arXiv subjects

Zehao Zhang

Publications and source records attributed to Zehao Zhang.

18 recordsLinked to original sources

arXiv2026

UMI-Bench 1.0: An Open and Reproducible Real-World Benchmark for Tabletop Robotic Manipulation with UMI Data

Real-robot evaluation is essential for understanding whether learned manipulation policies can operate reliably outside curated demonstrations. This need is particularly pressing for Universal Manipulation Interface (UMI)-style policies, whose performance depends on the coupling between wrist-view observations, action representation, data collection, and physical deployment. Existing real-world benchmarks have made important progress, but they are not designed around this UMI data-to-deployment setting. We present UMI-Bench 1.0, a local-first real-robot benchmark for standardized evaluation of UMI-style manipulation policies. To the best of our knowledge, this is the first benchmark dedicated to real-world evaluation of UMI-based manipulation models. UMI-Bench aligns data collection, scene reset, policy execution, result logging, and task-factor analysis within a unified protocol. By making the full evaluation process reproducible and auditable, UMI-Bench provides a practical testbed for measuring how UMI-trained policies generalize to real physical manipulation.

cs.RO↗

arXiv2026

An Enhanced Sample of Galactic Red Supergiants Reveals Spiral Structures

Red supergiants (RSGs), representing a kind of massive young stellar population, have rarely been used to probe the structure of the Milky Way, mainly due to the long-standing scarcity of Galactic RSG samples. The Gaia BP/RP spectra (hereafter XP), which cover a broad wavelength range, provide a powerful tool for identifying RSGs. In this work, we develop a feedforward neural network classifier that assigns to each XP spectrum a probability of being an RSG, denoted as $\mathrm{P(RSG)}$. We perform ten independent runs with randomly divided training and validation sets, and apply each run to all XP spectra of stars with $G < 12$ mag. By selecting sources with $\mathrm{P(RSG)} \geq 0.9$, ten high-confidence candidate samples are obtained. A star is considered a ture Galactic RSG only if it appears in at least eight of these samples, yielding a final catalog of 2,436 objects. These RSGs show a clear spatial correlation with OB stars and trace the Galactic spiral arms well, confirming the reliability of our classification, and highlighting their potential to serve as powerful tracers of the Milky Way's structure.

astro-ph.GA↗

arXiv2025

ACD: Direct Conditional Control for Video Diffusion Models via Attention Supervision

Controllability is a fundamental requirement in video synthesis, where accurate alignment with conditioning signals is essential. Existing classifier-free guidance methods typically achieve conditioning indirectly by modeling the joint distribution of data and conditions, which often results in limited controllability over the specified conditions. Classifier-based guidance enforces conditions through an external classifier, but the model may exploit this mechanism to raise the classifier score without genuinely satisfying the intended condition, resulting in adversarial artifacts and limited effective controllability. In this paper, we propose Attention-Conditional Diffusion (ACD), a novel framework for direct conditional control in video diffusion models via attention supervision. By aligning the model's attention maps with external control signals, ACD achieves better controllability. To support this, we introduce a sparse 3D-aware object layout as an efficient conditioning signal, along with a dedicated Layout ControlNet and an automated annotation pipeline for scalable layout integration. Extensive experiments on benchmark video generation datasets demonstrate that ACD delivers superior alignment with conditioning inputs while preserving temporal coherence and visual fidelity, establishing an effective paradigm for conditional video synthesis.

cs.CV↗

arXiv2025

HumanGenesis: Agent-Based Geometric and Generative Modeling for Synthetic Human Dynamics

\textbf{Synthetic human dynamics} aims to generate photorealistic videos of human subjects performing expressive, intention-driven motions. However, current approaches face two core challenges: (1) \emph{geometric inconsistency} and \emph{coarse reconstruction}, due to limited 3D modeling and detail preservation; and (2) \emph{motion generalization limitations} and \emph{scene inharmonization}, stemming from weak generative capabilities. To address these, we present \textbf{HumanGenesis}, a framework that integrates geometric and generative modeling through four collaborative agents: (1) \textbf{Reconstructor} builds 3D-consistent human-scene representations from monocular video using 3D Gaussian Splatting and deformation decomposition. (2) \textbf{Critique Agent} enhances reconstruction fidelity by identifying and refining poor regions via multi-round MLLM-based reflection. (3) \textbf{Pose Guider} enables motion generalization by generating expressive pose sequences using time-aware parametric encoders. (4) \textbf{Video Harmonizer} synthesizes photorealistic, coherent video via a hybrid rendering pipeline with diffusion, refining the Reconstructor through a Back-to-4D feedback loop. HumanGenesis achieves state-of-the-art performance on tasks including text-guided synthesis, video reenactment, and novel-pose generalization, significantly improving expressiveness, geometric fidelity, and scene integration.

