nils-herrmann/clean_scirepeval huggingface dataset:
Less-structured time in children's daily lives predicts self-directed executive functioning

Executive functions (EFs) in childhood predict important life outcomes. Thus, there is great interest in attempts to improve EFs early in life. Many interventions are led by trained adults, including structured training activities in the lab, and less-structured activities implemented in schools. Such programs have yielded gains in children's externally-driven executive functioning, where they are instructed on what goal-directed actions to carry out and when. However, it is less clear how children's experiences relate to their development of self-directed executive functioning, where they must determine on their own what goal-directed actions to carry out and when. We hypothesized that time spent in less-structured activities would give children opportunities to practice self-directed executive functioning, and lead to benefits. To investigate this possibility, we collected information from parents about their 6-7 year-old children's daily, annual, and typical schedules. We categorized children's activities as "structured" or "less-structured" based on categorization schemes from prior studies on child leisure time use. We assessed children's self-directed executive functioning using a well-established verbal fluency task, in which children generate members of a category and can decide on their own when to switch from one subcategory to another. The more time that children spent in less-structured activities, the better their self-directed executive functioning. The opposite was true of structured activities, which predicted poorer self-directed executive functioning. These relationships were robust (holding across increasingly strict classifications of structured and less-structured time) and specific (time use did not predict externally-driven executive functioning). We discuss implications, caveats, and ways in which potential interpretations can be distinguished in future work, to advance an understanding of this fundamental aspect of growing up.
Primal-Dual Group Convolutions for Deep Neural Networks

In this paper, we present a simple and modularized neural network architecture, named primal-dual group convolutional neural networks (PDGCNets). The main point lies in a novel building block, a pair of two successive group convolutions: primal group convolution and dual group convolution. The two group convolutions are complementary: (i) the convolution on each primal partition in primal group convolution is a spatial convolution, while on each dual partition in dual group convolution, the convolution is a point-wise convolution; (ii) the channels in the same dual partition come from different primal partitions. We discuss one representative advantage: Wider than a regular convolution with the number of parameters and the computation complexity preserved. We also show that regular convolutions, group convolution with summation fusion (as used in ResNeXt), and the Xception block are special cases of primal-dual group convolutions. Empirical results over standard benchmarks, CIFAR-10, CIFAR-100, SVHN and ImageNet demonstrate that our networks are more efficient in using parameters and computation complexity with similar or higher accuracy.
Forward-Backward Selection with Early Dropping

Forward-backward selection is one of the most basic and commonly-used feature selection algorithms available. It is also general and conceptually applicable to many different types of data. In this paper, we propose a heuristic that significantly improves its running time, while preserving predictive accuracy. The idea is to temporarily discard the variables that are conditionally independent with the outcome given the selected variable set. Depending on how those variables are reconsidered and reintroduced, this heuristic gives rise to a family of algorithms with increasingly stronger theoretical guarantees. In distributions that can be faithfully represented by Bayesian networks or maximal ancestral graphs, members of this algorithmic family are able to correctly identify the Markov blanket in the sample limit. In experiments we show that the proposed heuristic increases computational efficiency by about two orders of magnitude in high-dimensional problems, while selecting fewer variables and retaining predictive performance. Furthermore, we show that the proposed algorithm and feature selection with LASSO perform similarly when restricted to select the same number of variables, making the proposed algorithm an attractive alternative for problems where no (efficient) algorithm for LASSO exists.
Honda humanoid robots development.

Honda has been doing research on robotics since 1986 with a focus upon bipedal walking technology. The research started with straight and static walking of the first prototype two-legged robot. Now, the continuous transition from walking in a straight line to making a turn has been achieved with the latest humanoid robot ASIMO. ASIMO is the most advanced robot of Honda so far in the mechanism and the control system. ASIMO's configuration allows it to operate freely in the human living space. It could be of practical help to humans with its ability of five-finger arms as well as its walking function. The target of further development of ASIMO is to develop a robot to improve life in human society. Much development work will be continued both mechanically and electronically, staying true to Honda's 'challenging spirit'.
Adopting a Digital Business Operating System

The role of software in society and in industry in particular continues to grow exponentially. Most companies either have or are in the process of adoption continuous deployment of their software at products in the field and collect data concerning the performance of their systems. The continuous, fast feedback loops that companies now have available allow for a fundamentally different way of organizing. In fact, based on our work with dozens of companies, we have come to the conclusion that companies are moving towards a new, digital operating system. In this paper, we first present the key elements of the digital operating system and then discuss some of the challenges companies experience during the transformation.
Repeated labeling using multiple noisy labelers

