arXiv subjects

Zhe Li

Publications and source records attributed to Zhe Li.

At least 217 records · Page 12Linked to original sources

arXiv2023

Mixed $\text{QCD} \otimes \text{EW}$ corrections to charged Higgs pair production in THDM at electron-positron colliders

We calculate the two-loop mixed QCD$\otimes$EW corrections for the charged Higgs boson pair production within the framework of four types of Two Higgs Doublet Models (THDMs) with the $Z_2$ symmetry. We analyze in detail the dependences of our results on physical parameters, including the charged Higgs mass, $\tanβ$, the scattering angle, and the colliding energy. It is noticeable that the mixed QCD$\otimes$EW relative correction is independent of the scattering angle due to the topology of Feynman diagrams at $O(αα_s)$. Numerical results in most allowed regions of four types of THDMs are provided in the density plots on the $m_{H^{\pm}}$-$\tanβ$ plane. For type-I and type-X, the mixed QCD$\otimes$EW relative correction varies slightly near $1\%$ except in the vicinity of resonance. For type-II and type-Y, the corrections increase consistently in large $\tanβ$ region and reach up to $11.5\%$ at $\tanβ= 50$. We also compute the $O(α)$ corrections to obtain the corrected cross section up to $O(αα_s)$. The numerical results show that the corrected cross section can be larger than $80\ \mathrm{fb}$ in some parameter space region for type-I and type-X THDMs.

hep-ph↗

arXiv2023

Causality Analysis for Evaluating the Security of Large Language Models

Large Language Models (LLMs) such as GPT and Llama2 are increasingly adopted in many safety-critical applications. Their security is thus essential. Even with considerable efforts spent on reinforcement learning from human feedback (RLHF), recent studies have shown that LLMs are still subject to attacks such as adversarial perturbation and Trojan attacks. Further research is thus needed to evaluate their security and/or understand the lack of it. In this work, we propose a framework for conducting light-weight causality-analysis of LLMs at the token, layer, and neuron level. We applied our framework to open-source LLMs such as Llama2 and Vicuna and had multiple interesting discoveries. Based on a layer-level causality analysis, we show that RLHF has the effect of overfitting a model to harmful prompts. It implies that such security can be easily overcome by `unusual' harmful prompts. As evidence, we propose an adversarial perturbation method that achieves 100\% attack success rate on the red-teaming tasks of the Trojan Detection Competition 2023. Furthermore, we show the existence of one mysterious neuron in both Llama2 and Vicuna that has an unreasonably high causal effect on the output. While we are uncertain on why such a neuron exists, we show that it is possible to conduct a ``Trojan'' attack targeting that particular neuron to completely cripple the LLM, i.e., we can generate transferable suffixes to prompts that frequently make the LLM produce meaningless responses.

cs.AI↗

arXiv2023

Robust Learning Based Condition Diagnosis Method for Distribution Network Switchgear

This paper introduces a robust, learning-based method for diagnosing the state of distribution network switchgear, which is crucial for maintaining the power quality for end users. Traditional diagnostic models often rely heavily on expert knowledge and lack robustness. To address this, our method incorporates an expanded feature vector that includes environmental data, temperature readings, switch position, motor operation, insulation conditions, and local discharge information. We tackle the issue of high dimensionality through feature mapping. The method introduces a decision radius to categorize unlabeled samples and updates the model parameters using a combination of supervised and unsupervised loss, along with a consistency regularization function. This approach ensures robust learning even with a limited number of labeled samples. Comparative analysis demonstrates that this method significantly outperforms existing models in both accuracy and robustness.

