arXiv subjects

Yan Wang

Publications and source records attributed to Yan Wang.

At least 487 records · Page 27Linked to original sources

arXiv2024

A Novel RFID Authentication Protocol Based on A Block-Order-Modulus Variable Matrix Encryption Algorithm

In this paper, authentication for mobile radio frequency identification (RFID) systems with low-cost RFID sensor tags is studied. Firstly, an adaptive modulus (AM) encryption algorithm is proposed. Subsequently, in order to enhance the security without additional storage of new key matrices, a self-updating encryption order (SUEO) algorithm is designed. Furthermore, a diagonal block local transpose key matrix (DBLTKM) encryption algorithm is presented, which effectively expands the feasible domain of the key space. Based on the above three algorithms, a novel joint AM-SUEO-DBLTKM encryption algorithm is constructed. Making full use of the advantages of the proposed joint algorithm, a two-way RFID authentication protocol, named AM-SUEO-DBLTKM-RFID, is proposed for mobile RFID systems. In addition, the Burrows-Abadi-Needham (BAN) logic and security analysis indicate that the proposed AM-SUEO-DBLTKM-RFID protocol can effectively combat various typical attacks. Numerical results demonstrate that the proposed AM-SUEO-DBLTKM algorithm can save 99.59% of tag storage over traditional algorithms. Finally, the low computational complexity as well as the low storage cost of the proposed AM-SUEO-DBLTKM-RFID protocol facilitates deployment within low-cost RFID sensor tags.

cs.CR↗

arXiv2024

D2SP: Dynamic Dual-Stage Purification Framework for Dual Noise Mitigation in Vision-based Affective Recognition

The contemporary state-of-the-art of Dynamic Facial Expression Recognition (DFER) technology facilitates remarkable progress by deriving emotional mappings of facial expressions from video content, underpinned by training on voluminous datasets. Yet, the DFER datasets encompass a substantial volume of noise data. Noise arises from low-quality captures that defy logical labeling, and instances that suffer from mislabeling due to annotation bias, engendering two principal types of uncertainty: the uncertainty regarding data usability and the uncertainty concerning label reliability. Addressing the two types of uncertainty, we have meticulously crafted a two-stage framework aiming at \textbf{S}eeking \textbf{C}ertain data \textbf{I}n extensive \textbf{U}ncertain data (SCIU). This initiative aims to purge the DFER datasets of these uncertainties, thereby ensuring that only clean, verified data is employed in training processes. To mitigate the issue of low-quality samples, we introduce the Coarse-Grained Pruning (CGP) stage, which assesses sample weights and prunes those deemed unusable due to their low weight. For samples with incorrect annotations, the Fine-Grained Correction (FGC) stage evaluates prediction stability to rectify mislabeled data. Moreover, SCIU is conceived as a universally compatible, plug-and-play framework, tailored to integrate seamlessly with prevailing DFER methodologies. Rigorous experiments across prevalent DFER datasets and against numerous benchmark methods substantiates SCIU's capacity to markedly elevate performance metrics.

cs.CV↗

arXiv2024

AMPCliff: quantitative definition and benchmarking of activity cliffs in antimicrobial peptides

Since the mechanism of action of drug molecules in the human body is difficult to reproduce in the in vitro environment, it becomes difficult to reveal the causes of the activity cliff phenomenon of drug molecules. We found out the AC of small molecules has been extensively investigated but limited knowledge is accumulated about the AC phenomenon in peptides with canonical amino acids. Understanding the mechanism of AC in canonical amino acids might help understand the one in drug molecules. This study introduces a quantitative definition and benchmarking framework AMPCliff for the AC phenomenon in antimicrobial peptides (AMPs) composed by canonical amino acids. A comprehensive analysis of the existing AMP dataset reveals a significant prevalence of AC within AMPs. AMPCliff quantifies the activities of AMPs by the MIC, and defines 0.9 as the minimum threshold for the normalized BLOSUM62 similarity score between a pair of aligned peptides with at least two-fold MIC changes. This study establishes a benchmark dataset of paired AMPs in Staphylococcus aureus from the publicly available AMP dataset GRAMPA, and conducts a rigorous procedure to evaluate various AMP AC prediction models, including nine machine learning, four deep learning algorithms, four masked language models, and four generative language models. Our analysis reveals that these models are capable of detecting AMP AC events and the pre-trained protein language model ESM2 demonstrates superior performance across the evaluations. The predictive performance of AMP activity cliffs remains to be further improved, considering that ESM2 with 33 layers only achieves the Spearman correlation coefficient 0.4669 for the regression task of the MIC values on the benchmark dataset. Source code and additional resources are available at https://www.healthinformaticslab.org/supp/ or https://github.com/Kewei2023/AMPCliff-generation.

