arXiv subjects

Hao Zhang

Publications and source records attributed to Hao Zhang.

At least 757 records · Page 42Linked to original sources

arXiv2024

Crystal Transformer Based Universal Atomic Embedding for Accurate and Transferable Prediction of Materials Properties

In this work, we propose a novel approach to generate universal atomic embeddings, significantly enhancing the representational and accuracy aspects of atomic embeddings, which ultimately improves the accuracy of property prediction. Moreover, we demonstrate the excellent transferability of universal atomic embeddings across different databases and various property tasks. Our approach centers on developing the CrystalTransformer model. Unlike traditional methods, this model does not possess a fundamental graph network architecture but utilizes the Transformer architecture to extract latent atomic features. This allows the CrystalTransformer to mitigate the inherent topological information bias of graph neural networks while maximally preserving the atomic chemical information, making it more accurate in encoding complex atomic features and thereby offering a deeper understanding of the atoms in materials. In our research, we highlight the advantages of CrystalTransformer in generating universal atomic embeddings through comparisons with current mainstream graph neural network models. Furthermore, we validate the effectiveness of universal atomic embeddings in enhancing the accuracy of model predictions for properties and demonstrate their transferability across different databases and property tasks through various experiments. As another key aspect of our study, we discover the strong physical interpretability implied in universal atomic embeddings through clustering and correlation analysis, indicating the immense potential of our universal atomic embeddings as atomic fingerprints.

cond-mat.mtrl-sci↗

arXiv2024

Learning Implicit Representation for Reconstructing Articulated Objects

3D Reconstruction of moving articulated objects without additional information about object structure is a challenging problem. Current methods overcome such challenges by employing category-specific skeletal models. Consequently, they do not generalize well to articulated objects in the wild. We treat an articulated object as an unknown, semi-rigid skeletal structure surrounded by nonrigid material (e.g., skin). Our method simultaneously estimates the visible (explicit) representation (3D shapes, colors, camera parameters) and the implicit skeletal representation, from motion cues in the object video without 3D supervision. Our implicit representation consists of four parts. (1) Skeleton, which specifies how semi-rigid parts are connected. (2) \textcolor{black}{Skinning Weights}, which associates each surface vertex with semi-rigid parts with probability. (3) Rigidity Coefficients, specifying the articulation of the local surface. (4) Time-Varying Transformations, which specify the skeletal motion and surface deformation parameters. We introduce an algorithm that uses physical constraints as regularization terms and iteratively estimates both implicit and explicit representations. Our method is category-agnostic, thus eliminating the need for category-specific skeletons, we show that our method outperforms state-of-the-art across standard video datasets.

cs.CV↗

arXiv2024

Empirical Evidence for the Fragment level Understanding on Drug Molecular Structure of LLMs

AI for drug discovery has been a research hotspot in recent years, and SMILES-based language models has been increasingly applied in drug molecular design. However, no work has explored whether and how language models understand the chemical spatial structure from 1D sequences. In this work, we pre-train a transformer model on chemical language and fine-tune it toward drug design objectives, and investigate the correspondence between high-frequency SMILES substrings and molecular fragments. The results indicate that language models can understand chemical structures from the perspective of molecular fragments, and the structural knowledge learned through fine-tuning is reflected in the high-frequency SMILES substrings generated by the model.

cs.LG↗

arXiv2024

Real-time imaging of standing-wave patterns in microresonators

Real-time characterization of microresonator dynamics is important for many applications. In particular it is critical for near-field sensing and understanding light-matter interactions. Here, we report camera-facilitated imaging and analysis of standing wave patterns in optical ring resonators. The standing wave pattern is generated through bi-directional pumping of a microresonator and the scattered light from the microresonator is collected by a short-wave infrared (SWIR) camera. The recorded scattering patterns are wavelength dependent, and the scattered intensity exhibits a linear relation with the circulating power within the microresonator. By modulating the relative phase between the two pump waves, we can control the generated standing waves movements and characterize the resonator with the SWIR camera. The visualized standing wave enables subwavelength distance measurements of scattering targets with nanometer-level accuracy. This work opens new avenues for applications in on-chip near-field (bio-)sensing, real time characterization of photonic integrated circuits and backscattering control in telecom systems.