cs.CV↗

arXiv2025

The Mini-SiTian Array: Light Curves Analysis of Asteroids

The SiTian project, with its vast field of view, will become an ideal platform for asteroid scientific research. In this study, we develop a pipeline to analyze the photometry of asteroids and derive their periods from the data collected by the SiTian pathfinder project Mini-SiTian (MST). The pipeline is applied to the MST f02 region, a MST test region with a sky area of $2.29^{\circ} \times 1.53^{\circ}$. Rotation periods of 22 asteroids are derived by the obtained light curves analysis. Among them, there are 8 asteroids available in the Asteroid Lightcurve Photometry Database (ALCDEF), and 6 of them with more photometric points ($>$200) have similar period parameters as the ones in ALCDEF. Additionally, the periods for 14 of these asteroids are newly obtained and are not listed in ALCDEF. This study demonstrates the feasibility of asteroid photometric research by the SiTian project. It shows that future observations from the SiTian project will provide even more photometry of asteroids, significantly increasing the number of available light curves. The potential vast photometric data of asteroids will help us to further understand the physics of asteroids, their material composition, and the formation and evolution of the solar system.

astro-ph.EP↗

arXiv2025

New Red Supergiant Stars in the other side of our Galaxy

Red supergiant stars (RSGs) are massive stars in a late stage of evolution, crucial for understanding stellar life cycles and Galactic structure. However, RSGs on the far side of our Galaxy have been underexplored due to observational challenges. In this study, we introduce a novel method and present a new catalogue comprising 474 RSGs situated on the far side of the Milky Way, sourced from the OGLE-III catalogue of Variable Stars (OIII-CVS). The identification of these RSGs was made possible by analyzing the granulation parameters extracted from OGLE I-band time-series data and the stellar parameters from Gaia DR3. Additionally, we estimate the distances to these RSGs using an empirical relation between their characteristic amplitude, absolute magnitude, and intrinsic color, achieving a distance uncertainty of 13%. These newly identified RSGs are distributed at Galactocentric distances between 0 and 30kpc, and reside roughly 1 to 4kpc above and below the Galactic plane. This distribution provides new insights into the structure of the Milky Way, particularly at its outer boundaries. Our results reveal that the vertical distribution of these RSGs is consistent with the flare structure of the Galactic disk, confirming that the far side of the Milky Way exhibits a similar flaring pattern to the near side. This catalogue offers a valuable resource for future detailed studies of RSGs and contributes to a more comprehensive understanding of the Galactic structure and stellar evolution.

astro-ph.GA↗

arXiv2025

An Inorganic Liquid Crystalline Dispersion with 2D Ferroelectric Moieties

Electro-optical effect based liquid crystal devices have been extensively used in optical modulation techniques, in which the Kerr coefficient reflects the sensitivity of the liquid crystals and determines the strength of the device operational electric field. The Peterlin-Stuart theory and the O'Konski model jointly indicate that a giant Kerr coefficient could be obtained in a material with both a large geometrical anisotropy and an intrinsic polarization, but such a material is not yet reported. Here we reveal a ferroelectric effect in a monolayer two-dimensional mineral vermiculite. A large geometrical anisotropy factor and a large inherent electric dipole together raise the record value of Kerr coefficient by an order of magnitude, till $3.0\times 10^{-4}$ m V$^{-2}$. This finding enables an ultra-low operational electric field of $10^2$-$10^4$ V m$^{-1}$ and the fabrication of electro-optical devices with an inch-level electrode separation, which is not practical previously. Because of its high ultraviolet stability (decay <1% under ultraviolet exposure of 1000 hours), large-scale, and energy-efficiency, prototypical displayable billboards have been fabricated for outdoor interactive scenes. The work provides new insights for both liquid crystal optics and two-dimensional ferroelectrics.

