arXiv subjects

Yi Liu

Publications and source records attributed to Yi Liu.

At least 397 records · Page 22Linked to original sources

arXiv2024

Shining Light on the Dark Sector: Search for Axion-like Particles and Other New Physics in Photonic Final States with FASER

The first FASER search for a light, long-lived particle decaying into a pair of photons is reported. The search uses LHC proton-proton collision data at $\sqrt{s}=13.6~\text{TeV}$ collected in 2022 and 2023, corresponding to an integrated luminosity of $57.7\text{fb}^{-1}$. A model with axion-like particles (ALPs) dominantly coupled to weak gauge bosons is the primary target. Signal events are characterised by high-energy deposits in the electromagnetic calorimeter and no signal in the veto scintillators. One event is observed, compared to a background expectation of $0.44 \pm 0.39$ events, which is entirely dominated by neutrino interactions. World-leading constraints on ALPs are obtained for masses up to $300~\text{MeV}$ and couplings to the Standard Model W gauge boson, $g_{aWW}$, around $10^{-4}$ GeV$^{-1}$, testing a previously unexplored region of parameter space. Other new particle models that lead to the same experimental signature, including ALPs coupled to gluons or photons, U(1)$_B$ gauge bosons, up-philic scalars, and a Type-I two-Higgs doublet model, are also considered for interpretation, and new constraints on previously viable parameter space are presented in this paper.

hep-ex↗

arXiv2024

A MapReduce Approach to Effectively Utilize Long Context Information in Retrieval Augmented Language Models

While holding great promise for improving and facilitating healthcare, large language models (LLMs) struggle to produce up-to-date responses on evolving topics due to outdated knowledge or hallucination. Retrieval-augmented generation (RAG) is a pivotal innovation that improves the accuracy and relevance of LLM responses by integrating LLMs with a search engine and external sources of knowledge. However, the quality of RAG responses can be largely impacted by the rank and density of key information in the retrieval results, such as the "lost-in-the-middle" problem. In this work, we aim to improve the robustness and reliability of the RAG workflow in the medical domain. Specifically, we propose a map-reduce strategy, BriefContext, to combat the "lost-in-the-middle" issue without modifying the model weights. We demonstrated the advantage of the workflow with various LLM backbones and on multiple QA datasets. This method promises to improve the safety and reliability of LLMs deployed in healthcare domains.

cs.CL↗

arXiv2024

Exploring Enhanced Contextual Information for Video-Level Object Tracking

Contextual information at the video level has become increasingly crucial for visual object tracking. However, existing methods typically use only a few tokens to convey this information, which can lead to information loss and limit their ability to fully capture the context. To address this issue, we propose a new video-level visual object tracking framework called MCITrack. It leverages Mamba's hidden states to continuously record and transmit extensive contextual information throughout the video stream, resulting in more robust object tracking. The core component of MCITrack is the Contextual Information Fusion module, which consists of the mamba layer and the cross-attention layer. The mamba layer stores historical contextual information, while the cross-attention layer integrates this information into the current visual features of each backbone block. This module enhances the model's ability to capture and utilize contextual information at multiple levels through deep integration with the backbone. Experiments demonstrate that MCITrack achieves competitive performance across numerous benchmarks. For instance, it gets 76.6% AUC on LaSOT and 80.0% AO on GOT-10k, establishing a new state-of-the-art performance. Code and models are available at https://github.com/kangben258/MCITrack.

cs.CV↗

arXiv2024

TIGRE v3: Efficient and easy to use iterative computed tomographic reconstruction toolbox for real datasets

Computed Tomography (CT) has been widely adopted in medicine and it is increasingly being used in scientific and industrial applications. Parallelly, research in different mathematical areas concerning discrete inverse problems has led to the development of new sophisticated numerical solvers that can be applied in the context of CT. The Tomographic Iterative GPU-based Reconstruction (TIGRE) toolbox was born almost a decade ago precisely in the gap between mathematics and high performance computing for real CT data, providing user-friendly open-source software tools for image reconstruction. However, since its inception, the tools' features and codebase have had over a twenty-fold increase, and are now including greater geometric flexibility, a variety of modern algorithms for image reconstruction, high-performance computing features and support for other CT modalities, like proton CT. The purpose of this work is two-fold: first, it provides a structured overview of the current version of the TIGRE toolbox, providing appropriate descriptions and references, and serving as a comprehensive and peer-reviewed guide for the user; second, it is an opportunity to illustrate the performance of several of the available solvers showcasing real CT acquisitions, which are typically not be openly available to algorithm developers.

