nils-herrmann/clean_scirepeval huggingface dataset:
Determination of sun protection factor ( SPF ) of sunscreens by ultraviolet spectrophotometry

The aim of this research was to determine the sun protection factor (SPF) of sunscreens emulsions containing chemical and physical sunscreens by ultraviolet spectrophotometry. Ten different commercially available samples of sunscreen emulsions of various manufactures were evaluated. The SPF labeled values were in the range of 8 to 30. The SPF values of the 30% of the analyzed samples are in close agreement with the labeled SPF, 30% presented SPF values above the labeled amount and 40% presented SPF values under the labeled amount. The proposed spectrophotometric method is simple and rapid for the in vitro determination of SPF values of sunscreens emulsions.
Procedural House Generation : A method for dynamically generating floor plans

As the capability to render complex, realistic scenes in real time increases, many applications of computer graphics are hitting the problem of manual model creation. In virtual reality, researchers have found that creating and updating static models is a difficult and time-consuming task [Brooks 1999]. New games require massive amounts of content. Creating such content is such an expensive process that it may drive smaller companies out of business [Wright 2005]. Content creators cannot keep up with the increasing demand for many high-quality models; algorithms will have to be called upon to assist.
Automatic Detection of Depression in Speech Using Gaussian Mixture Modeling with Factor Analysis

1 Of increasing importance in the civilian and military population is the recognition of Major Depressive Disorder at its earliest stages and intervention before the onset of severe symptoms. Toward the goal of more effective monitoring of depression severity, we investigate automatic classifiers of depression state, that have the important property of mitigating nuisances due to data variability, such as speaker and channel effects, unrelated to levels of depression. To assess our measures, we use a 35-speaker free-response speech database of subjects treated for depression over a six-week duration, along with standard clinical HAMD depression ratings. Preliminary experiments indicate that by mitigating nuisances, thus focusing on depression severity as a class, we can significantly improve classification accuracy over baseline Gaussian-mixture-model-based classifiers.
Text analysis as a tool for analyzing conversation in online support groups

In this paper we describe a software tool that allows investigators to make comparisons across different online forums and media by analyzing word counts in user-specified categories. Using a large sample of messages from a bipolar support chatroom, we demonstrate how this tool can be used to characterize the nature of the discourse and compare it to other media, to analyze relationships among different word categories, and to characterize changes in visitors' discourse over time. Future plans for adding functionality to the software and using external data for additional validation are also discussed.
A Ku-band 20 W GaN-MMIC amplifier with built-in linearizer

This report describes a Ku-band amplifier GaN MMIC. The amplifier MMIC delivers a measured saturated power of 20 W and gain of 20 dB under CW operation. To enhance the linearity of the two stage amplifier composed of the MMIC and GaN Internally Matched FET, a diode linearizer has also been built into the MMIC. The linearizer offers 5dB better linear output power, defined the output power at IM3 of -25 dBc, compared with that of MMIC without the linearizer. To our knowledge, this is the first report to present GaN-based high power amplifier MMIC with a built-in linearizer which can enhance the linearity of a PA system.
Real time kinect based robotic arm manipulation with five degree of freedom

We propose a real time, gesture based robotic arm manipulation using kinect sensor. This method uses kinect depth data, skeletal data and joint orientation data for end effector movement including roll, pitch and pinch. For the initial data, we extracted skeletal of the person, hand position and hand state. In closed hand state condition, the position of the hand is extracted and the three dimension data is used for inverse kinematics of the joints of the robotic arm. In open hand state, tilting and rolling of hand is used for pitch and roll respectively. In lasso state, pinch of end effector is perceived. The robotic arm used for the experimentation is Scorbot ER III. The system shows promising results for non-invasive robotic arm manipulation.
Using Small Screen Space More Efficiently

This paper describes techniques for maximizing the efficient use of small screen space by combining delayed response with semi-transparency of control objects ("widgets") and on-screen text. Most research on the limitations of small display screens has focused on methods for optimizing concurrent display of text and widgets at the same level of transparency (that is, both are equally opaque). Prior research which proposes that widgets may be made semi-transparent is promising, but it does not, we feel, adequately address problems associated with user interaction with text that is partially obscured by the widgets. In this paper, we will propose that a variable delay in the response of overlapping widgets and text improves the effectiveness of the semi-transparent widget/text model. Our conclusions are based on usability studies of a prototype of an online newspaper that combined transparency and delayed-response techniques.
Model-based clustering for RNA-seq data

RNA-seq technology has been widely adopted as an attractive alternative to microarray-based methods to study global gene expression. However, robust statistical tools to analyze these complex datasets are still lacking. By grouping genes with similar expression profiles across treatments, cluster analysis provides insight into gene functions and networks, and hence is an important technique for RNA-seq data analysis.