eess.SP↗

arXiv2023

Nanowire Array Breath Acetone Sensor for Diabetes Monitoring

Diabetic ketoacidosis (DKA) is a life-threatening acute complication of diabetes in which ketone bodies accumulate in the blood. Breath acetone (a ketone) directly correlates with blood ketones, such that breath acetone monitoring could be used to improve safety in diabetes care. In this work, we report the design and fabrication of a chitosan/Pt/InP nanowire array based chemiresistive acetone sensor. By implementing chitosan as a surface functionalization layer and a Pt Schottky contact for efficient charge transfer processes and photovoltaic effect, self-powered, highly selective acetone sensing has been achieved. This sensor has an ultra-wide detection range from sub-ppb to >100,000 ppm levels at room temperature, incorporating the range from healthy individuals (300-800 ppb) to those at high-risk of DKA (> 75 ppm). The nanowire sensor has been further integrated into a handheld breath testing prototype, the Ketowhistle, which can successfully detect different ranges of acetone concentrations in simulated breath. The Ketowhistle demonstrates immediate potential for non-invasive ketone testing and monitoring for persons living with diabetes, in particular for DKA prevention.

physics.med-ph↗

arXiv2023

Distributed Estimation and Inference for Spatial Autoregression Model with Large Scale Networks

The rapid growth of online network platforms generates large-scale network data and it poses great challenges for statistical analysis using the spatial autoregression (SAR) model. In this work, we develop a novel distributed estimation and statistical inference framework for the SAR model on a distributed system. We first propose a distributed network least squares approximation (DNLSA) method. This enables us to obtain a one-step estimator by taking a weighted average of local estimators on each worker. Afterwards, a refined two-step estimation is designed to further reduce the estimation bias. For statistical inference, we utilize a random projection method to reduce the expensive communication cost. Theoretically, we show the consistency and asymptotic normality of both the one-step and two-step estimators. In addition, we provide theoretical guarantee of the distributed statistical inference procedure. The theoretical findings and computational advantages are validated by several numerical simulations implemented on the Spark system. Lastly, an experiment on the Yelp dataset further illustrates the usefulness of the proposed methodology.

stat.CO↗

arXiv2023

A Wi-Fi Signal-Based Human Activity Recognition Using High-Dimensional Factor Models

Passive sensing techniques based on Wi-Fi signals have emerged as a promising technology in advanced wireless communication systems due to their widespread application and cost-effectiveness. However, the proliferation of low-cost Internet of Things (IoT) devices has led to dense network deployments, resulting in increased levels of noise and interference in Wi-Fi environments. This, in turn, leads to noisy and redundant Channel State Information (CSI) data. As a consequence, the accuracy of human activity recognition based on Wi-Fi signals is compromised. To address this issue, we propose a novel CSI data signal extraction method. We established a human activity recognition system based on the Intel 5300 network interface cards (NICs) and collected a dataset containing six categories of human activities. Using our approach, signals extracted from the CSI data serve as inputs to machine learning (ML) classification algorithms to evaluate classification performance. In comparison to ML methods based on Principal Component Analysis (PCA), our proposed High-Dimensional Factor Model (HDFM) method improves recognition accuracy by 6.8%.

eess.SY↗

arXiv2023

An efficient modeling workflow for high-performance nanowire single-photon avalanche detector

Single-photon detector (SPD), an essential building block of the quantum communication system, plays a fundamental role in developing next-generation quantum technologies. In this work, we propose an efficient modeling workflow of nanowire SPDs utilizing avalanche breakdown at reverse-biased conditions. The proposed workflow is explored to maximize computational efficiency and balance time-consuming drift-diffusion simulation with fast script-based post-processing. Without excessive computational effort, we could predict a suite of key device performance metrics, including breakdown voltage, dark/light avalanche built-up time, photon detection efficiency, dark count rate, and the deterministic part of timing jitter due to device structures. Implementing the proposed workflow onto a single InP nanowire and comparing it to the extensively studied planar devices and superconducting nanowire SPDs, we showed the great potential of nanowire avalanche SPD to outperform their planar counterparts and obtain as superior performance as superconducting nanowires, i.e., achieve a high photon detection efficiency of 70% with a dark count rate less than 20 Hz at non-cryogenic temperature. The proposed workflow is not limited to single-nanowire or nanowire-based device modeling and can be readily extended to more complicated two-/three dimensional structures.