physics.med-ph↗

arXiv2024

MiniScope: Automated UI Exploration and Privacy Inconsistency Detection of MiniApps via Two-phase Iterative Hybrid Analysis

The advent of MiniApps, operating within larger SuperApps, has revolutionized user experiences by offering a wide range of services without the need for individual app downloads. However, this convenience has raised significant privacy concerns, as these MiniApps often require access to sensitive data, potentially leading to privacy violations. Despite existing privacy regulations and platform guidelines, there is a lack of effective mechanisms to safeguard user privacy fully. To address this critical gap, we introduce MiniScope, a novel two-phase hybrid analysis approach, specifically designed for the MiniApp environment. This approach overcomes the limitations of existing static analysis techniques by incorporating UI transition states analysis, cross-package callback control flow resolution, and automated iterative UI exploration. This allows for a comprehensive understanding of MiniApps' privacy practices, addressing the unique challenges of sub-package loading and event-driven callbacks. Our empirical evaluation of over 120K MiniApps using MiniScope demonstrates its effectiveness in identifying privacy inconsistencies. The results reveal significant issues, with 5.7% of MiniApps over-collecting private data and 33.4% overclaiming data collection. We have responsibly disclosed our findings to 2,282 developers, receiving 44 acknowledgments. These findings emphasize the urgent need for more precise privacy monitoring systems and highlight the responsibility of SuperApp operators to enforce stricter privacy measures.

cs.CR↗

arXiv2024

Fast Polypharmacy Side Effect Prediction Using Tensor Factorisation

Motivation: Adverse reactions from drug combinations are increasingly common, making their accurate prediction a crucial challenge in modern medicine. Laboratory-based identification of these reactions is insufficient due to the combinatorial nature of the problem. While many computational approaches have been proposed, tensor factorisation models have shown mixed results, necessitating a thorough investigation of their capabilities when properly optimized. Results: We demonstrate that tensor factorisation models can achieve state-of-the-art performance on polypharmacy side effect prediction, with our best model (SimplE) achieving median scores of 0.978 AUROC, 0.971 AUPRC, and 1.000 AP@50 across 963 side effects. Notably, this model reaches 98.3\% of its maximum performance after just two epochs of training (approximately 4 minutes), making it substantially faster than existing approaches while maintaining comparable accuracy. We also find that incorporating monopharmacy data as self-looping edges in the graph performs marginally better than using it to initialize embeddings. Availability and Implementation: All code used in the experiments is available in our GitHub repository (https://doi.org/10.5281/zenodo.10684402). The implementation was carried out using Python 3.8.12 with PyTorch 1.7.1, accelerated with CUDA 11.4 on NVIDIA GeForce RTX 2080 Ti GPUs. Contact: oliver.lloyd@bristol.ac.uk Supplementary information: Supplementary data, including precision-recall curves and F1 curves for the best performing model, are available at Bioinformatics online.

cs.LG↗

arXiv2024

Experimental electronic phase diagram in a diamond-lattice antiferromagnetic system

We report Ni-doping effect on the magnetic and electronic properties of thiospinel Co$_{1-x}$Ni$_x$[Co$_{0.3}$Ir$_{1.7}$]S$_4$ (0 $\leq x \leq$ 1). The parent compound Co[Co$_{0.3}$Ir$_{1.7}$]S$_4$ exhibits antiferromagnetic order below $T_\mathrm{N} \sim$ 292 K within the $A$-site diamond sublattice, along with a narrow charge-transfer gap. Upon Ni doping, an insulator-to-metal crossover occurs at $x \sim$ 0.35, and the antiferromagnetism is gradually suppressed, with $T_\mathrm{N}$ decreasing to 23 K at $x =$ 0.7. In the metallic state, a spin-glass-like transition emerges at low temperatures. The antiferromagnetic transition is completely suppressed at $x_\mathrm{c} \sim$ 0.95, around which a non-Fermi-liquid behavior emerges, evident from the $T^α$ temperature dependence with $α\approx$ 1.2-1.3 in resistivity and divergent behavior of $C/T$ in specific heat at low temperatures. Meanwhile, the electronic specific heat coefficient $γ$ increases substantially, signifying an enhancement of the quasiparticle effective mass. The magnetic phase diagram has been established, in which an antiferromagnetic quantum critical point is avoided at $x_\mathrm{c}$. Conversely, the observed glass-like tail above the critical concentration aligns more closely with theoretical predictions for an extended region of quantum Griffiths phase in the presence of strong disorder.

