nils-herrmann/clean_scirepeval huggingface dataset:
Mapping gray matter development: Implications for typical development and vulnerability to psychopathology

Recent studies with brain magnetic resonance imaging (MRI) have scanned large numbers of children and adolescents repeatedly over time, as their brains develop, tracking volumetric changes in gray and white matter in remarkable detail. Focusing on gray matter changes specifically, here we explain how earlier studies using lobar volumes of specific anatomical regions showed how different lobes of the brain matured at different rates. With the advent of more sophisticated brain mapping methods, it became possible to chart the dynamic trajectory of cortical maturation using detailed 3D and 4D (dynamic) models, showing spreading waves of changes evolving through the cortex. This led to a variety of time-lapse films revealing characteristic deviations from normal development in schizophrenia, bipolar illness, and even in siblings at genetic risk for these disorders. We describe how these methods have helped clarify how cortical development relates to cognitive performance, functional recovery or decline in illness, and ongoing myelination processes. These time-lapse maps have also been used to study effects of genotype and medication on cortical maturation, presenting a powerful framework to study factors that influence the developing brain.
Towards smart city: M2M communications with software agent intelligence

Recent advances in the fields of wireless technology have exhibited a strong potential and tendency on improving human life by means of ubiquitous communications devices that enable smart, distributed services. In fact, traditional human to human (H2H) communications are gradually falling behind the scale of necessity. Consequently, machine to machine (M2M) communications have surpassed H2H, thus drawing significant interest from industry and the research community recently. This paper first presents a four-layer architecture for internet of things (IoT). Based on this architecture, we employ the second generation RFID technology to propose a novel intelligent system for M2M communications.
Learning Fine-Grained Knowledge about Contingent Relations between Everyday Events

Much of the user-generated content on social media is provided by ordinary people telling stories about their daily lives. We develop and test a novel method for learning fine-grained common-sense knowledge from these stories about contingent (causal and conditional) relationships between everyday events. This type of knowledge is useful for text and story understanding, information extraction, question answering, and text summarization. We test and compare different methods for learning contingency relation, and compare what is learned from topic-sorted story collections vs. general-domain stories. Our experiments show that using topicspecific datasets enables learning finergrained knowledge about events and results in significant improvement over the baselines. An evaluation on Amazon Mechanical Turk shows 82% of the relations between events that we learn from topic-sorted stories are judged as contingent.
Automated Abdominal Multi-Organ Segmentation With Subject-Specific Atlas Generation

A robust automated segmentation of abdominal organs can be crucial for computer aided diagnosis and laparoscopic surgery assistance. Many existing methods are specialized to the segmentation of individual organs and struggle to deal with the variability of the shape and position of abdominal organs. We present a general, fully-automated method for multi-organ segmentation of abdominal computed tomography (CT) scans. The method is based on a hierarchical atlas registration and weighting scheme that generates target specific priors from an atlas database by combining aspects from multi-atlas registration and patch-based segmentation, two widely used methods in brain segmentation. The final segmentation is obtained by applying an automatically learned intensity model in a graph-cuts optimization step, incorporating high-level spatial knowledge. The proposed approach allows to deal with high inter-subject variation while being flexible enough to be applied to different organs. We have evaluated the segmentation on a database of 150 manually segmented CT images. The achieved results compare well to state-of-the-art methods, that are usually tailored to more specific questions, with Dice overlap values of 94%, 93%, 70%, and 92% for liver, kidneys, pancreas, and spleen, respectively.
Technology as an operant resource in service (eco)systems

In this paper, we explore the role and scope of technology in value co-creation, service innovation and service systems—value co-creation configurations of people technology and value propositions (Maglio and Spohrer in J Acad Mark Sci 36:18–20, 2008). We draw on a structurational model of technology (Orlikowsky in Organ Sci 3(3):398–427, 1992) to provide a framework for considering the role of technology in service systems and how it influences and is influenced by human actions (i.e., practices) and institutions. We broaden the scope of technology in this model, beyond a material artifact, or outcome of human actions, by applying an S-D logic, service ecosystems (Vargo and Lusch in J Market 68(1):1–17, 2004, Ind Mark Manag  2011a) approach, which focuses on the processes by which value is co-created and new ways of creating value (i.e., innovation) emerge. In this view, technology can be conceptualized as an operant resource—one that is capable of acting on other resources to create value—and, thus, becomes a critical resource for value co-creation, service innovation and systems (re)formation. We argue that the consideration of technology as an operant resource in service (eco)systems provides a more encompassing view for systematically studying the way in which technologies are integrated as resources, value is collaboratively created, and service is innovated.
Functional Near Infrared Spectroscope for Cognition Brain Tasks by Wavelets Analysis and Neural Networks

