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Hui Liu

Publications and source records attributed to Hui Liu.

At least 343 records · Page 19Linked to original sources
arXiv2024

Code-Based English Models Surprising Performance on Chinese QA Pair Extraction Task

In previous studies, code-based models have consistently outperformed text-based models in reasoning-intensive scenarios. When generating our knowledge base for Retrieval-Augmented Generation (RAG), we observed that code-based models also perform exceptionally well in Chinese QA Pair Extraction task. Further, our experiments and the metrics we designed discovered that code-based models containing a certain amount of Chinese data achieve even better performance. Additionally, the capabilities of code-based English models in specified Chinese tasks offer a distinct perspective for discussion on the philosophical "Chinese Room" thought experiment.

cs.CL↗
arXiv2024

Bug Priority Change: An Empirical Study on Apache Projects

In issue tracking systems, each bug is assigned a priority level (e.g., Blocker, Critical, Major, Minor, or Trivial in JIRA from highest to lowest), which indicates the urgency level of the bug. In this sense, understanding bug priority changes helps to arrange the work schedule of participants reasonably, and facilitates a better analysis and resolution of bugs. According to the data extracted from JIRA deployed by Apache, a proportion of bugs in each project underwent priority changes after such bugs were reported, which brings uncertainty to the bug fixing process. However, there is a lack of indepth investigation on the phenomenon of bug priority changes, which may negatively impact the bug fixing process. Thus, we conducted a quantitative empirical study on bugs with priority changes through analyzing 32 non-trivial Apache open source software projects. The results show that: (1) 8.3% of the bugs in the selected projects underwent priority changes; (2) the median priority change time interval is merely a few days for most (28 out of 32) projects, and half (50. 7%) of bug priority changes occurred before bugs were handled; (3) for all selected projects, 87.9% of the bugs with priority changes underwent only one priority change, most priority changes tend to shift the priority to its adjacent priority, and a higher priority has a greater probability to undergo priority change; (4) bugs that require bug-fixing changes of higher complexity or that have more comments are likely to undergo priority changes; and (5) priorities of bugs reported or allocated by a few specific participants are more likely to be modified, and maximally only one participant in each project tends to modify priorities.

cs.SE↗
arXiv2024

The Reflection Coefficient of a Reflectionless Kink

Classically, reflectionless kinks transmit all incident radiation. Recently, we have used an analyticity argument together with a solution of the Lippmann-Schwinger equation to write down the leading quantum correction to the reflection probability. The argument was fast but rather indirect. In the present paper, we calculate the reflection coefficient and probability by methodically grinding through the Schrodinger picture time evolution. We find the same answer. This answer contains contributions not considered in the traditional calculation of meson-kink scattering in 1991. However, as a result of these contributions, our total result is zero in the case of the Sine-Gordon model, and so is consistent with integrability.

hep-th↗
arXiv2024

Improving the JPEG-resistance of Adversarial Attacks on Face Recognition by Interpolation Smoothing

JPEG compression can significantly impair the performance of adversarial face examples, which previous adversarial attacks on face recognition (FR) have not adequately addressed. Considering this challenge, we propose a novel adversarial attack on FR that aims to improve the resistance of adversarial examples against JPEG compression. Specifically, during the iterative process of generating adversarial face examples, we interpolate the adversarial face examples into a smaller size. Then we utilize these interpolated adversarial face examples to create the adversarial examples in the next iteration. Subsequently, we restore the adversarial face examples to their original size by interpolating. Throughout the entire process, our proposed method can smooth the adversarial perturbations, effectively mitigating the presence of high-frequency signals in the crafted adversarial face examples that are typically eliminated by JPEG compression. Our experimental results demonstrate the effectiveness of our proposed method in improving the JPEG-resistance of adversarial face examples.

cs.CV↗
arXiv2024

Label-free Node Classification on Graphs with Large Language Models (LLMS)