physics.optics↗

arXiv2024

Crafter: Facial Feature Crafting against Inversion-based Identity Theft on Deep Models

With the increased capabilities at the edge (e.g., mobile device) and more stringent privacy requirement, it becomes a recent trend for deep learning-enabled applications to pre-process sensitive raw data at the edge and transmit the features to the backend cloud for further processing. A typical application is to run machine learning (ML) services on facial images collected from different individuals. To prevent identity theft, conventional methods commonly rely on an adversarial game-based approach to shed the identity information from the feature. However, such methods can not defend against adaptive attacks, in which an attacker takes a countermove against a known defence strategy. We propose Crafter, a feature crafting mechanism deployed at the edge, to protect the identity information from adaptive model inversion attacks while ensuring the ML tasks are properly carried out in the cloud. The key defence strategy is to mislead the attacker to a non-private prior from which the attacker gains little about the private identity. In this case, the crafted features act like poison training samples for attackers with adaptive model updates. Experimental results indicate that Crafter successfully defends both basic and possible adaptive attacks, which can not be achieved by state-of-the-art adversarial game-based methods.

cs.CR↗

arXiv2024

Shape-IoU: More Accurate Metric considering Bounding Box Shape and Scale

As an important component of the detector localization branch, bounding box regression loss plays a significant role in object detection tasks. The existing bounding box regression methods usually consider the geometric relationship between the GT box and the predicted box, and calculate the loss by using the relative position and shape of the bounding boxes, while ignoring the influence of inherent properties such as the shape and scale of the bounding boxes on bounding box regression. In order to make up for the shortcomings of existing research, this article proposes a bounding box regression method that focuses on the shape and scale of the bounding box itself. Firstly, we analyzed the regression characteristics of the bounding boxes and found that the shape and scale factors of the bounding boxes themselves will have an impact on the regression results. Based on the above conclusions, we propose the Shape IoU method, which can calculate the loss by focusing on the shape and scale of the bounding box itself, thereby making the bounding box regression more accurate. Finally, we validated our method through a large number of comparative experiments, which showed that our method can effectively improve detection performance and outperform existing methods, achieving state-of-the-art performance in different detection tasks.Code is available at https://github.com/malagoutou/Shape-IoU

cs.CV↗

arXiv2024

SnapCap: Efficient Snapshot Compressive Video Captioning

Video Captioning (VC) is a challenging multi-modal task since it requires describing the scene in language by understanding various and complex videos. For machines, the traditional VC follows the "imaging-compression-decoding-and-then-captioning" pipeline, where compression is pivot for storage and transmission. However, in such a pipeline, some potential shortcomings are inevitable, i.e., information redundancy resulting in low efficiency and information loss during the sampling process for captioning. To address these problems, in this paper, we propose a novel VC pipeline to generate captions directly from the compressed measurement, which can be captured by a snapshot compressive sensing camera and we dub our model SnapCap. To be more specific, benefiting from the signal simulation, we have access to obtain abundant measurement-video-annotation data pairs for our model. Besides, to better extract language-related visual representations from the compressed measurement, we propose to distill the knowledge from videos via a pre-trained CLIP with plentiful language-vision associations to guide the learning of our SnapCap. To demonstrate the effectiveness of SnapCap, we conduct experiments on two widely-used VC datasets. Both the qualitative and quantitative results verify the superiority of our pipeline over conventional VC pipelines. In particular, compared to the "caption-after-reconstruction" methods, our SnapCap can run at least 3$\times$ faster, and achieve better caption results.

