nils-herrmann/clean_scirepeval huggingface dataset:
Dual-Band Wide-Angle Scanning Planar Phased Array in X/Ku-Bands

A novel planar dual-band phased array, operational in the X/Ku-bands and with wide-angle scanning capability is presented. The design, development and experimental demonstration are described. A new single-layer crossed L-bar microstrip antenna is used for the array design. The antenna has low-profile architecture, measuring only 0.33λ×0.33λ, at the low frequency band of operation, with flexible resonance tuning capability offered by the use of a plate-through-hole and field-matching ring arrangement. A 49-element planar (7 × 7) array demonstrator has been built and its performance validated, exhibiting good agreement with full-wave simulations. The dual-band array supports a large frequency ratio of nearly 1.8:1, and also maintains good sub-band bandwidths. Wide-angle scanning up to a maximum of 60 ° and 50 ° are achieved at the low and high frequency bands of operation, respectively.
Joint Haar-like features for face detection

In this paper, we propose a new distinctive feature, called joint Haar-like feature, for detecting faces in images. This is based on co-occurrence of multiple Haar-like features. Feature co-occurrence, which captures the structural similarities within the face class, makes it possible to construct an effective classifier. The joint Haar-like feature can be calculated very fast and has robustness against addition of noise and change in illumination. A face detector is learned by stagewise selection of the joint Haar-like features using AdaBoost. A small number of distinctive features achieve both computational efficiency and accuracy. Experimental results with 5, 676 face images and 30,000 nonface images show that our detector yields higher classification performance than Viola and Jones' detector; which uses a single feature for each weak classifier. Given the same number of features, our method reduces the error by 37%. Our detector is 2.6 times as fast as Viola and Jones' detector to achieve the same performance
Graphical SLAM - a self-correcting map

We describe an approach to simultaneous localization and mapping, SLAM. This approach has the highly desirable property of robustness to data association errors. Another important advantage of our algorithm is that non-linearities are computed exactly, so that global constraints can be imposed even if they result in large shifts to the map. We represent the map as a graph and use the graph to find an efficient map update algorithm. We also show how topological consistency can be imposed on the map, such as, closing a loop. The algorithm has been implemented on an outdoor robot and we have experimental validation of our ideas. We also explain how the graph can be simplified leading to linear approximations of sections of the map. This reduction gives us a natural way to connect local map patches into a much larger global map.
BigDataScript: a scripting language for data pipelines

The analysis of large biological datasets often requires complex processing pipelines that run for a long time on large computational infrastructures. We designed and implemented a simple script-like programming language with a clean and minimalist syntax to develop and manage pipeline execution and provide robustness to various types of software and hardware failures as well as portability.



We introduce the BigDataScript (BDS) programming language for data processing pipelines, which improves abstraction from hardware resources and assists with robustness. Hardware abstraction allows BDS pipelines to run without modification on a wide range of computer architectures, from a small laptop to multi-core servers, server farms, clusters and clouds. BDS achieves robustness by incorporating the concepts of absolute serialization and lazy processing, thus allowing pipelines to recover from errors. By abstracting pipeline concepts at programming language level, BDS simplifies implementation, execution and management of complex bioinformatics pipelines, resulting in reduced development and debugging cycles as well as cleaner code.



BigDataScript is available under open-source license at http://pcingola.github.io/BigDataScript.
Remembering can cause forgetting: retrieval dynamics in long-term memory.

Three studies show that the retrieval process itself causes long-lasting forgetting. Ss studied 8 categories (e.g., Fruit). Half the members of half the categories were then repeatedly practiced through retrieval tests (e.g., Fruit Or_____). Category-cued recall of unpracticed members of practiced categories was impaired on a delayed test. Experiments 2 and 3 identified 2 significant features of this retrieval-induced forgetting: The impairment remains when output interference is controlled, suggesting a retrieval-based suppression that endures for 20 min or more, and the impairment appears restricted to high-frequency members. Low-frequency members show little impairment, even in the presence of strong, practiced competitors that might be expected to block access to those items. These findings suggest a critical role for suppression in models of retrieval inhibition and implicate the retrieval process itself in everyday forgetting.
Changing R&D models in research-based pharmaceutical companies

