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关键词： heat reservoir   work reservoir   entropy flow   entropy generation   lost work   heat  

摘要： In this paper, we discuss the concept of a reservoir in thermodynamics and its determining role in defining entropy flow, entropy generation, lost work, heat, and work. Also, a relation equivalent to that of Clausius is established, which explains why the statement 'the entropy generation for an irreversible engine is given by the lost work divided by the lowest temperature reservoir' is flawed, although such a statement is recognized in the literature. Addressing issues that are virtually absent from the literature, this study is expected to be mainly relevant from a scientific point of view but can also be educationally useful.

关键词： Maintenance engineering   Bandwidth   Switches   Random variables   Computer science   Fault tolerant systems   Fault tolerance   boldsymbol Distributed storage system   bandwidth   storage space   tradeoff   regenerating code  

摘要： In a fault-tolerant Distributed Storage System (DSS) that depends on regenerating codes, there may be a variety of motivations for the system or the user to switch from one set of code parameters (n , k , d , alpha, beta) to another. For example, the user may change their demand on reliability or the system may want to change the configuration due to implementation challenges, cost considerations and issues related to availability/ accessibility of geographically-distributed nodes. This can be managed by a well-designed DSS capable of dynamically scaling the parameters. In this letter, we present lower bounds on the bandwidth requirements for moving from a regenerating code configuration to another. In case of functional repair, our lower bounds are achievable, which helps the system designers identify the minimum-cost scaling strategy.

关键词： Human computer interaction   Computer science   Visual analytics   Psychology   Cognition   Planning   Problem-solving  

摘要： The growth of the field of visual analytics reflects a sustained, long-term commitment to applying convergent research principles to computer science research. Charting the future research directions that the field of visual analytics will evolve toward requires an understanding of the field's rich, productive history of collaboration and a discussion of current challenges.

关键词： Jan Kollar   capital punishment   Enlightenment   Cesare Beccaria   Michel Foucault   Habsburg monarchy  

摘要： This article analyses and assesses the arguments opposing capital punishment put forward by Jan Kollar (1793-1852), a representative of Central European Evangelical/Lutheran Enlightenment rationalism, using the definition of criminal practice in Europe at the turn of the nineteenth century as the basis. Consequently, the author pays attention to the movement for reform in criminal law and practices, initiated in Europe in the second half of the eighteenth century by Cesare Beccaria, including his argumentation against capital punishment. In this context, the author presents and investigates 13 arguments against capital punishment defined by Kollar in his 1815 diary. The author came to the conclusion that Kollar, in his arguments against capital punishment, followed, to a certain extent, the views of Beccaria and eighteenth -century adherents of the French Enlightenment;however, Kollar's actual argumentation is rationalistically based on ethical values of humanity and justice with significant space also dedicated to utilitarian aspects of rejecting capital punishment adopted from reformists of criminal law.

关键词： Computer science   Electrical and electronic engineering  

摘要： Traditional neural networks require enormous amounts of data to build their complex mappings during a slow training procedure that hinders their abilities for relearning and adapting to new data. Memory-augmented neural networks enhance neural networks with an explicit memory to overcome these issues. Access to this explicit memory, however, occurs via soft read and write operations involving every individual memory entry, resulting in a bottleneck when implemented using the conventional von Neumann computer architecture. To overcome this bottleneck, we propose a robust architecture that employs a computational memory unit as the explicit memory performing analog in-memory computation on high-dimensional (HD) vectors, while closely matching 32-bit software-equivalent accuracy. This is achieved by a content-based attention mechanism that represents unrelated items in the computational memory with uncorrelated HD vectors, whose real-valued components can be readily approximated by binary, or bipolar components. Experimental results demonstrate the efficacy of our approach on few-shot image classification tasks on the Omniglot dataset using more than 256,000 phase-change memory devices. Our approach effectively merges the richness of deep neural network representations with HD computing that paves the way for robust vector-symbolic manipulations applicable in reasoning, fusion, and compression. The implementation of memory-augmented neural networks using conventional computer architectures is challenging due to a large number of read and write operations. Here, Karunaratne, Schmuck et al. propose an architecture that enables analog in-memory computing on high-dimensional vectors at accuracy matching 32-bit software equivalent.

