nils-herrmann/clean_scirepeval huggingface dataset:
An Extension of Wilkinson&#8217;s Algorithm for Positioning Tick Labels on Axes

The non-data components of a visualization, such as axes and legends, can often be just as important as the data itself. They provide contextual information essential to interpreting the data. In this paper, we describe an automated system for choosing positions and labels for axis tick marks. Our system extends Wilkinson's optimization-based labeling approach to create a more robust, full-featured axis labeler. We define an expanded space of axis labelings by automatically generating additional nice numbers as needed and by permitting the extreme labels to occur inside the data range. These changes provide flexibility in problematic cases, without degrading quality elsewhere. We also propose an additional optimization criterion, legibility, which allows us to simultaneously optimize over label formatting, font size, and orientation. To solve this revised optimization problem, we describe the optimization function and an efficient search algorithm. Finally, we compare our method to previous work using both quantitative and qualitative metrics. This paper is a good example of how ideas from automated graphic design can be applied to information visualization.
600 GHz HD-TV transmission combining UTC-PD and heterodyne receiver

We propose a 600 GHz data transmission of high definition television using the combination of a photonic emission using an uni-travelling carrier photodiode and an electronic detection, featuring a very low power at the receiver. Only 10 nW of THz power at 600GHz were sufficient to ensure real-time error-free operation. This combination of photonics at emission and heterodyne detection lead to achieve THz wireless links with a safe level of electromagnetic exposure.
Multifocusing and Depth Estimation Using a Color Shift Model-Based Computational Camera

This paper presents a novel approach to depth estimation using a multiple color-filter aperture (MCA) camera and its application to multifocusing. An image acquired by the MCA camera contains spatially varying misalignment among RGB color channels, where the direction and length of the misalignment is a function of the distance of an object from the plane of focus. Therefore, if the misalignment is estimated from the MCA output image, multifocusing and depth estimation become possible using a set of image processing algorithms. We first segment the image into multiple clusters having approximately uniform misalignment using a color-based region classification method, and then find a rectangular region that encloses each cluster. For each of the rectangular regions in the RGB color channels, color shifting vectors are estimated using a phase correlation method. After the set of three clusters are aligned in the opposite direction of the estimated color shifting vectors, the aligned clusters are fused to produce an approximately in-focus image. Because of the finite size of the color-filter apertures, the fused image still contains a certain amount of spatially varying out-of-focus blur, which is removed by using a truncated constrained least-squares filter followed by a spatially adaptive artifacts removing filter. Experimental results show that the MCA-based multifocusing method significantly enhances the visual quality of an image containing multiple objects of different distances, and can be fully or partially incorporated into multifocusing or extended depth of field systems. The MCA camera also realizes single camera-based depth estimation, where the displacement between multiple apertures plays a role of the baseline of a stereo vision system. Experimental results show that the estimated depth is accurate enough to perform a variety of vision-based tasks, such as image understanding, description, and robot vision.
Lymphangiogenesis and lymphatic vessel remodelling in cancer

The generation of new lymphatic vessels through lymphangiogenesis and the remodelling of existing lymphatics are thought to be important steps in cancer metastasis. The past decade has been exciting in terms of research into the molecular and cellular biology of lymphatic vessels in cancer, and it has been shown that the molecular control of tumour lymphangiogenesis has similarities to that of tumour angiogenesis. Nevertheless, there are significant mechanistic differences between these biological processes. We are now developing a greater understanding of the specific roles of distinct lymphatic vessel subtypes in cancer, and this provides opportunities to improve diagnostic and therapeutic approaches that aim to restrict the progression of cancer.
Controlling Decoding for More Abstractive Summaries with Copy-Based Networks

Attention-based neural abstractive summarization systems equipped with copy mechanisms have shown promising results. Despite this success, it has been noticed that such a system generates a summary by mostly, if not entirely, copying over phrases, sentences, and sometimes multiple consecutive sentences from an input paragraph, effectively performing extractive summarization. In this paper, we verify this behavior using the latest neural abstractive summarization system a pointergenerator network (See et al., 2017). We propose a simple baseline method that allows us to control the amount of copying without retraining. Experiments indicate that the method provides a strong baseline for abstractive systems looking to obtain high ROUGE scores while minimizing overlap with the source article, substantially reducing the n-gram overlap with the original article while keeping within 2 points of the original model’s ROUGE score.
Interdisciplinary Research Issues in Music Information Retrieval: ISMIR 2000–2002

