arXiv subjects

Zhenhua Zhang

Publications and source records attributed to Zhenhua Zhang.

18 recordsLinked to original sources

arXiv2026

Clinician-Friendly Foundation Models for Ophthalmic Image Diagnostics without Fine-Tuning or Technical Barriers

Artificial intelligence (AI) shows remarkable potential in medical imaging diagnostics, yet most current models require retraining when applied across different clinical settings, limiting their scalability. We developed GlobeReady, a deployment-oriented platform powered by the RetiGlobe foun- dation model and local feature augmentation. RetiGlobe was pretrained in two stages: 1) self-supervised learning using DINOv2 on 38 million synthetic ophthalmic images, and 2) contrastive learning using CLIP on 475,845 real image-text pairs spanning diverse ethnicities, imaging devices, and geographic regions worldwide. We evaluate GlobeReady on 488,448 ophthalmic images, including color fundus photographs (CFPs) and optical coherence tomography scans, from multi-centres in China, Singapore, Vietnam and the UK. Prospective testing included usability assessment with 31 ophthalmologists. Exploratory analyses evaluated domain generalisability, Bayesian uncertainty quantification, out-of-distribution (OOD) detection, and feature-based case retrieval.

cs.CV↗

arXiv2026

Nearly Complete Charge--Spin Conversion via Strain-Eliminated Fermi Pockets in $d$-Wave Altermagnets

Ideal $d$-wave altermagnets with nearly orthogonal flat Fermi surfaces enable complete spin-channel separation and 100% theoretical charge--spin conversion efficiency (CSE). The metallic altermagnet $\mathrm{KV_2Se_2O}$ exemplifies this, but realistic samples host residual elliptical Fermi pockets that enhance charge conductivity while suppressing spin conductivity, drastically reducing CSE. Here we show that in-plane equibiaxial tensile strain systematically eliminates these parasitic pockets, restoring the flat-band geometry. Our first-principles calculations reveal that CSE increases monotonically with strain, reaching a record $\sim$96% at 4% strain. An effective tight-binding model confirms that pocket suppression, governed by reduced next-nearest-neighbor hoppings, is the dominant mechanism. We further identify an unconventional out-of-plane spin current component with CSE $\sim$55% at optimal orientations, enabling field-free perpendicular magnetization switching. Moreover, the same strain-driven removal of parasitic pockets yields giant TMR enhancement in $\mathrm{KV_2Se_2O}$-based magnetic tunnel junctions, from $10^{5}%$ to $10^{9}%$, and the giant TMR persists over a wide energy window near the Fermi level. These findings establish strain engineering as a clean, widely applicable strategy to maximize CSE and magnetoresistance in $d$-wave altermagnets, and provide predictive descriptors for screening high-efficiency spintronic materials.

cond-mat.mtrl-sci↗

arXiv2026

Anisotropic Spin Polarization and magnetic spin hall effect in Ferromagnets

Spin-dependent transport in ferromagnets underpins the development of high-density spintronic memories. Spin-dependent transport in strong spin-orbit-coupled ferromagnets exhibits a significant anisotropy. Both the overall spin polarization during charge transport and the magnetic spin Hall conductivity are found to exhibit pronounced anisotropy when the magnetization is tilted away from the crystallographic easy axis or when the electric field is rotated relative to the crystal axes. These anisotropic responses originate primarily from spin-orbit coupling, which is identified as the key driver of the large anisotropy observed in ferromagnet. Furthermore, strain tunability of the magnetic spin Hall anisotropy is demonstrated, with tensile strain progressively enhancing the oscillatory amplitude of the spin Hall conductivity. These findings establish strong spin-orbit-coupled ferromagnets as a platform for anisotropic spin-current generation and field-free spintronic devices that exploit intrinsic material anisotropy for improved performance and energy efficiency.