In recent years, there have been remarkable advancements in node classification achieved by Graph Neural Networks (GNNs). However, they necessitate abundant high-quality labels to ensure promising performance. In contrast, Large Language Models (LLMs) exhibit impressive zero-shot proficiency on text-attributed graphs. Yet, they face challenges in efficiently processing structural data and suffer from high inference costs. In light of these observations, this work introduces a label-free node classification on graphs with LLMs pipeline, LLM-GNN. It amalgamates the strengths of both GNNs and LLMs while mitigating their limitations. Specifically, LLMs are leveraged to annotate a small portion of nodes and then GNNs are trained on LLMs' annotations to make predictions for the remaining large portion of nodes. The implementation of LLM-GNN faces a unique challenge: how can we actively select nodes for LLMs to annotate and consequently enhance the GNN training? How can we leverage LLMs to obtain annotations of high quality, representativeness, and diversity, thereby enhancing GNN performance with less cost? To tackle this challenge, we develop an annotation quality heuristic and leverage the confidence scores derived from LLMs to advanced node selection. Comprehensive experimental results validate the effectiveness of LLM-GNN. In particular, LLM-GNN can achieve an accuracy of 74.9% on a vast-scale dataset \products with a cost less than 1 dollar.

cs.LG↗
arXiv2024

Generalized common index jump theorem with applications to closed characteristics on star-shaped hypersurfaces and beyond

In this paper, we first generalize the common index jump theorem of Long-Zhu in 2002 and Duan-Long-Wang in 2016 to the case where the mean indices of symplectic paths are not required to be all positive. As applications, we study closed characteristics on compact star-shaped hypersurfaces in ${\bf R}^{2n}$, when both positive and negative mean indices may appear simultaneously. Specially we establish the existence of at least $n$ geometrically distinct closed characteristics on every compact non-degenerate perfect star-shaped hypersurface $Σ$ in ${\bf R}^{2n}$ provided that every prime closed characteristic possesses nonzero mean index. Furthermore, in the case of ${\bf R}^6$ we remove the nonzero mean index condition by showing that the existence of only finitely many geometrically distinct closed characteristics implies that each of them must possess nonzero mean index. We also generalize the above results about closed characteristics on non-degenerate star-shaped hypersurfaces to closed Reeb orbits of non-degenerate contact forms on a broad class of prequantization bundles.

math.DS↗
arXiv2024

Are Large Language Models (LLMs) Good Social Predictors?

The prediction has served as a crucial scientific method in modern social studies. With the recent advancement of Large Language Models (LLMs), efforts have been made to leverage LLMs to predict the human features in social life, such as presidential voting. These works suggest that LLMs are capable of generating human-like responses. However, we find that the promising performance achieved by previous studies is because of the existence of input shortcut features to the response. In fact, by removing these shortcuts, the performance is reduced dramatically. To further revisit the ability of LLMs, we introduce a novel social prediction task, Soc-PRF Prediction, which utilizes general features as input and simulates real-world social study settings. With the comprehensive investigations on various LLMs, we reveal that LLMs cannot work as expected on social prediction when given general input features without shortcuts. We further investigate possible reasons for this phenomenon that suggest potential ways to enhance LLMs for social prediction.

cs.CY↗
arXiv2024

Single Cells Are Spatial Tokens: Transformers for Spatial Transcriptomic Data Imputation

Spatially resolved transcriptomics brings exciting breakthroughs to single-cell analysis by providing physical locations along with gene expression. However, as a cost of the extremely high spatial resolution, the cellular level spatial transcriptomic data suffer significantly from missing values. While a standard solution is to perform imputation on the missing values, most existing methods either overlook spatial information or only incorporate localized spatial context without the ability to capture long-range spatial information. Using multi-head self-attention mechanisms and positional encoding, transformer models can readily grasp the relationship between tokens and encode location information. In this paper, by treating single cells as spatial tokens, we study how to leverage transformers to facilitate spatial tanscriptomics imputation. In particular, investigate the following two key questions: (1) $\textit{how to encode spatial information of cells in transformers}$, and (2) $\textit{ how to train a transformer for transcriptomic imputation}$. By answering these two questions, we present a transformer-based imputation framework, SpaFormer, for cellular-level spatial transcriptomic data. Extensive experiments demonstrate that SpaFormer outperforms existing state-of-the-art imputation algorithms on three large-scale datasets while maintaining superior computational efficiency.

