nils-herrmann/clean_scirepeval huggingface dataset:
Machine Learning for the Geosciences: Challenges and Opportunities

Geosciences is a field of great societal relevance that requires solutions to several urgent problems facing our humanity and the planet. As geosciences enters the era of big data, machine learning (ML)— that has been widely successful in commercial domains—offers immense potential to contribute to problems in geosciences. However, problems in geosciences have several unique challenges that are seldom found in traditional applications, requiring novel problem formulations and methodologies in machine learning. This article introduces researchers in the machine learning (ML) community to these challenges offered by geoscience problems and the opportunities that exist for advancing both machine learning and geosciences. We first highlight typical sources of geoscience data and describe their properties that make it challenging to use traditional machine learning techniques. We then describe some of the common categories of geoscience problems where machine learning can play a role, and discuss some of the existing efforts and promising directions for methodological development in machine learning. We conclude by discussing some of the emerging research themes in machine learning that are applicable across all problems in the geosciences, and the importance of a deep collaboration between machine learning and geosciences for synergistic advancements in both disciplines.
A Leaf Recognition Technique for Plant Classification Using RBPNN and Zernike Moments

Plants are among the earth's most useful and beautiful products of nature. Plants have been crucial to mankind's survival. The urgent need is that many plants are at the risk of extinction. About 50% of ayurvedic medicines are prepared using plant leaves and many of these plant species belong to the endanger group. So it is indispensable to set up a database for plant protection. We believe that the first step is to teach a computer how to classify plants. Leaf /plant identification has been a challenge for many researchers. Several researchers have proposed various techniques. In this paper we have proposed a novel framework for recognizing and identifying plants using shape, vein, color, texture features which are combined with Zernike movements. Radial basis probabilistic neural network (RBPNN) has been used as a classifier. To train RBPNN we use a dual stage training algorithm which significantly enhances the performance of the classifier. Simulation results on the Flavia leaf dataset indicates that the proposed method for leaf recognition yields an accuracy rate of 93.82%
The Student ’ s Guide to Social Neuroscience

All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers.  Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging in Publication Data Ward, Jamie. The student's guide to social neuroscience / Jamie Ward. p. cm. Includes bibliographical references and index.
Independent drive of integrated multicolor (RGBY) micro-LED array using regularly arrayed InGaN based nanocolumns

We demonstrate monolithic integration of blue-, green-, yellow-, and red-emitting micro light emitting diodes (LEDs) and their independent drive using matrix wiring electrodes. The four-color (RGBY) micro-LEDs are prepared on the same template by using regularly arrayed InGaN based nanocolumns with different column diameters and periods via selective area growth.
Biorelevant Dissolution Methods and Their Applications in In Vitro-In Vivo Correlations for Oral Formulations

Dissolution tests that can predict the in vivo performance of drug products are usually called biorelevant dissolution tests. Biorelevant dissolution testing can be used to guide formulation development, to identify food effects on the dissolution and bioavailability of orally administered drugs, and to identify solubility limitations and stability issues. To develop a biorelevant dissolution test for oral dosage forms, the physiological conditions in the gastrointestinal (GI) tract that can affect drug dissolution are taken into consideration according to the properties of the drug and dosage form. A variety of biorelevant methods in terms of media and hydrodynamics to simulate the contents and the conditions of the GI tract are presented. The ability of biorelevant dissolution methods to predict in vivo performance and generate successful in vitro–in vivo correlations (IVIVC) for oral formulations are also discussed through several studies.
The interaction between sleep quality and academic performance.

Sleep quality has significant effects on cognitive performance and is influenced by multiple factors such as stress. Contrary to the ideal, medical students and residents suffer from sleep deprivation and stress at times when they should achieve the greatest amount of learning. In order to examine the relationship between sleep quality and academic performance, 144 medical students undertaking the pre-clinical board exam answered a survey regarding their subjective sleep quality (Pittsburgh sleep quality index, PSQI), grades and subjective stress for three different time points: semester, pre- and post-exam. Academic performance correlated with stress and sleep quality pre-exam (r = 0.276, p < 0.001 and r = 0.158, p < 0.03, note that low performance meant low sleep quality and high stress), however not with the stress or sleep quality during the semester and post-exam. 59% of all participants exhibited clinically relevant sleep disturbances (PSQI > 5) during exam preparation compared to 29% during the semester and 8% post-exam. This study shows that in medical students it is not the generally poor sleepers, who perform worse in the medical board exams. Instead students who will perform worse on their exams seem to be more stressed and suffer from poor sleep quality. However, poor sleep quality may negatively impact test performance as well, creating a vicious circle. Furthermore, the rate of sleep disturbances in medical students should be cause for intervention.
A Constrained Sequence-to-Sequence Neural Model for Sentence Simplification

