nils-herrmann/clean_scirepeval huggingface dataset:
Dietary Reference Intakes: summary of applications in dietary assessment.

To summarise the applications and appropriate use of Dietary Reference Intakes (DRIs) as guidance for nutrition and health research professionals in the dietary assessment of groups and individuals.



Key points from the Institute of Medicine report,  Applications in Dietary Assessment, are summarised in this paper. The different approaches for using DRIs to evaluate the intakes of groups vs. the intakes of individuals are highlighted.



Each of the new DRIs is defined and its role in the dietary assessment of groups and individuals is described. Two methods of group assessment and a new method for quantitative assessment of individuals are described. Illustrations are provided on appropriate use of the Estimated Average Requirement (EAR), the Adequate Intake (AI) and the Tolerable Upper Intake Level (UL) in dietary assessment.



Dietary assessment of groups or individuals must be based on estimates of usual (long-term) intake. The EAR is the appropriate DRI to use in assessing groups and individuals. The AI is of limited value in assessing nutrient adequacy, and cannot be used to assess the prevalence of inadequacy. The UL is the appropriate DRI to use in assessing the proportion of a group at risk of adverse health effects. It is inappropriate to use the Recommended Dietary Allowance (RDA) or a group mean intake to assess the nutrient adequacy of groups.
One-shot Learning with Memory-Augmented Neural Networks

Despite recent breakthroughs in the applications of deep neural networks, one setting that presents a persistent challenge is that of “one-shot learning.” Traditional gradient-based networks require a lot of data to learn, often through extensive iterative training. When new data is encountered, the models must inefficiently relearn their parameters to adequately incorporate the new information without catastrophic interference. Architectures with augmented memory capacities, such as Neural Turing Machines (NTMs), offer the ability to quickly encode and retrieve new information, and hence can potentially obviate the downsides of conventional models. Here, we demonstrate the ability of a memory-augmented neural network to rapidly assimilate new data, and leverage this data to make accurate predictions after only a few samples. We also introduce a new method for accessing an external memory that focuses on memory content, unlike previous methods that additionally use memory locationbased focusing mechanisms.
Review of Form-Finding Methods for Tensegrity Structures

Seven form-finding methods for tensegrity structures are reviewed and classified. The three kinematical methods include an analytical approach, a non-linear optimisation, and a pseudo-dynamic iteration. The four statical methods include an analytical method, the formulation of linear equations of equilibrium in terms of force densities, an energy minimisation, and a search for the equilibrium configurations of the struts of the structure connected by cables whose lengths are to be determined, using a reduced set of equilibrium equations. It is concluded that the kinematical methods are best suited to obtaining only configuration details of structures that are already essentially known, the force density method is best suited to searching for new configurations, but affords no control over the lengths of the elements of the structure. The reduced coordinates method offers a greater control on elements lengths, but requires more extensive symbolic manipulations.
A Psychometric Evaluation of the Facial Action Coding System for Assessing Spontaneous Expression

The Facial Action Coding System (FACS) (Ekman & Friesen, 1978) is a comprehensive and widely used method of objectively describing facial activity. Little is known, however, about inter-observer reliability in coding the occurrence, intensity, and timing of individual FACS action units. The present study evaluated the reliability of these measures. Observational data came from three independent laboratory studies designed to elicit a wide range of spontaneous expressions of emotion. Emotion challenges included olfactory stimulation, social stress, and cues related to nicotine craving. Facial behavior was video-recorded and independently scored by two FACS-certified coders. Overall, we found good to excellent reliability for the occurrence, intensity, and timing of individual action units and for corresponding measures of more global emotion-specified combinations.
Anatomical guidelines for miniscrew insertion: palatal sites.

A previous Overview described suitable sites for vestibular miniscrew insertion.1 Although these locations are commonly used in orthodontic applications because of their ease of access, the interradicular spaces are limited by the proximity of neighboring roots , presenting the following problems: • Risk of damaging the roots or the periodontium. • Possibility of miniscrew-root contact resulting in early screw failure. • Risk of screw fracture during placement, due to the narrower miniscrew dimensions needed for interradicular positions. • A loss rate as high as 25%.2 These risk factors can be avoided by using “rootless areas” such as the hard palate, the maxillary tuberosity, or the portions of the zygomatic arches adjacent to the maxilla. The tuberosity cannot be regarded as entirely safe, since unerupted third molars or thick layers of gingiva may prevent successful insertion3 . Insertion into the inferior portion of the zygomatic arch carries the risk of perforating the maxillary  Therefore, the only safe alternatives to buccal miniscrew placement are in the palate. In the mandible, where lingual screw insertion is associated with higher loss rates,5 the mentalis region is better suited for miniscrews and miniplates.6 In the maxilla, the hard palate appears to be an ideal insertion site. While the anterior palate definitely offers sufficient bone, consensus has yet to be reached regarding the minimal amount of bone required to avoid penetration into neighboring anatomical structures. Liou and colleagues suggested 2mm,7 Poggio and colleagues
Convolutional Neural Network on Three Orthogonal Planes for Dynamic Texture Classification

