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关键词： Optimization   Approximation algorithms   Scheduling   Scheduling algorithms   Data communication   Computer science   Internet of Things   Age of information   data collection   directional charging   peak AoI   wireless-powered network edge  

摘要： Age of Information (AoI) has emerged as a new metric to measure data freshness from the destination's perspective. To optimize the system AoI, most existing works focused on the point of scheduling of update transmissions. While at wireless-powered network edge, the source nodes can only transmit their updates after being charged ready, which means the system AoI is not only determined by the update transmission strategies, but also the charging strategies. Thus, in this article, we investigate the first work to optimize the weighted peak AoI from the point of charging at wireless-powered network edge. First, the problem of optimizing the weighted sum of average peak AoI with a directional charger is formulated, and then transformed to a charging time optimization problem with respect to the charging orientations and peak AoI, and an approximate algorithm is proposed to obtain the required charging time for each source node. Second, an age-based scheduling algorithm is proposed to compute the charging decisions and transmission decisions simultaneously, which can not only optimize the weighted sum of average peak AoI, but also guarantee the maximum peak AoI of each source node is bounded. The proposed algorithm is proved to have an approximation ratio of up to (1+ phi ), where phi is a small value related to the weight of each source node. When there exist multiple chargers, an approximate algorithm is also proposed to minimize the weighted sum of average peak AoI by scheduling the orientations of these chargers cooperatively. Finally, the extensive simulations demonstrate the high performance of the proposed algorithms in terms of peak AoI.

关键词： Computer science   OLS regression   Community college transfer   PhD interest   Computer science majors  

摘要： PurposeSupporting community college transfer students represents a critical strategy for broadening participation in STEM. In addition to being a racially diverse group, students who pursue STEM degrees by way of community college report frequent interests in graduate study and academic careers. Thus, supporting and expanding transfer students' PhD interests can help to diversify the STEM professoriate. This study aims to identify the experiences that predict PhD interests among students who transferred into the computer science major from a community ***/methodology/approachRelying on longitudinal survey data from over 150 community college transfer students throughout their first year at their receiving four-year university, we used regression analysis to identify the post-transfer college experiences that predict early interest in *** found that receiving information about PhDs from a professor strongly predicted PhD interest among transfer students. Relationships with other variables indicate that the provision of information about graduate school was more likely to occur for students who participated in undergraduate research experiences than for those participating in internships. Descriptive data document inequities in who has access to these types of ***/valueThis paper provides new insight into how STEM departments can develop targeted efforts to ensure that information about PhD training is equitably available to all transfer students. Working to ensure that faculty equitably communicate with students about PhD opportunities may go a long way in countering potential deterrents among transfer students who may be interested in such pathways.

关键词： Computer science   Electrical and electronic engineering  

摘要： Reinforcement learning (RL) is an effective method in training dialogue policies to steer the conversation towards successful task completion. However, most RL-based methods only rely on semantic inputs that lack empathy as they ignore the user emotional information. Moreover, these methods suffer from delayed rewards caused by the user simulator returning valuable results only at dialogue end. Recently, some methods have been proposed to learn the reward function together with user emotions, but they omit considering user emotion in each dialogue turn. In this paper, we proposed an emotion-sensitive dialogue policy model (ESDP), it incorporates user emotions information into dialogue policy and selects the optimal action by the combination of top-k actions with the user emotions. The user emotion information in each turn is used as an immediate reward for the current dialogue state to solve sparse rewards and the dependency on termination. Extensive experiments validate that our method outperforms the baseline approaches when combined with different Q-Learning algorithms, and also surpasses other popular existing dialog policies' performance.

关键词： Computer science   Information technology  

摘要： It is important to understand the mathematical foundations of neural networks and to include robustness in model evaluation. Here, we introduce algorithms based on manifold curvature estimation to assess neural network robustness. These algorithms rely solely on training data and do not require regular or adversarial test data. Initially, a metric is proposed to measure the curvature of discrete data manifolds by introducing weighted angles concept between subspaces. Following this, a robustness measure is introduced that is independent of network architecture or model parameters. Lastly, two additional methods are introduced, utilizing curvature estimation of special manifolds formed by using gradient vectors between output and input network layers, alongside manifold curvature estimation. A comprehensive evaluation is provided on multiple network models using the CIFAR-10 dataset. Manifold geometry-based robustness analysis may lead to the development of not only accurate but also robust neural network models.

关键词： Medical imaging   Institutions   Computer science  

摘要： Radiological imaging is a globally prevalent diagnostic method, yet the free text contained in radiology reports is not frequently used for secondary purposes. Natural Language Processing can provide structured data retrieved from these reports. This paper provides a summary of the current state of research on Large Language Model (LLM) based approaches for information extraction (IE) from radiology reports. We conduct a scoping review that follows the PRISMA-ScR guideline. Queries of five databases were conducted on August 1st 2023. Among the 34 studies that met inclusion criteria, only pre-transformer and encoder-based models are described. External validation shows a general performance decrease, although LLMs might improve generalizability of IE approaches. Reports related to CT and MRI examinations, as well as thoracic reports, prevail. Most common challenges reported are missing validation on external data and augmentation of the described methods. Different reporting granularities affect the comparability and transparency of approaches.

关键词： Computer science   Scientific data  

摘要： Robust object detection is crucial for automating underwater marine debris collection. While supervised deep learning achieves state-of-the-art performance in discriminative tasks, replicating this success on underwater data is challenging. The generalization of these methods suffers due to a lack of available annotated data considering different sources of variation in the unstructured underwater environment and imaging conditions. In this paper, we present the Seaclear Marine Debris Dataset, the first publicly available shallow-water marine debris dataset annotated for instance segmentation/object detection. The dataset contains 8610 images collected using ROVs at multiple locations and with different cameras, annotated for 40 object categories, encompassing not only litter but also observed animals, plants, and robot parts. As part of the technical validation, we provide baseline results for object detection using Faster RCNN and YOLOv6 models. Furthermore, we demonstrate the non-triviality of generalizing the trained model performance to unseen sites and cameras due to domain shift. This underscores the value of the presented dataset in further developing robust models for underwater debris detection.

关键词： Climate change   Computer science   Energy   Machine learning   Sustainability  

摘要： The rising energy and environmental cost of the artificial-intelligence boom is fuelling concern. Green policy mechanisms that already exist offer a path towards a solution.

关键词： Machine learning   Computer science  

摘要： Artificial neural networks become incapable of mastering new skills when they learn them one after the other. Researchers have only scratched the surface of why this phenomenon occurs — and how it can be fixed.

关键词： Routing   Electric vehicles   Transportation   Telecommunications   Open Access   Computer science   Biomedical engineering  

关键词： Neuroscience   Computer science   Engineering  

摘要： Skillful sequential action requires the delicate balance of sensorimotor control, encompassing both robustness and adaptability. However, it remains unknown whether both motor and neural responses triggered by sensory perturbation undergo plastic adaptation as a consequence of extensive sensorimotor experience. We assessed the effects of transiently delayed tone production on the subsequent motor actions and event-related potentials (ERPs) during piano performance by comparing pianists and non-musicians. Following the perturbation, the inter-keystroke interval was abnormally prolonged in non-musicians but not in pianists. By contrast, the keystroke velocity following the perturbation was increased only in the pianists. A regression model demonstrated that the change in the inter-keystroke interval covaried with the ERPs, particularly at the frontal and parietal regions. The alteration in the keystroke velocity was associated with the P300 component of the temporal region. These findings suggest that different neural mechanisms underlie robust and adaptive sensorimotor skills across proficiency level.