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关键词： Computational biology and bioinformatics   Software   Computer science   Information technology  

摘要： At Cambridge, VISS resources will help to rewrite the software behind the Scale Aware Sea Ice Project to better model the impact of climate change on sea ice. To counter that, CZI has since 2019 distributed $27.8 million as part of its Essential Open Source Software for Science initiative, the fifth round of which was announced in March. RSEs are specialists who work on software specifically to advance academic research; they're like at-large developers for a department or school. Since it was founded in 2018, the US Research Software Engineer Association has expanded its membership to more than 1,000. In 2021, the US National Science Foundation launched a new type of call, backed by $4 million, to help software projects transition to a sustainable basis. Since 2020, the US National Institutes of Health has awarded $6 million to $7 million per year to ensure that software created by existing awardees is robust, sustainable and scalable. [...]in the Netherlands, there has been a rise in hiring as attention has turned to the need for quality research software, says Maaike deJong, a scientific community manager at the Netherlands eScience Center in Amsterdam, which supports research software through funding projects and training.

关键词： Cancer imaging   Computer science   Gastric cancer   Multiphoton microscopy  

摘要： Gastroscopic biopsy provides the only effective method for gastric cancer diagnosis, but the gold standard histopathology is time-consuming and incompatible with gastroscopy. Conventional stimulated Raman scattering (SRS) microscopy has shown promise in label-free diagnosis on human tissues, yet it requires the tuning of picosecond lasers to achieve chemical specificity at the cost of time and complexity. Here, we demonstrate that single-shot femtosecond SRS (femto-SRS) reaches the maximum speed and sensitivity with preserved chemical resolution by integrating with U-Net. Fresh gastroscopic biopsy is imaged in <60 s, revealing essential histoarchitectural hallmarks perfectly agreed with standard histopathology. Moreover, a diagnostic neural network (CNN) is constructed based on images from 279 patients that predicts gastric cancer with accuracy >96%. We further demonstrate semantic segmentation of intratumor heterogeneity and evaluation of resection margins of endoscopic submucosal dissection (ESD) tissues to simulate rapid and automated intraoperative diagnosis. Our method holds potential for synchronizing gastroscopy and histopathological diagnosis. Diagnosis of gastric cancer currently requires gastroscopic biopsy, which requires time and expertize to perform. Here, the authors demonstrate a femto-SRS imaging method which showed high accuracy in diagnosing gastric cancer without the need for pathologistbased diagnosis.

关键词： Curricula  

摘要： Computer Science education has been evolving over the years to reflect applied realities. Until about a decade ago, theory of computation, algorithm design and system software dominated the curricula. Most courses were considered core and were hence mandatory;the programme structure did not allow much of a choice or variety. This column analyses why this changed Circa 2010 when elective subjects across scores of topics become part of mainstream education to reflect the on-going lateral acceleration of Computer Science. Fundamental discoveries in artificial intelligence, machine learning, virtualization and cloud computing are several decades old. Many core theories in data science are centuries old. Yet their leverage exploded only after Circa 2010, when the stage got set for people-centric problem solving in massive scale. This was due in part to the rush of innovative real-world applications that reached the common man through the ubiquitous smart phone. AI/ML modules arrived in popular programming languages;they could be used to build and train models on powerful - yet affordable - compute on public clouds reachable through high-speed Internet connectivity. Academia responded by adapting Computer Science curricula to align it with the changing technology landscape. The goal of this experiential piece is to trigger a lively discussion on the past and future of Computer Science education. Copyright © 2022, The Authors. All rights reserved.

摘要： No abstract available.

关键词： Computer science   Information technology  

摘要： This paper proposes an improved group teaching optimization algorithm (IGTOA) to improve the convergence speed and accuracy of the group teaching optimization algorithm. It assigns teachers independently for each individual, replacing the original way of sharing the same teacher, increasing the evolutionary direction and expanding the diversity of the population;it dynamically divides the students of the good group and the students of the average group to meet the different needs of convergence speed and population diversity in different evolutionary stages;in the student learning stage, the weak self-learning part is canceled, the mutual learning part is increased, and the population diversity is supplemented;for the average group students, a new sub-space search mode is proposed, and the teacher's teaching method is improved to reduce the diversity in the population evolution process. and propose a population reconstruction mechanism to expand the search range of the current population and ensure population diversity. Finally, the experimental results on the CEC2013 test suite show that IGTOA has clear advantages in convergence speed and accuracy over the other five excellent algorithms.

