nils-herrmann/clean_scirepeval huggingface dataset:
The stigma of obesity: a review and update.

Reviews epidemiology Obese individuals are highly stigmatized and face multiple forms of prejudice and discrimination because of their weight (1,2). The prevalence of weight discrimination in the United States has increased by 66% over the past decade (3), and is comparable to rates of racial discrimination , especially among women (4). Weight bias translates into inequities in employment settings, health-care facilities, and educational institutions, often due to widespread negative stereotypes that overweight and obese persons are lazy, unmotivated, lacking in self-discipline, less competent, non-compliant, and sloppy (2,5–7). These stereotypes are prevalent and are rarely challenged in Western society, leaving overweight and obese persons vulnerable to social injustice, unfair treatment, and impaired quality of life as a result of substantial disadvantages and stigma. In 2001, Puhl and Brownell published the first comprehensive review of several decades of research documenting bias and stigma toward overweight and obese persons (2). This review summarized weight stigma in domains of employment, health care, and education, demonstrating the vulnerability of obese persons to many forms of unfair treatment. Despite evidence of weight bias in important areas of living, the authors noted many gaps in research regarding the nature and extent of weight stigma in various settings, the lack of science on emotional and physical health consequences of weight bias, and the pau-city of interventions to reduce negative stigma. In recent years, attention to weight bias has increased, with a growing recognition of the pervasiveness of weight bias and stigma, and its potential harmful consequences for obese persons. The aim of this article is to provide an update of scientific evidence on weight bias toward overweight and obese adults through a systematic review of published literature since the 2001 article by Puhl and Brownell. This review expands upon previous findings of weight bias in major domains of living, documents new areas where weight bias has been studied, and highlights ongoing research questions that need to be addressed to advance this field of study. A systematic literature search of studies retrieved articles and books were also reviewed, and manual searches were conducted in the databases and journals for authors who had published in this field. Most studies retrieved for this review were published in the United States. Any articles published internationally are noted with their country of origin. Research on weight stigma in adolescents and children was excluded from this review, as this literature was recently reviewed elsewhere …
A compact ultra-wideband slot antenna with band notch characteristics

In this study, a compact wideband slot antenna with band notch performance is designed. An M-shaped slot on the feed line increases the impedance bandwidth of the square slot antenna. A stop-band notch can be achieved by using two L-shaped protruded strip inside the square-ring stub. The designed slot antenna has a compact size of 20×20 mm, showing wideband characteristics in the frequency band of 4.4 to over 10.7 GHz with a band rejection performance in the frequency band of 5.0 to 5.4 GHz.
VARIATIONAL RECURRENT ADVERSARIAL DEEP DOMAIN ADAPTATION

We study the problem of learning domain invariant representations for time series data while transferring the complex temporal latent dependencies between domains. Our model termed as Variational Recurrent Adversarial Deep Domain Adaptation (VRADA) is built atop a variational recurrent neural network (VRNN) and trains adversarially to capture complex temporal relationships that are domain-invariant. This is (as far as we know) the first to capture and transfer temporal latent dependencies of multivariate time-series data. Through experiments on real-world multivariate healthcare time-series datasets, we empirically demonstrate that learning temporal dependencies helps our model’s ability to create domain-invariant representations, allowing our model to outperform current state-of-the-art deep domain adaptation approaches.
Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables

This study compared two alternative techniques for predicting forest cover types from cartographic variables. The study evaluated four wilderness areas in the Roosevelt National Forest, located in the Front Range of northern Colorado. Cover type data came from US Forest Service inventory information, while the cartographic variables used to predict cover type consisted of elevation, aspect, and other information derived from standard digital spatial data processed in a geographic information system (GIS). The results of the comparison indicated that a feedforward artificial neural network model more accurately predicted forest cover type than did a traditional statistical model based on Gaussian discriminant analysis.
Dynamics of projective adaptive resonance theory model: the foundation of PART algorithm

Projective adaptive resonance theory (PART) neural network developed by Cao and Wu recently has been shown to be very effective in clustering data sets in high dimensional spaces. The PART algorithm is based on the assumptions that the model equations of PART (a large scale and singularly perturbed system of differential equations coupled with a reset mechanism) have quite regular computational performance. This paper provides a rigorous proof of these regular dynamics of the PART model when the signal functions are special step functions, and provides additional simulation results to illustrate the computational performance of PART.
Biomechanical strategies for mitigating collision damage in insect wings: structural design versus embedded elastic materials.