cs.CV↗

arXiv2024

MPN: Leveraging Multilingual Patch Neuron for Cross-lingual Model Editing

Large language models are known for encoding a vast amount of factual knowledge, but they often becomes outdated due to the ever-changing nature of external information. A promising solution to this challenge is the utilization of model editing methods to update the knowledge in an efficient manner. However, the majority of existing model editing techniques are limited to monolingual frameworks, thus failing to address the crucial issue of cross-lingual knowledge synchronization for multilingual models. To tackle this problem, we propose a simple yet effective method that trains multilingual patch neuron to store cross-lingual knowledge. It can be easily adapted to existing approaches to enhance their cross-lingual editing capabilities. To evaluate our method, we conduct experiments using both the XNLI dataset and a self-constructed XFEVER dataset. Experimental results demonstrate that our proposed method achieves improved performance in cross-lingual editing tasks without requiring excessive modifications to the original methodology, thereby showcasing its user-friendly characteristics. Codes will be released soon.

cs.CL↗

arXiv2024

FED-NeRF: Achieve High 3D Consistency and Temporal Coherence for Face Video Editing on Dynamic NeRF

The success of the GAN-NeRF structure has enabled face editing on NeRF to maintain 3D view consistency. However, achieving simultaneously multi-view consistency and temporal coherence while editing video sequences remains a formidable challenge. This paper proposes a novel face video editing architecture built upon the dynamic face GAN-NeRF structure, which effectively utilizes video sequences to restore the latent code and 3D face geometry. By editing the latent code, multi-view consistent editing on the face can be ensured, as validated by multiview stereo reconstruction on the resulting edited images in our dynamic NeRF. As the estimation of face geometries occurs on a frame-by-frame basis, this may introduce a jittering issue. We propose a stabilizer that maintains temporal coherence by preserving smooth changes of face expressions in consecutive frames. Quantitative and qualitative analyses reveal that our method, as the pioneering 4D face video editor, achieves state-of-the-art performance in comparison to existing 2D or 3D-based approaches independently addressing identity and motion. Codes will be released.

cs.CV↗

arXiv2024

Unlocking the Potential of Large Language Models for Explainable Recommendations

Generating user-friendly explanations regarding why an item is recommended has become increasingly common, largely due to advances in language generation technology, which can enhance user trust and facilitate more informed decision-making when using online services. However, existing explainable recommendation systems focus on using small-size language models. It remains uncertain what impact replacing the explanation generator with the recently emerging large language models (LLMs) would have. Can we expect unprecedented results? In this study, we propose LLMXRec, a simple yet effective two-stage explainable recommendation framework aimed at further boosting the explanation quality by employing LLMs. Unlike most existing LLM-based recommendation works, a key characteristic of LLMXRec is its emphasis on the close collaboration between previous recommender models and LLM-based explanation generators. Specifically, by adopting several key fine-tuning techniques, including parameter-efficient instructing tuning and personalized prompt techniques, controllable and fluent explanations can be well generated to achieve the goal of explanation recommendation. Most notably, we provide three different perspectives to evaluate the effectiveness of the explanations. Finally, we conduct extensive experiments over several benchmark recommender models and publicly available datasets. The experimental results not only yield positive results in terms of effectiveness and efficiency but also uncover some previously unknown outcomes. To facilitate further explorations in this area, the full code and detailed original results are open-sourced at https://github.com/GodFire66666/LLM_rec_explanation/.

cs.IR↗

arXiv2024

RF E-field enhanced sensing based on Rydberg-atom-based superheterodyne receiver

We present enhanced sensing of radio frequency (RF) electric fields (E-fields) by the combined polarizability of Rydberg atoms and the optimized local oscillator (LO) fields of supergheterodyne receiving. Our modified theoretical model reveals the dependencies of sensitivity of E-field amplitude measurement on the polarizability of Rydberg states and the strength of the LO RF field. The enhanced sensitivity of megahertz(MHz) E-field are demonstrated at an optimal LO field for three different Rydberg states $\rm 43D_{5/2}$, $\rm 60S_{1/2}$, and $\rm 90S_{1/2}$. The sensitivity of 63 MHz for the $\rm 90S_{1/2}$ state reaches 0.96 $μ\rm V/cm/\sqrt{Hz}$ that is about an order of magnitude higher than those already published. This result closely approaches the theoretical sensitivity limit of RF dipole antennas, and indicates the potential for breaking the limit in measuring sub-MHz E-fields. This atomic sensor based on Rydberg Stark effect with heterodyne technique is expected to boost an alternative solution to electric dipole antennas.

