snu-aidas/HVMap-dataset huggingface dataset:
Neighborhood playgrounds, fast food restaurants, and crime: relationships to overweight in low-income preschool children.

BACKGROUND
We examined the relationship between overweight in preschool children and three environmental factors--the proximity of the children's residences to playgrounds and to fast food restaurants and the safety of the children's neighborhoods. We hypothesized that children who lived farther from playgrounds, closer to fast food restaurants, and in unsafe neighborhoods were more likely to be overweight.


METHODS
This was a cross-sectional study of 7,020 low-income children, 36 through 59 months of age living in Cincinnati, OH. Overweight was defined as a measured body mass index > or =95th percentile. The distance between each child's residence and the nearest public playground and fast food restaurant was determined with geographic information systems. Neighborhood safety was defined by the number of police-reported crimes per 1,000 residents per year in each of 46 city neighborhoods.


RESULTS
Overall, 9.2% of the children were overweight, 76% black, and 23% white. The mean (+/- SD) distances from a child's home to the nearest playground and fast food restaurant were 0.31 (+/- 0.22) and 0.70 (+/- 0.38) miles, respectively. There was no association between child overweight and proximity to playgrounds, proximity to fast food restaurants, or level of neighborhood crime. The association between child overweight and playground proximity did not differ by neighborhood crime level.


CONCLUSIONS
Within a population of urban low-income preschoolers, overweight was not associated with proximity to playgrounds and fast food restaurants or with the level of neighborhood crime.
A Study on Evaluation of E-Government Service Quality

Service quality is the highest requirement by users, especially for the service in electronic government. During the past decades, it has become a major area of academic investigation. Considering this issue, there are a lot of researches that evaluated the dimensions and e-service contexts. This study also identified the dimensions of service quality, but focuses on a new concept and provides a new methodology in developing measurement scales of e-service quality such as information quality, service quality and organization quality. Finally, this study will suggest a key factor to evaluate e-government service quality better. Keywords—E-government, e-service, e-service quality, dimensionality.
A 28GHz Bulk-CMOS dual-polarization phased-array transceiver with 24 channels for 5G user and basestation equipment

Developing next-generation cellular technology (5G) in the mm-wave bands will require low-cost phased-array transceivers [1]. Even with the benefit of beamforming, due to space constraints in the mobile form-factor, increasing TX output power while maintaining acceptable PA PAE, LNA NF, and overall transceiver power consumption is important to maximizing link budget allowable path loss and minimizing handset case temperature. Further, the phased-array transceiver will need to be able to support dual-polarization communication. An IF interface to the analog baseband is desired for low power consumption in the handset or user equipment (UE) active antenna and to enable use of arrays of transceivers for customer premises equipment (CPE) or basestation (BS) antenna arrays with a low-loss IF power-combining/splitting network implemented on an antenna backplane carrying multiple tiled antenna modules.
7.2 A 28GHz 32-element phased-array transceiver IC with concurrent dual polarized beams and 1.4 degree beam-steering resolution for 5G communication

Next-generation mobile technology (5G) aims to provide an improved experience through higher data-rates, lower latency, and improved link robustness. Millimeter-wave phased arrays offer a path to support multiple users at high data-rates using high-bandwidth directional links between the base station and mobile devices. To realize this vision, a phased-array-based pico-cell must support a large number of precisely controlled beams, yet be compact and power efficient. These system goals have significant mm-wave radio interface implications, including scalability of the RFIC+antenna-array solution, increase in the number of concurrent beams by supporting dual polarization, precise beam steering, and high output power without sacrificing TX power efficiency. Packaged Si-based phased arrays [1–3] with nonconcurrent dual-polarized TX and RX operation [2,3], concurrent dual-polarized RX operation [3] and multi-IC scaling [3,4] have been demonstrated. However, support for concurrent dual-polarized operation in both RX and TX remains unaddressed, and high output power comes at the cost of power consumption, cooling complexity and increased size. The RFIC reported here addresses these challenges. It supports concurrent and independent dual-polarized operation in TX and RX modes, and is compatible with a volume-efficient, scaled, antenna-in-package array. A new TX/RX switch at the shared antenna interface enables high output power without sacrificing TX efficiency, and a t-line-based phase shifter achieves <1° RMS error and <5° phase steps for precise beam control.
Linking GloVe with word2vec

The Global Vectors for word representation (GloVe), introduced by Jeffrey Pennington et al. [3] is reported to be an efficient and effective method for learning vector representations of words. State-of-the-art performance is also provided by skip-gram with negative-sampling (SGNS) [2] implemented in the word2vec tool. In this note, we explain the similarities between the training objectives of the two models, and show that the objective of SGNS is similar to the objective of a specialized form of GloVe, though their cost functions are defined differently.
The effects of caffeine on heart rate variability in newborns with apnea of prematurity