The wings of many insects accumulate considerable wear and tear during their lifespan, and this irreversible structural damage can impose significant costs on insect flight performance and survivability. Wing wear in foraging bumblebees (and likely many other species) is caused by inadvertent, repeated collisions with vegetation during flight, suggesting the possibility that insect wings may display biomechanical adaptations to mitigate the damage associated with collisions. We used a novel experimental technique to artificially induce wing wear in bumblebees and yellowjacket wasps, closely related species with similar life histories but distinct wing morphologies. Wasps have a flexible resilin joint (the costal break) positioned distally along the leading edge of the wing, which allows the wing tip to crumple reversibly when it hits an obstacle, whereas bumblebees lack an analogous joint. Through experimental manipulation of its stiffness, we found that the costal break plays a critical role in mitigating collision damage in yellowjacket wings. However, bumblebee wings do not experience as much damage as would be expected based on their lack of a costal break, possibly due to differences in the spatial arrangement of supporting wing veins. Our results indicate that these two species utilize different wing design strategies for mitigating damage resulting from collisions. A simple inertial model of a flapping wing reveals the biomechanical constraints acting on the costal break, which may help explain its absence in bumblebee wings.
A whispered Mandarin corpus for speech technology applications

Whispered speech is a natural mode of speech in which voicing is absent – its acoustics differ significantly from normally spoken speech or so-called neutral speech, such that it is challenging to use only neutral speech to build speech processing and automatic recognition systems that can deal effectively with whisper. At the same time, humans can naturally produce and perceive whispered speech without explicit training. Tonal languages such as Mandarin present an interesting dilemma – tone is primarily encoded by pitch tracks which are absent during whispered speech, but humans can still tell tones apart. How humans manage to process whispered speech well without explicit training on it, whereas machine algorithms fail, is presently an unresolved question which could prove fruitful with study. This, however, is hindered by the lack of suitable, systematically collected corpora. We present iWhisper-Mandarin, a 25-hour parallel corpus of neutral and whispered Mandarin, designed to support research in linguistics and speech technology. We demonstrate and verify that earlier techniques applied to whispered speech from non-tonal languages also work with Mandarin, and present some preliminary studies on voice activity detection and whispered Mandarin speech recognition.
Hierarchical Modeling to Facilitate Personalized Word Prediction for Dialogue

The advent and ubiquity of mass-market portable computational devices has opened up new opportunities for the development of assistive technologies for disabilities, especially within the domain of augmentative and alternative communications (AAC) devices. Word prediction can facilitate everyday communication on mobile devices by reducing the physical interactions required to produce dialogue with them. To support personalized word prediction, a text prediction system should learn from the user’s own data to update the initial learned likelihoods that provide high quality “out of the box” performance. Within this lies an inherent trade-off: a larger corpus of initial training data can yield better default performance, but may also increase the amount of user data required for personalization of the system to be effective. We investigate a learning approach employing hierarchical modeling of phrases expected to offer sufficient “out of the box” performance relative to other learning approaches, while reducing the amount of initial training data required to facilitate on-line personalization of the text prediction system. The key insight of the proposed approach is the separation of stopwords, which primarily play syntactical roles in phrases, from keywords, which provide context and meaning in the phrase. This allows the abstraction of a phrase from an ordered list of all words to an ordered list of keywords. Thus the proposed hierarchical modeling of phrases employs two layers: keywords and stopwords. A third level abstracting the keywords to a single topic is also considered, combining the power of both topic modeling and trigrams to make predictions within and between layers. Empirically relaxed versions of the developed models are evaluated on training data composed of a mixture of slightly modified dialogues from the Santa Barbara Corpus of Spoken American English. Performance is measured in terms of the number of user interactions (keystroke or touch screen event) required to complete a phrase. We compare their performance against a system employing no prediction.
MedGAN: Medical Image Translation using GANs

Image-to-image translation is considered a next frontier in the field of medical image analysis, with numerous potential applications. However, recent advances in this field offer individualized solutions by utilizing specialized architectures which are taskspecific or by suffering from limited capacities and thus requiring refinement through non end-to-end training. In this paper, we propose a novel general purpose framework for medical image-to-image translation, titled MedGAN, which operates in an end-to-end manner on the image level. MedGAN builds upon recent advances in the field of generative adversarial networks (GANs) by combining the adversarial framework with a unique combination of non-adversarial losses which captures the high and low frequency components of the desired target modality. Namely, we utilize a discriminator network as a trainable feature extractor which penalizes the discrepancy between the translated medical images and the desired modalities in the pixel and perceptual sense. Moreover, style-transfer losses are utilized to match the textures and fine-structures of the desired target images to the outputs. Additionally, we present a novel generator architecture, titled CasNet, which enhances the sharpness of the translated medical outputs through progressive refinement via encoderdecoder pairs. To demonstrate the effectiveness of our approach, we apply MedGAN on three novel and challenging applications: PET-CT translation, correction of MR motion artefacts and PET image denoising. Qualitative and quantitative comparisons with stateof-the-art techniques have emphasized the superior performance of the proposed framework. MedGAN can be directly applied as a general framework for future medical translation tasks.
Adaptive median filters: new algorithms and results