cond-mat.mtrl-sci↗

arXiv2024

Searching for Galactic Red Supergiants with Gaia RVS Spectra

Red supergiants (RSGs) are essential to understanding the evolution and the contribution to the interstellar medium of massive stars. However, the number of identified RSGs within the Milky Way is still limited mainly due to the difficulty of measuring stellar extinction and distance. The release of approximately one million RVS spectra in Gaia DR3 presents new opportunity for identifying Galactic RSGs, because the equivalent width of the calcium triplet lines (EW(CaT)) in the spectra is an excellent indicator of stellar surface gravity. This work uses the RVS spectra with signal-to-noise ratio (SNR) greater than 100 to search for the Galactic RSGs. The dwarf stars and red giants are removed and the RSG candidates are selected by the location in the EW(CaT) vs. BP-RP diagram. The early-type RSG candidates (K0-M2) are then identified by BP-RP > 1.584 and EW(CaT) > 1.1 nm. To identify late-type RSG candidates (after M2), the criteria of the average equivalent widths of TiO in the XP spectra (EW(TiO)) > 10 nm, the color index K-W3 < 0.5 and the period-amplitude sequence from Gaia DR3 LPV catalog are further applied to reduce the contamination of late-type red giants and asymptotic giant branch stars. This method yields 30 early-type (K0-M2) and 6196 late-type (after M2) RSG candidates, which is a significant increase to the present Galactic RSG sample. The application of this approach to the spectra with SNR > 50 results in 48 early-type and 11,491 late-type RSG candidates. This preliminary analysis paves the way for more extensive research with Gaia DR4 when larger spectral datasets are expected to significantly enhance our understanding of Galactic RSG populations.

astro-ph.SR↗

arXiv2024

Utilizing Machine Learning Models to Predict Acute Kidney Injury in Septic Patients from MIMIC-III Database

Sepsis is a severe condition that causes the body to respond incorrectly to an infection. This reaction can subsequently cause organ failure, a major one being acute kidney injury (AKI). For septic patients, approximately 50% develop AKI, with a mortality rate above 40%. Creating models that can accurately predict AKI based on specific qualities of septic patients is crucial for early detection and intervention. Using medical data from septic patients during intensive care unit (ICU) admission from the Medical Information Mart for Intensive Care 3 (MIMIC-III) database, we extracted 3301 patients with sepsis, with 73% of patients developing AKI. The data was randomly divided into a training set (n = 1980, 40%), a test set (n = 661, 10%), and a validation set (n = 660, 50%). The proposed model was logistic regression, and it was compared against five baseline models: XGBoost, K Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest (RF), and LightGBM. Area Under the Curve (AUC), Accuracy, F1-Score, and Recall were calculated for each model. After analysis, we were able to select 23 features to include in our model, the top features being urine output, maximum bilirubin, minimum bilirubin, weight, maximum blood urea nitrogen, and minimum estimated glomerular filtration rate. The logistic regression model performed the best, achieving an AUC score of 0.887 (95% CI: [0.861-0.915]), an accuracy of 0.817, an F1 score of 0.866, a recall score of 0.827, and a Brier score of 0.13. Compared to the best existing literature in this field, our model achieved an 8.57% improvement in AUC while using 13 fewer variables, showcasing its effectiveness in determining AKI in septic patients. While the features selected for predicting AKI in septic patients are similar to previous literature, the top features that influenced our model's performance differ.

cs.LG↗

arXiv2024

Searching for radio late-type dwarf stars in the GLEAM-X DR1 catalog

We have developed a new method of multi-wavelength data combination for the search of late-type radio dwarfs, and have put it into practice using GLEAM-X DR1 data. The initial sample is selected by cross-matching the Gaia/DR3 objects with the probability of being a star no less than 99$\%$, and removing the extragalactic objects assigned by the SIMBAD database. The late-type dwarf stars are judged according to their location in the $(BP-RP)_0/M_{\rm G}$ color-magnitude diagram and in the $(J-H)_0/(K-W1)_0$ near-infrared color-color diagram. Furthermore, stellar activity is searched by ultraviolet excess in the GALEX/NUV band and the Rossby number in the TESS light curves. In total, 12 stars are found to be late-type dwarf stars associated with radio source, which is consisted of five stars with the UV excess and seven stars with the Rossby number less than 0.13. Three of these 12 stars are previously studied to be associated with radio objects. All these 12 stars are considered to be reliable counterparts of radio sources.

