arXiv subjects

Zheng Zhang

Publications and source records attributed to Zheng Zhang.

At least 289 records · Page 16Linked to original sources

arXiv2024

Crystalline electric field excitations and their nonlinear splitting under magnetic fields in YbOCl

Recently reported van der Waals layered honeycomb rare-earth chalcohalides REChX (RE = rare earth, Ch = chalcogen, and X = halogen) are considered to be promising Kitaev spin liquid (KSL) candidates. The high-quality single crystals of YbOCl, a representative member of the family with an effective spin of 1/2, are available now. The crystalline electric field (CEF) excitations in a rare-earth spin system are fundamentally important for understanding both finite-temperature and ground-state magnetism, but remain unexplored in YbOCl so far. In this paper, we conduct a comprehensive Raman scattering study to unambiguously identify the CEF excitations in YbOCl and determine the CEF parameters and wave functions. Our Raman experiments further reveal the anomalous nonlinear CEF splitting under magnetic fields. We have grown single crystals of YbOCl, the nonmagnetic LuOCl, and the diluted magnetic Lu_{0.86}Yb_{0.14}OCl to make a completely comparative investigation. Polarized Raman spectra on the samples at 1.8 K allow us to clearly assign all the Raman-active phonon modes and explicitly identify the CEF excitations in YbOCl. The CEF excitations are further examined using temperature-dependent Raman measurements and careful symmetry analysis based on Raman tensors related to CEF excitations. By applying the CEF Hamiltonian to the experimentally determined CEF excitations, we extract the CEF parameters and eventually determine the CEF wave functions. The study experimentally pins down the CEF excitations in the Kitaev compound YbOCl and sets a foundation for understanding its finite-temperature magnetism and exploring the possible nontrivial spin ground state.

cond-mat.str-el↗

arXiv2024

Integrating spoken instructions into flight trajectory prediction to optimize automation in air traffic control

The booming air transportation industry inevitably burdens air traffic controllers' workload, causing unexpected human factor-related incidents. Current air traffic control systems fail to consider spoken instructions for traffic prediction, bringing significant challenges in detecting human errors during real-time traffic operations. Here, we present an automation paradigm integrating controlling intent into the information processing loop through the spoken instruction-aware flight trajectory prediction framework. A 3-stage progressive multi-modal learning paradigm is proposed to address the modality gap between the trajectory and spoken instructions, as well as minimize the data requirements. Experiments on a real-world dataset show the proposed framework achieves flight trajectory prediction with high predictability and timeliness, obtaining over 20% relative reduction in mean deviation error. Moreover, the generalizability of the proposed framework is also confirmed by various model architectures. The proposed framework can formulate full-automated information processing in real-world air traffic applications, supporting human error detection and enhancing aviation safety.

cs.SD↗

arXiv2024

Transferable Unsupervised Outlier Detection Framework for Human Semantic Trajectories

Semantic trajectories, which enrich spatial-temporal data with textual information such as trip purposes or location activities, are key for identifying outlier behaviors critical to healthcare, social security, and urban planning. Traditional outlier detection relies on heuristic rules, which requires domain knowledge and limits its ability to identify unseen outliers. Besides, there lacks a comprehensive approach that can jointly consider multi-modal data across spatial, temporal, and textual dimensions. Addressing the need for a domain-agnostic model, we propose the Transferable Outlier Detection for Human Semantic Trajectories (TOD4Traj) framework.TOD4Traj first introduces a modality feature unification module to align diverse data feature representations, enabling the integration of multi-modal information and enhancing transferability across different datasets. A contrastive learning module is further pro-posed for identifying regular mobility patterns both temporally and across populations, allowing for a joint detection of outliers based on individual consistency and group majority patterns. Our experimental results have shown TOD4Traj's superior performance over existing models, demonstrating its effectiveness and adaptability in detecting human trajectory outliers across various datasets.

cs.LG↗

arXiv2024

VideoSAM: Open-World Video Segmentation

Video segmentation is essential for advancing robotics and autonomous driving, particularly in open-world settings where continuous perception and object association across video frames are critical. While the Segment Anything Model (SAM) has excelled in static image segmentation, extending its capabilities to video segmentation poses significant challenges. We tackle two major hurdles: a) SAM's embedding limitations in associating objects across frames, and b) granularity inconsistencies in object segmentation. To this end, we introduce VideoSAM, an end-to-end framework designed to address these challenges by improving object tracking and segmentation consistency in dynamic environments. VideoSAM integrates an agglomerated backbone, RADIO, enabling object association through similarity metrics and introduces Cycle-ack-Pairs Propagation with a memory mechanism for stable object tracking. Additionally, we incorporate an autoregressive object-token mechanism within the SAM decoder to maintain consistent granularity across frames. Our method is extensively evaluated on the UVO and BURST benchmarks, and robotic videos from RoboTAP, demonstrating its effectiveness and robustness in real-world scenarios. All codes will be available.

