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Zhenyu Zhang

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preprint2026arXiv

A Survey on Failure Analysis and Fault Injection in AI Systems

The rapid advancement of Artificial Intelligence (AI) has led to its integration into various areas, especially with Large Language Models (LLMs) significantly enhancing capabilities in Artificial Intelligence Generated Content (AIGC). However, the complexity of AI systems has also exposed their vulnerabilities, necessitating robust methods for failure analysis (FA) and fault injection (FI) to ensure resilience and reliability. Despite the importance of these techniques, there lacks a comprehensive review of FA and FI methodologies in AI systems. This study fills this gap by presenting a detailed survey of existing FA and FI approaches across six layers of AI systems. We systematically analyze 160 papers and repositories to answer three research questions including (1) what are the prevalent failures in AI systems, (2) what types of faults can current FI tools simulate, (3) what gaps exist between the simulated faults and real-world failures. Our findings reveal a taxonomy of AI system failures, assess the capabilities of existing FI tools, and highlight discrepancies between real-world and simulated failures. Moreover, this survey contributes to the field by providing a framework for fault diagnosis, evaluating the state-of-the-art in FI, and identifying areas for improvement in FI techniques to enhance the resilience of AI systems.

preprint2026arXiv

Interactive Evaluation Requires a Design Science

AI evaluation is undergoing a structural change. Large language models (LLMs) are increasingly deployed as systems that act over time through tools, environments, users, and other agents, while many evaluation practices still inherit assumptions from response-centered benchmarks (e.g., fixed inputs, isolated outputs, and outcome judgments that can be made from a single response). The field has begun to build interactive benchmarks, but the resulting landscape is fragmented: benchmarks differ in what interaction artifacts they admit, how trajectories are scored, and what claims their results support. This position paper argues that interactive evaluation should be treated as a principled evaluation paradigm, not merely a new family of agent benchmarks. Simply adopting previous evaluation paradigms does not suffice. We define evaluation as an autonomous mapping from evidence to judgments, and show that interactive evaluation changes both sides of this mapping: the evidence becomes interaction-generated trajectories, while the evaluation procedure must assess process, recoverability, coordination, robustness, and system-level performance. Building on this definition, we propose a two-axis taxonomy, derive design principles and reporting standards, examine representative scenarios, and analyze how longstanding evaluation challenges reappear at the trajectory level.

preprint2026arXiv

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training

We present Muses, the first training-free method for fantastic 3D creature generation in a feed-forward paradigm. Previous methods, which rely on part-aware optimization, manual assembly, or 2D image generation, often produce unrealistic or incoherent 3D assets due to the challenges of intricate part-level manipulation and limited out-of-domain generation. In contrast, Muses leverages the 3D skeleton, a fundamental representation of biological forms, to explicitly and rationally compose diverse elements. This skeletal foundation formalizes 3D content creation as a structure-aware pipeline of design, composition, and generation. Muses begins by constructing a creatively composed 3D skeleton with coherent layout and scale through graph-constrained reasoning. This skeleton then guides a voxel-based assembly process within a structured latent space, integrating regions from different objects. Finally, image-guided appearance modeling under skeletal conditions is applied to generate a style-consistent and harmonious texture for the assembled shape. Extensive experiments establish Muses' state-of-the-art performance in terms of visual fidelity and alignment with textual descriptions, and potential on flexible 3D object editing. Project page: https://luhexiao.github.io/Muses.github.io/.

preprint2025arXiv

Fantastic Reasoning Behaviors and Where to Find Them: Unsupervised Discovery of the Reasoning Process

Despite the growing reasoning capabilities of recent large language models (LLMs), their internal mechanisms during the reasoning process remain underexplored. Prior approaches often rely on human-defined concepts (e.g., overthinking, reflection) at the word level to analyze reasoning in a supervised manner. However, such methods are limited, as it is infeasible to capture the full spectrum of potential reasoning behaviors, many of which are difficult to define in token space. In this work, we propose an unsupervised framework (namely, RISE: Reasoning behavior Interpretability via Sparse auto-Encoder) for discovering reasoning vectors, which we define as directions in the activation space that encode distinct reasoning behaviors. By segmenting chain-of-thought traces into sentence-level 'steps' and training sparse auto-encoders (SAEs) on step-level activations, we uncover disentangled features corresponding to interpretable behaviors such as reflection and backtracking. Visualization and clustering analyses show that these behaviors occupy separable regions in the decoder column space. Moreover, targeted interventions on SAE-derived vectors can controllably amplify or suppress specific reasoning behaviors, altering inference trajectories without retraining. Beyond behavior-specific disentanglement, SAEs capture structural properties such as response length, revealing clusters of long versus short reasoning traces. More interestingly, SAEs enable the discovery of novel behaviors beyond human supervision. We demonstrate the ability to control response confidence by identifying confidence-related vectors in the SAE decoder space. These findings underscore the potential of unsupervised latent discovery for both interpreting and controllably steering reasoning in LLMs.

preprint2025arXiv

OpenGround: Active Cognition-based Reasoning for Open-World 3D Visual Grounding

3D visual grounding aims to locate objects based on natural language descriptions in 3D scenes. Existing methods rely on a pre-defined Object Lookup Table (OLT) to query Visual Language Models (VLMs) for reasoning about object locations, which limits the applications in scenarios with undefined or unforeseen targets. To address this problem, we present OpenGround, a novel zero-shot framework for open-world 3D visual grounding. Central to OpenGround is the Active Cognition-based Reasoning (ACR) module, which is designed to overcome the fundamental limitation of pre-defined OLTs by progressively augmenting the cognitive scope of VLMs. The ACR module performs human-like perception of the target via a cognitive task chain and actively reasons about contextually relevant objects, thereby extending VLM cognition through a dynamically updated OLT. This allows OpenGround to function with both pre-defined and open-world categories. We also propose a new dataset named OpenTarget, which contains over 7000 object-description pairs to evaluate our method in open-world scenarios. Extensive experiments demonstrate that OpenGround achieves competitive performance on Nr3D, state-of-the-art on ScanRefer, and delivers a substantial 17.6% improvement on OpenTarget. Project Page at https://why-102.github.io/openground.io/.

preprint2023arXiv

Sam-Guided Enhanced Fine-Grained Encoding with Mixed Semantic Learning for Medical Image Captioning

With the development of multimodality and large language models, the deep learning-based technique for medical image captioning holds the potential to offer valuable diagnostic recommendations. However, current generic text and image pre-trained models do not yield satisfactory results when it comes to describing intricate details within medical images. In this paper, we present a novel medical image captioning method guided by the segment anything model (SAM) to enable enhanced encoding with both general and detailed feature extraction. In addition, our approach employs a distinctive pre-training strategy with mixed semantic learning to simultaneously capture both the overall information and finer details within medical images. We demonstrate the effectiveness of this approach, as it outperforms the pre-trained BLIP2 model on various evaluation metrics for generating descriptions of medical images.

preprint2022arXiv

Accelerating Bayesian inference of dependency between complex biological traits

Inferring dependencies between complex biological traits while accounting for evolutionary relationships between specimens is of great scientific interest yet remains infeasible when trait and specimen counts grow large. The state-of-the-art approach uses a phylogenetic multivariate probit model to accommodate binary and continuous traits via a latent variable framework, and utilizes an efficient bouncy particle sampler (BPS) to tackle the computational bottleneck -- integrating many latent variables from a high-dimensional truncated normal distribution. This approach breaks down as the number of specimens grows and fails to reliably characterize conditional dependencies between traits. Here, we propose an inference pipeline for phylogenetic probit models that greatly outperforms BPS. The novelty lies in 1) a combination of the recent Zigzag Hamiltonian Monte Carlo (Zigzag-HMC) with linear-time gradient evaluations and 2) a joint sampling scheme for highly correlated latent variables and correlation matrix elements. In an application exploring HIV-1 evolution from 535 viruses, the inference requires joint sampling from an 11,235-dimensional truncated normal and a 24-dimensional covariance matrix. Our method yields a 5-fold speedup compared to BPS and makes it possible to learn partial correlations between candidate viral mutations and virulence. Computational speedup now enables us to tackle even larger problems: we study the evolution of influenza H1N1 glycosylations on around 900 viruses. For broader applicability, we extend the phylogenetic probit model to incorporate categorical traits, and demonstrate its use to study Aquilegia flower and pollinator co-evolution.

preprint2022arXiv

ASFD: Automatic and Scalable Face Detector

Along with current multi-scale based detectors, Feature Aggregation and Enhancement (FAE) modules have shown superior performance gains for cutting-edge object detection. However, these hand-crafted FAE modules show inconsistent improvements on face detection, which is mainly due to the significant distribution difference between its training and applying corpus, COCO vs. WIDER Face. To tackle this problem, we essentially analyse the effect of data distribution, and consequently propose to search an effective FAE architecture, termed AutoFAE by a differentiable architecture search, which outperforms all existing FAE modules in face detection with a considerable margin. Upon the found AutoFAE and existing backbones, a supernet is further built and trained, which automatically obtains a family of detectors under the different complexity constraints. Extensive experiments conducted on popular benchmarks, WIDER Face and FDDB, demonstrate the state-of-the-art performance-efficiency trade-off for the proposed automatic and scalable face detector (ASFD) family. In particular, our strong ASFD-D6 outperforms the best competitor with AP 96.7/96.2/92.1 on WIDER Face test, and the lightweight ASFD-D0 costs about 3.1 ms, more than 320 FPS, on the V100 GPU with VGA-resolution images.

