arXiv subjects

Cong Liu

Publications and source records attributed to Cong Liu.

At least 109 records · Page 6Linked to original sources

arXiv2024

Tele-Correlation: Calibrating Shear-Shear Correlation with Real Data

Tele-correlation refers to the correlation of galaxy shapes with large angular separations (e.g., $>100$ degrees). Since there are no astrophysical reasons causing such a correlation on cosmological scales, any detected tele-correlation could disclose systematic effects in shear-shear correlation measurement. If the shear estimators are measured on single exposures, we show that the field distortion (FD) signal associated with the galaxy position on the CCD can be retained and used in tele-correlation to help us directly calibrate the multiplicative and additive biases in shear-shear correlations. We use the DECaLS shear catalog produced by the Fourier\_Quad pipeline to demonstrate this idea. To our surprise, we find that significant multiplicative biases can arise (up to more than 10\%) due to redshift binning of the galaxies. Correction for this bias leads to about 1$σ$ increase of the best-fit value of $S_8$ from $0.760^{+0.015}_{-0.017}$ to $0.777^{+0.016}_{-0.019}$ in our tomography study.

astro-ph.CO↗

arXiv2024

GCAPS: GPU Context-Aware Preemptive Priority-based Scheduling for Real-Time Tasks

Scheduling real-time tasks that utilize GPUs with analyzable guarantees poses a significant challenge due to the intricate interaction between CPU and GPU resources, as well as the complex GPU hardware and software stack. While much research has been conducted in the real-time research community, several limitations persist, including the absence or limited availability of GPU-level preemption, extended blocking times, and/or the need for extensive modifications to program code. In this paper, we propose GCAPS, a GPU Context-Aware Preemptive Scheduling approach for real-time GPU tasks. Our approach exerts control over GPU context scheduling at the device driver level and enables preemption of GPU execution based on task priorities by simply adding one-line macros to GPU segment boundaries. In addition, we provide a comprehensive response time analysis of GPU-using tasks for both our proposed approach as well as the default Nvidia GPU driver scheduling that follows a work-conserving round-robin policy. Through empirical evaluations and case studies, we demonstrate the effectiveness of the proposed approaches in improving taskset schedulability and response time. The results highlight significant improvements over prior work as well as the default scheduling approach, with up to 40% higher schedulability, while also achieving predictable worst-case behavior on Nvidia Jetson embedded platforms.

cs.DC↗

arXiv2024

Safety Alignment in NLP Tasks: Weakly Aligned Summarization as an In-Context Attack

Recent developments in balancing the usefulness and safety of Large Language Models (LLMs) have raised a critical question: Are mainstream NLP tasks adequately aligned with safety consideration? Our study, focusing on safety-sensitive documents obtained through adversarial attacks, reveals significant disparities in the safety alignment of various NLP tasks. For instance, LLMs can effectively summarize malicious long documents but often refuse to translate them. This discrepancy highlights a previously unidentified vulnerability: attacks exploiting tasks with weaker safety alignment, like summarization, can potentially compromise the integrity of tasks traditionally deemed more robust, such as translation and question-answering (QA). Moreover, the concurrent use of multiple NLP tasks with lesser safety alignment increases the risk of LLMs inadvertently processing harmful content. We demonstrate these vulnerabilities in various safety-aligned LLMs, particularly Llama2 models, Gemini and GPT-4, indicating an urgent need for strengthening safety alignments across a broad spectrum of NLP tasks.

cs.CL↗

arXiv2024

Distantly-Supervised Joint Extraction with Noise-Robust Learning

Joint entity and relation extraction is a process that identifies entity pairs and their relations using a single model. We focus on the problem of joint extraction in distantly-labeled data, whose labels are generated by aligning entity mentions with the corresponding entity and relation tags using a knowledge base (KB). One key challenge is the presence of noisy labels arising from both incorrect entity and relation annotations, which significantly impairs the quality of supervised learning. Existing approaches, either considering only one source of noise or making decisions using external knowledge, cannot well-utilize significant information in the training data. We propose DENRL, a generalizable framework that 1) incorporates a lightweight transformer backbone into a sequence labeling scheme for joint tagging, and 2) employs a noise-robust framework that regularizes the tagging model with significant relation patterns and entity-relation dependencies, then iteratively self-adapts to instances with less noise from both sources. Surprisingly, experiments on two benchmark datasets show that DENRL, using merely its own parametric distribution and simple data-driven heuristics, outperforms large language model-based baselines by a large margin with better interpretability.

