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关键词： Cross modal retrieval   Semantics   Taxonomy   Computer science   Systematic literature review   Training   Statistical analysis   Space vehicles   Representation learning   Deep learning   Application and outlook   cross-modal retrieval   experimental evaluation   methodological taxonomy  

摘要： With the exponential surge in diverse multimodal data, traditional unimodal retrieval methods struggle to meet the needs of users seeking access to data across various modalities. To address this, cross-modal retrieval has emerged, enabling interaction across modalities, facilitating semantic matching, and leveraging complementarity and consistency between heterogeneous data. Although prior literature has reviewed the field of cross-modal retrieval, it suffers from numerous deficiencies in terms of timeliness, taxonomy, and comprehensiveness. This article conducts a comprehensive review of cross-modal retrieval's evolution, spanning from shallow statistical analysis techniques to vision-language pretraining (VLP) models. Commencing with a comprehensive taxonomy grounded in machine learning paradigms, mechanisms, and models, this article delves deeply into the principles and architectures underpinning existing cross-modal retrieval methods. Furthermore, it offers an overview of widely used benchmarks, metrics, and performances. Lastly, this article probes the prospects and challenges that confront contemporary cross-modal retrieval, while engaging in a discourse on potential directions for further progress in the field. To facilitate the ongoing research on cross-modal retrieval, we develop a user-friendly toolbox and an open-source repository at https://***.

关键词： black soil   straw resource utilization   cellulase   Bacillus cereus   Box-Behnken response surface methodology  

摘要： In order to solve the problem of low and poor straw degradation in typical black soil areas of Northeast China, the present study was carried out to screen the potential of in situ strains with cellulose degradation ability from black soils of Northeast China to play a role in the resourceful utilization of straw and the development of sustainable agriculture. The straw degradation potential of the strains was evaluated by combining sodium carboxymethyl cellulose plate screening and cellulase viability assay;the species identification of the strains was carried out by morphology, physiology, biochemistry, and molecular biology;and the basic medium formulation of the strains was optimized by Box-Behnken response surface methodology. Ten cellulose-degrading strains were identified: ZL-5, ZL-69, ZL-88, ZL-95, ZL-111, ZL-137, ZL-139, ZL-140, ZL-187, and ZL-216, of which ZL-139 had the highest cellulase production capacity, with a cellulase secretion of 7.8781 U/mL in the enzyme-producing medium. ZL-139 was identified as Bacillus cereus;the optimized best formulation was glucose-4.284 g/L, yeast extract-1.454 g/L, MgSO4-0.417 g/L, KH2PO4-0.5 g/L, KH2PO4-0.5 g/L, K2HPO4-1.5 g/L, and NaCl-1.0 g/L. In conclusion, strain ZL-139 has good potential for crop straw degradation and can be a candidate strain for a straw-rotting agent in northeast China, with promising prospects for development and utilization.

关键词： Collaboration   Training   Computational modeling   Data models   Servers   Computer science   Accuracy   Sustainable development   Federated learning   Convergence   Cross-Silo federated learning (FL)   efficient collaboration   fair aggregation   utopian fairness  

摘要： Although federated learning (FL) enables collaborative training across multiple data silos in a privacy-protected manner, naively minimizing the aggregated loss to facilitate an efficient federation may compromise its fairness. Many efforts have been devoted to maintaining similar average accuracy across clients by reweighing the loss function while clients' potential contributions are largely ignored. This, however, is often detrimental since treating all clients equally will harm the interests of those clients with more contribution. To tackle this issue, we introduce utopian fairness to expound the relationship between individual earning and collaborative productivity, and propose Federated-UtoPia (FedUP), a novel FL framework that balances both efficient collaboration and fair aggregation. For the distributed collaboration, we model the training process among strategic clients as a supermodular game, which facilitates a rational incentive design through the optimal reward. As for the model aggregation, we design a weight attention mechanism to compute the fair aggregation weights by minimizing the performance bias among heterogeneous clients. Particularly, we utilize the alternating optimization theory to bridge the gap between collaboration efficiency and utopian fairness, and theoretically prove that FedUP has fair model performance with fast-rate training convergence. Extensive experiments using both synthetic and real datasets demonstrate the superiority of FedUP.

