nils-herrmann/clean_scirepeval huggingface dataset:
The Distributed Nature of Working Memory

Studies in humans and non-human primates have provided evidence for storage of working memory contents in multiple regions ranging from sensory to parietal and prefrontal cortex. We discuss potential explanations for these distributed representations: (i) features in sensory regions versus prefrontal cortex differ in the level of abstractness and generalizability; and (ii) features in prefrontal cortex reflect representations that are transformed for guidance of upcoming behavioral actions. We propose that the propensity to produce persistent activity is a general feature of cortical networks. Future studies may have to shift focus from asking where working memory can be observed in the brain to how a range of specialized brain areas together transform sensory information into a delayed behavioral response.
Strategic performance measurement in a healthcare organisation : A multiple criteria approach based on balanced scorecard

The Balanced Scorecard (BSC) methodology focuses on major critical issues of modern business organisations: the effective measurement of corporate performance and the evaluation of the successful implementation of corporate strategy. Despite the increased adoption of the BSC methodology by numerous business organisations during the last decade, limited case studies concern non-profit organisations (e.g. public sector, educational institutions, healthcare organisations, etc.). The main aim of this study is to present the development of a performance measurement system for public health care organisations, in the context of BSC methodology. The proposed approach considers the distinguished characteristics of the aforementioned sector (e.g. lack of competition, social character of organisations, etc.). The proposed measurement system contains the most important financial performance indicators, as well as non-financial performance indicators that are able to examine the quality of the provided services, the satisfaction of internal and external customers, the selfimprovement system of the organisation and the ability of the organisation to adapt and change. These indicators play the role of Key Performance Indicators (KPIs), in the context of BSC methodology. The presented analysis is based on a MCDA approach, where the UTASTAR method is used in order to aggregate the marginal performance of KPIs. This approach is able to take into account the preferences of the management of the organisation regarding the achievement of the defined strategic objectives. The main results of the proposed approach refer to the evaluation of the overall scores for each one of the main dimensions of the BSC methodology (i.e. financial, customer, internal business process, and innovation-learning). These results are able to help the organisation to evaluate and revise its strategy, and generally to adopt modern management approaches in every day practise.
Finance Theory and Financial Strategy

Despite its major advances, finance theory has had scant impact on strategic planning. Strategic planning needs finance and should learn to apply finance theory correctly. Hov^ever, finance theory must be extended in order to reconcile financial and strategic analysis. S trategic planning is many things, but (1) Finance theory and traditional ap-it surely includes the process of de-proaches to strategic planning may be trategic planning is many things, but it surely includes the process of deciding how to commit the firm's resources across lines of business. The financial side of strategic planning allocates a particular resource, capital. Finance theory has made major advances in understanding how capital markets work and how risky real and financial assets are valued. Tools derived from finance theory, particularly discounted cash-flow analysis, are widely used. Yet finance theory has had scant impact on strategic planning. I attempt here to explain the gap between finance theory and strategic planning. Three explanations are offered: kept apart by differences in language and "culture." (2) Discounted cash flow analysis may have been misused, and consequently not accepted, in strategic applications. (3) Discounted cash flow analysis may fail in strategic applications even if it is properly applied. Each of these explanations is partly true. I do not claim that the three, taken together , add up to the whole truth. Nevertheless , I will describe both the problems encountered in applying finance theory to strategic planning, and the potential payoffs if the theory can be extended and
MOOCs inside Universities - An Analysis of MOOC Discourse as Represented in HE Magazines

Digital news media discourse on MOOCs has been pervasive in educational publications over recent years, and has often focused on debates over the disruptive potential of MOOCs at one extreme, and their survival at the other. Whether such articles reflect the concerns of academics and other internal university stakeholders is difficult to ascertain. This paper aims to determine the main concerns of internal university stakeholders in terms of their MOOC development and implementation work, and whether these concerns are reflected in the mainstream educational media. The study combines data from 2 previous studies (a content analysis of MOOC literature, and a grounded theory case study of internal university stakeholders) to establish key themes of concern for those working on MOOCs in Higher Education. An analysis of these themes in 3 educational media publications is then conducted for the year 2014. The findings indicate a clear focus in education media and among university stakeholders on new teaching practices and working dynamics in Higher Education as a result of involvement in MOOC development work. We argue that for many working on MOOCs in Higher Education, the debate about the future of MOOCs is over, and that more practical concerns of appropriate implementation and effective working practices are of greater importance.
A Circuit-Level Substrate Current Model for Smart-Power ICs

