nils-herrmann/clean_scirepeval huggingface dataset:
Lethargus is a Caenorhabditis elegans sleep-like state

There are fundamental similarities between sleep in mammals and quiescence in the arthropod Drosophila melanogaster, suggesting that sleep-like states are evolutionarily ancient. The nematode Caenorhabditis elegans also has a quiescent behavioural state during a period called lethargus, which occurs before each of the four moults. Like sleep, lethargus maintains a constant temporal relationship with the expression of the C. elegans Period homologue LIN-42 (ref. 5). Here we show that quiescence associated with lethargus has the additional sleep-like properties of reversibility, reduced responsiveness and homeostasis. We identify the cGMP-dependent protein kinase (PKG) gene egl-4 as a regulator of sleep-like behaviour, and show that egl-4 functions in sensory neurons to promote the C. elegans sleep-like state. Conserved effects on sleep-like behaviour of homologous genes in C. elegans and Drosophila suggest a common genetic regulation of sleep-like states in arthropods and nematodes. Our results indicate that C. elegans is a suitable model system for the study of sleep regulation. The association of this C. elegans sleep-like state with developmental changes that occur with larval moults suggests that sleep may have evolved to allow for developmental changes.
Consumption of animal source foods and dietary diversity reduce stunting in children in Cambodia

Malnutrition in children is a major public health concern. This study aimed to determine the association between dietary diversity and stunting, underweight, wasting, and diarrhea and that between consumption of each specific food group and these nutritional and health outcomes among children.



A nationally representative household survey of 6209 children aged 12 to 59 months was conducted in Cambodia. We examined the consumption of food in the 24 hours before the survey and stunting, underweight, wasting, and diarrhea that had occurred in the preceding 2 weeks. A food variety score (ranging from 0 to 9) was calculated to represent dietary diversity.



Stunting was negatively associated with dietary diversity (adjusted odd ratios [ORadj] 0.95, 95% confident interval [CI] 0.91-0.99, P = 0.01) after adjusting for socioeconomic and geographical factors. Consumption of animal source foods was associated with reduced risk of stunting (ORadj 0.69, 95% CI 0.54-0.89, P < 0.01) and underweight (ORadj 0.74, 95% CI 0.57-0.96, P = 0.03). On the other hand, the higher risk of diarrhea was significantly associated with consumption of milk products (ORadj 1.46, 95% CI 1.10-1.92, P = 0.02) and it was significantly pronounced among children from the poorer households (ORadj 1.85, 95% CI 1.17-2.93, P < 0.01).



Consumption of a diverse diet was associated with a reduction in stunting. In addition to dietary diversity, animal source food was a protective factor of stunting and underweight. Consumption of milk products was associated with an increase in the risk of diarrhea, particularly among the poorer households. Both dietary diversity and specific food types are important considerations of dietary recommendation.
The Solution of Two-stage Guillotine Cutting Stock Problems Having Extremely Varying Order Demands

In this paper the solution of two-stage guillotine cutting stock problems is considered. Especially such problems are under investigation where the sizes of the order demands diier in a large range. We propose a new approach dealing with such situations and compare it with the classical Gilmore/Gomory approach. We report results of extensive numerical experiments which show the advantages of the new approach.
Reward Reason Step Policy Based Agent Query Node : Band of Brothers Reason Task :

We study the problem of learning to reason in large scale knowledge graphs (KGs). More specifically, we describe a novel reinforcement learning framework for learning multi-hop relational paths: we use a policy-based agent with continuous states based on knowledge graph embeddings, which reasons in a KG vector space by sampling the most promising relation to extend its path. In contrast to prior work, our approach includes a reward function that takes the accuracy, diversity, and efficiency into consideration. Experimentally, we show that our proposed method outperforms a path-ranking based algorithm and knowledge graph embedding methods on Freebase and Never-Ending Language Learning
Scene text detection and recognition: recent advances and future trends

Text, as one of the most influential inventions of humanity, has played an important role in human life, so far from ancient times. The rich and precise information embodied in text is very useful in a wide range of vision-based applications, therefore text detection and recognition in natural scenes have become important and active research topics in computer vision and document analysis. Especially in recent years, the community has seen a surge of research efforts and substantial progresses in these fields, though a variety of challenges (e.g. noise, blur, distortion, occlusion and variation) still remain. The purposes of this survey are three-fold: 1) introduce up-to-date works, 2) identify state-of-the-art algorithms, and 3) predict potential research directions in the future. Moreover, this paper provides comprehensive links to publicly available resources, including benchmark datasets, source codes, and online demos. In summary, this literature review can serve as a good reference for researchers in the areas of scene text detection and recognition.
Effect of Growth Mindset on School Engagement and Psychological Well-Being of Chinese Primary and Middle School Students: The Mediating Role of Resilience