cond-mat.mes-hall↗

arXiv2026

Bridging the Last Mile of Time Series Forecasting with LLM Agents

Time series forecasting has advanced rapidly, especially with the emergence of foundation models that show strong zero-shot performance on numerical extrapolation. However, in real-world forecasting settings, a statistically plausible baseline is rarely the final forecast used in practice. Before a forecast becomes decision-ready, it often needs to be revised using weakly structured business context such as holiday effects, campaign plans, external events, historical analogs, and expert feedback. This practical stage remains underexplored in the forecasting literature. In this paper, we formulate this stage as the \textbf{last-mile forecasting} problem and present an LLM-agent framework that sits on top of a forecasting backbone. Our system maintains a unified forecast workspace, invokes tools to retrieve contextual evidence, and converts reasoning trajectories into explicit forecast revision actions under structural safety constraints. It also supports long-horizon forecasting through map-reduce-style decomposition and post-hoc reflection through a memory bank. The resulting system is designed to be controllable and auditable. Through real-world case studies, we show how LLM agents can bridge the gap between statistical prediction and business-ready forecasting.

cs.AI↗

arXiv2026

High-Efficiency Nonrelativistic Charge-Spin Conversion in X-Type Antiferromagnets

Antiferromagnetic materials with spin splitting have attracted considerable attention for their symmetry-enabled anisotropic spin textures that sustain a zero net magnetization, thereby facilitating efficient spin-current generation. In this work, the highly efficient generation of nonrelativistic spin currents is demonstrated to be facilitated by the distinctive Fermi surface geometry of X-type collinear antiferromagnets. As a prototype conducting X-type antiferromagnet, the Fermi surface of $β-\mathrm{Fe_2PO_5}$ exhibits a distinct $d$-wave altermagnetic characteristic, which compresses into a nearly X-shaped configuration. This results in highly efficient spin currents, achieving a charge-spin conversion efficiency of up to 90\%. Moreover, the spin current polarization is controlled by the orientation of the Néel vector. When the Néel vector tilts to the out-of-plane direction, an in-plane injected charge current can generate a special spin current component with both spin polarization and propagation along the out-of-plane direction, whose charge-spin conversion efficiency substantially exceeds that of known ferromagnets, altermagnets, noncollinear antiferromagnets, and low-symmetry materials. The highly efficient charge-spin conversion in X-type antiferromagnets provides a novel and highly effective spin source system for the development of low-power spintronic devices.

cond-mat.mtrl-sci↗

arXiv2025

Strain-induced nonrelativistic altermagnetic spin splitting effect

Recent studies reveal that $\mathcal{T}$-odd spin currents generated via the nonrelativistic altermagnetic spin splitting effect (ASSE) exhibit significant potential for spintronics applications, with both computational and experimental validations. Addressing the scarcity of conductive altermagnets, we propose strain engineering as a reliable method for inducing altermagnetism. Focusing on rutile-structured $\mathrm{OsO}_2$, first-principles calculations show that minor equibiaxial tensile strain ($\mathcal{E}_{\mathrm{ts}}$=3\%) induces nonmagnetic-to-altermagnetic transitions, achieving an ASSE-driven spin-charge conversion ratio ($θ_{\text{AS}}$) of $\sim$7\% -- far surpassing conventional spin Hall angles ($θ_{\text{IS}}$). Calculations reveal that substantial $θ_{\text{AS}}$ persists even in the absence of spin-orbit coupling, with its magnitude positively correlating to nonrelativistic spin splitting magnitude, which further confirms the strain-induced ASSE's nonrelativistic origin. Further investigation reveals that $\mathrm{RuO}_2$ exhibits analogous phenomena, which may resolve recent controversies regarding its magnetic properties. Our research opens new simple pathways for developing next-generation altermagnetic spintronic devices.

cond-mat.mtrl-sci↗

arXiv2025

Strain-Induced Non-alter Compensated Magnet and Its Application to Magnetic Tunnel Junction Device Design

The recent proposal of altermagnetism has drawn widespread attention to antiferromagnet (AFM) exhibiting spin splitting, extending beyond the realm of sign-alternating spin splitting in momentum space protected solely by rotational symmetry. Herrin, we propose a shear-strain strategy that enables significant modulation of d-wave altermagnets into an non-alter compensated magnets. A comprehensive analysis combining the magnetic moment compensation characteristics of opposite spin sublattices with the distribution of spin-resolved conduction channels in momentum space under the [001] crystal orientation reveals that shear strain breaks the rotational symmetry of alternatmagnets. To explore the application potential of non-alter compensated magnets, we designe RuO2/TiO2/RuO2 magnetic tunnel junctions (MTJ) with three crystallographic orientations ((001), (110), (100)) and investigated their transport properties under shear strain. This non-alter electronic structure not only enhances the tunneling magnetoresistance (TMR) in spin-split paths of intrinsic RuO2 (226% to 431%) but also enables substantial TMR in spin-degenerate paths (from 0-88%)). Our work provides guidelines for broadening magnetic materials and device platforms.