q-bio.BM↗

arXiv2024

VILA$^2$: VILA Augmented VILA

While visual language model architectures and training infrastructures advance rapidly, data curation remains under-explored where quantity and quality become a bottleneck. Existing work either crawls extra Internet data with a loose guarantee of quality or distills from black-box proprietary models, e.g., GPT-4V / Gemini that are API frequency and performance bounded. This work enables a VLM to improve itself via data enhancement, exploiting its generative nature. We introduce a simple yet effective VLM augmentation scheme that includes a self-augment step and a specialist-augment step to iteratively improve data quality and hence, model performance. In the self-augment step, the instruction-finetuned VLM recaptions its pretraining caption datasets and then retrains from scratch leveraging refined data. Without any expensive human-in-the-loop annotation, we observe improvements in data quality and downstream accuracy boosts with three self-augmentation rounds -- a viable free lunch to the current VLM training recipe. When self-augmentation saturates, we augment the caption diversity by leveraging specialty skills picked up from instruction finetuning. We finetune VLM specialists from the self-augmented VLM with domain-specific experts, including spatial, grounding, and OCR, to fuse task-aware synthetic data into the pretraining stage. Data quality improvements and hallucination reductions are cross-checked by VLM (GPT-4V, Gemini) and human judges. Combining self-augmentation and specialist-augmented training, VILA$^2$ consistently improves the accuracy on a wide range of benchmarks over the prior art, producing a reusable pretraining dataset that is 300x more cost-efficient than human labeling.

cs.CV↗

arXiv2024

Explainable Behavior Cloning: Teaching Large Language Model Agents through Learning by Demonstration

Autonomous mobile app interaction has become increasingly important with growing complexity of mobile applications. Developing intelligent agents that can effectively navigate and interact with mobile apps remains a significant challenge. In this paper, we propose an Explainable Behavior Cloning LLM Agent (EBC-LLMAgent), a novel approach that combines large language models (LLMs) with behavior cloning by learning demonstrations to create intelligent and explainable agents for autonomous mobile app interaction. EBC-LLMAgent consists of three core modules: Demonstration Encoding, Code Generation, and UI Mapping, which work synergistically to capture user demonstrations, generate executable codes, and establish accurate correspondence between code and UI elements. We introduce the Behavior Cloning Chain Fusion technique to enhance the generalization capabilities of the agent. Extensive experiments on five popular mobile applications from diverse domains demonstrate the superior performance of EBC-LLMAgent, achieving high success rates in task completion, efficient generalization to unseen scenarios, and the generation of meaningful explanations.

cs.CL↗

arXiv2024

Semicoherent Symmetric Quantum Processes: Theory and Applications

Discovering pragmatic and efficient approaches to construct $\varepsilon$-approximations of quantum operators such as real (imaginary) time-evolution propagators in terms of the basic quantum operations (gates) is challenging. Prior $\varepsilon$-approximations are invaluable, in that they enable the compilation of classical and quantum algorithm modeling of, e.g., dynamical and thermodynamic quantum properties. In parallel, symmetries are powerful tools concisely describing the fundamental laws of nature; the symmetric underpinnings of physical laws have consistently provided profound insights and substantially increased predictive power. In this work, we consider the interplay between the $\varepsilon$-approximate processes and the exact symmetries in a semicoherent context -- where measurements occur at each logical clock cycle. We draw inspiration from Pascual Jordan's groundbreaking formulation of nonassociative, but commutative, symmetric algebraic form. Our symmetrized formalism is then applied in various domains such as quantum random walks, real-time evolutions, variational algorithm ansatzes, and efficient entanglement verification. Our work paves the way for a deeper understanding and greater appreciation of how symmetries can be used to control quantum dynamics in settings where coherence is a limited resource.

