nils-herrmann/clean_scirepeval huggingface dataset:
Revisiting Classifier Two-Sample Tests

The goal of two-sample tests is to assess whether two samples, SP ∼ P and SQ ∼ Q, are drawn from the same distribution. Perhaps intriguingly, one relatively unexplored method to build two-sample tests is the use of binary classifiers. In particular, construct a dataset by pairing the n examples in SP with a positive label, and by pairing the m examples in SQ with a negative label. If the null hypothesis “P = Q” is true, then the classification accuracy of a binary classifier on a held-out subset of this dataset should remain near chance-level. As we will show, such Classifier Two-Sample Tests (C2ST) learn a suitable representation of the data on the fly, return test statistics in interpretable units, have a simple null distribution, and their predictive uncertainty allow to interpret where P and Q differ. The goal of this paper is to establish the properties, performance, and uses of C2ST. First, we analyze their main theoretical properties. Second, we compare their performance against a variety of state-of-the-art alternatives. Third, we propose their use to evaluate the sample quality of generative models with intractable likelihoods, such as Generative Adversarial Networks (GANs). Fourth, we showcase the novel application of GANs together with C2ST for causal discovery.
Learning Multiagent Communication with Backpropagation

Many tasks in AI require the collaboration of multiple agents. Typically, the communication protocol between agents is manually specified and not altered during training. In this paper we explore a simple neural model, called CommNN, that uses continuous communication for fully cooperative tasks. The model consists of multiple agents and the communication between them is learned alongside their policy. We apply this model to a diverse set of tasks, demonstrating the ability of the agents to learn to communicate amongst themselves, yielding improved performance over non-communicative agents and baselines. In some cases, it is possible to interpret the language devised by the agents, revealing simple but effective strategies for solving the task at hand.
Software-Defined and Virtualized Future Mobile and Wireless Networks: A Survey

With the proliferation of mobile demands and increasingly multifarious services and applications, mobile Internet has been an irreversible trend. Unfortunately, the current mobile and wireless network (MWN) faces a series of pressing challenges caused by the inherent design. In this paper, we extend two latest and promising innovations of Internet, software-defined networking and network virtualization, to mobile and wireless scenarios. We first describe the challenges and expectations of MWN, and analyze the opportunities provided by the software-defined wireless network (SDWN) and wireless network virtualization (WNV). Then, this paper focuses on SDWN and WNV by presenting the main ideas, advantages, ongoing researches and key technologies, and open issues respectively. Moreover, we interpret that these two technologies highly complement each other, and further investigate efficient joint design between them. This paper confirms that SDWN and WNV may efficiently address the crucial challenges of This work is supported by National Basic Research Program of China (973 Program Grant No. 2013CB329105), National Natural Science Foundation of China (Grants No. 61301080 and No. 61171065), Chinese National Major Scientific and Technological Specialized Project (No. 2013ZX03002001), Chinas Next Generation Internet (No. CNGI-12-03-007), and ZTE Corporation. M. Yang School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, P. R. China E-mail: yangmao210@gmail.com Y. Li · D. Jin · L. Zeng Department of Electronic Engineering, Tsinghua University, Beijing 100084, P. R. China Y. Li E-mail: liyong07@tsinghua.edu.cn D. Jin, L. Zeng E-mail: {jindp, zenglg}@mail.tsinghua.edu.cn Xin Wu Big Switch, USA E-mail: xin.wu@bigswitch.com A. V. Vasilakos Department of Computer and Telecommunications Engineering,University of Western Macedonia, Greece Electrical and Computer Engineering, National Technical University of Athens (NTUA), Greece E-mail: vasilako@ath.forthnet.gr MWN and significantly benefit the future mobile and wireless network.
A High-Performance Continuously Tunable MEMS Bandpass Filter at 1 GHz