This paper addresses the repeated acquisition of labels for data items when the labeling is imperfect. We examine the improvement (or lack thereof) in data quality via repeated labeling, and focus especially on the improvement of training labels for supervised induction of predictive models. With the outsourcing of small tasks becoming easier, for example via Amazon’s Mechanical Turk, it often is possible to obtain less-than-expert labeling at low cost. With low-cost labeling, preparing the unlabeled part of the data can become considerably more expensive than labeling. We present repeated-labeling strategies of increasing complexity, and show several main results. (i) Repeated-labeling can improve label quality and model quality, but not always. (ii) When labels are noisy, repeated labeling can be preferable to single labeling even in the traditional setting where labels are not particularly cheap. (iii) As soon as the cost of processing the unlabeled data is not free, even the simple strategy of labeling everything multiple times can give considerable advantage. (iv) Repeatedly labeling a carefully chosen set of points is generally preferable, and we present a set of robust techniques that combine different notions of uncertainty to select data points for which quality should be improved. The bottom line: the results show clearly that when labeling is not perfect, selective acquisition of multiple labels is a strategy that data miners should have in their repertoire; for certain label-quality/cost regimes, the benefit is substantial.
A qualitative meta-synthesis of emergency department staff experiences of violence and aggression.

Patient and visitor violence or aggression against healthcare workers in the Emergency Department (ED) is a significant issue worldwide. This review synthesises existing qualitative studies exploring the first-hand experiences of staff working in the ED to provide insight into preventing this issue.



A meta-ethnographic approach was used to review papers.



Four concepts were identified: 'The inevitability of violence and aggression'; 'Staff judgments about why they face violence and aggression'; 'Managing in isolation'; and 'Wounded heroes'.



Staff resigned themselves to the inevitability of violence and aggression, doing this due to a perceived lack of support from the organisation. Staff made judgements about the reasons for violent incidents which impacted on how they coped and subsequently tolerated the aggressor. Staff often felt isolated when managing violence and aggression.  Staff training in understanding violence and aggression and clinical supervision.



Violence and aggression in the ED can often be an overwhelming yet inevitable experience for staff. A strong organisational commitment to reducing violence and aggression is imperative.
Benefiting from Social Capital in Online Support Groups: An Empirical Study of Cancer Patients

With measures specific to the online cancer environment and data from an online survey of cancer patients, the current study finds support for the following model: asynchronous online communication --> social interaction --> social support --> positive health outcomes in terms of stress, depression, and coping. The findings suggest that the Internet can be a positive cyber venue for cancer patients as they confront illness, undergo treatment, and seek out support.
Imago: presence and emotion in virtual reality

As virtual reality becomes available to a wider audience, filmmakers are being challenged to create ever more immersive and creative narratives for this quickly evolving medium. While many of the early attempts at virtual reality filmmaking have been devoted to the creation of quick paced and aw-inspiring experiences, filmmakers such as Chris Milk and Oculus Story Studio have been more focused on more story driven experiences, using the medium to tell narratives in a new and exciting way.
 Project Hypnos was created at Carnegie Mellon University to explore immersive narrative filmmaking techniques for virtual reality. The resulting proof-of-concept film, Imago, was created using the techniques developed during this research. Imago plays with the concept of a viewer's presence in a film in an attempt to elicit both negative and positive emotions. The result is a story-driven experience that comes to life through live-action drama, dance, and abstract CG imagery.
Roles of Macro-Actions in Accelerating Reinforcement Learning

