Open-Domain Question Answering (ODQA) systems often struggle with the quality of retrieved passages, which may contain conflicting information and be misaligned with the reader's needs. Existing retrieval methods ...
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Directed evolution (DE) has been the most effective method for protein engineering that optimizes biological functionalities through a resource-intensive process of screening or selecting among a vast range of mutatio...
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Directed evolution (DE) has been the most effective method for protein engineering that optimizes biological functionalities through a resource-intensive process of screening or selecting among a vast range of mutations. To mitigate this extensive procedure, recent advancements in machine learning-guided methodologies center around the establishment of a surrogate sequence-function model. In this paper, we propose latent-based DE (LDE), an evolutionary algorithm designed to prioritize the exploration of high-fitness mutants in the latent space. At its core, LDE is a regularized variational autoencoder (VAE), harnessing the capabilities of the state-of-the-art protein language model, ESM-2, to construct a meaningful latent space of sequences. From this encoded representation, we present a novel approach for efficient traversal on the fitness landscape, employing a combination of gradient-based methods and DE. Experimental evaluations conducted on eight protein sequence design tasks demonstrate the superior performance of our proposed LDE over previous baseline algorithms. Our implementation is publicly available at https://***/HySonLab/LatentDE.
Multi-person pose estimation based on monocular cameras is one of the hot research topics in computer vision. Current monocular multi-person 3D pose estimation methods often treat individuals as independent entities f...
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With the rapid advancements of various machine learning models, there is a significant demand for model-agnostic explanation techniques, which can explain these models across different architectures. Mainstream model-...
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Quantum Computing (QC) offers significant potential to enhance scientific discovery in fields such as quantum chemistry, optimization, and artificial intelligence. Yet QC faces challenges due to the noisy intermediate...
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Graph Contrastive Learning (GCL) has recently emerged as a promising graph self-supervised learning framework for learning discriminative node representations without labels. The widely adopted objective function of G...
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Graph Contrastive Learning (GCL) has recently emerged as a promising graph self-supervised learning framework for learning discriminative node representations without labels. The widely adopted objective function of GCL benefits from two key properties: alignment and uniformity, which align representations of positive node pairs while uniformly distributing all representations on the hypersphere. The uniformity property plays a critical role in preventing representation collapse and is achieved by pushing apart augmented views of different nodes (negative pairs). As such, existing GCL methods inherently rely on increasing the quantity and quality of negative samples, resulting in heavy computational demands, memory overhead, and potential class collision issues. In this study, we propose a negative-free objective to achieve uniformity, inspired by the fact that points distributed according to a normalized isotropic Gaussian are uniformly spread across the unit hypersphere. Therefore, we can minimize the distance between the distribution of learned representations and the isotropic Gaussian distribution to promote the uniformity of node representations. Our method also distinguishes itself from other approaches by eliminating the need fora parameterized mutual information estimator, an additional projector, asymmetric structures, and, crucially, negative samples. Extensive experiments over seven graph benchmarks demonstrate that our proposal achieves competitive performance with fewer parameters, shorter training times, and lower memory consumption compared to existing GCL methods.
Traditional cell viability judgment methods are invasive and damaging to cells. Moreover, even under a microscope, it is difficult to distinguish live cells from dead cells by the naked eye alone. With the development...
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An event-based social network (EBSN) is a new type of social network that combines online and offline networks. In recent years, an important task in EBSN recommendation systems has been to design better and more reas...
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Current technology trends in high-performance computing (HPC) are pushing us towards accelerated systems. While GPU-based systems are the most common option, not all applications work well on such architectures. Solut...
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ISBN:
(纸本)9783031800832;9783031800849
Current technology trends in high-performance computing (HPC) are pushing us towards accelerated systems. While GPU-based systems are the most common option, not all applications work well on such architectures. Solutions, like programmable hardware in the form of FPGAs (Field Programmable Gate Arrays), can be a powerful alternative. However, the complexity of developing specialized computing units in FPGAs, which are optimized for a specific task, often limits their broad utilization. In this paper, we follow a co-design methodology to identify the key computational routines and to replace them by using user-friendly libraries that wrap complex FPGA access mechanisms. This simplifies the usage of specialized compute units in FPGAs. To demonstrate our approach, we focus on performance improvements for an HPC/BigData application called (MP)N, which is built around widely used data analytics algorithm computing the matrix profile for multidimensional time series. In this application, we identify a sorting kernel as one of the key time consumers and accelerate it designing a parallel sorting library and using it to offload sorting batches to the FPGA. At the same time, we enable efficient utilization of CPU resources through overlap and pipelining. We achieve a 2-fold run time improvement for computing a 128dimensional time series of 7 million records, with the performance gap increasing as the number of records grows, highlighting the potential of CPU-FPGA co-design in HPC.
Recently, many works have proposed various financial large language models (FinLLMs) by pre-training from scratch or fine-tuning open-sourced LLMs on financial corpora. However, existing FinLLMs exhibit unsatisfactory...
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