preprint2022arXiv

Capacity Bounds for the Two-User IM/DD Interference Channel

This paper studies the capacity of the two-user intensity-modulation/direct-detection (IM/DD) interference channel (IC), which is relevant in the context of multi-user optical wireless communications. Despite some known single-letter capacity characterizations for general discrete-memoryless ICs, a computable capacity expression for the IM/DD IC is missing. In this paper, we provide tight and easily computable inner and outer bounds for a general two-user IM/DD IC under peak and average optical intensity constraints. The bounds enable characterizing the asymptotic sum-rate capacity in the strong and weak interference regimes, as well as the generalized degrees of freedom (GDoF) in the symmetric case. Using the obtained bounds, the GDoF of the IM/DD IC is shown to have a `W' shape similar to the Gaussian IC with power constraints. The obtained bounds are also evaluated numerically in different interference regimes to show their tightness, and used to study the performance of on-chip and indoor OWC systems.

preprint2022arXiv

Data-Efficient Double-Win Lottery Tickets from Robust Pre-training

Pre-training serves as a broadly adopted starting point for transfer learning on various downstream tasks. Recent investigations of lottery tickets hypothesis (LTH) demonstrate such enormous pre-trained models can be replaced by extremely sparse subnetworks (a.k.a. matching subnetworks) without sacrificing transferability. However, practical security-crucial applications usually pose more challenging requirements beyond standard transfer, which also demand these subnetworks to overcome adversarial vulnerability. In this paper, we formulate a more rigorous concept, Double-Win Lottery Tickets, in which a located subnetwork from a pre-trained model can be independently transferred on diverse downstream tasks, to reach BOTH the same standard and robust generalization, under BOTH standard and adversarial training regimes, as the full pre-trained model can do. We comprehensively examine various pre-training mechanisms and find that robust pre-training tends to craft sparser double-win lottery tickets with superior performance over the standard counterparts. For example, on downstream CIFAR-10/100 datasets, we identify double-win matching subnetworks with the standard, fast adversarial, and adversarial pre-training from ImageNet, at 89.26%/73.79%, 89.26%/79.03%, and 91.41%/83.22% sparsity, respectively. Furthermore, we observe the obtained double-win lottery tickets can be more data-efficient to transfer, under practical data-limited (e.g., 1% and 10%) downstream schemes. Our results show that the benefits from robust pre-training are amplified by the lottery ticket scheme, as well as the data-limited transfer setting. Codes are available at https://github.com/VITA-Group/Double-Win-LTH.

preprint2022arXiv

Electromagnetic Dalitz Decays of $D_{(s)}^\ast$ Mesons

Rare electromagnetic decays of charmed mesons are useful laboratories to explore the structure of hadronic states and the interactions between photon and charmed mesons, to test the chiral perturbation theory in flavor sector and to search for new physics including dark photons. In this paper, we calculate the relative branching ratios of electromagnetic Dalitz decays $D_{(s)}^\ast\to D_{(s)}\ell^+\ell^-$ to their corresponding radiative decays $D_{(s)}^\ast\to D_{(s)}γ$, the dileptonic invariant mass spectra and the leptonic angular distributions with transition form factor in Vector-Meson Dominance model, where $D_{(s)}^\ast$ represents $D^\ast(2007)^0$, $D^\ast(2010)^\pm$, $D^\ast(2640)^\pm$, $D_s^{\ast\pm}$, $D_{s1}^\ast(2700)^\pm$ and $D_{s1}^\ast(2860)^\pm$.

preprint2022arXiv

Josephson-Coulomb drag effect between graphene and LaAlO3/SrTiO3 interfacial superconductor

Coulomb drag refers to the phenomenon that a charge current in one electronic circuit induces a responsive current in a neighboring circuit solely through Coulomb interactions. For conventional interactions between fermionic particles such as electrons, the as-induced drag current in the passive layer is orders of magnitude weaker than the active current due to strong dielectric screening effect between the two. Here we propose a 'super' Coulomb drag effect between an active normal conductor and a passive superconductor of Josephson junction arrays, whereby the passive current can greatly exceed the active. The drag force originates from the interactions between the substantially enhanced dynamical quantum fluctuations of the superconducting phases in the passive layer and the normal electrons in the active layer. We demonstrate this effect in the devices composed of monolayer graphene and LaAlO3/SrTiO3 heterointerface, an inherently non-uniform superconductor described by Josephson junction arrays. Remarkable drag signal is observed in the superconducting transition regime of the LaAlO3/SrTiO3 interface, with its sign independent of the carrier type in the graphene layer. The estimated passive-to-active ratio can reach about 0.3 at the optimal gate voltage and the temperature dependence follows that of the typical Josephson energy between superconducting puddles. Strikingly, the ratio ought to be as large as 10^5 at zero temperature by theoretical extrapolation. From engineering perspective, our device may work as current or voltage transformers, and the drag mechanism lays the foundation for synchronizing Josephson-junction-array-based terahertz radiators.

preprint2022arXiv

Label Anchored Contrastive Learning for Language Understanding

Contrastive learning (CL) has achieved astonishing progress in computer vision, speech, and natural language processing fields recently with self-supervised learning. However, CL approach to the supervised setting is not fully explored, especially for the natural language understanding classification task. Intuitively, the class label itself has the intrinsic ability to perform hard positive/negative mining, which is crucial for CL. Motivated by this, we propose a novel label anchored contrastive learning approach (denoted as LaCon) for language understanding. Specifically, three contrastive objectives are devised, including a multi-head instance-centered contrastive loss (ICL), a label-centered contrastive loss (LCL), and a label embedding regularizer (LER). Our approach does not require any specialized network architecture or any extra data augmentation, thus it can be easily plugged into existing powerful pre-trained language models. Compared to the state-of-the-art baselines, LaCon obtains up to 4.1% improvement on the popular datasets of GLUE and CLUE benchmarks. Besides, LaCon also demonstrates significant advantages under the few-shot and data imbalance settings, which obtains up to 9.4% improvement on the FewGLUE and FewCLUE benchmarking tasks.

preprint2022arXiv

Layout-Aware Information Extraction for Document-Grounded Dialogue: Dataset, Method and Demonstration

Building document-grounded dialogue systems have received growing interest as documents convey a wealth of human knowledge and commonly exist in enterprises. Wherein, how to comprehend and retrieve information from documents is a challenging research problem. Previous work ignores the visual property of documents and treats them as plain text, resulting in incomplete modality. In this paper, we propose a Layout-aware document-level Information Extraction dataset, LIE, to facilitate the study of extracting both structural and semantic knowledge from visually rich documents (VRDs), so as to generate accurate responses in dialogue systems. LIE contains 62k annotations of three extraction tasks from 4,061 pages in product and official documents, becoming the largest VRD-based information extraction dataset to the best of our knowledge. We also develop benchmark methods that extend the token-based language model to consider layout features like humans. Empirical results show that layout is critical for VRD-based extraction, and system demonstration also verifies that the extracted knowledge can help locate the answers that users care about.

preprint2022arXiv

Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness

Certifiable robustness is a highly desirable property for adopting deep neural networks (DNNs) in safety-critical scenarios, but often demands tedious computations to establish. The main hurdle lies in the massive amount of non-linearity in large DNNs. To trade off the DNN expressiveness (which calls for more non-linearity) and robustness certification scalability (which prefers more linearity), we propose a novel solution to strategically manipulate neurons, by "grafting" appropriate levels of linearity. The core of our proposal is to first linearize insignificant ReLU neurons, to eliminate the non-linear components that are both redundant for DNN performance and harmful to its certification. We then optimize the associated slopes and intercepts of the replaced linear activations for restoring model performance while maintaining certifiability. Hence, typical neuron pruning could be viewed as a special case of grafting a linear function of the fixed zero slopes and intercept, that might overly restrict the network flexibility and sacrifice its performance. Extensive experiments on multiple datasets and network backbones show that our linearity grafting can (1) effectively tighten certified bounds; (2) achieve competitive certifiable robustness without certified robust training (i.e., over 30% improvements on CIFAR-10 models); and (3) scale up complete verification to large adversarially trained models with 17M parameters. Codes are available at https://github.com/VITA-Group/Linearity-Grafting.

preprint2022arXiv

Multi-Modal Masked Pre-Training for Monocular Panoramic Depth Completion

In this paper, we formulate a potentially valuable panoramic depth completion (PDC) task as panoramic 3D cameras often produce 360° depth with missing data in complex scenes. Its goal is to recover dense panoramic depths from raw sparse ones and panoramic RGB images. To deal with the PDC task, we train a deep network that takes both depth and image as inputs for the dense panoramic depth recovery. However, it needs to face a challenging optimization problem of the network parameters due to its non-convex objective function. To address this problem, we propose a simple yet effective approach termed M{^3}PT: multi-modal masked pre-training. Specifically, during pre-training, we simultaneously cover up patches of the panoramic RGB image and sparse depth by shared random mask, then reconstruct the sparse depth in the masked regions. To our best knowledge, it is the first time that we show the effectiveness of masked pre-training in a multi-modal vision task, instead of the single-modal task resolved by masked autoencoders (MAE). Different from MAE where fine-tuning completely discards the decoder part of pre-training, there is no architectural difference between the pre-training and fine-tuning stages in our M$^{3}$PT as they only differ in the prediction density, which potentially makes the transfer learning more convenient and effective. Extensive experiments verify the effectiveness of M{^3}PT on three panoramic datasets. Notably, we improve the state-of-the-art baselines by averagely 26.2% in RMSE, 51.7% in MRE, 49.7% in MAE, and 37.5% in RMSElog on three benchmark datasets.

