Background With the widespread adoption of electronic medical records, massive prescription data can be digitized and systematically stored. This provides a solid foundation for intelligent traditional Chinese medicine (TCM) diagnosis systems. TCM syndrome classification is the core of syndrome differentiation and treatment. Developing an effective classification framework remains a major challenge for intelligent diagnosis systems. Recent progress in natural language processing has introduced new approaches and tools for semantic understanding and knowledge extraction from prescription texts. However, traditional machine learning methods rely on hand-crafted features and struggle to process high-dimensional, sparse, and intricate TCM prescription texts. The single text-based model can capture semantic features but ignore the structural connections in prescription data. The single graph-based model emphasizes structural associations but fails to incorporate rich contextual semantics. Methods To address the challenges, we propose a new dual-channel TCM syndrome classification model (DC-TSCM) in healthcare applications. The text channel extracts deep representations from clinical description and physique detection texts. We developed a TCM differentiation-guided attention fusion module to dynamically learn the optimal weighting between prescription texts. The graph channel constructs a unique TCM differentiation heterogeneous graph and uses hybrid graph neural networks to model the complex semantic associations among clinical entities. Additionally, we extracted 8,280 prescriptions from real electronic medical records, covering 24 different syndrome types. The prescription data were standardized according to clinical diagnostic terminology and divided into training, validation, and test sets in an 8:1:1 ratio. Results Experiments were conducted on a structured multi-label syndrome differentiation dataset. The results indicate that the model achieves superior performance and strong generalization ability in multi-class syndrome classification. Its interpretability is further validated through visualization analysis, including the co-occurrence relationship heat map, confusion matrix, and receiver operating characteristic curve. The dual-channel model achieved an accuracy of 0.8919, precision of 0.9012, recall of 0.8947, and F1-score of 0.8930. Conclusion Overall, DC-TSCM bridges semantic understanding with structural reasoning and incorporates the principles of TCM differentiation. It significantly improves the accuracy of syndrome differentiation and suggests potential applicability beyond TCM, which could be explored in future work. It also provides a robust and interpretable framework for intelligent auxiliary diagnosis systems and lays a foundation for the integration of clinical knowledge with advanced deep learning methodologies.
Modern Fortran is a standardized language that includes object-oriented and parallel programming paradigms. The Fortran-lang community, created at the end of 2019, is actively working to modernize its ecosystem. New compilers are under development. And the fourth Fortran standard of the 21st century is due to be published in autumn 2023.
Mohamed Lemine El Bechir, Cheikh Sad Bouh, Abobakr Shuwail
This review paper synthesizes the latest research on performance optimization strategies for serverless applications deployed on AWS Lambda. By examining recent studies, we highlight the challenges, solutions, and best practices for enhancing the performance, cost efficiency, and scalability of serverless applications. The review covers a range of optimization techniques including resource management, runtime selection, observability improvements, and workload aware operations.
Nowadays, all sectors utilize devices that are part of the Internet of Things (IoT) for the purpose of connecting and exchanging information with other devices and systems over the Internet. This increases the diversity of devices and their working environments, which, in turn, creates new challenges, such as real-time interaction, security, interoperability, performance, and robustness of IoT systems. To address these, many applications protocols were adopted and developed for devices with constrained resources. This paper surveys communication protocols divided according to their goals along with their merits, demerits, and suitability towards IoT applications. We summarize the challenges of communication protocols as well as some relevant solutions.
COOL (Chen'21) is an error-free and deterministic Byzantine agreement protocol that achieves consensus on an $\ell$-bit message with a communication complexity of $O(\max\{n\ell, n t \log t \})$ bits in four phases, given $n\geq 3t + 1$, for a network of $n$ nodes, where up to $t$ nodes may be dishonest. In this work we show that COOL can be optimized by reducing one communication round. The new protocol is called OciorCOOL. Additionally, building on OciorCOOL, we design an optimal reliable broadcast protocol that requires only six communication rounds.
This paper addresses the problem of scheduling non-preemptive tasks with release jitter and execution time variation on a uniprocessor. We show that the schedulability analysis based on schedule graph generation, proposed by Nasri and Brandenburg [RTSS 2017], produces negative results when it could be easily avoided by slightly reformalizing the notion of non-work-conserving policies. In this work, we develop a schedulability analysis that constructs the schedule graph using new job-eligibility rules and is exact and sustainable for both work-conserving and enhanced formalization of non-work-conserving policies. Besides, the experimental evaluation shows that our schedulability analysis is substantially faster.