physics.atom-ph↗

arXiv2024

Quantum sensing of microwave electric fields based on Rydberg atoms

Microwave electric field sensing is of importance for a wide range of applications in areas of remote sensing, radar astronomy and communications. Over the past decade, Rydberg atoms, owing to their exaggerated response to microwave electric fields, plentiful optional energy levels and integratable preparation methods, have been used in ultra-sensitive, wide broadband, traceable, stealthy microwave electric field sensing. This review first introduces the basic concept of quantum sensing, properties of Rydberg atoms and principles of quantum sensing of microwave electric fields with Rydberg atoms. Then an overview of this very active research direction is gradually expanded, covering progresses of sensitivity and bandwidth in Rydberg atoms based icrowavesensing,uperheterodyne quantum sensing with microwave-dressed Rydberg atoms, quantum-enhanced sensing of microwave electric field, recent advanced quantum measurement systems and approaches to further improve the performance of microwave electric field sensing. Finally, a brief outlook on future development directions is discussed.

physics.atom-ph↗

arXiv2023

BRICS: Bi-level feature Representation of Image CollectionS

We present BRICS, a bi-level feature representation for image collections, which consists of a key code space on top of a feature grid space. Specifically, our representation is learned by an autoencoder to encode images into continuous key codes, which are used to retrieve features from groups of multi-resolution feature grids. Our key codes and feature grids are jointly trained continuously with well-defined gradient flows, leading to high usage rates of the feature grids and improved generative modeling compared to discrete Vector Quantization (VQ). Differently from existing continuous representations such as KL-regularized latent codes, our key codes are strictly bounded in scale and variance. Overall, feature encoding by BRICS is compact, efficient to train, and enables generative modeling over key codes using the diffusion model. Experimental results show that our method achieves comparable reconstruction results to VQ while having a smaller and more efficient decoder network (50% fewer GFlops). By applying the diffusion model over our key code space, we achieve state-of-the-art performance on image synthesis on the FFHQ and LSUN-Church (29% lower than LDM, 32% lower than StyleGAN2, 44% lower than Projected GAN on CLIP-FID) datasets.

cs.CV↗

arXiv2023

NeuralKalman: A Learnable Kalman Filter for Acoustic Echo Cancellation

The robustness of the Kalman filter to double talk and its rapid convergence make it a popular approach for addressing acoustic echo cancellation (AEC) challenges. However, the inability to model nonlinearity and the need to tune control parameters cast limitations on such adaptive filtering algorithms. In this paper, we integrate the frequency domain Kalman filter (FDKF) and deep neural networks (DNNs) into a hybrid method, called NeuralKalman, to leverage the advantages of deep learning and adaptive filtering algorithms. Specifically, we employ a DNN to estimate nonlinearly distorted far-end signals, a transition factor, and the nonlinear transition function in the state equation of the FDKF algorithm. Experimental results show that the proposed NeuralKalman improves the performance of FDKF significantly and outperforms strong baseline methods.

eess.AS↗

arXiv2023

On derivatives of zeta and $L$-functions near the 1-line

We study the conditional upper bounds and extreme values of derivatives of the Riemann zeta function and Dirichlet $L$-functions near the 1-line. Let $\ell$ be a fixed natural number. We show that, if $|σ-1|\ll1/\log_2t$, then $|ζ^{(\ell)}(σ+ it)|$ has the same maximal order (up to the leading coefficients) as $|ζ^{(\ell)}(1+ it)|$ when $t\to\infty$. The range $1-σ\ll1/\log_2t$ is wide enough, since we also show that $(1-σ) \log_2t \to \infty\; (t \to \infty)$ implies $\limsup_{t\to\infty}|ζ^{(\ell)}(σ+ it)| / (\log_2t)^{\ell+1} = \infty$. Similar results can be obtained for Dirichlet $L$-functions $L^{(\ell)}(σ,χ)$ with $χ\pmod q$.