Dynamic Textures (DTs) are sequences of images of moving scenes that exhibit certain stationarity properties in time such as smoke, vegetation and fire. The analysis of DT is important for recognition, segmentation, synthesis or retrieval for a range of applications including surveillance, medical imaging and remote sensing. Deep learning methods have shown impressive results and are now the new state of the art for a wide range of computer vision tasks including image and video recognition and segmentation. In particular, Convolutional Neural Networks (CNNs) have recently proven to be well suited for texture analysis with a design similar to a filter bank approach. In this paper, we develop a new approach to DT analysis based on a CNN method applied on three orthogonal planes x y , xt and y t . We train CNNs on spatial frames and temporal slices extracted from the DT sequences and combine their outputs to obtain a competitive DT classifier. Our results on a wide range of commonly used DT classification benchmark datasets prove the robustness of our approach. Significant improvement of the state of the art is shown on the larger datasets.
Representation Learning over Dynamic Graphs

How can we effectively encode evolving information over dynamic graphs into low-dimensional representations? In this paper, we propose DyRep – an inductive deep representation learning framework that learns a set of functions to efficiently produce low-dimensional node embeddings that evolves over time. The learned embeddings drive the dynamics of two key processes namely, communication and association between nodes in dynamic graphs. These processes exhibit complex nonlinear dynamics that evolve at different time scales and subsequently contribute to the update of node embeddings. We employ a time-scale dependent multivariate point process model to capture these dynamics. We devise an efficient unsupervised learning procedure and demonstrate that our approach significantly outperforms representative baselines on two real-world datasets for the problem of dynamic link prediction and event time prediction.
Profession of neuropsychology in Latin America.

The purpose of this study was to analyze characteristics of individuals working in the profession of neuropsychology in Latin America in order to understand their background, professional training, current work situation, assessment and diagnostic procedures used, rehabilitation techniques employed, population targeted, teaching responsibilities, and research activities. A total of 808 professionals working in neuropsychology from 17 countries in Latin America completed an online survey between July 2013 and January 2014. The majority of participants were female and the mean age was 36.76 years (range 21-74 years). The majority of professionals working in neuropsychology in Latin America have a background in psychology, with some additional specialized training and supervised clinical practice. Over half work in private practice, universities, or private clinics and are quite satisfied with their work. Those who identify themselves as clinicians primarily work with individuals with learning problems, ADHD, mental retardation, TBI, dementia, and stroke. The majority respondents cite the top barrier in the use of neuropsychological instruments to be the lack of normative data for their countries. The top perceived barriers to the field include: lack of academic training programs, lack of clinical training opportunities, lack of willingness to collaborate between professionals, and lack of access to neuropsychological instruments. There is a need in Latin America to increase regulation, improve graduate curriculums, enhance existing clinical training, develop professional certification programs, validate existing neuropsychological tests, and create new, culturally-relevant instruments.
Generative Image Inpainting with Contextual Attention

Recent deep learning based approaches have shown promising results for the challenging task of inpainting large missing regions in an image. These methods can generate visually plausible image structures and textures, but often create distorted structures or blurry textures inconsistent with surrounding areas. This is mainly due to ineffectiveness of convolutional neural networks in explicitly borrowing or copying information from distant spatial locations. On the other hand, traditional texture and patch synthesis approaches are particularly suitable when it needs to borrow textures from the surrounding regions. Motivated by these observations, we propose a new deep generative model-based approach which can not only synthesize novel image structures but also explicitly utilize surrounding image features as references during network training to make better predictions. The model is a feedforward, fully convolutional neural network which can process images with multiple holes at arbitrary locations and with variable sizes during the test time. Experiments on multiple datasets including faces (CelebA, CelebA-HQ), textures (DTD) and natural images (ImageNet, Places2) demonstrate that our proposed approach generates higher-quality inpainting results than existing ones. Code, demo and models are available at: https://github.com/JiahuiYu/generative_inpainting.
A Lean Carrier for LTE

The next major step in the evolution of LTE targets the rapidly increasing demand for mobile broadband services and traffic volumes. One of the key technologies is a new carrier type, referred to in this article as a Lean Carrier, an LTE carrier with minimized control channel overhead and cell-specific reference signals. The Lean Carrier can enhance spectral efficiency, increase spectrum flexibility, and reduce energy consumption. This article provides an overview of the motivations and main use cases of the Lean Carrier. Technical challenges are highlighted, and design options are discussed; finally, a performance evaluation quantifies the benefits of the Lean Carrier.
Neural Machine Translation via Binary Code Prediction