q-bio.GN↗
arXiv2024

Rethinking Large Language Model Architectures for Sequential Recommendations

Recently, sequential recommendation has been adapted to the LLM paradigm to enjoy the power of LLMs. LLM-based methods usually formulate recommendation information into natural language and the model is trained to predict the next item in an auto-regressive manner. Despite their notable success, the substantial computational overhead of inference poses a significant obstacle to their real-world applicability. In this work, we endeavor to streamline existing LLM-based recommendation models and propose a simple yet highly effective model Lite-LLM4Rec. The primary goal of Lite-LLM4Rec is to achieve efficient inference for the sequential recommendation task. Lite-LLM4Rec circumvents the beam search decoding by using a straight item projection head for ranking scores generation. This design stems from our empirical observation that beam search decoding is ultimately unnecessary for sequential recommendations. Additionally, Lite-LLM4Rec introduces a hierarchical LLM structure tailored to efficiently handle the extensive contextual information associated with items, thereby reducing computational overhead while enjoying the capabilities of LLMs. Experiments on three publicly available datasets corroborate the effectiveness of Lite-LLM4Rec in both performance and inference efficiency (notably 46.8% performance improvement and 97.28% efficiency improvement on ML-1m) over existing LLM-based methods. Our implementations will be open sourced.

cs.IR↗
arXiv2024

A conformal mapping approach to broadband nonlinear optics on chip

Integrated nonlinear optical devices play an important role in modern optical communications. However, conventional on-chip optical devices with homogeneous or periodic translation dimensions generally have limited bandwidth when applied to nonlinear optical applications. Up today, there lacks a general method to design compact nonlinear optical devices over a broadband continuous frequency range. In this work, we propose a general strategy based on transformation optics (TO) to design curved accelerating waveguides (CAWs) with spatially gradient curvatures able to achieve broadband nonlinear frequency conversion on chip. Through rigorous analytical calculation, we show that increasing the acceleration (i.e. gradient in the waveguide curvature) broadens the output signal spectrum in the nonlinear process. In the experiment, we take the sum-frequency generation for infrared signal upconversion (SFG-ISU) as the example and fabricated a variety of CAWs using thin-film lithium niobate on insulator (LNOI). Efficient SFG is observed over a broadband continuous spectrum. Our conformal mapping approach offers a platform for various nonlinear optical processes and works in any frequency range, including visible, infrared and terahertz bands. Apart from LNOI, our approach is also compatible with other nonlinear materials, such as silicon, silicon nitride and chalcogenide glasses etc.

physics.optics↗
arXiv2024

Neutral pion masses within a hot and magnetized medium in a lattice-improved soft-wall AdS/QCD model

We investigate chiral phase transitions and the screening masses, pole masses, and thermal widths of neutral pion meson with finite temperature $T$ and magnetic field $B$ in a lattice-improved AdS/QCD model, which is constructed by fitting the lattice results of the pseudo-critical temperatures ( T_{\text{pc}}(B) ). Specifically, we have that the chiral condensate (σ) undergoes a crossover phase transition demonstrating distinct magnetic catalysis and inverse magnetic catalysis effects in very low and high-temperature regions with fixed finite $B$, respectively. For the screening masses, we find that the longitudinal component decreases with $B$ at very low and high temperatures and increases with $B$ near $T_{\text{pc}}$. The transverse component always increases with $B$ at fixed $T$. However, both the longitudinal and transverse screening masses increase with $T$ at fixed $B$. Furthermore, we find that the pole mass decreases with the increasing of $B$ or $T$. Besides, it is interesting to note that the thermal width shows similar behavior to the longitudinal screening masses in the very high temperature region.

hep-th↗
arXiv2024

Ultralight scalar and axion dark matter detection with atom interferometers

The detection of dark matter is a challenging problem in modern physics. The ultralight scalar and axion dark matter could induce the oscillation of the nuclear charge radii and then oscillate the atomic transition frequency by interacting with standard model particles. We compute the differential phase shift caused by the scalar and axion dark matter in a pair of separated atom interferometers and give the proposed constraints on the scalar dark matter coupling parameters $d_g$ and $d_{\hat{m}}$ as well as the axion dark matter coupling parameter $1/f_a$. Our results are expected to improve the current detection level and complement with other experiments.

hep-ph↗
arXiv2024

How Are Paid and Volunteer Open Source Developers Different? A Study of the Rust Project