关键词： Human behaviour   Computational science   Computer science  

摘要： Measuring sentiment in social media text has become an important practice in studying emotions at the macroscopic level. However, this approach can suffer from methodological issues like sampling biases and measurement errors. To date, it has not been validated if social media sentiment can actually measure the temporal dynamics of mood and emotions aggregated at the level of communities. We ran a large-scale survey at an online newspaper to gather daily mood self-reports from its users, and compare these with aggregated results of sentiment analysis of user discussions. We find strong correlations between text analysis results and levels of self-reported mood, as well as between inter-day changes of both measurements. We replicate these results using sentiment data from Twitter. We show that a combination of supervised text analysis methods based on novel deep learning architectures and unsupervised dictionary-based methods have high agreement with the time series of aggregated mood measured with self-reports. Our findings indicate that macro level dynamics of mood expressed on an online platform can be tracked with social media text, especially in situations of high mood variability.

关键词： Gender equity   research through design   intertextuality   computer science   games   HCI design approach   critical design  

摘要： Click to increase image sizeClick to decrease image size

关键词： Neuroscience   Computer science  

摘要： How does the mind organize thoughts? The hippocampal-entorhinal complex is thought to support domain-general representation and processing of structural knowledge of arbitrary state, feature and concept spaces. In particular, it enables the formation of cognitive maps, and navigation on these maps, thereby broadly contributing to cognition. It has been proposed that the concept of multi-scale successor representations provides an explanation of the underlying computations performed by place and grid cells. Here, we present a neural network based approach to learn such representations, and its application to different scenarios: a spatial exploration task based on supervised learning, a spatial navigation task based on reinforcement learning, and a non-spatial task where linguistic constructions have to be inferred by observing sample sentences. In all scenarios, the neural network correctly learns and approximates the underlying structure by building successor representations. Furthermore, the resulting neural firing patterns are strikingly similar to experimentally observed place and grid cell firing patterns. We conclude that cognitive maps and neural network-based successor representations of structured knowledge provide a promising way to overcome some of the short comings of deep learning towards artificial general intelligence.

关键词： 糖尿病   网络互动平台   系统化护理   血糖水平   自我效能   生活质量  

摘要： 目的 分析基于网络互动平台下系统化护理对糖尿病患者血糖水平、自我效能及生活质量的影响。方法选取2019年12月—2021年10月黑龙江省医院收治的糖尿病患者158例,按照随机数表法将其分为对照组和研究组,每组79例,对照组实施常规护理干预,研究组实施基于网络互动下系统化护理干预,观察两组患者血糖水平、自我效能以及生活质量的变化情况。结果 干预后,研究组患者血糖水平、自我效能评分及生活质量水平明显优于对照组,差异有统计学意义(P<0.05)。结论 对糖尿病患者实施基于网络互动平台下系统化护理干预,可有效改善患者血糖水平,加强自我护理效能水平,提高生活质量。

关键词： Task analysis   Tensile stress   Crowdsourcing   History   Electronic mail   Performance evaluation   Computer science   Spatial crowdsourcing   task assignment   worker preference  

摘要： With the ubiquity of smart devices, Spatial Crowdsourcing (SC) has emerged as a new transformative platform that engages mobile users to perform spatio-temporal tasks by physically traveling to specified locations. Thus, various SC techniques have been studied for performance optimization, among which one of the major challenges is how to assign workers the tasks that they are really interested in and willing to perform. In this paper, we propose a novel preference-aware spatial task assignment system based on workers' temporal preferences, which consists of two components: History-based Context-aware Tensor Decomposition (HCTD) for workers' temporal preferences modeling and preference-aware task assignment. We model workers' preferences with a three-dimension tensor (worker-task-time). Supplementing the missing entries of the tensor through HCTD with the assistant of historical data and other two context matrices, we recover workers' preferences for different categories of tasks in different time slots. Several preference-aware individual task assignment algorithms are then devised, aiming to maximize the total number of task assignments at every time instance, in which we give higher priorities to the workers who are more interested in the tasks. In order to make our proposed framework applicable to more scenarios, we further optimize the original framework by proposing strategies to allow each task to be assigned to a group of workers such that the task can be completed by these workers simultaneously, wherein workers' tolerable waiting time, consensus, and tasks' rewards are taken into consideration. We conduct extensive experiments using a real dataset, verifying the practicability of our proposed methods.