quant-ph↗

arXiv2024

Study on Theoretical Derivations in CP Violation of Bottom Hadrons' Weak Decays

We investigate the phenomenon of CP violation in the weak decay of bottom hadrons (B mesons) through comprehensive theoretical analysis and mathematical derivations. We analyze the fundamental mechanisms of B meson decay, emphasizing the interplay between CP symmetry breaking and the contributions from the quark level. By examining the role of the weak Hamiltonian, we reveal the significance of both short-range and long-range interactions in the decay process. Our theoretical framework highlights the close relationship between differential decay distributions and CP violation, providing a robust foundation for understanding CP violation phenomena in B meson decays. We discuss the implications of our findings for future research, particularly the necessity of improving the precision of CP violation measurements to validate existing theoretical models and explore potential new physics. Through a combination of mathematical derivations and theoretical insights, this study aims to contribute to a deeper understanding of the matter-antimatter asymmetry problem in particle physics.

physics.gen-ph↗

arXiv2024

In-architecture X-ray assisted C-Br dissociation for on-surface fabrication of diamondoid chains

The fabrication of well-defined, low-dimensional diamondoid-based materials is a promising approach for tailoring diamond properties such as superconductivity. On-surface self-assembly of halogenated diamondoids under ultrahigh vacuum conditions represents an effective strategy in this direction, enabling reactivity exploration and on-surface synthesis approaches. Here we demonstrate through scanning probe microscopy, time-of-flight mass spectrometry and photoelectron spectroscopy, that self-assembled layers of dibromodiamantanes on gold can be debrominated at atomic wavelengths (Al K$α$ at 8.87 $\mathring{A}$ and Mg K$α$ at 9.89 $\mathring{A}$) and low temperatures without affecting their well-defined arrangement. The resulting 'in-architecture' debromination enables the fabrication of diamantane chains from self-assembled precursors in close proximity, which is otherwise inaccessible through annealing on metal surfaces. Our work introduces a novel approach for the fabrication of nanodiamond chains, with significant implications for in-architecture and layer-by-layer synthesis.

cond-mat.mtrl-sci↗

arXiv2024

Label Filling via Mixed Supervision for Medical Image Segmentation from Noisy Annotations

The success of medical image segmentation usually requires a large number of high-quality labels. But since the labeling process is usually affected by the raters' varying skill levels and characteristics, the estimated masks provided by different raters usually suffer from high inter-rater variability. In this paper, we propose a simple yet effective Label Filling framework, termed as LF-Net, predicting the groundtruth segmentation label given only noisy annotations during training. The fundamental idea of label filling is to supervise the segmentation model by a subset of pixels with trustworthy labels, meanwhile filling labels of other pixels by mixed supervision. More concretely, we propose a qualified majority voting strategy, i.e., a threshold voting scheme is designed to model agreement among raters and the majority-voted labels of the selected subset of pixels are regarded as supervision. To fill labels of other pixels, two types of mixed auxiliary supervision are proposed: a soft label learned from intrinsic structures of noisy annotations, and raters' characteristics labels which propagate individual rater's characteristics information. LF-Net has two main advantages. 1) Training with trustworthy pixels incorporates training with confident supervision, guiding the direction of groundtruth label learning. 2) Two types of mixed supervision prevent over-fitting issues when the network is supervised by a subset of pixels, and guarantee high fidelity with the true label. Results on five datasets of diverse imaging modalities show that our LF-Net boosts segmentation accuracy in all datasets compared with state-of-the-art methods, with even a 7% improvement in DSC for MS lesion segmentation.

cs.CV↗

arXiv2024

Instruction-Driven Game Engine: A Poker Case Study

The Instruction-Driven Game Engine (IDGE) project aims to democratize game development by enabling a large language model (LLM) to follow free-form game descriptions and generate game-play processes. The IDGE allows users to create games simply by natural language instructions, which significantly lowers the barrier for game development. We approach the learning process for IDGEs as a Next State Prediction task, wherein the model autoregressively predicts the game states given player actions. The computation of game states must be precise; otherwise, slight errors could corrupt the game-play experience. This is challenging because of the gap between stability and diversity. To address this, we train the IDGE in a curriculum manner that progressively increases its exposure to complex scenarios. Our initial progress lies in developing an IDGE for Poker, which not only supports a wide range of poker variants but also allows for highly individualized new poker games through natural language inputs. This work lays the groundwork for future advancements in transforming how games are created and played.

cs.AI↗

arXiv2024

Data stewardship: case studies from North-American, Dutch, and Finnish universities

