nils-herrmann/clean_scirepeval huggingface dataset:
Macrophage biology in development, homeostasis and disease

Macrophages, the most plastic cells of the haematopoietic system, are found in all tissues and show great functional diversity. They have roles in development, homeostasis, tissue repair and immunity. Although tissue macrophages are anatomically distinct from one another, and have different transcriptional profiles and functional capabilities, they are all required for the maintenance of homeostasis. However, these reparative and homeostatic functions can be subverted by chronic insults, resulting in a causal association of macrophages with disease states. In this Review, we discuss how macrophages regulate normal physiology and development, and provide several examples of their pathophysiological roles in disease. We define the ‘hallmarks’ of macrophages according to the states that they adopt during the performance of their various roles, taking into account new insights into the diversity of their lineages, identities and regulation. It is essential to understand this diversity because macrophages have emerged as important therapeutic targets in many human diseases.
Conflict-Aware Event-Participant Arrangement and Its Variant for Online Setting

With the rapid development of Web 2.0 and Online To Offline (O2O) marketing model, various <italic>online <underline> e</underline>vent-<underline>b</underline>ased <underline>s</underline>ocial <underline>n</underline>etwork<underline>s </underline></italic> (EBSNs) are getting popular. An important task of EBSNs is to facilitate the most satisfactory event-participant arrangement for both sides, i.e., events enroll more participants and participants are arranged with personally interesting events. Existing approaches usually focus on the arrangement of each single event to a set of potential users, or ignore the conflicts between different events, which leads to infeasible or redundant arrangements. In this paper, to address the shortcomings of existing approaches, we first identify a more general and useful event-participant arrangement problem, called <italic><underline>G</underline>lobal <underline>E</underline> vent-participant <underline>A</underline>rrangement with <underline>C</underline>onflict and <underline>C</underline> apacity</italic> (<inline-formula><tex-math notation="LaTeX">$GEACC$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="chen-ieq1-2565468.gif"/></alternatives></inline-formula>) problem, focusing on the conflicts of different events and making event-participant arrangements in a global view. We find that the GEACC problem is NP-hard due to the conflicts among events. Thus, we design two approximation algorithms with provable approximation ratios and an exact algorithm with pruning technique to address this problem. In addition, we propose an online setting of GEACC, called OnlineGEACC, which is also practical in real-world scenarios. We further design an online algorithm with provable performance guarantee. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments on real and synthetic datasets.
On the Design and Scalability of Distributed Shared-Data Databases

Database scale-out is commonly implemented by partitioning data across several database instances. This approach, however, has several restrictions. In particular, partitioned databases are inflexible in large-scale deployments and assume a partition-friendly workload in order to scale. In this paper, we analyze an alternative architecture design for distributed relational databases that overcomes the limitations of partitioned databases. The architecture is based on two fundamental principles: We decouple query processing and transaction management from data storage, and we share data across query processing nodes. The combination of these design choices provides scalability, elasticity, and operational flexibility without making any assumptions on the workload. As a drawback, sharing data among multiple database nodes causes synchronization overhead. To address this limitation, we introduce techniques for scalable transaction processing in shared-data environments. Specifically, we describe mechanisms for efficient data access, concurrency control, and data buffering. In combination with new hardware trends, the techniques enable performance characteristics that top state-of-the-art partitioned databases.
An inertial-based human motion tracking system with twists and exponential maps

Wearable inertial tracking is well accepted due to its convenience for free-style motion tracking with high accuracy. Traditionally, complicated high-order calculations for human kinematic modeling and inaccurate estimation of sensor placement are interfering the efficiency of real-time tracking. In order to tackle the challenges, a wearable human motion tracking system is developed by applying twists and exponential maps techniques. When the body segments are articulated by product of exponential maps, joint positions are continuously updated based on these techniques and their rotational angles are represented individually within the global frame. It is more efficient to achieve real-time motion tracking with low-order calculations. Meanwhile by applying the well-designed calibration procedure, it is more convenient to estimate a sensor's position and orientation regardless of knowing its placement. This paper presents our approach and exemplifies the assessment of proposed motion tracking system by several tests of limb and full body motion tracking. The comparisons with Vicon and OptiTrack motion capture systems verify satisfactorily high accuracy.
A Novel Multi-Stack Device Structure and its Analysis for High Power CMOS Switch Design

