nils-herrmann/clean_scirepeval huggingface dataset:
Distributed Optimization for Model Predictive Control of Linear Dynamic Networks With Control-Input and Output Constraints

A linear dynamic network is a system of subsystems that approximates the dynamic model of large, geographically distributed systems such as the power grid and traffic networks. A favorite technique to operate such networks is distributed model predictive control (DMPC), which advocates the distribution of decision-making while handling constraints in a systematic way. This paper contributes to the state-of-the-art of DMPC of linear dynamic networks in two ways. First, it extends a baseline model by introducing constraints on the output of the subsystems and by letting subsystem dynamics to depend on the state besides the control signals of the subsystems in the neighborhood. With these extensions, constraints on queue lengths and delayed dynamic effects can be modeled in traffic networks. Second, this paper develops a distributed interior-point algorithm for solving DMPC optimization problems with a network of agents, one for each subsystem, which is shown to converge to an optimal solution. In a traffic network, this distributed algorithm permits the subsystem of an intersection to be reconfigured by only coordinating with the subsystems in its vicinity.
Genetic heritability and shared environmental factors among twin pairs with autism.

Autism is considered the most heritable of neurodevelopmental disorders, mainly because of the large difference in concordance rates between monozygotic and dizygotic twins.



To provide rigorous quantitative estimates of genetic heritability of autism and the effects of shared environment.



Twin pairs with at least 1 twin with an autism spectrum disorder (ASD) born between 1987 and 2004 were identified through the California Department of Developmental Services.



Structured diagnostic assessments (Autism Diagnostic Interview-Revised and Autism Diagnostic Observation Schedule) were completed on 192 twin pairs. Concordance rates were calculated and parametric models were fitted for 2 definitions, 1 narrow (strict autism) and 1 broad (ASD).



For strict autism, probandwise concordance for male twins was 0.58 for 40 monozygotic pairs (95% confidence interval [CI], 0.42-0.74) and 0.21 for 31 dizygotic pairs (95% CI, 0.09-0.43); for female twins, the concordance was 0.60 for 7 monozygotic pairs (95% CI, 0.28-0.90) and 0.27 for 10 dizygotic pairs (95% CI, 0.09-0.69). For ASD, the probandwise concordance for male twins was 0.77 for 45 monozygotic pairs (95% CI, 0.65-0.86) and 0.31 for 45 dizygotic pairs (95% CI, 0.16-0.46); for female twins, the concordance was 0.50 for 9 monozygotic pairs (95% CI, 0.16-0.84) and 0.36 for 13 dizygotic pairs (95% CI, 0.11-0.60). A large proportion of the variance in liability can be explained by shared environmental factors (55%; 95% CI, 9%-81% for autism and 58%; 95% CI, 30%-80% for ASD) in addition to moderate genetic heritability (37%; 95% CI, 8%-84% for autism and 38%; 95% CI, 14%-67% for ASD).



Susceptibility to ASD has moderate genetic heritability and a substantial shared twin environmental component.
EpicFlow: Edge-preserving interpolation of correspondences for optical flow

We propose a novel approach for optical flow estimation, targeted at large displacements with significant occlusions. It consists of two steps: (i) dense matching by edge-preserving interpolation from a sparse set of matches; (ii) variational energy minimization initialized with the dense matches. The sparse-to-dense interpolation relies on an appropriate choice of the distance, namely an edge-aware geodesic distance. This distance is tailored to handle occlusions and motion boundaries - two common and difficult issues for optical flow computation. We also propose an approximation scheme for the geodesic distance to allow fast computation without loss of performance. Subsequent to the dense interpolation step, standard one-level variational energy minimization is carried out on the dense matches to obtain the final flow estimation. The proposed approach, called Edge-Preserving Interpolation of Correspondences (EpicFlow) is fast and robust to large displacements. It significantly outperforms the state of the art on MPI-Sintel and performs on par on Kitti and Middlebury.
The Relationship Between Facebook and the Well-Being of Undergraduate College Students

We investigated how Facebook use and attitudes relate to self-esteem and college adjustment, and expected to find a positive relationship between Facebook and social adjustment, and a negative relationship between Facebook, self-esteem, and emotional adjustment. We examined these relationships in first-year and upper-class students and expected to find differences between the groups. Seventy undergraduate students completed Facebook measures (time, number of friends, emotional and social connection to Facebook), the Rosenberg Self-Esteem Scale, and the Student Adaptation to College Scale. First-year students had a stronger emotional connection to and spent more time on Facebook while they reported fewer friends than upper-class students did. The groups did not differ in the adjustment scores. The number of Facebook friends potentially hinders academic adjustment, and spending a lot of time on Facebook is related to low self-esteem. The number of Facebook friends was negatively associated with emotional and academic adjustment among first-year students but positively related to social adjustment and attachment to institution among upper-class students. The results suggest that the relationship becomes positive later in college life when students use Facebook effectively to connect socially with their peers. Lastly, the number of Facebook friends and not the time spent on Facebook predicted college adjustment, suggesting the value of studying further the notion of Facebook friends.
Statistical Parametric Maps in Functional Imaging : A General Linear Approach