Based on two types of image models corrupted by impulse noise, we propose two new algorithms for adaptive median filters. They have variable window size for removal of impulses while preserving sharpness. The first one, called the ranked-order based adaptive median filter (RAMF), is based on a test for the presence of impulses in the center pixel itself followed by a test for the presence of residual impulses in the median filter output. The second one, called the impulse size based adaptive median filter (SAMF), is based on the detection of the size of the impulse noise. It is shown that the RAMF is superior to the nonlinear mean L(p) filter in removing positive and negative impulses while simultaneously preserving sharpness; the SAMF is superior to Lin's (1988) adaptive scheme because it is simpler with better performance in removing the high density impulsive noise as well as nonimpulsive noise and in preserving the fine details. Simulations on standard images confirm that these algorithms are superior to standard median filters.
Computational Design of Rubber Balloons

This paper presents an automatic process for fabrication-oriented design of custom-shaped rubber balloons. We cast computational balloon design as an inverse problem: given a target shape, we compute an optimal balloon that, when inflated, approximates the target as closely as possible. To solve this problem numerically, we propose a novel physics-driven shape optimization method, which combines physical simulation of inflatable elastic membranes with a dedicated constrained optimization algorithm. We validate our approach by fabricating balloons designed with our method and comparing their inflated shapes to the results predicted by simulation. An extensive set of manufactured sample balloons demonstrates the shape diversity that can be achieved by our method.
New insights into churn prediction in the telecommunication sector: A profit driven data mining approach

Customer churn prediction models aim to indicate the customers with the highest propensity to attrite, allowing to improve the efficiency of customer retention campaigns and to reduce the costs associated with churn. Although cost reduction is their prime objective, churn prediction models are typically evaluated using statistically based performance measures, resulting in suboptimal model selection. Therefore, in the first part of this paper, a novel, profit centric performance measure is developed, by calculating the maximum profit that can be generated by including the optimal fraction of customers with the highest predicted probabilities to attrite in a retention campaign. The novel measure selects the optimal model and fraction of customers to include, yielding a significant increase in profits compared to statistical mea-
Detection of leukemia in human blood sample based on microscopic images: A study

At the moment, identification of blood disorders is through visual inspection of microscopic images of blood cells. From the identification of blood disor de s, it can lead to classification of certain dise ases related to blood. This paper describes a preliminar y study of developing a detection of leukemia types using microscopic blood sample images. Analyzing through images is very important as from images, diseases can be detected and diagnosed at earlier stage. Fro m there, further actions like controlling, monitori ng and prevention of diseases can be done. Images are used as they are cheap and do not require expensive tes ting and lab equipments. The system will focus on white blood cells disease, leukemia. The system will use features in microscopic images and examine changes on texture, geometry, color and statistical analysi s. Changes in these features will be used as a classif ier input. A literature review has been done and Reinforcement Learning is proposed to classify type s of leukemia. A little discussion about issues inv olved by researchers also has been prepared.
RaspiReader: Open Source Fingerprint Reader

We open source an easy to assemble, spoof resistant, high resolution, optical fingerprint reader, called RaspiReader, using ubiquitous components. By using our open source STL files and software, RaspiReader can be built in under one hour for only US $175. As such, RaspiReader provides the fingerprint research community a seamless and simple method for quickly prototyping new ideas involving fingerprint reader hardware. In particular, we posit that this open source fingerprint reader will facilitate the exploration of novel fingerprint spoof detection techniques involving both hardware and software. We demonstrate one such spoof detection technique by specially customizing RaspiReader with two cameras for fingerprint image acquisition. One camera provides high contrast, frustrated total internal reflection (FTIR) fingerprint images, and the other outputs direct images of the finger in contact with the platen. Using both of these image streams, we extract complementary information which, when fused together and used for spoof detection, results in marked performance improvement over previous methods relying only on grayscale FTIR images provided by COTS optical readers. Finally, fingerprint matching experiments between images acquired from the FTIR output of RaspiReader and images acquired from a COTS reader verify the interoperability of the RaspiReader with existing COTS optical readers.
Reverse Word Order Models

