Vectorization using reversible data dependences

Data dependences between statements have long been used for detecting parallelism and converting sequential programs into parallel forms. However, some data dependences can be reversed and the transformed program still produces the same results. In this paper, we revisit vectorization and propose a new vectorization algorithm using reversible data dependences. The new algorithm can generate more or thicker vector statements than traditional algorithm. The techniques presented in this paper can be incorporated in all the existing vectorizing compilers for supercomputers

Guiding AMR Parsing with Reverse Graph Linearization

Abstract Meaning Representation (AMR) parsing aims to extract an abstract semantic graph from a given sentence. The sequence-to-sequence approaches, which linearize the semantic graph into a sequence of nodes and edges and generate the linearized graph directly, have achieved good performance. However, we observed that these approaches suffer from structure loss accumulation during the decoding process, leading to a much lower F1-score for nodes and edges decoded later compared to those decoded earlier. To address this issue, we propose a novel Reverse Graph Linearization (RGL) enhanced framework. RGL defines both default and reverse linearization orders of an AMR graph, where most structures at the back part of the default order appear at the front part of the reversed order and vice versa. RGL incorporates the reversed linearization to the original AMR parser through a two-pass self-distillation mechanism, which guides the model when generating the default linearizations. Our analysis shows that our proposed method significantly mitigates the problem of structure loss accumulation, outperforming the previously best AMR parsing model by 0.8 and 0.5 Smatch scores on the AMR 2.0 and AMR 3.0 dataset, respectively. The code are available at https://github.com/pkunlp-icler/AMR_reverse_graph_linearization.Comment: Findings of EMNLP202

Are prominent medullary veins better than prominent cortical veins as predictors of early clinical outcome in patients with acute ischemic stroke?

PURPOSEThe prominent vessel sign (PVS) on susceptibility-weighted imaging (SWI) can be dichotomized into prominent cortical veins (PCV) and prominent medullary veins (PMV). This study was designed to compare the predictive value of PCV and PMV in the evaluation of the severity of acute ischemic stroke (AIS) in patients within the reperfusion window.METHODSForty-seven consecutive patients with AIS within the middle cerebral artery territory were recruited. Magnetic resonance imaging was performed within 8 hours of symptom onset and at 7 days after stroke onset. Infarct volume was measured, and the early clinical outcome at 7 days was assessed using the modified Rankin Scale. PVS was dichotomized into cases with both PCV and PMV and cases with only PCV according to location.RESULTSPatients with both PCV and PMV (n=32) had higher admission National Institutes of Health Stroke Scale scores (p = 0.020), larger infarct volumes at baseline (p = 0.026) and 7 days (p = 0.007), and larger infarct growth at 7 days (p = 0.050) than those with PCV only. Multivariate regression analysis showed that both the time of onset at baseline (p = 0.013) and infarct growth at 7 days (p = 0.014) could independently predict poor early clinical outcome.CONCLUSIONPMV may predict poor early clinical outcome in AIS patients, and reperfusion therapy may, therefore, be required more urgently in patients with PMV

Epidemiological characteristics of noise-induced hearing loss among workers in five automobile manufacturing enterprises in Zhejiang Province

BackgroundNoise is the most common occupational hazard in the automobile manufacturing industry with the most workers exposed. Automobile manufacturing industry is a high-risk industry for noise-induced hearing loss. ObjectiveTo understand the epidemiological characteristics of noise-induced hearing loss among workers in automobile manufacturing industry and explore related influencing factors. MethodsA questionnaire survey, individual noise recording, and pure tone audiometry were conducted among workers (n=656) exposed to noise from five automobile manufacturing enterprises. The data on age, sex, exposure duration, noise intensity, kurtosis, and hearing loss were obtained. The positive rates of high-frequency noise-induced hearing loss (HFNIHL) and speech-frequency noise-induced hearing loss (SFNIHL) were calculated, and each factor was compared between workers with and without HFNIHL. Chi-square test and analysis of trend were conducted among different groups of age, sex, exposure duration, A-weighted equivalent continuous sound pressure level normalized to a nominal 8-hour working day (LAeq,8h), and kurtosis. Logistic regression analysis was conducted to analyze the factors influencing the positive rates of HFNIHL and SFNIHL. ResultsThe exposure rates of non-Gaussian noise was 73.6%. The positive rates of HFNIHL and SFNIHL were 32.6% (214 workers) and 6.7% (44 workers), respectively. The HFNIHL workers showed older age, higher proportion of male, longer exposure duration, higher noise intensity (LAeq,8 h), and increased kurtosis than those without HFNIHL (P<0.05). The positive rates of HFNIHL increased with the increase of age, exposure duration, LAeq,8 h, and kurtosis (\begin{document}${\chi ^2}$\end{document}trend-age=49.25, P<0.001; \begin{document}${\chi ^2}$\end{document}trend-duration=22.19, P<0.001; \begin{document}${\chi ^2}$\end{document}trend-LAeq=6.91, P=0.009; \begin{document}${\chi ^2}$\end{document}trend-kurtosis=8.56, P=0.003). The results of logistic regression showed that age (OR=2.13, 95%CI: 1.67-2.71, P<0.001), sex (OR=2.29, 95%CI: 1.44-3.62, P<0.001), exposure duration (OR=1.43, 95%CI: 1.11-1.85, P=0.006), LAeq,8h (OR=1.37, 95%CI: 1.08~1.76, P=0.011), and kurtosis (OR=1.37, 95%CI: 1.14-1.63, P=0.001) were factors associated with the risk of HFNIHL, while only age was associated with the risk of SFNIHL (OR=2.15, 95%CI: 1.33-3.33, P=0.001). ConclusionWorkers exposed to noise in automobile manufacturing industry are at a high risk of hearing loss. Age, sex, exposure duration, LAeq,8 h, and kurtosis are key influencing factors of hearing loss

