nils-herrmann/clean_scirepeval huggingface dataset:
The PREP pipeline: standardized preprocessing for large-scale EEG analysis

The technology to collect brain imaging and physiological measures has become portable and ubiquitous, opening the possibility of large-scale analysis of real-world human imaging. By its nature, such data is large and complex, making automated processing essential. This paper shows how lack of attention to the very early stages of an EEG preprocessing pipeline can reduce the signal-to-noise ratio and introduce unwanted artifacts into the data, particularly for computations done in single precision. We demonstrate that ordinary average referencing improves the signal-to-noise ratio, but that noisy channels can contaminate the results. We also show that identification of noisy channels depends on the reference and examine the complex interaction of filtering, noisy channel identification, and referencing. We introduce a multi-stage robust referencing scheme to deal with the noisy channel-reference interaction. We propose a standardized early-stage EEG processing pipeline (PREP) and discuss the application of the pipeline to more than 600 EEG datasets. The pipeline includes an automatically generated report for each dataset processed. Users can download the PREP pipeline as a freely available MATLAB library from http://eegstudy.org/prepcode.
On Exploiting Transient Social Contact Patterns for Data Forwarding in Delay-Tolerant Networks

Unpredictable node mobility, low node density, and lack of global information make it challenging to achieve effective data forwarding in Delay-Tolerant Networks (DTNs). Most of the current data forwarding schemes choose the nodes with the best cumulative capability of contacting others as relays to carry and forward data, but these nodes may not be the best relay choices within a short time period due to the heterogeneity of transient node contact characteristics. In this paper, we propose a novel approach to improve the performance of data forwarding with a short time constraint in DTNs by exploiting the transient social contact patterns. These patterns represent the transient characteristics of contact distribution, network connectivity and social community structure in DTNs, and we provide analytical formulations on these patterns based on experimental studies of realistic DTN traces. We then propose appropriate forwarding metrics based on these patterns to improve the effectiveness of data forwarding. When applied to various data forwarding strategies, our proposed forwarding metrics achieve much better performance compared to existing schemes with similar forwarding cost.
Texture Classification Approach Based on Combination of Edge & Co-occurrence and Local Binary Pattern

Texture classification is one of the problems which has been paid much attention on by computer scientists since late 90s. If texture classification is done correctly and accurately, it can be used in many cases such as Pattern recognition, object tracking, and shape recognition. So far, there have been so many methods offered to solve this problem. Near all these methods have tried to extract and define features to separate different labels of textures really well. This article has offered an approach which has an overall process on the images of textures based on Local binary pattern and Gray Level Co-occurrence matrix and then by edge detection, and finally, extracting the statistical features from the images would classify them. Although, this approach is a general one and is could be used in different applications, the method has been tested on the stone texture and the results have been compared with some of the previous approaches to prove the quality of proposed approach. Texture Classification, Gray level Co occurrence, Local Binary Pattern, Statistical Features
Low-Resource Named Entity Recognition with Cross-lingual, Character-Level Neural Conditional Random Fields

Low-resource named entity recognition is still an open problem in NLP. Most stateof-the-art systems require tens of thousands of annotated sentences in order to obtain high performance. However, for most of the world’s languages it is unfeasible to obtain such annotation. In this paper, we present a transfer learning scheme, whereby we train character-level neural CRFs to predict named entities for both high-resource languages and low-resource languages jointly. Learning character representations for multiple related languages allows transfer among the languages, improving F1 by up to 9.8 points over a loglinear CRF baseline.
Predicting 30-Day Risk and Cost of "All-Cause" Hospital Readmissions

The hospital readmission rate of patients within 30 days after discharge is broadly accepted as a healthcare quality measure and cost driver in the United States. The ability to estimate hospitalization costs alongside 30 day risk-stratification for such readmissions provides additional benefit for accountable care, now a global issue and foundation for the U.S. government mandate under the Affordable Care Act. Recent data mining efforts either predict healthcare costs or risk of hospital readmission, but not both. In this paper we present a dual predictive modeling effort that utilizes healthcare data to predict the risk and cost of any hospital readmission (“all-cause”). For this purpose, we explore machine learning algorithms to do accurate predictions of healthcare costs and risk of 30-day readmission. Results on risk prediction for “all-cause” readmission compared to the standardized readmission tool (LACE) are promising, and the proposed techniques for cost prediction consistently outperform baseline models and demonstrate substantially lower mean absolute error (MAE).
An Analysis of the Skype Peer-to-Peer Internet Telephony Protocol