cs.CL↗

arXiv2024

A Hierarchical PSF Reconstruction Method

Reconstruction of the point spread function (PSF) plays an important role in many areas of astronomy, including photometry, astrometry, galaxy morphology, and shear measurement. The atmospheric and instrumental effects are the two main contributors to the PSF, both of which may exhibit complex spatial features. Current PSF reconstruction schemes typically rely on individual exposures, and its ability of reproducing the complicated features of the PSF distribution is therefore limited by the number of stars. Interestingly, in conventional methods, after stacking the model residuals of the PSF ellipticities and (relative) sizes from a large number of exposures, one can often observe some stable and nontrivial spatial patterns on the entire focal plane, which could be quite detrimental to, e.g., weak lensing measurements. These PSF residual patterns are caused by instrumental effects as they consistently appear in different exposures. Taking this as an advantage, we propose a multi-layer PSF reconstruction method to remove such PSF residuals, the second and third layers of which make use of all available exposures together. We test our method on the i-band data of the second release of Hyper Suprime-Cam. Our method successfully eliminates most of the PSF residuals. Using the Fourier\_Quad shear measurement method, we further test the performance of the resulting PSF fields on shear recovery using the field distortion effect. The PSF residuals have strong correlations with the shear residuals, and our new multi-layer PSF reconstruction method can remove most of such systematic errors related to PSF, leading to much smaller shear biases.

astro-ph.IM↗

arXiv2024

Assessing the Utility of Large Language Models for Phenotype-Driven Gene Prioritization in Rare Genetic Disorder Diagnosis

Phenotype-driven gene prioritization is a critical process in the diagnosis of rare genetic disorders for identifying and ranking potential disease-causing genes based on observed physical traits or phenotypes. While traditional approaches rely on curated knowledge graphs with phenotype-gene relations, recent advancements in large language models have opened doors to the potential of AI predictions through extensive training on diverse corpora and complex models. This study conducted a comprehensive evaluation of five large language models, including two Generative Pre-trained Transformers series, and three Llama2 series, assessing their performance across three key metrics: task completeness, gene prediction accuracy, and adherence to required output structures. Various experiments explored combinations of models, prompts, input types, and task difficulty levels. Our findings reveal that even the best-performing LLM, GPT-4, achieved an accuracy of 16.0%, which still lags behind traditional bioinformatics tools. Prediction accuracy increased with the parameter/model size. A similar increasing trend was observed for the task completion rate, with complicated prompts more likely to increase task completeness in models smaller than GPT-4. However, complicated prompts are more likely to decrease the structure compliance rate, but no prompt effects on GPT-4. Compared to HPO term-based input, LLM was also able to achieve better than random prediction accuracy by taking free-text input, but slightly lower than with the HPO input. Bias analysis showed that certain genes, such as MECP2, CDKL5, and SCN1A, are more likely to be top-ranked, potentially explaining the variances observed across different datasets. This study provides valuable insights into the integration of LLMs within genomic analysis, contributing to the ongoing discussion on the utilization of advanced LLMs in clinical workflows.

q-bio.QM↗

arXiv2024

BundledSLAM: An Accurate Visual SLAM System Using Multiple Cameras

Multi-camera SLAM systems offer a plethora of advantages, primarily stemming from their capacity to amalgamate information from a broader field of view, thereby resulting in heightened robustness and improved localization accuracy. In this research, we present a significant extension and refinement of the state-of-the-art stereo SLAM system, known as ORB-SLAM2, with the objective of attaining even higher precision. To accomplish this objective, we commence by mapping measurements from all cameras onto a virtual camera termed BundledFrame. This virtual camera is meticulously engineered to seamlessly adapt to multi-camera configurations, facilitating the effective fusion of data captured from multiple cameras. Additionally, we harness extrinsic parameters in the bundle adjustment (BA) process to achieve precise trajectory estimation.Furthermore, we conduct an extensive analysis of the role of bundle adjustment (BA) in the context of multi-camera scenarios, delving into its impact on tracking, local mapping, and global optimization. Our experimental evaluation entails comprehensive comparisons between ground truth data and the state-of-the-art SLAM system. To rigorously assess the system's performance, we utilize the EuRoC datasets. The consistent results of our evaluations demonstrate the superior accuracy of our system in comparison to existing approaches.

cs.RO↗

arXiv2024

VGTS: Visually Guided Text Spotting for Novel Categories in Historical Manuscripts