We analyze the use of built-in policies, or macro-actions, as a form of domain knowledge that can improve the speed and scaling of reinforcement learning algorithms. Such macro-actions are often used in robotics, and macro-operators are also well-known as an aid to state-space search in AI systems. The macro-actions we consider are closed-loop policies with termination conditions. The macro-actions can be chosen at the same level as primitive actions. Macro-actions commit the learning agent to act in a particular, purposeful way for a sustained period of time. Overall, macro-actions may either accelerate or retard learning , depending on the appropriateness of the macro-actions to the particular task. We analyze their eeect in a simple example, breaking the acceleration eeect into two parts: 1) the eeect of the macro-action in changing exploratory behavior, independent of learning , and 2) the eeect of the macro-action on learning, independent of its eeect on behavior. In our example, both eeects are signiicant, but the latter appears to be larger. Finally, we provide a more complex gridworld illustration of how appropriately chosen macro-actions can accelerate overall learning. Many problems in artiicial intelligence (AI) are too large to be solved practically by searching the state-space using available primitive operators. By searching for the goal using only primitive operators, the AI system is bounded by both the depth and the breadth of the search tree. One way to overcome this diiculty is through macro-actions (or macros). By chunking together primitive actions into macro-actions, the eeective length of the solution is shortened. Both Korf, 1985] and Iba, 1989] have demonstrated that using macro-actions to search for a solution has resulted in solutions in cases where the system was unable to nd answers by searching in primitive state-space, and in nding faster solutions in cases where both systems could solve the problem. Reinforcement learning (RL) is a collection of methods for discovering near-optimal solutions to stochas-tic sequential decision problems Watkins, 1989]. An RL system interacts with the environment by executing actions and receiving rewards from the environment. Unlike supervised learning, RL does not rely on an outside teacher to specify the correct action for a given state. Instead, an RL system tries diierent actions and uses the feedback from the environment to determine a closed loop policy which maximizes reward. In this work, we treat macro-actions as closed-loop policies with termination conditions. Prior work that has included closed-loop macro-In …
Learning Mixed Initiative Dialog Strategies By Using Reinforcement Learning On Both Conversants

This paper describes an application of reinforcement learning to determine a dialog policy for a complex collaborative task where policies for both the system and a proxy for a user of the system are learned simultaneously. With this approach a useful dialog policy is learned without the drawbacks of other approaches that require significant human interaction. The specific task that the agents were trained on was chosen for its complexity and requirement that both conversants bring task knowledge to the interaction, thus ensuring its collaborative nature. The results of our experiment show that you can use reinforcement learning to create an effective dialog policy, which employs a mixed initiative strategy, without the drawbacks of large amounts of data or significant human input.
Exploiting users' social relations to forward data in opportunistic networks: The HiBOp solution

Opportunistic networks, in which nodes opportunistically exploit any pair-wise contact to identify next hops towards the destination, are one of the most interesting technologies to support the pervasive networking vision. Opportunistic networks allow content sharing between mobile users without requiring any pre-existing Internet infrastructure, and tolerate partitions, long disconnections, and topology instability in general. In this paperwe propose a context-aware framework for routing and forwarding in opportunistic networks. The framework is general, and able to host various flavors of context-aware routing. In this work we also present a particular protocol, HiBOp, which, by exploiting the framework, learns and represents through context information, the users’ behavior and their social relations, and uses this knowledge to drive the forwarding process. The comparison of HiBOp with reference to alternative solutions shows that a context-aware approach based on users’ social relations turns out to be a very efficient solution for forwarding in opportunistic networks.We showperformance improvements over the reference solutions both in terms of resource utilization and in terms of user perceived QoS.
A Novel 60 GHz Wideband Coupled Half-Mode/Quarter-Mode Substrate Integrated Waveguide Antenna

A novel wideband substrate integrated waveguide (SIW) antenna topology, consisting of coupled half-mode and quarter-mode SIW resonant cavities, is proposed for operation in the 60 GHz band. This innovative topology combines a considerable bandwidth enhancement and a low form factor with compatibility with low-cost printed circuit board manufacturing processes, making it excellently suited for the next generation, high data rate wireless applications. Moreover, exploiting SIW technology, a high antenna-platform isolation is obtained, enabling dense integration with active electronics without harmful coupling. The computer-aided design process yields an antenna that covers the entire 57–64 GHz IEEE 802.11ad band with a measured fractional impedance bandwidth of 11.7% (7 GHz). The measured maximum gain and radiation efficiency of the prototype are larger than 5.1 dBi and 65%, respectively, within the entire impedance bandwidth.
Fine-Grained Proper Noun Ontologies For Question Answering

The WordNet lexical ontology, which is primarily composed of common nouns, has been widely used in retrieval tasks. Here, we explore the notion of a finegrained proper noun ontology and argue for the utility of such an ontology in retrieval tasks. To support this claim, we build a fine-grained proper noun ontology from unrestricted news text and use this ontology to improve performance on a question answering task.
A Regularized Singular Value Decomposition-Based Approach for Failure Pattern Classification on Fail Bit Map in a DRAM Wafer