In this work, we study the impact of the word order decoding direction for statistical machine translation (SMT). Both phrase-based and hierarchical phrasebased SMT systems are investigated by reversing the word order of the source and/or target language and comparing the translation results with the normal direction. Analysis are done on several components such as alignment model, language model and phrase table to see which of them accounts for the differences generated by various translation directions. Furthermore, we propose to use system combination, alignment combinations and phrase table combinations to take benefit from systems trained with different translation directions. Experimental results show improvements of up to 1.7 points in BLEU and 3.1 points in TER compared to the normal direction systems for the NTCIR9 Japanese-English and Chinese-English tasks.
Detect, Replace, Refine: Deep Structured Prediction for Pixel Wise Labeling

Pixel wise image labeling is an interesting and challenging problem with great significance in the computer vision community. In order for a dense labeling algorithm to be able to achieve accurate and precise results, it has to consider the dependencies that exist in the joint space of both the input and the output variables. An implicit approach for modeling those dependencies is by training a deep neural network that, given as input an initial estimate of the output labels and the input image, it will be able to predict a new refined estimate for the labels. In this context, our work is concerned with what is the optimal architecture for performing the label improvement task. We argue that the prior approaches of either directly predicting new label estimates or predicting residual corrections w.r.t. the initial labels with feed-forward deep network architectures are sub-optimal. Instead, we propose a generic architecture that decomposes the label improvement task to three steps: 1) detecting the initial label estimates that are incorrect, 2) replacing the incorrect labels with new ones, and finally 3) refining the renewed labels by predicting residual corrections w.r.t. them. Furthermore, we explore and compare various other alternative architectures that consist of the aforementioned Detection, Replace, and Refine components. We extensively evaluate the examined architectures in the challenging task of dense disparity estimation (stereo matching) and we report both quantitative and qualitative results on three different datasets. Finally, our dense disparity estimation network that implements the proposed generic architecture, achieves state-of-the-art results in the KITTI 2015 test surpassing prior approaches by a significant margin. We plan to release the Torch code that implements the paper in: https://github.com/gidariss/DRR_struct_pred/.
A Flexible Model-Driven Game Development Approach

Game developers are facing an increasing demand for new games every year. Game development tools can be of great help, but require highly specialized professionals. Also, just as any software development effort, game development has some challenges. Model-Driven Game Development (MDGD) is suggested as a means to solve some of these challenges, but with a loss in flexibility. We propose a MDGD approach that combines multiple domain-specific languages (DSLs) with design patterns to provide flexibility and allow generated code to be integrated with manual code. After experimentation, we observed that, with the approach, less experienced developers can create games faster and more easily, and the product of code generation can be customized with manually written code, providing flexibility. However, with MDGD, developers become less familiar with the code, making manual codification more difficult.
A 5-Gbps CMOS Burst-Mode CDR Circuit With an Analog Phase Interpolator for PONs

This paper presents a 5-Gb/s low-power burst-mode clock and data recovery circuit based on analog phase interpolator for passive optical network applications. The proposed clock recovery unit consists of two double-edge triggered sample-and-holds (DTSHs) and a phase interpolator. The PI instantaneously locks the recovered clock to incoming burst-mode data by coefficients generated at the DT-SHs’ outputs. To reduce power dissipation in clock recovery unit, instead of two buffers, only one is utilized for the DT-SH. The proposed PI-based BM-CDR has been designed and simulated in 0.18-μm standard CMOS technology. The Results show that reduction in power dissipation of 40% for the clock recovery unit has been achieved. The proposed BM-CDR circuit retimes data at 5Gb/s for a 210-1 pseudo-random binary sequence within the first UI. The recovered data shows jitter at 14ps (pp). The circuit, including 1:2 data demux, draws 29mW power from a 1.8-V supply.
Grid-based mapping and tracking in dynamic environments using a uniform evidential environment representation

Mapping and tracking in dynamic environments for autonomously-moving robots is still challenging, despite being essential tasks. They are often done separately using occupancy grids and established object tracking algorithms. In this work, an approach is presented that estimates a uniform, low-level, grid-based world model including dynamic and static objects, their uncertainties, as well as their velocities. It does not require existing object tracks to filter out data points not used for creating and updating the map. Nor does it require that measurements can be classified into belonging to a static or to a moving object. Promising results from experiments with an autonomous vehicle equipped with a laser scanner demonstrate the usefulness of the approach.