nils-herrmann/clean_scirepeval huggingface dataset:
From Monocytes to M1/M2 Macrophages: Phenotypical vs. Functional Differentiation

Studies on monocyte and macrophage biology and differentiation have revealed the pleiotropic activities of these cells. Macrophages are tissue sentinels that maintain tissue integrity by eliminating/repairing damaged cells and matrices. In this M2-like mode, they can also promote tumor growth. Conversely, M1-like macrophages are key effector cells for the elimination of pathogens, virally infected, and cancer cells. Macrophage differentiation from monocytes occurs in the tissue in concomitance with the acquisition of a functional phenotype that depends on microenvironmental signals, thereby accounting for the many and apparently opposed macrophage functions. Many questions arise. When monocytes differentiate into macrophages in a tissue (concomitantly adopting a specific functional program, M1 or M2), do they all die during the inflammatory reaction, or do some of them survive? Do those that survive become quiescent tissue macrophages, able to react as naïve cells to a new challenge? Or, do monocyte-derived tissue macrophages conserve a "memory" of their past inflammatory activation? This review will address some of these important questions under the general framework of the role of monocytes and macrophages in the initiation, development, resolution, and chronicization of inflammation.
Highway State Gating for Recurrent Highway Networks: improving information flow through time

Recurrent Neural Networks (RNNs) play a major role in the field of sequential learning, and have outperformed traditional algorithms on many benchmarks. Training deep RNNs still remains a challenge, and most of the state-of-the-art models are structured with a transition depth of 2-4 layers. Recurrent Highway Networks (RHNs) were introduced in order to tackle this issue. These have achieved state-of-the-art performance on a few benchmarks using a depth of 10 layers. However, the performance of this architecture suffers from a bottleneck, and ceases to improve when an attempt is made to add more layers. In this work, we analyze the causes for this, and postulate that the main source is the way that the information flows through time. We introduce a novel and simple variation for the RHN cell, called Highway State Gating (HSG), which allows adding more layers, while continuing to improve performance. By using a gating mechanism for the state, we allow the net to ”choose” whether to pass information directly through time, or to gate it. This mechanism also allows the gradient to back-propagate directly through time and, therefore, results in a slightly faster convergence. We use the Penn Treebank (PTB) dataset as a platform for empirical proof of concept. Empirical results show that the improvement due to Highway State Gating is for all depths, and as the depth increases, the improvement also increases.
Feed-forward neural networks

One critical aspect neural network designers face today is choosing an appropriate network size for a given application. Network size involves in the case of layered neural network architectures, the number of layers in a network, the number of nodes per layer, and the number of connections. Roughly speaking, a neural network implements a nonlinear mapping of u=G(x). The mapping function G is established during a training phase where the network learns to correctly associate input patterns x to output patterns u. Given a set of training examples (x, u), there is probably an infinite number of different size networks that can learn to map input patterns x into output patterns u. The question is, which network size is more appropriate for a given problem? Unfortunately, the answer to this question is not always obvious. Many researchers agree that the quality of a solution found by a neural network depends strongly on the network size used. In general, network size affects network complexity, and learning time. It also affects the generalization capabilities of the network; that is, its ability-to produce accurate results on patterns outside its training set.<<ETX>>
Reading Strategies , Learning Styles and Reading Comprehension : A Correlation Study

This study aimed to measure the correlations between reading strategies, learning styles and reading comprehension of the Saudi EFL college learners' English reading comprehension. This study used a survey and two IELTS reading passages that vary in difficulty levels. The purpose was to show how two different reading strategies affect EFL students' reading comprehension. The study further examines the correlations between learning styles and reading strategies, and whether this affects the students' comprehension in a sample of seventy-five EFL Saudi college students enrolled in the English Department. Participants were randomly assigned to two groups: an oral reading group (n = 37) and a silent reading group (n = 38). The learning strategies were 'visual learner' and 'auditory learner', with three performance grades, 'low', 'average' and 'high'; while the reading methods were 'oral' and 'silent'. The findings showed that the variation of reading strategies, namely oral reading versus silent reading strategies, did not produce any statistically significant differences on EFL learners' reading comprehension. Findings also showed that high visual learners did not perform significantly differently from the silent reading group or the oral reading group. There were no statistically significant differences between silent reading participants and oral reading participants in their performance on either text from the IELTS. More detailed findings were also presented and discussed against a background of prior research. Pedagogical implications were drawn, and recommendations for further research were proposed.
Gradient Adversarial Training of Neural Networks

