Domain generalization refers to the problem of training a model from collection different source domains that can directly generalize unseen target domains. A promising solution is contrastive learning, which attempts learn domain-invariant representations by exploiting rich semantic relations among sample-to-sample pairs simple approach pull positive sample closer while pushing other negative further apart. In this paper, we find applying contrastive-based methods (e.g., supervised...

Pursuing accurate and robust recognizers has been a long-lasting goal for scene text recognition (STR) researchers. Recently, attention-based methods have demonstrated their effectiveness achieved impressive results on public benchmarks. The attention mechanism enables models to recognize with severe visual distortions by leveraging contextual information. However, recent studies revealed that the implicit over-reliance of context leads catastrophic out-of-vocabulary performance. On contrary...

The integration of a complex set Electronic Design Automation (EDA) tools to enhance interoperability is critical concern for circuit designers. Recent advancements in large language models (LLMs) have showcased their exceptional capabilities natural processing and comprehension, offering novel approach interfacing with EDA tools. This research paper introduces ChatEDA, an autonomous agent empowered by model, AutoMage, complemented serving as executors. ChatEDA streamlines the design flow...

The integration of a complex set Electronic Design Automation (EDA) tools to enhance interoperability is critical concern for circuit designers. Recent advancements in large language models (LLMs) have showcased their exceptional capabilities natural processing and comprehension, offering novel approach interfacing with EDA tools. This research paper introduces ChatEDA, an autonomous agent empowered by model, AutoMage, complemented serving as executors. ChatEDA streamlines the design flow...

ABSTRACT As global life expectancy increases, the focus has shifted from merely extending lifespan to promoting healthy aging. GSTA1, GSTA2, and GSTA3 (GSTA1‐3), members of alpha class glutathione S‐transferases, are involved in diverse biological processes, including metabolism immune regulation, highlighting their potential influence on human health span lifespan. In this study, we employed Caenorhabditis elegans as a model organism investigate role gst‐35 , an ortholog mammalian GSTA1‐3,...

Now that the human genome has been sequenced, measurement, processing, and analysis of specific genomic information in real time are gaining considerable interest because their importance to better understanding inherent function, early diagnosis disease, discovery new drugs. Traditional methods process analyze deoxyribonucleic acid (DNA) or ribonucleic data, based on statistical Fourier theories, not robust enough time-consuming, thus well suited for future routine rapid medical...

Remaining useful life (RUL) estimation is an important part of prognostic health management (PHM) technology. Traditional RUL methods need to define thresholds with the help experience, and affect precision test results. In this paper, a hybrid method convolutional recurrent neural network (CNN-RNN) proposed for estimation. This can accurately predict by using trained without setting threshold. The prediction accuracy model further improved processing, clustering, classifying data. CNN-RNN...

Alzheimer's disease is the predominant form of dementia, and disulfidptosis latest reported mode cell death that impacts various processes. This study used bioinformatics to analyze genes associated with in comprehensively. Based on public datasets, differentially expressed were identified, immune infiltration was investigated through correlation analysis. Subsequently, hub determined by a randomforest model. A prediction model constructed using logistic regression. In addition, drug-target...

Machinery operates well under normal conditions in most cases; far fewer samples are collected a fault state (minority samples) than state, resulting an imbalance of samples. Common machine learning algorithms such as deep neural networks require significant amount data during training to avoid overfitting. These models often fail detect minority when the input imbalanced, which results missed diagnoses equipment faults. As effective method enhance samples, Deep Convolution Generative...

With the rapid development of semiconductors and continuous scaling-down circuit feature size, hotspot detection has become much more challenging crucial as a critical step in physical verification flow. In recent years, advanced deep learning techniques have spawned many frameworks for detection. However, most existing detectors can only detect defects arising central region small clips, making whole process time-consuming on large layouts. Some multiple hotspots area but need to propose...

Design Rule Checking (DRC) is a critical step in integrated circuit design. DRC requires formatted scripts as the input to design rule checkers. However, these are manually generated foundry, which tedious and error prone for generation of thousands rules advanced technology nodes. To mitigate this issue, we propose first script framework, leveraging deep learning-based key information extractor automatically identify essential arguments from translator organize extracted into executable...

Meetings typically involve multiple participants and lengthy conversations, resulting in redundant trivial content. To overcome these challenges, we propose a two-step framework, Reconstruct before Summarize (RbS), for effective efficient meeting summarization. RbS first leverages self-supervised paradigm to annotate essential contents by reconstructing the transcripts. Secondly, relative positional bucketing (RPB) algorithm equip (conventional) summarization models generate summary. Despite...

<abstract> <p>Currently, with the rapid growth of online media, more people are obtaining information from it. However, traditional hotspot mining algorithms cannot achieve precise and fast control hot topics. Aiming at problem poor accuracy timeliness in current news media methods, this paper proposes a method based on co-occurrence word model. First, new model weight is proposed. Then, for key phrase extraction, algorithm improved smooth inverse frequency rank (SIFRANK)...

We introduce the exact approximation order in dynamics of $ \beta $-expansions which has its analogy classic Diophantine approximation. More precisely, let E_{\beta}(\psi) be set real numbers [0, 1) are approximable by their convergents to \psi but no better order. The Hausdorff dimension is given for any monotonic function $.

Thangka has been a crucial aspect of Buddhism for thousands years. It acts as visualization the myths and legends that shaped attracted many devotees to worship. Furthermore, also serves reflection cultural, political social Tibetan society in history. However, paintings, especially outlines, became extremely fragile after years harsh environments. I utilized Deep learning, specifically Convolutional neural network (CNN) main method processing data. After numerous training loss gradually...