physics.app-ph↗

arXiv2023

General Point Model with Autoencoding and Autoregressive

The pre-training architectures of large language models encompass various types, including autoencoding models, autoregressive models, and encoder-decoder models. We posit that any modality can potentially benefit from a large language model, as long as it undergoes vector quantization to become discrete tokens. Inspired by GLM, we propose a General Point Model (GPM) which seamlessly integrates autoencoding and autoregressive tasks in point cloud transformer. This model is versatile, allowing fine-tuning for downstream point cloud representation tasks, as well as unconditional and conditional generation tasks. GPM enhances masked prediction in autoencoding through various forms of mask padding tasks, leading to improved performance in point cloud understanding. Additionally, GPM demonstrates highly competitive results in unconditional point cloud generation tasks, even exhibiting the potential for conditional generation tasks by modifying the input's conditional information. Compared to models like Point-BERT, MaskPoint and PointMAE, our GPM achieves superior performance in point cloud understanding tasks. Furthermore, the integration of autoregressive and autoencoding within the same transformer underscores its versatility across different downstream tasks.

cs.LG↗

arXiv2023

Inferring Inference

Patterns of microcircuitry suggest that the brain has an array of repeated canonical computational units. Yet neural representations are distributed, so the relevant computations may only be related indirectly to single-neuron transformations. It thus remains an open challenge how to define canonical distributed computations. We integrate normative and algorithmic theories of neural computation into a mathematical framework for inferring canonical distributed computations from large-scale neural activity patterns. At the normative level, we hypothesize that the brain creates a structured internal model of its environment, positing latent causes that explain its sensory inputs, and uses those sensory inputs to infer the latent causes. At the algorithmic level, we propose that this inference process is a nonlinear message-passing algorithm on a graph-structured model of the world. Given a time series of neural activity during a perceptual inference task, our framework finds (i) the neural representation of relevant latent variables, (ii) interactions between these variables that define the brain's internal model of the world, and (iii) message-functions specifying the inference algorithm. These targeted computational properties are then statistically distinguishable due to the symmetries inherent in any canonical computation, up to a global transformation. As a demonstration, we simulate recordings for a model brain that implicitly implements an approximate inference algorithm on a probabilistic graphical model. Given its external inputs and noisy neural activity, we recover the latent variables, their neural representation and dynamics, and canonical message-functions. We highlight features of experimental design needed to successfully extract canonical computations from neural data. Overall, this framework provides a new tool for discovering interpretable structure in neural recordings.

q-bio.NC↗

arXiv2023

Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis

Breast cancer is a major concern for women's health globally, with axillary lymph node (ALN) metastasis identification being critical for prognosis evaluation and treatment guidance. This paper presents a deep learning (DL) classification pipeline for quantifying clinical information from digital core-needle biopsy (CNB) images, with one step less than existing methods. A publicly available dataset of 1058 patients was used to evaluate the performance of different baseline state-of-the-art (SOTA) DL models in classifying ALN metastatic status based on CNB images. An extensive ablation study of various data augmentation techniques was also conducted. Finally, the manual tumor segmentation and annotation step performed by the pathologists was assessed.

eess.IV↗

arXiv2023

A Graph Reconstruction by Dynamic Signal Coefficient for Fault Classification

To improve the performance in identifying the faults under strong noise for rotating machinery, this paper presents a dynamic feature reconstruction signal graph method, which plays the key role of the proposed end-to-end fault diagnosis model. Specifically, the original mechanical signal is first decomposed by wavelet packet decomposition (WPD) to obtain multiple subbands including coefficient matrix. Then, with originally defined two feature extraction factors MDD and DDD, a dynamic feature selection method based on L2 energy norm (DFSL) is proposed, which can dynamically select the feature coefficient matrix of WPD based on the difference in the distribution of norm energy, enabling each sub-signal to take adaptive signal reconstruction. Next the coefficient matrices of the optimal feature sub-bands are reconstructed and reorganized to obtain the feature signal graphs. Finally, deep features are extracted from the feature signal graphs by 2D-Convolutional neural network (2D-CNN). Experimental results on a public data platform of a bearing and our laboratory platform of robot grinding show that this method is better than the existing methods under different noise intensities.

eess.SP↗

arXiv2023

Controlled Ion Transport in the Subsurface: A Coupled Advection-Diffusion-Electromigration System