cs.CV↗

arXiv2024

Formation of Anisotropic Polarons in Antimony Selenide

Antimony Selenide (Sb$_2$Se$_3$) is an attractive candidate of photovoltaics with not yet satisfying efficiency. Beside defects, polaron formation originated from lattice distortion was proposed to account for trapping free carriers, and the subsequent photoexcitation dynamics and optoelectronic properties, but such a mechanism is still lack of structural observations. Here we directly track the pathways of carrier and lattice evolutions after photoexcitation through optical and electron diffraction pump-probe methods, revealing the temporal correlations between dynamics of both degrees of freedom. The observed opposite separation changes of Se2-Sb2 and Sb2-Sb1 atom pairs in a few picoseconds, and the intermediate state induced by local structural distortions lasting several tens of picoseconds, coinciding with the optical phonons population and coupling, and the trapping process of carriers, respectively, together with the analyses of modulation on diffuse scattering by the atomic displacement fields of polaron model, indicate the formation of anisotropic polarons with large size. Our findings provide carrier and structural information for helping the elucidation of polaron scenario in Sb2Se3, and probably in materials with anisotropic structure and soft lattice which are popular in developing novel optoelectronics.

cond-mat.mtrl-sci↗

arXiv2024

ECon: On the Detection and Resolution of Evidence Conflicts

The rise of large language models (LLMs) has significantly influenced the quality of information in decision-making systems, leading to the prevalence of AI-generated content and challenges in detecting misinformation and managing conflicting information, or "inter-evidence conflicts." This study introduces a method for generating diverse, validated evidence conflicts to simulate real-world misinformation scenarios. We evaluate conflict detection methods, including Natural Language Inference (NLI) models, factual consistency (FC) models, and LLMs, on these conflicts (RQ1) and analyze LLMs' conflict resolution behaviors (RQ2). Our key findings include: (1) NLI and LLM models exhibit high precision in detecting answer conflicts, though weaker models suffer from low recall; (2) FC models struggle with lexically similar answer conflicts, while NLI and LLM models handle these better; and (3) stronger models like GPT-4 show robust performance, especially with nuanced conflicts. For conflict resolution, LLMs often favor one piece of conflicting evidence without justification and rely on internal knowledge if they have prior beliefs.

cs.CL↗

arXiv2024

Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval

Deep hashing has been intensively studied and successfully applied in large-scale image retrieval systems due to its efficiency and effectiveness. Recent studies have recognized that the existence of adversarial examples poses a security threat to deep hashing models, that is, adversarial vulnerability. Notably, it is challenging to efficiently distill reliable semantic representatives for deep hashing to guide adversarial learning, and thereby it hinders the enhancement of adversarial robustness of deep hashing-based retrieval models. Moreover, current researches on adversarial training for deep hashing are hard to be formalized into a unified minimax structure. In this paper, we explore Semantic-Aware Adversarial Training (SAAT) for improving the adversarial robustness of deep hashing models. Specifically, we conceive a discriminative mainstay features learning (DMFL) scheme to construct semantic representatives for guiding adversarial learning in deep hashing. Particularly, our DMFL with the strict theoretical guarantee is adaptively optimized in a discriminative learning manner, where both discriminative and semantic properties are jointly considered. Moreover, adversarial examples are fabricated by maximizing the Hamming distance between the hash codes of adversarial samples and mainstay features, the efficacy of which is validated in the adversarial attack trials. Further, we, for the first time, formulate the formalized adversarial training of deep hashing into a unified minimax optimization under the guidance of the generated mainstay codes. Extensive experiments on benchmark datasets show superb attack performance against the state-of-the-art algorithms, meanwhile, the proposed adversarial training can effectively eliminate adversarial perturbations for trustworthy deep hashing-based retrieval. Our code is available at https://github.com/xandery-geek/SAAT.