preprint2022arXiv

NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results

This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results. In this challenge, the new Large-scale Diverse Video (LDV) dataset is employed. The challenge has three tracks. Tracks 1 and 2 aim at enhancing the videos compressed by HEVC at a fixed QP, while Track 3 is designed for enhancing the videos compressed by x265 at a fixed bit-rate. Besides, the quality enhancement of Tracks 1 and 3 targets at improving the fidelity (PSNR), and Track 2 targets at enhancing the perceptual quality. The three tracks totally attract 482 registrations. In the test phase, 12 teams, 8 teams and 11 teams submitted the final results of Tracks 1, 2 and 3, respectively. The proposed methods and solutions gauge the state-of-the-art of video quality enhancement. The homepage of the challenge: https://github.com/RenYang-home/NTIRE21_VEnh

preprint2022arXiv

Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free

Trojan attacks threaten deep neural networks (DNNs) by poisoning them to behave normally on most samples, yet to produce manipulated results for inputs attached with a particular trigger. Several works attempt to detect whether a given DNN has been injected with a specific trigger during the training. In a parallel line of research, the lottery ticket hypothesis reveals the existence of sparse subnetworks which are capable of reaching competitive performance as the dense network after independent training. Connecting these two dots, we investigate the problem of Trojan DNN detection from the brand new lens of sparsity, even when no clean training data is available. Our crucial observation is that the Trojan features are significantly more stable to network pruning than benign features. Leveraging that, we propose a novel Trojan network detection regime: first locating a "winning Trojan lottery ticket" which preserves nearly full Trojan information yet only chance-level performance on clean inputs; then recovering the trigger embedded in this already isolated subnetwork. Extensive experiments on various datasets, i.e., CIFAR-10, CIFAR-100, and ImageNet, with different network architectures, i.e., VGG-16, ResNet-18, ResNet-20s, and DenseNet-100 demonstrate the effectiveness of our proposal. Codes are available at https://github.com/VITA-Group/Backdoor-LTH.

preprint2022arXiv

RigNet: Repetitive Image Guided Network for Depth Completion

Depth completion deals with the problem of recovering dense depth maps from sparse ones, where color images are often used to facilitate this task. Recent approaches mainly focus on image guided learning frameworks to predict dense depth. However, blurry guidance in the image and unclear structure in the depth still impede the performance of the image guided frameworks. To tackle these problems, we explore a repetitive design in our image guided network to gradually and sufficiently recover depth values. Specifically, the repetition is embodied in both the image guidance branch and depth generation branch. In the former branch, we design a repetitive hourglass network to extract discriminative image features of complex environments, which can provide powerful contextual instruction for depth prediction. In the latter branch, we introduce a repetitive guidance module based on dynamic convolution, in which an efficient convolution factorization is proposed to simultaneously reduce its complexity and progressively model high-frequency structures. Extensive experiments show that our method achieves superior or competitive results on KITTI benchmark and NYUv2 dataset.

preprint2022arXiv

Sparsity Winning Twice: Better Robust Generalization from More Efficient Training

Recent studies demonstrate that deep networks, even robustified by the state-of-the-art adversarial training (AT), still suffer from large robust generalization gaps, in addition to the much more expensive training costs than standard training. In this paper, we investigate this intriguing problem from a new perspective, i.e., injecting appropriate forms of sparsity during adversarial training. We introduce two alternatives for sparse adversarial training: (i) static sparsity, by leveraging recent results from the lottery ticket hypothesis to identify critical sparse subnetworks arising from the early training; (ii) dynamic sparsity, by allowing the sparse subnetwork to adaptively adjust its connectivity pattern (while sticking to the same sparsity ratio) throughout training. We find both static and dynamic sparse methods to yield win-win: substantially shrinking the robust generalization gap and alleviating the robust overfitting, meanwhile significantly saving training and inference FLOPs. Extensive experiments validate our proposals with multiple network architectures on diverse datasets, including CIFAR-10/100 and Tiny-ImageNet. For example, our methods reduce robust generalization gap and overfitting by 34.44% and 4.02%, with comparable robust/standard accuracy boosts and 87.83%/87.82% training/inference FLOPs savings on CIFAR-100 with ResNet-18. Besides, our approaches can be organically combined with existing regularizers, establishing new state-of-the-art results in AT. Codes are available in https://github.com/VITA-Group/Sparsity-Win-Robust-Generalization.

preprint2022arXiv

Study of exotic hadrons with machine learning

We analyzed the invariant mass spectrum of near-threshold exotic states for one-channel candidates with a deep neural network. It can extract the scattering length and effective range, which would shed light on the nature of given states, from the experimental mass spectrum. As an application, the mass spectrum of the $X(3872)$ and the $T_{cc}^+$ are studied. The obtained scattering lengths, effective ranges, and most relevant thresholds are consistent with those from fitting to the experimental data. The advantage of the neural network is that it is more stable than the fitting, especially for low-statistic data. The network, which provides another way to analyze the experimental data, can also be applied to other one-channel near-threshold exotic candidates.

preprint2022arXiv

The Principle of Diversity: Training Stronger Vision Transformers Calls for Reducing All Levels of Redundancy

Vision transformers (ViTs) have gained increasing popularity as they are commonly believed to own higher modeling capacity and representation flexibility, than traditional convolutional networks. However, it is questionable whether such potential has been fully unleashed in practice, as the learned ViTs often suffer from over-smoothening, yielding likely redundant models. Recent works made preliminary attempts to identify and alleviate such redundancy, e.g., via regularizing embedding similarity or re-injecting convolution-like structures. However, a "head-to-toe assessment" regarding the extent of redundancy in ViTs, and how much we could gain by thoroughly mitigating such, has been absent for this field. This paper, for the first time, systematically studies the ubiquitous existence of redundancy at all three levels: patch embedding, attention map, and weight space. In view of them, we advocate a principle of diversity for training ViTs, by presenting corresponding regularizers that encourage the representation diversity and coverage at each of those levels, that enabling capturing more discriminative information. Extensive experiments on ImageNet with a number of ViT backbones validate the effectiveness of our proposals, largely eliminating the observed ViT redundancy and significantly boosting the model generalization. For example, our diversified DeiT obtains 0.70%~1.76% accuracy boosts on ImageNet with highly reduced similarity. Our codes are fully available in https://github.com/VITA-Group/Diverse-ViT.

preprint2021arXiv

JUNO Physics and Detector

The Jiangmen Underground Neutrino Observatory (JUNO) is a 20 kton LS detector at 700-m underground. An excellent energy resolution and a large fiducial volume offer exciting opportunities for addressing many important topics in neutrino and astro-particle physics. With 6 years of data, the neutrino mass ordering can be determined at 3-4 sigma and three oscillation parameters can be measured to a precision of 0.6% or better by detecting reactor antineutrinos. With 10 years of data, DSNB could be observed at 3-sigma; a lower limit of the proton lifetime of 8.34e33 years (90% C.L.) can be set by searching for p->nu_bar K^+; detection of solar neutrinos would shed new light on the solar metallicity problem and examine the vacuum-matter transition region. A core-collapse supernova at 10 kpc would lead to ~5000 IBD and ~2000 (300) all-flavor neutrino-proton (electron) scattering events. Geo-neutrinos can be detected with a rate of ~400 events/year. We also summarize the final design of the JUNO detector and the key R&D achievements. All 20-inch PMTs have been tested. The average photon detection efficiency is 28.9% for the 15,000 MCP PMTs and 28.1% for the 5,000 dynode PMTs, higher than the JUNO requirement of 27%. Together with the >20 m attenuation length of LS, we expect a yield of 1345 p.e. per MeV and an effective energy resolution of 3.02%/\sqrt{E (MeV)}$ in simulations. The underwater electronics is designed to have a loss rate <0.5% in 6 years. With degassing membranes and a micro-bubble system, the radon concentration in the 35-kton water pool could be lowered to <10 mBq/m^3. Acrylic panels of radiopurity <0.5 ppt U/Th are produced. The 20-kton LS will be purified onsite. Singles in the fiducial volume can be controlled to ~10 Hz. The JUNO experiment also features a double calorimeter system with 25,600 3-inch PMTs, a LS testing facility OSIRIS, and a near detector TAO.

preprint2021arXiv

Magnetic moment preservation and emergent Kondo resonance of Co-phthalocyanine on semimetallic Sb(111)

Magnetic molecules on surfaces have been widely investigated to reveal delicate interfacial couplings and for potential technological applications. In these endeavors, one prevailing challenge is how to preserve or recover the molecular spins, especially on highly metallic substrates that can readily quench the magnetic moments of the ad-molecules. Here we use scanning tunneling microscopy/spectroscopy to exploit the semimetallic nature of antimony and observe, surprisingly yet pleasantly, that the spin of Co-phthalocyanine is well preserved on Sb(111), as unambiguously evidenced by the emergent strong Kondo resonance across the molecule. Our first-principles calculations further confirm that the optimal density of states near the Fermi level of the semimetal is a decisive factor, weakening the overall interfacial coupling, while still ensuring sufficiently effective electron-spin scattering in the many-body system. Beyond isolated ad-molecules, we discover that each of the magnetic moments in a molecular dimer or a densely packed island is distinctly preserved as well, rendering such molecular magnets immense potentials for ultra-high density memory devices.