A novel multi-stack CMOS device structure is proposed, the operation of the structure is fully analyzed for high power CMOS switch design. The structure is also implemented in a standard 0.18-um triple-well CMOS process, and its performance is fully characterized. The proposed switch device incorporates multi-stack NMOS switches, one of which has a switch at the bulk and the others of which have a connection between the bulk and the source in order to provide high power handling capability to the transmit switch side. In order to demonstrate the improvement of power handling capability, performance of the conventional structure and the proposed structure are fully analyzed, compared from various simulation results, and verified with measurement results in detail. Experimental data show that the isolation of the proposed test structure is 25 dB higher at 30 dBm input power level than that of conventional structures, which was caused by the significant reduction of leakage current of the switch device in OFF state. In addition, insertion loss of the Rx switch can be maintained as 1.5 dB by applying body switching technique in 900 MHz.
Indian Sign Language gesture classification as single or double handed gestures

The development of a sign language recognition system can have a great impact on the daily lives of humans with hearing disabilities. Recognizing gestures from the Indian Sign Language (ISL) with a camera can be difficult due to complexity of various gestures. The motivation behind the paper is to develop an approach to successfully classify gestures in the ISL under ambiguous conditions from static images. A novel approach involving the decomposition of gestures into single handed or double handed gesture has been presented in this paper. Classifying gesture into these subcategories simplifies the process of gesture recognition in the ISL due to presence of lesser number of gestures in each subcategory. Various approaches making use of Histogram of Gradients (HOG) features and geometric descriptors using KNN and SVM classifiers were tried on a dataset consisting of images of all 26 English alphabets present in the ISL under variable background. HOG features when classified with Support Vector Machine were found to be the most efficient approach resulting in an accuracy of 94.23%.
Flow-Guided Feature Aggregation for Video Object Detection

Extending state-of-the-art object detectors from image to video is challenging. The accuracy of detection suffers from degenerated object appearances in videos, e.g., motion blur, video defocus, rare poses, etc. Existing work attempts to exploit temporal information on box level, but such methods are not trained end-to-end. We present flow-guided feature aggregation, an accurate and end-to-end learning framework for video object detection. It leverages temporal coherence on feature level instead. It improves the per-frame features by aggregation of nearby features along the motion paths, and thus improves the video recognition accuracy. Our method significantly improves upon strong singleframe baselines in ImageNet VID [33], especially for more challenging fast moving objects. Our framework is principled, and on par with the best engineered systems winning the ImageNet VID challenges 2016, without additional bells-and-whistles. The code would be released.
THE SOCIAL ORGANIZATION OF CONSPIRACY : ILLEGAL NETWORKS IN THE HEAVY ELECTRICAL EQUIPMENT INDUSTRY *

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org. American Sociological Association is collaborating with JSTOR to digitize, preserve and extend access to American Sociological Review. We analyze the social organization of three well-known price-fixing conspiracies in the heavy electrical equipment industry. Although aspects of collusion have been studied by industrial organization economists and organizational criminologists, the organization of conspiracies has remained virtually unexplored. Using archival data, we reconstruct the actual communication networks involved in conspiracies in switchgear transformers , and turbines. We find that the structure of illegal networks is driven primarily by the need to maximize concealment, rather than the need to maximize efficiency. However, network structure is also contingent on information-processing requirements imposed by product and market characteristics. Our individual-level model predicts verdict (guilt or innocence), sentence, and fine as functions of personal centrality in the illegal network, network structure, management level, and company size. "The fact that secrets do not remain guarded forever is the weakness of the secret society. "-Georg Simmel, The Secret Society ree enterprise," "open markets," and similar expressions are standard business rhetoric, but in practice economic organizations strive to limit, curtail, and restrict the operation of competitive markets. direct manipulation of market ties (Baker 1990), and embeddeding business decisions in social relationships (Granovetter 1985). These market-restricting tactics are legal , but business organizations also indulge in practices proscribed by law that flagrantly sub-vert the market mechanism. We analyze the social organization of a prevalent illegal corporate practice-price-fixing. Aspects of price-fixing conspiracies have been studied extensively by industrial organization economists and organizational criminologists, but the constituent features of the conspiracies are virtually unexplored. For example, the conspiracies in the heavy electrical equipment industry have been called "the most carefully studied corporate offenses in the history of the United States" (Geis and Meier 1977, p. 68), yet insights into the social structure of these conspiracies are limited to anecdotes and journalis-cago. We benefited from the insightful and thorough reviews provided by anonymous ASR reviewers. We are grateful for comments received during presentations at the 1992 annual meeting of the In-837 838 AMERICAN SOCIOLOGICAL REVIEW tic descriptions. Even researchers who had access to confidential data …
Straight to the Tree: Constituency Parsing with Neural Syntactic Distance

