nils-herrmann/clean_scirepeval huggingface dataset:
Specific language impairment: a convenient label for whom?

The term 'specific language impairment' (SLI), in use since the 1980s, describes children with language impairment whose cognitive skills are within normal limits where there is no identifiable reason for the language impairment. SLI is determined by applying exclusionary criteria, so that it is defined by what it is not rather than by what it is. The recent decision to not include SLI in DSM-5 provoked much debate and concern from researchers and clinicians.



To explore how the term 'specific language impairment' emerged, to consider how disorders, including SLI, are generally defined and to explore how societal changes might impact on use the term.


 PROCEDURES
We reviewed the literature to explore the origins of the term 'specific language impairment' and present published evidence, as well as new analyses of population data, to explore the validity of continuing to use the term.



We support the decision to exclude the term 'specific language impairment' from DSM-5 and conclude that the term has been a convenient label for researchers, but that the current classification is unacceptably arbitrary. Furthermore, we argue there is no empirical evidence to support the continued use of the term SLI and limited evidence that it has provided any real benefits for children and their families. In fact, the term may be disadvantageous to some due to the use of exclusionary criteria to determine eligibility for and access to speech pathology services. We propose the following recommendations. First, that the word 'specific' be removed and the label 'language impairment' be used. Second, that the exclusionary criteria be relaxed and in their place inclusionary criteria be adopted that take into account the fluid nature of language development particularly in the preschool period. Building on the goodwill and collaborations between the clinical and research communities we propose the establishment of an international consensus panel to develop an agreed definition and set of criteria for language impairment. Given the rich data now available in population studies it is possible to test the validity of these definitions and criteria. Consultation with service users and policy-makers should be incorporated into the decision-making process.
Marker-controlled watershed segmentation of nuclei in H&E stained breast cancer biopsy images

In this paper we present an unsupervised automatic method for segmentation of nuclei in H&E stained breast cancer biopsy images. Colour deconvolution and morphological operations are used to preprocess the images in order to remove irrelevant structures. Candidate nuclei locations, obtained with the fast radial symmetry transform, act as markers for a marker-controlled watershed segmentation. Watershed regions that are unlikely to represent nuclei are removed in the postprocessing stage. The proposed algorithm is evaluated on a number of images that are representative of the diversity in pathology in this type of tissue. The method shows good performance in terms of the number of segmented nuclei and segmentation accuracy.
A Distributed Multi Agents Based Platform for High Performance Computing Infrastructures

This work introduces a novel, modular, layered web based platform for managing machine learning experiments on grid-based High Performance Computing infrastructures. The coupling of the communication services offered by the grid, with an administration layer and conventional web server programming, via a data synchronization utility, leads to the straightforward development of a web-based user interface that allows the monitoring and managing of diverse online distributed computing applications. It also introduces an experiment generation and monitoring tool particularly suitable for investigating machine learning in game playing. The platform is demonstrated with experiments for two different games.
Diffusion dynamics on multiplex networks

We study the time scales associated with diffusion processes that take place on multiplex networks, i.e., on a set of networks linked through interconnected layers. To this end, we propose the construction of a supra-laplacian matrix, which consists of a dimensional lifting of the laplacian matrix of each layer of the multiplex network. We use perturbative analysis to reveal analytically the structure of eigenvectors and eigenvalues of the complete network in terms of the spectral properties of the individual layers. The spectrum of the supra-laplacian allows us to understand the physics of diffusionlike processes on top of multiplex networks.
DC Bus Voltage Control for a Distributed Power System