关键词： Convolutional neural networks   Computer vision   Deep learning   Image recognition   Machine learning   Wood identification   Wood anatomy  

摘要： The remarkable developments in computer vision and machine learning have changed the methodologies of many scientific disciplines. They have also created a new research field in wood science called computer vision-based wood identification, which is making steady progress towards the goal of building automated wood identification systems to meet the needs of the wood industry and market. Nevertheless, computer vision-based wood identification is still only a small area in wood science and is still unfamiliar to many wood anatomists. To familiarize wood scientists with the artificial intelligence-assisted wood anatomy and engineering methods, we have reviewed the published mainstream studies that used or developed machine learning procedures. This review could help researchers understand computer vision and machine learning techniques for wood identification and choose appropriate techniques or strategies for their study objectives in wood science.

关键词： Computer Science   Artificial Intelligence   Human-Computer Interaction  

摘要： Global coordination is required to solve a wide variety of challenging collective action problems from network colorings to the tragedy of the commons. Recent empirical study shows that the presence of a few noisy autonomous agents can greatly improve collective performance of humans in solving networked color coordination games. To provide analytical insights into the role of behavioral randomness, here we study myopic artificial agents attempting to solve similar network coloring problems using decision update rules that are only based on local information but allow random choices at various stages of their heuristic reasonings. We show that the resulting efficacy of resolving color conflicts is dependent on the implementation of random behavior of agents and specific population characteristics. Our work demonstrates that distributed greedy optimization algorithms exploiting local information should be deployed in combination with occasional exploration via random choices in order to overcome local minima and achieve global coordination.

关键词： Complex System Biology   Systems Biology   Transcriptomics   Computer Science  

摘要： With the advent of deep generative models in computational chemistry, in-silico drug design is undergoing an unprecedented transformation. Although deep learning approaches have shown potential in generating compounds with desired chemical properties, they disregard the cellular environment of target diseases. Bridging systems biology and drug design, we present a reinforcement learning method for de novo molecular design from gene expression profiles. We construct a hybrid Variational Autoencoder that tailors molecules to target-specific transcriptomic profiles, using an anticancer drug sensitivity prediction model (PaccMann) as reward function. Without incorporating information about anticancer drugs, the molecule generation is biased toward compounds with high predicted efficacy against cell lines or cancer types. The generation can be further refined by subsidiary constraints such as toxicity. Our cancer-type-specific candidate drugs are similar to cancer drugs in drug-likeness, synthesizability, and solubility and frequently exhibit the highest structural similarity to compounds with known efficacy against these cancer types.

关键词： Computer aided instruction  

摘要： This is the accepted version, to read the final version published in 2021 in the IEEE Transactions on Learning Technologies (IEEE TLT), please go to: http://***/10.1109/TLT.2021.3056907. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Manually determining concepts present in a group of questions is a challenging and time-consuming process. However, the process is an essential step while modeling a virtual learning environment since a mapping between concepts and questions using mastery level assessment and recommendation engines are required. We investigated unsupervised semantic models (known as topic modeling techniques) to assist computer science teachers in this task and propose a method to transform Computer Science 1 teacher-provided code solutions into representative text documents, including the code structure information. By applying non-negative matrix factorization and latent Dirichlet allocation techniques, we extract the underlying relationship between questions and validate the results using an external dataset. We consider the interpretability of the learned concepts using 14 university professors' data, and the results confirm six semantically coherent clusters using the current dataset. Moreover, the six topics comprise the main concepts present in the test dataset, achieving 0.75 in the normalized pointwise mutual information metric. The metric correlates with human ratings, making the proposed method useful and providing semantics for large amounts of unannotated code. © 2021, CC BY.