New drugs serving unmet medical needs are one of the key value drivers of research-based pharmaceutical companies. The efficiency of research and development (R&D), defined as the successful approval and launch of new medicines (output) in the rate of the monetary investments required for R&D (input), has declined since decades. We aimed to identify, analyze and describe the factors that impact the R&D efficiency. Based on publicly available information, we reviewed the R&D models of major research-based pharmaceutical companies and analyzed the key challenges and success factors of a sustainable R&D output. We calculated that the R&D efficiencies of major research-based pharmaceutical companies were in the range of USD 3.2–32.3 billion (2006–2014). As these numbers challenge the model of an innovation-driven pharmaceutical industry, we analyzed the concepts that companies are following to increase their R&D efficiencies: (A) Activities to reduce portfolio and project risk, (B) activities to reduce R&D costs, and (C) activities to increase the innovation potential. While category A comprises measures such as portfolio management and licensing, measures grouped in category B are outsourcing and risk-sharing in late-stage development. Companies made diverse steps to increase their innovation potential and open innovation, exemplified by open source, innovation centers, or crowdsourcing, plays a key role in doing so. In conclusion, research-based pharmaceutical companies need to be aware of the key factors, which impact the rate of innovation, R&D cost and probability of success. Depending on their company strategy and their R&D set-up they can opt for one of the following open innovators: knowledge creator, knowledge integrator or knowledge leverager.
Modeling social influence through network autocorrelation: constructing the weight matrix

Many physical and social phenomena are embedded within networks of interdependencies, the so-called ‘context’ of these phenomena. In network analysis, this type of process is typically modeled as a network autocorrelation model. Parameter estimates and inferences based on autocorrelation models, hinge upon the chosen specification of weight matrix W, the elements of which represent the influence pattern present in the network. In this paper I discuss how social influence processes can be incorporated in the specification of W. Theories of social influence center around ‘communication’ and ‘comparison’; it is discussed how these can be operationalized in a network analysis context. Starting from that, a series of operationalizations of W is discussed. Finally, statistical tests are presented that allow an analyst to test various specifications against one another or pick the best fitting model from a set of models.
A Reduction of the Elastic Net to Support Vector Machines with an Application to GPU Computing

The past years have witnessed many dedicated open-source pr ojects that built and maintain implementations of Support Vector Machines (SVM) , parallelized for GPU, multi-core CPUs and distributed systems. Up to this poi nt, no comparable effort has been made to parallelize the Elastic Net, despite its popularity in many high impact applications, including genetics, neuroscien ce and systems biology. The first contribution in this paper is of theoretical nature . We establish a tight link between two seemingly different algorithms and prove that E l stic Net regression can be reduced to SVM with squared hinge loss classification. Our second contribution is to derive a practical algorithm based on this re duction. The reduction enables us to utilize prior efforts in speeding up and parall elizing SVMs to obtain a highly optimized and parallel solver for the Elastic Net an d Lasso. With a simple wrapper, consisting of only 11 lines of MATLAB TM code, we obtain an Elastic Net implementation that naturally utilizes GPU and multi-c ore CPUs. We demonstrate on twelve real world data sets, that our algorithm yie lds identical results as the popular (and highly optimized) glmnet implementation but is one or several orders of magnitude faster.
'Leave me alone' lesions of the petrous apex.

When troublesome MR imaging findings are noted in the petrous apex, the radiologist must determine if the area in question needs surgical therapy. Two nonsurgical entities, asymmetric fatty marrow and fluid-filled petrous air cells (trapped fluid), can be noted on conventional brain MR images and confused with pathologic lesions. Our observation that radiologists do not always confidently define the nonsurgical petrous apex lesions precipitated this investigation.



Twenty-three patients with either asymmetric fatty marrow (six) or unilateral effusion in a pneumatized petrous apex (17) on MR images were studied. Eighteen patients underwent high-resolution temporal bone CT. For all patients, the medical charts were reviewed retrospectively and/or the surgical and clinical follow-up findings were reviewed with the referring physician.



In the patients with asymmetric fatty marrow, MR signal intensity followed fat on all sequences. The questioned apex in the patients with trapped fluid showed mixed MR signal characteristics (low to high T1 signal, high T2 signal). CT scans confirmed nonexpansile air-cell opacification.