Music Information Retrieval (MIR) is an interdisciplinary research area that has grown out of the need to manage burgeoning collections of music in digital form. Its diverse disciplinary communities, exemplified by the recently established ISMIR conference series, have yet to articulate a common research agenda or agree on methodological principles and metrics of success. In order for MIR to succeed, researchers need to work with real user communities and develop research resources such as reference music collections , so that the wide variety of techniques being developed in MIR can be meaningfully compared with one another. Out of these efforts, a common MIR practice can emerge.
Real-Time Salient Object Detection with a Minimum Spanning Tree

In this paper, we present a real-time salient object detection system based on the minimum spanning tree. Due to the fact that background regions are typically connected to the image boundaries, salient objects can be extracted by computing the distances to the boundaries. However, measuring the image boundary connectivity efficiently is a challenging problem. Existing methods either rely on superpixel representation to reduce the processing units or approximate the distance transform. Instead, we propose an exact and iteration free solution on a minimum spanning tree. The minimum spanning tree representation of an image inherently reveals the object geometry information in a scene. Meanwhile, it largely reduces the search space of shortest paths, resulting an efficient and high quality distance transform algorithm. We further introduce a boundary dissimilarity measure to compliment the shortage of distance transform for salient object detection. Extensive evaluations show that the proposed algorithm achieves the leading performance compared to the state-of-the-art methods in terms of efficiency and accuracy.
MAPO: mining API usages from open source repositories

To improve software productivity, when constructing new software systems, developers often reuse existing class libraries or frameworks by invoking their APIs. Those APIs, however, are often complex and not well documented, posing barriers for developers to use them in new client code. To get familiar with how those APIs are used, developers may search the Web using a general search engine to find relevant documents or code examples. Developers can also use a source code search engine to search open source repositories for source files that use the same APIs. Nevertheless, the number of returned source files is often large. It is difficult for developers to learn API usages from a large number of returned results. In order to help developers understand API usages and write API client code more effectively, we have developed an API usage mining framework and its supporting tool called MAPO (for <u>M</u>ining <u>AP</u>I usages from <u>O</u>pen source repositories). Given a query that describes a method, class, or package for an API, MAPO leverages the existing source code search engines to gather relevant source files and conducts data mining. The mining leads to a short list of frequent API usages for developers to inspect. MAPO currently consists of five components: a code search engine, a source code analyzer, a sequence preprocessor, a frequent sequence miner, and a frequent sequence post processor. We have examined the effectiveness of MAPO using a set of various queries. The preliminary results show that the framework is practical for providing informative and succinct API usage patterns.
A Cost-Value Approach for Prioritizing Requirements

eveloping software systems that meet stakeholders' needs and expectations is the ultimate goal of any software provider seeking a competitive edge. To achieve this, you must effectively and accurately manage your stakeholders' system requirements: the features, functions , and attributes they need in their software system. 1 Once you agree on these requirements, you can use them as a focal point for the development process and produce a software system that meets the expectations of both customers and users. However, in real-world software development, there are usually more requirements than you can implement given stakeholders' time and resource constraints. Thus, project managers face a dilemma: How do you select a subset of the customers' requirements and still produce a system that meets their needs? Deciding which requirements really matter is a difficult task and one increasingly demanded because of time and budget constraints. The authors developed a cost–value approach for prioritizing requirements and applied it to two commercial projects.
Lens adhering contaminant detection using spatio-temporal blur

In this paper, we present a hierarchical spatiotemporal blur-based approach to automatically detect contaminants on the camera lens. Contaminants adhering to camera lens corresponds to blur regions in digital image, as camera is focused on scene. We use kurtosis for a first level analysis to detect blur regions and filter them out. Next level of analysis computes lowpass energy and singular values to further validate blur regions. These analyses detect blur regions in an image efficiently and temporal consistency of blur is additionally incorporated to remove false detections. Once the presence of a contaminant is detected, we use an appearance-based classifier to categorize the type of contaminant on the lens. Our results are promising in terms of performance and latency when compared with state-of-the-art methods under a variety of real-world conditions.
Automatic model-based tracing algorithm for vessel segmentation and diameter estimation