Groundwater pollution poses a significant threat to environmental sustainability during urbanization. Existing remediation methods like pump-and-treat and electrokinetics have limited ion transport control. This study introduces a coupled advection-diffusion-electromigration system for controlled ion transport in the subsurface. Using the Lattice-Boltzmann-Poisson method, we simulate ion transport in various two- and three-dimensional porous media. We establish an ion transport regime classification based on the Peclet number (Pe) and a novel Electrodiffusivity index (EDI). By manipulating the electric potential, hydrostatic pressure, and ion concentration, we identify four transport regimes: large channeling, uniform flow, small channeling, and no flow. Large channeling occurs when advection dominates, while uniform flow arises when diffusion and electromigration are more prevalent. Small channeling happens when the advection opposes electromigration and diffusion, and no flow occurs when the advection or electromigration impedes ion transport via diffusion. Simulations in heterogeneous models confirm these transport regimes, highlighting the influence of pore size variation on transport regimes. Consequently, $Pe$ and $EDI$ must be tailored for optimal transport control. These findings enable better control over ion transport, optimizing processes such as heavy metal removal, bioremediation, and contaminant degradation in groundwater management.

physics.flu-dyn↗

arXiv2023

From Knowledge Distillation to Self-Knowledge Distillation: A Unified Approach with Normalized Loss and Customized Soft Labels

Knowledge Distillation (KD) uses the teacher's prediction logits as soft labels to guide the student, while self-KD does not need a real teacher to require the soft labels. This work unifies the formulations of the two tasks by decomposing and reorganizing the generic KD loss into a Normalized KD (NKD) loss and customized soft labels for both target class (image's category) and non-target classes named Universal Self-Knowledge Distillation (USKD). We decompose the KD loss and find the non-target loss from it forces the student's non-target logits to match the teacher's, but the sum of the two non-target logits is different, preventing them from being identical. NKD normalizes the non-target logits to equalize their sum. It can be generally used for KD and self-KD to better use the soft labels for distillation loss. USKD generates customized soft labels for both target and non-target classes without a teacher. It smooths the target logit of the student as the soft target label and uses the rank of the intermediate feature to generate the soft non-target labels with Zipf's law. For KD with teachers, our NKD achieves state-of-the-art performance on CIFAR-100 and ImageNet datasets, boosting the ImageNet Top-1 accuracy of ResNet18 from 69.90% to 71.96% with a ResNet-34 teacher. For self-KD without teachers, USKD is the first self-KD method that can be effectively applied to both CNN and ViT models with negligible additional time and memory cost, resulting in new state-of-the-art results, such as 1.17% and 0.55% accuracy gains on ImageNet for MobileNet and DeiT-Tiny, respectively. Our codes are available at https://github.com/yzd-v/cls_KD.

cs.CV↗

arXiv2023

Forward Laplacian: A New Computational Framework for Neural Network-based Variational Monte Carlo

Neural network-based variational Monte Carlo (NN-VMC) has emerged as a promising cutting-edge technique of ab initio quantum chemistry. However, the high computational cost of existing approaches hinders their applications in realistic chemistry problems. Here, we report the development of a new NN-VMC method that achieves a remarkable speed-up by more than one order of magnitude, thereby greatly extending the applicability of NN-VMC to larger systems. Our key design is a novel computational framework named Forward Laplacian, which computes the Laplacian associated with neural networks, the bottleneck of NN-VMC, through an efficient forward propagation process. We then demonstrate that Forward Laplacian is not only versatile but also facilitates more developments of acceleration methods across various aspects, including optimization for sparse derivative matrix and efficient neural network design. Empirically, our approach enables NN-VMC to investigate a broader range of atoms, molecules and chemical reactions for the first time, providing valuable references to other ab initio methods. The results demonstrate a great potential in applying deep learning methods to solve general quantum mechanical problems.