This paper addresses voltage control of distributed dc power systems. DC power systems have been discussed as a result of the introduction of renewable, small-scale power generation units. Also, telecommunication power systems featuring UPS properties might benefit from a broader introduction of dc power systems. Droop control is utilized to distribute the load between the source converters. In order to make the loading of the source converters equal, in per unit, the voltage control algorithm for each converter has to be designed to act similar. The dc side capacitor of each converter, needed for filtering, is also determined as a consequence. The root locus is investigated for varying dc bus impedance. It is found that the risk of entering converter over-modulation is a stronger limitation than stability, at least for reasonable dc bus cable parameters. The stationary and dynamic properties during load variations are also investigated.
The impact of waveform on the efficiency of RF to DC conversion using prefabricated energy harvesting device

This paper is dedicated to the investigation of the influence of different waveforms on the efficiency of radio frequency (RF) to direct current (DC) conversion in wireless power transfer (WPT) systems. Single-tone, multi-tone, random and chaotic signals are used and compared as waveforms for wireless power transfer. The influence of peak-to-average power ratio (PAPR) and average input power of the waveform on the efficiency of RF-to-DC conversion is measured in laboratory conditions and studied. A commercially available RF energy harvesting device — Powercast P2110B is used as RF-to-DC conversion circuitry.
Response to Susilo and Duchaine: beyond neuropsychological dissociations in understanding face and word representations

We welcome the opportunity to clarify our theoretical position in light of comments by Susilo and Duchaine. Our central claim is that face and word processing are carried out by a distributed network of partially specialized cortical regions, with the degree of specialization varying across individuals (partly as a function of language lateralization). Thus, and perhaps not surprisingly, Susilo and Duchaine are adopting an overly all-or-none perspective when they mischaracterize our views as implying that cortical regions are ‘not specialized for particular categories’ or that ‘individuals with prosopagnosia will always have some deficits in word recognition while individuals with alexia will always have some deficits in face recognition’ (emphasis added). Rather, on our view, cortical regions are not dedicated to categories, and patients with severe face or word impairments will, as a population, tend to be more moderately impaired in the other domain, as well. To be clear, Susilo and Duchaine are absolutely correct to emphasize the importance of considering neuropsychological dissociations of face and word processing in evaluating our theory. It is worth pointing out that our claims concern the representations that support visual recognition of faces and words; we certainly do not claim that there are no mechanisms of any sort that are relevant to the processing of words, but not of faces (or vice versa). Thus, depending on the nature of the testing, performance on faces and words might dissociate for reasons outside the scope of our theory. More generally, however, as theories confront the complexities of individual differences among preand post-morbid populations, traditional neuropsychological inference based on single-case studies does not suffice  and a broader consideration of the full distribution of effects caused by brain damage is needed (see, e.g., ). Thus, it is difficult to interpret Farah’s  report of cases of prosopagnosia
How STEM Education Improves Student Learning

The intent of this article is to examine current taxonomies used to design and deliver Science, Technology, Engineering, and Mathematics (i.e., STEM) curriculum in an effort to attract a greater variety of students to the STEM field of study in the K-12 public school environment. I analyzed specific aspects of STEM-based programs to compare STEM education components to traditional college-preparatory methods of instruction, including looking for environmental practices that may attract female and first-generation college attendees toward developing a positive attitude toward attaining a STEM education. I also made connections between current instructional trends and their impact on student mastery.
A predictive model of pathologic response based on tumor cellularity and tumor-infiltrating lymphocytes (CelTIL) in HER2-positive breast cancer treated with chemo-free dual HER2 blockade.

The presence of stromal tumor-infiltrating lymphocytes (TILs) is associated with increased pathologic complete response (pCR) and improved outcomes in HER2-positive early-breast cancer (BC) treated with anti-HER2-based chemotherapy. In the absence of chemotherapy, the association of TILs with pCR following anti-HER2 therapy-only is largely unknown.



The PAMELA neoadjuvant trial treated 151 women with HER2-positive BC with lapatinib and trastuzumab [and hormonal therapy if hormone receptor (HR)-positive] for 18 weeks. Percentage of TILs and tumor cellularity were determined at baseline (N = 148) and at day 15 (D15) of treatment (N = 134). Associations of TILs and tumor cellularity with pCR in the breast were evaluated. A combined score based on tumor cellularity and TILs (CelTIL) measured at D15 was derived in PAMELA, and validated in D15 samples from 65 patients with HER2-positive disease recruited in the LPT109096 neoadjuvant trial, where anti-HER2 therapy-only was administer for 2 weeks, then standard chemotherapy was added for 24 weeks.