The objective of positive education is not only to improve students' well-being but also their academic performance. As an important concept in positive education, growth mindset refers to core assumptions about the malleability of a person's intellectual abilities. The present study investigates the relation of growth mindsets to psychological well-being and school engagement. The study also explores the mediating function of resilience in this relation. We recruited a total of 1260 (658 males and 602 females) Chinese students from five diversified primary and middle schools. Results from the structural equation model show that the development of high levels of growth mindsets in students predicts higher psychological well-being and school engagement through the enhancement of resilience. The current study contributes to our understanding of the potential mechanisms by which positive education (e.g., altering the mindset of students) can impact psychological well-being and school engagement.
Introduction to Spark 2.0 for Database Researchers

Originally started as an academic research project at UC Berkeley, Apache Spark is one of the most popular open source projects for big data analytics. Over 1000 volunteers have contributed code to the project; it is supported by virtually every commercial vendor; many universities are now offering courses on Spark. Spark has evolved significantly since the 2010 research paper: its foundational APIs are becoming more relational and structural with the introduction of the Catalyst relational optimizer, and its execution engine is developing quickly to adopt the latest research advances in database systems such as whole-stage code generation.
 This tutorial is designed for database researchers (graduate students, faculty members, and industrial researchers) interested in a brief hands-on overview of Spark. This tutorial covers the core APIs for using Spark 2.0, including DataFrames, Datasets, SQL, streaming and machine learning pipelines. Each topic includes slide and lecture content along with hands-on use of a Spark cluster through a web-based notebook environment.
 In addition, we will dive into the engine internals to discuss architectural design choices and their implications in practice. We will guide the audience to "hack" Spark by extending its query optimizer to speed up distributed join execution.
TURNOVER CONTAGION : HOW COWORKERS ’ JOB EMBEDDEDNESS AND JOB SEARCH BEHAVIORS INFLUENCE QUITTING

This research developed and tested a model of turnover contagion in which the job embeddedness and job search behaviors of coworkers influence employees’ decisions to quit. In a sample of 45 branches of a regional bank and 1,038 departments of a national hospitality firm, multilevel analysis revealed that coworkers’ job embeddedness and job search behaviors explain variance in individual “voluntary turnover” over and above that explained by other individual and group-level predictors. Broadly speaking, these results suggest that coworkers’ job embeddedness and job search behaviors play critical roles in explaining why people quit their jobs. Implications are discussed.
Predicting Student Grade based on Free-style Comments using Word2Vec and ANN by Considering Prediction Results Obtained in Consecutive Lessons

Continuously tracking students during a whole semester plays a vital role to enable a teacher to grasp their learning situation, attitude and motivation. It also helps to give correct assessment and useful feedback to them. To this end, we ask students to write their comments just after each lesson, because student comments re ect their learning attitude towards the lesson, understanding of course contents, and di culties of learning. In this paper, we propose a new method to predict nal student grades. The method employs Word2Vec and Arti cial Neural Network (ANN) to predict student grade in each lesson based on their comments freely written just after the lesson. In addition, we apply a window function to the predicted results obtained in consecutive lessons to keep track of each student's learning situation. The experiment results show that the prediction correct rate reached 80% by considering the predicted student grades from six consecutive lessons, and a nal rate became 94% from all 15 lessons. The results illustrate that our proposed method continuously tracked student learning situation and improved prediction performance of nal student grades as the lessons go by.
A Handful of Heuristics and Some Propositions for Understanding Resilience in Social-Ecological Systems

This paper is a work-in-progress account of ideas and propositions about resilience in socialecological systems. It articulates our understanding of how these complex systems change and what determines their ability to absorb disturbances in either their ecological or their social domains. We call them “propositions” because, although they are useful in helping us understand and compare different social-ecological systems, they are not sufficiently well defined to be considered formal hypotheses. These propositions were developed in two workshops, in 2003 and 2004, in which participants compared the dynamics of 15 case studies in a wide range of regions around the world. The propositions raise many questions, and we present a list of some that could help define the next phase of resilience-related research.
An Overview of Cognitive Radio Networks

Radio spectrum needed for applications such as mobile telephony, digital video broadcasting (DVB), wireless local area networks (WiFi), wireless sensor networks (ZigBee), and internet of things is enormous and continues to grow exponentially. Since spectrum is limited and the current usage can be inefficient, cognitive radio paradigm has emerged to exploit the licensed and/or underutilized spectrum much more effectively. In this article, we present the motivation for and details of cognitive radio. A critical requirement for cognitive radio is the accurate, real-time estimation of spectrum usage. We thus review various spectrum sensing techniques, propagation effects, interference modeling, spatial randomness, upper layer details, and several existing cognitive radio standards.
Recent Advances in Deep Learning: An Overview