cond-mat.str-el↗

arXiv2024

Self and Cross-Model Distillation for LLMs: Effective Methods for Refusal Pattern Alignment

Large Language Models (LLMs) like OpenAI's GPT series, Anthropic's Claude, and Meta's LLaMa have shown remarkable capabilities in text generation. However, their susceptibility to toxic prompts presents significant security challenges. This paper investigates alignment techniques, including Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF), to mitigate these risks. We conduct an empirical study on refusal patterns across nine LLMs, revealing that models with uniform refusal patterns, such as Claude3, exhibit higher security. Based on these findings, we propose self-distilling and cross-model distilling methods to enhance LLM security. Our results show that these methods significantly improve refusal rates and reduce unsafe content, with cross-model distilling achieving refusal rates close to Claude3's 94.51%. These findings underscore the potential of distillation-based alignment in securing LLMs against toxic prompts.

cs.CR↗

arXiv2024

Quantum Griffiths singularity in a three-dimensional superconductor to Anderson critical insulator transition

Disorder is ubiquitous in real materials and can have dramatic effects on quantum phase transitions. Originating from the disorder enhanced quantum fluctuation, quantum Griffiths singularity (QGS) has been revealed as a universal phenomenon in quantum criticality of low-dimensional superconductors. However, due to the weak fluctuation effect, QGS is very challenging to detect experimentally in three-dimensional (3D) superconducting systems. Here we report the discovery of QGS associated with the quantum phase transition from 3D superconductor to Anderson critical insulator in a spinel oxide MgTi2O4 (MTO). Under both perpendicular and parallel magnetic field, the dynamical critical exponent diverges when approaching the quantum critical point, demonstrating the existence of 3D QGS. Among 3D superconductors, MTO shows a relatively strong fluctuation effect featured as a wide superconducting transition region. The enhanced fluctuation, which may arise from the mobility edge of Anderson localization, finally leads to the occurrence of 3D quantum phase transition and QGS. Our findings offer a new perspective to understand quantum phase transitions in strongly disordered 3D systems.

cond-mat.supr-con↗

arXiv2024

A criterion for virtual Euler class one

Let $M$ be an oriented closed hyperbolic $3$--manifold. Suppose that $w$ is a rational second cohomology class of $M$ with dual Thurston norm $1$. Upon the existence of certain nonvanishing Alexander polynomials, the author shows that the pullback of $w$ to some finite cover of $M$ is the real Euler class of some transversely oriented taut foliation on that cover. As application, the author constructs examples with first Betti number either $2$ or $3$, and partial examples with any first Betti number at least $4$, supporting Yazdi's virtual Euler class one conjecture.

math.GT↗

arXiv2024

Underwater Image Enhancement with Cascaded Contrastive Learning

Underwater image enhancement (UIE) is a highly challenging task due to the complexity of underwater environment and the diversity of underwater image degradation. Due to the application of deep learning, current UIE methods have made significant progress. Most of the existing deep learning-based UIE methods follow a single-stage network which cannot effectively address the diverse degradations simultaneously. In this paper, we propose to address this issue by designing a two-stage deep learning framework and taking advantage of cascaded contrastive learning to guide the network training of each stage. The proposed method is called CCL-Net in short. Specifically, the proposed CCL-Net involves two cascaded stages, i.e., a color correction stage tailored to the color deviation issue and a haze removal stage tailored to improve the visibility and contrast of underwater images. To guarantee the underwater image can be progressively enhanced, we also apply contrastive loss as an additional constraint to guide the training of each stage. In the first stage, the raw underwater images are used as negative samples for building the first contrastive loss, ensuring the enhanced results of the first color correction stage are better than the original inputs. While in the second stage, the enhanced results rather than the raw underwater images of the first color correction stage are used as the negative samples for building the second contrastive loss, thus ensuring the final enhanced results of the second haze removal stage are better than the intermediate color corrected results. Extensive experiments on multiple benchmark datasets demonstrate that our CCL-Net can achieve superior performance compared to many state-of-the-art methods. The source code of CCL-Net will be released at https://github.com/lewis081/CCL-Net.