In PAMELA, baseline and D15 TILs were significantly associated with pCR in univariate analysis. In multivariable analysis, D15 TILs, but not baseline TILs, were significantly associated with pCR. At D15, TILs and tumor cellularity were found independently associated with pCR. A combined score (CelTIL) taking into account both variables was derived. CelTIL at D15 as a continuous variable was significantly associated with pCR, and patients with CelTIL-low and CelTIL-high scores had a pCR rate of 0% and 33%, respectively. In LPT109096, CelTIL at D15 was found associated with pCR both as a continuous variable and as group categories using a pre-defined cut-off (75.0% versus 33.3%).



On-treatment TILs, but not baseline TILs, are independently associated with response following anti-HER2 therapy-only. A combined score of TILs and tumor cellularity measured at D15 provides independent predictive information upon completion of neoadjuvant anti-HER2-based therapy.
Removal of High Density Salt and Pepper Noise Through Modified Decision Based Unsymmetric Trimmed Median Filter

A modified decision based unsymmetrical trimmed median filter algorithm for the restoration of gray scale, and color images that are highly corrupted by salt and pepper noise is proposed in this paper. The proposed algorithm replaces the noisy pixel by trimmed median value when other pixel values, 0's and 255's are present in the selected window and when all the pixel values are 0's and 255's then the noise pixel is replaced by mean value of all the elements present in the selected window. This proposed algorithm shows better results than the Standard Median Filter (MF), Decision Based Algorithm (DBA), Modified Decision Based Algorithm (MDBA), and Progressive Switched Median Filter (PSMF). The proposed algorithm is tested against different grayscale and color images and it gives better Peak Signal-to-Noise Ratio (PSNR) and Image Enhancement Factor (IEF).
Low Complexity Damped Gauss-Newton Algorithms for CANDECOMP/PARAFAC

The damped Gauss-Newton (dGN) algorithm for CANDECOMP /PARAFAC (CP) decomposition has been successfully applied for di fficult tensor factorization such as collinearity of factors, different magnitudes of factors. Nevertheless, for factorization of an N-D tensor of sizeI1 × . . . × IN with rank R, the algorithm is computationally demanding due to construction of large app roximate Hessian of size (( R ∑ n In) × (R ∑ n In)) and its inversion. In this paper, we propose a fast implementation o f the dGN algorithm which is based on novel expressions of the inverse approximate Hessian in block form. The new imp le entation has a lower computational complexity, which beside computation of the gradient (this part is commo n to both methods) involves inversion of a matrix of the sizeNR2 × NR2, much smaller than the Hessian, if n In ≫ NR. In addition, the implementation has a lower memory requirements, because neither the Hessian nor its in verse need to be stored entire in one time. A variant of the algorithm working with complex valued data is proposed a s well. Complexity and performance of the proposed algorithm is compared with those of dGN and ALS with line sear ch on examples with di fficult benchmark tensors.
U-shaped slot-array antenna for RFID shelf in the UHF

This paper presents a linearly polarized slot-array antenna that consists of slots series-fed by a microstrip line for RFID shelf applications in the UHF band in which the end of the line is shorted to the ground plane by the via-hole. The radiating element of the antenna is the U-shaped slot fed by the travelling and standing current wave formed on the microstrip line left shorted-end. The gain of the designed 3×6 slot-array antenna is about 4dBi and the fractional return-loss bandwidth of less than −10dB about 5.5% at 920MHz.
Graphene Reflectarray Metasurface for Terahertz Beam Steering and Phase Modulation

