arXiv subjects

Tao Ding

Publications and source records attributed to Tao Ding.

14 recordsLinked to original sources

arXiv2026

Technical Report: A Hierarchical Dynamically Weighting Deep Reinforcement Learning Method for Multi-UAV Multi-Task Coordination

This paper investigates the multi-UAV multi-task coordination problem in infrastructure-less emergency scenarios, where UAVs collaboratively are required to jointly perform aerial image acquisition and ground-user communication. To tackle the challenge of balancing heterogeneous tasks within dynamic environments, we propose a hierarchical dynamic weighting Deep Reinforcement Learning (DRL) framework. Specifically, an episode-level module is introduced to capture global task preferences, while a step-level module adaptively adjusts the objective weights according to real-time system conditions. By integrating global and instantaneous weights, the proposed framework improves decision stability and responsiveness during task execution. Simulation results demonstrate that the proposed method achieves faster convergence, more stable training, and higher task completion efficiency than conventional works.

cs.NI↗

arXiv2026

Atomic-scale order enables high thermal boundary conductance at $\beta$-Ga$_2$O$_3$/4H-SiC interfaces

Thermal boundary conductance (TBC) at dissimilar interfaces imposes a fundamental limit on electronic device performance, yet predicting and understanding heat transport across realistic, disordered boundaries remains elusive. Here, we develop a computational framework that combines machine-learned interatomic potentials with lattice dynamics to address the long-standing problem of how interfacial structure, from disordered to atomically sharp, affects thermal transport in the technologically important $\beta$-Ga$_2$O$_3$/4H-SiC heterostructure. By explicitly accounting for phonon wave-particle duality, we show that interfacial disorder introduces additional interfacial phonon modes that facilitate vibrational impedance matching between the two highly dissimilar crystals, yet it simultaneously disrupts interfacial phonon coherence and limits the potential heat-transport benefit. Our atomistic simulations further indicate that restoring atomic-scale order preserves coherence and yields markedly higher conductance. These insights motivate the controlled epitaxial growth of $\beta$-Ga$_2$O$_3$/4H-SiC heterostructures with systematically tuned interfacial order. Experimental measurements validate our predictions, achieving a record-high TBC of 231 MW m$^{-2}$ K$^{-1}$ at atomically sharp junctions. Beyond the immediate implications for $\beta$-Ga$_2$O$_3$-based power electronics, our results establish the preservation of interfacial phonon coherence as an effective strategy for mitigating thermal bottlenecks in mismatched systems.

cond-mat.mtrl-sci↗

arXiv2025

Discussion on the vanishment of solar atmospheric structures during magnetic reconnection

In solar atmosphere, magnetic reconnection alters the topological connectivity, and magnetic energy is released. However, the length change of the reconnecting structures has rarely been reported. To identify the evolution of the topological structures, we search for reconnection events which should satisfy 3 criteria. (1) Each event displays an explicit X-type configuration, and the configuration consists of two sets of independent atmospheric structures, (2) the reconnection process is clearly observed, and (3) the topological connectivity of the structures can be tracked from at least 5 minutes prior to the occurrence of magnetic reconnection to 5 minutes after the reconnection. In this work, 3 events are selected and studied. During the reconnection moment, the total length of the two topological structures in each event shortens suddenly, and the decrements for events 1--3 are 47 Mm, 3.7 Mm, and 8.2 Mm, respectively, implying that partial structures vanish observationally during magnetic reconnection process. Several possibilities about the vanishment, e.g. the shrinkage of the reconnecting structures due to magnetic tension, the bizarre change in the third dimension, and magnetic field annihilation, have been discussed.

astro-ph.SR↗

arXiv2025

Nonlinear Chiroptical Effect in Refractory Plasmonic Molybdenum Metasurface: Chiral Saturated Absorption and Chiral Reverse Saturable Absorption