Brain Computer Interface (BCI) has been recently increased in research.  Because near infrared technology allows design of safe, portable, wearable, non-invasive and wireless qualities monitoring systems, fNIRs monitoring of brain hemodynamics can be value in helping to understand brain tasks. In this paper, we present results of fNIRs signal analysis indicating that there exist distinct patterns of hemodynamic responses which recognize brain tasks toward developing a BCI. We applied two different mathematics tools separately, Wavelets analysis for preprocessing as signal filters and feature extractions and Neural networks for cognition brain tasks as a classification module. We also discuss and compare with other methods while our proposals perform better with an average accuracy of 99.9% for classification. functional near infrared spectroscope (fNIRs), braincomputer interface (BCI), wavelets, neural networks, brain activity, neuroimaging.
Improving read performance of STT-MRAM based main memories through Smash Read and Flexible Read

Spin Transfer Torque Magnetoresistive RAM (STT-MRAM) has been recently deemed as one promising main memory alternative for high-end mobile processors. With process technology scaling, the amplitude of write current approaches that of read current in deep sub-micrometer STT-MRAM arrays. As a result, read disturbance errors (RDEs) emerge. Both high current restore required (HCRR) reads and low current long latency (LCLL) reads can guarantee read reliability and utterly remove RDEs. However, both of them degrade system performance, because of extra restores or a longer read latency. And neither of them always achieves the better performance when running a wide variety of applications. In this paper, we present two architectural techniques to boost read performance for STT-MRAM based main memories in the presence of RDEs. We first propose Smash Read (S-RD) to shorten the latency of HCRR reads by injecting a larger read current. We further introduce Flexible Read (F-RD) to dynamically adopt different types of read schemes, S-RD and LCLL, to maximize main memory system performance. On average, our techniques improve system performance by 9~13% and reduces total energy by 4~8% over all existing read schemes including HCRR and LCLL.
A Framework for Passengers Demand Prediction and Recommendation

With the rapid development of mobile internet and wireless network technologies, more and more people use the mobile app to call a taxicab to pick them up. Therefore, understanding the passengers' travel demand becomes crucial to improve the utilization of the taxicabs and reduce their cost. In this paper, based on spatio-temporal clustering, we propose a demand hotspots prediction framework to generate recommendation for taxi drivers. Specially, an adaptive prediction approach is presented to demand hotspots and their hotness, and then combing the driver's location and the hotness, top candidates are recommended and visually presented to drivers. Based on the dataset provided by CAR INC., the experiment shows that our approach gains a significant improvement in hotspots prediction and recommendation, with 15.21% improvement on average f-measure for prediction and 79.6% hit ratio for recommendation.
Selectively compliant underactuated hand for mobile manipulation

The demands of mobile manipulation are leading to a new class of multi-fingered hands with a premium on being lightweight and robust as well as being able to grasp and perform basic manipulations with a wide range of objects. A promising approach to addressing these goals is to use compliant, underactuated hands with selectively lockable degrees of freedom. This paper presents the design of one such hand that combines series-elastic actuation and electrostatic braking at the joints. A numerical analysis shows how the maximum pullout force varies as a function of kinematic parameters, spring forces at the joints and brake torques.
Travel Q: Questifying Micro Activities Using Travel Photos to Enhance Travel Experience

Travel is a series of micro activities. With the proliferation of digital cameras and smartphones, it is now common for travelers to record their micro activities in fine detail by taking photos without ever missing a moment. However, how to enhance the travel experience by utilizing such recorded micro activities has not been determined. Thus, we aim to 1) investigate how travelers record micro activities in photos through a user study and 2) design a system to enhance travel experiences utilizing travel photos. Based on the user study, we devise a "questification" strategy that motivates travelers to change their photos into quests. We implement a research prototype, Travel Q, a community platform that allows users to share their micro activities through photos. The results of a field study with the prototype suggest that questification is an applicable strategy for enhancing travel experiences.
Ontological based interoperability and integration framework for heterogeneous weather systems

