nils-herrmann/clean_scirepeval huggingface dataset:
Compressed Sensing MRI Reconstruction with Cyclic Loss in Generative Adversarial Networks

Compressed Sensing MRI (CS-MRI) has provided theoretical foundations upon which the time-consuming MRI acquisition process can be accelerated. However, it primarily relies on iterative numerical solvers which still hinders their adaptation in time-critical applications. In addition, recent advances in deep neural networks have shown their potential in computer vision and image processing, but their adaptation to MRI reconstruction is still in an early stage. In this paper, we propose a novel deep learning-based generative adversarial model, RefineGAN, for fast and accurate CS-MRI reconstruction. The proposed model is a variant of fully-residual convolutional autoencoder and generative adversarial networks (GANs), specifically designed for CS-MRI formulation; it employs deeper generator and discriminator networks with cyclic data consistency loss for faithful interpolation in the given under-sampled k-space data. In addition, our solution leverages a chained network to further enhance the reconstruction quality. RefineGAN is fast and accurate – the reconstruction process is extremely rapid, as low as tens of milliseconds for reconstruction of a 256×256 image, because it is one-way deployment on a feed-forward network, and the image quality is superior even for extremely low sampling rate (as low as 10%) due to the data-driven nature of the method. We demonstrate that RefineGAN outperforms the state-of-the-art CS-MRI methods by a large margin in terms of both running time and image quality via evaluation using several open-source MRI databases.
Design Thinking Methods and Tools for Innovation

Design thinking (DT) is regarded as a system of three overlapping spaces—viability, desirability, and feasibility—where innovation increases when all three perspectives are addressed. Understanding how innovation within teams can be supported by DT methods and tools captivates the interest of business communities. This paper aims to examine how DT methods and tools foster innovation in teams. A case study approach, based on two workshops, examined three DT methods with a software tool. The findings support the use of DT methods and tools as a way of incubating ideas and creating innovative solutions within teams when team collaboration and software limitations are balanced. The paper proposes guidelines for utilizing DT methods and tools in innovation
An Annotated Corpus for Machine Reading of Instructions in Wet Lab Protocols

1. Andrew Borthwick and Ralph Grishman. 1999. A maximum entropy approach to named entity recognition. Ph. D. Thesis, Dept. of Computer Science, New York University. 2. Alan Graves, Abdel-rahman Mohamed, and Geoffrey Hinton. 2013. Speech recognition with deep recurrent neural networks. In  Xuezhe Ma and Eduard Hovy. 2016. End-to-end se-quence labeling via bi-directional lstm-cnns-crf. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL). The Ohio State University
MOND – theoretical aspects

I discuss open theoretical questions pertaining to the modified dynamics (MOND)–a proposed alternative to dark matter, which posits a breakdown of Newtonian dynamics in the limit of small accelerations. In particular, I point the reasons for thinking that MOND is an effective theory–perhaps, despite appearance, not even in conflict with GR. I then contrast the two interpretations of MOND as modified gravity and as modified inertia. I describe two mechanical models that are described by potential theories similar to (non-relativistic) MOND: a potential-flow model, and a membrane model. These might shed some light on a possible origin of MOND. The possible involvement of vacuum effects is also speculated on.
Prototyping and measurements for a LiFi system

We are witnessing a revolution in wireless technology where Light Fidelity (LiFi) emerges as one potential candidate. In this paper we present a LiFi prototype that allows us to verify the feasibility of deploying this technology. The prototype is based on two Spartan 6 FPGAs and uses a Light Emitting Diode (LED) to transport the information through amplitude changes of the light. The receiver uses a low dark current PIN photodiode. We describe the system design, the receiver algorithms and the measurement set-up. We present some measurements where in a Line of Sight (LOS) channel the received pulses are shown to match the transmitted ones.
Ontology Population and Enrichment: State of the Art

Ontology learning is the process of acquiring (constructing or integrating) an ontology (semi-) automatically. Being a knowledge acquisition task, it is a complex activity, which becomes even more complex in the context of the BOEMIE project, due to the management of multimedia resources and the multi-modal semantic interpretation that they require. The purpose of this chapter is to present a survey of the most relevant methods, techniques and tools used for the task of ontology learning. Adopting a practical perspective, an overview of the main activities involved in ontology learning is presented. This breakdown of the learning process is used as a basis for the comparative analysis of existing tools and approaches. The comparison is done along dimensions that emphasize the particular interests of the BOEMIE project. In this context, ontology learning in BOEMIE is treated and compared to the state of the art, explaining how BOEMIE addresses problems observed in existing systems and contributes to issues that are not frequently considered by existing approaches.
The Wikipedia Corpus