The Fast Fourier Transform (FFT) is a numerical operation that transforms a function into a form comprised of its constituent frequencies and is an integral part of scientific computation and data analysis. The objective of our work is to enable use of the FFT as part of a scientific in situ processing chain to facilitate the analysis of data in the spectral regime. We describe the implementation of an FFT endpoint for the transformation of multi-dimensional data within the SENSEI infrastructure. Our results show its use on a sample problem in the context of a multi-stage in situ processing workflow.
Asynchronous Byzantine Atomic Broadcast (ABAB) promises simplicity in implementation as well as increased performance and robustness in comparison to partially synchronous approaches. We adapt the recently proposed DAG-Rider approach to achieve ABAB with $n\geq 2f+1$ processes, of which $f$ are faulty, with only a constant increase in message size. We leverage a small Trusted Execution Environment (TEE) that provides a unique sequential identifier generator (USIG) to implement Reliable Broadcast with $n>f$ processes and show that the quorum-critical proofs still hold when adapting the quorum size to $\lfloor \frac{n}{2} \rfloor + 1$. This first USIG-based ABAB preserves the simplicity of DAG-Rider and serves as starting point for further research on TEE-based ABAB.
This paper proposes that two distinct types of structures are present in the brain: Symbolic Knowledge Structures (SKSs), used for formal symbolic reasoning, and Intuitive Knowledge Structures (IKSs), used for drawing informal associations. The paper contains ideas for modeling and analyzing these structures in an algorithmic style based on Spiking Neural Networks, following the paradigm used in earlier work by Lynch, Musco, Parter, and co-workers. The paper also contains two examples of use of these structures, involving counting through a memorized sequence, and understanding simple stylized sentences. The ideas presented here are preliminary and speculative, and do not (yet) comprise a complete, coherent, algorithmic theory. I hope that posting this preliminary version will help the ideas to evolve into such a theory.
El presente artículo de investigación pretende develar algunos efectos de las representaciones socialmente racializadas y sexualizadas en las experiencias de vida de las mujeres afrodescendientes residentes en Cali y las formas como estas se materializan en la vida cotidiana. De igual modo, intenta propiciar una lectura crítica sobre la violencia hacia las mujeres, la cual posibilite nuevas interpretaciones a la hora de realizar estudios de género.
In the age of big data, more and more applications need to query and analyse large volumes of continuously updated data in real-time. In response, cloud-scale storage systems can extend their interface that allows fast lookups on the primary key with the ability to retrieve data based on non-primary attributes. However, the need to ingest content rapidly and make it searchable immediately while supporting low-latency, high-throughput query evaluation, as well as the geo-distributed nature and weak consistency guarantees of modern storage systems pose several challenges to the implementation of indexing and search systems. We present our early-stage work on the design and implementation of an indexing and query processing system that enables realtime queries on secondary attributes of data stored in geo-distributed, weakly consistent storage systems.
We demonstrate that training ResNet-50 on ImageNet for 90 epochs can be achieved in 15 minutes with 1024 Tesla P100 GPUs. This was made possible by using a large minibatch size of 32k. To maintain accuracy with this large minibatch size, we employed several techniques such as RMSprop warm-up, batch normalization without moving averages, and a slow-start learning rate schedule. This paper also describes the details of the hardware and software of the system used to achieve the above performance.
Energy harvesting is a promising solution to power Internet of Things (IoT) devices. Due to the intermittent nature of these energy sources, one cannot guarantee forward progress of program execution. Prior work has advocated for checkpointing the intermediate state to off-chip non-volatile memory (NVM). Encrypting checkpoints addresses the security concern, but significantly increases the checkpointing overheads. In this paper, we propose a new online checkpointing policy that judiciously determines when to checkpoint so as to minimize application time to completion while guaranteeing security. Compared to state-of-the-art checkpointing schemes that do not account for the overheads of encrypted checkpoints we improve execution time up to 1.4x.
Application-layer multicast implements the multicast functionality at the application layer. The main goal of application-layer multicast is to construct and maintain efficient distribution structures between endhosts. In this paper we focus on the implementation of an application-layer multicast network using PlanetLab. We observe that the total time required to measure network latency over TCP is influenced dramatically by the TCP connection time. We argue that end-host distribution is not only influenced by the quality of network links but also by the time required to make connections between nodes. We provide several solutions to decrease the total end-host distribution time.