In this manuscript, we derive clustering algorithms based on appropriate probability models for RNA-seq data. An expectation-maximization algorithm and another two stochastic versions of expectation-maximization algorithms are described. In addition, a strategy for initialization based on likelihood is proposed to improve the clustering algorithms. Moreover, we present a model-based hybrid-hierarchical clustering method to generate a tree structure that allows visualization of relationships among clusters as well as flexibility of choosing the number of clusters. Results from both simulation studies and analysis of a maize RNA-seq dataset show that our proposed methods provide better clustering results than alternative methods such as the K-means algorithm and hierarchical clustering methods that are not based on probability models.



An R package, MBCluster.Seq, has been developed to implement our proposed algorithms. This R package provides fast computation and is publicly available at http://www.r-project.org
Neural data science: accelerating the experiment-analysis-theory cycle in large-scale neuroscience

Modern large-scale multineuronal recording methodologies, including multielectrode arrays, calcium imaging, and optogenetic techniques, produce single-neuron resolution data of a magnitude and precision that were the realm of science fiction twenty years ago. The major bottlenecks in systems and circuit neuroscience no longer lie in simply collecting data from large neural populations, but also in understanding this data: developing novel scientific questions, with corresponding analysis techniques and experimental designs to fully harness these new capabilities and meaningfully interrogate these questions. Advances in methods for signal processing, network analysis, dimensionality reduction, and optimal control-developed in lockstep with advances in experimental neurotechnology-promise major breakthroughs in multiple fundamental neuroscience problems. These trends are clear in a broad array of subfields of modern neuroscience; this review focuses on recent advances in methods for analyzing neural time-series data with single-neuronal precision.
Language Understanding in the Wild: Combining Crowdsourcing and Machine Learning

Social media has led to the democratisation of opinion sharing. A wealth of information about public opinions, current events, and authors' insights into specific topics can be gained by understanding the text written by users. However, there is a wide variation in the language used by different authors in different contexts on the web. This diversity in language makes interpretation an extremely challenging task. Crowdsourcing presents an opportunity to interpret the sentiment, or topic, of free-text. However, the subjectivity and bias of human interpreters raise challenges in inferring the semantics expressed by the text. To overcome this problem, we present a novel Bayesian approach to language understanding that relies on aggregated crowdsourced judgements. Our model encodes the relationships between labels and text features in documents, such as tweets, web articles, and blog posts, accounting for the varying reliability of human labellers. It allows inference of annotations that scales to arbitrarily large pools of documents. Our evaluation using two challenging crowdsourcing datasets shows that by efficiently exploiting language models learnt from aggregated crowdsourced labels, we can provide up to 25% improved classifications when only a small portion, less than 4% of documents has been labelled. Compared to the six state-of-the-art methods, we reduce by up to 67% the number of crowd responses required to achieve comparable accuracy. Our method was a joint winner of the CrowdFlower - CrowdScale 2013 Shared Task challenge at the conference on Human Computation and Crowdsourcing (HCOMP 2013).
Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control

The Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control, a fleet of quadrotor helicopters, has been developed as a testbed for novel algorithms that enable autonomous operation of aerial vehicles. The testbed has been used to validate multiple algorithms such as reactive collision avoidance, collision avoidance through Nash Bargaining, path planning, cooperative search and aggressive maneuvering. This article briefly describes the algorithms presented and provides references for a more in-depth formulation, and the accompanying movie shows the demonstration of the algorithms on the testbed.
A survey of unit testing practices

Unit testing is testing of individual units or groups of related units. What are a company's typical strengths and weaknesses when applying unit testing? Per Beremark and the author surveyed unit testing practices on the basis of focus group discussions in software process improvement network (SPIN) and launched a questionnaire to validate the results. This survey is an indication of unit testing in several companies. You can use the questionnaire at your own company to clarify what you mean by unit testing, to identify the strengths and weaknesses of your unit testing practices, and to compare with other organizations to improve those practices
UHF RFID localization system based on a phased array antenna

This paper presents new experimental results of angle of arrival (AoA) measurements for localizing passive RFID tags in the UHF frequency range. The localization system is based on the principle of a phased array with electronic beam steering mechanism. This approach has been successfully applied within a UHF RFID system and it allows the precise determination of the angle and the position of small passive RFID tags. The paper explains the basic principle, the experimental setup with the phased array and shows results of the measurements.
Double Dielectric Slab-Loaded Air-Filled SIW Phase Shifters for High-Performance Millimeter-Wave Integration