Objective:Apnea of prematurity is a common complication in premature newborns and caffeine is a widespread medication used to treat this complication. Caffeine may have adverse effects on the cardiovascular and central nervous system, yet its effects on the autonomic nervous system modulation of heart rate have not been studied in premature newborns, which was the objective of our study.Study Design:We prospectively studied 21 premature newborns who were treated with caffeine. We analyzed heart rate variability by power spectral density and by dynamic nonlinear analyses methods.Result:There were no changes in heart rate, blood pressure or the autonomic nervous system tone following administration of caffeine, nor were the nonlinear dynamical properties of the system altered by caffeine.Conclusion:Caffeine does not have detrimental effects on heart rate variablility, heart rate or blood pressure in conventional doses given to premature newborns.
Compact low-voltage power-efficient operational amplifier cells for VLSI

“Some Design Aspects of a Two-Stage Rail-to-Rail CMOS Op Amp' by Sander L. J. Gierkink, Peter J. Holzmann, Remco J. Wiegerink and Roelof F. Wassenaar, Analog Integrated Circuits and Signal Processing, Vol. 21, No. 2, Nov. 1999, pp 143-152. “Compact Low-Voltage Power-Efficient Operational Amplifier Cells for VLSI by Klaas-Jan de Langen and Johan H. Huijsing, IEEE Journal of Solid State Circuits, vol. 33, No. 10, Oct. 1998, pp. 1482–1496. “Design Aspects of a Rail-to-Rail CMOS Op Amp' by Glierkink et al., Mesa Research institute, ECT-97-36, pp. 23-28.
A framework and graphical development environment for robust NLP tools and applications.
Colony of NPUs : Scaling the Efficiency of Neural Accelerators

Trading small amounts of output accuracy to obtain significant performance improvement or reduce power consumption is a promising research direction. Many modern applications from different domains such as image and signal processing, computer vision, data mining, machine learning and speech recognition are amenable to approximate computing. In many cases, their output is interpreted by humans, so infrequent variation in outputs are not noticeable by users. Moreover, the input of these applications is noisy which prevents strict constraints on the output of the system. Hence, these domains have significant potential for the aforementioned trade-offs, and these applications are ideal targets for approximate computing. Existing approaches for approximate computing include modifying the ISA [6], compiler [1], programming language [2, 4], underlying hardware [14] or the entire framework [3, 8, 10]. Approximation accelerators [5, 7, 13] use some of these methods to trade off accuracy for higher performance or energy savings. These accelerators require the programmer to annotate code sections which are amenable for approximation. Approximate parts are offloaded to the accelerator and other parts are executed by the CPU at run time. Esmaeilzadeh et. al. [7] proposed a Neural Processing Unit (NPU) as a programmable approximate accelerator. The key idea is to train a neural network (nn) to mimic an approximable region of original code and replace that with an efficient computation of the learned model. Although the proposed approximate accelerators such as NPU demonstrate acceptable results on the benchmarks from different domains, they have some shortcomings. Firstly, different invocations of a program might produce varying output qualities because the output quality is dependent on the input values. Consequently, using an NPU with fixed configuration for a wide range of inputs may produce outputs with poor accuracies. On the other hand, if the output quality drops below a determined threshold, one way to improve the quality is re-executing the whole program on the exact hardware. However, the overhead of this recovery process may offset the gains of approximation. Secondly, existing techniques usually measure the output quality by averaging errors in individual output elements, e.g., pixels in an image. Previous works in approximate computing [9, 11, 12] show that although most of the output elements have small errors, there exist a few output elements that have considerably large errors which may degrade the whole user experience. Therefore, some inputs might need more specialized methods to deal with the issue of output quality. Lastly, it is challenging to tune the output quality of an approximate hardware dynamically. If different NPUs with different configurations are available in the system, defining the number of active NPUs per invocation will be a reasonable knob for changing output quality based on users preferences. NPU Output Input Monolithic NPU
Artificial Neural Networks Modeling in Water Resources Engineering : Infrastructure and Applications

The use of artificial neural network (ANN) modeling for prediction and forecasting variables in water r esources engineering are being increasing rapidly. Infrastru ctural applications of ANN in terms of selection of inputs, architectur e of networks, training algorithms, and selection of training para meters in different types of neural networks used in water resources en gin ering have been reported. ANN modeling conducted for water res ources engineering variables (river sediment and discharge ) published in high impact journals since 2002 to 2011 have been e xamined and presented in this review. ANN is a vigorous techniq ue to develop immense relationship between the input and output v ariables, and able to extract complex behavior between the water resources variables such as river sediment and discharge. It can produce robust prediction results for many of the water resources engineering problems by appropriate learning from a set of exam ples. It is important to have a good understanding of the input and output variables from a statistical analysis of the data b efore network modeling, which can facilitate to design an efficie nt network. An appropriate training based ANN model is able to ado pt the physical understanding between the variables and may generat more effective results than conventional prediction techniques. Keywords—ANN, discharge, modeling, prediction, sediment,
Sexually transmitted diseases treatment guidelines, 2010.