This paper reports a continuously tunable lumped bandpass filter implemented in a third-order coupled resonator configuration. The filter is fabricated on a Borosilicate glass substrate using a surface micromachining technology that offers hightunable passive components. Continuous electrostatic tuning is achieved using three tunable capacitor banks, each consisting of one continuously tunable capacitor and three switched capacitors with pull-in voltage of less than 40 V. The center frequency of the filter is tuned from 1 GHz down to 600 MHz while maintaining a 3-dB bandwidth of 13%-14% and insertion loss of less than 4 dB. The maximum group delay is less than 10 ns across the entire tuning range. The temperature stability of the center frequency from -50°C to 50°C is better than 2%. The measured tuning speed of the filter is better than 80 s, and the is better than 20 dBm, which are in good agreement with simulations. The filter occupies a small size of less than 1.5 cm × 1.1 cm. The implemented filter shows the highest performance amongst the fully integrated microelectromechanical systems filters operating at sub-gigahertz range.
Automatic patch generation by learning correct code

We present Prophet, a novel patch generation system that works with a set of successful human patches obtained from open- source software repositories to learn a probabilistic, application-independent model of correct code. It generates a space of candidate patches, uses the model to rank the candidate patches in order of likely correctness, and validates the ranked patches against a suite of test cases to find correct patches. Experimental results show that, on a benchmark set of 69 real-world defects drawn from eight open-source projects, Prophet significantly outperforms the previous state-of-the-art patch generation system.
Machine Learned Sentence Selection Strategies for Query-Biased Summarization

It has become standard for search engines to augment result lists with document summaries. Each document summary consists of a title, abstract, and a URL. In this work, we focus on the task of selecting relevant sentences for inclusion in the abstract. In particular, we investigate how machine learning-based approaches can effectively be applied to the problem. We analyze and evaluate several learning to rank approaches, such as ranking support vector machines (SVMs), support vector regression (SVR), and gradient boosted decision trees (GBDTs). Our work is the first to evaluate SVR and GBDTs for the sentence selection task. Using standard TREC test collections, we rigorously evaluate various aspects of the sentence selection problem. Our results show that the effectiveness of the machine learning approaches varies across collections with different characteristics. Furthermore, the results show that GBDTs provide a robust and powerful framework for the sentence selection task and significantly outperform SVR and ranking SVMs on several data sets.
Topical Influence on Twitter : A Feature Construction Approach

In this paper we discuss the task of discovering topical influ ence within the online social network T WITTER. The main goal of this research is to discover who the influenti al users are with respect to a certain given topic. For this research we have sampled a portion of the T WIT ER social graph, from which we have distilled topics and topical activity, and constructed a se t of diverse features which we believe are useful in capturing the concept of topical influence. We will use sev eral correlation and classification techniques to determine which features perform best with respect to the TWITTER network. Our findings support the claim that only looking at simple popularity features such a s the number of followers is not enough to capture the concept of topical influence. It appears that mor e int icate features are required.
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

Spatial pyramid pooling module or encode-decoder structure are used in deep neural networks for semantic segmentation task. The former networks are able to encode multi-scale contextual information by probing the incoming features with filters or pooling operations at multiple rates and multiple effective fields-of-view, while the latter networks can capture sharper object boundaries by gradually recovering the spatial information. In this work, we propose to combine the advantages from both methods. Specifically, our proposed model, DeepLabv3+, extends DeepLabv3 by adding a simple yet effective decoder module to refine the segmentation results especially along object boundaries. We further explore the Xception model and apply the depthwise separable convolution to both Atrous Spatial Pyramid Pooling and decoder modules, resulting in a faster and stronger encoder-decoder network. We demonstrate the effectiveness of the proposed model on PASCAL VOC 2012 and Cityscapes datasets, achieving the test set performance of 89.0% and 82.1% without any post-processing. Our paper is accompanied with a publicly available reference implementation of the proposed models in Tensorflow at https: //github.com/tensorflow/models/tree/master/research/deeplab.
High-Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications

This paper presents a complete approach to a successful utilization of a high-performance extreme learning machines (ELMs) Toolbox for Big Data. It summarizes recent advantages in algorithmic performance; gives a fresh view on the ELM solution in relation to the traditional linear algebraic performance; and reaps the latest software and hardware performance achievements. The results are applicable to a wide range of machine learning problems and thus provide a solid ground for tackling numerous Big Data challenges. The included toolbox is targeted at enabling the full potential of ELMs to the widest range of users.
Feature Selection Based on Enhanced Cuckoo Search for Breast Cancer Classification in Mammogram Image