ASSISTGUI: Task-Oriented Desktop Graphical User Interface Automation

Graphical User Interface (GUI) automation holds significant promise for assisting users with complex tasks, thereby boosting human productivity. Existing works leveraging Large Language Model (LLM) or LLM-based AI agents have shown capabilities in automating tasks on Android and Web platforms. However, these tasks are primarily aimed at simple device usage and entertainment operations. This paper presents a novel benchmark, AssistGUI, to evaluate whether models are capable of manipulating the mouse and keyboard on the Windows platform in response to user-requested tasks. We carefully collected a set of 100 tasks from nine widely-used software applications, such as, After Effects and MS Word, each accompanied by the necessary project files for better evaluation. Moreover, we propose an advanced Actor-Critic Embodied Agent framework, which incorporates a sophisticated GUI parser driven by an LLM-agent and an enhanced reasoning mechanism adept at handling lengthy procedural tasks. Our experimental results reveal that our GUI Parser and Reasoning mechanism outshine existing methods in performance. Nevertheless, the potential remains substantial, with the best model attaining only a 46% success rate on our benchmark. We conclude with a thorough analysis of the current methods' limitations, setting the stage for future breakthroughs in this domain.Comment: Project Page: https://showlab.github.io/assistgui

Risk Factors For Recurrent Stroke After Coronary Artery Bypass Grafting

<p>Abstract</p> <p>Objectives</p> <p>Preventing stroke after coronary artery bypass grafting (CABG) remains a therapeutic goal, due in part to the lack of identifiable risk factors. The aim of this study, accordingly, was to identify risk factors in CABG patients with a previous history of stroke.</p> <p>Methods</p> <p>Patients with a history of stroke who underwent CABG at Beijing An Zhen hospital from January 2007 to July 2010 were selected (n = 430), and divided into two groups according to the occurrence of postoperative stroke. Pre-operative and post-operative data were retrospectively collected and analyzed by univariate and multivariate logistic regression analyses.</p> <p>Results</p> <p>Thirty-two patients (7.4%) suffered post-operative stroke. Univariate analysis identified several statistically significant risk factors in the post-operative stroke group, including pre-surgical left ventricular ejection fractions (LVEF) ≤50%, on-pump surgery, post-operative atrial fibrillation (AF), and hypotension. Multivariable analysis identified 4 independent risk factors for recurrent stroke: unstable angina (odds ratio (OR) = 2.95, 95% CI: 1.05-8.28), LVEF ≤50% (OR = 2.77, 95% CI: 1.23-6.27), AF (OR = 4.69, 95% CI: 1.89-11.63), and hypotension (OR = 2.55, 95% CI: 1.07-6.04).</p> <p>Conclusion</p> <p>Unstable angina, LVEF ≤50%, post-operative AF, and post-operative hypotension are independent risk factors of recurrent stroke in CABG patients with a previous history of stroke.</p

DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

The rapid development of open-source large language models (LLMs) has been truly remarkable. However, the scaling law described in previous literature presents varying conclusions, which casts a dark cloud over scaling LLMs. We delve into the study of scaling laws and present our distinctive findings that facilitate scaling of large scale models in two commonly used open-source configurations, 7B and 67B. Guided by the scaling laws, we introduce DeepSeek LLM, a project dedicated to advancing open-source language models with a long-term perspective. To support the pre-training phase, we have developed a dataset that currently consists of 2 trillion tokens and is continuously expanding. We further conduct supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) on DeepSeek LLM Base models, resulting in the creation of DeepSeek Chat models. Our evaluation results demonstrate that DeepSeek LLM 67B surpasses LLaMA-2 70B on various benchmarks, particularly in the domains of code, mathematics, and reasoning. Furthermore, open-ended evaluations reveal that DeepSeek LLM 67B Chat exhibits superior performance compared to GPT-3.5

Advances in acute ischemic stroke imaging research

DOI：10.3969/j.issn.1672-6731.2011.03.00

Vectorization Using Reversible Data

Data dependences between statements have long been used for detecting parallelism and converting sequential programs into parallel forms. However, some data dependences can be reversed and the transformed program still produces the same results. In this paper, we revisit vectorization and propose a new vectorization algorithm using reversible data dependences. The new algorithm can generate more or thicker vector statements than traditional algorithm. The techniques presented in this paper can be incorporated in all the existing vectorizing compilers for supercomputers

Vectorization Using Reversible Data Dependences

Data dependences between statements have long been used for detecting parallelism and converting sequential programs into parallel forms. However, some data dependences can be reversed and the transformed program still produces the same results. In this paper, we revisit vectorization and propose a new vectorization algorithm using reversible data dependences. The new algorithm can generate more or thicker vector statements than traditional algorithm. The techniques presented in this paper can be incorporated in all the existing vectorizing compilers for supercomputers. This work was supported in part by the Australian Research Council under Grant No. A49232251 ii 1 Introduction Data dependences between statements have long been used by vectorizing and parallelizing compilers to detect parallelism and convert sequential programs into parallel forms [1, 2]. Two statement instances 1 are said to be data dependent if they access the same data element and at least one of the accesse..