Skype is a peer-to-peer VoIP client developed by KaZaa in 2003. Skype claims that it can work almost seamlessly across NATs and firewalls and has better voice quality than the MSN and Yahoo IM applications. It encrypts calls end-to-end, and stores user information in a decentralized fashion. Skype also supports instant messaging and conferencing. This report analyzes key Skype functions such as login, NAT and firewall traversal, call establishment, media transfer, codecs, and conferencing under three different network setups. Analysis is performed by careful study of Skype network traffic.
Beyond trees: common-factor models for 2D human pose recovery

Tree structured models have been widely used for determining the pose of a human body, from either 2D or 3D data. While such models can effectively represent the kinematic constraints of the skeletal structure, they do not capture additional constraints such as coordination of the limbs. Tree structured models thus miss an important source of information about human body pose, as limb coordination is necessary for balance while standing, walking, or running, as well as being evident in other activities such as dancing and throwing. In this paper, we consider the use of undirected graphical models that augment a tree structure with latent variables in order to account for coordination between limbs. We refer to these as common-factor models, since they are constructed by using factor analysis to identify additional correlations in limb position that are not accounted for by the kinematic tree structure. These common-factor models have an underlying tree structure and thus a variant of the standard Viterbi algorithm for a tree can be applied for efficient estimation. We present some experimental results contrasting common-factor models with tree models, and quantify the improvement in pose estimation for 2D image data.
The anatomy of choice: active inference and agency

This paper considers agency in the setting of embodied or active inference. In brief, we associate a sense of agency with prior beliefs about action and ask what sorts of beliefs underlie optimal behavior. In particular, we consider prior beliefs that action minimizes the Kullback-Leibler (KL) divergence between desired states and attainable states in the future. This allows one to formulate bounded rationality as approximate Bayesian inference that optimizes a free energy bound on model evidence. We show that constructs like expected utility, exploration bonuses, softmax choice rules and optimism bias emerge as natural consequences of this formulation. Previous accounts of active inference have focused on predictive coding and Bayesian filtering schemes for minimizing free energy. Here, we consider variational Bayes as an alternative scheme that provides formal constraints on the computational anatomy of inference and action-constraints that are remarkably consistent with neuroanatomy. Furthermore, this scheme contextualizes optimal decision theory and economic (utilitarian) formulations as pure inference problems. For example, expected utility theory emerges as a special case of free energy minimization, where the sensitivity or inverse temperature (of softmax functions and quantal response equilibria) has a unique and Bayes-optimal solution-that minimizes free energy. This sensitivity corresponds to the precision of beliefs about behavior, such that attainable goals are afforded a higher precision or confidence. In turn, this means that optimal behavior entails a representation of confidence about outcomes that are under an agent's control.
High-efficiency low-voltage dc-dc conversion for portable applications

2 The design and measured results are reported on three prototype DC-DC converters which successfully demonstrate the design techniques of this thesis and the low-power enabling capabilities of DC-DC converters in portable applications. Voltage scaling for low-power throughput-constrained digital signal processing is reviewed and is shown to provide up to an order of magnitude power reduction compared to existing 3.3 V standards when enabled by high-efficiency low-voltage DC-DC conversion. A new ultra-low-swing I/O strategy, enabled by an ultra-low-voltage and low-power DCDC converter, is used to reduce the power of high-speed inter-chip communication by greater than two orders of magnitude. Dynamic voltage scaling is proposed to dynamically trade general-purpose processor throughput for energy-efficiency, yielding up to an order of magnitude improvement in the average energy per operation of the processor. This is made possible by a new class of voltage converter, called the dynamic DC-DC converter, whose primary performance objectives and design considerations are introduced in this thesis. Robert W. Brodersen, Chairman of Committee Table of
A dynamic programming offloading algorithm for mobile cloud computing

Computational offloading is an effective method to address the limited battery power of a mobile device, by executing some components of a mobile application in the cloud. In this paper, a novel offloading algorithm called `Dynamic Programming with Hamming Distance Termination' (denoted DPH) is presented. Our algorithm uses randomization and a hamming distance termination criterion to find a nearly optimal offloading solution quickly. The algorithm will offload as many tasks as possible to the cloud when the network transmission bandwidth is high, thereby improving the total execution time of all tasks and minimizing the energy use of the mobile device. The algorithm can find very good solutions with low computational overhead. A novel and innovative approach to fill the dynamic programming table is used to avoid unnecessary computations, resulting in lower computation times compared to other schemes. Furthermore, the algorithm is extensible to handle larger offloading problems without a loss of computational efficiency. Performance evaluation shows that the proposed DPH algorithm can achieve near minimal energy while meeting an application's execution time constraints, and it can find a nearly optimal offloading decision within a few iterations.
Circularly-Polarized Wide Slot Antenna Loaded With a Parasitic Patch

