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Yu-Ting Huang

Publications and source records attributed to Yu-Ting Huang.

14 recordsLinked to original sources
arXiv2026

Unconventional crystallization pathway bypassing the intermediate cubic phase in phase-change superlattices

The Ge-Sb-Te (GST) superlattice phase-change material is a promising candidate for overcoming the high power-consumption of phase-change memory (PCM). However, the working mechanism of the superlattice PCM remains controversial. Partial amorphization, which is currently considered the most plausible mechanism, remains hotly debated: how does the partially amorphized GST recrystallize into its superlattice phase instead of the conventionally expected cubic phase? Here, we address this issue using large-scale molecular dynamics simulations enabled by a machine-learning interatomic potential. Starting from a partially melted GST superlattice, we demonstrate that the residual crystalline regions serve as nuclei, enabling the amorphous GST to recrystallize directly into the superlattice phase without passing through the intermediate cubic phase. Moreover, the recrystallized phase is not an ideal superlattice, but rather a structurally ordered and chemically disordered defective superlattice characterized by anti-site defects and stacking faults. The defective superlattice region is also more susceptible to melting than the defect-free superlattice, and thereby can act as the active region of the PCM device. These results help to clarify the longstanding debates concerning the mechanism of superlattice-based PCM.

cond-mat.mtrl-sci↗
arXiv2024

Direct and inverse spectral problems for the Schrodinger operator with double generalized Regge boundary conditions

In this paper, we study the direct and inverse spectral problems for the Schrodinger operator with two generalized Regge boundary conditions. For the direct problem, we give the properties of the spectrum, including the asymptotic distribution of the eigenvalues. For the inverse problems, we prove several uniqueness theorems, including the cases: even potential, two-spectra, as well as the general partial inverse problem.

math.SP↗
arXiv2023

Privacy-Preserving Video Conferencing via Thermal-Generative Images

Due to the COVID-19 epidemic, video conferencing has evolved as a new paradigm of communication and teamwork. However, private and personal information can be easily leaked through cameras during video conferencing. This includes leakage of a person's appearance as well as the contents in the background. This paper proposes a novel way of using online low-resolution thermal images as conditions to guide the synthesis of RGB images, bringing a promising solution for real-time video conferencing when privacy leakage is a concern. SPADE-SR (Spatially-Adaptive De-normalization with Self Resampling), a variant of SPADE, is adopted to incorporate the spatial property of a thermal heatmap and the non-thermal property of a normal, privacy-free pre-recorded RGB image provided in a form of latent code. We create a PAIR-LRT-Human (LRT = Low-Resolution Thermal) dataset to validate our claims. The result enables a convenient way of video conferencing where users no longer need to groom themselves and tidy up backgrounds for a short meeting. Additionally, it allows a user to switch to a different appearance and background during a conference.

cs.CR↗
arXiv2022

Revealing directed effective connectivity of cortical neuronal networks from measurements

In the study of biological networks, one of the major challenges is to understand the relationships between network structure and dynamics. In this paper, we model in vitro cortical neuronal cultures as stochastic dynamical systems and apply a method that reconstructs directed networks from dynamics [Ching and Tam, Phys. Rev. E 95, 010301(R), 2017] to reveal directed effective connectivity, namely the directed links and synaptic weights, of the neuronal cultures from voltage measurements recorded by a multielectrode array. The effective connectivity so obtained reproduces several features of cortical regions in rats and monkeys and has similar network properties as the synaptic network of the nematode C. elegans, the only organism whose entire nervous system has been mapped out as of today. The distribution of the incoming degree is bimodal and the distributions of the average incoming and outgoing synaptic strength are non-Gaussian with long tails. The effective connectivity captures different information from the commonly studied functional connectivity, estimated using statistical correlation between spiking activities. The average synaptic strengths of excitatory incoming and outgoing links are found to increase with the spiking activity in the estimated effective connectivity but not in the functional connectivity estimated using the same sets of voltage measurements. These results thus demonstrate that the reconstructed effective connectivity can capture the general properties of synaptic connections and better reveal relationships between network structure and dynamics.