M. Tamagnone 1,2,∗,†, S. Capdevila1,†, A. Lombardo, J. Wu, A. Centeno, A. Zurutuza, A. M. Ionescu, A. C. Ferrari, J. R. Mosig Laboratory of Electromagnetics and Antennas, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, USA Cambridge Graphene Centre, University of Cambridge, 9 J.J. Thompson Avenue, Cambridge CB3 OFA, UK Graphenea SA, 20018 Donostia-San Sebastián, Spain Nanoelectronic Devices Laboratory, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland ∗Corresponding authors and †These Authors contributed equally
Driver Sleepiness Detection System Based on Eye Movements Variables

Driver sleepiness is a hazard state, which can easily lead to traffic accidents. To detect driver sleepiness in real time, a novel driver sleepiness detection system using support vector machine (SVM) based on eye movements is proposed. Eye movements data are collected using SmartEye system in a driving simulator experiment. Characteristic parameters, which include blinking frequency, gaze direction, fixation time, and PERCLOS, are extracted based on the data using a statistical method. 13 sleepiness detection models including 12 specific models and 1 general model are developed based on SVM. Experimental results demonstrate that eye movements can be used to detect driver sleepiness in real time. The detecting accuracy of the specific models significantly exceeds the general model (P < 0.001), suggesting that individual differences are an important consideration when building detection algorithms for different drivers.
Reverse Engineering and Evaluation of Prediction Models for Progression to Type 2 Diabetes: An Application of Machine Learning Using Electronic Health Records.

Application of novel machine learning approaches to electronic health record (EHR) data could provide valuable insights into disease processes. We utilized this approach to build predictive models for progression to prediabetes and type 2 diabetes (T2D).



Using a novel analytical platform (Reverse Engineering and Forward Simulation [REFS]), we built prediction model ensembles for progression to prediabetes or T2D from an aggregated EHR data sample. REFS relies on a Bayesian scoring algorithm to explore a wide model space, and outputs a distribution of risk estimates from an ensemble of prediction models. We retrospectively followed 24 331 adults for transitions to prediabetes or T2D, 2007-2012. Accuracy of prediction models was assessed using an area under the curve (AUC) statistic, and validated in an independent data set.



Our primary ensemble of models accurately predicted progression to T2D (AUC = 0.76), and was validated out of sample (AUC = 0.78). Models of progression to T2D consisted primarily of established risk factors (blood glucose, blood pressure, triglycerides, hypertension, lipid disorders, socioeconomic factors), whereas models of progression to prediabetes included novel factors (high-density lipoprotein, alanine aminotransferase, C-reactive protein, body temperature; AUC = 0.70).



We constructed accurate prediction models from EHR data using a hypothesis-free machine learning approach. Identification of established risk factors for T2D serves as proof of concept for this analytical approach, while novel factors selected by REFS represent emerging areas of T2D research. This methodology has potentially valuable downstream applications to personalized medicine and clinical research.
Reading Scene Text with Attention Convolutional Sequence Modeling

Reading text in the wild is a challenging task in the field of computer vision. Existing approaches mainly adopted Connectionist Temporal Classification (CTC) or Attention models based on Recurrent Neural Network (RNN), which is computationally expensive and hard to train. In this paper, we present an end-to-end Attention Convolutional Network for scene text recognition. Firstly, instead of RNN, we adopt the stacked convolutional layers to effectively capture the contextual dependencies of the input sequence, which is characterized by lower computational complexity and easier parallel computation. Compared to the chain structure of recurrent networks, the Convolutional Neural Network (CNN) provides a natural way to capture long-term dependencies between elements, which is 9 times faster than Bidirectional Long ShortTerm Memory (BLSTM). Furthermore, in order to enhance the representation of foreground text and suppress the background noise, we incorporate the residual attention modules into a small densely connected network to improve the discriminability of CNN features. We validate the performance of our approach on the standard benchmarks, including the Street View Text, IIIT5K and ICDAR datasets. As a result, state-of-the-art or highly-competitive performance and efficiency show the superiority of the proposed approach.
IoTScanner: Detecting and Classifying Privacy Threats in IoT Neighborhoods

