nils-herrmann/clean_scirepeval huggingface dataset:
Stochastic Geometry Methods for Modeling Automotive Radar Interference

As the use of automotive radar increases, performance limitations associated with radar-to-radar interference will become more significant. In this paper, we employ tools from stochastic geometry to characterize the statistics of radar interference. Specifically, using two different models for the spatial distributions of vehicles, namely, a Poisson point process and a Bernoulli lattice process, we calculate for each case the interference statistics and obtain analytical expressions for the probability of successful range estimation. This paper shows that the regularity of the geometrical model appears to have limited effect on the interference statistics, and so it is possible to obtain tractable tight bounds for the worst case performance. A technique is proposed for designing the duty cycle for the random spectrum access, which optimizes the total performance. This analytical framework is verified using Monte Carlo simulations.
Precise and Scalable Detection of Double-Fetch Bugs in OS Kernels

During system call execution, it is common for operating system kernels to read userspace memory multiple times (multi-reads). A critical bug may exist if the fetched userspace memory is subject to change across these reads, i.e., a race condition, which is known as a double-fetch bug. Prior works have attempted to detect these bugs both statically and dynamically. However, due to their improper assumptions and imprecise definitions regarding double-fetch bugs, their multi-read detection is inherently limited and suffers from significant false positives and false negatives. For example, their approach is unable to support device emulation, inter-procedural analysis, loop handling, etc. More importantly, they completely leave the task of finding real double-fetch bugs from the haystack of multi-reads to manual verification, which is expensive if possible at all. In this paper, we first present a formal and precise definition of double-fetch bugs and then implement a static analysis system - Deadline - to automatically detect double-fetch bugs in OS kernels. Deadline uses static program analysis techniques to systematically find multi-reads throughout the kernel and employs specialized symbolic checking to vet each multi-read for double-fetch bugs. We apply Deadline to Linux and FreeBSD kernels and find 23 new bugs in Linux and one new bug in FreeBSD. We further propose four generic strategies to patch and prevent double-fetch bugs based on our study and the discussion with kernel maintainers.
Carbon Nanotube Based NH3 Gas Sensor: Ab-Initio Study

The electronic and transport properties of carbon nanotube has been investigated in presence of ammonia gas molecule, using Density Functional Theory (DFT) based ab-initio approach. The model of CNT sensor has been build using zigzag (7, 0) CNT with a NH3 molecule adsorbed on its surface. The presence of NH3 molecule results in increase of CNT band gap. From the analysis of I-V curve, it is observed that the adsorption of NH3 leads to different voltage and current curve in comparison to its pristine state confirms the presence of NH3.
Breaking Monotony with Meaning: Motivation in Crowdsourcing Markets

We conduct a natural field experiment that explores the relationship between the “meaningfulness” of a task and people’s willingness to work. Our study uses workers from Amazon’s Mechanical Turk (MTurk), an online marketplace for task-based work. All participants are given an identical task of labeling medical images. However, the task is presented differently depending on treatment. Subjects assigned to the meaningful treatment are told they would be helping researchers label tumor cells, whereas subjects in the zero-context treatment are not told the purpose of their task and only told that they would be labeling “objects of interest”. Our experimental design specifically hires US and Indian workers in order to test for heterogeneous effects. We find that US, but not Indian, workers are induced to work at a higher proportion when given cues that their task was meaningful. However, conditional on working, whether a task was framed as meaningful does not induce greater or higher quality output in either the US or in India.
Impact of Online Social Networking on Employees Productivity at Work Place in University of Gondar - A Case Study

Employees are considered as one of the most important assets of any institution. University of Gondar is one of the known universities in Ethiopia and has large number employees. Success of this University depends on the productivity of its employees. Social media, which has become very popular, has infiltrated the workplace and most employees are utilizing social media in the workplace without any access restriction. The purpose of this study is to examine the extent of social media participation by employees and its effect on their productivity. A sample was stratified randomly selected from a population that has internet connectivity in the workplace. Primary data was collected by using a questionnaire and interview. The research found both negative and positive relationship between social media participation and employee productivity. The negative relationship was however found to be stronger as 68.4 % employees spend most of their time on social media enhancing personal networks and 86 % of employees use office hours to visit online social networks. Positive relationship exists in employee, who use of social media for seeking and viewing work related information. The study concluded that employees participate in social media in the workplace for both work and non-work related
Deformable models in medical image analysis: a survey

