nils-herrmann/clean_scirepeval huggingface dataset:
Communication disorders: prevalence and comorbid intellectual disability, autism, and emotional/behavioral disorders.

To determine a population-based estimate of communication disorders (CDs) in children; the co-occurrence of intellectual disability (ID), autism, and emotional/behavioral disorders; and the impact of these conditions on the prevalence of CDs.



Surveillance targeted 8-year-olds born in 1994 residing in 2002 in the 3 most populous counties in Utah (n = 26,315). A multiple-source record review was conducted at all major health and educational facilities.



A total of 1,667 children met the criteria of CD. The prevalence of CD was estimated to be 63.4 per 1,000 8-year-olds (95% confidence interval = 60.4-66.2). The ratio of boys to girls was 1.8:1. Four percent of the CD cases were identified with an ID and 3.7% with autism spectrum disorders (ASD). Adjusting the CD prevalence to exclude ASD and/or ID cases significantly affected the CD prevalence rate. Other frequently co-occurring emotional/behavioral disorders with CD were attention deficit/hyperactivity disorder, anxiety, and conduct disorder.



Findings affirm that CDs and co-occurring mental health conditions are a major educational and public health concern.
Sampling algorithms for stochastic graphs: A learning automata approach

Recently, there has been growing interest in social network analysis. Graph models for social network analysis are usually assumed to be a deterministic graph with fixed weights for its edges or nodes. As activities of users in online social networks are changed with time, however, this assumption is too restrictive because of uncertainty, unpredictability and the time-varying nature of such real networks. The existing network measures and network sampling algorithms for complex social networks are designed basically for deterministic binary graphs with fixed weights. This results in loss of much of the information about the behavior of the network contained in its time-varying edge weights of network, such that is not an appropriate measure or sample for unveiling the important natural properties of the original network embedded in the varying edge weights. In this paper, we suggest that using stochastic graphs, in which weights associated with the edges are random variables, can be a suitable model for complex social network. Once the network model is chosen to be stochastic graphs, every aspect of the network such as path, clique, spanning tree, network measures and sampling algorithms should be treated stochastically. In particular, the network measures should be reformulated and new network sampling algorithms must be designed to reflect the stochastic nature of the network. In this paper, we first define some network measures for stochastic graphs, and then we propose four sampling algorithms based on learning automata for stochastic graphs. In order to study the performance of the proposed sampling algorithms, several experiments are conducted on real and synthetic stochastic graphs. The performances of these algorithms are studied in terms of Kolmogorov-Smirnov D statistics, relative error, Kendall’s rank correlation coefficient and relative cost.
Design and analysis of 1-to-4 wilkinson power divider for antenna array feeding network

In this paper, A Novel 1 to 4 modified Wilkinson power divider operating over the frequency range of (3 GHz to 8 GHz) is proposed. The design perception of the proposed divider based on two different stages and printed on FR4 (Epoxy laminate material) with the thickness of 1.57mm and єr =4.3 respectively. The modified design of this power divider including curved corners instead of the sharp edges and some modification in the length of matching stubs. In addition, this paper contain the power divider with equal power split at all ports, reasonable insertion loss, acceptable return loss below −10 dB, good impedance matching at all ports and satisfactory isolation performance has been obtained over the mentioned frequency range. The design concept and optimization development is practicable through CST simulation software.
Tractable dual optimal stochastic model predictive control: An example in healthcare

Output-Feedback Stochastic Model Predictive Control based on Stochastic Optimal Control for nonlinear systems is computationally intractable because of the need to solve a Finite Horizon Stochastic Optimal Control Problem. However, solving this problem leads to a control law possessing optimal probing properties, called dual control, which trades off benefits of exploration and exploitation. In practice, intractability of Stochastic Model Predictive Control is typically overcome by replacement of the underlying Stochastic Optimal Control problem by more amenable approximate surrogate problems, which however come at a loss of the optimal probing nature of the control signals. While probing can be superimposed in some approaches, this is done sub-optimally. In this paper, we examine approximation of the system dynamics by a Partially Observable Markov Decision Process with its own Finite Horizon Stochastic Optimal Control Problem, which can be solved for an optimal control policy, implemented in receding horizon fashion. This procedure enables maintaining probing in the control actions. We further discuss a numerical example in healthcare decision making, highlighting the duality in stochastic optimal receding horizon control.
Robust and compact key generator using physically unclonable function based on logic-transistor-compatible poly-crystalline-Si channel FinFET technology