preprint2020arXiv

Artificial Intelligence for High-Throughput Discovery of Topological Insulators: the Example of Alloyed Tetradymites

Significant advances have been made in predicting new topological materials using high-throughput empirical descriptors or symmetry-based indicators. To date, these approaches have been applied to materials in existing databases, and are severely limited to systems with well-defined symmetries, leaving a much larger materials space unexplored. Using tetradymites as a prototypical class of examples, we uncover a novel two-dimensional descriptor by applying an artificial intelligence (AI) based approach for fast and reliable identification of the topological characters of a drastically expanded range of materials, without prior determination of their specific symmetries and detailed band structures. By leveraging this descriptor that contains only the atomic number and electronegativity of the constituent species, we have readily scanned a huge number of alloys in the tetradymite family. Strikingly, nearly half of which are identified to be topological insulators, revealing a much larger territory of the topological materials world. The present work also attests the increasingly important role of such AI-based approaches in modern materials discovery.

preprint2020arXiv

Feasibility and physics potential of detecting $^8$B solar neutrinos at JUNO

The Jiangmen Underground Neutrino Observatory~(JUNO) features a 20~kt multi-purpose underground liquid scintillator sphere as its main detector. Some of JUNO's features make it an excellent experiment for $^8$B solar neutrino measurements, such as its low-energy threshold, its high energy resolution compared to water Cherenkov detectors, and its much large target mass compared to previous liquid scintillator detectors. In this paper we present a comprehensive assessment of JUNO's potential for detecting $^8$B solar neutrinos via the neutrino-electron elastic scattering process. A reduced 2~MeV threshold on the recoil electron energy is found to be achievable assuming the intrinsic radioactive background $^{238}$U and $^{232}$Th in the liquid scintillator can be controlled to 10$^{-17}$~g/g. With ten years of data taking, about 60,000 signal and 30,000 background events are expected. This large sample will enable an examination of the distortion of the recoil electron spectrum that is dominated by the neutrino flavor transformation in the dense solar matter, which will shed new light on the tension between the measured electron spectra and the predictions of the standard three-flavor neutrino oscillation framework. If $Δm^{2}_{21}=4.8\times10^{-5}~(7.5\times10^{-5})$~eV$^{2}$, JUNO can provide evidence of neutrino oscillation in the Earth at the about 3$σ$~(2$σ$) level by measuring the non-zero signal rate variation with respect to the solar zenith angle. Moveover, JUNO can simultaneously measure $Δm^2_{21}$ using $^8$B solar neutrinos to a precision of 20\% or better depending on the central value and to sub-percent precision using reactor antineutrinos. A comparison of these two measurements from the same detector will help elucidate the current tension between the value of $Δm^2_{21}$ reported by solar neutrino experiments and the KamLAND experiment.

preprint2020arXiv

HIN: Hierarchical Inference Network for Document-Level Relation Extraction

Document-level RE requires reading, inferring and aggregating over multiple sentences. From our point of view, it is necessary for document-level RE to take advantage of multi-granularity inference information: entity level, sentence level and document level. Thus, how to obtain and aggregate the inference information with different granularity is challenging for document-level RE, which has not been considered by previous work. In this paper, we propose a Hierarchical Inference Network (HIN) to make full use of the abundant information from entity level, sentence level and document level. Translation constraint and bilinear transformation are applied to target entity pair in multiple subspaces to get entity-level inference information. Next, we model the inference between entity-level information and sentence representation to achieve sentence-level inference information. Finally, a hierarchical aggregation approach is adopted to obtain the document-level inference information. In this way, our model can effectively aggregate inference information from these three different granularities. Experimental results show that our method achieves state-of-the-art performance on the large-scale DocRED dataset. We also demonstrate that using BERT representations can further substantially boost the performance.

preprint2020arXiv

Joint Extraction of Entities and Relations Based on a Novel Decomposition Strategy

Joint extraction of entities and relations aims to detect entity pairs along with their relations using a single model. Prior work typically solves this task in the extract-then-classify or unified labeling manner. However, these methods either suffer from the redundant entity pairs, or ignore the important inner structure in the process of extracting entities and relations. To address these limitations, in this paper, we first decompose the joint extraction task into two interrelated subtasks, namely HE extraction and TER extraction. The former subtask is to distinguish all head-entities that may be involved with target relations, and the latter is to identify corresponding tail-entities and relations for each extracted head-entity. Next, these two subtasks are further deconstructed into several sequence labeling problems based on our proposed span-based tagging scheme, which are conveniently solved by a hierarchical boundary tagger and a multi-span decoding algorithm. Owing to the reasonable decomposition strategy, our model can fully capture the semantic interdependency between different steps, as well as reduce noise from irrelevant entity pairs. Experimental results show that our method outperforms previous work by 5.2%, 5.9% and 21.5% (F1 score), achieving a new state-of-the-art on three public datasets

preprint2020arXiv

Residual Clipping Noise in Multi-layer Optical OFDM: Modeling, Analysis, and Application

Optical orthogonal frequency division multiplexing (O-OFDM) schemes are variations of OFDM schemes which produce non-negative signals. Asymmetrically-clipped O-OFDM (ACO-OFDM) is a single-layer O-OFDM scheme, whose spectral efficiency can be enhanced by adopting multiple ACO-OFDM layers or a combination of ACO-OFDM and other O-OFDM schemes. However, since symbol detection in such enhanced ACO-OFDM (eACO-OFDM) is done iteratively, erroneous detection leads to residual clipping noise (RCN) which can degrade performance in practice. Thus, it is important to develop an accurate model for RCN which can be used to design RCN-aware eACO-OFDM schemes. To this end, this paper provides a mathematical analysis of RCN leading to an accurate model of RCN power. The obtained model is used to analyze the performance of various eACO-OFDM schemes. It is shown that the model provides an accurate evaluation of symbol error rate (SER), which would be underestimated if RCN is ignored. Moreover, the model is shown to be useful for designing an RCN-aware resource allocation that increases the robustness of the system in terms of meeting a target SER, compared to an RCN-unaware design.

preprint2020arXiv

TAO Conceptual Design Report: A Precision Measurement of the Reactor Antineutrino Spectrum with Sub-percent Energy Resolution

The Taishan Antineutrino Observatory (TAO, also known as JUNO-TAO) is a satellite experiment of the Jiangmen Underground Neutrino Observatory (JUNO). A ton-level liquid scintillator detector will be placed at about 30 m from a core of the Taishan Nuclear Power Plant. The reactor antineutrino spectrum will be measured with sub-percent energy resolution, to provide a reference spectrum for future reactor neutrino experiments, and to provide a benchmark measurement to test nuclear databases. A spherical acrylic vessel containing 2.8 ton gadolinium-doped liquid scintillator will be viewed by 10 m^2 Silicon Photomultipliers (SiPMs) of >50% photon detection efficiency with almost full coverage. The photoelectron yield is about 4500 per MeV, an order higher than any existing large-scale liquid scintillator detectors. The detector operates at -50 degree C to lower the dark noise of SiPMs to an acceptable level. The detector will measure about 2000 reactor antineutrinos per day, and is designed to be well shielded from cosmogenic backgrounds and ambient radioactivities to have about 10% background-to-signal ratio. The experiment is expected to start operation in 2022.

preprint2019arXiv

Large-area, periodic, and tunable intrinsic pseudo-magnetic fields in low-angle twisted bilayer graphene

A properly strained graphene monolayer or bilayer is expected to harbour periodic pseudo-magnetic fields with high symmetry, yet to date, a convincing demonstration of such pseudo-magnetic fields has been lacking, especially for bilayer graphene. Here, we report the first definitive experimental proof for the existence of large-area, periodic pseudo-magnetic fields, as manifested by vortex lattices in commensurability with the moiré patterns of low-angle twisted bilayer graphene. The pseudo-magnetic fields are strong enough to confine the massive Dirac electrons into circularly localized pseudo-Landau levels, as observed by scanning tunneling microscopy/spectroscopy, and also corroborated by tight-binding calculations. We further demonstrate that the geometry, amplitude, and periodicity of the pseudo-magnetic field can be fine-tuned by both the rotation angle and heterostrain applied to the system. Collectively, the present study substantially enriches twisted bilayer graphene as a powerful enabling platform for exploration of new and exotic physical phenomena, including quantum valley Hall effects and quantum anomalous Hall effects.

preprint2016arXiv

Dirac node lines in pure alkali earth metals

Beryllium is a simple alkali earth metal, but has been the target of intensive studies for decades because of its unusual electron behaviors at surfaces. Puzzling aspects include (i) severe deviations from the description of the nearly free electron picture, (ii) anomalously large electron-phonon coupling effect, and (iii) giant Friedal oscillations. The underlying origins for such anomalous surface electron behaviors have been under active debate, but with no consensus. Here, by means of first-principle calculations, we discover that this pure metal system, surprisingly, harbors the Dirac node line (DNL) that in turn helps to rationalize many of the existing puzzles. The DNL is featured by a closed line consisting of linear band crossings and its induced topological surface band agrees well with previous photoemission spectroscopy observation on Be (0001) surface. We further reveal that each of the elemental alakali earth metals of Mg, Ca, and Sr also harbors the DNL, and speculate that the fascinating topological property of DNL might naturally exist in other elemental metals as well.