cs.CV↗

arXiv2024

Superconducting Energy Gap Structure of CsV$_3$Sb$_5$ from Magnetic Penetration Depth Measurements

Experimental determination of the structure of the superconducting order parameter in the kagome lattice compound CsV$_3$Sb$_5$ is an essential step towards understanding the nature of the superconducting pairing in this material. Here we report measurements of the temperature dependence of the in-plane magnetic penetration depth, $λ(T)$, in crystals of CsV$_3$Sb$_5$ down to $\sim 60\,\mathrm{mK}$. We find that $λ(T)$ is consistent with a fully-gapped state but with significant gap anisotropy. The magnitude of the gap minima are in the range $\sim 0.2 - 0.3 T_\mathrm{c}$ for the measured samples, markedly smaller than previous estimates. We discuss different forms of potential anisotropy and how these can be linked to the V and Sb Fermi surface sheets. We highlight a significant discrepancy between the calculated and measured values of $λ(T=0)$ which we suggest is caused by spatially suppressed superconductivity.

cond-mat.supr-con↗

arXiv2024

Weak antilocalization in the transition metal telluride Ta$_2$Pd$_3$Te$_5$

We report transport studies on the layered van der Waals topological crystalline insulator Ta$_2$Pd$_3$Te$_5$. The temperature-dependent resistance at high temperature is dominated by a bulk insulating gap and tend to saturate at low temperatures. Low temperature magnetotransport shows that Ta$_2$Pd$_3$Te$_5$ exhibits weak antilocatization (WAL) effect in both perpendicular orientation and parallel orientation, suggesting an contribution of the WAL effect from both topological edge states and bulk states. By measuring the anisotropic magnetoconductance and then subtracting the contribution of bulk states, the WAL effect associated with topological edge states can be revealed and analyzed quantitatively based on the two-dimensional Hikami-Larkin-Nagaoka model. Our results have important implications in understanding the WAL phenomena in Ta$_2$Pd$_3$Te$_5$.

cond-mat.mtrl-sci↗

arXiv2024

LLM-Based Misconfiguration Detection for AWS Serverless Computing

Serverless computing is an emerging cloud computing paradigm that enables developers to build applications at the function level, known as serverless applications. Amazon Web Services (AWS), the leading provider in this domain, provides the Serverless Application Model (AWS SAM), the most widely adopted configuration schema for configuring and managing serverless applications through a specified file. However, misconfigurations pose a significant challenge in serverless development. Traditional data-driven techniques may struggle with serverless applications because the complexity of serverless configurations hinders pattern recognition, and it is challenging to gather complete datasets that cover all possible configurations. Leveraging vast amounts of publicly available data during pre-training, LLMs can have the potential to assist in identifying and explaining misconfigurations in serverless applications. In this paper, we introduce SlsDetector, the first framework leveraging LLMs to detect misconfigurations in serverless applications. SlsDetector utilizes effective prompt engineering with zero-shot learning to identify configuration issues. It designs multi-dimensional constraints specifically tailored to the configuration characteristics of serverless applications and leverages the Chain of Thought technique to enhance LLMs inferences. We evaluate SlsDetector on a curated dataset of 110 configuration files. Our results show that SlsDetector, based on ChatGPT-4o, achieves a precision of 72.88%, recall of 88.18%, and F1-score of 79.75%, outperforming state-of-the-art data-driven approaches by 53.82, 17.40, and 49.72 percentage points, respectively. Furthermore, we investigate the generalization capability of SlsDetector by applying recent LLMs, including Llama 3.1 (405B) Instruct Turbo and Gemini 1.5 Pro, with results showing consistently high effectiveness across these models.

cs.SE↗

arXiv2024

Minions: Accelerating Large Language Model Inference with Aggregated Speculative Execution