This article surveys deformable models, a promising and vigorously researched computer-assisted medical image analysis technique. Among model-based techniques, deformable models offer a unique and powerful approach to image analysis that combines geometry, physics and approximation theory. They have proven to be effective in segmenting, matching and tracking anatomic structures by exploiting (bottom-up) constraints derived from the image data together with (top-down) a priori knowledge about the location, size and shape of these structures. Deformable models are capable of accommodating the significant variability of biological structures over time and across different individuals. Furthermore, they support highly intuitive interaction mechanisms that, when necessary, allow medical scientists and practitioners to bring their expertise to bear on the model-based image interpretation task. This article reviews the rapidly expanding body of work on the development and application of deformable models to problems of fundamental importance in medical image analysis, including segmentation, shape representation, matching and motion tracking.
Design and synthesis of sarolaner, a novel, once-a-month, oral isoxazoline for the control of fleas and ticks on dogs.

Over the last decade, the isoxazoline motif has become the intense focus of crop protection and animal health companies in their search for novel pesticides and ectoparasiticides. Herein we report the discovery of sarolaner, a proprietary, optimized-for-animal health use isoxazoline, for once-a-month oral treatment of flea and tick infestation on dogs.
Daily Routine in Cosmetic Dermatology

Increasingly, patients are requesting information and treatments to ameliorate the effects of skin aging. Skin aging is a biological process determinated by endogenous and exogenous factors. Cosmetic interventions may have a significant impact on the health and well-being of patients. Over the last years, several antiaging strategies have been developed, and doctors must be up to date with these constant advances. Interventions, for the facial aging mainly, may be categorized into four “Rs” of facial rejuvenation: resurfacing (chemical peels, dermabrasion, microneedling, and ablative and non-ablative lasers), redraping (the various pulling and lifting facial surgical procedures), relaxing (chemodenervation with paralytic agents), and replacement/recontouring (the use of filling agent for superficial and deep soft tissue augmentation). However, before delving into the techniques to perform cosmetic procedures, it is essential to have full knowledge of the histology, physiology, and topographic particularities of the skin. This first chapter is also the first and essential step for the construction of your knowledge on cosmetic procedures. So, make sure you understand every detail before you
MATCHING LIGHT FIELD DATASETS FROM PLENOPTIC CAMERAS 1.0 AND 2.0

The capturing of angular and spatial information of the scene using single camera is made possible by new emerging technology referred to as plenoptic camera. Both angular and spatial information, enable various post-processing applications, e.g. refocusing, synthetic aperture, super-resolution, and 3D scene reconstruction. In the past, multiple traditional cameras were used to capture the angular and spatial information of the scene. However, recently with the advancement in optical technology, plenoptic cameras have been introduced to capture the scene information. In a plenoptic camera, a lenslet array is placed between the main lens and the image sensor that allows multiplexing of the spatial and angular information onto a single image, also referred to as plenoptic image. The placement of the lenslet array relative to the main lens and the image sensor, results in two different optical designs of a plenoptic camera, also referred to as plenoptic 1.0 and plenoptic 2.0. In this work, we present a novel dataset captured with plenoptic 1.0 (Lytro Illum) and plenoptic 2.0 (Raytrix R29) cameras for the same scenes under the same conditions. The dataset provides the benchmark contents for various research and development activities for plenoptic images.
OBJECT DETECTION IN AIRBORNE LIDAR DATA FOR IMPROVED SOLAR RADIATION MODELING IN URBAN AREAS

In times of higher market prices of fossil fuels and to meet th increasingly environmental and economic threads of clima te change renewable energy must play a major role for global energy sup ply. This paper focuses on a new method for fully automated so lar potential assessment of roof planes from airborne LiDAR dat a and uses the full 3D information for both, roof plane detect ion and solar potential analysis. An image based candidate region detect ion algorithm reduces the data volume of the point cloud and i dentifies potential areas containing buildings with high completene ss (97%). Three dimensional roof planes are extracted from t he building candidate regions and their aspect and slope are calculated . Th horizon of each roof plane is calculated within the 3D po int cloud and thus shadowing effects of nearby objects such as vegetation , ro fs, chimneys, dormers etc. are respected in a proper way . In contrast to other objects such as walls or buildings vegetation is cha racterized by transparent properties. Thus, in a further st ep vegetation is detected within the remaining non-roof points and transpar ent shadow values are introduced by calculating a local tran sp rency measure averaged per tree segment. The following solar potential an alysis is performed for regularly distributed roof points a nd results in both, (i) the annual sum of the direct and diffuse radiation for eac h roof plane and (ii) in a detailed information about the dist ribution of radiation within one roof. By calculating a clear sky index, cloud cover effects are considered using data from a nearby m eteorological ground station.
Electronic books versus adult readers: effects on children's emergent literacy as a function of social class