A design is described of a broadband circularly-polarized (CP) slot antenna. A conventional annular-ring slot antenna is first analyzed, and it is found that two adjacent CP modes can be simultaneously excited through the proximity coupling of an L-shaped feed line. By tuning the dimensions of this L-shaped feed line, the two CP modes can be coupled together and a broad CP bandwidth is thus formed. The design method is also valid when the inner circular patch of the annular-ring slot antenna is vertically raised from the ground plane. In this case, the original band-limited ring slot antenna is converted into a wide-band structure that is composed of a circular wide slot and a parasitic patch, and consequently the CP bandwidth is further enhanced. For the patch-loaded wide slot antenna, its key parameters are investigated to show how to couple the two CP modes and achieve impedance matching. The effects of the distance between the parasitic patch and wide slot on the CP bandwidth and antenna gain are also presented and discussed in details.
Gender Differences in Emotion Regulation: An fMRI Study of Cognitive Reappraisal.

Despite strong popular conceptions of gender differences in emotionality and striking gender differences in the prevalence of disorders thought to involve emotion dysregulation, the literature on the neural bases of emotion regulation is nearly silent regarding gender differences (Gross, 2007; Ochsner & Gross, in press). The purpose of the present study was to address this gap in the literature. Using functional magnetic resonance imaging, we asked male and female participants to use a cognitive emotion regulation strategy (reappraisal) to down-regulate their emotional responses to negatively valenced pictures. Behaviorally, men and women evidenced comparable decreases in negative emotion experience. Neurally, however, gender differences emerged. Compared with women, men showed (a) lesser increases in prefrontal regions that are associated with reappraisal, (b) greater decreases in the amygdala, which is associated with emotional responding, and (c) lesser engagement of ventral striatal regions, which are associated with reward processing. We consider two non-competing explanations for these differences. First, men may expend less effort when using cognitive regulation, perhaps due to greater use of automatic emotion regulation. Second, women may use positive emotions in the service of reappraising negative emotions to a greater degree. We then consider the implications of gender differences in emotion regulation for understanding gender differences in emotional processing in general, and gender differences in affective disorders.
Analysis and Design of Conformal Tapered Leaky-Wave Antennas

The theory to analyze and design tapered conformal line-source leaky-wave antennas is described in this letter. The deformation in the radiation pattern associated to the bending of the antenna is accurately predicted, and useful expressions are derived to taper the leaky-mode complex propagation constant in order to restore the high-directive radiated beam scanning at the desired angle. The analysis method and the design approach are validated with full-wave simulations of a curved tapered leaky-wave antenna in hybrid technology.
Color Invariance

ÐThis paper presents the measurement of colored object reflectance, under different, general assumptions regarding the imaging conditions. We exploit the Gaussian scale-space paradigm for color images to define a framework for the robust measurement of object reflectance from color images. Object reflectance is derived from a physical reflectance model based on the Kubelka-Munk theory for colorant layers. Illumination and geometrical invariant properties are derived from the reflectance model. Invariance and discriminative power of the color invariants is experimentally investigated, showing the invariants to be successful in discounting shadow, illumination, highlights, and noise. Extensive experiments show the different invariants to be highly discriminative, while maintaining invariance properties. The presented framework for color measurement is well-founded in the physics of color as well as in measurement science. Hence, the proposed invariants are considered more adequate for the measurement of invariant color features than existing methods. Index TermsÐPhotometric invariance, color constancy, measurement theory, scale-space, differential invariants, differential geometry, multispectral imaging, Kubelka-Munk theory, photometric models, Gaussian color model.
Controlled Lagrangians and the stabilization of mechanical systems. I. The first matching theorem

We develop a method for the stabilization of mechanical systems with symmetry based on the technique of controlled Lagrangians. The procedure involves making structured modifications to the Lagrangian for the uncontrolled system, thereby constructing the controlled Lagrangian. The Euler–Lagrange equations derived from the controlled Lagrangian describe the closed-loop system, where new terms in these equations are identified with control forces. Since the controlled system is Lagrangian by construction, energy methods can be used to find control gains that yield closed-loop stability. In this paper we usekinetic shapingto preserve symmetry and only stabilize systems modulo the symmetry group. In the sequel to this paper (Part II), we extend the technique to includepotential shaping and we achieve stabilization in the full phase space. The procedure is demonstrated for several underactuated balance problems, including the stabilization of an inverted planar pendulum on a cart moving on a line and an inverted spherical pendulum on a cart moving in the plane.
Further Meta-Evaluation of Machine Translation