preprint2016arXiv

Distinct Reconstruction Patterns and Spin-Resolved Electronic States along the Zigzag Edges of Transition Metal Dichalcogenides

Two-dimensional transition metal dichalcogenides represent an emerging class of materials exhibiting various intriguing properties, and integration of such materials for potential device applications will necessarily encounter creation of different boundaries. Using first-principles approaches, here we investigate the structural, electronic, and magnetic properties along two inequivalent zigzag M and X edges of MX$_{2}$ (M=Mo, W; X=S, Se). Along the M edges, we reveal a previously unrecognized but energetically strongly preferred (2x1) reconstruction pattern, which is universally operative for all the MX$_{2}$, characterized by a self-passivation mechanism through place exchanges of the outmost X and M edge atoms. In contrast, the X edges undergo a more moderate (2x1) or (3x1) reconstruction for MoX$_{2}$ or WX$_{2}$, respectively. We further use the prototypical zigzag MoX$_{2}$ nanoribbons to demonstrate that the M and X edges possess distinctly different electronic and magnetic properties, which are discussed for spintronic and catalytic applications.

preprint2016arXiv

Maximizing the thermoelectric performance of topological insulator Bi2Te3 films in the few-quintuple layer regime

Using first-principles calculations and Boltzmann theory, we explore the feasibility to maximize the thermoelectric figure of merit (ZT) of topological insulator Bi2Te3 films in the few-quintuple layer regime. We discover that the delicate competitions between the surface and bulk contributions, coupled with the overall quantum size effects, lead to a novel and generic non-monotonous dependence of ZT on the film thickness. In particular, when the system crosses into the topologically non-trivial regime upon increasing the film thickness, the much longer surface relaxation time associated with the robust nature of the topological surface states results in a maximal ZT value, which can be further optimized to ~2.0 under physically realistic conditions. We also reveal the appealing potential of bridging the long-standing ZT asymmetry of p- and n-type Bi2Te3 systems.

preprint2015arXiv

Ab-initio Studies of (Li$_{0.8}$Fe$_{0.2}$)OHFeSe Superconductors: Revealing the Dual Roles of Fe$_{0.2}$ in Structural Stability and Charge Transfer

The recently discovered (Li$_{0.8}$Fe$_{0.2}$)OHFeSe superconductor provides a new platform for exploiting the microscopic mechanisms of high-$T_c$ superconductivity in FeSe-derived systems. Using density functional theory calculations, we first show that substitution of Li by Fe not only significantly strengthens the attraction between the (Li$_{0.8}$Fe$_{0.2}$)OH spacing layers and the FeSe superconducting layers along the \emph{c} axis, but also minimizes the lattice mismatch between the two in the \emph{ab} plane, both favorable for stabilizing the overall structure. Next we explore the electron injection into FeSe from the spacing layers, and unambiguously identify the Fe$_{0.2}$ components to be the dominant atomic origin of the dramatically enhanced interlayer charge transfer. We further reveal that the system strongly favors collinear antiferromagnetic ordering in the FeSe layers, but the spacing layers can be either antiferromagnetic or ferromagnetic depending on the Fe$_{0.2}$ spatial distribution. Based on these understandings, we also predict (Li$_{0.8}$Co$_{0.2}$)OHFeSe to be structurally stable with even larger electron injection and potentially higher $T_c$.

preprint2015arXiv

Competing Magnetic Orderings and Tunable Topological States in Two-Dimensional Hexagonal Organometallic Lattices

The exploration of topological states is of significant fundamental and practical importance in contemporary condensed matter physics, for which the extension to two-dimensional (2D) organometallic systems is particularly attractive. Using first-principles calculations, we show that a 2D hexagonal triphenyl-lead lattice composed of only main group elements is susceptible to a magnetic instability, characterized by a considerably more stable antiferromagnetic (AFM) insulating state rather than the topologically nontrivial quantum spin Hall state proposed recently. Even though this AFM phase is topologically trivial, it possesses an intricate emergent degree of freedom, defined by the product of spin and valley indices, leading to Berry curvature-induced spin and valley currents under electron or hole doping. Furthermore, such a trivial band insulator can be tuned into a topologically nontrivial matter by the application of an out-of-plane electric field, which destroys the AFM order, favoring instead ferrimagnetic spin ordering and a quantum anomalous Hall state with a non-zero topological invariant. These findings further enrich our understanding of 2D hexagonal organometallic lattices for potential applications in spintronics and valleytronics.

preprint2015arXiv

Converting a topologically trivial superconductor into a topological superconductor via magnetic doping

We present a comparative theoretical study of the effects of standard Anderson and magnetic disorders on the topological phases of two-dimensional Rashba spin-orbit coupled superconductors, with the initial state to be either topologically trivial or nontrivial. Using the self-consistent Born approximation approach, we show that the presence of Anderson disorders will drive a topological superconductor into a topologically trivial superconductor in the weak coupling limit. Even more strikingly, a topologically trivial superconductor can be driven into a topological superconductor upon diluted doping of independent magnetic disorders, which gradually narrow, close, and reopen the quasi-particle gap in a nontrivial manner. These topological phase transitions are distinctly characterized by the changes in the corresponding topological invariants. The central findings made here are also confirmed using a complementary numerical approach by solving the Bogoliubov-de Gennes equations self-consistently within a tight-binding model. The present study offers appealing new schemes for potential experimental realization of topological superconductors.

preprint2015arXiv

Densities, isobaric thermal expansion coefficients and isothermal compressibilities of linear alkylbenzene

We report the measurements of the densities of linear alkylbenzene at three temperatures over 4 to 23 Celsius degree with pressures up to 10 MPa. The measurements have been analysed to yield the isobaric thermal expansion coefficients and, so far for the first time, isothermal compressibilities of linear alkylbenzene. Relevance of results for current generation (i.e. Daya Bay) and next generation (i.e. JUNO) large liquid scintillator neutrino detectors are discussed.

preprint2015arXiv

Equivalence of Electronic and Mechanical Stresses in Structural Phase Stabilization: A Case Study of Indium Wires on Si(111)

It was recently proposed that the stress state of a material can also be altered via electron or hole doping, a concept termed electronic stress (ES), which is different from the traditional mechanical stress (MS) due to lattice contraction or expansion. Here we demonstrate the equivalence of ES and MS in structural stabilization, using In wires on Si(111) as a prototypical example. Our systematic density-functional theory calculations reveal that, first, for the same degrees of carrier doping into the In wires, the ES of the high-temperature metallic 4x1 structure is only slightly compressive, while that of the low-temperature insulating 8x2 structure is much larger and highly anisotropic. As a consequence, the intrinsic energy difference between the two phases is significantly reduced towards electronically phase-separated ground states. Our calculations further demonstrate quantitatively that such intriguing phase tunabilities can be achieved equivalently via lattice-contraction induced MS in the absence of charge doping. We also validate the equivalence through our detailed scanning tunneling microscopy experiments. The present findings have important implications in understanding the underlying driving forces involved in various phase transitions of simple and complex systems alike.

preprint2015arXiv

High-Temperature Quantum Anomalous Hall Effect in n-p Codoped Topological Insulators

The quantum anomalous Hall effect (QAHE) is a fundamental quantum transport phenomenon that manifests as a quantized transverse conductance in response to a longitudinally applied electric field in the absence of an external magnetic field, and promises to have immense application potentials in future dissipation-less quantum electronics. Here we present a novel kinetic pathway to realize the QAHE at high temperatures by $n$-$p$ codoping of three-dimensional topological insulators. We provide proof-of-principle numerical demonstration of this approach using vanadium-iodine (V-I) codoped Sb$_2$Te$_3$ and demonstrate that, strikingly, even at low concentrations of $\sim$2\% V and $\sim$1\% I, the system exhibits a quantized Hall conductance, the tell-tale hallmark of QAHE, at temperatures of at least $\sim$ 50 Kelvin, which is three orders of magnitude higher than the typical temperatures at which it has been realized so far. The proposed approach is conceptually general and may shed new light in experimental realization of high-temperature QAHE.

preprint2015arXiv

Neutrino Physics with JUNO

The Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton multi-purpose underground liquid scintillator detector, was proposed with the determination of the neutrino mass hierarchy as a primary physics goal. It is also capable of observing neutrinos from terrestrial and extra-terrestrial sources, including supernova burst neutrinos, diffuse supernova neutrino background, geoneutrinos, atmospheric neutrinos, solar neutrinos, as well as exotic searches such as nucleon decays, dark matter, sterile neutrinos, etc. We present the physics motivations and the anticipated performance of the JUNO detector for various proposed measurements. By detecting reactor antineutrinos from two power plants at 53-km distance, JUNO will determine the neutrino mass hierarchy at a 3-4 sigma significance with six years of running. The measurement of antineutrino spectrum will also lead to the precise determination of three out of the six oscillation parameters to an accuracy of better than 1\%. Neutrino burst from a typical core-collapse supernova at 10 kpc would lead to ~5000 inverse-beta-decay events and ~2000 all-flavor neutrino-proton elastic scattering events in JUNO. Detection of DSNB would provide valuable information on the cosmic star-formation rate and the average core-collapsed neutrino energy spectrum. Geo-neutrinos can be detected in JUNO with a rate of ~400 events per year, significantly improving the statistics of existing geoneutrino samples. The JUNO detector is sensitive to several exotic searches, e.g. proton decay via the $p\to K^++\barν$ decay channel. The JUNO detector will provide a unique facility to address many outstanding crucial questions in particle and astrophysics. It holds the great potential for further advancing our quest to understanding the fundamental properties of neutrinos, one of the building blocks of our Universe.