In this paper, we propose a new method for calculating the output layer in neural machine translation systems. The method is based on predicting a binary code for each word and can reduce computation time/memory requirements of the output layer to be logarithmic in vocabulary size in the best case. In addition, we also introduce two advanced approaches to improve the robustness of the proposed model: using error-correcting codes and combining softmax and binary codes. Experiments on two English ↔ Japanese bidirectional translation tasks show proposed models achieve BLEU scores that approach the softmax, while reducing memory usage to the order of less than 1/10 and improving decoding speed on CPUs by x5 to x10.
RATIONAL EXPECTATIONS , AND EFFICIENT MARKET HYPOTHESIS : OVERVIEWS AND EXPOSITION OF INTERCONNECTIONS

This paper provides overviews of interesting topics of game theory, information economics, rational expectations, and efficient market hypothesis. Then, the paper shows how these topics are interconnected, with the rational expectations topic playing the pivotal role. Finally, by way of proving a theorem in the context of the well-known Kyle's [75] rational expectations equilibrium model, the paper provides an exposition of the interconnectedness of the topics.
Single HIV-1 Imaging Reveals Progression of Infection through CA-Dependent Steps of Docking at the Nuclear Pore, Uncoating, and Nuclear Transport.

The HIV-1 core consists of capsid proteins (CA) surrounding viral genomic RNA. After virus-cell fusion, the core enters the cytoplasm and the capsid shell is lost through uncoating. CA loss precedes nuclear import and HIV integration into the host genome, but the timing and location of uncoating remain unclear. By visualizing single HIV-1 infection, we find that CA is required for core docking at the nuclear envelope (NE), whereas early uncoating in the cytoplasm promotes proteasomal degradation of viral complexes. Only docked cores exhibiting accelerated loss of CA at the NE enter the nucleus. Interestingly, a CA mutation (N74D) altering virus engagement of host factors involved in nuclear transport does not alter the uncoating site at the NE but reduces the nuclear penetration depth. Thus, CA protects HIV-1 complexes from degradation, mediates docking at the nuclear pore before uncoating, and determines the depth of nuclear penetration en route to integration.
Authorship Attribution in Bengali Language

We describe Authorship Attribution of Bengali literary text. Our contributions include a new corpus of 3,000 passages written by three Bengali authors, an end-to-end system for authorship classification based on character n-grams, feature selection for authorship attribution, feature ranking and analysis, and learning curve to assess the relationship between amount of training data and test accuracy. We achieve state-of-theart results on held-out dataset, thus indicating that lexical n-gram features are unarguably the best discriminators for authorship attribution of Bengali literary text.
Agricultural Robotics: The Future of Robotic Agriculture

Agri-Food is the largest manufacturing sector in the UK. It supports a food chain that generates over {\pounds}108bn p.a., with 3.9m employees in a truly international industry and exports {\pounds}20bn of UK manufactured goods. However, the global food chain is under pressure from population growth, climate change, political pressures affecting migration, population drift from rural to urban regions and the demographics of an aging global population. These challenges are recognised in the UK Industrial Strategy white paper and backed by significant investment via a Wave 2 Industrial Challenge Fund Investment ("Transforming Food Production: from Farm to Fork"). Robotics and Autonomous Systems (RAS) and associated digital technologies are now seen as enablers of this critical food chain transformation. To meet these challenges, this white paper reviews the state of the art in the application of RAS in Agri-Food production and explores research and innovation needs to ensure these technologies reach their full potential and deliver the necessary impacts in the Agri-Food sector.
- 0-The Role of Trust in Customer Online Shopping Behavior : Perspective of Technology Acceptance Model

Online shopping, different from traditional shopping behavior, is characterized with uncertainty, anonymity, and lack of control and potential opportunism. Therefore, trust is an important factor to facilitate online transactions. The purpose of this study is to explore the role of trust in consumer online purchase behavior. This study undertook a comprehensive survey of online customers having e-shopping experiences in Taiwan and we received 1258 valid questionnaires. The empirical results, using structural equation modeling, indicated that perceived ease of use and perceived usefulness affect have a significant impact on trust in e-commerce. Trust also has a significant influence on attitude towards online purchase. However, there is no significant impact from trust on the intention of online purchase.
Exact and Metaheuristic Approaches for a Bi-Objective School Bus Scheduling Problem