Sentence simplification reduces semantic complexity to benefit people with language impairments. Previous simplification studies on the sentence level and word level have achieved promising results but also meet great challenges. For sentencelevel studies, sentences after simplification are fluent but sometimes are not really simplified. For word-level studies, words are simplified but also have potential grammar errors due to different usages of words before and after simplification. In this paper, we propose a two-step simplification framework by combining both the word-level and the sentence-level simplifications, making use of their corresponding advantages. Based on the twostep framework, we implement a novel constrained neural generation model to simplify sentences given simplified words. The final results on Wikipedia and Simple Wikipedia aligned datasets indicate that our method yields better performance than various baselines.
Embedded Platform for Gas Applications Using Hardware/Software Co-Design and RFID

This paper presents the development of a wireless low power reconfigurable self-calibrated multi-sensing platform for gas sensing applications. The proposed electronic nose (EN) system monitors gas temperatures, concentrations, and mixtures wirelessly using the radio-frequency identification (RFID) technology. The EN takes the form of a set of gas and temperature sensors and multiple pattern recognition algorithms implemented on the Zynq system on chip (SoC) platform. The gas and temperature sensors are integrated on a semi-passive RFID tag to reduce the consumed power. Various gas sensors are tested, including an in-house fabricated <inline-formula> <tex-math notation="LaTeX">$4\times 4$ </tex-math></inline-formula> <italic>SnO</italic><sub>2</sub>based sensor and seven commercial Figaro sensors. The data is transmitted to the Zynq based processing unit using a RFID reader, where it is processed using multiple pattern recognition algorithms for dimensionality reduction and classification. Multiple algorithms are explored for optimum performance, including principal component analysis (PCA) and linear discriminant analysis (LDA) for dimensionality reduction while decision tree (DT) and k-nearest neighbors (KNN) are assessed for classification purpose. Different gases are targeted at diverse concentration, including carbon monoxide (<italic>CO</italic>), ethanol (<italic>C</italic><sub>2</sub><italic>H</italic><sub>6</sub><italic>O</italic>), carbon dioxide (<italic>CO</italic><sub>2</sub>), propane (<italic>C</italic><sub>3</sub><italic>H</italic><sub>8</sub>), ammonia (<italic>NH</italic><sub>3</sub>), and hydrogen (<italic>H</italic><sub>2</sub>). An accuracy of 100% is achieved in many cases with an overall accuracy above 90% in most scenarios. Finally, the hardware/software heterogeneous solution to implementation PCA, LDA, DT, and KNN on the Zynq SoC shows promising results in terms of resources usage, power consumption, and processing time.
Six-Degree-of-Freedom Digital Simulations for Missile Guidance , Navigation , and Control

Models and simulations are further classified by the DoD into four levels: campaign, mission, engagement, and engineering. These four levels are shown and defined in Fig. 1. A campaign-level simulation includes such a large number of model elements that using engineering-level models on a single computer would probably take years of execution time. Typically for guidance, navigation, and control (GNC) analysis, the number of assets is more limited, and the simulation is at the engineering level—although the level of sophistication of the models used varies with the system questions to be answered. This article discusses the engineering questions, model implementations, and simulation architectures used in a GNC simulation. We start with a brief historical review of GNC simulations and their uses, and then we examine the requirements of a digital simulation independent of the models and outline current simulation designs. Finally, we characterize the essential models for a GNC simulation and the different levels of detail for these models. Additionally, we consider some his article presents a brief history of missile simulations and a discussion of the programming languages and paradigms used for developing them. Evolving language and programming paradigms elicit requirements for new simulation architectures. Within this execution framework, engineering-level guidance, navigation, and control simulations must include certain functional modules to capture the performance characteristics of the missile system. The level of model sophistication required depends on the particular engineering question to be answered. Six-degree-offreedom simulations are effective tools for cost and risk reduction during the development and deployment of missile systems. Six-Degree-of-Freedom Digital Simulations for Missile Guidance, Navigation, and Control
iPose : Instance-Aware 6 D Pose Estimation of Partly Occluded Objects Omid