Asymmetric fatty marrow in the petrous apex and petrous air-cell effusions have characteristic MR and CT features that facilitate their correct diagnosis. Effusions with intermediate or high T1 signal are most frequently confused with cholesterol granulomas. In those patients, long-term CT follow-up may be helpful to confirm their stability.
On the Use of College Students in Social Science Research : Insights from a Second-Order Meta-analysis

A second-order meta-analysis was conducted to assess the implications of using college student subjects in social science research. Four meta-analyses investigating response homogeneity (cumulative N 1 650,000) and 30 meta-analyses reporting effect sizes for 65 behavioral or psychological relationships (cumulative N 1 350,000) provided comparative data for college student subjects and nonstudent (adult) subjects for the present research. In general, responses of college student subjects were found to be slightly more homogeneous than those of nonstudent subjects.Moreover, effect sizes derived from college student subjects frequently differed from those derived from nonstudent subjects both directionally and in magnitude. Because there was no systematic pattern to the differences observed, caution must be exercised when attempting to extend any relationship found using college student subjects to a nonstudent (adult) population. The results augur in favor of, and emphasize the importance of, replicating research based on college student subjects withnonstudent subjects before attempting any generalizations.
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns

ÐThis paper presents a theoretically very simple, yet efficient, multiresolution approach to gray-scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions. The method is based on recognizing that certain local binary patterns, termed auniform,o are fundamental properties of local image texture and their occurrence histogram is proven to be a very powerful texture feature. We derive a generalized gray-scale and rotation invariant operator presentation that allows for detecting the auniformo patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis. The proposed approach is very robust in terms of gray-scale variations since the operator is, by definition, invariant against any monotonic transformation of the gray scale. Another advantage is computational simplicity as the operator can be realized with a few operations in a small neighborhood and a lookup table. Excellent experimental results obtained in true problems of rotation invariance, where the classifier is trained at one particular rotation angle and tested with samples from other rotation angles, demonstrate that good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary patterns. These operators characterize the spatial configuration of local image texture and the performance can be further improved by combining them with rotation invariant variance measures that characterize the contrast of local image texture. The joint distributions of these orthogonal measures are shown to be very powerful tools for rotation invariant texture analysis. Index TermsÐNonparametric, texture analysis, Outex, Brodatz, distribution, histogram, contrast.
Anti-Solvent Crystallization Strategies for Highly Efficient Perovskite Solar Cells

Solution-processed organic-inorganic halide perovskites are currently established as the hottest area of interest in the world of photovoltaics, ensuring low manufacturing cost and high conversion efficiencies. Even though various fabrication/deposition approaches and device architectures have been tested, researchers quickly realized that the key for the excellent solar cell operation was the quality of the crystallization of the perovskite film, employed to assure efficient photogeneration of carriers, charge separation and transport of the separated carriers at the contacts. One of the most typical methods in chemistry to crystallize a material is anti-solvent precipitation. Indeed, this classical precipitation method worked really well for the growth of single crystals of perovskite. Fortunately, the method was also effective for the preparation of perovskite films by adopting an anti-solvent dripping technique during spin-coating the perovskite precursor solution on the substrate. With this, polycrystalline perovskite films with pure and stable crystal phases accompanied with excellent surface coverage were prepared, leading to highly reproducible efficiencies close to 22%. In this review, we discuss recent results on highly efficient solar cells, obtained by the anti-solvent dripping method, always in the presence of Lewis base adducts of lead(II) iodide. We present all the anti-solvents that can be used and what is the impact of them on device efficiencies. Finally, we analyze the critical challenges that currently limit the efficacy/reproducibility of this crystallization method and propose prospects for future directions.
Unsupervised Modeling of Twitter Conversations

We propose the first unsupervised approach to the problem of modeling dialogue acts in an open domain. Trained on a corpus of noisy Twitter conversations, our method discovers dialogue acts by clustering raw utterances. Because it accounts for the sequential behaviour of these acts, the learned model can provide insight into the shape of communication in a new medium. We address the challenge of evaluating the emergent model with a qualitative visualization and an intrinsic conversation ordering task. This work is inspired by a corpus of 1.3 million Twitter conversations, which will be made publicly available. This huge amount of data, available only because Twitter blurs the line between chatting and publishing, highlights the need to be able to adapt quickly to a new medium.
PIP Distance: A Unitary-invariant Metric for Understanding Functionality and Dimensionality of Vector Embeddings