These guidelines for the treatment of persons who have or are at risk for sexually transmitted diseases (STDs) were updated by CDC after consultation with a group of professionals knowledgeable in the field of STDs who met in Atlanta on April 18-30, 2009. The information in this report updates the 2006 Guidelines for Treatment of Sexually Transmitted Diseases (MMWR 2006;55[No. RR-11]). Included in these updated guidelines is new information regarding 1) the expanded diagnostic evaluation for cervicitis and trichomoniasis; 2) new treatment recommendations for bacterial vaginosis and genital warts; 3) the clinical efficacy of azithromycin for chlamydial infections in pregnancy; 4) the role of Mycoplasma genitalium and trichomoniasis in urethritis/cervicitis and treatment-related implications; 5) lymphogranuloma venereum proctocolitis among men who have sex with men; 6) the criteria for spinal fluid examination to evaluate for neurosyphilis; 7) the emergence of azithromycin-resistant Treponema pallidum; 8) the increasing prevalence of antimicrobial-resistant Neisseria gonorrhoeae; 9) the sexual transmission of hepatitis C; 10) diagnostic evaluation after sexual assault; and 11) STD prevention approaches.
A Character Model with Moral Emotions: Preliminary Evaluation

In literary and drama criticism, emotions, and moral emotions in particular, have been pointed out as one of characterizing features of stories. In this paper, we propose to model story characters as value-based emotional agents, who appraise their own and others’ actions based on their desires and values, and feel the appropriate moral emotions in response to narrative situations that challenge their goals and values. In order to validate the appropriateness of the agent model for narrative characters, we ran an experiment with human participants aimed at comparing their expectations about characters’ emotions with the predictions of the value-based model of emotional agent. The results of the experiment show that the participants’ expectations meet the predictions of the model. 1998 ACM Subject Classification I.2.11 Intelligent Agents, I.2.0 Cognitive Simulation
Social neuroscience evidence for dehumanised perception
Statistical Decision Theory and Bayesian Analysis, 2nd Edition
1 Wave Dispersion in High-Rise Buildings due to Soil-Structure Interaction

Nonparametric techniques for estimation of wave dispersion in buildings by seismic interferometry are applied to a simple model of a soil-structure interaction (SSI) system with coupled horizontal and rocking response. The system consists of a viscously damped shearbeam, representing a building, on a rigid foundation embedded in a half-space. The analysis shows that (1) wave propagation through the system is dispersive. The dispersion is characterized by lower phase velocity (softening) in the band containing the fundamental system mode of vibration, and little change in the higher frequency bands, relative to the building shear wave velocity. This mirrors its well known effect on the frequencies of vibration, i.e. reduction for the fundamental mode (softening) and no significant change for the higher modes of vibration, in agreement with the duality of the wave and vibrational nature of structural response. Nevertheless, the phase velocity identified from broader band IRFs is very close to the superstructure shear wave velocity, as found by the earlier study. The analysis reveals that (2) the reason for this apparent paradox is that the latter estimates are biased towards the higher values, representative of the higher frequencies in the band, where the response is less affected by SSI. It is also discussed that (3) bending flexibility and soil flexibility produce similar effects on the phase velocities and frequencies of vibration of a building. 1 Ph.D. Candidate, University of Southern California, Dept. of Civil Eng., Los Angeles, CA 90089-2531, Email: mrahmani@usc.edu 2 Ph.D. Candidate, University of Southern California, Dept. of Civil Eng., Los Angeles, CA 90089-2531, Email: mebrahim@usc.edu 3 Research Professor, University of Southern California, Dept. of Civil Eng., Los Angeles, CA 90089-2531, Email: mtodorov@usc.edu Earthquake Engineering and Structural Dynamics. DOI: 10.1002/eqe.2454, Final Draft. First published online on June 23, 2014, in press Article available at: http://onlinelibrary.wiley.com/doi/10.1002/eqe.2454/abstract.
TopP-S: Persistent homology-based multi-task deep neural networks for simultaneous predictions of partition coefficient and aqueous solubility