In this work, we propose a novel constituency parsing scheme. The model predicts a vector of real-valued scalars, named syntactic distances, for each split position in the input sentence. The syntactic distances specify the order in which the split points will be selected, recursively partitioning the input, in a top-down fashion. Compared to traditional shiftreduce parsing schemes, our approach is free from the potential problem of compounding errors, while being faster and easier to parallelize. Our model achieves competitive performance amongst single model, discriminative parsers in the PTB dataset and outperforms previous models
Visual reaction time and high-speed ball games.

Laboratory measures of visual reaction time suggest that some aspects of high-speed ball games such as cricket are 'impossible' because there is insufficient time for the player to respond to unpredictable movements of the ball. Given the success with which some people perform these supposedly impossible acts, it has been assumed by some commentators that laboratory measures of reaction time are not applicable to skilled performers. An analysis of high-speed film of international cricketers batting on a specially prepared pitch which produced unpredictable movement of the ball is reported, and it is shown that, when batting, highly skilled professional cricketers show reaction times of around 200 ms, times similar to those found in traditional laboratory studies. Furthermore, professional cricketers take roughly as long as casual players to pick up ball flight information from film of bowlers. These two sets of results suggest that the dramatic contrast between the ability of skilled and unskilled sportsmen to act on the basis of visual information does not lie in differences in the speed of operation of the perceptual system. It lies in the organisation of the motor system that uses the output of the perceptual system.
Image Inpainting – An Inclusive Review of the Underlying Algorithm and Comparative Study of the Associated Techniques

Image In-painting, the technique that aims to revert deterioration (scratches, artifacts in photographs and videos) in images in an undetectable form, is as ancient as artistic creation itself. Digital Image In painting, a relatively young research area is an art of filling in the missing or corrupted regions in an image using information from the neighbouring pixels in a visually plausible manner, while restoring its unity. In painting which is essentially an image interpolation problem has numerous applications. It is helpfully used for object removal in digital photographs, image reconstruction, text removal, video restoration, special effects in movies disocclusion and so on. Several approaches have been proposed by the researchers to correct the occlusion. This proposed work presents a comparative study to provide a comprehensive visualization of different image in painting techniques. In this paper different types of image in painting algorithms are placed in juxtaposition. The algorithms are analysed theoretically as well as experimentally, based on which a ranking of algorithms will be established over different kinds of applications in diverse areas .
Backhauling 5G small cells: A radio resource management perspective

Developing efficient backhauling solutions for small cells is considered as one of the most significant challenges in the rollout of the upcoming 5G cellular systems where massive numbers of small cells will be deployed. In this article, we first review small cell evolutions and the consequent radio access network architecture evolution toward heterogeneity and centralized baseband processing. The backhaul solution, which is mainly determined by the availability of existing backhaul infrastructures and service demand, will lead to a heterogeneous backhaul network architecture. Radio resource management issues including cell association, and interference management and scheduling with inter-node coordination, as well as how they are related to backhaul solutions are discussed. Based on the existing RRM solutions and their limitations in multi-tier 5G systems with heterogeneous backhaul network architecture, backhaul- aware RRM strategies are envisioned. Results from a simple case study of joint cell association and bandwidth allocation with a massive MIMO-based in-band wireless backhauling framework are presented to demonstrate the performance gain with backhaul-aware RRM in a multi-tier cellular RAN. Several related open research issues are highlighted, and possible solution approaches are briefly discussed.
Learning scale-variant and scale-invariant features for deep image classification

Convolutional Neural Networks (CNNs) require large image corpora to be trained on classification tasks. The variation in image resolutions, sizes of objects and patterns depicted, and image scales, hampers CNN training and performance, because the task-relevant information varies over spatial scales. Previous work attempting to deal with such scale variations focused on encouraging scale-invariant CNN representations. However, scale-invariant representations are incomplete representations of images, because images contain scale-variant information as well. This paper addresses the combined development of scaleinvariant and scale-variant representations. We propose a multiscale CNN method to encourage the recognition of both types of features and evaluate it on a challenging image classification task involving task-relevant characteristics at multiple scales. The results show that our multi-scale CNN outperforms single-scale CNN. This leads to the conclusion that encouraging the combined development of a scale-invariant and scale-variant representation in CNNs is beneficial to image recognition performance.
Humanlike Driving: Empirical Decision-Making System for Autonomous Vehicles