We compared the effects of children’s reading of an educational electronic storybook on their emergent literacy with those of being read the same story in its printed version by an adult. We investigated 128 5to 6-year-old kindergarteners; 64 children from each of two socio-economic status (SES) groups: low (LSES) and middle (MSES). In each group, children were randomly assigned to one of three subgroups. The two intervention groups included three book reading sessions each; children in one group individually read the electronic book; in the second group, the children were read the same printed book by an adult; children in the third group, which served as a control, received the regular kindergarten programme. Preand post-intervention emergent literacy measures included vocabulary, word recognition and phonological awareness. Compared with the control group, the children’s vocabulary scores in both intervention groups improved following reading activity. Children from both interventions groups and both SES groups showed a similarly good level of story comprehension. In both SES groups, compared with the control group, children’s phonological awareness and word recognition did not improve following both reading interventions. Implications for future research and for education are discussed.
Semantic Labeling of Indoor Environments from 3D RGB Maps

We present an approach to automatically assign semantic labels to rooms reconstructed from 3D RGB maps of apartments. Evidence for the room types is generated using state-of-the-art deep-learning techniques for scene classification and object detection based on automatically generated virtual RGB views, as well as from a geometric analysis of the map's 3D structure. The evidence is merged in a conditional random field, using statistics mined from different datasets of indoor environments. We evaluate our approach qualitatively and quantitatively and compare it to related methods.
SAFE: A clean-slate architecture for secure systems

SAFE is a large-scale, clean-slate co-design project encompassing hardware architecture, programming languages, and operating systems. Funded by DARPA, the goal of SAFE is to create a secure computing system from the ground up. SAFE hardware provides memory safety, dynamic type checking, and native support for dynamic information flow control. The Breeze programming language leverages the security features of the underlying machine, and the “zero kernel” operating system avoids relying on any single privileged component for overall system security. The SAFE project is working towards formally verifying security properties of the runtime software. The SAFE system sets a new high-water mark for system security, allowing secure applications to be built on a solid foundation rather than on the inherently vulnerable conventional platforms available today.
An Experimental Study on Speech Enhancement Based on Deep Neural Networks

This letter presents a regression-based speech enhancement framework using deep neural networks (DNNs) with a multiple-layer deep architecture. In the DNN learning process, a large training set ensures a powerful modeling capability to estimate the complicated nonlinear mapping from observed noisy speech to desired clean signals. Acoustic context was found to improve the continuity of speech to be separated from the background noises successfully without the annoying musical artifact commonly observed in conventional speech enhancement algorithms. A series of pilot experiments were conducted under multi-condition training with more than 100 hours of simulated speech data, resulting in a good generalization capability even in mismatched testing conditions. When compared with the logarithmic minimum mean square error approach, the proposed DNN-based algorithm tends to achieve significant improvements in terms of various objective quality measures. Furthermore, in a subjective preference evaluation with 10 listeners, 76.35% of the subjects were found to prefer DNN-based enhanced speech to that obtained with other conventional technique.
Face Super-Resolution Guided by Facial Component Heatmaps

State-of-the-art face super-resolution methods leverage deep convolutional neural networks to learn a mapping between low-resolution (LR) facial patterns and their corresponding high-resolution (HR) counterparts by exploring local appearance information. However, most of these methods do not account for facial structure and suffer from degradations due to large pose variations and misalignments. In this paper, we propose a method that explicitly incorporates structural information of faces into the face super-resolution process by using a multi-task convolutional neural network (CNN). Our CNN has two branches: one for super-resolving face images and the other branch for predicting salient regions of a face coined facial component heatmaps. These heatmaps encourage the upsampling stream to generate super-resolved faces with higher-quality details. Our method not only uses low-level information (i.e., intensity similarity), but also middle-level information (i.e., face structure) to further explore spatial constraints of facial components from LR inputs images. Therefore, we are able to super-resolve very small unaligned face images (16×16 pixels) with a large upscaling factor of 8×, while preserving face structure. Extensive experiments demonstrate that our network achieves superior face hallucination results and outperforms the state-of-the-art.
Evidence Based Health Informatics: 10 Years of Efforts to Promote the Principle. Joint Contribution of IMIA WG EVAL and EFMI WG EVAL.

To present the importance of Evidence-based Health Informatics (EBHI) and the ethical imperative of this approach; to highlight the work of the IMIA Working Group on Technology Assessment and Quality Improvement and the EFMI Working Group on Assessment of Health Information Systems; and to introduce the further important evaluation and evidence aspects being addressed.



Reviews of IMIA, EFMA and other initiatives, together with literature reviews on evaluation methods and on published systematic reviews.