Aqueous solubility and partition coefficient are important physical properties of small molecules. Accurate theoretical prediction of aqueous solubility and partition coefficient plays an important role in drug design and discovery. The prediction accuracy depends crucially on molecular descriptors which are typically derived from a theoretical understanding of the chemistry and physics of small molecules. This work introduces an algebraic topology-based method, called element-specific persistent homology (ESPH), as a new representation of small molecules that is entirely different from conventional chemical and/or physical representations. ESPH describes molecular properties in terms of multiscale and multicomponent topological invariants. Such topological representation is systematical, comprehensive, and scalable with respect to molecular size and composition variations. However, it cannot be literally translated into a physical interpretation. Fortunately, it is readily suitable for machine learning methods, rendering topological learning algorithms. Due to the inherent correlation between solubility and partition coefficient, a uniform ESPH representation is developed for both properties, which facilitates multi-task deep neural networks for their simultaneous predictions. This strategy leads to a more accurate prediction of relatively small datasets. A total of six datasets is considered in this work to validate the proposed topological and multitask deep learning approaches. It is demonstrated that the proposed approaches achieve some of the most accurate predictions of aqueous solubility and partition coefficient. Our software is available online at http://weilab.math.msu.edu/TopP-S/. © 2018 Wiley Periodicals, Inc.
Clump splitting via bottleneck detection and shape classification

Under-segmentation of an image with multiple objects is a common problem in image segmentation algorithms. This paper presents a novel approach for splitting clumps formed by multiple objects due to under-segmentation. The proposed algorithm includes three steps: (1) decide whether to split a candidate connected component by application-specific shape classification; (2) find a pair of points for clump splitting and (3) join the pair of selected points. In the first step, a shape classifier is applied to determine whether a connected component should be split. In the second step, a pair of points for splitting is detected using a bottleneck rule, under the assumption that the desired objects have roughly a convex shape. In the third step, the selected splitting points from step two are joined by finding the optimal splitting line between them, based on minimizing an image energy. The shape classifier is built offline via various shape features and a support vector machine. Steps two and three are applicationindependent. The performance of this method is evaluated using images from various applications. Experimental results show that the proposed approach outperforms the state-of-the-art algorithms for the clump splitting problem. & 2011 Elsevier Ltd. All rights reserved.
ADLs and dynamic architecture changes

Existing ADLs typically support only static architecture specification and do not provide facilities for the support of dynamically changing architectures. This paper presents a possible solution to this problem: in order to adequately support dynamic architecture changes, ADLs can leverage techniques used in dynamic programming languages. In particular, changes to ADL specifications should be interpreted. To enable interpretation, an ADL should have an architecture construction component that supports explicit and incremental specification of architectural changes, in addition to the traditional architecture description facilities. This will allow software architects to specify the changes to an architecture after it has been built. The paper expands upon the results from an ongoing project -building a development environment for CZstyle architectures.’
Optimizing distributions over molecular space . An Objective-Reinforced Generative Adversarial Network for Inverse-design Chemistry ( ORGANIC )

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design. 1
Extending the Hierarchical Deep Reinforcement Learning framework

iv
Process scheduling under uncertainty: Review and challenges

Uncertainty is a very important concern in production scheduling since it can cause infeasibilities and production disturbances. Thus scheduling nder uncertainty has received a lot of attention in the open literature in recent years from chemical engineering and operations research communities. he purpose of this paper is to review the main methodologies that have been developed to address the problem of uncertainty in production cheduling as well as to identify the main challenges in this area. The uncertainties in process scheduling are first analyzed, and the different athematical approaches that exist to describe process uncertainties are classified. Based on the different descriptions for the uncertainties, lternative scheduling approaches and relevant optimization models are reviewed and discussed. Further research challenges in the field of process cheduling under uncertainty are identified and some new ideas are discussed. 2007 Elsevier Ltd. All rights reserved.
Designing and implementing an integrated technological pedagogical science knowledge framework for science teachers professional development

This paper reports on the design and the implementation of the Technological Pedagogical Science Knowledge (TPASK), a new model for science teachers professional development built on an integrated framework determined by the Technological Pedagogical Content Knowledge (TPACK) model and the authentic learning approach. The TPASK curriculum dimensions and the related course sessions are also elaborated and applied in the context of a teacher trainers’ preparation program aiming at ICT integration in science classroom practice. A brief description of the project, its accomplishments, and perceptions of the participants, through the lens of TPASK professional development model, are presented. This is followed by the presentation of the evaluation results on the impact of the program which demonstrates that science teachers reported meaningful TPASK knowledge and increased willingness to adopt and apply this framework in their instruction. Finally, we draw on the need to expand TPACK by incorporating a fourth dimension, the Educational Context within Pedagogy, Content and Technology mutually interact, in order to address future policy models concerning teacher preparation to integrate ICT in education. 2010 Elsevier Ltd. All rights reserved.