This paper analyzes the translation quality of machine translation systems for 10 language pairs translating between Czech, English, French, German, Hungarian, and Spanish. We report the translation quality of over 30 diverse translation systems based on a large-scale manual evaluation involving hundreds of hours of effort. We use the human judgments of the systems to analyze automatic evaluation metrics for translation quality, and we report the strength of the correlation with human judgments at both the system-level and at the sentence-level. We validate our manual evaluation methodology by measuring intraand inter-annotator agreement, and collecting timing information.
"You Might Also Like:" Privacy Risks of Collaborative Filtering

Many commercial websites use recommender systems to help customers locate products and content. Modern recommenders are based on collaborative filtering: they use patterns learned from users' behavior to make recommendations, usually in the form of related-items lists. The scale and complexity of these systems, along with the fact that their outputs reveal only relationships between items (as opposed to information about users), may suggest that they pose no meaningful privacy risk. In this paper, we develop algorithms which take a moderate amount of auxiliary information about a customer and infer this customer's transactions from temporal changes in the public outputs of a recommender system. Our inference attacks are passive and can be carried out by any Internet user. We evaluate their feasibility using public data from popular websites Hunch, Last. fm, Library Thing, and Amazon.
Augmented Reality Meets Computer Vision: Efficient Data Generation for Urban Driving Scenes

The success of deep learning in computer vision is based on the availability of large annotated datasets. To lower the need for hand labeled images, virtually rendered 3D worlds have recently gained popularity. Unfortunately, creating realistic 3D content is challenging on its own and requires significant human effort. In this work, we propose an alternative paradigm which combines real and synthetic data for learning semantic instance segmentation and object detection models. Exploiting the fact that not all aspects of the scene are equally important for this task, we propose to augment real-world imagery with virtual objects of the target category. Capturing real-world images at large scale is easy and cheap, and directly provides real background appearances without the need for creating complex 3D models of the environment. We present an efficient procedure to augment these images with virtual objects. In contrast to modeling complete 3D environments, our data augmentation approach requires only a few user interactions in combination with 3D models of the target object category. Leveraging our approach, we introduce a novel dataset of augmented urban driving scenes with 360 degree images that are used as environment maps to create realistic lighting and reflections on rendered objects. We analyze the significance of realistic object placement by comparing manual placement by humans to automatic methods based on semantic scene analysis. This allows us to create composite images which exhibit both realistic background appearance as well as a large number of complex object arrangements. Through an extensive set of experiments, we conclude the right set of parameters to produce augmented data which can maximally enhance the performance of instance segmentation models. Further, we demonstrate the utility of the proposed approach on training standard deep models for semantic instance segmentation and object detection of cars in outdoor driving scenarios. We test the models trained on our augmented data on the KITTI 2015 dataset, which we have annotated with pixel-accurate ground truth, and on the Cityscapes dataset. Our experiments demonstrate that the models trained on augmented imagery generalize better than those trained on fully synthetic data or models trained on limited amounts of annotated real data.
An Adaptation of the Vector-Space Model for Ontology-Based Information Retrieval

Semantic search has been one of the motivations of the semantic Web since it was envisioned. We propose a model for the exploitation of ontology-based knowledge bases to improve search over large document repositories. In our view of information retrieval on the semantic Web, a search engine returns documents rather than, or in addition to, exact values in response to user queries. For this purpose, our approach includes an ontology-based scheme for the semiautomatic annotation of documents and a retrieval system. The retrieval model is based on an adaptation of the classic vector-space model, including an annotation weighting algorithm, and a ranking algorithm. Semantic search is combined with conventional keyword-based retrieval to achieve tolerance to knowledge base incompleteness. Experiments are shown where our approach is tested on corpora of significant scale, showing clear improvements with respect to keyword-based search
Wireless M-Bus sensor nodes in smart water grids: The energy issue

Smart Metering is one of the key issues in modern energy efficiency technologies. Several efforts have been recently made in developing suitable communication protocols for metering data management and transmission, and the Metering-Bus (M-Bus) is a relevant standard example, with a wide diffusion in the European market. This paper deals with its wireless evolution, namely Wireless M-Bus (WM-Bus), and in particular looks at it from the energy consumption perspective. Indeed, specially in those applicative scenarios where the grid powering is not available, like in water and gas metering settings, it is fundamental to guarantee the sustainability of the meter itself, by means of long-life batteries or suitable energy harvesting technologies. The present work analyzes all these aspects directly referring to a specific HW/SW implementation of the WM-Bus variants, providing some useful guidelines for its application in the smart water grid context.