preprint2015arXiv

Optical Control of Fluorescence through Plasmonic Eigenmode Extinction

We introduce the concept of optical control of the fluorescence yield of CdSe quantum dots through plasmon-induced structural changes in random semicontinuous nanostructured gold films. We demonstrate that the wavelength- and polarization dependent coupling between quantum dots and the semicontinuous films, and thus the fluorescent emission spectrum, can be controlled and significantly increased through the optical extinction of a selective band of eigenmodes in the films. This optical method of effecting controlled changes in the metal nanostructure allows for versatile functionality in a single sample and opens a pathway to in situ control over the fluorescence spectrum.

preprint2015arXiv

Orientationally Misaligned Zipping of Lateral Graphene and Boron Nitride Nanoribbons with Minimized Strain Energy and Enhanced Half-Metallicity

Lateral heterostructures of two-dimensional materials may exhibit various intriguing emergent properties. Yet when specified to the orientationally aligned heterojunctions of zigzag graphene and hexagonal boron nitride (hBN) nanoribbons, realizations of the high expectations on their properties encounter two standing hurtles. First, the rapid accumulation of strain energy prevents large- scale fabrication. Secondly, the pronounced half-metallicity predicted for freestanding graphene nanoribbons is severely suppressed. By properly tailoring orientational misalignment between zigzag graphene and chiral hBN nanoribbons, here we present a facile approach to overcome both obstacles. Our first-principles calculations show that the strain energy accumulation in such heterojunctions is significantly diminished for a range of misalignments. More strikingly, the half-metallicity is substantially enhanced from the orientationally aligned case, back to be comparable in magnitude with the freestanding case. The restored half-metallicity is largely attributed to the recovered superexchange interaction between the opposite heterojunction interfaces. The present findings may have important implications in eventual realization of graphene-based spintronics.

preprint2015arXiv

Quantum Anomalous Hall Effect in Graphene Proximity Coupled to an Antiferromagnetic Insulator

We propose realizing the quantum anomalous Hall effect by proximity coupling graphene to an antiferromagnetic insulator that provides both broken time-reversal symmetry and spin-orbit coupling. We illustrate our idea by performing ab initio calculations for graphene adsorbed on the (111) surface of BiFeO3. In this case, we find that the proximity-induced exchange field in graphene is about 70 meV, and that a topologically nontrivial band gap is opened by Rashba spin-orbit coupling. The size of the gap depends on the separation between the graphene and the thin film substrate, which can be tuned experimentally by applying external pressure.

preprint2015arXiv

Rayleigh scattering of linear alkylbenzene in large liquid scintillator detectors

Rayleigh scattering poses an intrinsic limit for the transparency of organic liquid scintillators. This work focuses on the Rayleigh scattering length of linear alkylbenzene (LAB), which will be used as the solvent of the liquid scintillator in the central detector of the Jiangmen Underground Neutrino Observatory. We investigate the anisotropy of the Rayleigh scattering in LAB, showing that the resulting Rayleigh scattering length will be significantly shorter than reported before. Given the same overall light attenuation, this will result in a more efficient transmission of photons through the scintillator, increasing the amount of light collected by the photosensors and thereby the energy resolution of the detector.

preprint2015arXiv

Single-Valley Engineering in Graphene Superlattices

The two inequivalent valleys in graphene preclude the protection against inter-valley scattering offered by an odd-number of Dirac cones characteristic of Z2 topological insulator phases. Here we propose a way to engineer a chiral single-valley metallic phase with quadratic crossover in a honeycomb lattice through tailored \sqrt{3}N *\sqrt{3}N or 3N *3N superlattices. The possibility of tuning valley-polarization via pseudo-Zeeman field and the emergence of Dresselhaus-type valley-orbit coupling are proposed in adatom decorated graphene superlattices. Such valley manipulation mechanisms and metallic phase can also find applications in honeycomb photonic crystals.

preprint2015arXiv

Spectroscopic study of light scattering in linear alkylbenzene for liquid scintillator neutrino detectors

We has set up a light scattering spectrometer to study the depolarization of light scattering in linear alkylbenzene. From the scattering spectra it can be unambiguously shown that the depolarized part of light scattering belongs to Rayleigh scattering. The additional depolarized Rayleigh scattering can make the effective transparency of linear alkylbenzene much better than it was expected. Therefore sufficient scintillation photons can transmit through the large liquid scintillator detector of JUNO. Our study is crucial to achieving the unprecedented energy resolution 3\%/$\sqrt{E\mathrm{(MeV)}}$ for JUNO experiment to determine the neutrino mass hierarchy. The spectroscopic method can also be used to judge the attribution of the depolarization of other organic solvents used in neutrino experiments.

preprint2014arXiv

Persistent ferromagnetism and topological phase transition at the interface of a superconductor and a topological insulator

At the interface of an s-wave superconductor and a three-dimensional topological insulator, Ma- jorana zero modes and Majorana helical states have been proposed to exist respectively around magnetic vortices and geometrical edges. Here we first show that a single magnetic impurity at such an interface splits each resonance state of a given spin channel outside the superconducting gap, and also induces two new symmetric impurity states inside the gap. Next we find that an increase in the superconducting gap suppresses both the oscillation magnitude and period of the RKKY inter- action between two interface magnetic impurities mediated by BCS quasi-particles. Within a mean field approximation, the ferromagnetic Curie temperature is found to be essentially independent of the superconducting gap, an intriguing phenomenon due to a compensation effect between the short-range ferromagnetic and long-range anti-ferromagnetic interactions. The existence of persis- tent ferromagnetism at the interface allows realization of a novel topological phase transition from a non-chiral to a chiral superconducting state at sufficiently low temperatures, providing a new platform for topological quantum computation.

preprint2014arXiv

Proximity Effects in Topological Insulator Heterostructures

Topological insulators (TIs) are bulk insulators that possess robust helical conducting states along their interfaces with conventional insulators. A tremendous research effort has recently been devoted to TI-based heterostructures, in which conventional proximity effects give rise to a series of exotic physical phenomena. This paper reviews our recent works on the potential existence of topological proximity effects at the interface between a topological insulator and a normal insulator or other topologically trivial systems. Using first-principles approaches, we have established the tunability of the vertical location of the topological helical state via intriguing dual-proximity effects. To further elucidate the control parameters of this effect, we have used the graphene-based heterostructures as prototypical systems to reveal a more complete phase diagram. On the application side of the topological helical states, we have presented a catalysis example, where the topological helical state plays an essential role in facilitating surface reactions by serving as an effective electron bath. These discoveries lay the foundation for accurate manipulation of the real space properties of the topological helical state in TI-based heterostructures and pave the way for realization of the salient functionality of topological insulators in future device applications.

preprint2013arXiv

Gate-Tunable Exchange Coupling Between Cobalt Clusters on Graphene

We use spin-density-functional theory (SDFT) ab initio calculations to theoretically explore the possibility of achieving useful gate control over exchange coupling between cobalt clusters placed on a graphene sheet. By applying an electric field across supercells we demonstrate that the exchange interaction is strongly dependent on gate voltage, but find that it is also sensitive to the relative sublattice registration of the cobalt clusters. We use our results to discuss strategies for achieving strong and reproducible magneto-electric effects in graphene/transition-metal hybrid systems.

preprint2013arXiv

NV-Center Based Digital Quantum Simulation of a Quantum Phase Transition in Topological Insulators

Nitrogen-vacancy centers in diamond are ideal platforms for quantum simulation, which allows one to handle problems that are intractable theoretically or experimentally. Here we propose a digital quantum simulation scheme to simulate the quantum phase transition occurring in an ultrathin topological insulator film placed in a parallel magnetic field [Zyuzin \textit{et al.}, Phys. Rev. B \textbf{83}, 245428 (2011)]. The quantum simulator employs high quality spin qubits achievable in nitrogen-vacancy centers and can be realized with existing technology. The problem can be mapped onto the Hamiltonian of two entangled qubits represented by the electron and nuclear spins. The simulation uses the Trotter algorithm, with an operation time of the order of 100 $μ$s for each individual run.

preprint2013arXiv

Topological Proximity Effects in Graphene Nanoribbon Heterostructures

Topological insulators (TI) are bulk insulators that possess robust chiral conducting states along their interfaces with normal insulators. A tremendous research effort has recently been devoted to TI-based heterostructures, in which conventional proximity effects give rise to many exotic physical phenomena. Here we establish the potential existence of "topological proximity effects" at the interface of a topological graphene nanoribbon (GNR) and a normal GNR. Specifically, we show that the location of the topological edge states exhibits versatile tunability as a function of the interface orientation, as well as the strengths of the interface coupling and spin-orbit coupling in the normal GNR. For zigzag and bearded GNRs, the topological edge state can be tuned to be either at the interface or outer edge of the normal ribbon. For armchair GNR, the potential location of the topological edge state can be further enriched to be at the edge of or within the normal ribbon, at the interface, or diving into the topological GNR. We also discuss potential experimental realization of the predicted topological proximity effects, which may pave the way for integrating the salient functionality of TI and graphene in future device applications.