astro-ph.SR↗

arXiv2024

Accelerated Gradient Descent by Concatenation of Stepsize Schedules

This work considers stepsize schedules for gradient descent on smooth convex objectives. We extend the existing literature and propose a unified technique for constructing stepsizes with analytic bounds for an arbitrary number of iterations. This technique constructs new stepsize schedules by concatenating two stepsize schedules with fewer steps. Using this approach, we introduce two new families of stepsize schedules, achieving a convergence rate of $O(n^{-\log_2(\sqrt 2+1)})$ with state-of-the-art constants for the objective value and gradient norm of the last iterate, respectively. Furthermore, our analytically derived stepsize schedules either match or surpass the existing best numerically computed stepsize schedules.

math.OC↗

arXiv2024

Modeling of Granulation in Red Supergiants in the Magellanic Clouds with the Gaussian Process Regressions

The granulation of red supergiants (RSGs) in the Magellanic Clouds are systematically investigated by combining the latest RSGs samples and light curves from the Optical Gravitational Lensing Experiment and the All-Sky Automated Survey for Supernovae. The present RSGs samples are firstly examined for foreground stars and possible misidentified sources, and the light curves are sequentially checked to remove the outliers by white noise and photometric quality. The Gaussian Process regression is used to model the granulation, and the Markov Chain Monte Carlo is applied to derive the granulation amplitude $\sigma$ and the period of the undamped oscillator $\rho$, as well as the damping timescale $\tau$. The dimensionless quality factor $Q$ is then calculated through $Q=\pi \tau/\rho$. RSGs around $Q = 1/\sqrt{2}$ are considered to have significant granulation signals and are used for further analysis. Combining granulation parameters with stellar parameters, robust scaling relations for the timescale $\rho$ are established, while the scaling relations for amplitude $\sigma$ are represented by a piecewise function, possibly related to the tendency of amplitudes in faint RSGs to converge towards a certain value. Comparing results between the SMC and LMC confirms that amplitudes and timescales become larger with metallicity. In examining the scaling relations between the two galaxies, it is found that $\rho$ is nearly independent of metallicity, whereas $\sigma$ is more significantly affected by metallicity. The Gaussian Process method is compared with the periodogram fitting of the granulations, and the advantages of either are discussed.

astro-ph.SR↗

arXiv2023

Cold & Warm Net: Addressing Cold-Start Users in Recommender Systems

Cold-start recommendation is one of the major challenges faced by recommender systems (RS). Herein, we focus on the user cold-start problem. Recently, methods utilizing side information or meta-learning have been used to model cold-start users. However, it is difficult to deploy these methods to industrial RS. There has not been much research that pays attention to the user cold-start problem in the matching stage. In this paper, we propose Cold & Warm Net based on expert models who are responsible for modeling cold-start and warm-up users respectively. A gate network is applied to incorporate the results from two experts. Furthermore, dynamic knowledge distillation acting as a teacher selector is introduced to assist experts in better learning user representation. With comprehensive mutual information, features highly relevant to user behavior are selected for the bias net which explicitly models user behavior bias. Finally, we evaluate our Cold & Warm Net on public datasets in comparison to models commonly applied in the matching stage and it outperforms other models on all user types. The proposed model has also been deployed on an industrial short video platform and achieves a significant increase in app dwell time and user retention rate.

cs.IR↗

arXiv2023

The Exploration and Evaluation of Generating Affective 360$^\circ$ Panoramic VR Environments Through Neural Style Transfer

Affective virtual reality (VR) environments with varying visual style can impact users' valence and arousal responses. We applied Neural Style Transfer (NST) to generate 360$^\circ$ VR environments that elicited users' varied valence and arousal responses. From a user study with 30 participants, findings suggested that generative VR environments changed participants' arousal responses but not their valence levels. The generated visual features, e.g., textures and colors, also altered participants' affective perceptions. Our work contributes novel insights about how users respond to generative VR environments and provided a strategy for creating affective VR environments without altering content.