This paper presents a robust and compact SRAM physically-unclonable-function (PUF) cell using a polycrystalline-Si channel (poly-Si) FinFET, for the first time. Its process is identical to that of a crystalline-Si FinFET except channel material. A systematic comparison between poly- and crystalline-Si FinFET PUF cells, reveals that the poly-Si cell improves the intra-PUF hamming distance to 1/3.4 of that of the crystalline-Si cell and 15k logic-transistors for stabilizing PUF reproducibility are reduced with keeping the same stability. For this analysis, a newly defined noise margin for SRAM PUFs, which is different from the SRAM static noise margin, is introduced.
Health-ATM: A Deep Architecture for Multifaceted Patient Health Record Representation and Risk Prediction

Leveraging massive electronic health records (EHR) brings tremendous promises to advance clinical and precision medicine informatics research. However, it is very challenging to directly work with multifaceted patient information encoded in their EHR data. Deriving effective representations of patient EHRs is a crucial step to bridge raw EHR information and the endpoint analytical tasks, such as risk prediction or disease subtyping. In this paper, we propose Health-ATM, a novel and integrated deep architecture to uncover patients’ comprehensive health information from their noisy, longitudinal, heterogeneous and irregular EHR data. HealthATM extracts comprehensive multifaceted patient information patterns with attentive and time-aware modulars (ATM) and a hybrid network structure composed of both Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN). The learned features are finally fed into a prediction layer to conduct the risk prediction task. We evaluated the Health-ATM on both artificial and real world EHR corpus and demonstrated its promising utility and efficacy on representation learning and disease onset predictions.
Automatic Hybridization of Runtime Systems

The hybrid runtime (HRT) model offers a plausible path towards high performance and efficiency. By integrating the OS kernel, parallel runtime, and application, an HRT allows the runtime developer to leverage the full privileged feature set of the hardware and specialize OS services to the runtime's needs. However, conforming to the HRT model currently requires a complete port of the runtime and application to the kernel level, for example to our Nautilus kernel framework, and this requires knowledge of kernel internals. In response, we developed Multiverse, a system that bridges the gap between a built-from-scratch HRT and a legacy runtime system. Multiverse allows existing, unmodified applications and runtimes to be brought into the HRT model without any porting effort whatsoever. Developers simply recompile their package with our compiler toolchain, and Multiverse automatically splits the execution of the application between the domains of a legacy OS and an HRT environment. To the user, the package appears to run as usual on Linux, but the bulk of it now runs as a kernel. The developer can then incrementally extend the runtime and application to take advantage of the HRT model. We describe the design and implementation of Multiverse, and illustrate its capabilities using the Racket runtime system.
Morphological predictors of restenosis after coronary stenting in humans.

Experimental studies suggest that arterial injury and inflammation lead to increased neointimal growth after stenting. Despite the increased use of coronary stents in humans, there are only limited pathological data on the morphological features of in-stent restenosis.



Detailed histology was performed on 116 stents, implanted > or =90 days in 87 coronary arteries, from 56 patients (mean age, 59+/-13 years). The mean duration of stent implant was 10 months. In-stent restenosis was defined as a stent area stenosis of >75%. Lumen area increased as stent area increased (r2=0.27, P=0.0001), but there was a much stronger correlation between stent area and neointimal area (r2=0.70, P<0.0001). Arterial medial fracture was associated with a 29% increase (P<0.01) in neointimal thickness compared with arteries with an intact media. Neointimal thickness (P=0.0001), inflammatory cell density (P<0.0001), and neointimal vascular channel density (P<0.0001) were greater when struts were in contact with a ruptured arterial media compared with fibrous plaque or an intact fibrous cap. Stent strut penetration into a lipid core was associated with increased neointimal thickness (P=0.04) and inflammatory cell density (P=0.03). Neointimal inflammatory cell content was 2.4-fold greater in stents with restenosis versus no restenosis, and inflammation was associated with increased neoangiogenesis.