Presentation of the rationale for the health informatics domain to adopt a scientific approach by assessing impact, avoiding harm, and empirically demonstrating benefit and best use; reporting of the origins and rationale of the IMIA- and EQUATOR-endorsed Statement on Reporting of Evaluation Studies in Health Informatics (STARE-HI) and of the IMIA WG's Guideline for Good Evaluation Practice in Health Informatics (GEP-HI); presentation of other initiatives for objective evaluation; and outlining of further work in hand on usability and indicators; together with the case for development of relevant evaluation methods in newer applications such as telemedicine. The focus is on scientific evaluation as a reliable source of evidence, and on structured presentation of results to enable easy retrieval of evidence.



EBHI is feasible, necessary for efficiency and safety, and ethically essential. Given the significant impact of health informatics on health systems, care delivery and personal health, it is vital that cultures change to insist on evidence-based policies and investment, and that emergent global moves for this are supported.
Asymmetric Binary Coding for Image Search

Learning to hash has attracted broad research interests in recent computer vision and machine learning studies, due to its ability to accomplish efficient approximate nearest neighbor search. However, the closely related task, maximum inner product search (MIPS), has rarely been studied in this literature. To facilitate the MIPS study, in this paper, we introduce a general binary coding framework based on asymmetric hash functions, named asymmetric inner-product binary coding (AIBC). In particular, AIBC learns two different hash functions, which can reveal the inner products between original data vectors by the generated binary vectors. Although conceptually simple, the associated optimization is very challenging due to the highly nonsmooth nature of the objective that involves sign functions. We tackle the nonsmooth optimization in an alternating manner, by which each single coding function is optimized in an efficient discrete manner. We also simplify the objective by discarding the quadratic regularization term which significantly boosts the learning efficiency. Both problems are optimized in an effective discrete way without continuous relaxations, which produces high-quality hash codes. In addition, we extend the AIBC approach to the supervised hashing scenario, where the inner products of learned binary codes are forced to fit the supervised similarities. Extensive experiments on several benchmark image retrieval databases validate the superiority of the AIBC approaches over many recently proposed hashing algorithms.
Shoe-integrated sensor system for wireless gait analysis and real-time feedback

We are developing a sensor system for use in clinical gait analysis. This research involves the development of an on-shoe device that can be used for continuous and real -time monitoring of gait. This paper presents the design of an instrumente d insole and a removable instrumented shoe attachment. Transmission of the data is in real -time and wireless, providing information about the three-dimensional motion, position, and pressure distribution of the foot. Using pattern recognition and numerical analysis of the calibrated sensor outputs, algorithms will be developed to analyze the data in real -time. Results will be validated by comparison to results from a commerical optical gait analysis system at the Massachusetts General Hospital (MGH) Biomoti on Lab.
Design of 3dB wideband multisection quadrature directional coupler

The paper present a three-section quadrature directional coupler which is bandwide coupled though a slot. In the middle region of the slot, a small ellipticall conducting disk is used to extend bandwidth and increase return loss and isolation. Result of simulation show that the coupler exhibits a coupling equal to 3.3dB±0.9dB accomanied by the return loss and isolation of more than 20dB across the band 2.5GHz-9.2GHz.
Coordinated Reinforcement Learning

We present several new algorithms for multiagent reinforcement learning. A common feature of these algorithms i a parameterized, structured representation of a policy or value function. This structure is leveraged in an approach we call coordinated reinforcement learning, by which agents coordinate both their action selection activities and their parameter updates. Within the limits of our parametric representations, the agents will determine a jointly optimal action without explicitly considering every possible action in their exponentially large joint action space. Our methods iffer from many previous reinforcement learning approaches to multiagent coordination in that structured communication and coordination between agents appears at the core of both the learning algorithm and the execution architecture.
High Gain and High CMRR Two-Stage Folded Cascode OTA with Nested Miller Compensation

By using Gm-C compensation (GCC) technique, a two-stage recycling folded cascode (FC) operational transconductance ampli ̄er (OTA) is designed. The proposed con ̄guration consists of recycling structure, positive feedback and feed-forward compensation path. In comparison with the typical folded cascode CMOS Miller ampli ̄er, this design has higher DC gain, unitygain frequency (UGF), slew rate and common mode rejection ratio (CMRR). The presented OTA is simulated in 0.18m CMOS technology and the simulation results con ̄rm the theoretical analyses. Finally, the proposed ampli ̄er has a 111 dB open-loop DC gain, 20MHz UGF and 145 dB CMRR@ 1.2V supply voltage while the power consumption is 400 Wwhich makes it suitable for low-voltage applications.