Proposed system has been developed to extract the optimal features from the breast tumors using Enhanced Cuckoo Search (ECS) and presented in this paper. The texture feature, intensity histogram feature, radial distance feature and shape features have been extracted and the optimal feature set has been obtained using ECS. The overall accuracy of a minimum distance classifier and k-Nearest Neighbor (k-NN) on validation samples is used as a fitness value for ECS. The new approach is carried out on the extracted feature dataset. The proposed system selects only the minimum number of features and performed the accuracy of 98.75% with Minimum Distance Classifier and 99.13% with k-NN Classifier. The performance of the new ECS is compared with the Cuckoo Search and Harmony Search. This result shows that the ECS algorithm is more accurate than the other algorithm. The proposed system can provide valuable information to the physician in medical pathology.
Exploring the importance of citizen participation and involvement in e-government projects : Practice , incentives , and organization

The purpose of this research is to investigate if, and in that case, how and what the egovernment field can learn from user participation concepts and theories in general IS research. We aim to contribute with further understanding of the importance of citizen participation and involvement within the e-government research body of knowledge and when developing public eservices in practice.  The analysis in the article is made from a comparative, qualitative case study of two e-government projects. Three analysis themes are induced from the literature review; practice of participation, incentives for participation, and organization of participation. These themes are guiding the comparative analysis of our data with a concurrent openness to interpretations from the field.  The main results in this article are that the e-government field can get inspiration and learn from methods and approaches in traditional IS projects concerning user participation, but in egovernment we also need methods to handle the challenges that arise when designing public e-services for large, heterogeneous user groups. Citizen engagement cannot be seen as a separate challenge in egovernment, but rather as an integrated part of the process of organizing, managing, and performing egovernment projects. Our analysis themes of participation generated from literature; practice, incentives and organization can be used in order to highlight, analyze, and discuss main issues regarding the challenges of citizen participation within e-government. This is an important implication based on our study that contributes both to theory on and practice of e-government.  Lessons to learn from this study concern that many e-government projects have a public e-service as one outcome and an internal e-administration system as another outcome. A dominating internal, agency perspective in such projects might imply that citizens as the user group of the e-service are only seen as passive receivers of the outcome – not as active participants in the development. By applying the analysis themes, proposed in this article, citizens as active participants can be thoroughly discussed when initiating (or evaluating) an e-government project.  This article addresses challenges regarding citizen participation in e-government development projects. User participation is well-researched within the IS discipline, but the egovernment setting implies new challenges, that are not explored enough.
Crowdsourced coordination through online games

We have conducted two investigations on the ability of human participants to solve challenging collective coordination tasks in a distributed fashion with limited perception and communication capabilities similar to those of a simple ground robot. In these investigations, participants were gathered in a laboratory of networked workstations and were given a series of different collective tasks with varying communication and perception capabilities. Here, we focus on our latest investigation and describe our methodology, platform design considerations, and highlight some interesting observed behaviors. These investigations are the preliminary phase in designing a formal strategy for learning human-inspired behaviors for solving complex distributed multirobot problems, such as pattern formation.
Automatical Storyline Generation with Help from Twitter

Storyline detection aims to connect seemly irrelevant single documents into meaningful chains, which provides opportunities for understanding how events evolve over time and what triggers such evolutions. Most previous work generated the storylines through unsupervised methods that can hardly reveal underlying factors driving the evolution process. This paper introduces a Bayesian model to generate storylines from massive documents and infer the corresponding hidden relations and topics. In addition, our model is the first attempt that utilizes Twitter data as human input to ``supervise'' the generation of storylines. Through extensive experiments, we demonstrate our proposed model can achieve significant improvement over baseline methods and can be used to discover interesting patterns for real world cases.
Leaf vein segmentation using Odd Gabor filters and morphological operations

Leaf vein forms the basis of leaf characterization and classification. Different species have different leaf vein patterns. It is seen that leaf vein segmentation will help in maintaining a record of all the leaves according to their specific pattern of veins thus provide an effective way to retrieve and store information regarding various plant species in database as well as provide an effective means to characterize plants on the basis of leaf vein structure which is unique for every species. The algorithm proposes a new way of segmentation of leaf veins with the use of Odd Gabor filters and the use of morphological operations for producing a better output. The Odd Gabor filter gives an efficient output and is robust and scalable as compared with the existing techniques as it detects the fine fiber like veins present in leaves much more efficiently.
Control of Microgrids