Deep Learning is one of the newest trends in Machine Learning and Artificial Intelligence research. It is also one of the most popular scientific research trends now-a-days. Deep learning methods have brought revolutionary advances in computer vision and machine learning. Every now and then, new and new deep learning techniques are being born, outperforming state-of-the-art machine learning and even existing deep learning techniques. In recent years, the world has seen many major breakthroughs in this field. Since deep learning is evolving at a huge speed, its kind of hard to keep track of the regular advances especially for new researchers. In this paper, we are going to briefly discuss about recent advances in Deep Learning for past few years.
Trusted 5G Vehicular Networks: Blockchains and Content-Centric Networking

Vehicular communications, though a reality, must continue to evolve to support higher throughput and, above all, ultralow latency to accommodate new use cases, such as the fully autonomous vehicle. Cybersecurity must be assured since the risk of losing control of vehicles if a country were to come under attack is a matter of national security. This article presents the technological enablers that ensure security requirements are met. Under the umbrella of a dedicated network slice, this article proposes the use of content-centric networking (CCN), instead of conventional transmission control protocol/Internet protocol (TCP/IP) routing and permissioned blockchains that allow for the dynamic control of the source reliability, and the integrity and validity of the information exchanged.
Establishing Natural Nootropics: Recent Molecular Enhancement Influenced by Natural Nootropic

Nootropics or smart drugs are well-known compounds or supplements that enhance the cognitive performance. They work by increasing the mental function such as memory, creativity, motivation, and attention. Recent researches were focused on establishing a new potential nootropic derived from synthetic and natural products. The influence of nootropic in the brain has been studied widely. The nootropic affects the brain performances through number of mechanisms or pathways, for example, dopaminergic pathway. Previous researches have reported the influence of nootropics on treating memory disorders, such as Alzheimer's, Parkinson's, and Huntington's diseases. Those disorders are observed to impair the same pathways of the nootropics. Thus, recent established nootropics are designed sensitively and effectively towards the pathways. Natural nootropics such as Ginkgo biloba have been widely studied to support the beneficial effects of the compounds. Present review is concentrated on the main pathways, namely, dopaminergic and cholinergic system, and the involvement of amyloid precursor protein and secondary messenger in improving the cognitive performance.
Application of Data Mining in Agriculture 27 Definition of the Term

MiloviC, B. and v. RadojeviC, 2015. application of data mining in agriculture. Bulg.  21: 26-34 Today, agricultural organizations work with large amounts of data. Processing and retrieval of significant data in this abundance of agricultural information is necessary. Utilization of information and communications technology enables automation of extracting significant data in an effort to obtain knowledge and trends, which enables the elimination of manual tasks and easier data extraction directly from electronic sources, transfer to secure electronic system of documentation which will enable production cost reduction, higher yield and higher market price. Data mining in addition to information about crops enables agricultural enterprises to predict trends about customer’s conditions or their behavior, which is achieved by analyzing data from different perspectives and finding connections and relationships in seemingly unrelated data. Raw data of agricultural enterprises are very ample and diverse. it is necessary to collect and store them in an organized form, and their integration enables the creation of agricultural information system. Data mining in agriculture provides many opportunities for exploring hidden patterns in these collections of data. These patterns can be used to determine the condition of customers in agricultural organizations.
ENG: End-to-end Neural Geometry for Robust Depth and Pose Estimation using CNNs

Recovering structure and motion parameters given a image pair or a sequence of images is a well studied problem in computer vision. This is often achieved by employing Structure from Motion (SfM) or Simultaneous Localization and Mapping (SLAM) algorithms based on the real-time requirements. Recently, with the advent of Convolutional Neural Networks (CNNs) researchers have explored the possibility of using machine learning techniques to reconstruct the 3D structure of a scene and jointly predict the camera pose. In this work, we present a framework that achieves state-of-the-art performance on single image depth prediction for both indoor and outdoor scenes. The depth prediction system is then extended to predict optical flow and ultimately the camera pose and trained end-to-end. Our framework outperforms previous deep-learning based motion prediction approaches, and we also demonstrate that the state-of-the-art metric depths can be further improved using the knowledge of pose.
Single-Image Super-Resolution: A Benchmark

Single-image super-resolution is of great importance for vision applications, and numerous algorithms have been proposed in recent years. Despite the demonstrated success, these results are often generated based on different assumptions using different datasets and metrics. In this paper, we present a systematic benchmark evaluation for state-of-the-art single-image super-resolution algorithms. In addition to quantitative evaluations based on conventional full-reference metrics, human subject studies are carried out to evaluate image quality based on visual perception. The benchmark evaluations demonstrate the performance and limitations of state-of-the-art algorithms which sheds light on future research in single-image super-resolution.
Inkjet printing of silver nanowire networks.