Purpose - As national legislation, federated national services, institutional policies and institutional research service arrangements may differ, data stewardship programs may be organized differently in higher education institutions across the world. This work seeks to elaborate the picture of different data stewardship programs running in different institutional and national research environments. Design/methodology/approach - Utilizing a case study design, this study described three distinct data stewardship programs from Purdue University (United States), Delft Technical University (Netherlands) and Aalto University (Finland). In addition, this work investigated the institutional and national research environments of the programs. The focus was on initiatives led by academic libraries or similar services.Findings - This work demonstrates that data stewardship programs may be organized differently within varying national and institutional contexts. The data stewardship programs varied in terms of roles, organization and funding structures. Furthermore, policies and legislation, organizational structures, and national infrastructures differed. Originality - This work broadens the current literature on data stewardship by not only providing detailed descriptions of three distinct data stewardship programs, but also highlighting how research environments may affect their organization. We present a summary of key factors in the organization of data stewardship programs. Research limitations/implications - The data stewardship programs and their contexts develop, and the descriptions presented in this work should be considered as snapshots.

cs.DL↗

arXiv2024

CrossQuant: A Post-Training Quantization Method with Smaller Quantization Kernel for Precise Large Language Model Compression

Post-Training Quantization (PTQ) is an effective technique for compressing Large Language Models (LLMs). While many studies focus on quantizing both weights and activations, it is still a challenge to maintain the accuracy of LLM after activating quantization. To investigate the primary cause, we extend the concept of kernel from linear algebra to quantization functions to define a new term, "quantization kernel", which refers to the set of elements in activations that are quantized to zero. Through quantitative analysis of the quantization kernel, we find that these elements are crucial for maintaining the accuracy of quantized LLMs. With the decrease of quantization kernel, the precision of quantized LLMs increases. If the quantization kernel proportion is kept below 19% for OPT models and below 1% for LLaMA models, the precision loss from quantizing activations to INT8 becomes negligible. Motivated by the goal of developing a quantization method with small quantization kernel, we propose CrossQuant: a simple yet effective method for quantizing activations. CrossQuant cross-quantizes elements using row and column-wise absolute maximum vectors, achieving a quantization kernel of approximately 16% for OPT models and less than 0.1% for LLaMA models. Experimental results on LLMs (LLaMA, OPT) ranging from 6.7B to 70B parameters demonstrate that CrossQuant improves or maintains perplexity and accuracy in language modeling, zero-shot, and few-shot tasks.

cs.LG↗

arXiv2024

FDDM: Frequency-Decomposed Diffusion Model for Rectum Cancer Dose Prediction in Radiotherapy

Accurate dose distribution prediction is crucial in the radiotherapy planning. Although previous methods based on convolutional neural network have shown promising performance, they have the problem of over-smoothing, leading to prediction without important high-frequency details. Recently, diffusion model has achieved great success in computer vision, which excels in generating images with more high-frequency details, yet suffers from time-consuming and extensive computational resource consumption. To alleviate these problems, we propose Frequency-Decomposed Diffusion Model (FDDM) that refines the high-frequency subbands of the dose map. To be specific, we design a Coarse Dose Prediction Module (CDPM) to first predict a coarse dose map and then utilize discrete wavelet transform to decompose the coarse dose map into a low-frequency subband and three high-frequency subbands. There is a notable difference between the coarse predicted results and ground truth in high-frequency subbands. Therefore, we design a diffusion-based module called High-Frequency Refinement Module (HFRM) that performs diffusion operation in the high-frequency components of the dose map instead of the original dose map. Extensive experiments on an in-house dataset verify the effectiveness of our approach.

eess.IV↗

arXiv2024

Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate

Modern large language models (LLMs) like ChatGPT have shown remarkable performance on general language tasks but still struggle on complex reasoning tasks, which drives the research on cognitive behaviors of LLMs to explore human-like problem-solving strategies. Along this direction, one representative strategy is self-reflection, which asks an LLM to refine the solution with the feedback generated by itself iteratively. However, our study shows that such reflection-style methods suffer from the Degeneration-of-Thought (DoT) problem: once the LLM has established confidence in its solutions, it is unable to generate novel thoughts later through reflection even if its initial stance is incorrect. To address the DoT problem, we propose a Multi-Agent Debate (MAD) framework, in which multiple agents express their arguments in the state of "tit for tat" and a judge manages the debate process to obtain a final solution. Clearly, our MAD framework encourages divergent thinking in LLMs which would be helpful for tasks that require deep levels of contemplation. Experiment results on two challenging datasets, commonsense machine translation and counter-intuitive arithmetic reasoning, demonstrate the effectiveness of our MAD framework. Extensive analyses suggest that the adaptive break of debate and the modest level of "tit for tat" state are required for MAD to obtain good performance. Moreover, we find that LLMs might not be a fair judge if different LLMs are used for agents. Code is available at https://github.com/Skytliang/Multi-Agents-Debate.