The autonomous vehicle, as an emerging and rapidly growing field, has received extensive attention for its futuristic driving experiences. Although the fast developing depth sensors and machine learning methods have given a huge boost to self-driving research, existing autonomous driving vehicles do meet with several avoidable accidents during their road testings. The major cause is the misunderstanding between self-driving systems and human drivers. To solve this problem, we propose a humanlike driving system in this paper to give autonomous vehicles the ability to make decisions like a human. In our method, a convolutional neural network model is used to detect, recognize, and abstract the information in the input road scene, which is captured by the on-board sensors. And then a decision-making system calculates the specific commands to control the vehicles based on the abstractions. The biggest advantage of our work is that we implement a decision-making system which can well adapt to real-life road conditions, in which a massive number of human drivers exist. In addition, we build our perception system with only the depth information, rather than the unstable RGB data. The experimental results give a good demonstration of the efficiency and robustness of the proposed method.
MaJIC: Compiling MATLAB for Speed and Responsiveness

This paper presents and evaluates techniques to improve the execution performance of MATLAB. Previous efforts concentrated on source to source translation and batch compilation; MaJIC provides an interactive frontend that looks like MATLAB and compiles/optimizes code behind the scenes in real time, employing a combination of just-in-time and speculative ahead-of-time compilation. Performance results show that the proper mixture of these two techniques can yield near-zero response time as well as performance gains previously achieved only by batch compilers.
Suppression by Selecting Wavelets for Feature Compression in Distributed Speech Recognition

Distributed speech recognition DSR splits the processing of data between a mobile device and a network server. In the front-end, features are extracted and compressed to transmit over a wireless channel to a back-end server, where the incoming stream is received and reconstructed for recognition tasks. In this paper, we propose a feature compression algorithm termed suppression by selecting wavelets SSW to achieve the two main goals of DSR: Minimizing memory and device requirements while also maintaining or even improving the recognition performance. The SSW approach first applies the discrete wavelet transform DWT to filter the incoming speech feature sequence into two temporal subsequences at the client terminal. Feature compression is achieved by keeping the low modulation frequency subsequence while discarding the high frequency counterpart. The low-frequency subsequence is then transmitted across the remote network for specific feature statistics normalization. Wavelets are favorable for resolving the temporal properties of the feature sequence, and the down-sampling process in DWT achieves data compression by reducing the amount of data at the terminal prior to transmission across the network. Once the compressed features have arrived at the server, the feature sequence can be enhanced by statistics normalization, reconstructed with inverse DWT, and compensated with a simple post filter to alleviate any over-smoothing effects from the compression stage. Results on a standard robustness task Aurora-4 and on a Mandarin Chinese news corpus showed SSW outperforms conventional noise-robustness techniques while also providing nearly a 50% compression rate during the transmission stage of DSR systems.
Children's engagement with educational iPad apps: Insights from a Spanish classroom

This study investigates the effects of a story-making app called Our Story and a selection of other educational apps on the learning engagement of forty-one Spanish 4–5-year-olds. Children were observed interacting in small groups with the story-making app and this was compared to their engagement with a selection of construction and drawing apps. Children’s engagement was analysed in two ways: it was categorised using Bangert-Drowns and Pyke’s taxonomy for individual hands-on engagement with educational software, and using the concept of exploratory talk as developed by Mercer et al. to analyse peer engagement. For both approaches, quantitative and qualitative indices of children’s engagement were considered. The overall findings suggested that in terms of the BangertDrowns and Pyke taxonomy, the quality of children’s individual engagement was higher with the OS app in contrast to their engagement with other app software. The frequency of children’s use of exploratory talk was similar with the OS and colouring and drawing apps, and a detailed qualitative analysis of the interaction transcripts revealed several instances of the OS and drawing apps supporting joint problem-solving and collaborative engagement. We suggest that critical indices of an app’s educational value are the extent to which the app supports opportunities for open-ended content and children’s independent use of increasingly difficult features.