arXiv subjects

Tong Guan

Publications and source records attributed to Tong Guan.

12 recordsLinked to original sources

arXiv2026

IRIS: Visual-Semantic Binding for Forgery-Resistant Watermarking of Diffusion Images

Most in-generation diffusion watermarks embed patterns independent of the image that carries them, and attackers transplant the marks onto images the generator did not produce, resulting in forgery. Binding the mark to visual semantics prevents such transplantation, yet existing bindings anchor to a proxy image rather than the image they mark. Realizing visual-semantic binding inside generation faces two challenges. The mark derives from the image itself yet enters the sampling trajectory before that image exists, and may itself shift the semantics it binds. The binding also meets opposite sensitivity demands, breaking under semantic change while holding through common processing. We present IRIS, a training-free watermarking scheme that embeds an Intrinsic Ring Identifier from Semantics. IRIS reads a content code from the non-watermarked generated image, derives a one-time ring from the code and a secret key, returns to the final low-noise steps of the same trajectory and blends the ring in, after the semantics it binds are settled. To meet the opposite sensitivity demands, the code is read through a canonicalization shared between embedding and detection, holding through common distortions and mild regeneration while flipping under semantic change. Detection recomputes the ring from the query image and the key alone, and the mark therefore fails on a foreign or spliced image, with acceptance tracking semantic displacement. On three prompt datasets IRIS detects reliably and stays close to its same-seed non-watermarked counterpart, a fidelity prior in-generation marks do not reach. While forgeries transfer fixed-pattern marks and regeneration strips post-hoc marks, IRIS alone among the compared marks withstands both.

cs.CV↗

arXiv2026

AION: Next-Generation Tasks and Practical Harness for Time Series

Time series research is moving beyond fixed forecasting benchmarks toward realistic tasks that combine prediction, contextual reasoning, tool use, and structured decision support. Most benchmarks are built around clean data and short evaluation loops; agents alone may miss temporal constraints, evidence checks, or review before finalizing outputs. We first formalize next-generation time series tasks as three-component tuples consisting of a task file, a workspace, and a validation interface. We then present AION, a time series harness built from six component groups: agents, skills, rules, memory, evaluation, and protocols. In this harness, we use three design principles: temporal grounding, temporal knowledge-grounded reasoning, and reliability mechanisms such as post-experiment analysis and layered review. A Kaggle Store Sales case study shows that the harness produces more detailed process traces, more artifacts, and more review steps than the same base agent operating in OpenCode direct build mode. Taken together, these results argue for a paradigm shift from fixed tasks to realistic ones under real-world constraints.

cs.AI↗

arXiv2026

PhySE: A Psychological Framework for Real-Time AR-LLM Social Engineering Attacks

The emerging threat of AR-LLM-based Social Engineering (AR-LLM-SE) attacks (e.g. SEAR) poses a significant risk to real-world social interactions. In such an attack, a malicious actor uses Augmented Reality (AR) glasses to capture a target visual and vocal data. A Large Language Model (LLM) then analyzes this data to identify the individual and generate a detailed social profile. Subsequently, LLM-powered agents employ social engineering strategies, providing real-time conversation suggestions, to gain the target trust and ultimately execute phishing or other malicious acts. Despite its potential, the practical application of AR-LLM-SE faces two major bottlenecks, (1) Cold-start personalization, Current Retrieval-Augmented Generation (RAG) methods introduce critical delays in the earliest turns, slowing initial profile formation and disrupting real-time interaction, (2) Static Attack Strategies, Existing approaches rely on fixed-stage, handcrafted social engineering tactics that lack foundation in established psychological theory. To address these limitations, we propose PhySE, a novel framework with two core innovations, (1) VLM-Based SocialContext Training, To eliminate profiling delays, we efficiently pre-train a Visual Language Model (VLM) with social-context data, enabling rapid, on-the-fly profile generation, (2) Adaptive Psychological Agent, We introduce a psychological LLM that dynamically deploys distinct classes of psychological strategies based on target response, moving beyond static, handcrafted scripts. We evaluated PhySE through an IRB-approved user study with 60 participants, collecting a novel dataset of 360 annotated conversations across diverse social scenarios.

cs.AI↗

arXiv2026

TimeOmni-VL: Unified Models for Time Series Understanding and Generation

Recent time series modeling faces a sharp divide between numerical generation and semantic understanding, with research showing that generation models often rely on superficial pattern matching, while understanding-oriented models struggle with high-fidelity numerical output. Although unified multimodal models (UMMs) have bridged this gap in vision, their potential for time series remains untapped. We propose TimeOmni-VL, the first vision-centric framework that unifies time series understanding and generation through two key innovations: (1) Fidelity-preserving bidirectional mapping between time series and images (Bi-TSI), which advances Time Series-to-Image (TS2I) and Image-to-Time Series (I2TS) conversions to ensure near-lossless transformations. (2) Understanding-guided generation. We introduce TSUMM-Suite, a novel dataset consisting of six understanding tasks rooted in time series analytics and coupled with two generation tasks. With a calibrated Chain-of-Thought, TimeOmni-VL is the first to leverage time series understanding as an explicit control signal for high-fidelity generation. Experiments confirm that this unified approach significantly improves semantic understanding and numerical precision, establishing a new frontier for multimodal time series modeling.