In the context of the emerging Internet of Things (IoT), a proliferation of wireless connectivity can be expected. That ubiquitous wireless communication will be hard to centrally manage and control, and can be expected to be opaque to end users. As a result, owners and users of physical space are threatened to lose control over their digital environments. In this work, we propose the idea of an IoTScanner. The IoTScanner integrates a range of radios to allow local reconnaissance of existing wireless infrastructure and participating nodes. It enumerates such devices, identifies connection patterns, and provides valuable insights for technical support and home users alike. Using our IoTScanner, we attempt to classify actively streaming IP cameras from other non-camera devices using simple heuristics. We show that our classification approach achieves a high accuracy in an IoT setting consisting of a large number of IoT devices. While related work usually focuses on detecting either the infrastructure, or eavesdropping on traffic from a specific node, we focus on providing a general overview of operations in all observed networks. We do not assume prior knowledge of used SSIDs, preshared passwords, or similar.
Using Patterns to Capture Architectural Decisions

Throughout the software design process, developers must make decisions and reify them in code. The decisions made during software architecting are particularly significant in that they have system-wide implications, especially on the quality attributes. However, architects often fail to adequately document their decisions because they don't appreciate the benefits, don't know how to document decisions, or don't recognize that they're making decisions. This lack of thorough documentation. This paper provides information about a decision's rationale and consequences, architecture patterns can help architects better understand and more easily record their decisions.
Health chair: implicitly sensing heart and respiratory rate

People interact with chairs frequently, making them a potential location to perform implicit health sensing that requires no additional effort by users. We surveyed 550 participants to understand how people sit in chairs and inform the design of a chair that detects heart and respiratory rate from the armrests and backrests of the chair respectively. In a laboratory study with 18 participants, we evaluated a range of common sitting positions to determine when heart rate and respiratory rate detection was possible (32% of the time for heart rate, 52% for respiratory rate) and evaluate the accuracy of the detected rate (83% for heart rate, 73% for respiratory rate). We discuss the challenges of moving this sensing to the wild by evaluating an in-situ study totaling 40 hours with 11 participants. We show that, as an implicit sensor, the chair can collect vital signs data from its occupant through natural interaction with the chair.
Design of an HF-Band RFID System with Multiple Readers and Passive Tags for Indoor Mobile Robot Self-Localization

Radio frequency identification (RFID) technology has already been explored for efficient self-localization of indoor mobile robots. A mobile robot equipped with RFID readers detects passive RFID tags installed on the floor in order to locate itself. The Monte-Carlo localization (MCL) method enables the localization of a mobile robot equipped with an RFID system with reasonable accuracy, sufficient robustness and low computational cost. The arrangements of RFID readers and tags and the size of antennas are important design parameters for realizing accurate and robust self-localization using a low-cost RFID system. The design of a likelihood model of RFID tag detection is also crucial for the accurate self-localization. This paper presents a novel design and arrangement of RFID readers and tags for indoor mobile robot self-localization. First, by considering small-sized and large-sized antennas of an RFID reader, we show how the design of the likelihood model affects the accuracy of self-localization. We also design a novel likelihood model by taking into consideration the characteristics of the communication range of an RFID system with a large antenna. Second, we propose a novel arrangement of RFID tags with eight RFID readers, which results in the RFID system configuration requiring much fewer readers and tags while retaining reasonable accuracy of self-localization. We verify the performances of MCL-based self-localization realized using the high-frequency (HF)-band RFID system with eight RFID readers and a lower density of RFID tags installed on the floor based on MCL in simulated and real environments. The results of simulations and real environment experiments demonstrate that our proposed low-cost HF-band RFID system realizes accurate and robust self-localization of an indoor mobile robot.