This paper presents a new modeling methodology accounting for generation and propagation of minority carriers that can be used directly in circuit-level simulators in order to estimate coupled parasitic currents. The method is based on a new compact model of basic components (p-n junction and resistance) and takes into account minority carriers at the boundary. An equivalent circuit schematic of the substrate is built by identifying these basic elements in the substrate and interconnecting them. Parasitic effects such as bipolar or latch-up effects result from the continuity of minority carriers guaranteed by the components' models. A structure similar to a half-bridge perturbing sensitive n-wells has been simulated. It is composed by four p-n junctions connected together by their common p-doped sides. The results are in good agreement with those obtained from physical device simulations.
A Survey of Technologies in Internet of Things

Internet of Things (IoT) is a fast-growing innovation that will greatly change the way humans live. It can be thought of as the next big step in Internet technology. What really enable IoT to be a possibility are the various technologies that build it up. The IoT architecture mainly requires two types of technologies: data acquisition technologies and networking technologies. Many technologies are currently present that aim to serve as components to the IoT paradigm. This paper aims to categorize the various technologies present that are commonly used by Internet of Things.
Deep Reasoning with Knowledge Graph for Social Relationship Understanding

Social relationships (e.g., friends, couple etc.) form the basis of the social network in our daily life. Automatically interpreting such relationships bears a great potential for the intelligent systems to understand human behavior in depth and to better interact with people at a social level. Human beings interpret the social relationships within a group not only based on the people alone, and the interplay between such social relationships and the contextual information around the people also plays a significant role. However, these additional cues are largely overlooked by the previous studies. We found that the interplay between these two factors can be effectively modeled by a novel structured knowledge graph with proper message propagation and attention. And this structured knowledge can be efficiently integrated into the deep neural network architecture to promote social relationship understanding by an end-to-end trainable Graph Reasoning Model (GRM), in which a propagation mechanism is learned to propagate node message through the graph to explore the interaction between persons of interest and the contextual objects. Meanwhile, a graph attentional mechanism is introduced to explicitly reason about the discriminative objects to promote recognition. Extensive experiments on the public benchmarks demonstrate the superiority of our method over the existing leading competitors.
Examining the Effect of Social Media Marketing in Tourism

A growing number of travel agencies in the tourism industry use social media to promote their services and reach target customers despite some doubt regarding the effectiveness of these tools. Nevertheless, few researchers have examined the effects of social media marketing, and especially its effects on sales. Therefore, our study aims to assess the impact of social media on the purchase of tourism products. Additionally, to explore whether the influence of social media marketing changes among different types of tourism products, we develop a typology of tourism products that classifies tourism along the following five dimensions: (1) the structure of tourism, (2) the involvement of tourists, (3) the scope of tourism, (4) the price of products, and (5) the length of a tour. All tours with Facebook campaigns conducted by our case company from February 1, 2012 to November 30, 2013 are selected as our target tourism products. Moreover, we choose certain products without Facebook campaigns that were available for purchase during the same time period as a control group. We obtain the sales data from the case company and calculate the sales of each product before and after the Facebook campaigns. We then apply a difference-in-difference approach, comparing the average changes in sales performance of the treatment group with those of the control group. The results show that Facebook campaign activities have a positive impact on purchases of tourism products. Furthermore, sales are more likely to increase when a travel agency promotes products that are less structured, limited in scope, relatively lower-priced, or require less tourist involvement.
Dense In Dense : Training Segmentation from Scratch

In recent years, training image segmentation networks often needs fine-tuning the model which comes from the initial training upon large-scale classification datasets like ImageNet. Such fine-tuning methods are confronted with three problems: (1) domain gap. (2) mismatch between data size and model size. (3) poor controllability. A more practical solution is to train the segmentation model from scratch, which motivates our Dense In Dense (DID) network. In DID, we put forward an efficient architecture based on DenseNet to further accelerate the information flow inside and outside the dense block. Deep supervision also applies to a progressive upsampling rather than the traditional straightforward upsampling. Our DID Network performs favorably on Camvid dataset, Inria Aerial Image Labeling dataset and Cityscapes by training from scratch with less parameters.
Challenges in Networking to Support Augmented Reality and Virtual Reality