Wikipedia, the popular online encyclopedia, has in just six years grown from an adjunct to the now-defunct Nupedia to over 31 million pages and 429 million revisions in 256 languages and spawned sister projects such as Wiktionary and Wikisource.  Its articles are largely consistent in structure and organized into category hierarchies. However, the wiki method of collaborative editing creates challenges that must be addressed. Wikipedia’s accuracy is frequently questioned, and systemic bias means that quality and coverage are uneven, while even the variety of English dialects juxtaposed can sabotage the unwary with differences in semantics, diction and spelling. This paper examines Wikipedia from a research perspective, providing basic background knowledge and an understanding of its strengths and weaknesses. We also solve a technical challenge posed by the enormity of text (1.04TB for the English version) made available with a simple, easily-implemented dictionary compression algorithm that permits time-efficient random access to the data with a twenty-eight-fold reduction in size.
Biogas production: current state and perspectives

Anaerobic digestion of energy crops, residues, and wastes is of increasing interest in order to reduce the greenhouse gas emissions and to facilitate a sustainable development of energy supply. Production of biogas provides a versatile carrier of renewable energy, as methane can be used for replacement of fossil fuels in both heat and power generation and as a vehicle fuel. For biogas production, various process types are applied which can be classified in wet and dry fermentation systems. Most often applied are wet digester systems using vertical stirred tank digester with different stirrer types dependent on the origin of the feedstock. Biogas is mainly utilized in engine-based combined heat and power plants, whereas microgas turbines and fuel cells are expensive alternatives which need further development work for reducing the costs and increasing their reliability. Gas upgrading and utilization as renewable vehicle fuel or injection into the natural gas grid is of increasing interest because the gas can be used in a more efficient way. The digestate from anaerobic fermentation is a valuable fertilizer due to the increased availability of nitrogen and the better short-term fertilization effect. Anaerobic treatment minimizes the survival of pathogens which is important for using the digested residue as fertilizer. This paper reviews the current state and perspectives of biogas production, including the biochemical parameters and feedstocks which influence the efficiency and reliability of the microbial conversion and gas yield.
Weak models for insider threat detection

This paper describes the design for a content based approach to detecting insider misuse by an analyst producing reports in an environment supported by a document control system. The approach makes use of Hidden Markov Models to represent stages in the EvidenceBased Intelligence Analysis Process Model (EBIAPM). This approach is seen as a potential application for the Process Query System / Tracking and Fusion Engine (PQS/TRAFEN). Actions taken by the insider are viewed as processes that can be detected in PQS/TRAFEN. Text categorization of the content of analyst’s queries, documents accessed, and work product are used to disambiguate multiple EBIAPM processes.
Covariance Matrix Adaptation Revisited - The CMSA Evolution Strategy -

The covariance matrix adaptation evolution strategy (CMA-ES) rates among the most successful evolutionary algorithms for continuous parameter optimization. Nevertheless, it is plagued with some drawbacks like the complexity of the adaptation process and the reliance on a number of sophisticatedly constructed strategy parameter formulae for which no or little theoretical substantiation is available. Furthermore, the CMA-ES does not work well for large population sizes. In this paper, we propose an alternative – simpler – adaptation step of the covariance matrix which is closer to the ”traditional” mutative self-adaptation. We compare the newly proposed algorithm, which we term the CMSA-ES, with the CMA-ES on a number of different test functions and are able to demonstrate its superiority in particular for large population sizes.
A Deep Neural Network for Modeling Music

We propose a convolutional neural network architecture with k-max pooling layer for semantic modeling of music. The aim of a music model is to analyze and represent the semantic content of music for purposes of classification, discovery, or clustering. The k-max pooling layer is used in the network to make it possible to pool the k most active features, capturing the semantic-rich and time-varying information about music. Our network takes an input music as a sequence of audio words, where each audio word is associated with a distributed feature vector that can be fine-tuned by backpropagating errors during the training. The architecture allows us to take advantage of the better trained audio word embeddings and the deep structures to produce more robust music representations. Experiment results with two different music collections show that our neural networks achieved the best accuracy in music genre classification comparing with three state-of-art systems.
Trustless Machine Learning Contracts; Evaluating and Exchanging Machine Learning Models on the Ethereum Blockchain