Coronary stenting that is accompanied by medial damage or penetration of the stent into a lipid core induces increased arterial inflammation, which is associated with increased neointimal growth. These data suggest the use of stenting strategies that reduce inflammation and neoangiogenesis to reduce the incidence of restenosis.
Few Shot Learning with Simplex

Deep learning has made remarkable achievement in many fields. However, learning the parameters of neural networks usually demands a large amount of labeled data. The algorithms of deep learning, therefore, encounter difficulties when applied to supervised learning where only little data are available. This specific task is called few-shot learning. To address it, we propose a novel algorithm for fewshot learning using discrete geometry, in the sense that the samples in a class are modeled as a reduced simplex. The volume of the simplex is used for the measurement of class scatter. During testing, combined with the test sample and the points in the class, a new simplex is formed. Then the similarity between the test sample and the class can be quantized with the ratio of volumes of the new simplex to the original class simplex. Moreover, we present an approach to constructing simplices using local regions of feature maps yielded by convolutional neural networks. Experiments on Omniglot and miniImageNet verify the effectiveness of our simplex algorithm on few-shot learning.
Food packaging history and innovations.

Food packaging has evolved from simply a container to hold food to something today that can play an active role in food quality. Many packages are still simply containers, but they have properties that have been developed to protect the food. These include barriers to oxygen, moisture, and flavors. Active packaging, or that which plays an active role in food quality, includes some microwave packaging as well as packaging that has absorbers built in to remove oxygen from the atmosphere surrounding the product or to provide antimicrobials to the surface of the food. Packaging has allowed access to many foods year-round that otherwise could not be preserved. It is interesting to note that some packages have actually allowed the creation of new categories in the supermarket. Examples include microwave popcorn and fresh-cut produce, which owe their existence to the unique packaging that has been developed.
Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques

Recommender systems are becoming increasingly important to individual users and businesses for providing personalized recommendations. However, while the majority of algorithms proposed in recommender systems literature have focused on improving recommendation accuracy (as exemplified by the recent Netflix Prize competition), other important aspects of recommendation quality, such as the diversity of recommendations, have often been overlooked. In this paper, we introduce and explore a number of item ranking techniques that can generate substantially more diverse recommendations across all users while maintaining comparable levels of recommendation accuracy. Comprehensive empirical evaluation consistently shows the diversity gains of the proposed techniques using several real-world rating data sets and different rating prediction algorithms.
A New Metric To Evaluate Ontology Modularization

As ontologies are the backbone of the Semantic Web, they attract much attention from researchers and engineers in many domains. This results in an increasing number of ontologies and semantic web applications. The number and complexity of such ontologies makes it hard for developers of ontologies and tools to decide which ontologies to use and reuse. To simplify the problem, a modularization algorithm can be used to partition ontologies into sets of modules. In order to evaluate the quality of modularization, we propose a new evaluation metric that quantifies the goodness of ontology modularization. In particular, we investigate the ontology module homogeneity, which assesses module cohesion, and the ontology module heterogeneity, which appraises module coupling. The experimental results demonstrate that the proposed metric is effective.
Filter based alpha matting for depth image based rendering

In this paper, we propose a free viewpoint image rendering method combined with filter based alpha matting for improving the image quality of image boundaries. When we synthesize a free viewpoint image, blur around object boundaries in an input image spills foreground/background color in the synthesized image. To generate smooth boundaries, alpha matting is a solution. In our method based on filtering, we make a boundary map from input images and depth maps, and then feather the map by using guided filter. In addition, we extend view synthesis method to deal the alpha channel. Experiment results show that the proposed method synthesizes 0.4 dB higher quality images than the conventional method without the matting. Also the proposed method synthesizes 0.2 dB higher quality images than the conventional method of robust matting. In addition, the computational cost of the proposed method is 100x faster than the conventional matting.
Expectation-based syntactic comprehension

This paper investigates the role of resource allocation as a source of processing difficulty in human sentence comprehension. The paper proposes a simple information-theoretic characterization of processing difficulty as the work incurred by resource reallocation during parallel, incremental, probabilistic disambiguation in sentence comprehension, and demonstrates its equivalence to the theory of Hale [Hale, J. (2001). A probabilistic Earley parser as a psycholinguistic model. In ], in which the difficulty of a word is proportional to its surprisal (its negative log-probability) in the context within which it appears. This proposal subsumes and clarifies findings that high-constraint contexts can facilitate lexical processing, and connects these findings to well-known models of parallel constraint-based comprehension. In addition, the theory leads to a number of specific predictions about the role of expectation in syntactic comprehension, including the reversal of locality-based difficulty patterns in syntactically constrained contexts, and conditions under which increased ambiguity facilitates processing. The paper examines a range of established results bearing on these predictions, and shows that they are largely consistent with the surprisal theory.
Urban area characterization based on crowd behavioral lifelogs over Twitter