We propose gradient adversarial training, an auxiliary deep learning framework applicable to different machine learning problems. In gradient adversarial training, we leverage a prior belief that in many contexts, simultaneous gradient updates should be statistically indistinguishable from each other. We enforce this consistency using an auxiliary network that classifies the origin of the gradient tensor, and the main network serves as an adversary to the auxiliary network in addition to performing standard task-based training. We demonstrate gradient adversarial training for three different scenarios: (1) as a defense to adversarial examples we classify gradient tensors and tune them to be agnostic to the class of their corresponding example, (2) for knowledge distillation, we do binary classification of gradient tensors derived from the student or teacher network and tune the student gradient tensor to mimic the teacher’s gradient tensor; and (3) for multi-task learning we classify the gradient tensors derived from different task loss functions and tune them to be statistically indistinguishable. For each of the three scenarios we show the potential of gradient adversarial training procedure. Specifically, gradient adversarial training increases the robustness of a network to adversarial attacks, is able to better distill the knowledge from a teacher network to a student network compared to soft targets, and boosts multi-task learning by aligning the gradient tensors derived from the task specific loss functions. Overall, our experiments demonstrate that gradient tensors contain latent information about whatever tasks are being trained, and can support diverse machine learning problems when intelligently guided through adversarialization using a auxiliary network.
On the universality of cognitive tests

The analysis of the adaptive behaviour of many different kinds of systems such as humans, animals and machines, requires more general ways of assessing their cognitive abilities. This need is strengthened by increasingly more tasks being analysed for and completed by a wider diversity of systems, including swarms and hybrids. The notion of universal test has recently emerged in the context of machine intelligence evaluation as a way to define and use the same cognitive test for a variety of systems, using some principled tasks and adapting the interface to each particular subject. However, how far can universal tests be taken? This paper analyses this question in terms of subjects, environments, space-time resolution, rewards and interfaces. This leads to a number of findings, insights and caveats, according to several levels where universal tests may be progressively more difficult to conceive, implement and administer. One of the most significant contributions is given by the realisation that more universal tests are defined as maximisations of less universal tests for a variety of configurations. This means that universal tests must be necessarily adaptive.
Proof Protocol for a Machine Learning Technique Making Longitudinal Predictions in Dynamic Contexts

We demonstrate a protocol for proving strongly that a black-box machine learning technique robustly predicts the future in dynamic, indefinite contexts. We propose necessary components of the proof protocol and demonstrate results visualizations to support evaluation of the proof components. Components include contemporaneously verifiable discrete predictions, deterministic computability of longitudinal predictions, imposition of realistic costs and domain constraints, exposure to diverse contexts, statistically significant excess benefits relative to a priori benchmarks and Monte Carlo trials, insignificant decay of excess benefits, pathology detection and an extended real-time trial "in the wild." We apply the protocol to a big data machine learning technique deployed since 2011 that finds persistent, exploitable opportunities in many of 41 segments of US financial markets, the existence of which opportunities substantially contradict the Efficient Market Hypothesis.
A reconfigurable flight control system based on the EMMAE method

The ability of the multiple model adaptive estimation method (MMAE) to detect faults based on a predefined hypothesis and the parameter-estimating ability of an extended Kalman filter (EKF) results in an efficient fault detection approach. This extended multiple model adaptive estimation method (EMMAE) has been investigated on a nonlinear model of an aircraft to estimate on-line the state vector of the system and the control surface deflection in case of failed actuators. A supervision module has been designed to enhance the performance of the EMMAE method and to appropriately change settings in a control allocation module. The results show that this reconfigurable flight control system is capable of detecting, isolating, and compensating for actuator faults of various types, without any need to add additional sensors to measure control-surface deflections or to change the flight controller
An Integrated Neural Network Model for Domain Action Determination in Goal-Oriented Dialogues