Statistical parametric maps are spatially extended statistical processes that are used to test hypotheses about regionally specific effects in neuroimaging data. The most established sorts of statistical parametric maps (e.g., Friston et al. [1991]: J Cereb Blood Flow Metab 11:690-699; Worsley et al. 119921: J Cereb Blood Flow Metab 12:YOO-918) are based on linear models, for example ANCOVA, correlation coefficients and t tests. In the sense that these examples are all special cases of the general linear model it should be possible to implement them (and many others) within a unified framework. We present here a general approach that accommodates most forms of experimental layout and ensuing analysis (designed experiments with fixed effects for factors, covariates and interaction of factors). This approach brings together two well established bodies of theory (the general linear model and the theory of Gaussian fields) to provide a complete and simple framework for the analysis of imaging data. The importance of this framework is twofold: (i) Conceptual and mathematical simplicity, in that the same small number of operational equations is used irrespective of the complexity of the experiment or nature of the statistical model and (ii) the generality of the framework provides for great latitude in experimental design and analysis.
Neuroergonomics: a review of applications to physical and cognitive work

Neuroergonomics is an emerging science that is defined as the study of the human brain in relation to performance at work and in everyday settings. This paper provides a critical review of the neuroergonomic approach to evaluating physical and cognitive work, particularly in mobile settings. Neuroergonomics research employing mobile and immobile brain imaging techniques are discussed in the following areas of physical and cognitive work: (1) physical work parameters; (2) physical fatigue; (3) vigilance and mental fatigue; (4) training and neuroadaptive systems; and (5) assessment of concurrent physical and cognitive work. Finally, the integration of brain and body measurements in investigating workload and fatigue, in the context of mobile brain/body imaging ("MoBI"), is discussed.
On Multi-Layer Basis Pursuit, Efficient Algorithms and Convolutional Neural Networks

Parsimonious representations are ubiquitous in modeling and processing information. Motivated by the recent Multi-Layer Convolutional Sparse Coding (ML-CSC) model, we herein generalize the traditional Basis Pursuit problem to a multi-layer setting, introducing similar sparse enforcing penalties at different representation layers in a symbiotic relation between synthesis and analysis sparse priors. We explore different iterative methods to solve this new problem in practice, and we propose a new Multi-Layer Iterative Soft Thresholding Algorithm (ML-ISTA), as well as a fast version (ML-FISTA). We show that these nested first order algorithms converge, in the sense that the function value of near-fixed points can get arbitrarily close to the solution of the original problem. We further show how these algorithms effectively implement particular recurrent convolutional neural networks (CNNs) that generalize feed-forward ones without introducing any parameters. We present and analyze different architectures resulting unfolding the iterations of the proposed pursuit algorithms, including a new Learned ML-ISTA, providing a principled way to construct deep recurrent CNNs. Unlike other similar constructions, these architectures unfold a global pursuit holistically for the entire network. We demonstrate the emerging constructions in a supervised learning setting, consistently improving the performance of classical CNNs while maintaining the number of parameters constant.
Sensitivity of Support Vector Machines to Random Feature Selection in Classification of Hyperspectral Data

The accuracy of supervised land cover classifications depends on factors such as the chosen classification algorithm, adequate training data, the input data characteristics, and the selection of features. Hyperspectral imaging provides more detailed spectral and spatial information on the land cover than other remote sensing resources. Over the past ten years, traditional and formerly widely accepted statistical classification methods have been superseded by more recent machine learning algorithms, e.g., support vector machines (SVMs), or by multiple classifier systems (MCS). This can be explained by limitations of statistical approaches with regard to high-dimensional data, multimodal classes, and often limited availability of training data. In the presented study, MCSs based on SVM and random feature selection (RFS) are applied to explore the potential of a synergetic use of the two concepts. We investigated how the number of selected features and the size of the MCS influence classification accuracy using two hyperspectral data sets, from different environmental settings. In addition, experiments were conducted with a varying number of training samples. Accuracies are compared with regular SVM and random forests. Experimental results clearly demonstrate that the generation of an SVM-based classifier system with RFS significantly improves overall classification accuracy as well as producer's and user's accuracies. In addition, the ensemble strategy results in smoother, i.e., more realistic, classification maps than those from stand-alone SVM. Findings from the experiments were successfully transferred onto an additional hyperspectral data set.
Clinical application of the Hybrid Assistive Limb (HAL) for gait training—a systematic review

The aim of this study was to review the literature on clinical applications of the Hybrid Assistive Limb system for gait training.