physics.comp-ph↗

arXiv2023

Caption Anything: Interactive Image Description with Diverse Multimodal Controls

Controllable image captioning is an emerging multimodal topic that aims to describe the image with natural language following human purpose, $\textit{e.g.}$, looking at the specified regions or telling in a particular text style. State-of-the-art methods are trained on annotated pairs of input controls and output captions. However, the scarcity of such well-annotated multimodal data largely limits their usability and scalability for interactive AI systems. Leveraging unimodal instruction-following foundation models is a promising alternative that benefits from broader sources of data. In this paper, we present Caption AnyThing (CAT), a foundation model augmented image captioning framework supporting a wide range of multimodel controls: 1) visual controls, including points, boxes, and trajectories; 2) language controls, such as sentiment, length, language, and factuality. Powered by Segment Anything Model (SAM) and ChatGPT, we unify the visual and language prompts into a modularized framework, enabling the flexible combination between different controls. Extensive case studies demonstrate the user intention alignment capabilities of our framework, shedding light on effective user interaction modeling in vision-language applications. Our code is publicly available at https://github.com/ttengwang/Caption-Anything.

cs.CV↗

arXiv2023

Intrinsic and tunable quantum anomalous Hall effect and magnetic topological phases in XYBi2Te5

By first-principles calculations, we study the magnetic and topological properties of XYBi2Te5-family (X, Y = Mn, Ni, V, Eu) compounds. The strongly coupled double magnetic atom-layers can significantly enhance the magnetic ordering temperature while keeping the topologically nontrivial properties. Particularly, NiVBi2Te5 is found to be a magnetic Weyl semimetal in bulk and a Chern insulator in thin film with both the Curie temperature (~150 K) and full gap well above 77 K. Ni2Bi2Te5, MnNiBi2Te5, NiVBi2Te5 and NiEuBi2Te5 exhibits intrinsic dynamic axion state. Among them, MnNiBi2Te5 has a Neel temperature over 200 K and Ni2Bi2Te5 even demonstrates antiferromagnetic order above room temperature. These results indicate an approach to realize high temperature quantum anomalous Hall effect and other topological quantum effects for practical applications.

cond-mat.mtrl-sci↗

arXiv2023

PoseVocab: Learning Joint-structured Pose Embeddings for Human Avatar Modeling

Creating pose-driven human avatars is about modeling the mapping from the low-frequency driving pose to high-frequency dynamic human appearances, so an effective pose encoding method that can encode high-fidelity human details is essential to human avatar modeling. To this end, we present PoseVocab, a novel pose encoding method that encourages the network to discover the optimal pose embeddings for learning the dynamic human appearance. Given multi-view RGB videos of a character, PoseVocab constructs key poses and latent embeddings based on the training poses. To achieve pose generalization and temporal consistency, we sample key rotations in $so(3)$ of each joint rather than the global pose vectors, and assign a pose embedding to each sampled key rotation. These joint-structured pose embeddings not only encode the dynamic appearances under different key poses, but also factorize the global pose embedding into joint-structured ones to better learn the appearance variation related to the motion of each joint. To improve the representation ability of the pose embedding while maintaining memory efficiency, we introduce feature lines, a compact yet effective 3D representation, to model more fine-grained details of human appearances. Furthermore, given a query pose and a spatial position, a hierarchical query strategy is introduced to interpolate pose embeddings and acquire the conditional pose feature for dynamic human synthesis. Overall, PoseVocab effectively encodes the dynamic details of human appearance and enables realistic and generalized animation under novel poses. Experiments show that our method outperforms other state-of-the-art baselines both qualitatively and quantitatively in terms of synthesis quality. Code is available at https://github.com/lizhe00/PoseVocab.

cs.CV↗

arXiv2023

Track Anything: Segment Anything Meets Videos

Recently, the Segment Anything Model (SAM) gains lots of attention rapidly due to its impressive segmentation performance on images. Regarding its strong ability on image segmentation and high interactivity with different prompts, we found that it performs poorly on consistent segmentation in videos. Therefore, in this report, we propose Track Anything Model (TAM), which achieves high-performance interactive tracking and segmentation in videos. To be detailed, given a video sequence, only with very little human participation, i.e., several clicks, people can track anything they are interested in, and get satisfactory results in one-pass inference. Without additional training, such an interactive design performs impressively on video object tracking and segmentation. All resources are available on {https://github.com/gaomingqi/Track-Anything}. We hope this work can facilitate related research.

cs.CV↗