nils-herrmann/clean_scirepeval huggingface dataset:
Theoretical Robopsychology: Samu Has Learned Turing Machines

From the point of view of a programmer, the robopsychology is a synonym for the activity is done by developers to implement their machine learning applications. This robopsychological approach raises some fundamental theoretical questions of machine learning. Our discussion of these questions is constrained to Turing machines. Alan Turing had given an algorithm (aka the Turing Machine) to describe algorithms. If it has been applied to describe itself then this brings us to Turing’s notion of the universal machine. In the present paper, we investigate algorithms to write algorithms. From a pedagogy point of view, this way of writing programs can be considered as a combination of learning by listening and learning by doing due to it is based on applying agent technology and machine learning. As the main result we introduce the problem of learning and then we show that it cannot easily be handled in reality therefore it is reasonable to use machine learning algorithm for learning Turing machines.
Semantic Complex Event Processing over End-to-End Data Flows

Emerging Complex Event Processing (CEP) applications in cyber physical systems like Smart Power Grids present novel challenges for end-to-end analysis over events, flowing from heterogeneous information sources to persistent knowledge repositories. CEP for these applications must support two distinctive features – easy specification patterns over diverse information streams, and integrated pattern detection over realtime and historical events. Existing work on CEP has been limited to relational query patterns, and engines that match events arriving after the query has been registered. We propose SCEPter, a semantic complex event processing framework which uniformly processes queries over continuous and archived events. SCEPteris built around an existing CEP engine with innovative support for semantic event pattern specification and allows their seamless detection over past, present and future events. Specifically, we describe a unified semantic query model that can operate over data flowing through event streams to event repositories. Compile-time and runtime semantic patterns are distinguished and addressed separately for efficiency. Query rewriting is examined and analyzed in the context of temporal boundaries that exist between event streams and their repository to avoid duplicate or missing results. The design and prototype implementation of SCEPterare analyzed using latency and throughput metrics for scenarios from the Smart Grid domain.
User interpretations of future information system use: A snapshot with technological frames

Integrated information systems for managing patient data transform the nature of hospital work to the extent that the work practices, the responsibilities, even the professional identities are likely to undergo major changes. Therefore, during the organizational implementation of the IS, attention should be paid to the future users and how they understand and see what is going on. Here the focus is on these interpretation processes, analyzed as technological frames. That is, people develop different assumptions, expectations and knowledge concerning new technology. During this sense-making process they build their idea of that technology, its technological frame. We analyzed the pre-implementation frames that could be discerned in 24 interviews of hospital personnel. Main influences on the frames in this case were the work role in the organization, knowledge about the new system, and attitudes toward the old systems. The social context appeared to have a significant influence in the users' interpretation processes and the frames seemed to be congruent within one group. So far, the incongruence between groups appeared to have caused no major problems for the implementation.
Policymakers ’ Horizon and Economic Reforms ∗

This paper investigates the relation between policymakers’ term length and their willingness to support economic reforms. We describe a model in which office-motivated legislators have mandates of different length and consider the introduction of a trade liberalization reform, which gives rise to distributional effects that only become known over time. We show that legislators’ voting behavior depends on their political horizon and on the trade policy interests of their constituencies. In particular, legislators with shorter mandates are less likely to support the reform, if they represent import-competing constituencies. To assess the validity of these results, we examine the determinants of all votes on major U.S. trade liberalization bills cast between 1973 and 2005. We exploit the particular features of the U.S. Congress, in which House and Senate representatives have respectively two-year and six-year mandates, with one-third of the Senate being up for election every two years. We find that members of the House are generally more protectionist than members of the Senate. However, this difference in voting behavior disappears for senators in the last two years of their mandate and for representatives of export constituencies.
Tables, Counters, and Shelves: Semantic Mapping of Surfaces in 3D