A speaker’s intentions can be represented by domain actions (domainindependent speech act and domain-dependent concept sequence pairs). Therefore, it is essential that domain actions be determined when implementing dialogue systems because a dialogue system should determine users’ intentions from their utterances and should create counterpart intentions to the users’ intentions. In this paper, a neural network model is proposed for classifying a user’s domain actions and planning a system’s domain actions. An integrated neural network model is proposed for simultaneously determining user and system domain actions using the same framework. The proposed model performed better than previous non-integrated models in an experiment using a goal-oriented dialogue corpus. This result shows that the proposed integration method contributes to improving domain action determination performance. Domain Action, Speech Act, Concept Sequence, Neural Network
Vortex Visualization in Ultra Low Reynolds Number Insect Flight

We present the visual analysis of a biologically inspired CFD simulation of the deformable flapping wings of a dragonfly as it takes off and begins to maneuver, using vortex detection and integration-based flow lines. The additional seed placement and perceptual challenges introduced by having multiple dynamically deforming objects in the highly unsteady 3D flow domain are addressed. A brief overview of the high speed photogrammetry setup used to capture the dragonfly takeoff, parametric surfaces used for wing reconstruction, CFD solver and underlying flapping flight theory is presented to clarify the importance of several unsteady flight mechanisms, such as the leading edge vortex, that are captured visually. A novel interactive seed placement method is used to simplify the generation of seed curves that stay in the vicinity of relevant flow phenomena as they move with the flapping wings. This method allows a user to define and evaluate the quality of a seed's trajectory over time while working with a single time step. The seed curves are then used to place particles, streamlines and generalized streak lines. The novel concept of flowing seeds is also introduced in order to add visual context about the instantaneous vector fields surrounding smoothly animate streak lines. Tests show this method to be particularly effective at visually capturing vortices that move quickly or that exist for a very brief period of time. In addition, an automatic camera animation method is used to address occlusion issues caused when animating the immersed wing boundaries alongside many geometric flow lines. Each visualization method is presented at multiple time steps during the up-stroke and down-stroke to highlight the formation, attachment and shedding of the leading edge vortices in pairs of wings. Also, the visualizations show evidence of wake capture at stroke reversal which suggests the existence of previously unknown unsteady lift generation mechanisms that are unique to quad wing insects.
Composing Distributed Representations of Relational Patterns

Learning distributed representations for relation instances is a central technique in downstream NLP applications. In order to address semantic modeling of relational patterns, this paper constructs a new dataset that provides multiple similarity ratings for every pair of relational patterns on the existing dataset (Zeichner et al., 2012). In addition, we conduct a comparative study of different encoders including additive composition, RNN, LSTM, and GRU for composing distributed representations of relational patterns. We also present Gated Additive Composition, which is an enhancement of additive composition with the gating mechanism. Experiments show that the new dataset does not only enable detailed analyses of the different encoders, but also provides a gauge to predict successes of distributed representations of relational patterns in the relation classification task.
Hierarchical Clustering: Objective Functions and Algorithms

Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly finer granularity. Motivated by the fact that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective, Dasgupta framed similarity-based hierarchical clustering as a combinatorial optimization problem, where a ‘good’ hierarchical clustering is one that minimizes a particular cost function [21]. He showed that this cost function has certain desirable properties: in order to achieve optimal cost, disconnected components (namely, dissimilar elements) must be separated at higher levels of the hierarchy and when the similarity between data elements is identical, all clusterings achieve the same cost. We take an axiomatic approach to defining ‘good’ objective functions for both similarity and dissimilarity-based hierarchical clustering. We characterize a set of admissible objective functions having the property that when the input admits a ‘natural’ ground-truth hierarchical clustering, the ground-truth clustering has an optimal value. We show that this set includes the objective function introduced by Dasgupta. Equipped with a suitable objective function, we analyze the performance of practical algorithms, as well as develop better and faster algorithms for hierarchical clustering. We also initiate a beyond worst-case analysis of the complexity of the problem, and design algorithms for this scenario.
Emotion Analysis and Classification: Understanding the Performers' Emotions Using the LMA Entities