In the field of historical manuscript research, scholars frequently encounter novel symbols in ancient texts, investing considerable effort in their identification and documentation. Although existing object detection methods achieve impressive performance on known categories, they struggle to recognize novel symbols without retraining. To address this limitation, we propose a Visually Guided Text Spotting (VGTS) approach that accurately spots novel characters using just one annotated support sample. The core of VGTS is a spatial alignment module consisting of a Dual Spatial Attention (DSA) block and a Geometric Matching (GM) block. The DSA block aims to identify, focus on, and learn discriminative spatial regions in the support and query images, mimicking the human visual spotting process. It first refines the support image by analyzing inter-channel relationships to identify critical areas, and then refines the query image by focusing on informative key points. The GM block, on the other hand, establishes the spatial correspondence between the two images, enabling accurate localization of the target character in the query image. To tackle the example imbalance problem in low-resource spotting tasks, we develop a novel torus loss function that enhances the discriminative power of the embedding space for distance metric learning. To further validate our approach, we introduce a new dataset featuring ancient Dongba hieroglyphics (DBH) associated with the Naxi minority of China. Extensive experiments on the DBH dataset and other public datasets, including EGY, VML-HD, TKH, and NC, show that VGTS consistently surpasses state-of-the-art methods. The proposed framework exhibits great potential for application in historical manuscript text spotting, enabling scholars to efficiently identify and document novel symbols with minimal annotation effort.

cs.CV↗

arXiv2024

Quasi-2D Weak Lensing Cosmological Constraints Using the PDF-SYM method

Cosmic shear statistics, such as the two-point correlation function (2PCF), can be evaluated with the PDF-SYM method instead of the traditional weighted-sum approach. It makes use of the full PDF information of the shear estimators, and does not require weightings on the shear estimators, which can in principle introduce additional systematic biases. This work presents our constraints on $S_8$ and $Ω_m$ from the shear-shear correlations using the PDF-SYM method. The data we use is from the z-band images of the Dark Energy Camera Legacy Survey (DECaLS), which covers about 10000 deg$^2$ with more than 100 million galaxies. The shear catalog is produced by the Fourier\_Quad method, and well tested on the real data itself with the field-distortion effect. Our main approach is called quasi-2D as we do use the photo-$z$ information of each individual galaxy, but without dividing the galaxies into redshift bins. We mainly use galaxy pairs within the redshift interval between 0.2 and 1.3, and the angular range from $4.7$ to $180$ arcmin. Our analysis yields $S_8=0.762 \pm 0.026$ and $Ω_{\rm m}=0.234 \pm 0.075$, with the baryon effects and the intrinsic alignments included. The results are robust against redshift uncertainties. We check the consistency of our results by deriving the cosmological constraints from auto-correlations of $γ_1$ and $γ_2$ separately, and find that they are consistent with each other, but the constraints from the $γ_1$ component is much weaker than that from $γ_2$. It implies a much worse data quality of $γ_1$, which is likely due to additional shear uncertainties caused by CCD electronics (according to the survey strategy of DECaLS). We also perform a pure 2D analysis, which gives $S_8=0.81^{+0.03}_{-0.04}$ and $Ω_{\rm m}=0.25^{+0.06}_{-0.05}$. Our findings demonstrate the potential of the PDF-SYM method for precision cosmology.

astro-ph.CO↗

arXiv2024

Clifford Group Equivariant Simplicial Message Passing Networks

We introduce Clifford Group Equivariant Simplicial Message Passing Networks, a method for steerable E(n)-equivariant message passing on simplicial complexes. Our method integrates the expressivity of Clifford group-equivariant layers with simplicial message passing, which is topologically more intricate than regular graph message passing. Clifford algebras include higher-order objects such as bivectors and trivectors, which express geometric features (e.g., areas, volumes) derived from vectors. Using this knowledge, we represent simplex features through geometric products of their vertices. To achieve efficient simplicial message passing, we share the parameters of the message network across different dimensions. Additionally, we restrict the final message to an aggregation of the incoming messages from different dimensions, leading to what we term shared simplicial message passing. Experimental results show that our method is able to outperform both equivariant and simplicial graph neural networks on a variety of geometric tasks.

cs.AI↗

arXiv2024

The stability and instability of the language control network: a longitudinal resting-state functional magnetic resonance imaging study

The language control network is vital among language-related networks responsible for solving the problem of multiple language switching. Researchers have expressed concerns about the instability of the language control network when exposed to external influences (e.g., Long-term second language learning). However, some studies have suggested that the language control network is stable. Therefore, whether the language control network is stable or not remains unclear. In the present study, we directly evaluated the stability and instability of the language control network using resting-state functional magnetic resonance imaging (rs-fMRI). We employed cohorts of Chinese first-year college students majoring in English who underwent second language (L2) acquisition courses at a university and those who did not. Two resting-state fMRI scans were acquired approximately 1 year apart. We found that the language control network was both moderately stable and unstable. We further investigated the morphological coexistence patterns of stability and instability within the language control network. First, we extracted connections representing stability and plasticity from the entire network. We then evaluated whether the coexistence patterns were modular (stability and instability involve different brain regions) or non-modular (stability and plasticity involve the same brain regions but have unique connectivity patterns). We found that both stability and instability coexisted in a non-modular pattern. Compared with the non-English major group, the English major group has a more non-modular coexistence pattern.. These findings provide preliminary evidence of the coexistence of stability and instability in the language control network.