We consider the network implications of virtual reality (VR) and augmented reality (AR). While there are intrinsic challenges for AR/VR applications to deliver on their promise, their impact on the underlying infrastructure will be undeniable. We look at augmented and virtual reality and consider a few use cases where they could be deployed. These use cases define a set of requirements for the underlying network. We take a brief look at potential network architectures. We then make the case for Information-centric networks as a potential architecture to assist the deployment of AR/VR and draw a list of challenges and future research directions for next generation networks to better support AR/VR.
A gm/ID based design of high PSR low dropout regulator for SoC applications

A design methodology by including the finite PSR of the error amplifier to improve the low frequency PSR of the Low dropout regulator with improved voltage subtractor circuit is proposed. The gm/ID method based on exploiting the all regions of operation of the MOS transistor is utilized for the design of LDO regulator. The PSR of the LDO regulator is better than -50dB up to 10MHz frequency for the load currents up to 20mA with 0.15V drop-out voltage. A comparison is made between different schematics of the LDO regulator and proposed methodology for the LDO regulator with improved voltage subtractor circuit. Low frequency PSR of the regulator can be significantly improved with proposed methodology.
Translated Learning: Transfer Learning across Different Feature Spaces

This paper investigates a new machine learning strategy cal led translated learning. Unlike many previous learning tasks, we focus on how to use l ab led data from one feature space to enhance the classification of other entirely different learning spaces. For example, we might wish to use labeled te xt ata to help learn a model for classifying image data, when the labeled images a r difficult to obtain. An important aspect of translated learning is to build a “bridge” to link one feature space (known as the “source space”) to another space (known as the “target space”) through a translator in order to migrate the know ledge from source to target. The translated learning solution uses a language mo del t link the class labels to the features in the source spaces, which in turn is t an lated to the features in the target spaces. Finally, this chain of linkages i s completed by tracing back to the instances in the target spaces. We show that this p ath of linkage can be modeled using a Markov chain and risk minimization. Throu gh experiments on the text-aided image classification and cross-language c l ssification tasks, we demonstrate that our translated learning framework can gre atly outperform many state-of-the-art baseline methods.
simNet: Stepwise Image-Topic Merging Network for Generating Detailed and Comprehensive Image Captions

The encode-decoder framework has shown recent success in image captioning. Visual attention, which is good at detailedness, and semantic attention, which is good at comprehensiveness, have been separately proposed to ground the caption on the image. In this paper, we propose the Stepwise Image-Topic Merging Network (simNet) that makes use of the two kinds of attention at the same time. At each time step when generating the caption, the decoder adaptively merges the attentive information in the extracted topics and the image according to the generated context, so that the visual information and the semantic information can be effectively combined. The proposed approach is evaluated on two benchmark datasets and reaches the state-of-the-art
ADC: Automated Deep Compression and Acceleration with Reinforcement Learning

Model compression is an effective technique facilitating the deployment of neural network models on mobile devices that have limited computation resources and a tight power budget. However, conventional model compression techniques [19, 20, 23] use hand-crafted features and require domain experts to explore the large design space trading off model size, speed, and accuracy, which is usually suboptimal and time-consuming. In this paper, we propose Automated Deep Compression (ADC) that leverages reinforcement learning in order to efficiently sample the design space and greatly improve the model compression quality. We achieved state-of-the-art model compression results in a fully automated way without any human efforts. Under 4× FLOPs reduction, we achieved 2.7% better accuracy than hand-crafted model compression method for VGG-16 on ImageNet. We applied this automated, push-the-button compression pipeline to MobileNet and achieved a 2× reduction in FLOPs, and a speedup of 1.49× on Titan Xp and 1.65× on an Android phone (Samsung Galaxy S7), with negligible loss of accuracy.
Metric learning with spectral graph convolutions on brain connectivity networks