A systematic literature search was conducted using Web of Science, PubMed, CINAHL and clinicaltrials.gov and additional search was made using reference lists in identified reports. Abstracts were screened, relevant articles were reviewed and subject to quality assessment.



Out of 37 studies, 7 studies fulfilled inclusion criteria. Six studies were single group studies and 1 was an explorative randomized controlled trial. In total, these studies involved 140 participants of whom 118 completed the interventions and 107 used HAL for gait training. Five studies concerned gait training after stroke, 1 after spinal cord injury (SCI) and 1 study after stroke, SCI or other diseases affecting walking ability. Minor and transient side effects occurred but no serious adverse events were reported in the studies. Beneficial effects on gait function variables and independence in walking were observed.



The accumulated findings demonstrate that the HAL system is feasible when used for gait training of patients with lower extremity paresis in a professional setting. Beneficial effects on gait function and independence in walking were observed but data do not allow conclusions. Further controlled studies are recommended.
A method for learning matching errors for stereo computation

This paper describes a novel learning-based approach for improving the performance of stereo computation. It is based on the observation that whether the image matching scores lead to true or erroneous depth values is dependent on the original stereo images and the underlying scene structure. This function is learned from training data and is integrated into a depth estimation algorithm using the MAP-MRF framework. Because the resultant likelihood function is dependent on the states of a large neighboring region around each pixel, we propose to solve the high-order MRF inference problem using the simulated annealing algorithm combined with a Metropolis-Hastings sampler. A segmentation-based approach is proposed to accelerate the computational speed and improve the performance. Preliminary experimental results show that the learning process captures common errors in SSD matching including the fattening effect, the aperture effect, and mismatches in occluded or low texture regions. It is also demonstrated that the proposed approach significantly improves the accuracy of the depth computation.
Bubble-up: Increasing utilization in modern warehouse scale computers via sensible co-locations

As much of the world's computing continues to move into the cloud, the overprovisioning of computing resources to ensure the performance isolation of latency-sensitive tasks, such as web search, in modern datacenters is a major contributor to low machine utilization. Being unable to accurately predict performance degradation due to contention for shared resources on multicore systems has led to the heavy handed approach of simply disallowing the co-location of high-priority, latency-sensitive tasks with other tasks. Performing this precise prediction has been a challenging and unsolved problem.
 In this paper, we present Bubble-Up, a characterization methodology that enables the accurate prediction of the performance degradation that results from contention for shared resources in the memory subsystem. By using a bubble to apply a tunable amount of "pressure" to the memory subsystem on processors in production datacenters, our methodology can predict the performance interference between co-locate applications with an accuracy within 1% to 2% of the actual performance degradation. Using this methodology to arrive at "sensible" co-locations in Google's production datacenters with real-world large-scale applications, we can improve the utilization of a 500-machine cluster by 50% to 90% while guaranteeing a high quality of service of latency-sensitive applications.
Dietary carbohydrate, muscle glycogen, and exercise performance during 7 d of training.

The effects of moderate- or high-carbohydrate diets on muscle glycogen and performance in runners and cyclists over 7 consecutive days of training were determined. Muscle biopsies were performed on 4 separate days before exercise for 1 h at 75% peak oxygen consumption (VO2) followed by five, 1-min sprints. After the training session on day 7, subjects ran or cycled to exhaustion at 80% peak VO2. Muscle glycogen for cyclists and runners was maintained with the high-carbohydrate diet but was reduced 30-36% (P < 0.05) with the moderate-carbohydrate diet. All subjects completed all training sessions, and there were no differences in times to exhaustion on day 7. For cyclists and runners, consuming a moderate-carbohydrate diet over 7 d of intense training reduces muscle glycogen but has no apparent deleterious effect on training capability or high-intensity exercise performance. A high-carbohydrate diet maintains muscle glycogen, but this has no apparent benefit on training capability or high-intensity exercise performance.
Exploring Appropriation of Enterprise Wikis:

The purpose of this paper is to provide both application-oriented researchers and practitioners with detailed insights into conception, implementation, and utilization of intra-organizational wikis to support knowledge management and group work. Firstly, we report on three case studies and describe how wikis have been appropriated in the context of a concrete practice. Our study reveals that the wikis have been used as Knowledge Base, Encyclopedia and Support Base, respectively. We present the identified practices as a result of the wiki appropriation process and argue that due to their open and flexible nature these wikis have been appropriated according to the users’ needs. Our contribution helps to understand how platforms support working practices that have not been supported by groupware before, or at least not in the same way. Secondly, three detailed implementation reports uncover many aspects of wiki projects, e.g., different viewpoints of managers and users, an investigation of other sources containing business-relevant information, and perceived obstacles to wiki projects. In this context, our study generates a series of lessons learned for people who intend to implement wikis in their own organizations, including the awareness of usage potential, the need for additional managerial support, and clear communication strategies to promote wiki usage.
View-based and modular eigenspaces for face recognition

In this work we describe experiments with eigenfaces for recognition and interactive search in a large-scale face database. Accurate visual recognition is demonstrated using a database of O(10) faces. The problem of recognition under general viewing orientation is also examined. A view-based multiple-observer eigenspace technique is proposed for use in face recognition under variable pose. In addition, a modular eigenspace description technique is used which incorporates salient features such as the eyes, nose and mouth, in an eigenfeature layer. This modular representation yields higher recognition rates as well as a more robust framework for face recognition. An automatic feature extraction technique using feature eigentemplates is also demonstrated.
An Analysis of the AskMSR Question-Answering System

We describe the architecture of the AskMSR question answering system and systematically evaluate contributions of different system components to accuracy. The system differs from most question answering systems in its dependency on data redundancy rather than sophisticated linguistic analyses of either questions or candidate answers. Because a wrong answer is often worse than no answer, we also explore strategies for predicting when the question answering system is likely to give an incorrect answer.
Engineering Self-Adaptive Systems through Feedback Loops

To deal with the increasing complexity of software systems and uncertainty of their environments, software engineers have turned to self-adaptivity. Self-adaptive systems are capable of dealing with a continuously changing environment and emerging requirements that may be unknown at design-time. However, building such systems cost-effectively and in a predictable manner is a major engineering challenge. In this paper, we explore the state-of-the-art in engineering self-adaptive systems and identify potential improvements in the design process. Our most important finding is that in designing self-adaptive systems, the feedback loops that control self-adaptation must become first-class entities. We explore feedback loops from the perspective of control engineering and within existing self-adaptive systems in nature and biology. Finally, we identify the critical challenges our community must address to enable systematic and well-organized engineering of self-adaptive and self-managing software systems.
Cost-based placement of vDPI functions in NFV infrastructures

Network Functions Virtualization (NFV) is transforming how networks are architected and network services delivered. The network is more flexible and adaptable, it can scale with traffic demands. To manage video traffic in the network, or get protection from cyber-attacks, Deep Packet Inspection is increasingly deployed at specific locations in the network. The virtual Deep Packet Inspection (vDPI) engines can be dynamically deployed as software on commodity servers within emerging NFV infrastructures. For a network operator, deploying a set of vDPIs over the network is a matter of finding the appropriate placement that meets the traffic management or cyber-security targets (such as the number of inspected flows) and operational cost constraints (license fees, network efficiency or power consumption). In this work, we formulate the vDPI placement problem as a cost minimization problem. The cost captures the different objectives the operator is pursuing. A placement of vDPIs on the network nodes realizes a trade-off between these possibly conflicting goals. We cast the problem as a multi-commodity flow problem and solve it as an Integer Linear Program (ILP). We then devise a centrality-based greedy algorithm and assess its validity by comparing it with the ILP optimal solution on a real data set (GEANT network with 22 nodes and real traffic matrix). We further analyze the scalability of the heuristic by applying it to larger random networks of up to 100 nodes. The results show the network structure and the costs strongly influence time performance. They also show that after a size limit (between 40 to 80 nodes in our case), the execution time increases exponentially due to combinatorial issues. Finally, they demonstrate that the heuristic well approximate the optimal on smaller problem instances.
Importance filtering for image retargeting

Content-aware image retargeting has attracted a lot of interests recently. The key and most challenging issue for this task is how to balance the tradeoff between preserving the important contents and minimizing the visual distortions on the consistency of the image structure. In this paper we present a novel filtering-based technique to tackle this issue, called ”importance filtering”. Specifically, we first filter the image saliency guided by the image itself to achieve a structure-consistent importance map. We then use the pixel importance as the key constraint to compute the gradient map of pixel shifts from the original resolution to the target. Finally, we integrate the shift gradient across the image using a weighted filter to construct a smooth shift map and render the target image. The weight is again controlled by the pixel importance. The two filtering processes enforce to maintain the structural consistency and yet preserve the important contents in the target image. Furthermore, the simple nature of filter operations allows highly efficient implementation for real-time applications and easy extension to video retargeting, as the structural constraints from the original image naturally convey the temporal coherence between frames. The effectiveness and efficiency of our importance filtering algorithm are confirmed in extensive experiments.