An automatic algorithm capable of segmenting the whole vessel tree and calculate vessel diameter and orientation in a digital ophthalmologic image is presented in this work. The algorithm is based on a parametric model of a vessel that can assume arbitrarily complex shape and a simple measure of match that quantifies how well the vessel model matches a given angiographic image. An automatic vessel tracing algorithm is described that exploits the geometric model and actively seeks vessel bifurcation, without user intervention. The proposed algorithm uses the geometric vessel model to determine the vessel diameter at each detected central axis pixel. For this reason, the algorithm is fine tuned using a subset of ophthalmologic images of the publically available DRIVE database, by maximizing vessel segmentation accuracy. The proposed algorithm is then applied to the remaining ophthalmological images of the DRIVE database. The segmentation results of the proposed algorithm compare favorably in terms of accuracy with six other well established vessel detection techniques, outperforming three of them in the majority of the available ophthalmologic images. The proposed algorithm achieves subpixel root mean square central axis positioning error that outperforms the non-expert based vessel segmentation, whereas the accuracy of vessel diameter estimation is comparable to that of the non-expert based vessel segmentation.
Automated testing of mixed-signal integrated circuits by topology modification

A general method is proposed to automatically generate a DfT solution aiming at the detection of catastrophic faults in analog and mixed-signal integrated circuits. The approach consists in modifying the topology of the circuit by pulling up (down) nodes and then probing differentiating node voltages. The method generates a set of optimal hardware implementations addressing the multi-objective problem such that the fault coverage is maximized and the silicon overhead is minimized. The new method was applied to a real-case industrial circuit, demonstrating a nearly 100 percent coverage at the expense of an area increase of about 5 percent.
Prevention of accidental exposure in radiotherapy: the risk matrix approach.

Knowledge and lessons from past accidental exposures in radiotherapy are very helpful in finding safety provisions to prevent recurrence. Disseminating lessons is necessary but not sufficient. There may be additional latent risks for other accidental exposures, which have not been reported or have not occurred, but are possible and may occur in the future if not identified, analyzed, and prevented by safety provisions. Proactive methods are available for anticipating and quantifying risk from potential event sequences. In this work, proactive methods, successfully used in industry, have been adapted and used in radiotherapy. Risk matrix is a tool that can be used in individual hospitals to classify event sequences in levels of risk. As with any anticipative method, the risk matrix involves a systematic search for potential risks; that is, any situation that can cause an accidental exposure. The method contributes new insights: The application of the risk matrix approach has identified that another group of less catastrophic but still severe single-patient events may have a higher probability, resulting in higher risk. The use of the risk matrix approach for safety assessment in individual hospitals would provide an opportunity for self-evaluation and managing the safety measures that are most suitable to the hospital's own conditions.
LibD3C: Ensemble classifiers with a clustering and dynamic selection strategy

Selective ensemble is a learning paradigm that follows an “overproduce and choose” strategy, where a number of candidate classifiers are trained, and a set of several classifiers that are accurate and diverse are selected to solve a problem. In this paper, the hybrid approach called D3C is presented; this approach is a hybrid model of ensemble pruning that is based on k-means clustering and the framework of dynamic selection and circulating in combination with a sequential search method. Additionally, a multilabel D3C is derived from D3C through employing a problem transformation for multi-label classification. Empirical study shows that D3C exhibits competitive performance against other high-performance methods, and experiments in multi-label datasets verify the feasibility of multi-label D3C. & 2013 Elsevier B.V. All rights reserved.
A low-voltage low-power sigma-delta modulator for broadband analog-to-digital conversion

A cascade of sigma-delta modulator stages that employ a feedforward architecture to reduce the signal ranges required at the integrator inputs and outputs has been used to implement a broadband, high-resolution oversampling CMOS analog-to-digital converter capable of operating from low-supply voltages. An experimental prototype of the proposed architecture has been integrated in a 0.25-/spl mu/m CMOS technology and operates from an analog supply of only 1.2 V. At a sampling rate of 40 MSamples/sec, it achieves a dynamic range of 96 dB for a 1.25-MHz signal bandwidth. The analog power dissipation is 44 mW.
Toward a theory of sustainable entrepreneurship : Reducing environmental degradation through entrepreneurial action

This article explains how entrepreneurship can help resolve the environmental problems of global socio-economic systems. Environmental economics concludes that environmental degradation results from the failure of markets, whereas the entrepreneurship literature argues that opportunities are inherent in market failure. A synthesis of these literatures suggests that environmentally relevant market failures represent opportunities for achieving profitability while simultaneously reducing environmentally degrading economic behaviors. It also implies conceptualizations of sustainable and environmental entrepreneurship which detail how entrepreneurs seize the opportunities that are inherent in environmentally relevant market failures. Finally, the article examines the ability of the proposed theoretical framework to transcend its environmental context and provide insight into expanding the domain of the study of entrepreneurship. D 2005 Elsevier Inc. All rights reserved.
Six-Port Based Interferometry for Precise Radar and Sensing Applications