preprint2013arXiv

Tuning the vertical location of helical surface states in topological insulator heterostructures via dual-proximity effects

In integrating topological insulators (TIs) with conventional materials, one crucial issue is how the topological surface states (TSS) will behave in such heterostructures. We use first-principles approaches to establish accurate tunability of the vertical location of the TSS via intriguing dual-proximity effects. By depositing a conventional insulator (CI) overlayer onto a TI substrate (Bi2Se3 or Bi2Te3), we demonstrate that, the TSS can float to the top of the CI film, or stay put at the CI/TI interface, or be pushed down deeper into the otherwise structurally homogeneous TI substrate. These contrasting behaviors imply a rich variety of possible quantum phase transitions in the hybrid systems, dictated by key material-specific properties of the CI. These discoveries lay the foundation for accurate manipulation of the real space properties of TSS in TI heterostructures of diverse technological significance.

preprint2012arXiv

Quantum Efficiency of Intermediate-Band Solar Cells Based on Non-Compensated n-p Codoped TiO2

As an appealing concept for developing next-generation solar cells, intermediate-band solar cells (IBSCs) promise to drastically increase the quantum efficiency of photovoltaic conversion. Yet to date, a standing challenge lies in the lack of materials suitable for developing IBSCs. Recently, a new doping approach, termed non-compensated n-p codoping, has been proposed to construct intermediate bands (IBs) in the intrinsic energy band gaps of oxide semiconductors such as TiO$_2$. We explore theoretically the optimal quantum efficiency of IBSCs based on non-compensated n-p codoped TiO$_2$ under two different design schemes. The first preserves the ideal condition that no electrical current be extracted from the IB. The corresponding maximum quantum efficiency for the codoped TiO$_2$ can reach 52.7%. In the second scheme, current is also extracted from the IB, resulting in a further enhancement in the maximum efficiency to 56.7%. Our findings also relax the stringent requirement that the IB location be close to the optimum value, making it more feasible to realize IBSCs with high quantum efficiencies.

preprint2011arXiv

Activated Vibrational Modes and Fermi Resonance in Tip-Enhanced Raman Spectroscopy

Using p-aminothiophenol (PATP) molecules on a gold substrate as prototypical examples and high vacuum tip-enhanced Raman spectroscopy (HV-TERS), we show that the vibrational spectra of those molecules are distinctly different from those in typical surface-enhanced Raman spectroscopy. Detailed first-principles calculations help to assign the Raman peaks in the TERS measurements as Raman active and infrared (IR) active vibrational modes of dimercaptoazobenzene (DMAB), thus providing strong spectroscopic evidence for the conversion of PATP dimerization to DMAB. The activation of the IR active modes is due to enhanced electromagnetic field gradient effects within the gap region of the highly asymmetric tip-surface geometry. Our TERS measurements also realize splitting of certain vibrational modes due to Fermi resonance between a fundamental mode and the overtone of a different mode or a combinational mode. These findings help to broaden the versatility of TERS as a promising technique for ultrasensitive molecular spectroscopy.

preprint2011arXiv

Atomic structure, energetics, and dynamics of topological solitons in Indium chains on Si(111) surfaces

Based on scanning tunneling microscopy and first-principles theoretical studies, we characterize the precise atomic structure of a topological soliton in In chains grown on Si(111) surfaces. Variable-temperature measurements of the soliton population allow us to determine the soliton formation energy to be ~60 meV, smaller than one half of the band gap of ~200 meV. Once created, these solitons have very low mobility, even though the activation energy is only about 20 meV; the sluggish nature is attributed to the exceptionally low attempt frequency for soliton migration. We further demonstrate local electric field-enhanced soliton dynamics.

preprint2011arXiv

CO Oxidation Facilitated by Robust Surface States on Au-Covered Topological Insulators

Surface states refer to electronic states emerging as a solid material terminates at a surface, and can be present in many systems. Despite their spatial proximity to material surfaces, surface states have been largely overlooked in fundamental understanding of surface catalysis and potential real-world applications, because of their vulnerability to local impurities or defects. In contrast, the recently discovered three-dimensional topological insulators (3DTI) have exceptionally robust metallic surface states that are topologically protected against surface contamination and imperfection. The robust topological surface state(s) (TSS) provides a perfect platform for exploiting novel physical phenomena and potential applications of surface states in less stringent environments. Here we employ first-principles density functional theory to demonstrate that the TSS can play a vital and elegant role in facilitating surface reactions by serving as an effective electron bath. We use CO oxidation on gold-covered Bi2Se3 as a prototype example, and first show that the TSS is preserved when a stable ultrathin Au film is deposited onto a Bi-terminated Bi2Se3 substrate. Furthermore, the TSS can significantly enhance the adsorption energy of both CO and O2 molecules, by promoting different directions of electron transfer. For CO, the TSS accepts electrons from the CO-Au system, thereby decreasing the undesirable occupation of the CO antibonding states. For O2, the TSS donates the needed electrons to promote the molecule towards dissociative adsorption. The present study adds a new arena to the technological potentials of 3DTI, and the central concept of TSS as an electron bath as revealed here may lead to new design principles beyond the conventional d-band theory of heterogeneous catalysis.

preprint2011arXiv

Quantum Size Effect and Electronic Stability of Freestanding Metal Atom Wires

Using DFT calculations, we present a thorough study of the quantum size effects on the stability of freestanding metal atom wires. Our systems include Na, Ag, Au, In, Ga and Pb atom wires, i.e. $s$, $sd$, and $sp$ electron prototypes. We found that the total energy always oscillates with the wire length, which clearly indicates the existence of preferred lengths. Increasing the length, the s-system exhibits even-odd oscillations following a $a/x +b/x^2$ decay law in the stability, which can be attributed to electron band filling and quantum confinement along the wire. The $sd$-system exhibits a similar oscillation pattern, even in the presence of $sd$ hybridization. In $sp$-system, the energy oscillations are beyond the simple even-odd nature, likely due to unpaired p orbitals and the corresponding nontrival band filling. Our findings clearly demonstrate that electronic contribution is quite critical to the stability of freestanding wires, and this stability may be important even when wires are deposited on substrates or strained. This study sheds light on the underlying formation mechanism of metal atom wires.

preprint2011arXiv

Suppression of Grain Boundaries in Graphene Growth on Superstructured Mn-Cu(111) Surface

As undesirable defects, grain boundaries (GBs) are widespread in epitaxial graphene using existing growth methods on metal substrates. Employing density functional theory calculations, we first identify that the misorientations of carbon islands nucleated on a Cu(111) surface lead to the formation of GBs as the islands coalesce. We then propose a two-step kinetic pathway to effectively suppress the formation of GBs. In the first step, large aromatic hydrocarbon molecules are deposited onto a $\sqrt{3}\times\sqrt{3}$ superstructured Cu-Mn alloyed surface to seed the initial carbon clusters of a single orientation; in the second step, the seeded islands are enlarged through normal chemical vapor deposition of methane to form a complete graphene sheet. The present approach promises to overcome a standing obstacle in large scale single-crystal graphene fabrication.

preprint2010arXiv

$0^{++}$ scalar glueball in finite-width Gaussian sum rules

Based on a semiclassical expansion for quantum chromodynamics in the instanton liquid background, the correlation function of the $0^{++}$ scalar glueball current is given, and the properties of the $0^{++}$ scalar glueball are studied in the framework of Gaussian sum rules. Besides the pure classical and quantum contributions, the contributions arising from the interactions between the classical instanton fields and quantum gluons are come into play. Instead of the usual zero-width approximation for the resonance, the Breit-Wigner form for the spectral function of the finite-width resonance is adopted. The family of the Gaussian sum rules for the scalar glueball in quantum chromodynamics with and without light quarks is studied. A consistency between the subtracted and unsubtracted sum rules is very well justified, and the values of the decay width and the coupling to the corresponding current for the $0^{++}$ resonance, in which the scalar glueball fraction is dominant, are obtained.

preprint2010arXiv

Atomistic mechanisms and diameter selection during nanorod growth

We study in this paper the atomic mechanisms of nanorod growth and propose the way of diameter selection of nanorod. A characteristic radius is demonstrated to be crucial in nanorod growth, which increases proportional to one fifth power of the ratio of the interlayer hopping rate of adatoms across the monolayer steps to the deposition rate. When the radius of the initial island is larger than this characteristic radius, the growth morphology evolves from a taper-like structure to a nanorod with radius equal to the characteristic radius after some transient layers. Otherwise the nanorod morphology can be maintained during the growth, with stable radius being limited by both the radius of the initial island and the three-dimensional Ehrlich-Schwoebel barrier. Therefore different growth modes and diameter of nanorod can be selected by tuning the characteristic radius. The theoretical predictions are in good agreement with experimental observations of ZnO growth.

preprint2010arXiv

Half-Heusler Compounds as a New Class of Three-Dimensional Topological Insulators