q-bio.NC↗
arXiv2022

Robust Learning from Observation with Model Misspecification

Imitation learning (IL) is a popular paradigm for training policies in robotic systems when specifying the reward function is difficult. However, despite the success of IL algorithms, they impose the somewhat unrealistic requirement that the expert demonstrations must come from the same domain in which a new imitator policy is to be learned. We consider a practical setting, where (i) state-only expert demonstrations from the real (deployment) environment are given to the learner, (ii) the imitation learner policy is trained in a simulation (training) environment whose transition dynamics is slightly different from the real environment, and (iii) the learner does not have any access to the real environment during the training phase beyond the batch of demonstrations given. Most of the current IL methods, such as generative adversarial imitation learning and its state-only variants, fail to imitate the optimal expert behavior under the above setting. By leveraging insights from the Robust reinforcement learning (RL) literature and building on recent adversarial imitation approaches, we propose a robust IL algorithm to learn policies that can effectively transfer to the real environment without fine-tuning. Furthermore, we empirically demonstrate on continuous-control benchmarks that our method outperforms the state-of-the-art state-only IL method in terms of the zero-shot transfer performance in the real environment and robust performance under different testing conditions.

cs.LG↗
arXiv2021

Detection of Anticipatory Dynamics Between a Pair of Zebrafish

Trajectories from a pair of interacting zebrafish are used to test for the existence of anticipatory dynamics in natural systems. Anticipatory dynamics (AD) is unusual in that causal events are not necessarily ordered by their temporal order. However, their causal order can still be established if the direction of information flow (DIF) is known. In order to obtain DIF between trajectories of the two fish, we have made use of the difference of the transfer entropy between the trajectories with a history length established by experiments with known DIF. Our experimental results indicate that AD can be observed much more often in fish pairs of different genders. The use of DIF to determine causal order is further verified by the simulation of two chaotic Lorenz oscillators with anticipatory coupling; mimicking the interaction between the fish. Our simulation results further suggest that the two fish are interacting with their own internal dynamics, not by adaptation.

physics.bio-ph↗
arXiv2021

Robust Inverse Reinforcement Learning under Transition Dynamics Mismatch

We study the inverse reinforcement learning (IRL) problem under a transition dynamics mismatch between the expert and the learner. Specifically, we consider the Maximum Causal Entropy (MCE) IRL learner model and provide a tight upper bound on the learner's performance degradation based on the $\ell_1$-distance between the transition dynamics of the expert and the learner. Leveraging insights from the Robust RL literature, we propose a robust MCE IRL algorithm, which is a principled approach to help with this mismatch. Finally, we empirically demonstrate the stable performance of our algorithm compared to the standard MCE IRL algorithm under transition dynamics mismatches in both finite and continuous MDP problems.

cs.LG↗
arXiv2021

Reinforcement Learning Trajectory Generation and Control for Aggressive Perching on Vertical Walls with Quadrotors

Micro aerial vehicles are widely being researched and employed due to their relative low operation costs and high flexibility in various applications. We study the under-actuated quadrotor perching problem, designing a trajectory planner and controller which generates feasible trajectories and drives quadrotors to desired state in state space. This paper proposes a trajectory generating and tracking method for quadrotor perching that takes the advantages of reinforcement learning controller and traditional controller. The trained low-level reinforcement learning controller would manipulate quadrotor toward the perching point in simulation environment. Once the simulated quadrotor has successfully perched, the relative trajectory information in simulation will be sent to tracking controller on real quadrotor and start the actual perching task. Generating feasible trajectories via the trained reinforcement learning controller requires less time, and the traditional trajectory tracking controller could easily be modified to control the quadrotor and mathematically analysis its stability and robustness. We show that this approach permits the control structure of trajectories and controllers enabling such aggressive maneuvers perching on vertical surfaces with high precision.

cs.RO↗
arXiv2020

Anticipation and Negative Group Delay in a Retina

The mechanism of negative group delay (NGD) is used to understand the anticipatory capability of a retina. Experiments with retinas from bull frogs are performed to compare with the predictions of the NGD model. In particulars, whole field stochastic stimulation with various time correlations are used to probe anticipatory responses from the retina. We find that the NGD model can reproduce essential features of experimental observations characterized by the cross correlations between the stimulation and the retinal responses. The prediction horizon of a retina is found to depend on the correlation time of the stimulation as predicted by the NGD model. Experiments with dark and bright Gaussian light pulses further support the NGD mechanism; but only for the dark pulses indicating that the NGD effect of a retina might originate from its OFF response. Our finding suggests that sensory systems capable of using negative feedback for adaptation can give rise to anticipation as a consequence of the delay in the system.