We address the task of 6D pose estimation of known rigid objects from single input images in scenarios where the objects are partly occluded. Recent RGB-D-based methods are robust to moderate degrees of occlusion. For RGB inputs, no previous method works well for partly occluded objects. Our main contribution is to present the first deep learning-based system that estimates accurate poses for partly occluded objects from RGB-D and RGB input. We achieve this with a new instance-aware pipeline that decomposes 6D object pose estimation into a sequence of simpler steps, where each step removes specific aspects of the problem. The first step localizes all known objects in the image using an instance segmentation network, and hence eliminates surrounding clutter and occluders. The second step densely maps pixels to 3D object surface positions, so called object coordinates, using an encoder-decoder network, and hence eliminates object appearance. The third, and final, step predicts the 6D pose using geometric optimization. We demonstrate that we significantly outperform the state-of-the-art for pose estimation of partly occluded objects for both RGB and RGB-D input.
Data mining based crime investigation systems: Taxonomy and relevance

The number of crime incidents that is reported per day in India is increasing dramatically. The criminals today use various advanced technologies and commit crimes in really tactful ways. This makes crime investigation a more complicated process. Thus the police officers have to perform a lot of manual tasks to get a thread for investigation. This paper deals with the study of data mining based systems for analyzing crime information and thus automates the crime investigation procedure of the police officers. The majority of these frameworks utilize a blend of data mining methods such as clustering and classification for the effective investigation of the criminal acts.
Advancing Grounded Theory : Using Theoretical Frameworks within Grounded Theory Studies

The founding fathers of grounded theory (GT) claimed it is an inductive methodological approach. Yet, some scholars argue that purely inductive GTs are not possible given researchers’ involvement in data collection and analysis. Subsequently, a constructivist GT approach was introduced. Still, full-length methodological articles that include rationales or detailed explanations for using constructivist GT approaches are limited in peerreviewed journals. The purpose of this article is to highlight the ways in which the author used a constructivist GT approach in his dissertation. Within the article, the author provides concrete examples and a rationale for the ways in which he used a theoretical framework within a constructivist GT study. First, the author introduces literature on GT. Next, the author introduces the theoretical framework used in the study, highlighting the introduction of a theoretical framework as a departure from the traditional tenets of GT. Finally, the author highlights the ways in which he used the theoretical framework to shape the research questions, data collection and analysis, and findings.
"Facebook depression?" social networking site use and depression in older adolescents.

To evaluate the association between social networking site (SNS) use and depression in older adolescents using an experience sample method (ESM) approach.



Older adolescent university students completed an online survey containing the Patient Health Questionnaire-9 depression screen (PHQ) and a week-long ESM data collection period to assess SNS use.



Participants (N = 190) included in the study were 58% female and 91% Caucasian. The mean age was 18.9 years (standard deviation = .8). Most used SNSs for either <30 minutes (n = 100, 53%) or between 30 minutes and 2 hours (n = 74, 39%); a minority of participants reported daily use of SNS >2 hours (n = 16, 8%). The mean PHQ score was 5.4 (standard deviation = 4.2). No associations were seen between SNS use and either any depression (p = .519) or moderate to severe depression (p = .470).



We did not find evidence supporting a relationship between SNS use and clinical depression. Counseling patients or parents regarding the risk of "Facebook Depression" may be premature.
Structured Low-Rank Matrix Factorization for Point-Cloud Denoising

In this work we address the problem of point-cloud denoising where we assume that a given point-cloud comprises (noisy) points that were sampled from an underlying surface that is to be denoised. We phrase the point-cloud denoising problem in terms of a dictionary learning framework. To this end, for a given point-cloud we (robustly) extract planar patches covering the entire point-cloud, where each patch contains a (noisy) description of the local structure of the underlying surface. Based on the general assumption that many of the local patches (in the noise-free point-cloud) contain redundant information (e.g. due to smoothness of the surface, or due to repetitive structures), we find a low-dimensional affine subspace that (approximately) explains the extracted (noisy) patches. Computationally, this is achieved by solving a structured low-rank matrix factorization problem, where we impose smoothness on the patch dictionary and sparsity on the coefficients. We experimentally demonstrate that our method outperforms existing denoising approaches in various noise scenarios.
A Systematic Review of Healthcare Applications for Smartphones

Advanced mobile communications and portable computation are now combined in handheld devices called "smartphones", which are also capable of running third-party software. The number of smartphone users is growing rapidly, including among healthcare professionals. The purpose of this study was to classify smartphone-based healthcare technologies as discussed in academic literature according to their functionalities, and summarize articles in each category.



In April 2011, MEDLINE was searched to identify articles that discussed the design, development, evaluation, or use of smartphone-based software for healthcare professionals, medical or nursing students, or patients. A total of 55 articles discussing 83 applications were selected for this study from 2,894 articles initially obtained from the MEDLINE searches.