Using first-principles calculations within density functional theory, we explore the feasibility of converting ternary half-Heusler compounds into a new class of three-dimensional topological insulators (3DTI). We demonstrate that the electronic structure of unstrained LaPtBi as a prototype system exhibits distinct band-inversion feature. The 3DTI phase is realized by applying a uniaxial strain along the [001] direction, which opens a bandgap while preserving the inverted band order. A definitive proof of the strained LaPtBi as a 3DTI is provided by directly calculating the topological Z2 invariants in systems without inversion symmetry. We discuss the implications of the present study to other half-Heusler compounds as 3DTI, which, together with the magnetic and superconducting properties of these materials, may provide a rich platform for novel quantum phenomena.

preprint2010arXiv

The finite-width Laplace sum rules for $0^{++}$ scalar glueball in instanton liquid model

In the framework of a semi-classical expansion for quantum chromodynamics in the instanton liquid background, the correlation function of the $0^{++}$ scalar glueball current is given. Besides the pure classical and quantum contributions, the contributions arising from the interactions between the classical instanton fields and quantum gluons are taken into account as well. Instead of the usual zero-width approximation for the resonance, the Brite-Wigner form for the spectral function of the finite-width resonance is adopted. The family of the Laplace sum rules for the scalar glueball in quantum chromodynamics with and without light quarks are studed. A consistency between the subtracted and unsubtracted sum rules are very well justified, and the values of the mass, decay width, and the coupling to the corresponding current for the $0^{++}$ resonance in which the glueball fraction is dominant, are obtained.

preprint2009arXiv

Adsorbate-induced Restructuring of Pb mesas Grown on Vicinal Si(111) in the Quantum Regime

Using scanning tunneling microscopy and spectroscopy, we demonstrate that the adsorption of a minute amount of Cs on a Pb mesa grown in the quantum regime can induce dramatic morphological changes of the mesa, characterized by the appearance of populous monatomic-layer-high Pb nano-islands on top of the mesa. The edges of the Pb nano-islands are decorated with Cs adatoms, and the nano-islands preferentially nucleate and grow on the quantum mechanically unstable regions of the mesa. Furthermore, first-principles calculations within density functional theory show that the Pb atoms forming these nano-islands were expelled by the adsorbed Cs atoms via a kinetically accessible place exchange process when the Cs atoms alloyed into the top layer of the Pb mesa.

preprint2009arXiv

Contrasting Behavior of Carbon Nucleation in the Initial Stages of Graphene Epitaxial Growth on Stepped Metal Surfaces

Using first-principles calculations within density functional theory, we study the energetics and kinetics of carbon nucleation in the early stages of epitaxial graphene growth on three representative stepped metal surfaces: Ir(111), Ru(0001), and Cu(111). We find that on the flat surfaces of Ir(111) and Ru(0001), two carbon atoms repel each other, while they prefer to form a dimer on Cu(111). Moreover, the step edges on Ir and Ru surfaces cannot serve as effective trapping centers for single carbon adatoms, but can readily facilitate the formation of carbon dimers. These contrasting behaviors are attributed to the delicate competition between C-C bonding and C-metal bonding, and a simple generic principle is proposed to predict the nucleation sites of C adatoms on many other metal substrates with the C-metal bond strengths as the minimal inputs.

preprint1999arXiv

Two-dimensional pattern formation in surfactant-mediated epitaxial growth

The effects of a surfactant on two-dimensional pattern formation in epitaxial growth are explored theoretically using a simple model, in which an adatom becomes immobile only after overcoming a large energy barrier as it exchanges positions with a surfactant atom, and subsequent growth from such a seed is further shielded. Within this model, a fractal-to-compact island shape transition can be induced by either decreasing the growth temperature or increasing the deposition flux. This and other intriguing findings are in excellent qualitative agreement with recent experiments.

Zhenyu Zhang

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66 published item(s)

A Survey on Failure Analysis and Fault Injection in AI Systems

Interactive Evaluation Requires a Design Science

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training

Fantastic Reasoning Behaviors and Where to Find Them: Unsupervised Discovery of the Reasoning Process

OpenGround: Active Cognition-based Reasoning for Open-World 3D Visual Grounding

Sam-Guided Enhanced Fine-Grained Encoding with Mixed Semantic Learning for Medical Image Captioning

Accelerating Bayesian inference of dependency between complex biological traits

ASFD: Automatic and Scalable Face Detector

Capacity Bounds for the Two-User IM/DD Interference Channel

Data-Efficient Double-Win Lottery Tickets from Robust Pre-training

Electromagnetic Dalitz Decays of $D_{(s)}^\ast$ Mesons

Josephson-Coulomb drag effect between graphene and LaAlO3/SrTiO3 interfacial superconductor

Label Anchored Contrastive Learning for Language Understanding

Layout-Aware Information Extraction for Document-Grounded Dialogue: Dataset, Method and Demonstration

Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness

Multi-Modal Masked Pre-Training for Monocular Panoramic Depth Completion

NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results

Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free

RigNet: Repetitive Image Guided Network for Depth Completion

Sparsity Winning Twice: Better Robust Generalization from More Efficient Training

Study of exotic hadrons with machine learning

The Principle of Diversity: Training Stronger Vision Transformers Calls for Reducing All Levels of Redundancy

JUNO Physics and Detector

Magnetic moment preservation and emergent Kondo resonance of Co-phthalocyanine on semimetallic Sb(111)

Artificial Intelligence for High-Throughput Discovery of Topological Insulators: the Example of Alloyed Tetradymites

Feasibility and physics potential of detecting $^8$B solar neutrinos at JUNO

HIN: Hierarchical Inference Network for Document-Level Relation Extraction

Joint Extraction of Entities and Relations Based on a Novel Decomposition Strategy

Residual Clipping Noise in Multi-layer Optical OFDM: Modeling, Analysis, and Application

TAO Conceptual Design Report: A Precision Measurement of the Reactor Antineutrino Spectrum with Sub-percent Energy Resolution

Large-area, periodic, and tunable intrinsic pseudo-magnetic fields in low-angle twisted bilayer graphene

Dirac node lines in pure alkali earth metals

Distinct Reconstruction Patterns and Spin-Resolved Electronic States along the Zigzag Edges of Transition Metal Dichalcogenides

Maximizing the thermoelectric performance of topological insulator Bi2Te3 films in the few-quintuple layer regime

Ab-initio Studies of (Li$_{0.8}$Fe$_{0.2}$)OHFeSe Superconductors: Revealing the Dual Roles of Fe$_{0.2}$ in Structural Stability and Charge Transfer

Competing Magnetic Orderings and Tunable Topological States in Two-Dimensional Hexagonal Organometallic Lattices

Converting a topologically trivial superconductor into a topological superconductor via magnetic doping

Densities, isobaric thermal expansion coefficients and isothermal compressibilities of linear alkylbenzene

Equivalence of Electronic and Mechanical Stresses in Structural Phase Stabilization: A Case Study of Indium Wires on Si(111)

High-Temperature Quantum Anomalous Hall Effect in n-p Codoped Topological Insulators

Neutrino Physics with JUNO

Optical Control of Fluorescence through Plasmonic Eigenmode Extinction

Orientationally Misaligned Zipping of Lateral Graphene and Boron Nitride Nanoribbons with Minimized Strain Energy and Enhanced Half-Metallicity

Quantum Anomalous Hall Effect in Graphene Proximity Coupled to an Antiferromagnetic Insulator

Rayleigh scattering of linear alkylbenzene in large liquid scintillator detectors

Single-Valley Engineering in Graphene Superlattices

Spectroscopic study of light scattering in linear alkylbenzene for liquid scintillator neutrino detectors

Persistent ferromagnetism and topological phase transition at the interface of a superconductor and a topological insulator

Proximity Effects in Topological Insulator Heterostructures

Gate-Tunable Exchange Coupling Between Cobalt Clusters on Graphene

NV-Center Based Digital Quantum Simulation of a Quantum Phase Transition in Topological Insulators

Topological Proximity Effects in Graphene Nanoribbon Heterostructures

Tuning the vertical location of helical surface states in topological insulator heterostructures via dual-proximity effects

Quantum Efficiency of Intermediate-Band Solar Cells Based on Non-Compensated n-p Codoped TiO2

Activated Vibrational Modes and Fermi Resonance in Tip-Enhanced Raman Spectroscopy

Atomic structure, energetics, and dynamics of topological solitons in Indium chains on Si(111) surfaces

CO Oxidation Facilitated by Robust Surface States on Au-Covered Topological Insulators

Quantum Size Effect and Electronic Stability of Freestanding Metal Atom Wires

Suppression of Grain Boundaries in Graphene Growth on Superstructured Mn-Cu(111) Surface

$0^{++}$ scalar glueball in finite-width Gaussian sum rules

Atomistic mechanisms and diameter selection during nanorod growth

Half-Heusler Compounds as a New Class of Three-Dimensional Topological Insulators

The finite-width Laplace sum rules for $0^{++}$ scalar glueball in instanton liquid model

Adsorbate-induced Restructuring of Pb mesas Grown on Vicinal Si(111) in the Quantum Regime

Contrasting Behavior of Carbon Nucleation in the Initial Stages of Graphene Epitaxial Growth on Stepped Metal Surfaces

Two-dimensional pattern formation in surfactant-mediated epitaxial growth