Using blockchain technology, it is possible to create contracts that offer a reward in exchange for a trained machine learning model for a particular data set. This would allow users to train machine learning models for a reward in a trustless manner. The smart contract will use the blockchain to automatically validate the solution, so there would be no debate about whether the solution was correct or not. Users who submit the solutions wont have counterparty risk that they wont get paid for their work. Contracts can be created easily by anyone with a dataset, even programmatically by software agents. This creates a market where parties who are good at solving machine learning problems can directly monetize their skillset, and where any organization or software agent that has a problem to solve with AI can solicit solutions from all over the world. This will incentivize the creation of better machine learning models, and make AI more accessible to companies and software agents. A consequence of creating this market is that there will be a well defined price of GPU training for machine learning models. Crypto-currency mining also uses GPUs in many cases. We can envision a world where at any given moment, miners can choose to direct their hardware to work on whichever workload is more profitable: cryptocurrency mining, or machine learning training. 1.  Bitcoin and cryptocurrencies Bitcoin was first introduced in 2008 to create a decentralized method of storing and transferring funds from one account to another. It enforced ownership using public key cryptography. Funds are stored in various addresses, and anyone with the private key for an address would be able to transfer funds from this account. To create such a system in a decentralized fashion required innovation on how to achieve consensus between participants, which was solved using a blockchain. This created an ecosystem that enabled fast and trusted transactions between untrusted users. Bitcoin implemented a scripting language for simple tasks. This language wasnt designed to be turing complete. Over time, people wanted to implement more complicated programming tasks on blockchains. Ethereum introduced a turing-complete language to support a wider range of applications. This language was designed to utilize the decentralized nature of the blockchain. Essentially its an application layer on top of the ethereum blockchain. By having a more powerful, turing-complete programming language, it became possible to build new types of applications on top of the ethereum blockchain: from escrow systems, minting new coins, decentralized corporations, and more. The Ethereum whitepaper talks about creating decentralized marketplaces, but focuses on things like identities and reputations to facilitate these transactions.
Deep Convolutional Framelets: A General Deep Learning for Inverse Problems

Recently, deep learning approaches with various network architectures have achieved significant performance improvement over existing iterative reconstruction methods in various imaging problems. However, it is still unclear why these deep learning architectures work for specific inverse problems. Moreover, unlike the usual evolution of signal processing theory around the classical theories, the link between the deep learning and the classical signal processing approaches such as wavelet, non-local processing, compressed sensing, etc, is still not well understood, which often makes signal processors in deep troubles. To address these issues, here we show that the long-searched-for missing link is the convolutional framelet for representing a signal by convolving local and non-local bases. The convolutional framelets was originally developed to generalize the recent theory of low-rank Hankel matrix approaches, and this paper significantly extends the idea to derive a deep neural network using multi-layer convolutional framelets with perfect reconstruction (PR) under rectified linear unit (ReLU) nonlinearity. Our analysis also shows that the popular deep network components such as residual block, redundant filter channels, and concatenated ReLU (CReLU) indeed help to achieve the PR, while the pooling and unpooling layers should be augmented with multi-resolution convolutional framelets to achieve PR condition. This discovery reveals the limitations of many existing deep learning architectures for inverse problems, and leads us to propose a novel deep convolutional framelets neural network. Using numerical experiments with sparse view x-ray computed tomography (CT), we demonstrated that our deep convolution framelets network shows consistent improvement over existing deep architectures at all downsampling factors. This discovery suggests that the success of deep learning is not from a magical power of a black-box, but rather comes from the power of a novel signal representation using non-local basis combined with data-driven local basis, which is indeed a natural extension of classical signal processing theory. Index Terms Convolutional framelets, deep learning, inverse problems, ReLU, perfect reconstruction condition Correspondence to: Jong Chul Ye, Ph.D KAIST Endowed Chair Professor Department of Bio and Brain Engineering Department of Mathematical Sciences Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea Tel: +82-42-350-4320  jong.ye@kaist.ac.kr J.C. Ye and Y. S. Han are with the Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea (e-mail: {jong.ye,hanyoseob}@kaist.ac.kr). ar X iv :1 70 7. 00 37 2v 1 [ st at .M L ] 3 J ul 2 01 7
Variable stiffness actuators: The user's point of view

Since their introduction in the early years of this century, Variable Stiffness Actuators (VSA) witnessed a sustain ed growth of interest in the research community, as shown by the growing number of publications. While many consider VSA very interesting for applications, one of the factors hindering their further diffusion is the relatively new conceptual structure of this technology. In choosing a VSA for his/her application, the educated practitioner, used to choosing robot actuators based on standardized procedures and uniformly presented data, would be confronted with an inhomogeneous and rather disorganized mass of information coming mostly from scientific publications. In this paper, the authors consider how the design procedures and data presentation of a generic VS actuator could be organized so as to minimize the engineer’s effort in choosing the actuator type and size that would best fit the application needs. The reader is led through the list of the most important parameters that will determine the ultimate performance of his/her VSA robot, and influence both the mechanical design and the controller shape. This set of parameters extends the description of a traditional electric actuator with quantities describing the capability of the VSA to change its output stiffness. As an instrument for the end-user, the VSA datasheet is intended to be a compact, self-contained description of an actuator that summarizes all the salient characteristics that the user must be aware of when choosing a device for his/her application. At the end some example of compiled VSA datasheets are reported, as well as a few examples of actuator selection procedures.
턴이 바뀌지 않는 경우가 있는 게임에서의 Monte Carlo Tree Search를 이용한 게임 전략