cs.CL↗

arXiv2024

Challenges and Possible Strategies to Address Them in Rare Disease Drug Development: A Statistical Perspective

Developing drugs for rare diseases presents unique challenges from a statistical perspective. These challenges may include slowly progressive diseases with unmet medical needs, poorly understood natural history, small population size, diversified phenotypes and geneotypes within a disorder, and lack of appropriate surrogate endpoints to measure clinical benefits. The Real-World Evidence (RWE) Scientific Working Group of the American Statistical Association Biopharmaceutical Section has assembled a research team to assess the landscape including challenges and possible strategies to address these challenges and the role of real-world data (RWD) and RWE in rare disease drug development. This paper first reviews the current regulations by regulatory agencies worldwide and then discusses in more details the challenges from a statistical perspective in the design, conduct, and analysis of rare disease clinical trials. After outlining an overall development pathway for rare disease drugs, corresponding strategies to address the aforementioned challenges are presented. Other considerations are also discussed for generating relevant evidence for regulatory decision-making on drugs for rare diseases. The accompanying paper discusses how RWD and RWE can be used to improve the efficiency of rare disease drug development.

stat.AP↗

arXiv2024

Use of Real-World Data and Real-World Evidence in Rare Disease Drug Development: A Statistical Perspective

Real-world data (RWD) and real-world evidence (RWE) have been increasingly used in medical product development and regulatory decision-making, especially for rare diseases. After outlining the challenges and possible strategies to address the challenges in rare disease drug development (see the accompanying paper), the Real-World Evidence (RWE) Scientific Working Group of the American Statistical Association Biopharmaceutical Section reviews the roles of RWD and RWE in clinical trials for drugs treating rare diseases. This paper summarizes relevant guidance documents and frameworks by selected regulatory agencies and the current practice on the use of RWD and RWE in natural history studies and the design, conduct, and analysis of rare disease clinical trials. A targeted learning roadmap for rare disease trials is described, followed by case studies on the use of RWD and RWE to support a natural history study and marketing applications in various settings.

stat.AP↗

arXiv2024

Performance Analysis of Local Partial MMSE Precoding Based User-Centric Cell-Free Massive MIMO Systems and Deployment Optimization

Cell-free massive multiple-input multiple-output (MIMO) systems, leveraging tight cooperation among wireless access points, exhibit remarkable signal enhancement and interference suppression capabilities, demonstrating significant performance advantages over traditional cellular networks. This paper investigates the performance and deployment optimization of a user-centric scalable cell-free massive MIMO system with imperfect channel information over correlated Rayleigh fading channels. Based on the large-dimensional random matrix theory, this paper presents the deterministic equivalent of the ergodic sum rate for this system when applying the local partial minimum mean square error (LP-MMSE) precoding method, along with its derivative with respect to the channel correlation matrix. Furthermore, utilizing the derivative of the ergodic sum rate, this paper designs a Barzilai-Borwein based gradient descent method to improve system deployment. Simulation experiments demonstrate that under various parameter settings and large-scale antenna configurations, the deterministic equivalent of the ergodic sum rate accurately approximates the Monte Carlo ergodic sum rate of the system. Furthermore, the deployment optimization algorithm effectively enhances the ergodic sum rate of this system by optimizing the positions of access points.

eess.SP↗

arXiv2024

Observing tight triple uncertainty relations in two-qubit systems

As the fundamental tool in quantum information science, the uncertainty principle is essential for manifesting nonclassical properties of quantum systems. Plenty of efforts on the uncertainty principle with two observables have been achieved, making it an appealing challenge to extend the scenario to multiple observables. Here, based on an optical setup, we demonstrate the uncertainty relations in two-qubit systems involving three physical components with the tight constant $2/\sqrt{3}$, which signifies a more precise limit in the measurement of multiple quantum components and offers deeper insights into the trade-offs between observables. Furthermore, we reveal the correspondence of the maximal values of the uncertainty functions and the degree of entanglement, where the more uncertainty is proportional to the higher degree of entanglement. Our results provide a new insight into understanding the uncertainty relations with multiple observables and may motivate more innovative applications in quantum information science.

quant-ph↗