Recent location-based social networking sites are attractively providing us with a novel capability of monitoring massive crowd lifelogs in the real-world space. In particular, they make it easier to collect publicly shared crowd lifelogs in a large scale of geographic area reflecting the crowd’s daily lives and even more characterizing urban space through what they have in minds and how they behave in the space. In this paper, we challenge to analyze urban characteristics in terms of crowd behavior by utilizing crowd lifelogs in urban area over the social networking sites. In order to collect crowd behavioral data, we exploit the most famous microblogging site, Twitter, where a great deal of geo-tagged micro lifelogs emitted by massive crowds can be easily acquired. We first present a model to deal with crowds’ behavioral logs on the social network sites as a representing feature of urban space’s characteristics, which will be used to conduct crowd-based urban characterization. Based on this crowd behavioral feature, we will extract significant crowd behavioral patterns in a period of time. In the experiment, we conducted the urban characterization by extracting the crowd behavioral patterns and examined the relation between the regions of common crowd activity patterns and the major categories of local facilities.
AN EFFICIENT AUTOMATED SYSTEM FOR GLAUCOMA DETECTION USING FUNDUS IMAGE

This paper proposes a new method for the detection of glaucoma using fundus image which mainly affects the optic disc by increasing the cup size is proposed. The ratio of the optic cup to disc (CDR) in retinal fundus images is one of the primary physiological parameter for the diagnosis of glaucoma. The Kmeans clustering technique is recursively applied to extract the optic disc and optic cup region and an elliptical fitting technique is applied to find the CDR values. The blood vessels in the optic disc region are detected by using local entropy thresholding approach. The ratio of area of blood vessels in the inferiorsuperior side to area of blood vessels in the nasal-temporal side (ISNT) is combined with the CDR for the classification of fundus image as normal or glaucoma by using K-Nearest neighbor , Support Vector Machine and Bayes classifier. A batch of 36 retinal images obtained from the Aravind Eye Hospital, Madurai, Tamilnadu, India is used to assess the performance of the proposed system and a classification rate of 95% is achieved.
Issues in Assembly Process of Next-Generation Fine-Pitch Chip-On-Flex Packages for LCD Applications

Some of the current assembly issues of fine-pitch chip-on-flex (COF) packages for LCD applications are reviewed. Traditional underfill material, anisotropic conductive adhesive (ACA), and nonconductive adhesive (NCA) are considered in conjunction with two applicable bonding methods including thermal and laser bonding. Advantages and disadvantages of each material/process combination are identified. Their applicability is further investigated to identify a process most suitable to the next-generation fine-pitch packages (less than 35 mum). Numerical results and subsequent testing results indicate that the NCA/laser bonding process is advantageous for preventing both lead crack and excessive misalignment compared to the conventional bonding process
Context-Sensitive Generation of Open-Domain Conversational Responses

Despite the success of existing works on single-turn conversation generation, taking the coherence in consideration, human conversing is actually a context-sensitive process. Inspired by the existing studies, this paper proposed the static and dynamic attention based approaches for context-sensitive generation of open-domain conversational responses. Experimental results on two public datasets show that the proposed static attention based approach outperforms all the baselines on automatic and human evaluation.
MINKOWSKI QUESTION MARK FUNCTION AND ITS GENERALIZATIONS, ASSOCIATED WITH p−CONTINUED FRACTIONS: FRACTALS, EXPLICIT SERIES FOR THE DYADIC PERIOD FUNCTION AND MOMENTS

Previously, several natural integral transforms of Minkowski question mark function F (x) were introduced by the author. Each of them is uniquely characterized by certain regularity conditions and the functional equation, thus encoding intrinsic information about F (x). One of them the dyadic period function G(z) was defined via certain transcendental integral. In this paper we introduce a family of “distributions” Fp(x) for R p ≥ 1, such that F1(x) is the question mark function and F2(x) is a discrete distribution with support on x = 1. Thus, all the aforementioned integral transforms are calculated for such p. As a consequence, the generating function of moments of F p(x) satisfies the three term functional equation. This has an independent interest, though our main concern is the information it provides about F (x). This approach yields certain explicit series for G(z). This also solves the problem in expressing the moments of F (x) in closed form.
Evolutionof indirect reciprocity by imagescoring

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