In this paper, we present a theoretical framework for understanding vector embedding, a fundamental building block of many deep learning models, especially in NLP. We discover a natural unitary-invariance in vector embeddings, which is required by the distributional hypothesis. This unitary-invariance states the fact that two embeddings are essentially equivalent if one can be obtained from the other by performing a relative-geometry preserving transformation, for example a rotation. This idea leads to the Pairwise Inner Product (PIP) loss, a natural unitary-invariant metric for the distance between two embeddings. We demonstrate that the PIP loss captures the difference in functionality between embeddings. By formulating the embedding training process as matrix factorization under noise, we reveal a fundamental bias-variance tradeoff in dimensionality selection. With tools from perturbation and stability theory, we provide an upper bound on the PIP loss using the signal spectrum and noise variance, both of which can be readily inferred from data. Our framework sheds light on many empirical phenomena, including the existence of an optimal dimension, and the robustness of embeddings against over-parametrization. The bias-variance tradeoff of PIP loss explicitly answers the fundamental open problem of dimensionality selection for vector embeddings.
Autonomous human-robot proxemics: socially aware navigation based on interaction potential

To enable situated human–robot interaction (HRI), an autonomous robot must both understand and control proxemics—the social use of space—to employ natural communication mechanisms analogous to those used by humans. This work presents a computational framework of proxemics based on data-driven probabilistic models of how social signals (speech and gesture) are produced (by a human) and perceived (by a robot). The framework and modelswere implemented as autonomous proxemic behavior systems for sociable robots, including: (1) a sampling-based method for robot proxemic goal state estimation with respect to human–robot distance and orientation parameters, (2) a reactive proxemic controller for goal state realization, and (3) a cost-based trajectory planner for maximizing automated robot speech and gesture recognition rates along a path to the goal state. Evaluation results indicate that the goal state estimation and realization significantly improve upon past work in human–robot proxemics with respect to “interaction potential”—predicted automated speech and gesture recognition rates as the robot enters into and engages in faceto-face social encounters with a human user—illustrating their efficacy to support richer robot perception and autonomy in HRI. Electronic supplementary material The online version of this article (doi:10.1007/s10514-016-9572-2) contains supplementary material, which is available to authorized users.
Characterizing the software process: a maturity framework

A description is given of a software-process maturity framework that has been developed to provide the US Department of Defense with a means to characterize the capabilities of software-development organizations. This software-development process-maturity model reasonably represents the actual ways in which software-development organizations improve. It provides a framework for assessing these organizations and identifying the priority areas for immediate improvement. It also helps identify those places where advanced technology can be most valuable in improving the software-development process. The framework can be used by any software organization to assess its own capabilities and identify the most important areas for improvement.<<ETX>>
A Tutorial on Logistic Regression

Many procedures in SAs/STAT~ can be used to perform 10' gistic regression analysis: CATMOD, GENMOD,LOGISTIC, al)d PROBIT. Each procedure has special features that make. ~ useful for gertain applications. For most applica· tions, PROC LOGISTIC is the preferred choice,. It fits binary response or proportional odds models, provides various model·selection methods to identify important prognostic variables from a large number of candidate variables, and computes regression diagnostic statistics. This tutorial dis· cusses some of the problems users encountered when they used the LOGISTIC procedure.
Modeling Commonsense Reasoning via Analogical Chaining: A Preliminary Report

Understanding the nature of commonsense reasoning is one of the deepest questions of cognitive science. Prior work has proposed analogy as a mechanism for commonsense reasoning, with prior simulations focusing on reasoning about continuous behavior of physical systems. This paper examines how analogy might be used in commonsense more broadly. The two contributions are (1) the idea of common sense units, intermediate-sized collections of facts extracted from experience (including cultural experience) which improves analogical retrieval and simplifies inferencing, and (2) analogical chaining, where multiple rounds of analogical retrieval and mapping are used to rapidly construct explanations and predictions. We illustrate these ideas via an implemented computational model, tested on examples from an independently-developed test of commonsense reasoning.
Contextualizing agile software development

This paper presents a contextual model for software-intensive systems development to guide the adoption and adaptation of agile software development practices. This model was found especially useful when the project context departs significantly from the “agile sweet spot”, i.e., the ideal conditions in which agile software development practices originated from, and where they are most likely to succeed, “out of the box”. This is the case for large systems, distributed development environment, safety-critical systems, system requiring a novel architecture, or systems with an unorthodox business model or governance model.
Analysis of keyword spotting performance across IARPA babel languages

With the completion of the IARPA Babel program, it is possible to systematically analyze the performance of speech recognition systems across a wide variety of languages. We select 16 languages from the dataset and compare performance using a deep neural network-based acoustic model. The focus is on keyword spotting using the actual term-weighted value (ATWV) metric. We demonstrate that ATWV is keyword dependent, and that this must be accounted for in any cross-language analysis. Further, we show that while performance across languages does not track with any particular feature of the language, it is correlated with inter-annotator agreement.