Abslracl Semantic mapping aims to create maps that include meaningful features, both to robots nnd humans. We prescnt :10 extens ion to our feature based mapping technique that includes information about the locations of horizontl.lJ surfaces such as tables, shelves, or counters in the map. The surfaces a rc detected in 3D point clouds, the locations of which arc optimized by our SLAM algorithm. The resulting scans of surfaces :lrc then analyzed to segment them into distinct surfaces, which may include measurements of a single surface across multiple scans. Preliminary rl'Sults arc presented in the form of a feature based map augmented with a sct of 3D point clouds in a consistent global map frame that represent all detected surfaces within the mapped area.
Grasp recognition in virtual reality for robot pregrasp planning by demonstration

This paper describes a virtual reality based programming by demonstration system for grasp recognition in manipulation tasks and robot pregrasp planning. The system classifies the human hand postures taking advantage of virtual grasping and information about the contact points and normals computed in the virtual reality environment. A pregrasp planning algorithm mimicking the human hand motion is also proposed. Reconstruction of human hand trajectories, approaching the objects in the environment, is based on NURBS curves and a data smoothing algorithm. Some experiments involving grasp classification and pregrasp planning, while avoiding obstacles in the workspace, show the viability and effectiveness of the approach
Learning compound multi-step controllers under unknown dynamics

Applications of reinforcement learning for robotic manipulation often assume an episodic setting. However, controllers trained with reinforcement learning are often situated in the context of a more complex compound task, where multiple controllers might be invoked in sequence to accomplish a higher-level goal. Furthermore, training such controllers typically requires resetting the environment between episodes, which is typically handled manually. We describe an approach for training chains of controllers with reinforcement learning. This requires taking into account the state distributions induced by preceding controllers in the chain, as well as automatically training reset controllers that can reset the task between episodes. The initial state of each controller is determined by the controller that precedes it, resulting in a non-stationary learning problem. We demonstrate that a recently developed method that optimizes linear-Gaussian controllers under learned local linear models can tackle this sort of non-stationary problem, and that training controllers concurrently with a corresponding reset controller only minimally increases training time. We also demonstrate this method on a complex tool use task that consists of seven stages and requires using a toy wrench to screw in a bolt. This compound task requires grasping and handling complex contact dynamics. After training, the controllers can execute the entire task quickly and efficiently. Finally, we show that this method can be combined with guided policy search to automatically train nonlinear neural network controllers for a grasping task with considerable variation in target position.
Hardware Implementations of SVM on FPGA: A State-of-the-Art Review of Current Practice

The Support Vector Machine (SVM) is a common machine learning tool that is widely used because of its high classification accuracy. Implementing SVM for embedded real-time applications is very challenging because of the intensive computations required. This increases the attractiveness of implementing SVM on hardware platforms for reaching high performance computing with low cost and power consumption. This paper provides the first comprehensive survey of current literature (2010-2015) of different hardware implementations of SVM classifier on Field-Programmable Gate Array (FPGA). A classification of existing techniques is presented, along with a critical analysis and discussion. A challenging trade-off between meeting embedded real-time systems constraints and high classification accuracy has been observed. Finally, some key future research directions are suggested.
Fenestration in the P 1 Segment of the Posterior Cerebral Artery

The posterior cerebral artery (PCA) has been noted in literature to have anatomical variations, specifically fenestration. Cerebral arteries with fenestrations are uncommon, especially when associated with other vascular pathologies. We report a case here of fenestrations within the P1 segment of the right PCA associated with a right middle cerebral artery (MCA) aneurysm in an elder adult male who presented with a new onset of headaches. The patient was treated with vascular clipping of the MCA and has recovered well. Identifying anatomical variations with appropriate imaging is of particular importance in neuro-interventional procedures as it may have an impact on the procedure itself and consequently post-interventional outcomes.  Neurology, Neurosurgery
User Preference Learning for Online Social Recommendation