Microwave technology plays a more important role in modern industrial sensing applications. Pushed by the significant progress in monolithic microwave integrated circuit technology over the past decades, complex sensing systems operating in the microwave and even millimeter-wave range are available for reasonable costs combined with exquisite performance. In the context of industrial sensing, this stimulates new approaches for metrology based on microwave technology. An old measurement principle nearly forgotten over the years has recently gained more and more attention in both academia and industry: the six-port interferometer. This paper reviews the basic concept, investigates promising applications in remote, as well as contact-based sensing and compares the system with state-of-the-art metrology. The significant advantages will be discussed just as the limitations of the six-port architecture. Particular attention will be paid to impairment effects and non-ideal behavior, as well as compensation and linearization concepts. It will be shown that in application fields, like remote distance sensing, precise alignment measurements, as well as interferometrically-evaluated mechanical strain analysis, the six-port architecture delivers extraordinary measurement results combined with high measurement data update rates for reasonable system costs. This makes the six-port architecture a promising candidate for industrial metrology.
PoTrojan: powerful neural-level trojan designs in deep learning models

With the popularity of deep learning (DL), artificial intelligence (AI) has been applied in many areas of human life. Artificial neural network or neural network (NN), the main technique behind DL, has been extensively studied to facilitate computer vision and natural language processing. However, the more we rely on information technology, the more vulnerable we are. That is, malicious NNs could bring huge threat in the so-called coming AI era. In this paper, for the first time in the literature, we propose a novel approach to design and insert powerful neural-level trojans or PoTrojan in pre-trained NN models. Most of the time, PoTrojans remain inactive, not affecting the normal functions of their host NN models. PoTrojans could only be triggered in very rare conditions. Once activated, however, the PoTrojans could cause the host NN models to malfunction, either falsely predicting or classifying, which is a significant threat to human society of the AI era. We would explain the principles of PoTrojans and the easiness of designing and inserting them in pre-trained deep learning models. PoTrojans doesn’t modify the existing architecture or parameters of the pre-trained models, without re-training. Hence, the proposed method is very efficient.
76.5 GHz millimeter-wave radar for Foreign Object Debris detection on airport runways

The paper is a joint work between the LEAT (France) and the ENRI (Japan) in the framework of a Sakura project supported by the JSPS and the French Ministry of Foreign Affairs. The purpose is the study of a FOD (Foreign Object Debris) detection system on airport runways. A FM-CW mm-Wave radar working between 76.25 and 76.75 GHz is used together with a high directivity printed reflectarray. Measurement results show detection capabilities of a -20 tlBsm cylinder up to 35 m which is 10 m less than the FAA recommendations. Antenna improvements are discussed for reaching the requirements and system performance as well as the use of calibration objects.
Implicit theories and ability emotional intelligence

Previous research has shown that people differ in their implicit theories about the essential characteristics of intelligence and emotions. Some people believe these characteristics to be predetermined and immutable (entity theorists), whereas others believe that these characteristics can be changed through learning and behavior training (incremental theorists). The present study provides evidence that in healthy adults (N = 688), implicit beliefs about emotions and emotional intelligence (EI) may influence performance on the ability-based Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT). Adults in our sample with incremental theories about emotions and EI scored higher on the MSCEIT than entity theorists, with implicit theories about EI showing a stronger relationship to scores than theories about emotions. Although our participants perceived both emotion and EI as malleable, they viewed emotions as more malleable than EI. Women and young adults in general were more likely to be incremental theorists than men and older adults. Furthermore, we found that emotion and EI theories mediated the relationship of gender and age with ability EI. Our findings suggest that people's implicit theories about EI may influence their emotional abilities, which may have important consequences for personal and professional EI training.
Mambo: a full system simulator for the PowerPC architecture

Mambo is a full-system simulator for modeling PowerPC-based systems. It provides building blocks for creating simulators that range from purely functional to timing-accurate. Functional versions support fast emulation of individual PowerPC instructions and the devices necessary for executing operating systems. Timing-accurate versions add the ability to account for device timing delays, and support the modeling of the PowerPC processor microarchitecture. We describe our experience in implementing the simulator and its uses within IBM to model future systems, support early software development, and design new system software.
Multi-layer Neural Networks for Image Analysis of Agricultural Products

Neural networks have recently gained popularity as an alternative to regression models to characterize biological processes. Their decision-making capabilities can be best used in image analysis of biological products where the shape and size classi"cation is not governed by any mathematical function. This paper reviews the technique of image analysis of agricultural products with reference to use of neural network classi"ers for decision making. A thorough review of published literature reveals that, although many neural network classi"ers have been used and evaluated for classifying agricultural products, multi-layer neural network classi"ers perform the best for such tasks. ( 2000 Silsoe Research Institute