关键词： Data analysis   Cells (biology)   Task analysis   Computer science   Biology   Data mining   Clustering algorithms   Coreset   large-scale datasets   ScCoreset   single - cell RNA-seq data  

摘要： The recent boom in single-cell sequencing technologies provides valuable insights into the transcriptomes of individual cells. Through single-cell data analyses, a number of biological discoveries, such as novel cell types, developmental cell lineage trajectories, and gene regulatory networks, have been uncovered. However, the massive and increasingly accumulated single-cell datasets have also posed a seriously computational and analytical challenge for researchers. To address this issue, one typically applies dimensionality reduction approaches to reduce the large-scale datasets. However, these approaches are generally computationally infeasible for tall matrices. In addition, the downstream data analysis tasks such as clustering still take a large time complexity even on the dimension-reduced datasets. We present single-cell Coreset (scCoreset), a data summarization framework that extracts a small weighted subset of cells from a huge sparse single-cell RNA-seq data to facilitate the downstream data analysis tasks. Single-cell data analyses run on the extracted subset yield similar results to those derived from the original uncompressed data. Tests on various single-cell datasets show that scCoreset outperforms the existing data summarization approaches for common downstream tasks such as visualization and clustering. We believe that scCoreset can serve as a useful plug-in tool to improve the efficiency of current single-cell RNA-seq data analyses.

关键词： Cloning   Task analysis   Organizations   Software   Manuals   Computer science   Prototypes   Software product lines, variability management   clone management   re-engineering   framework  

摘要： Customization is a general trend in software engineering, demanding systems that support variable stakeholder requirements. Two opposing strategies are commonly used to create variants: software clone & own and software configuration with an integrated platform. Organizations often start with the former, which is cheap and agile, but does not scale. The latter scales by establishing an integrated platform that shares software assets between variants, but requires high up-front investments or risky migration processes. So, could we have a method that allows an easy transition or even combine the benefits of both strategies? We propose a method and tool that supports a truly incremental development of variant-rich systems, exploiting a spectrum between the opposing strategies. We design, formalize, and prototype a variability-management framework: the virtual platform. Virtual platform bridges clone & own and platform-oriented development. Relying on programming-language independent conceptual structures representing software assets, it offers operators for engineering and evolving a system, comprising: traditional, asset-oriented operators and novel, feature-oriented operators for incrementally adopting concepts of an integrated platform. The operators record meta-data that is exploited by other operators to support the transition. Among others, they eliminate expensive feature-location effort or the need to trace clones. A cost-and-benefit analysis of using the virtual platform to simulate the development of a real-world variant-rich system shows that it leads to benefits in terms of saved effort and time for clone detection and feature location. Furthermore, we present a user study indicating that the virtual platform effectively supports exploratory and hands-on tasks, outperforming manual development concerning correctness. We also observed that participants were significantly faster when performing typical variability management tasks using the virtual platform.

关键词： artificial intelligence   medical imaging   neural networks   machine learning   deep learning  