The motivation to develop microgrids, as a particular form of active networks is explained and presented as an effective solution for the control of grids with high levels of distibuted energy resources. The operation, more in particular the voltage and frequency control, is discussed. Control concepts useful with microgrids are detailed and implemented. Besides technical control aspects, also economical ones are developed. Primary, secondary and tertiary control algorithms are designed operating in a completely distributed way. The theoretical concepts are tested in a extensive laboratory experiment implementing a realistic scenario by using a setup of four inverters able to communicate through an Internet connection.
Feature The Singing Neanderthals : the Origins of Music , Language , Mind and Body by

Anthropologists and archaeologists have paid little attention to the origin of music and musicality — far less than for either language or ‘art’. While art has been seen as an index of cognitive complexity and language as an essential tool of communication, music has suffered from our perception that it is an epiphenomenal ‘leisure activity’, and archaeologically inaccessible to boot. Nothing could be further from the truth, according to Steven Mithen; music is integral to human social life, he argues, and we can investigate its ancestry with the same rich range of analyses — neurological, physiological, ethnographic, linguistic, ethological and even archaeological — which have been deployed to study language.
Cookies and web browser design: toward realizing informed consent online

We first provide criteria for assessing informed consent online. Then we examine how cookie technology and Web browser designs have responded to concerns about informed consent. Specifically, we document relevant design changes in Netscape Navigator and Internet Explorer over a 5-year period, starting in 1995. Our retrospective analyses leads us to conclude that while cookie technology has improved over time regarding informed consent, some startling problems remain. We specify six of these problems and offer design remedies. This work fits within the emerging field of Value-Sensitive Design.
Parallelizing WFST speech decoders

The performance-intensive part of a large-vocabulary continuous speech-recognition system is the Viterbi computation that determines the sequence of words that are most likely to generate the acoustic-state scores extracted from an input utterance. This paper presents an efficient parallel algorithm for Viterbi. The key idea is to partition the per-frame computation among threads to minimize inter-thread communication despite traversing a large irregular acoustic and language model graphs. Together with a per-thread beam search, load balancing language-model lookups, and memory optimizations, we achieve a 6.67× speedup over an highly-optimized production-quality WFST-based speech decoder. On a 200,000 word vocabulary and a 59 million ngram model, our decoder runs at 0.27× real time while achieving a word-error rate of 14.81% on 6214 labeled utterances from Voice Search data.
Selective visual attention enables learning and recognition of multiple objects in cluttered scenes

A key problem in learning representations of multiple objects from unlabeled images is that it is a priori impossible to tell which part of the image corresponds to each individual object, and which part is irrelevant clutter. Distinguishing individual objects in a scene would allow unsupervised learning of multiple objects from unlabeled images. There is psychophysical and neurophysiological evidence that the brain employs visual attention to select relevant parts of the image and to serialize the perception of individual objects. We propose a method for the selection of salient regions likely to contain objects, based on bottom-up visual attention. By comparing the performance of David Lowe s recognition algorithm with and without attention, we demonstrate in our experiments that the proposed approach can enable one-shot learning of multiple objects from complex scenes, and that it can strongly improve learning and recognition performance in the presence of large amounts of clutter.
The RCSB Protein Data Bank: views of structural biology for basic and applied research and education

The RCSB Protein Data Bank (RCSB PDB, http://www.rcsb.org) provides access to 3D structures of biological macromolecules and is one of the leading resources in biology and biomedicine worldwide. Our efforts over the past 2 years focused on enabling a deeper understanding of structural biology and providing new structural views of biology that support both basic and applied research and education. Herein, we describe recently introduced data annotations including integration with external biological resources, such as gene and drug databases, new visualization tools and improved support for the mobile web. We also describe access to data files, web services and open access software components to enable software developers to more effectively mine the PDB archive and related annotations. Our efforts are aimed at expanding the role of 3D structure in understanding biology and medicine.