A total of 83 applications were documented: 57 applications for healthcare professionals focusing on disease diagnosis (21), drug reference (6), medical calculators (8), literature search (6), clinical communication (3), Hospital Information System (HIS) client applications (4), medical training (2) and general healthcare applications (7); 11 applications for medical or nursing students focusing on medical education; and 15 applications for patients focusing on disease management with chronic illness (6), ENT-related (4), fall-related (3), and two other conditions (2). The disease diagnosis, drug reference, and medical calculator applications were reported as most useful by healthcare professionals and medical or nursing students.



Many medical applications for smartphones have been developed and widely used by health professionals and patients. The use of smartphones is getting more attention in healthcare day by day. Medical applications make smartphones useful tools in the practice of evidence-based medicine at the point of care, in addition to their use in mobile clinical communication. Also, smartphones can play a very important role in patient education, disease self-management, and remote monitoring of patients.
TFLMS: Large Model Support in TensorFlow by Graph Rewriting

While accelerators such as GPUs have limited memory, deep neural networks are becoming larger and will not fit with the memory limitation of accelerators for training. We propose an approach to tackle this problem by rewriting the computational graph of a neural network, in which swap-out and swap-in operations are inserted to temporarily store intermediate results on CPU memory. In particular, we first revise the concept of a computational graph by defining a concrete semantics for variables in a graph. We then formally show how to derive swap-out and swap-in operations from an existing graph and present rules to optimize the graph. To realize our approach, we developed a module in TensorFlow, named TFLMS. TFLMS is published as a pull request in the TensorFlow repository for contributing to the TensorFlow community. With TFLMS, we were able to train ResNet-50 and 3DUnet with 4.7x and 2x larger batch size, respectively. In particular, we were able to train 3DUNet using images of size of 1923 for image segmentation, which, without TFLMS, had been done only by dividing the images to smaller images, which affects the accuracy.
IMPLICIT AND EXPLICIT KNOWLEDGE ABOUT LANGUAGE

Children acquire their first language (L1) by engaging with their caretakers in natural meaningful communication. From this “evidence” they automatically acquire complex knowledge of the structure of their language. Yet paradoxically they cannot describe this knowledge, the discovery of which forms the object of the disciplines of theoretical linguistics, psycholinguistics, and child language acquisition. This is a difference between explicit and implicit knowledge—ask a young child how to form a plural and she says she does not know; ask her “here is a wug, here is another wug, what have you got?” and she is able to reply, “two wugs.” The acquisition of L1 grammar is implicit and is extracted from experience of usage rather than from explicit rules—simple exposure o normal linguistic input suffices and no explicit instruction is needed. Adult acquisition of second language (L2) is a different matter in that what can be acquired implicitly from communicative contexts is typically quite limited in comparison to native speaker norms, and adult attainment of L2 accuracy usually requires additional resources of explicit learning. The various roles of consciousness in second language acquisition (SLA) include: the learner noticing negative evidence; their attending to language form, their perception focused by social scaffolding or explicit instruction; their voluntary use of pedagogical grammatical descriptions and analogical reasoning; their reflective induction of metalinguistic insights about language; and their consciously guided practice which results, eventually, in unconscious, automatized skill. From various divisions of cognitive neuroscience, we know that implicit and explicit learning are distinct processes, that humans have separate implicit and explicit memory systems, that there are different types of knowledge of and about language, that these are stored in different areas of the brain, and that different educational experiences generate different types of knowledge.
Specific flexion-related low back pain and sitting: comparison of seated discomfort on two different chairs.

No study has examined the effectiveness of prescribing seating modifications according to the individual clinical presentation of people with low back pain (LBP). A dynamic, forward-inclined chair ('Back App') can reduce seated paraspinal muscle activation among pain-free participants. This study examined 21 participants whose LBP was specifically aggravated by prolonged sitting and was eased by standing. Low back discomfort (LBD) and overall body discomfort (OBD) were assessed every 15 min while participants sat for 1 h on both the dynamic, forward-inclined chair and a standard office chair. LBD increased significantly more (p = 0.005) on the standard office chair, with no significant difference (p = 0.178) in OBD between the chairs. The results demonstrate that, in a specific flexion-related subgroup of people with LBP, increased LBD during sitting can be minimised through modifying chair design. Mechanisms that minimise seated discomfort may be of relevance in LBP management, as part of a biopsychosocial management plan.



This study examined low back discomfort (LBD) during a typing task among people with low back pain (LBP). Sitting on a dynamic, forward-inclined chair resulted in less seated LBD than sitting on a standard office chair. Further research is required to examine the long-term effectiveness of ergonomics interventions in LBP.