The development of printed electronics will require the ability to deposit a wide range of nanomaterials using printing techniques. Here we demonstrate the controlled deposition of networks of silver nanowires in well-defined patterns by inkjet printing from an optimized isopropyl alcohol-diethylene glycol dispersion. We find that great care must be taken while producing the ink and during solvent evaporation. The resultant networks have good electrical properties, displaying sheet resistances as low as 8 Ω/□ and conductivities as high as 10(5) S/m. Such optimized performances were achieved for line widths of 1-10 mm and network thicknesses of 0.5-2 μm deposited from ∼10-20 passes while using processing temperatures of no more than 110 °C. Thin networks are semitransparent with dc to optical conductivity ratios of ∼40.
Slicer: Auto-Sharding for Datacenter Applications

Sharding is a fundamental building block of large-scale applications, but most have their own custom, ad-hoc implementations. Our goal is to make sharding as easily reusable as a filesystem or lock manager. Slicer is Google’s general purpose sharding service. It monitors signals such as load hotspots and server health to dynamically shard work over a set of servers. Its goals are to maintain high availability and reduce load imbalance while minimizing churn from moved work. In this paper, we describe Slicer’s design and implementation. Slicer has the consistency and global optimization of a centralized sharder while approaching the high availability, scalability, and low latency of systems that make local decisions. It achieves this by separating concerns: a reliable data plane forwards requests, and a smart control plane makes load-balancing decisions off the critical path. Slicer’s small but powerful API has proven useful and easy to adopt in dozens of Google applications. It is used to allocate resources for web service front-ends, coalesce writes to increase storage bandwidth, and increase the efficiency of a web cache. It currently handles 2-7M req/s of production traffic. The median production Slicer-managed workload uses 63% fewer resources than it would with static sharding.
Optimal speech enhancement under signal presence uncertainty using log-spectral amplitude estimator

We present an optimally modified log-spectral amplitude estimator, which minimizes the mean-square error of the log-spectra for speech signals under signal presence uncertainty. We propose an estimator for the a priori signal-to-noise ratio (SNR), and introduce an efficient estimator for the a priori speech absence probability. Speech presence probability is estimated for each frequency bin and each frame by a soft-decision approach, which exploits the strong correlation of speech presence in neighboring frequency bins of consecutive frames. Objective and subjective evaluation confirm superiority in noise suppression and quality of the enhanced speech.
Multiple Object Recognition with Visual Attention

We present an attention-based model for recognizing multiple objects in images. The proposed model is a deep recurrent neural network trained with reinforcement learning to attend to the most relevant regions of the input image. We show that the model learns to both localize and recognize multiple objects despite being given only class labels during training. We evaluate the model on the challenging task of transcribing house number sequences from Google Street View images and show that it is both more accurate than the state-of-the-art convolutional networks and uses fewer parameters and less computation.
Reservoir computing using dynamic memristors for temporal information processing

Reservoir computing systems utilize dynamic reservoirs having short-term memory to project features from the temporal inputs into a high-dimensional feature space. A readout function layer can then effectively analyze the projected features for tasks, such as classification and time-series analysis. The system can efficiently compute complex and temporal data with low-training cost, since only the readout function needs to be trained. Here we experimentally implement a reservoir computing system using a dynamic memristor array. We show that the internal ionic dynamic processes of memristors allow the memristor-based reservoir to directly process information in the temporal domain, and demonstrate that even a small hardware system with only 88 memristors can already be used for tasks, such as handwritten digit recognition. The system is also used to experimentally solve a second-order nonlinear task, and can successfully predict the expected output without knowing the form of the original dynamic transfer function. Reservoir computing facilitates the projection of temporal input signals onto a high-dimensional feature space via a dynamic system, known as the reservoir. Du et al. realise this concept using metal-oxide-based memristors with short-term memory to perform digit recognition tasks and solve non-linear problems.
SampleRNN: An Unconditional End-to-End Neural Audio Generation Model

In this paper we propose a novel model for unconditional audio generation based on generating one audio sample at a time. We show that our model, which profits from combining memory-less modules, namely autoregressive multilayer perceptrons, and stateful recurrent neural networks in a hierarchical structure is able to capture underlying sources of variations in the temporal sequences over very long time spans, on three datasets of different nature. Human evaluation on the generated samples indicate that our model is preferred over competing models. We also show how each component of the model contributes to the exhibited performance.