cs.HC↗

arXiv2022

Robust One Round Federated Learning with Predictive Space Bayesian Inference

Making predictions robust is an important challenge. A separate challenge in federated learning (FL) is to reduce the number of communication rounds, particularly since doing so reduces performance in heterogeneous data settings. To tackle both issues, we take a Bayesian perspective on the problem of learning a global model. We show how the global predictive posterior can be approximated using client predictive posteriors. This is unlike other works which aggregate the local model space posteriors into the global model space posterior, and are susceptible to high approximation errors due to the posterior's high dimensional multimodal nature. In contrast, our method performs the aggregation on the predictive posteriors, which are typically easier to approximate owing to the low-dimensionality of the output space. We present an algorithm based on this idea, which performs MCMC sampling at each client to obtain an estimate of the local posterior, and then aggregates these in one round to obtain a global ensemble model. Through empirical evaluation on several classification and regression tasks, we show that despite using one round of communication, the method is competitive with other FL techniques, and outperforms them on heterogeneous settings. The code is publicly available at https://github.com/hasanmohsin/FedPredSpace_1Round.

cs.LG↗

arXiv2022

Dependence of pulsation mode of Cepheids on metallicity

The Cepheid variables in SMC, LMC, the Milky Way, M33 and M31 are used to examine the dependence of pulsation mode on metallicity which was previously found in red supergiants. The initial samples of Cepheids are collected from the Cepheid catalogs identified from the OGLE, PS1, DIRECT, WISE and ZTF surveys. The contaminants are removed with the help of the Gaia/EDR3 astrometric information for extra galaxies or by comparing the geometric distance and the distance from the P-L relation for the Milky Way. The division of fundamental and first-overtone mode is refined according to the gap between the two modes in the P-L diagram of the objects in each galaxy. The ratio of FU/(FU+1O) is found to be 0.59, 0.60, 0.69, 0.83 and 0.85 for SMC, LMC, the Milky Way, M33 and M31 respectively in order of metallicity, which confirms that the pulsation mode depends on metallicity in the way that the ratio of FU/(FU+1O) increases with metallicity. This dependence is not changed if the incompleteness of the samples is taken into account.

astro-ph.SR↗

arXiv2022

Two-dimensional Functional Minerals for Sustainable Optics

Optical device is a key component in our lives and organic liquid crystals are nowadays widely used to reduce human imprint. However, this technology still suffers from relatively high costs, toxicity and other environmental impacts, and cannot fully meet the demand of future sustainable society. Here we describe an alternative approach to colour-tuneable optical devices, which is based on sustainable inorganic liquid crystals derived from two-dimensional mineral materials abundant in nature. The prototypical two-dimensional mineral of vermiculite is massively produced by a green method, possessing size-to-thickness ratios of >103, in-plane magnetisation of >10 emu g-1, and an optical bandgap of >3 eV. These characteristics endow two-dimensional vermiculite with sensitive magneto-birefringence response, which is several orders of magnitude larger than organic counterparts, as well as capability of broad-spectrum modulation. Our finding consequently permits the fabrication of various chromic devices with low or even zero-energy consumption, which can be used for sustainable optics.

physics.optics↗

arXiv2019

Arithmetic addition of two integers by deep image classification networks: experiments to quantify their autonomous reasoning ability

The unprecedented performance achieved by deep convolutional neural networks for image classification is linked primarily to their ability of capturing rich structural features at various layers within networks. Here we design a series of experiments, inspired by children's learning of the arithmetic addition of two integers, to showcase that such deep networks can go beyond the structural features to learn deeper knowledge. In our experiments, a set of images is constructed, each image containing an arithmetic addition $n+m$ in its central area, and several classification networks are then trained over a subset of images, using the sum as the label. Tests on the excluded images show that, as the image set gets larger, the networks have well learnt the law of arithmetic additions so as to build up their autonomous reasoning ability strongly. For instance, networks trained over a small percentage of images can classify a big majority of the remaining images correctly, and many arithmetic additions involving some integers that have never been seen during the training can also be solved correctly by the trained networks.

cs.CV↗