摘要： Artificial intelligence (AI), the wide spectrum of technologies aiming to give machines or computers the ability to perform human-like cognitive functions, began in the 1940s with the first abstract models of intelligent machines. Soon after, in the 1950s and 1960s, machine learning algorithms such as neural networks and decision trees ignited significant enthusiasm. More recent advancements include the refinement of learning algorithms, the development of convolutional neural networks to efficiently analyze images, and methods to synthesize new images. This renewed enthusiasm was also due to the increase in computational power with graphical processing units and the availability of large digital databases to be mined by neural networks. AI soon began to be applied in medicine, first through expert systems designed to support the clinician's decision and later with neural networks for the detection, classification, or segmentation of malignant lesions in medical images. A recent prospective clinical trial demonstrated the non-inferiority of AI alone compared with a double reading by two radiologists on screening mammography. Natural language processing, recurrent neural networks, transformers, and generative models have both improved the capabilities of making an automated reading of medical images and moved AI to new domains, including the text analysis of electronic health records, image self-labeling, and self-reporting. The availability of open-source and free libraries, as well as powerful computing resources, has greatly facilitated the adoption of deep learning by researchers and clinicians. Key concerns surrounding AI in healthcare include the need for clinical trials to demonstrate efficacy, the perception of AI tools as 'black boxes' that require greater interpretability and explainability, and ethical issues related to ensuring fairness and trustworthiness in AI systems. Thanks to its versatility and impressive results, AI is one of the most promising res

关键词： computer science   control   virtual reality   brain-computer interface   artificial intelligence   cognitive function   information integration  

摘要： Featured Application Potential applications of the work related to advanced hands-free control systems based on the interaction of BCI and *** This article examines state-of-the-art research into the impact of virtual reality (VR) on brain-computer interface (BCI) performance: how the use of virtual reality can affect brain activity and neural plasticity in ways that can improve the performance of brain-computer interfaces in IoT control, e.g., for smart home purposes. Integrating BCI with VR improves the performance of brain-computer interfaces in IoT control by providing immersive, adaptive training environments that increase signal accuracy and user control. VR offers real-time feedback and simulations that help users refine their interactions with smart home systems, making the interface more intuitive and responsive. This combination ultimately leads to greater independence, efficiency, and ease of use, especially for users with mobility issues, in managing IoT-connected devices. The integration of BCI and VR shows great potential for transformative applications ranging from neurorehabilitation and human-computer interaction to cognitive assessment and personalized therapeutic interventions for a variety of neurological and cognitive disorders. The literature review highlights the significant advances and multifaceted challenges in this rapidly evolving field. Particularly noteworthy is the emphasis on the importance of adaptive signal processing techniques, which are key to enhancing the overall control and immersion experienced by individuals in virtual environments. The value of multimodal integration, in which BCI technology is combined with complementary biosensors such as gaze tracking and motion capture, is also highlighted. The incorporation of advanced artificial intelligence (AI) techniques will revolutionize the way we approach the diagnosis and treatment of neurodegenerative conditions.

关键词： Obituaries   Carver Doris L   Seismic   Eastern Europe   Computer Science   Masters Degree   Digital Library   Central And Eastern Europe   Japan Society   Science Degree   Sense Of Humor   Editor In Chief   Amicable   Great Wall   Model For Others   Past President   IEEE Transactions   IT Professionals  

摘要： Recounts the career and contributions of Doris L. Carver.

摘要： While abstraction is critical for the transferability of automated laboratory science in (bio)chemical and materials sciences, its improper implementation is a technical debt taken against the reproducibility of experimental results. Over the decades, computer science has developed guidelines and strategies for how abstractions are captured in programming languages-particularly concerning the substitutability of implementations of abstracted ideas and the clear definition of the contexts in which abstractions are used. However, few programming languages developed for automated experiments fully leverage the wisdom learned in computer science. To achieve collaborative sharing of scientific knowledge via automated laboratories, the way that experimental protocols are codified and interpreted by machine agents must use abstractions responsibly and with reproducibility, rather than solely transferability, at its core. This Review discusses how computer science principles of abstraction can be translated to create more reproducible automation as an enabler for the acceleration of collaborative research with self-driving laboratories. Digital workflow representations in automated and autonomous chemistry laboratories can achieve transferability by using abstract concepts. However, such abstractions must abide by certain rules to ensure reproducibility. Lessons learned from computer science for responsible abstraction are translated into an automated chemistry laboratory context to guide digital workflow development towards reproducibility.