-driven spintronic applications.

cond-mat.mtrl-sci↗

arXiv2018

Large anomalous Nernst effect in thin films of the Weyl semimetal Co2MnGa

The magneto-thermoelectric properties of Heusler compound thin films are very diverse. Here, we discuss the anomalous Nernst response of Co$_2$MnGa thin films. We systematically study the anomalous Nernst coefficient as a function of temperature, and we show that unlike the anomalous Hall effect, the anomalous Nernst effect in Co$_2$MnGa strongly varies with temperature. We exploit the on-chip thermometry technique to quantify the thermal gradient, which enables us to directly evaluate the anomalous Nernst coefficient. We compare these results to a reference CoFeB thin film. We show that the 50-nm-thick Co$_2$MnGa films exhibit a large anomalous Nernst effect of -2$μ$V/K at 300 K, whereas the 10-nm-thick Co$_2$MnGa film exhibits a significantly smaller anomalous Nernst coefficient despite having similar volume magnetizations. These findings suggest that the microscopic origin of the anomalous Nernst effect in Co$_2$MnGa is complex and may contain contributions from skew-scattering, side-jump or intrinsic Berry phase. In any case, the large anomalous Nernst coefficent of Co$_2$MnGa thin films at room temperature makes this material system a very promising candidate for efficient spin-caloritronic devices.

cond-mat.mes-hall↗