cs.CV↗

arXiv2024

BadCM: Invisible Backdoor Attack Against Cross-Modal Learning

Despite remarkable successes in unimodal learning tasks, backdoor attacks against cross-modal learning are still underexplored due to the limited generalization and inferior stealthiness when involving multiple modalities. Notably, since works in this area mainly inherit ideas from unimodal visual attacks, they struggle with dealing with diverse cross-modal attack circumstances and manipulating imperceptible trigger samples, which hinders their practicability in real-world applications. In this paper, we introduce a novel bilateral backdoor to fill in the missing pieces of the puzzle in the cross-modal backdoor and propose a generalized invisible backdoor framework against cross-modal learning (BadCM). Specifically, a cross-modal mining scheme is developed to capture the modality-invariant components as target poisoning areas, where well-designed trigger patterns injected into these regions can be efficiently recognized by the victim models. This strategy is adapted to different image-text cross-modal models, making our framework available to various attack scenarios. Furthermore, for generating poisoned samples of high stealthiness, we conceive modality-specific generators for visual and linguistic modalities that facilitate hiding explicit trigger patterns in modality-invariant regions. To the best of our knowledge, BadCM is the first invisible backdoor method deliberately designed for diverse cross-modal attacks within one unified framework. Comprehensive experimental evaluations on two typical applications, i.e., cross-modal retrieval and VQA, demonstrate the effectiveness and generalization of our method under multiple kinds of attack scenarios. Moreover, we show that BadCM can robustly evade existing backdoor defenses. Our code is available at https://github.com/xandery-geek/BadCM.

cs.CV↗

arXiv2024

Mitigating Training Imbalance in LLM Fine-Tuning via Selective Parameter Merging

Supervised fine-tuning (SFT) is crucial for adapting Large Language Models (LLMs) to specific tasks. In this work, we demonstrate that the order of training data can lead to significant training imbalances, potentially resulting in performance degradation. Consequently, we propose to mitigate this imbalance by merging SFT models fine-tuned with different data orders, thereby enhancing the overall effectiveness of SFT. Additionally, we introduce a novel technique, "parameter-selection merging," which outperforms traditional weighted-average methods on five datasets. Further, through analysis and ablation studies, we validate the effectiveness of our method and identify the sources of performance improvements.

cs.CL↗

arXiv2024

Cross-Modal Retrieval: A Systematic Review of Methods and Future Directions

With the exponential surge in diverse multi-modal data, traditional uni-modal retrieval methods struggle to meet the needs of users seeking access to data across various modalities. To address this, cross-modal retrieval has emerged, enabling interaction across modalities, facilitating semantic matching, and leveraging complementarity and consistency between heterogeneous data. Although prior literature has reviewed the field of cross-modal retrieval, it suffers from numerous deficiencies in terms of timeliness, taxonomy, and comprehensiveness. This paper conducts a comprehensive review of cross-modal retrieval's evolution, spanning from shallow statistical analysis techniques to vision-language pre-training models. Commencing with a comprehensive taxonomy grounded in machine learning paradigms, mechanisms, and models, the paper delves deeply into the principles and architectures underpinning existing cross-modal retrieval methods. Furthermore, it offers an overview of widely-used benchmarks, metrics, and performances. Lastly, the paper probes the prospects and challenges that confront contemporary cross-modal retrieval, while engaging in a discourse on potential directions for further progress in the field. To facilitate the ongoing research on cross-modal retrieval, we develop a user-friendly toolbox and an open-source repository at https://cross-modal-retrieval.github.io.

cs.IR↗

arXiv2024

One Token to Seg Them All: Language Instructed Reasoning Segmentation in Videos

We introduce VideoLISA, a video-based multimodal large language model designed to tackle the problem of language-instructed reasoning segmentation in videos. Leveraging the reasoning capabilities and world knowledge of large language models, and augmented by the Segment Anything Model, VideoLISA generates temporally consistent segmentation masks in videos based on language instructions. Existing image-based methods, such as LISA, struggle with video tasks due to the additional temporal dimension, which requires temporal dynamic understanding and consistent segmentation across frames. VideoLISA addresses these challenges by integrating a Sparse Dense Sampling strategy into the video-LLM, which balances temporal context and spatial detail within computational constraints. Additionally, we propose a One-Token-Seg-All approach using a specially designed token, enabling the model to segment and track objects across multiple frames. Extensive evaluations on diverse benchmarks, including our newly introduced ReasonVOS benchmark, demonstrate VideoLISA's superior performance in video object segmentation tasks involving complex reasoning, temporal understanding, and object tracking. While optimized for videos, VideoLISA also shows promising generalization to image segmentation, revealing its potential as a unified foundation model for language-instructed object segmentation. Code and model will be available at: https://github.com/showlab/VideoLISA.

cs.CV↗

arXiv2024

VascularPilot3D: Toward a 3D fully autonomous navigation for endovascular robotics