cs.LG↗

arXiv2025

Synecdoche: Efficient and Accurate In-Network Traffic Classification via Direct Packet Sequential Pattern Matching

Traffic classification on programmable data plane holds great promise for line-rate processing, with methods evolving from per-packet to flow-level analysis for higher accuracy. However, a trade-off between accuracy and efficiency persists. Statistical feature-based methods align with hardware constraints but often exhibit limited accuracy, while online deep learning methods using packet sequential features achieve superior accuracy but require substantial computational resources. This paper presents Synecdoche, the first traffic classification framework that successfully deploys packet sequential features on a programmable data plane via pattern matching, achieving both high accuracy and efficiency. Our key insight is that discriminative information concentrates in short sub-sequences--termed Key Segments--that serve as compact traffic features for efficient data plane matching. Synecdoche employs an "offline discovery, online matching" paradigm: deep learning models automatically discover Key Segment patterns offline, which are then compiled into optimized table entries for direct data plane matching. Extensive experiments demonstrate Synecdoche's superior accuracy, improving F1-scores by up to 26.4% against statistical methods and 18.3% against online deep learning methods, while reducing latency by 13.0% and achieving 79.2% reduction in SRAM usage. The source code of Synecdoche is publicly available to facilitate reproducibility and further research.

cs.NI↗

arXiv2025

TimeOmni-1: Incentivizing Complex Reasoning with Time Series in Large Language Models

Recent advances in multimodal time series learning underscore a paradigm shift from analytics centered on basic patterns toward advanced time series understanding and reasoning. However, existing multimodal time series datasets mostly remain at the level of surface alignment and question answering, without reaching the depth of genuine reasoning. The absence of well-defined tasks that genuinely require time series reasoning, along with the scarcity of high-quality data, has limited progress in building practical time series reasoning models (TSRMs). To this end, we introduce Time Series Reasoning Suite (TSR-Suite), which formalizes four atomic tasks that span three fundamental capabilities for reasoning with time series: (1) perception, acquired through scenario understanding and causality discovery; (2) extrapolation, realized via event-aware forecasting; and (3) decision-making, developed through deliberation over perception and extrapolation. TSR-Suite is the first comprehensive time series reasoning suite that supports not only thorough evaluation but also the data pipeline and training of TSRMs. It contains more than 23K samples, of which 2.3K are carefully curated through a human-guided hierarchical annotation process. Building on this foundation, we introduce TimeOmni-1, the first unified reasoning model designed to address diverse real-world problems demanding time series reasoning. The model is trained in multiple stages, integrating a mixture of task scenarios, novel reward functions, and tailored optimizations. Experiments show that TimeOmni-1 delivers strong out-of-distribution generalization across all tasks and achieves a high rate of valid responses. It significantly improves causality discovery accuracy (64.0% vs. 35.9% with GPT-4.1) and raises the valid response rate by over 6% compared to GPT-4.1 on the event-aware forecasting task.

cs.LG↗

arXiv2021

Thermal dynamics of charge density wave pinning in ZrTe3

Impurity pinning has long been discussed to have a profound effect on the dynamics of an incommensurate charge density wave (CDW), which would otherwise slide through the lattice without resistance. Here we visualize the impurity pinning evolution of the CDW in ZrTe3 using the variable temperature scanning tunneling microscopy (STM). At low temperatures, we observe a quasi-1D incommensurate CDW modulation moderately correlated to the impurity positions, indicating a weak impurity pinning. As we raise the sample temperature, the CDW modulation gets progressively weakened and distorted, while the correlation with the impurities becomes stronger. Above the CDW transition temperature, short-range modulations persist with the phase almost all pinned by impurities. The evolution from weak to strong impurity pinning through the CDW transition can be understood as a result of losing phase rigidity.

cond-mat.str-el↗

arXiv2020

Robust Gapless Surface State against Surface Magnetic Impurities on (Bi$_{0.5}$Sb$_{0.5}$)$_2$Te$_3$ Evidenced by In Situ Magnetotransport Measurements

Despite extensive experimental and theoretical efforts, the important issue of the effects of surface magnetic impurities on the topological surface state of a topological insulator (TI) remains unresolved. We elucidate the effects of Cr impurities on epitaxial thin films of (Bi$_{0.5}$Sb$_{0.5}$)$_{2}$Te$_{3}$: Cr adatoms are incrementally deposited onto the TI held in ultrahigh vacuum at low temperatures, and \textit{in situ} magnetoconductivity and Hall effect measurements are performed at each increment with electrostatic gating. In the experimentally identified surface transport regime, the measured minimum electron density shows a non-monotonic evolution with the Cr density ($n_{\mathrm{Cr}}$): it first increases and then decreases with $n_{\mathrm{Cr}}$. This unusual behavior is ascribed to the dual roles of the Cr as ionized impurities and electron donors, having competing effects of enhancing and decreasing the electronic inhomogeneities in the surface state at low and high $n_{\mathrm{Cr}}$ respectively. The magnetoconductivity is obtained for different $n_{\mathrm{Cr}}$ on one and the same sample, which yields clear evidence that the weak antilocalization effect persists and the surface state remains gapless up to the highest $n_{\mathrm{Cr}}$, contrary to the expectation that the deposited Cr should break the time reversal symmetry and induce a gap opening at the Dirac point.