As a class of hard combinatorial optimization problems, the school bus routing problem has received considerable attention in the last decades. For a multi-school system, given the bus trips for each school, the school bus scheduling problem aims at optimizing bus schedules to serve all the trips within the school time windows. In this paper, we propose two approaches for solving the bi-objective school bus scheduling problem: an exact method of mixed integer programming (MIP) and a metaheuristic method which combines simulated annealing with local search. We develop MIP formulations for homogenous and heterogeneous fleet problems respectively and solve the models by MIP solver CPLEX. The bus type-based formulation for heterogeneous fleet problem reduces the model complexity in terms of the number of decision variables and constraints. The metaheuristic method is a two-stage framework for minimizing the number of buses to be used as well as the total travel distance of buses. We evaluate the proposed MIP and the metaheuristic method on two benchmark datasets, showing that on both instances, our metaheuristic method significantly outperforms the respective state-of-the-art methods.
name-brand sneakers: social imPlicaTions

Relatively little is known about how commodity consumption amongst African-Americans affirms issues of social organization within society. Moreover, the lack of primary documentation on the attitudes of African-American (A-A) commodity consumers contributes to the distorting image of A-A adolescents who actively engage in name-brand sneaker consumption; consequently maintaining the stigma of A-A adolescents being ‘addicted to brands’ (Chin, 2001). This qualitative study sought to employ the attitudes of African-Americans from an urban/metropolitan high school in dialogue on the subject of commodity consumption; while addressing the concepts of structure and agency with respect to name-brand sneaker consumption. Additionally, this study integrated three theoretical frameworks that were used to assess the participants’ engagement as consumers of name-brand sneakers. Through a focus group and analysis of surveys, it was discovered that amongst the African-American adolescent population, sneaker consumption imparted a means of attaining a higher socio-economic status, while concurrently providing an outlet for ‘acting’ as agents within the constraints of a constructed social structure. This study develops a practical method of analyzing several issues within commodity consumption, specifically among African-American adolescents. Prior to an empirical application of several theoretical frameworks, the researcher assessed the role of sneaker production as it predates sneaker consumption. Labor-intensive production of name-brand footwear is almost exclusively located in Asia (Vanderbilt, 1998), and has become the formula for efficient, profitable production in name-brand sneaker factories. Moreover, the production of such footwear is controlled by the demand for commodified products in the global economy. Southeast Asian manufacturing facilities owned by popular athletic footwear companies generate between $830 million and $5 billion a year from sneaker consumption (Vanderbilt, 1998). The researcher asks, What are the characteristics that determine the role of African-American consumers within the name-brand sneaker industry? The manner in which athletic name-brand footwear is consumed is a process that is directly associated with the social satisfaction of the consumer (Stabile, 2000). In this study, the researcher investigated the attitudes of adolescents towards name-brand sneaker consumption and production in order to determine how their perceived socioeconomic status affected by their consumption. Miller (2002) suggests that the consumption practices of young African-Americans present a central understanding of the act of consumption itself. While an analysis of consumption is vital in determining how and to whom a product is marketed Chin (2001), whose argument will be discussed further into this study, McNair ScholarS JourNal • VoluMe 8 111 explicates that (commodity) consumption is significant because it provides an understanding of the socially constructed society in which economically disadvantaged children are a part of.
Induction motor versus permanent magnet synchronous motor in motion control applications : a comparative study

Thesis for the degree of Doctor of Science (Technology) to be presented with due permission for the public examination and criticism in the Auditorium 1382 at High dynamic performance of an electric motor is a fundamental prerequisite in motion control applications, also known as servo drives. Recent developments in the field of microprocessors and power electronics have enabled faster and faster movements with an electric motor. In such a dynamically demanding application, the dimensioning of the motor differs substantially from the industrial motor design, where feasible characteristics of the motor are for example high efficiency, a high power factor, and a low price. In motion control instead, such characteristics as high overloading capability, high-speed operation, high torque density and low inertia are required. The thesis investigates how the dimensioning of a high-performance servomotor differs from the dimensioning of industrial motors. The two most common servomotor types are examined; an induction motor and a permanent magnet synchronous motor. The suitability of these two motor types in dynamically demanding servo applications is assessed, and the design aspects that optimize the servo characteristics of the motors are analyzed. Operating characteristics of a high performance motor are studied, and some methods for improvements are suggested. The main focus is on the induction machine, which is frequently compared to the permanent magnet synchronous motor. A 4 kW prototype induction motor was designed and manufactured for the verification of the simulation results in the laboratory conditions. Also a dynamic simulation model for estimating the thermal behaviour of the induction motor in servo applications was constructed. The accuracy of the model was improved by coupling it with the electromagnetic motor model in order to take into account the variations in the motor electromagnetic characteristics due to the temperature rise.