q-bio.NC↗

arXiv2024

ChatMusician: Understanding and Generating Music Intrinsically with LLM

While Large Language Models (LLMs) demonstrate impressive capabilities in text generation, we find that their ability has yet to be generalized to music, humanity's creative language. We introduce ChatMusician, an open-source LLM that integrates intrinsic musical abilities. It is based on continual pre-training and finetuning LLaMA2 on a text-compatible music representation, ABC notation, and the music is treated as a second language. ChatMusician can understand and generate music with a pure text tokenizer without any external multi-modal neural structures or tokenizers. Interestingly, endowing musical abilities does not harm language abilities, even achieving a slightly higher MMLU score. Our model is capable of composing well-structured, full-length music, conditioned on texts, chords, melodies, motifs, musical forms, etc, surpassing GPT-4 baseline. On our meticulously curated college-level music understanding benchmark, MusicTheoryBench, ChatMusician surpasses LLaMA2 and GPT-3.5 on zero-shot setting by a noticeable margin. Our work reveals that LLMs can be an excellent compressor for music, but there remains significant territory to be conquered. We release our 4B token music-language corpora MusicPile, the collected MusicTheoryBench, code, model and demo in GitHub.

cs.SD↗

arXiv2024

1DFormer: a Transformer Architecture Learning 1D Landmark Representations for Facial Landmark Tracking

Recently, heatmap regression methods based on 1D landmark representations have shown prominent performance on locating facial landmarks. However, previous methods ignored to make deep explorations on the good potentials of 1D landmark representations for sequential and structural modeling of multiple landmarks to track facial landmarks. To address this limitation, we propose a Transformer architecture, namely 1DFormer, which learns informative 1D landmark representations by capturing the dynamic and the geometric patterns of landmarks via token communications in both temporal and spatial dimensions for facial landmark tracking. For temporal modeling, we propose a recurrent token mixing mechanism, an axis-landmark-positional embedding mechanism, as well as a confidence-enhanced multi-head attention mechanism to adaptively and robustly embed long-term landmark dynamics into their 1D representations; for structure modeling, we design intra-group and inter-group structure modeling mechanisms to encode the component-level as well as global-level facial structure patterns as a refinement for the 1D representations of landmarks through token communications in the spatial dimension via 1D convolutional layers. Experimental results on the 300VW and the TF databases show that 1DFormer successfully models the long-range sequential patterns as well as the inherent facial structures to learn informative 1D representations of landmark sequences, and achieves state-of-the-art performance on facial landmark tracking.

cs.CV↗

arXiv2024

Unleashing the Power of Preemptive Priority-based Scheduling for Real-Time GPU Tasks

Scheduling real-time tasks that utilize GPUs with analyzable guarantees poses a significant challenge due to the intricate interaction between CPU and GPU resources, as well as the complex GPU hardware and software stack. While much research has been conducted in the real-time research community, several limitations persist, including the absence or limited availability of preemption, extended blocking times, and/or the need for extensive modifications to program code. In this paper, we propose two novel techniques, namely the kernel thread and IOCTL-based approaches, to enable preemptive priority-based scheduling for real-time GPU tasks. Our approaches exert control over GPU context scheduling at the device driver level and enable preemptive GPU scheduling based on task priorities. The kernel thread-based approach achieves this without requiring modifications to user-level programs, while the IOCTL-based approach needs only a single macro at the boundaries of GPU access segments. In addition, we provide a comprehensive response time analysis that takes into account overlaps between different task segments, mitigating pessimism in worst-case estimates. Through empirical evaluations and case studies, we demonstrate the effectiveness of the proposed approaches in improving taskset schedulability and timeliness of real-time tasks. The results highlight significant improvements over prior work, with up to 40\% higher schedulability, while also achieving predictable worst-case behavior on Nvidia Jetson embedded platforms.

cs.DC↗

arXiv2024

PPM: Automated Generation of Diverse Programming Problems for Benchmarking Code Generation Models