This research reports VascularPilot3D, the first 3D fully autonomous endovascular robot navigation system. As an exploration toward autonomous guidewire navigation, VascularPilot3D is developed as a complete navigation system based on intra-operative imaging systems (fluoroscopic X-ray in this study) and typical endovascular robots. VascularPilot3D adopts previously researched fast 3D-2D vessel registration algorithms and guidewire segmentation methods as its perception modules. We additionally propose three modules: a topology-constrained 2D-3D instrument end-point lifting method, a tree-based fast path planning algorithm, and a prior-free endovascular navigation strategy. VascularPilot3D is compatible with most mainstream endovascular robots. Ex-vivo experiments validate that VascularPilot3D achieves 100% success rate among 25 trials. It reduces the human surgeon's overall control loops by 18.38%. VascularPilot3D is promising for general clinical autonomous endovascular navigations.

cs.RO↗

arXiv2024

Planning and Rendering: Towards Product Poster Generation with Diffusion Models

Product poster generation significantly optimizes design efficiency and reduces production costs. Prevailing methods predominantly rely on image-inpainting methods to generate clean background images for given products. Subsequently, poster layout generation methods are employed to produce corresponding layout results. However, the background images may not be suitable for accommodating textual content due to their complexity, and the fixed location of products limits the diversity of layout results. To alleviate these issues, we propose a novel product poster generation framework based on diffusion models named P\&R. The P\&R draws inspiration from the workflow of designers in creating posters, which consists of two stages: Planning and Rendering. At the planning stage, we propose a PlanNet to generate the layout of the product and other visual components considering both the appearance features of the product and semantic features of the text, which improves the diversity and rationality of the layouts. At the rendering stage, we propose a RenderNet to generate the background for the product while considering the generated layout, where a spatial fusion module is introduced to fuse the layout of different visual components. To foster the advancement of this field, we propose the first product poster generation dataset PPG30k, comprising 30k exquisite product poster images along with comprehensive image and text annotations. Our method outperforms the state-of-the-art product poster generation methods on PPG30k. The PPG30k will be released soon.

cs.CV↗

arXiv2024

Conversation Disentanglement with Bi-Level Contrastive Learning

Conversation disentanglement aims to group utterances into detached sessions, which is a fundamental task in processing multi-party conversations. Existing methods have two main drawbacks. First, they overemphasize pairwise utterance relations but pay inadequate attention to the utterance-to-context relation modeling. Second, huge amount of human annotated data is required for training, which is expensive to obtain in practice. To address these issues, we propose a general disentangle model based on bi-level contrastive learning. It brings closer utterances in the same session while encourages each utterance to be near its clustered session prototypes in the representation space. Unlike existing approaches, our disentangle model works in both supervised setting with labeled data and unsupervised setting when no such data is available. The proposed method achieves new state-of-the-art performance on both settings across several public datasets.

cs.CL↗

arXiv2024

Dual-Functional Artificial Noise (DFAN) Aided Robust Covert Communications in Integrated Sensing and Communications

This paper investigates covert communications in an integrated sensing and communications system, where a dual-functional base station (called Alice) covertly transmits signals to a covert user (called Bob) while sensing multiple targets, with one of them acting as a potential watcher (called Willie) and maliciously eavesdropping on legitimate communications. To shelter the covert communications, Alice transmits additional dual-functional artificial noise (DFAN) with a varying power not only to create uncertainty at Willie's signal reception to confuse Willie but also to sense the targets simultaneously. Based on this framework, the weighted sum of the sensing beampattern means square error (MSE) and cross correlation is minimized by jointly optimizing the covert communication and DFAN signals subject to the minimum covert rate requirement. The robust design considers both cases of imperfect Willie's CSI (WCSI) and statistical WCSI. Under the worst-case assumption that Willie can adaptively adjust the detection threshold to achieve the best detection performance, the minimum detection error probability (DEP) at Willie is analytically derived in the closed-form expression. The formulated covertness constrained optimization problems are tackled by a feasibility-checking based difference-of-convex relaxation (DC) algorithm utilizing the S-procedure, Bernstein-type inequality, and the DC method. Simulation results validate the feasibility of the proposed scheme and demonstrate the covertness performance gains achieved by our proposed design over various benchmarks.

cs.IT↗

arXiv2024

Three approaches to a categorical Torelli theorem for cubic threefolds of non-Eckardt type via the equivariant Kuznetsov components