Weather predictions are used in various sectors viz. agriculture, air traffic, marine, forestry, energy, and severe weather alerts advisories etc. The weather information is widely provided by various systems with different formats and parameters. For the effective utilization of the heterogeneous weather data, various processing techniques are required to resolve the information heterogeneity and to enrich data integration in order to provide a uniform access for various agencies, research institutions for better decision making. To address this issue, there is a need to resolve semantic heterogeneities between information systems. An integrated system is formed based on the semantic correspondence between semantically similar elements of different information systems. Ontologies are used as a method to solve the semantic heterogeneity issue, which provides formal, explicit representation of information. Ontology mapping finds semantic correspondence between ontologies and ontology merging integrates mapped ontologies into a single ontology. This integrated system ensures fast and vast information access from a single source. In this paper a novel framework is proposed for integrating heterogeneous information systems, using ontology merging which is based on ontology mapping relations. To evaluate our framework weather systems are taken and the result shows enhanced information retrieval from the integrated system.
Metacognitive strategies in student learning: do students practise retrieval when they study on their own?

Basic research on human learning and memory has shown that practising retrieval of information (by testing the information) has powerful effects on learning and long-term retention. Repeated testing enhances learning more than repeated reading, which often confers limited benefit beyond that gained from the initial reading of the material. Laboratory research also suggests that students lack metacognitive awareness of the mnemonic benefits of testing. The implication is that in real-world educational settings students may not engage in retrieval practise to enhance learning. To investigate students' real-world study behaviours, we surveyed 177 college students and asked them (1) to list strategies they used when studying (an open-ended free report question) and (2) to choose whether they would reread or practise recall after studying a textbook chapter (a forced report question). The results of both questions point to the same conclusion: A majority of students repeatedly read their notes or textbook (despite the limited benefits of this strategy), but relatively few engage in self-testing or retrieval practise while studying. We propose that many students experience illusions of competence while studying and that these illusions have significant consequences for the strategies students select when they monitor and regulate their own learning.
Novel paradigms for advanced distribution grid energy management

The electricity distribution grid was not designed to cope with load dynamics imposed by high penetration of electric vehicles, neither to deal with the increasing deployment of distributed Renewable Energy Sources. Distribution System Operators (DSO) will increasingly rely on flexible Distributed Energy Resources (flexible loads, controllable generation and storage) to keep the grid stable and to ensure quality of supply. In order to properly integrate demand-side flexibility, DSOs need new energy management architectures, capable of fostering collaboration with wholesale market actors and prosumers. We propose the creation of Virtual Distribution Grids (VDG) over a common physical infrastructure, to cope with heterogeneity of resources and actors, and with the increasing complexity of distribution grid management and related resources allocation problems. Focusing on residential VDG, we propose an agent-based hierarchical architecture for providing Demand-Side Management services through a market-based approach, where households transact their surplus/lack of energy and their flexibility with neighbours, aggregators, utilities and DSOs. For implementing the overall solution, we consider fine-grained control of smart homes based on Internet of Things technology. Homes seamlessly transact self-enforcing smart contracts over a blockchainbased generic platform. Finally, we extend the architecture to solve existing problems on smart home control, beyond energy management.
LSTM-Based Predictions for Proactive Information Retrieval

We describe a method for proactive information retrieval targeted at retrieving relevant information during a writing task. In our method, the current task and the needs of the user are estimated, and the potential next steps are unobtrusively predicted based on the user’s past actions. We focus on the task of writing, in which the user is coalescing previously collected information into a text. Our proactive system automatically recommends the user relevant background information. The proposed system incorporates text input prediction using a long short-term memory (LSTM) network. We present simulations, which show that the system is able to reach higher precision values in an exploratory search setting compared to both a baseline and a comparison system.
Secret Sharing Made Short