Phase shifters based on double dielectric slab-loaded air-filled substrate-integrated waveguide (SIW) are proposed for high-performance applications at millimeter-wave frequencies. The three-layered air-filled SIW, made of a low-cost multilayer printed circuit board process, allows for substantial loss reduction and power handling capability enhancement compared with the conventional dielectric-filled counterpart. It is of particular interest for millimeter-wave applications that generally require low-loss transmission and high-density power handling. Its top and bottom layers may make use of a low-cost standard substrate, such as FR-4, on which baseband analog or digital circuits can be implemented so to obtain very compact, low cost, and self-packaged millimeter-wave integrated systems compared with the systems based on rectangular waveguide while achieving higher performance than the systems based on the conventional SIW. In this paper, it is demonstrated that transmission loss can be further improved at millimeter-wave frequencies with an additional polishing of the top and bottom conductor surfaces. Over Ka-band, an improvement of average 1.56 dB/m is experimentally demonstrated. Using the air-filled SIW fabrication process, dielectric slabs can be implemented along conductive via rows without any additional process. Based on the propagation properties of the obtained double dielectric slab-loaded air-filled SIW, phase shifters are proposed. To obtain a broadband response, an equal-length compensated phase shifter made of two air-filled SIW structures, offering a reverse varying propagation constant difference against frequency, is proposed and demonstrated at Ka-band. Finally, a single dielectric slab phase shifter is investigated for comparison and its bandwidth limitation is highlighted.
Machine Learning from Imbalanced Data Sets 101 Extended

For research to progress most effectively, we first should establish common ground regarding just what is the problem that imbalanced data sets present to machine learning systems. Why and when should imbalanced data sets be problematic? When is the problem simply an artifact of easily rectified design choices? I will try to pick the low-hanging fruit and share them with the rest of the workshop participants. Specifically, I would like to discuss what the problem is not. I hope this will lead to a profitable discussion of what the problem indeed is, and how it might be addressed most effectively. A common notion in machine learning causes the most basic problem, and indeed often has stymied both research-oriented and practical attempts to learn from imbalanced data sets. Fortunately the problem is straightforward to fix. The stumbling block is the notion that an inductive learner produces a black box that acts as a categorical (e.g., binary) labeling function. Of course, many of our learning algorithms in fact do produce such classifiers, which gets us into trouble when faced with imbalanced class distributions. The assumptions built into (most of) these algorithms are: 1. that maximizing accuracy is the goal, and 2. that, in use, the classifier will operate on data drawn from the same distribution as the training data. The result of these two assumptions is that machine learning on unbalanced data sets produces unsatisfactory classifiers. The reason why should be clear: if 99% of the data are from one class, for most realistic problems a learning algorithm will be hard pressed to do better than the 99% accuracy achievable by the trivial classifier that labels everything with the majority class. Based on the underlying assumptions, this is the intelligent thing to do. It is more striking when one of our algorithms, operating under these assumptions, behaves otherwise. This apparent problem nothwithstanding, it would be premature to conclude that there is a fundamental difficulty with learning from imbalanced data sets. We first must probe deeper and ask whether the algorithms are robust to the weakening of the assumptions that cause the problem. When designing algorithms, some assumptions are fundamental. Changing them would entail redesigning the algorithm completely. Other assumptions are made for convenience, and can be changed with little consequence. So, which is the nature of the assumptions (1 & 2) in question? Investigating this is (tacitly perhaps) one of the main …
Fast, unconstrained camera motion estimation from stereo without tracking and robust statistics

Camera motion estimation is useful for a range of applications. Usually, feature tracking is performed through the sequence of images to determine correspondences. Furthermore, robust statistical techniques are normally used to handle large number of outliers in correspondences. This paper proposes a new method that avoids both. Motion is calculated between two consecutive stereo images without any pre-knowledge or prediction about feature location or the possibly large camera movement. This permits a lower frame rate and almost arbitrary movements. Euclidean constraints are used to incrementally select inliers from a set of initial correspondences, instead of using robust statistics that has to handle all inliers and outliers together. These constraints are so strong that the set of initial correspondences can contain several times more outliers than inliers. Experiments on a worst-case stereo sequence show that the method is robust, accurate and can be used in real-time.
Predicting the drape of woven cloth using interacting particles