representation of the game. It was programmed in LI SP. Further use of abstraction was also studied by Friedenbach (1980). The combination of s earch, heuristics, and expert systems led to the best programs in the eighties. At the end of the eighties a new type of Go programs emerged. Th ese programs made an intensive use of pattern recognition. This approach was dis cussed in detail by Boon (1990). In the following years, different AI techniques, such as Rei nforcement Learning (Schraudolph, Dayan, and Sejnowski, 1993), Monte Carlo (Br ügmann, 1993), and Neural Networks (Richards, Moriarty, and Miikkulainen, 1998), were tested in Go. However, programs applying these techniques were not able to surpass the level of the best programs. The combination of search, heuristics, expert systems, and pattern r ecognition remained the winning methodology. Brügmann (1993) proposed to use Monte-Carlo evaluations as an lter ative technique for Computer Go. His idea did not got many followers in the 199 0s. In the following decade, Bouzy and Helmstetter (2003) and Bouzy (2006) combined Mont e-Carlo evaluations and search in Indigo. The program won three bronze medals at the O lympiads of 2004, 2005, and 2006. Their pioneering research inspired the developme nt of Monte-Carlo Tree Search (MCTS) (Coulom, 2006; Kocsis and Szepesv ári, 2006; Chaslot et al., 2006a). Since 2007, MCTS programs are dominating the Computer Go field. MCTS will be explained in the next chapter. 2.6 Go Programs MANGO and MOGO In this subsection, we briefly describe the Go programs M ANGO and MOGO that we use for the experiments in the thesis. Their performance in vari ous tournaments is discussed as
ToriLLE: Learning Environment for Hand-to-Hand Combat

We present Toribash Learning Environment (ToriLLE), a learning environment for machine learning agents based on the video game Toribash. Toribash is a MuJoCo-like environment of two humanoid character fighting each other hand-tohand, controlled by changing actuation modes of the joints. Competitive nature of Toribash as well its focused domain provide a platform for evaluating self-play methods, and evaluating machine learning agents against human players. In this paper we describe the environment with ToriLLE’s capabilities and limitations, and experimentally show its applicability as a learning environment. The source code of the environment and conducted experiments can be found at
Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models

Volatility plays crucial roles in financial markets, such as in derivative pricing, portfolio risk management, and hedging strategies. Therefore, accurate prediction of volatility is critical. We propose a new hybrid long short-term memory (LSTM) model to forecast stock price volatility that combines the LSTM model with various generalized autoregressive conditional heteroscedasticity (GARCH)-type models. We use KOSPI 200 index data to discover proposed hybrid models that combine an LSTM with one to three GARCH-type models. In addition, we compare their performance with existing methodologies by analyzing single models, such as the GARCH, exponential GARCH, exponentially weighted moving average, a deep feedforward neural network (DFN), and the LSTM, as well as the hybrid DFN models combining a DFN with one GARCH-type model. Their performance is compared with that of the proposed hybrid LSTM models. We discover that GEW-LSTM, a proposed hybrid model combining the LSTM model with three GARCH-type models, has the lowest prediction errors in terms of mean absolute error (MAE), mean squared error (MSE), heteroscedasticity adjusted MAE (HMAE), and heteroscedasticity adjusted MSE (HMSE). The MAE of GEW-LSTM is 0.0107, which is 37.2% less than that of the E-DFN (0.017), the model combining EGARCH and DFN and the best model among those existing. In addition, the GEWLSTM has 57.3%, 24.7%, and 48% smaller MSE, HMAE, and HMSE, respectively. The first contribution of this study is its hybrid LSTM model that combines excellent sequential pattern learning with improved prediction performance in stock market volatility. Second, our proposed model markedly enhances prediction performance of the existing literature by combining a neural network model with multiple econometric models rather than only a single econometric model. Finally, the proposed methodology can be extended to various fields as an integrated model combining time-series and neural network models as well as forecasting stock market volatility.
Self-Attention Generative Adversarial Networks

In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks. Traditional convolutional GANs generate high-resolution details as a function of only spatially local points in lower-resolution feature maps. In SAGAN, details can be generated using cues from all feature locations. Moreover, the discriminator can check that highly detailed features in distant portions of the image are consistent with each other. Furthermore, recent work has shown that generator conditioning affects GAN performance. Leveraging this insight, we apply spectral normalization to the GAN generator and find that this improves training dynamics. The proposed SAGAN achieves the state-ofthe-art results, boosting the best published Inception score from 36.8 to 52.52 and reducing Fréchet Inception distance from 27.62 to 18.65 on the challenging ImageNet dataset. Visualization of the attention layers shows that the generator leverages neighborhoods that correspond to object shapes rather than local regions of fixed shape.