In semiconductor manufacturing processes, monitoring the quality of wafers is one of the most important steps to quickly detect process faults and significantly reduce yield loss. Fail bit maps (FBMs) represent the failed cell count during wafer functional tests and have been popularly used as one of the diagnosis tools in semiconductor manufacturing for process monitoring, root cause analysis, and yield improvements. However, the visual inspection process is costly and time-consuming. Therefore, this paper proposes an automated classification procedure for failure patterns on FBMs in dynamic random access memory (DRAM) wafers. The novel matrix factorization approach, called regularized singular value decomposition, is proposed to decompose binarized FBMs into several eigen-images to extract features that can provide the characteristics of the failure patterns on FBMs. By using the extracted features, k-nearest neighbor classifier is employed to classify feature patterns on FBMs into single bit failure maps and non-single bit failure ones. The proposed procedure is tested on real-life DRAM wafer data set provided by a semiconductor manufacturing industry, and promising results have been obtained for the automatic classification of single bit and non-single bit failure maps.
Leveraging app usage contexts for app recommendation: a neural approach

The large volume and variety of apps pose a great challenge for people to choose appropriate apps. As a consequence, app recommendation is becoming increasingly important. Recently, app usage data which record the sequence of apps being used by a user have become increasingly available. Such data record the usage context of each instance of app use, i.e., the app instances being used together with this app (within a short time window). Our empirical data analysis shows that a user has a pattern of app usage contexts. More importantly, the similarity in the two users’ preferences over mobile apps is correlated with the similarity in their app usage context patterns. Inspired by these important observations, this paper tries to leverage the predictive power of app usage context patterns for effective app recommendation. To this end, we propose a novel neural approach which learns the embeddings of both users and apps and then predicts a user’s preference for a given app. Our neural network structure models both a user’s preference over apps and the user’s app usage context pattern in a unified way. To address the issue of unbalanced training data, we introduce several sampling methods to sample user-app interactions and app usage contexts effectively. We conduct extensive experiments using a large real app usage data. Comparative results demonstrate that our approach achieves higher precision and recall, compared with the state-of-the-art recommendation methods.
Parallelization of DC Analysis through Multiport Decomposition

Physical problems offer scope for macro level parallelization of solution by their essential structure. For parallelization of electrical network simulation, the most natural structure based method is that of multiport decomposition. In this paper this method is used for the simulation of electrical networks consisting of resistances, voltage and current sources using a distributed cluster of weakly coupled processors. At the two levels in which equations are solved in this method the authors have used sparse LU for both levels in the first scheme and sparse LU in the inner level and conjugate gradient in the outer level in the second scheme. Results are presented for planar networks, for the cases where the numbers of slave processors are 1 and 2, and for circuit sizes up to 8.2 million nodes and 16.4 million edges using 8 slave processors. The authors use a cluster of Pentium IV processors linked through a 10/100MBPS Ethernet switch
Method to find community structures based on information centrality.

Community structures are an important feature of many social, biological, and technological networks. Here we study a variation on the method for detecting such communities proposed by Girvan and Newman and based on the idea of using centrality measures to define the community boundaries [M. Girvan and M. E. J. Newman,  (2002)]. We develop an algorithm of hierarchical clustering that consists in finding and removing iteratively the edge with the highest information centrality. We test the algorithm on computer generated and real-world networks whose community structure is already known or has been studied by means of other methods. We show that our algorithm, although it runs to completion in a time O(n4) , is very effective especially when the communities are very mixed and hardly detectable by the other methods.
Analysis of Blockage Effects on Urban Cellular Networks

Large-scale blockages such as buildings affect the performance of urban cellular networks, especially at higher frequencies. Unfortunately, such blockage effects are either neglected or characterized by oversimplified models in the analysis of cellular networks. Leveraging concepts from random shape theory, this paper proposes a mathematical framework to model random blockages and analyze their impact on cellular network performance. Random buildings are modeled as a process of rectangles with random sizes and orientations whose centers form a Poisson point process on the plane. The distribution of the number of blockages in a link is proven to be a Poisson random variable with parameter dependent on the length of the link. Our analysis shows that the probability that a link is not intersected by any blockages decays exponentially with the link length. A path loss model that incorporates the blockage effects is also proposed, which matches experimental trends observed in prior work. The model is applied to analyze the performance of cellular networks in urban areas with the presence of buildings, in terms of connectivity, coverage probability, and average rate. Our results show that the base station density should scale superlinearly with the blockage density to maintain the network connectivity. Our analyses also show that while buildings may block the desired signal, they may still have a positive impact on the SIR coverage probability and achievable rate since they can block significantly more interference.