We demonstrate a physically-based technique for predicting the drape of a wide variety of woven fabrics. The approach exploits a theoretical model that explicitly represents the microstructure of woven cloth with interacting particles, rather than utilizing a continuum approximation. By testing a cloth sample in a Kawabata fabric testing device, we obtain data that is used to tune the model's energy functions, so that it reproduces the draping behavior of the original material. Photographs, comparing the drape of actual cloth with visualizations of simulation results, show that we are able to reliably model the unique large-scale draping characteristics of distinctly different fabric types.
Image Features from Phase Congruency Image Features from Phase Congruency

Image features such as step edges, lines and Mach bands all give rise to points where the Fourier components of the image are maximally in phase. The use of phase congruency for marking features has signiicant advantages over gradient based methods. It is a dimension-less quantity that is invariant to changes in image brightness or contrast, hence it provides an absolute measure of the signiicance of feature points. This allows the use of universal threshold values that can be applied over wide classes of images. This paper presents a new way of calculating phase congruency through the use of wavelets. The existing theory that has been developed for 1D signals is extended to allow the calculation of phase congruency in 2D images. It is shown that for good localization it is important to consider the spread of frequencies present at a point of phase congruency. An eeective method for identifying, and compensating for, the level of noise in an image is presented. Finally, it is argued that high-pass ltering should be used to obtain image information at diierent scales. With this approach the choice of scale only aaects the relative signiicance of features without degrading their localization.  Image features such as step edges, lines and Mach bands all give rise to points where the Fourier components of the image are maximally in phase. The use of phase congruency for marking features has signiicant advantages over gradient based methods. It is a dimensionless quantity that is invariant to changes in image brightness or contrast, hence it provides an absolute measure of the signiicance of feature points. This allows the use of universal threshold values that can be applied over wide classes of images. This paper presents a new way of calculating phase congruency through the use of wavelets. The existing theory that has been developed for 1D signals is extended to allow the calculation of phase congruency in 2D images. It is shown that for good localization it is important to consider the spread of frequencies present at a point of phase congruency. An eeective method for identifying, and compensating for, the level of noise in an image is presented. Finally, it is argued that high-pass ltering should be used to obtain image information at diierent scales. With this approach the choice of scale only aaects the relative signiicance of features without degrading their localization.
A psychometric toolbox for testing validity and reliability.

To review the concepts of reliability and validity, provide examples of how the concepts have been used in nursing research, provide guidance for improving the psychometric soundness of instruments, and report suggestions from editors of nursing journals for incorporating psychometric data into manuscripts.



CINAHL, MEDLINE, and PsycINFO databases were searched using key words: validity, reliability, and psychometrics. Nursing research articles were eligible for inclusion if they were published in the last 5 years, quantitative methods were used, and statistical evidence of psychometric properties were reported. Reports of strong psychometric properties of instruments were identified as well as those with little supporting evidence of psychometric soundness.



Reports frequently indicated content validity but sometimes the studies had fewer than five experts for review. Criterion validity was rarely reported and errors in the measurement of the criterion were identified. Construct validity remains underreported. Most reports indicated internal consistency reliability (alpha) but few reports included reliability testing for stability. When retest reliability was asserted, time intervals and correlations were frequently not included.



Planning for psychometric testing through design and reducing nonrandom error in measurement will add to the reliability and validity of instruments and increase the strength of study findings. Underreporting of validity might occur because of small sample size, poor design, or lack of resources. Lack of information on psychometric properties and misapplication of psychometric testing is common in the literature.
Real-time multi-stereo depth estimation on GPU with approximative discontinuity handling

This paper describes a system for dense depth estimation for multiple images in real-time. The algorithm runs almost entirely on standard graphics hardware, leaving the main CPU free for other tasks as image capture, compression and storage during scene capture. We follow a plain-sweep approach extended by truncated SSD scores, shiftable windows and best camera selection. We do not need specialized hardware and exploit the computational power of freely programmable PC graphics hardware. Dense depth maps are computed with up to 20 fps.
Set Variability and Failure Induced by Complementary Switching in Bipolar RRAM

The resistive switching memory (RRAM) offers fast switching, low-voltage operation, and scalable device area. However, reliability and variability issues must be understood, particularly in the low-current operation regime. This letter addresses set-state variability and presents a new set failure phenomenon in RRAM, leading to a high-resistance tail in the set-state distribution. The set failure is due to complementary switching of the RRAM, causing an increase of resistance soon after the set transition. The dependence of set failure on the programing current is explained by the increasing voltage stress across the RRAM device causing filament disconnection.