A well-known fact in the theory of secret sharing schemes is that shares must be of length at least as the secret itself. However, the proof of this lower bound uses the notion of information theoretic secrecy. A natural (and very practical) question is whether one can do better for secret sharing if the notion of secrecy is computational, namely, against resource bounded adversaries. In this note we observe that, indeed, one can do much better in the computational model (which is the one used in most applications). We present an m-threshold scheme, where m shares recover the secret but m 1 shares give no (computational) information on the secret, in which shares corresponding to a secret S are of size $ plus a short piece of information whose length does not depend on the secret size but just in the security parameter. (The bound of 5 is clearly optimal if the secret is to be recovered from m shares). Therefore, for moderately large secrets (a confidential file, a long message, a large data base) the savings in space and communication over traditional schemes is remarkable. The scheme is very simple and combines in a natural way traditiond (perfect) secret sharing schemes, encryption, and information dispersal. It is provable secure given a secure (e.g., private key) encryption function.
Iron Maiden While Jogging, Debussy for Dinner? - An Analysis of Music Listening Behavior in Context

Contextual information of the listener is only slowly being integrated into music retrieval and recommendation systems. Given the enormous rise in mobile music consumption and the many sensors integrated into today’s smart-phones, at the same time, an unprecedented source for user context data of different kinds is becoming available. Equipped with a smart-phone application, which had been developed to monitor contextual aspects of users when listening to music, we collected contextual data of listening events for 48 users. About 100 different user features, in addition to music meta-data have been recorded. In this paper, we analyze the relationship between aspects of the user context and music listening preference. The goals are to assess (i) whether user context factors allow predicting the song, artist, mood, or genre of a listened track, and (ii) which contextual aspects are most promising for an accurate prediction. To this end, we investigate various classifiers to learn relations between user context aspects and music meta-data. We show that the user context allows to predict artist and genre to some extent, but can hardly be used for song or mood prediction. Our study further reveals that the level of listening activity has little influence on the accuracy of predictions.
Anatomy of deductive reasoning

Much of cognitive research on deductive reasoning has been preoccupied with advocating for or against visuospatial (mental model theory) or linguistic/syntactic (mental logic theory) models of logical reasoning. Neuroimaging studies bear on this issue by pointing to both language-based and visuospatial systems being engaged during logical reasoning, and by raising additional issues not anticipated by these cognitive theories. Here, the literature on the neural basis of deductive reasoning from the past decade is reviewed. Although these results might seem chaotic and inconsistent, we identify several interesting patterns and articulate their implications for cognitive theories of reasoning. Cognitive neuroscience data point away from a unitary system for logical reasoning and towards a fractionated system dynamically reconfigured in response to specific task and environmental cues.
Review of Silicon Power Semiconductor Technologies for Power Supply on Chip and Power Supply in Package Applications

This paper provides an overview of the fundamental operating principles of power semiconductor devices and reviews the state-of-the-art power device technologies which are most relevant to the realization of power supply on chip and power supply in package solutions. Power switching devices are divided into four categories low-voltage discrete transistors, high-voltage discrete transistors, low-voltage power ICs, and high-voltage power ICs. Advantages and disadvantages of different device technologies are compared and analyzed. Future technology trends are discussed.
Image resizing via non-homogeneous warping

Image resizing aims to adapt images to displays with different sizes and aspect ratios. In this paper, we provide a new image resizing approach for efficiently determining the non-homogeneous warp that better preserves the global image configuration and concentrates the distortion in regions of the image where they are least-likely to be noticed. Considering the different properties of large displays and small displays, we design different strategies for upsizing and downsizing. We define a variety of quadratic metrics to measure image distortion. We introduce a patch-linking scheme that can better preserve the global image configuration. We formulate image resizing as a quadratic minimization problem, which can be efficiently solved. We experiment with our method on a variety category of images and compare our results to the state of the art.
Gene therapy : what it is , what it is not and what it will be

Gene therapy is the therapeutic procedure based on the introduction of healthy genes using recombinant DNA techniques. the first successful clinical trial of this technique was published in 1990. Despite the occurrence of adverse effects in certain clinical trials, some of them serious, both laboratories and companies are continuously developing novel materials and establishing both safer and more effective procedures. Although still in experimental stages, recent progress points to growing opportunities for investment by industry, as well as justify the expectation that, in some cases, this technology may reach clinical practice within a few years.  Genes, Genetic diseases, Genetic engineering, Genome manipulation, Advanced therapies, Biotechnology. Rafael Linden is an mD, holds a PhD in sciences and is a full professor at Carlos Chagas institute of Biophysics, UFrJ. @ – rlinden@biof.ufrj.br received on 29 July 2010.  the original contents of the manuscript provided by the author were written and published in Portuguese. the paper was translated into English by tag Comunicação, as per publisher’s request. the author is not responsible for translation or interpretation of content.