Large language models (LLM) have recently attracted surging interest due to their outstanding capabilities across various domains. However, enabling efficient LLM inference is challenging due to its autoregressive decoding that generates tokens only one at a time. Although research works apply pruning or quantization to speed up LLM inference, they typically require fine-tuning the LLM, incurring significant time and economic costs. Meanwhile, speculative decoding has been proposed to use small speculative models (SSMs) to accelerate the inference of LLM. However, the low acceptance rate of SSM and the high verification cost of LLM prohibit further performance improvement of inference. In this paper, we propose Minions, an LLM inference system that accelerates LLM inference with a collective and adaptive speculative generation. Specifically, Minions proposes a majority-voted mechanism to leverage multiple SSMs to jointly speculate the outputs of LLM, which improves the inference performance without introducing prohibitive computation costs for LLM. To better trade off the number of tokens speculated from SSM and the verification cost of LLM, Minions proposes an adaptive mechanism to dynamically determine the optimal speculation length of SSM, which can achieve better inference performance across different models, datasets, and hyper-parameters. In addition, Minions decouples the SSM decoding and LLM verification efficiently and adopts a pipelined execution mechanism to further improve the inference performance of LLM. By comparing with the state-of-the-art LLM inference systems, we demonstrate that Minions can achieve higher inference throughput and lower inference time.

cs.DC↗

arXiv2024

Evaluating Text-to-Image Generative Models: An Empirical Study on Human Image Synthesis

In this paper, we present an empirical study introducing a nuanced evaluation framework for text-to-image (T2I) generative models, applied to human image synthesis. Our framework categorizes evaluations into two distinct groups: first, focusing on image qualities such as aesthetics and realism, and second, examining text conditions through concept coverage and fairness. We introduce an innovative aesthetic score prediction model that assesses the visual appeal of generated images and unveils the first dataset marked with low-quality regions in generated human images to facilitate automatic defect detection. Our exploration into concept coverage probes the model's effectiveness in interpreting and rendering text-based concepts accurately, while our analysis of fairness reveals biases in model outputs, with an emphasis on gender, race, and age. While our study is grounded in human imagery, this dual-faceted approach is designed with the flexibility to be applicable to other forms of image generation, enhancing our understanding of generative models and paving the way to the next generation of more sophisticated, contextually aware, and ethically attuned generative models. Code and data, including the dataset annotated with defective areas, are available at \href{https://github.com/cure-lab/EvaluateAIGC}{https://github.com/cure-lab/EvaluateAIGC}.

cs.CV↗

arXiv2024

Nonconforming virtual element method for general second-order elliptic problems on curved domain

This paper introduces a nonconforming virtual element method for general second-order elliptic problems with variable coefficients on domains with curved boundaries and curved internal interfaces. We prove arbitrary order optimal convergence in the energy and $L^2$ norms, confirmed by numerical experiments on a set of polygonal meshes. The accuracy of the numerical approximation provided by the method is shown to be comparable with the theoretical analysis.

math.NA↗

arXiv2024

Inter-Cation Charge Transfer Mediated Antiferromagnetism in Co$_{1+x}$Ir$_{2-x}$S$_4$

The antiferromagnetism in transition metal compounds is mostly mediated by the bridging anions through a so-called superexchange mechanism. However, in materials like normal spinels $AB_2X_4$ with local moments only at the $A$ site, such an anion-mediated superexchange needs to be modified. Here we report a new spinel compound Co$_{1+x}$Ir$_{2-x}$S$_4$ ($x$ = 0.3). The physical property measurements strongly suggest an antiferromagnetic-like transition at 292 K in the Co($A$) diamond sublattice. The first-principle calculations reveal that the nearest-neighbor Co($A$) spins align antiferromagnetically with an ordered magnetic moment of 1.67 $μ_\mathrm{B}$, smaller than the expected $S = 3/2$ for Co$^{2+}$. In the antiferromagnetic state, there exists an inter-cation charge-transfer gap between the non-bonding Ir-$t_\mathrm{2g}$ orbitals at the valence band maximum and the Co-S antibonding molecular orbitals at the conduction band minimum. The small charge transfer energy significantly enhances the virtual hopping between these two states, facilitating a robust long-range superexchange interaction between two neighboring CoS$_4$ complexes, which accounts for the high Néel temperature in Co$_{1+x}$Ir$_{2-x}$S$_4$. This inter-cation charge transfer mediated magnetic interaction expands the traditional superexchange theory, which could be applicable in complex magnetic materials with multiple cations.

cond-mat.mtrl-sci↗