Graph representations are often used to model structured data at an individual or population level and have numerous applications in pattern recognition problems. In the field of neuroscience, where such representations are commonly used to model structural or functional connectivity between a set of brain regions, graphs have proven to be of great importance. This is mainly due to the capability of revealing patterns related to brain development and disease, which were previously unknown. Evaluating similarity between these brain connectivity networks in a manner that accounts for the graph structure and is tailored for a particular application is, however, non-trivial. Most existing methods fail to accommodate the graph structure, discarding information that could be beneficial for further classification or regression analyses based on these similarities. We propose to learn a graph similarity metric using a siamese graph convolutional neural network (s-GCN) in a supervised setting. The proposed framework takes into consideration the graph structure for the evaluation of similarity between a pair of graphs, by employing spectral graph convolutions that allow the generalisation of traditional convolutions to irregular graphs and operates in the graph spectral domain. We apply the proposed model on two datasets: the challenging ABIDE database, which comprises functional MRI data of 403 patients with autism spectrum disorder (ASD) and 468 healthy controls aggregated from multiple acquisition sites, and a set of 2500 subjects from UK Biobank. We demonstrate the performance of the method for the tasks of classification between matching and non-matching graphs, as well as individual subject classification and manifold learning, showing that it leads to significantly improved results compared to traditional methods.
On the Choice-Based Linear Programming Model for Network Revenue Management

G et al. [Gallego, G., G. Iyengar, R. Phillips, A. Dubey. 2004. Managing flexible products on a network.  Technical Report TR-2004-01, Department of Industrial Engineering and Operations Research, Columbia University, New York.] recently proposed a choice-based deterministic linear programming model (CDLP) for network revenue management (RM) that parallels the widely used deterministic linear programming (DLP) model. While they focused on analyzing “flexible products”—a situation in which the provider has the flexibility of using a collection of products (e.g., different flight times and/or itineraries) to serve the same market demand (e.g., an origin-destination connection)—their approach has broader implications for understanding choice-based RM on a network. In this paper, we explore the implications in detail. Specifically, we characterize optimal offer sets (sets of available network products) by extending to the network case a notion of “efficiency” developed by Talluri and van Ryzin [Talluri, K. T., G. J. van Ryzin. 2004. Revenue management under a general discrete choice model of consumer behavior.  for the single-leg, choice-based RM problem. We show that, asymptotically, as demand and capacity are scaled up, only these efficient sets are used in an optimal policy. This analysis suggests that efficiency is a potentially useful approach for identifying “good” offer sets on networks, as it is in the case of single-leg problems. Second, we propose a practical decomposition heuristic for converting the static CDLP solution into a dynamic control policy. The heuristic is quite similar to the familiar displacement-adjusted virtual nesting (DAVN) approximation used in traditional network RM, and it significantly improves on the performance of the static LP solution. We illustrate the heuristic on several numerical examples.
Enabling Docker Containers for High-Performance and Many-Task Computing

Docker is the most popular and user friendlyplatform for running and managing Linux containers. This isproven by the fact that vast majority of containerized tools arepackaged as Docker images. A demanding functionality is toenable running Docker containers inside HPC job scripts forresearchers to make use of the flexibility offered by containersin their real-life computational and data intensive jobs. The maintwo questions before implementing such functionality are: how tosecurely run Docker containers within cluster jobs? and how tolimit the resource usage of a Docker job to the borders defined bythe HPC queuing system? This paper presents Socker, a securewrapper for running Docker containers on Slurm and similarqueuing systems. Socker enforces the execution of containerswithin Slurm jobs as the submitting user instead of root, as wellas enforcing the inclusion of containers in the cgroups assignedby the queuing system to the parent jobs. Different from otherDocker supported containers-for-hpc platform, socker uses theunderlaying Docker engine instead of replacing it. To eveluatesocker, it has been tested for running MPI Docker jobs on Slurm. It has been also tested for Many-task computing (MTC) on interconnectedclusters. Socker has proven to be secure, as well asintroducing no additional overhead to the one introduced alreadyby the Docker engine.
Mobile Edge Computation Offloading Using Game Theory and Reinforcement Learning

Due to the ever-increasing popularity of resourcehungry and delay-constrained mobile applications, the computation and storage capabilities of remote cloud has partially migrated towards the mobile edge, giving rise to the concept known as Mobile Edge Computing (MEC). While MEC servers enjoy the close proximity to the end-users to provide services at reduced latency and lower energy costs, they suffer from limitations in computational and radio resources, which calls for fair efficient resource management in the MEC servers. The problem is however challenging due to the ultra-high density, distributed nature, and intrinsic randomness of next generation wireless networks. In this article, we focus on the application of game theory and reinforcement learning for efficient distributed resource management in MEC, in particular, for computation offloading. We briefly review the cutting-edge research and discuss future challenges. Furthermore, we develop a gametheoretical model for energy-efficient distributed edge server activation and study several learning techniques. Numerical results are provided to illustrate the performance of these distributed learning techniques. Also, open research issues in the context of resource management in MEC servers are discussed.