The absorptive chiral metasurface shows evidence of giant nonlinear chiroptical effects, including chiral third harmonic generation, chiral saturated absorption, and chiral reverse saturable absorption. We finally demonstrate proof of concept circular polarized light limiter based on the refractory chiral Mo metasurface and paves the way for nonlinear chiral sensing, nonlinear hot electron generation, and XOR gate of the all-optical full-adder, showcasing the potential of refractory chiral Mo metasurfaces in overcoming the limitations of traditional chiral plasmonic materials.

physics.optics↗

arXiv2024

Manifold-valued models for analysis of EEG time series data

We propose a model for time series taking values on a Riemannian manifold and fit it to time series of covariance matrices derived from EEG data for patients suffering from epilepsy. The aim of the study is two-fold: to develop a model with interpretable parameters for different possible modes of EEG dynamics, and to explore the extent to which modelling results are affected by the choice of manifold and its associated geometry. The model specifies a distribution for the tangent direction vector at any time point, combining an autoregressive term, a mean reverting term and a form of Gaussian noise. Parameter inference is carried out by maximum likelihood estimation, and we compare modelling results obtained using the standard Euclidean geometry on covariance matrices and the affine invariant geometry. Results distinguish between epileptic seizures and interictal periods between seizures in patients: between seizures the dynamics have a strong mean reverting component and the autoregressive component is missing, while for the majority of seizures there is a significant autoregressive component and the mean reverting effect is weak. The fitted models are also used to compare seizures within and between patients. The affine invariant geometry is advantageous and it provides a better fit to the data.

stat.ME↗

arXiv2019

Social Media-based User Embedding: A Literature Review

Automated representation learning is behind many recent success stories in machine learning. It is often used to transfer knowledge learned from a large dataset (e.g., raw text) to tasks for which only a small number of training examples are available. In this paper, we review recent advance in learning to represent social media users in low-dimensional embeddings. The technology is critical for creating high performance social media-based human traits and behavior models since the ground truth for assessing latent human traits and behavior is often expensive to acquire at a large scale. In this survey, we review typical methods for learning a unified user embeddings from heterogeneous user data (e.g., combines social media texts with images to learn a unified user representation). Finally we point out some current issues and future directions.

cs.SI↗

arXiv2019

Detecting retail products in situ using CNN without human effort labeling

CNN is a powerful tool for many computer vision tasks, achieving much better result than traditional methods. Since CNN has a very large capacity, training such a neural network often requires many data, but it is often expensive to obtain labeled images in real practice, especially for object detection, where collecting bounding box of every object in training set requires many human efforts. This is the case in detection of retail products where there can be many different categories. In this paper, we focus on applying CNN to detect 324-categories products in situ, while requiring no extra effort of labeling bounding box for any image. Our approach is based on an algorithm that extracts bounding box from in-vitro dataset and an algorithm to simulate occlusion. We have successfully shown the effectiveness and usefulness of our methods to build up a Faster RCNN detection model. Similar idea is also applicable in other scenarios.

cs.CV↗

arXiv2018

Automatically Infer Human Traits and Behavior from Social Media Data

Given the complexity of human minds and their behavioral flexibility, it requires sophisticated data analysis to sift through a large amount of human behavioral evidence to model human minds and to predict human behavior. People currently spend a significant amount of time on social media such as Twitter and Facebook. Thus many aspects of their lives and behaviors have been digitally captured and continuously archived on these platforms. This makes social media a great source of large, rich and diverse human behavioral evidence. In this paper, we survey the recent work on applying machine learning to infer human traits and behavior from social media data. We will also point out several future research directions.

cs.SI↗

arXiv2018

Causal Feature Selection for Individual Characteristics Prediction

People can be characterized by their demographic information and personality traits. Characterizing people accurately can help predict their preferences, and aid recommendations and advertising. A growing number of studies infer people's characteristics from behavioral data. However, context factors make behavioral data noisy, making these data harder to use for predictive analytics. In this paper, we demonstrate how to employ causal identification on feature selection and how to predict individuals' characteristics based on these selected features. We use visitors' choice data from a large theme park, combined with personality measurements, to investigate the causal relationship between visitors' characteristics and their choices in the park. We demonstrate the benefit of feature selection based on causal identification in a supervised prediction task for individual characteristics. Based on our evaluation, our models that trained with features selected based on causal identification outperformed existing methods.