cond-mat.mes-hall↗

arXiv2019

PocketCare: Tracking the Flu with Mobile Phones using Partial Observations of Proximity and Symptoms

Mobile phones provide a powerful sensing platform that researchers may adopt to understand proximity interactions among people and the diffusion, through these interactions, of diseases, behaviors, and opinions. However, it remains a challenge to track the proximity-based interactions of a whole community and then model the social diffusion of diseases and behaviors starting from the observations of a small fraction of the volunteer population. In this paper, we propose a novel approach that tries to connect together these sparse observations using a model of how individuals interact with each other and how social interactions happen in terms of a sequence of proximity interactions. We apply our approach to track the spreading of flu in the spatial-proximity network of a 3000-people university campus by mobilizing 300 volunteers from this population to monitor nearby mobile phones through Bluetooth scanning and to daily report flu symptoms about and around them. Our aim is to predict the likelihood for an individual to get flu based on how often her/his daily routine intersects with those of the volunteers. Thus, we use the daily routines of the volunteers to build a model of the volunteers as well as of the non-volunteers. Our results show that we can predict flu infection two weeks ahead of time with an average precision from 0.24 to 0.35 depending on the amount of information. This precision is six to nine times higher than with a random guess model. At the population level, we can predict infectious population in a two-week window with an r-squared value of 0.95 (a random-guess model obtains an r-squared value of 0.2). These results point to an innovative approach for tracking individuals who have interacted with people showing symptoms, allowing us to warn those in danger of infection and to inform health researchers about the progression of contact-induced diseases.

cs.SI↗

arXiv2017

VehSense: Slippery Road Detection Using Smartphones

This paper investigates a new application of vehicular sensing: detecting and reporting the slippery road conditions. We describe a system and associated algorithm to monitor vehicle skidding events using smartphones and OBD-II (On board Diagnostics) adaptors. This system, which we call the VehSense, gathers data from smartphone inertial sensors and vehicle wheel speed sensors, and processes the data to monitor slippery road conditions in real-time. Specifically, two speed readings are collected: 1) ground speed, which is estimated by vehicle acceleration and rotation, and 2) wheel speed, which is retrieved from the OBD-II interface. The mismatch between these two speeds is used to infer a skidding event. Without tapping into vehicle manufactures' proprietary data (e.g., antilock braking system), VehSense is compatible with most of the passenger vehicles, and thus can be easily deployed. We evaluate our system on snow-covered roads at Buffalo, and show that it can detect vehicle skidding effectively.

eess.SY↗

arXiv2015

Evidence for Half-Metallicity in n-type HgCr2Se4

High quality HgCr$_2$Se$_4$ single crystals have been investigated by magnetization, electron transport and Andreev reflection spectroscopy. In the ferromagnetic ground state, the saturation magnetic moment of each unit cell corresponds to an integer number of electron spins (3 $μ_B$/Cr$^{3+}$), and the Hall effect measurements suggest n-type charge carriers. Spin polarizations as high as $97\%$ were obtained from fits of the differential conductance spectra of HgCr$_2$Se$_4$/Pb junctions with the modified Blonder-Tinkham-Klapwijk (BTK) theory. The temperature and bias-voltage dependencies of the sub-gap conductance are consistent with recent theoretical calculations based on spin active scatterings at a superconductor/half metal interface. Our results suggest that n-HgCr$_2$Se$_4$ is a half metal, in agreement with theoretical calculations that also predict undoped HgCr$_2$Se$_4$ is a magnetic Weyl semimetal.

cond-mat.mes-hall↗

arXiv2015

Disorder Enhanced Nuclear Spin Relaxation at Landau Level Filling Factor One

The nuclear spin relaxation rate (1/$T_1$) is measured for GaAs two-dimensional electron systems in the quantum Hall regime with an all-electrical technique for agitating and probing the nuclear spins. A "tilted plateau" feature is observed near the Landau level filling factor $ν=1$ in 1/$T_1$ versus $ν$. Both width and magnitude of the plateau increase with decreasing electron density. At low temperatures, 1/$T_1$ exhibits an Arrhenius temperature dependence within the tilted plateau regime. The extracted energy gaps are up to two orders of magnitude smaller than the corresponding charge transport gaps. These results point to a nontrivial mechanism for the disorder enhanced nuclear spin relaxation, in which microscopic inhomogeneities play a key role for the low energy spin excitations related to skyrmions.

cond-mat.mes-hall↗