math.NT↗

arXiv2023

Interference of cavity light by a single atom acting as a double slit

Young's double-slit interference experiment is central to quantum mechanics. While it has been demonstrated that an array of atoms can produce interference in light, it is a fundamental question to ask whether a single atom can act as a double slit when prepared in a superposition of two separate positions. Cohen-Tannoudji et al. [Proceedings of the Tenth International Conference on Laser Spectroscopy, edited by M. Ducloy, E. Giacobino, and G. Camy (World Scientific, Singapore, 1992), pp. 3-14] showed that the cross section of the light scattered by a single atom is independent of the spatial separation. In this work, however, we show that when a single atom tunneling in a double well is coupled to an optical ring cavity, the interference phenomena arise if the tunneling rate is comparable to the cavity linewidth. Being driven by an external laser in the dispersive regime, the field emitted by the atom into the cavity exhibits an interference pattern when varying the double-well spacing. Super-Poissonian bunched light can also be generated near the destructive interference. Furthermore, we show that the atomic flux of the coherent tunneling motion generates directional cavity emission, which oscillates for many cycles before the decoherence of the atomic motion and the decay of the cavity photons. Our work opens ways to manipulate photons with controllable external states of atoms for quantum information applications and use cavity light as nondestructive measurements for many-body states of atomic systems.

quant-ph↗

arXiv2023

Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena

Evaluating large language model (LLM) based chat assistants is challenging due to their broad capabilities and the inadequacy of existing benchmarks in measuring human preferences. To address this, we explore using strong LLMs as judges to evaluate these models on more open-ended questions. We examine the usage and limitations of LLM-as-a-judge, including position, verbosity, and self-enhancement biases, as well as limited reasoning ability, and propose solutions to mitigate some of them. We then verify the agreement between LLM judges and human preferences by introducing two benchmarks: MT-bench, a multi-turn question set; and Chatbot Arena, a crowdsourced battle platform. Our results reveal that strong LLM judges like GPT-4 can match both controlled and crowdsourced human preferences well, achieving over 80% agreement, the same level of agreement between humans. Hence, LLM-as-a-judge is a scalable and explainable way to approximate human preferences, which are otherwise very expensive to obtain. Additionally, we show our benchmark and traditional benchmarks complement each other by evaluating several variants of LLaMA and Vicuna. The MT-bench questions, 3K expert votes, and 30K conversations with human preferences are publicly available at https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge.

cs.CL↗

arXiv2023

Microresonator soliton frequency combs via cascaded Brillouin scattering

We demonstrate Kerr soliton frequency comb generation that is seeded by a cascaded Brillouin scattering process. In this process, a pump laser is used to generate multiple orders of Brillouin sidebands in a microresonator, which in turn generate the soliton. In such a process, even orders of Brillouin scattering sidebands are co-propagating with respect to the pump laser while odd orders of Brillouin scattering are backwards propagating. In this work we present the generation of forward propagating Kerr solitons via a forward propagating second order Brillouin scattering process in a fused silica rod resonator. Importantly, we show that the Brillouin scattering process can bridge the gap between different microresonator mode families, such that the repetition rate of the Kerr soliton is independent from the Brillouin gain frequency shift (about 10 GHz in fused silica). In our work we demonstrate this by generating soliton pulse trains with a repetition rate of 107 GHz. Our work opens up a new way for using cascaded Brillouin lasing as a seed for microresonator frequency comb generation. This can be of particular interest for the realization of soliton frequency combs with low noise properties from Brillouin lasing while still having arbitrary repetition rates that are determined by the resonator size. Applications range from optical communication to LIDAR systems and photonic signal generation.

physics.optics↗