It is now commonplace for organizations to pay developers to work on specific open source software (OSS) projects to pursue their business goals. Such paid developers work alongside voluntary contributors, but given the different motivations of these two groups of developers, conflict may arise, which may pose a threat to a project's sustainability. This paper presents an empirical study of paid developers and volunteers in Rust, a popular open source programming language project. Rust is a particularly interesting case given considerable concerns about corporate participation. We compare volunteers and paid developers through contribution characteristics and long-term participation, and solicit volunteers' perceptions on paid developers. We find that core paid developers tend to contribute more frequently; commits contributed by one-time paid developers have bigger sizes; peripheral paid developers implement more features; and being paid plays a positive role in becoming a long-term contributor. We also find that volunteers do have some prejudices against paid developers. This study suggests that the dichotomous view of paid vs. volunteer developers is too simplistic and that further subgroups can be identified. Companies should become more sensitive to how they engage with OSS communities, in certain ways as suggested by this study.

cs.SE↗
arXiv2024

Controlling thermal emission with metasurfaces and its applications

Thermal emission caused by the thermal motion of the charged particles is commonly broadband, un-polarized, and incoherent, like a melting pot of electromagnetic waves, which makes it unsuitable for infrared applications in many cases requiring specific thermal emission properties. Metasurfaces, characterized by two-dimensional subwavelength artificial nanostructures, have been extensively investigated for their flexibility in tuning optical properties, which provide an ideal platform for shaping thermal emission. Recently, remarkable progress was achieved not only in tuning thermal emission in multiple degrees of freedom, such as wavelength, polarization, radiation angle, coherence, and so on but also in applications of compact and integrated optical devices. Here, we review the recent advances in the regulation of thermal emission through metasurfaces and corresponding infrared applications, such as infrared sensing, radiative cooling, and thermophotovoltaic devices.

physics.optics↗
arXiv2024

Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs

Learning on Graphs has attracted immense attention due to its wide real-world applications. The most popular pipeline for learning on graphs with textual node attributes primarily relies on Graph Neural Networks (GNNs), and utilizes shallow text embedding as initial node representations, which has limitations in general knowledge and profound semantic understanding. In recent years, Large Language Models (LLMs) have been proven to possess extensive common knowledge and powerful semantic comprehension abilities that have revolutionized existing workflows to handle text data. In this paper, we aim to explore the potential of LLMs in graph machine learning, especially the node classification task, and investigate two possible pipelines: LLMs-as-Enhancers and LLMs-as-Predictors. The former leverages LLMs to enhance nodes' text attributes with their massive knowledge and then generate predictions through GNNs. The latter attempts to directly employ LLMs as standalone predictors. We conduct comprehensive and systematical studies on these two pipelines under various settings. From comprehensive empirical results, we make original observations and find new insights that open new possibilities and suggest promising directions to leverage LLMs for learning on graphs. Our codes and datasets are available at https://github.com/CurryTang/Graph-LLM.

cs.LG↗
arXiv2023

Applications of Gorenstein projective $τ$-rigid modules

We first introduce the notion of $CM$-$τ$-tilting free algebras as the generalization of $CM$-free algebras and show the homological properties of $CM$-$τ$-tilting free algebras. Then we give a bijection between Gorenstein projective $τ$-rigid modules and certain modules by using an equivalence established by Kong and Zhang. Finally, we give a partial answer to Tachikawa's first conjecture by using Gorenstein projective $τ$-rigid modules.

math.RT↗
arXiv2023

T-SciQ: Teaching Multimodal Chain-of-Thought Reasoning via Mixed Large Language Model Signals for Science Question Answering

Large Language Models (LLMs) have recently demonstrated exceptional performance in various Natural Language Processing (NLP) tasks. They have also shown the ability to perform chain-of-thought (CoT) reasoning to solve complex problems. Recent studies have explored CoT reasoning in complex multimodal scenarios, such as the science question answering task, by fine-tuning multimodal models with high-quality human-annotated CoT rationales. However, collecting high-quality COT rationales is usually time-consuming and costly. Besides, the annotated rationales are hardly accurate due to the external essential information missed. To address these issues, we propose a novel method termed T-SciQ that aims at teaching science question answering with LLM signals. The T-SciQ approach generates high-quality CoT rationales as teaching signals and is advanced to train much smaller models to perform CoT reasoning in complex modalities. Additionally, we introduce a novel data mixing strategy to produce more effective teaching data samples for simple and complex science question answer problems. Extensive experimental results show that our T-SciQ method achieves a new state-of-the-art performance on the ScienceQA benchmark, with an accuracy of 96.18%. Moreover, our approach outperforms the most powerful fine-tuned baseline by 4.5%. The code is publicly available at https://github.com/T-SciQ/T-SciQ.

cs.CL↗