cond-mat.mtrl-sci↗

arXiv2024

Extremely Large Anisotropy of Effective Gilbert Damping in Half-Metallic CrO2

Half-metals are a class of quantum materials with 100% spin-polarization at the Fermi level and have attracted a lot of attention for future spintronic device applications. CrO2 is one of the most promising half-metal candidates, for which the electrical and magnetic properties have been intensively studied in the last several decades. Here, we report the observation of a giant anisotropy (~1600%) of effective Gilbert damping in the single crystalline half metallic (100)-CrO2 thin films, which is significantly larger than the values observed on conventional ferromagnetic Fe and CoFe thin films. Furthermore, the effective Gilbert damping exhibits opposite temperature-dependent behaviors below 50 K with magnetic field along [010] direction and near [001] direction. These experimental results suggest the strong spin-orbit coupling anisotropy of the half-metallic CrO2 and might pave the way for future magnonic computing applications.

cond-mat.mtrl-sci↗

arXiv2024

TripCast: Pre-training of Masked 2D Transformers for Trip Time Series Forecasting

Deep learning and pre-trained models have shown great success in time series forecasting. However, in the tourism industry, time series data often exhibit a leading time property, presenting a 2D structure. This introduces unique challenges for forecasting in this sector. In this study, we propose a novel modelling paradigm, TripCast, which treats trip time series as 2D data and learns representations through masking and reconstruction processes. Pre-trained on large-scale real-world data, TripCast notably outperforms other state-of-the-art baselines in in-domain forecasting scenarios and demonstrates strong scalability and transferability in out-domain forecasting scenarios.

cs.LG↗

arXiv2024

Nuclear mass table in deformed relativistic Hartree-Bogoliubov theory in continuum, II: Even-$Z$ nuclei

The mass table in the deformed relativistic Hartree-Bogoliubov theory in continuum (DRHBc) with the PC-PK1 density functional has been established for even-$Z$ nuclei with $8\le Z\le120$, extended from the previous work for even-even nuclei [Zhang $\it{et.~al.}$ (DRHBc Mass Table Collaboration), At. Data Nucl. Data Tables 144, 101488 (2022)]. The calculated binding energies, two-nucleon and one-neutron separation energies, root-mean-square (rms) radii of neutron, proton, matter, and charge distributions, quadrupole deformations, and neutron and proton Fermi surfaces are tabulated and compared with available experimental data. A total of 4829 even-$Z$ nuclei are predicted to be bound, with an rms deviation of 1.477 MeV from the 1244 mass data. Good agreement with the available experimental odd-even mass differences, $α$ decay energies, and charge radii is also achieved. The description accuracy for nuclear masses and nucleon separation energies as well as the prediction for drip lines is compared with the results obtained from other relativistic and nonrelativistic density functional. The comparison shows that the DRHBc theory with PC-PK1 provides an excellent microscopic description for the masses of even-$Z$ nuclei. The systematics of the nucleon separation energies, odd-even mass differences, pairing energies, two-nucleon gaps, $α$ decay energies, rms radii, quadrupole deformations, potential energy curves, neutron density distributions, and neutron mean-field potentials are discussed.

nucl-th↗

arXiv2021

Deriving the Traveler Behavior Information from Social Media: A Case Study in Manhattan with Twitter

Social media platforms, such as Twitter, provide a totally new perspective in dealing with the traffic problems and is anticipated to complement the traditional methods. The geo-tagged tweets can provide the Twitter users' location information and is being applied in traveler behavior analysis. This paper explores the full potentials of Twitter in deriving travel behavior information and conducts a case study in Manhattan Area. A systematic method is proposed to extract displacement information from Twitter locations. Our study shows that Twitter has a unique demographics which combine not only local residents but also the tourists or passengers. For individual user, Twitter can uncover his/her travel behavior features including the time-of-day and location distributions on both weekdays and weekends. For all Twitter users, the aggregated travel behavior results also show that the time-of-day travel patterns in Manhattan Island resemble that of the traffic flow; the identification of OD pattern is also promising by comparing with the results of travel survey.