The increasing availability of large motion databases, in addition to advancements in motion synthesis, has made motion indexing and classification essential for better motion composition. However, in order to achieve good connectivity in motion graphs, it is important to understand human behaviour; human movement though is complex and difficult to completely describe. In this paper, we investigate the similarities between various emotional states with regards to the arousal and valence of the Russell’s circumplex model. We use a variety of features that encode, in addition to the raw geometry, stylistic characteristics of motion based on Laban Movement Analysis (LMA). Motion capture data from acted dance performances were used for training and classification purposes. The experimental results show that the proposed features can partially extract the LMA components, providing a representative space for indexing and classification of dance movements with regards to the emotion. This work contributes to the understanding of human behaviour and actions, providing insights on how people express emotional states using their body, while the proposed features can be used as complement to the standard motion similarity, synthesis and classification methods.
A Review on Multipurpose Plant: Psidium Guajava

Psidium guajava Linn. (Guava) is used not only as food but also as folk medicine in subtropical areas around the world because of its pharmacologic activities. In particular, the leaf extract of guava has traditionally been used for the treatment of diabetes in East Asia and other countries. Many pharmacological studies have demonstrated the ability of this plant to exhibit antioxidant, hepatoprotective, anti-allergy, antimicrobial, antigenotoxic, antiplasmodial, cytotoxic, antispasmodic, cardioactive, anticough, antidiabetic, antiinflamatory and antinociceptive activities, supporting its traditional uses. Suggesting a wide range of clinical applications for the treatment of infantile rotaviral enteritis, diarrhoea and diabetes.
Interpretation and aggregation of marks in classroom learning partner

This thesis explores the mark understanding problem in the context of a Tablet-PC-based classroom interaction system. It presents a novel method for interpreting digital ink strokes on background images, and aggregating those interpretations. It addresses complexity of mark interpreters and development and acquisition of a representation of a contextual background. It details the design, implementation, testing, and plans for future extension of a mark interpreter and aggregator in the Classroom Learning Partner, our classroom interaction system.  Kimberle Koile, Ph.D. Title: Research Scientist
DéjàVu: a map of code duplicates on GitHub

Previous studies have shown that there is a non-trivial amount of duplication in source code. This paper analyzes a corpus of 4.5 million non-fork projects hosted on GitHub representing over 428 million files written in Java, C++, Python, and JavaScript. We found that this corpus has a mere 85 million unique files. In other words, 70% of the code on GitHub consists of clones of previously created files. There is considerable variation between language ecosystems. JavaScript has the highest rate of file duplication, only 6% of the files are distinct. Java, on the other hand, has the least duplication, 60% of files are distinct. Lastly, a project-level analysis shows that between 9% and 31% of the projects contain at least 80% of files that can be found elsewhere. These rates of duplication have implications for systems built on open source software as well as for researchers interested in analyzing large code bases. As a concrete artifact of this study, we have created DéjàVu, a publicly available map of code duplicates in GitHub repositories.
Learning together to teach together: interprofessional education and faculty development.

Interprofessional education for collaborative patient-centered practice has been identified as a key mechanism to address health care needs and priorities. Faculty development can play a unique role in promoting interprofessional education (IPE) by addressing some of the barriers to teaching and learning that exist at both the individual and the organizational level, and by providing individuals with the knowledge and skills needed to design and facilitate IPE. This article highlights a number of approaches and strategies that can facilitate IPE. In particular, it is recommended that faculty development initiatives aim to bring about change at the individual and the organizational level; target diverse stakeholders; address three main content areas, notably interprofessional education and collaborative patient-centred practice, teaching and learning, and leadership and organizational change; take place in a variety of settings, using diverse formats and educational strategies; model the principles and premises of interprofessional education and collaborative practice; incorporate principles of effective educational design; and consider the adoption of a dissemination model to implementation. Clearly, faculty members play a critical role in the teaching and learning of IPE and they must be prepared to meet this challenge.
Clinical practice. Precocious puberty.

From the Department of Pediatric Endocrinology and Diabetology, INSERM Unité 690, and Centre de Référence des Maladies Endocriniennes de la Croissance, Robert Debré Hospital and University of Paris 7 — Denis Diderot, Paris (J.-C.C., J.L.). Address reprint requests to Dr. Carel at Endocrinologie Diabétologie Pédiatrique and INSERM U690, Hôpital Robert Debré, 48, Blvd. Sérurier, 75935 Paris CEDEX 19, France, or at jean-claude.