cs.SI↗

arXiv2017

Social Media-based Substance Use Prediction

In this paper, we demonstrate how the state-of-the-art machine learning and text mining techniques can be used to build effective social media-based substance use detection systems. Since a substance use ground truth is difficult to obtain on a large scale, to maximize system performance, we explore different feature learning methods to take advantage of a large amount of unsupervised social media data. We also demonstrate the benefit of using multi-view unsupervised feature learning to combine heterogeneous user information such as Facebook `"likes" and "status updates" to enhance system performance. Based on our evaluation, our best models achieved 86% AUC for predicting tobacco use, 81% for alcohol use and 84% for drug use, all of which significantly outperformed existing methods. Our investigation has also uncovered interesting relations between a user's social media behavior (e.g., word usage) and substance use.

cs.CL↗

arXiv2017

\$1 Today or \$2 Tomorrow? The Answer is in Your Facebook Likes

In economics and psychology, delay discounting is often used to characterize how individuals choose between a smaller immediate reward and a larger delayed reward. People with higher delay discounting rate (DDR) often choose smaller but more immediate rewards (a "today person"). In contrast, people with a lower discounting rate often choose a larger future rewards (a "tomorrow person"). Since the ability to modulate the desire of immediate gratification for long term rewards plays an important role in our decision-making, the lower discounting rate often predicts better social, academic and health outcomes. In contrast, the higher discounting rate is often associated with problematic behaviors such as alcohol/drug abuse, pathological gambling and credit card default. Thus, research on understanding and moderating delay discounting has the potential to produce substantial societal benefits.

cs.AI↗

arXiv2016

Experimental and theoretical analyses of strongly polarized photon emission from non-polar InGaN quantum dots

We present a comprehensive investigation of the polarization properties of non-polar a-plane InGaN quantum dots (QDs) and their origin with statistically significant experimental data and rigorous k.p modelling. The unbiased selection and study of 180 individual QDs allow us to compute an average polarization degree of 0.90, with a standard deviation of only 0.08. When coupled with theoretical insights, we show that a-plane InGaN QDs are highly insensitive to size differences, shape anisotropies, and indium content fluctuations. Furthermore, 91% of the studied QDs exhibit a polarization axis along the crystal [1-100] axis, with the other 9% polarized orthogonal to this direction. When coupled with their ability to emit single-photons, a-plane QDs are good candidates for the generation of linearly polarized single-photons, a feature attractive for quantum cryptography protocols.

cond-mat.mes-hall↗

arXiv2016

Personalized Emphasis Framing for Persuasive Message Generation

In this paper, we present a study on personalized emphasis framing which can be used to tailor the content of a message to enhance its appeal to different individuals. With this framework, we directly model content selection decisions based on a set of psychologically-motivated domain-independent personal traits including personality (e.g., extraversion and conscientiousness) and basic human values (e.g., self-transcendence and hedonism). We also demonstrate how the analysis results can be used in automated personalized content selection for persuasive message generation.

cs.AI↗

arXiv2015

A Modification of Sufficient Conditions to Ensure the Exact Conic Relaxation

To solve the AC optimal power flow problem, it is proposed in [1,2] that a convex conic approximation to branch flow model (BFM) can be obtained if we first eliminate phase angles of voltages and currents and then relax a set of equality constraints to second order conic ones. In particular, provided a set of sufficient conditions are satisfied, the conic relaxation is exact. We note, however, that those conditions do not always guarantee the exactness. In this letter, we analyze the argument of exactness and include a new condition that there is no line with negative reactance to ensure the conic formulation's exactness.

math.OC↗