A social recommendation system has attracted a lot of attention recently in the research communities of information retrieval, machine learning, and data mining. Traditional social recommendation algorithms are often based on batch machine learning methods which suffer from several critical limitations, e.g., extremely expensive model retraining cost whenever new user ratings arrive, unable to capture the change of user preferences over time. Therefore, it is important to make social recommendation system suitable for real-world online applications where data often arrives sequentially and user preferences may change dynamically and rapidly. In this paper, we present a new framework of online social recommendation from the viewpoint of online graph regularized user preference learning (OGRPL), which incorporates both collaborative user-item relationship as well as item content features into an unified preference learning process. We further develop an efficient iterative procedure, OGRPL-FW which utilizes the Frank-Wolfe algorithm, to solve the proposed online optimization problem. We conduct extensive experiments on several large-scale datasets, in which the encouraging results demonstrate that the proposed algorithms obtain significantly lower errors (in terms of both RMSE and MAE) than the state-of-the-art online recommendation methods when receiving the same amount of training data in the online learning process.
Towards computer game studies

It is relatively stress-free to write about computer games as nothing too much has been said yet, and almost anything goes. The situation is pretty much the same when it comes to writing about games and gaming in general. The sad fact with alarming cumulative consequences is that they are undertheorized; there are Huizinga, Caillois and Ehrmann of course, and libraries full of board game studies,in addition to game theory and bits and pieces of philosophy—most notably those of Wittgenstein— but they won’t get us very far with computer games. So if there already is or soon will be a legitimate field for computer game studies, this field is also very open to intrusions and colonisations from the already organized scholarly tribes. Resisting and beating them is the goal of our first survival game in this paper, as what these emerging studies need is independence, or at least relative independence.
A planar parallel 3-DOF cable-driven haptic interface

In this paper, a cable-driven planar parallel haptic interface is presented. First, the velocity equations are derived and the forces in the cables are obtained by the principle of virtual work. Then, an analysis of the wrench-closure workspace is performed and a geometric arrangement of the cables is proposed. Control issues are then discussed and a control scheme is presented. The calibration of the attachment points is also discussed. Finally, the prototype is described and experimental results are provided.
Partial deletion 9p syndrome in Malaysian children.

We report the first two Malaysian children with partial deletion 9p syndrome, a well delineated but rare clinical entity. Both patients had trigonocephaly, arching eyebrows, anteverted nares, long philtrum, abnormal ear lobules, congenital heart lesions and digital anomalies. In addition, the first patient had underdeveloped female genitalia and anterior anus. The second patient had hypocalcaemia and high arched palate and was initially diagnosed with DiGeorge syndrome. Chromosomal analysis revealed a partial deletion at the short arm of chromosome 9. Karyotyping should be performed in patients with craniostenosis and multiple abnormalities as an early syndromic diagnosis confers prognostic, counselling and management implications.
Theoretical Analyses and Design of Circular Array to Generate Orbital Angular Momentum

Uniform circular array (UCA) is a pioneering approach to generate radio twisted beams carrying orbital angular momentum (OAM). However, faulty antenna elements in a UCA could render the array into an imperfect UCA (IUCA), consequently creating distortion for OAM-based communication or radar systems. To analyze the effects of an IUCA on generating OAM, elementary wave function and wave transformation are derived to obtain field equations of a general circular array. Further derivation of IUCA shows that the field of an IUCA is a superposition of OAM modes, which is caused by polluted orthogonalities of OAM modes. To achieve pure OAM modes, the orthogonal matching pursuit algorithm, the convex optimization tool, and the analytical solution are employed to design a feeding network to retrieve the orthogonal property at the cost of strong gain loss. Then, the reduced constraints method is proposed for the IUCA to achieve high-purity and low-loss OAM mode. Finally, four circular arrays with different shapes are explored for limited installation space to generate twisted beams.
A Data Model for Supporting On-Line Analytical Processing