Let $Y$ be a cubic threefold with a non-Eckardt type involution $τ$. Our first main result is that the $τ$-equivariant category of the Kuznetsov component $\mathcal{K}u_{\mathbb{Z}_2}(Y)$ determines the isomorphism class of $Y$ for general $(Y,τ)$. We shall prove this categorical Torelli theorem via three approaches: a noncommutative Hodge theoretical one (using a generalization of the intermediate Jacobian construction due to Alexander Perry), a Bridgeland moduli theoretical one (using equivariant stability conditions), and a Chow theoretical one (using some techniques in [kuznetsovnonclodedfield2021]).The remaining part of the paper is devoted to proving an equivariant infinitesimal categorical Torelli for non-Eckardt cubic threefolds $(Y,τ)$. To accomplish it, we prove a compatibility theorem on the algebra structures of the Hochschild cohomology of the bounded derived category $D^b(X)$ of a smooth projective variety $X$ and on the Hochschild cohomology of a semi-orthogonal component of $D^b(X)$. Another key ingredient is a generalization of a result in [macri2009infinitesima] which shows that the twisted Hochschild-Kostant-Rosenberg isomorphism is compatible with the actions on the Hochschild cohomology and on the singular cohomology induced by an automorphism of $X$. In appendix, we prove an equivariant categorical Torelli theorem for arbitrary cubic threefold with a geometric involution under a natural assumption.

math.AG↗

arXiv2024

Ground State Magnetic Structure and Magnetic Field Effects in the Layered Honeycomb Antiferromagnet YbOCl

YbOCl is a representative member of the van der Waals layered honeycomb rare-earth chalcohalide REChX (RE = rare earth, Ch = O, S, Se, and Te, and X = F, Cl, Br, and I) family reported recently. Its spin ground state remains to be explored experimentally. In this paper, we have grown high-quality single crystals of YbOCl and conducted comprehensive thermodynamic, elastic, and inelastic neutron scattering experiments down to 50 mK. The experiments reveal an antiferromagnetic phase below 1.3 K, which is identified as a spin ground state with an intralayer ferromagnetic and interlayer antiferromagnetic ordering. By applying sophisticated numerical techniques to a honeycomb (nearest-neighbor)-triangle (next-nearest-neighbor) model Hamiltonian which accurately describes the highly anisotropic spin system, we are able to well simulate the experiments and determine the diagonal and off-diagonal spin-exchange interactions. The simulations give an antiferromagnetic Kitaev term comparable to the Heisenberg one. The experiments under magnetic fields allow us to establish a magnetic field-temperature phase diagram around the spin ground state. Most interestingly, a relatively small magnetic field (~ 0.3 to 3 T) can significantly suppress the antiferromagnetic order, suggesting an intriguing interplay of the Kitaev interaction and magnetic fields in the spin system. The present study provides fundamental insights into the highly anisotropic spin systems and opens a new window to look into Kitaev spin physics in a rare-earth-based system.

cond-mat.str-el↗

arXiv2024

Finite Temperature Magnetism in the Triangular Lattice Antiferromagnet KErTe2

After the discovery of the ARECh2 (A=alkali or monovalent ions, RE=rare-earth, Ch= chalcogen) triangular lattice quantum spin liquid (QSL) family, a series of its oxide, sulfide, and selenide counterparts has been consistently reported and extensively investigated. While KErTe2 represents the initial synthesized telluride member, preserving its triangular spin lattice, it was anticipated that the substantial tellurium ions could impart more pronounced magnetic attributes and electronic structures to this material class. This study delves into the magnetism of KErTe2 at finite temperatures through magnetization and electron spin resonance (ESR) measurements. Based on the angular momentum $\hat{J}$ after spin-orbit coupling (SOC) and symmetry analysis, we obtain the magnetic effective Hamiltonian to describe the magnetism of Er3+ in R-3m space group. Applying the mean-field approximation to the Hamiltonian, we can simulate the magnetization and magnetic heat capacity of KErTe2 in paramagnetic state and determine the crystalline electric field (CEF) parameters and partial exchange interactions. The relatively narrow energy gaps between CEF ground state and excited states exert a significant influence on the magnetism. For example, small CEF excitations can result in a significant broadening of the ESR linewidth at 2 K. For the fitted exchange interactions, although the values are small, given a large angular momentum J = 15/2 after SOC, they still have a noticeable effect at finite temperatures. Notably, the heat capacity data under different magnetic fields along the c-axis direction also roughly match our calculated results, further validating the reliability of our analytical approach. These derived parameters serve as crucial tools for future investigations into the ground state magnetism of KErTe2.

cond-mat.str-el↗