cs.SI↗

arXiv2019

Voltage-controlled skyrmion-based artificial synapse in a synthetic antiferromagnet

Spintronics exhibits significant potential in neuromorphic computing system with high speed, high integration density, and low dissipation. In this letter, we propose an ultralow-dissipation spintronic memristor composed of a synthetic antiferromagnet (SAF) and a piezoelectric substrate. Skyrmions/skyrmion bubbles can be generated in the upper layer of SAF with weak anisotropy energy (Ea). With a weak electric field on the heterostructure, the interlayer antiferromagnetic coupling can be manipulated, giving rise to a continuous transition between a large skyrmion bubble and a small skyrmion. This thus induces the variation of the resistance of a magnetic tunneling junction. The synapse based on this principle may manipulate the weight in a wide range at a cost of a very low energy consumption of 0.3 fJ. These results pave a way to ultralow power neuromorphic computing applications.

physics.comp-ph↗

arXiv2018

A deep learning approach for detecting traffic accidents from social media data

This paper employs deep learning in detecting the traffic accident from social media data. First, we thoroughly investigate the 1-year over 3 million tweet contents in two metropolitan areas: Northern Virginia and New York City. Our results show that paired tokens can capture the association rules inherent in the accident-related tweets and further increase the accuracy of the traffic accident detection. Second, two deep learning methods: Deep Belief Network (DBN) and Long Short-Term Memory (LSTM) are investigated and implemented on the extracted token. Results show that DBN can obtain an overall accuracy of 85% with about 44 individual token features and 17 paired token features. The classification results from DBN outperform those of Support Vector Machines (SVMs) and supervised Latent Dirichlet allocation (sLDA). Finally, to validate this study, we compare the accident-related tweets with both the traffic accident log on freeways and traffic data on local roads from 15,000 loop detectors. It is found that nearly 66% of the accident-related tweets can be located by the accident log and more than 80% of them can be tied to nearby abnormal traffic data. Several important issues of using Twitter to detect traffic accidents have been brought up by the comparison including the location and time bias, as well as the characteristics of influential users and hashtags.

cs.SI↗

arXiv2017

Nature of the growth of plasma electrolyte oxidation coating on Aluminum

The localized dielectric breakdown had been considered as the driving force for growth of plasma electrolytic oxide (PEO) coatings for several decades. However, the TEM study here reveals the dielectric breakdown behavior has little contribution for coating thickening. The presented evidences show the nature of PEO coating growth in all three consecutive stages I-III is ionic migration behavior inside the amorphous alumina layer (AAL) at the coating/matrix interface. The evolution of morphological characterizations in the PEO process is attributed to the interfacial reactions of AAL/alkaline (stage I), AAL/discharge-on-surface (stage II) and AAL/discharge-in-pore (stage III).

physics.app-ph↗

arXiv2017

Bi-National Delay Pattern Analysis For Commercial and Passenger Vehicles at Niagara Frontier Border

Border crossing delays between New York State and Southern Ontario cause problems like enormous economic loss and massive environmental pollutions. In this area, there are three border-crossing ports: Peace Bridge (PB), Rainbow Bridge (RB) and Lewiston-Queenston Bridge (LQ) at Niagara Frontier border. The goals of this paper are to figure out whether the distributions of bi-national wait times for commercial and passenger vehicles are evenly distributed among the three ports and uncover the hidden significant influential factors that result in the possible insufficient utilization. The historical border wait time data from 7:00 to 21:00 between 08/22/2016 and 06/20/2017 are archived, as well as the corresponding temporal and weather data. For each vehicle type towards each direction, a Decision Tree is built to identify the various border delay patterns over the three bridges. We find that for the passenger vehicles to the USA, the convenient connections between the Canada freeways with USA I-190 by LQ and PB may cause these two bridges more congested than RB, especially when it is a holiday in Canada. For the passenger vehicles in the other bound, RB is much more congested than LQ and PB in some cases, and the visitors to Niagara Falls in the USA in summer may be a reason. For the commercial trucks to the USA, the various delay patterns show PB is always more congested than LQ. Hour interval and weekend are the most significant factors appearing in all the four Decision Trees. These Decision Trees can help the authorities to make specific routing suggestions when the corresponding conditions are satisfied.