physics.bio-ph↗
arXiv2020

Robust Reinforcement Learning via Adversarial training with Langevin Dynamics

We introduce a sampling perspective to tackle the challenging task of training robust Reinforcement Learning (RL) agents. Leveraging the powerful Stochastic Gradient Langevin Dynamics, we present a novel, scalable two-player RL algorithm, which is a sampling variant of the two-player policy gradient method. Our algorithm consistently outperforms existing baselines, in terms of generalization across different training and testing conditions, on several MuJoCo environments. Our experiments also show that, even for objective functions that entirely ignore potential environmental shifts, our sampling approach remains highly robust in comparison to standard RL algorithms.

cs.LG↗
arXiv2018

High-performance InSe Transistors with Ohmic Contact Enabled by Nonrectifying-barrier-type Indium Electrodes

The electrical contact to two-dimensional (2D)-semiconductor materials are decisive to the electronic performance of 2D-semiconductor field-effect devices (FEDs). The presence of a Schottky barrier often leads to a large contact resistance, which seriously limits the channel conductance and carrier mobility measured in a two-terminal geometry. In contrast, ohmic contact is desirable and can be achieved by the presence of a nonrectifying or tunneling barrier. Here, we demonstrate that an nonrectifying barrier can be realized by contacting indium (In), a low work function metal, with layered InSe because of a favorable band alignment at the In-InSe interface. The nonrectifying barrier is manifested by ohmic contact behavior at T=2 K and a low barrier height, Φ$_B$=50 meV. This ohmic contact enables demonstration of an ON-current as large as 410 μA/μm, which is among the highest values achieved in FEDs based on layered semiconductors. A high electron mobility of 3,700 and 1,000 cm$^2$/Vs is achieved with the two-terminal In-InSe FEDs at T=2 K and room temperature, respectively, which can be attributed to enhanced quality of both conduction channel and the contacts. The improvement in the contact quality is further proven by an X-ray photoelectron spectroscopy study, which suggests that a reduction effect occurs at the In-InSe interface. The demonstration of high-performance In-InSe FEDs indicates a viable interface engineering method for next-generation, 2D-semiconductor-based electronics.

cond-mat.mes-hall↗
arXiv2016

Positive Feedback and Synchronized Bursts in Neuronal Cultures

Synchronized bursts (SBs) with complex structures are common in neuronal cultures. Although the origin of SBs is still unclear, they have been studied for their information processing capabilities. Here, we investigate the properties of these SBs in a culture on multi-electrode array system. We find that structures of these SBs are related to the different developmental stages of the cultures. A model based on short term synaptic plasticity, recurrent connections and astrocytic recycling of neurotransmitters has been developed successfully to understand these structures. A phase diagram obtained from this model shows that networks exhibiting SBs are in an oscillatory state due to large enough positive feedback provided by synaptic facilitation and recurrent connections. In this model, the structures of the SBs are the results of intrinsic synaptic interactions; not information stored in the network.

q-bio.NC↗
arXiv2016

Propagation and synchronization of reverberatory bursts in developing cultured networks

Developing networks of neural systems can exhibit spontaneous, synchronous activities called neural bursts, which can be important in the organization of functional neural circuits. Before the network matures, the activity level of a burst can reverberate in repeated rise-and-falls in periods of hundreds of milliseconds following an initial wave-like propagation of spiking activity, while the burst itself lasts for seconds. To investigate the spatiotemporal structure of the reverberatory bursts, we culture dissociated, rat cortical neurons on a high-density multi-electrode array to record the dynamics of neural activity over the growth and maturation of the network. We find the synchrony of the spiking significantly reduced following the initial wave and the activities become broadly distributed spatially. The synchrony recovers as the system reverberates until the end of the burst. Using a propagation model we infer the spreading speed of the spiking activity, which increases as the culture ages. We perform computer simulations of the system using a physiological model of spiking networks in two spatial dimensions and find the parameters that reproduce the observed resynchronization of spiking in the bursts. An analysis of the simulated dynamics suggests that the depletion of synaptic resources causes the resynchronization. The spatial propagation dynamics of the simulations match well with observations over the course of a burst and point to an interplay of the synaptic efficacy and the noisy neural self-activation in producing the morphology of the bursts.

q-bio.NC↗