In recent times, a plethora of Large Code Generation Models (LCGMs) have been proposed, showcasing significant potential in assisting developers with complex programming tasks. Benchmarking LCGMs necessitates the creation of a set of diverse programming problems, and each problem comprises the prompt (including the task description), canonical solution, and test inputs. The existing methods for constructing such a problem set can be categorized into two main types: manual methods and perturbation-based methods. However, manual methods demand high effort and lack scalability, while also risking data integrity due to LCGMs' potentially contaminated data collection, and perturbation-based approaches mainly generate semantically homogeneous problems with the same canonical solutions and introduce typos that can be easily auto-corrected by IDE, making them ineffective and unrealistic. In this work, we propose the idea of programming problem merging (PPM) and provide two implementation of this idea, we utilize our tool on two widely-used datasets and compare it against nine baseline methods using eight code generation models. The results demonstrate the effectiveness of our tool in generating more challenging, diverse, and natural programming problems, comparing to the baselines.

cs.SE↗

arXiv2024

SEMv2: Table Separation Line Detection Based on Instance Segmentation

Table structure recognition is an indispensable element for enabling machines to comprehend tables. Its primary purpose is to identify the internal structure of a table. Nevertheless, due to the complexity and diversity of their structure and style, it is highly challenging to parse the tabular data into a structured format that machines can comprehend. In this work, we adhere to the principle of the split-and-merge based methods and propose an accurate table structure recognizer, termed SEMv2 (SEM: Split, Embed and Merge). Unlike the previous works in the ``split'' stage, we aim to address the table separation line instance-level discrimination problem and introduce a table separation line detection strategy based on conditional convolution. Specifically, we design the ``split'' in a top-down manner that detects the table separation line instance first and then dynamically predicts the table separation line mask for each instance. The final table separation line shape can be accurately obtained by processing the table separation line mask in a row-wise/column-wise manner. To comprehensively evaluate the SEMv2, we also present a more challenging dataset for table structure recognition, dubbed iFLYTAB, which encompasses multiple style tables in various scenarios such as photos, scanned documents, etc. Extensive experiments on publicly available datasets (e.g. SciTSR, PubTabNet and iFLYTAB) demonstrate the efficacy of our proposed approach. The code and iFLYTAB dataset are available at https://github.com/ZZR8066/SEMv2.

cs.CV↗

arXiv2024

Uncertainty Awareness of Large Language Models Under Code Distribution Shifts: A Benchmark Study

Large Language Models (LLMs) have been widely employed in programming language analysis to enhance human productivity. Yet, their reliability can be compromised by various code distribution shifts, leading to inconsistent outputs. While probabilistic methods are known to mitigate such impact through uncertainty calibration and estimation, their efficacy in the language domain remains underexplored compared to their application in image-based tasks. In this work, we first introduce a large-scale benchmark dataset, incorporating three realistic patterns of code distribution shifts at varying intensities. Then we thoroughly investigate state-of-the-art probabilistic methods applied to CodeLlama using these shifted code snippets. We observe that these methods generally improve the uncertainty awareness of CodeLlama, with increased calibration quality and higher uncertainty estimation~(UE) precision. However, our study further reveals varied performance dynamics across different criteria (e.g., calibration error vs misclassification detection) and trade-off between efficacy and efficiency, highlighting necessary methodological selection tailored to specific contexts.

cs.SE↗

arXiv2023

Ferromagnetism and insulating behavior with a logarithmic temperature dependence of resistivity in $Pb_{10-x}Cu_x\left( PO_4 \right) _6O$

Recent claim of discovering above-room-temperature superconductivity (Tc about 400 K) at ambient pressure in copper doped apatite $Pb_{10}\left( PO_4 \right) _6O$ has stimulated world-wide enthusiasm and impulse of motion. A lot of follow-up works have been carried out with controversial conclusions. To check whether superconductivity is really present or absent in the material, we need samples which should have a rather pure phase of $Pb_{10-x}Cu_x\left( PO_4 \right) _6O$. Here we report the characterization results from the $Pb_{10-x}Cu_x\left( PO_4 \right) _6O$ with a fraction of about 97 wt.% inferred from the fitting to the x-ray diffraction pattern. The resistivity measurements show that it is a semiconductor characterized roughly by a ln(1/T) temperature dependence in wide temperature region without trace of superconductivity. Magnetization measurements show that it has a general ferromagnetic signal with a weak superparamagnetic background. Many grains of the sample show clear interactions with a NbFeB magnet. The detected Cu concentration is much lower than the expected nominal one and the conduction may be improved if more Cu atoms are successfully doped into the system. Our results show the absence of metallicity and superconductivity in $Pb_{10-x}Cu_x\left( PO_4 \right) _6O$ at ambient pressure, and suggest the presence of strong correlation effect.

cond-mat.supr-con↗