cs.CY↗

arXiv2017

Abnormal Spatial-Temporal Pattern Analysis for Niagara Frontier Border Wait Times

Border crossing delays cause problems like huge economics loss and heavy environmental pollutions. To understand more about the nature of border crossing delay, this study applies a dictionary-based compression algorithm to process the historical Niagara Frontier border wait times data. It can identify the abnormal spatial-temporal patterns for both passenger vehicles and trucks at three bridges connecting US and Canada. Furthermore, it provides a quantitate anomaly score to rank the wait times patterns across the three bridges for each vehicle type and each direction. By analyzing the top three most abnormal patterns, we find that there are at least two factors contributing the anomaly of the patterns. The weekends and holidays may cause unusual heave congestions at the three bridges at the same time, and the freight transportation demand may be uneven from Canada to the USA at Peace Bridge and Lewiston-Queenston Bridge, which may lead to a high anomaly score. By calculating the frequency of the top 5% abnormal patterns by hour of the day, the results show that for cars from the USA to Canada, the frequency of abnormal waiting time patterns is the highest during noon while for trucks in the same direction, it is the highest during the afternoon peak hours. For Canada to US direction, the frequency of abnormal border wait time patterns for both cars and trucks reaches to the peak during the afternoon. The analysis of abnormal spatial-temporal wait times patterns is promising to improve the border crossing management

cs.AI↗

arXiv2016

Deep Extreme Feature Extraction: New MVA Method for Searching Particles in High Energy Physics

In this paper, we present Deep Extreme Feature Extraction (DEFE), a new ensemble MVA method for searching $τ^{+}τ^{-}$ channel of Higgs bosons in high energy physics. DEFE can be viewed as a deep ensemble learning scheme that trains a strongly diverse set of neural feature learners without explicitly encouraging diversity and penalizing correlations. This is achieved by adopting an implicit neural controller (not involved in feedforward compuation) that directly controls and distributes gradient flows from higher level deep prediction network. Such model-independent controller results in that every single local feature learned are used in the feature-to-output mapping stage, avoiding the blind averaging of features. DEFE makes the ensembles 'deep' in the sense that it allows deep post-process of these features that tries to learn to select and abstract the ensemble of neural feature learners. With the application of this model, a selection regions full of signal process can be obtained through the training of a miniature collision events set. In comparison of the Classic Deep Neural Network, DEFE shows a state-of-the-art performance: the error rate has decreased by about 37\%, the accuracy has broken through 90\% for the first time, along with the discovery significance has reached a standard deviation of 6.0 $σ$. Experimental data shows that, DEFE is able to train an ensemble of discriminative feature learners that boosts the overperformance of final prediction.

cs.LG↗

arXiv2014

A Scalable Framework for Spatiotemporal Analysis of Location-based Social Media Data

In the past several years, social media (e.g., Twitter and Facebook) has been experiencing a spectacular rise and popularity, and becoming a ubiquitous discourse for content sharing and social networking. With the widespread of mobile devices and location-based services, social media typically allows users to share whereabouts of daily activities (e.g., check-ins and taking photos), and thus strengthens the roles of social media as a proxy to understand human behaviors and complex social dynamics in geographic spaces. Unlike conventional spatiotemporal data, this new modality of data is dynamic, massive, and typically represented in stream of unstructured media (e.g., texts and photos), which pose fundamental representation, modeling and computational challenges to conventional spatiotemporal analysis and geographic information science. In this paper, we describe a scalable computational framework to harness massive location-based social media data for efficient and systematic spatiotemporal data analysis. Within this framework, the concept of space-time trajectories (or paths) is applied to represent activity profiles of social media users. A hierarchical spatiotemporal data model, namely a spatiotemporal data cube model, is developed based on collections of space-time trajectories to represent the collective dynamics of social media users across aggregation boundaries at multiple spatiotemporal scales. The framework is implemented based upon a public data stream of Twitter feeds posted on the continent of North America. To demonstrate the advantages and performance of this framework, an interactive flow mapping interface (including both single-source and multiple-source flow mapping) is developed to allow real-time, and interactive visual exploration of movement dynamics in massive location-based social media at multiple scales.

cs.SI↗