A database application, called “on-line analytical processing” (or OLAP) and aimed at providing business intelligence through on-line multidimensional data analysis, has become increasingly important due to the existence of huge amounts of on-line data. This paper formalizes a multidimensional data (MDD) model for OLAP, and develops an algebraic query language called grouping algebra. The basic component of the MDD model is a multidimensional cube, consisting of a number of relations (called dimensions) and for each combination of tuples (called a coordinate), one from each dimension, there is an associated data value. Each dimension is viewed as a basic grouping, i.e., each tuple in the dimension comesponds to the group consisting of all the coordinates that contain this tuple. In order to express user queries, relational algebra expressions are then extended to those on basic groupings for obtaining complex groupings, including orderoriented groupings (for expressing, e.g., cumulative sum). The paper then considers the environment where the multidimensional cubes are materialized views derived from base data situated at remote sites. A multidimensional cube algebra is introduced in order to facilitate the data derivation. The putpose of the paper is to establish a formal foundation for further research regarding databasesupport for OLAP applications.
From ERPs to MVPA Using the Amsterdam Decoding and Modeling Toolbox (ADAM)

In recent years, time-resolved multivariate pattern analysis (MVPA) has gained much popularity in the analysis of electroencephalography (EEG) and magnetoencephalography (MEG) data. However, MVPA may appear daunting to those who have been applying traditional analyses using event-related potentials (ERPs) or event-related fields (ERFs). To ease this transition, we recently developed the Amsterdam Decoding and Modeling (ADAM) toolbox in MATLAB. ADAM is an entry-level toolbox that allows a direct comparison of ERP/ERF results to MVPA results using any dataset in standard EEGLAB or Fieldtrip format. The toolbox performs and visualizes multiple-comparison corrected group decoding and forward encoding results in a variety of ways, such as classifier performance across time, temporal generalization (time-by-time) matrices of classifier performance, channel tuning functions (CTFs) and topographical maps of (forward-transformed) classifier weights. All analyses can be performed directly on raw data or can be preceded by a time-frequency decomposition of the data in which case the analyses are performed separately on different frequency bands. The figures ADAM produces are publication-ready. In the current manuscript, we provide a cookbook in which we apply a decoding analysis to a publicly available MEG/EEG dataset involving the perception of famous, non-famous and scrambled faces. The manuscript covers the steps involved in single subject analysis and shows how to perform and visualize a subsequent group-level statistical analysis. The processing pipeline covers computation and visualization of group ERPs, ERP difference waves, as well as MVPA decoding results. It ends with a comparison of the differences and similarities between EEG and MEG decoding results. The manuscript has a level of description that allows application of these analyses to any dataset in EEGLAB or Fieldtrip format.
Two simple full-text indexes based on the suffix array

We propose two suffix array inspired full-text indexes. One, called SAhash, augments the suffix array with a hash table to speed up pattern searches due to significantly narrowed search interval before the binary search phase. The other, called FBCSA, is a compact data structure, similar to Mäkinen’s compact suffix array, but working on fixed sized blocks. Experiments on the Pizza & Chili 200MB datasets show that SA-hash is about 2–3 times faster in pattern searches (counts) than the standard suffix array, for the price of requiring 0.2n− 1.1n bytes of extra space, where n is the text length, and setting a minimum pattern length. FBCSA is relatively fast in single cell accesses (a few times faster than related indexes at about the same or better compression), but not competitive if many consecutive cells are to be extracted. Still, for the task of extracting, e.g., 10 successive cells its time-space relation remains attractive.
Image-based procedural modeling of facades

This paper describes algorithms to automatically derive 3D models of high visual quality from single facade images of arbitrary resolutions. We combine the procedural modeling pipeline of shape grammars with image analysis to derive a meaningful hierarchical facade subdivision. Our system gives rise to three exciting applications: urban reconstruction based on low resolution oblique aerial imagery, reconstruction of facades based on higher resolution ground-based imagery, and the automatic derivation of shape grammar rules from facade images to build a rule base for procedural modeling technology.
A nested microphone array for broadband audio signal processing

The application of a recently developed broadband beamformer to distinguish audio signals received from different directions is experimentally tested. The beamformer combines spatial and temporal subsampling using a nested array and multirate techniques which leads to the same region of support in the frequency domain for all subbands. This allows using the same beamformer for all subbands. The experimental set-up is presented and the recorded signals are analyzed. Results indicate that the proposed approach can be used to distinguish plane waves propagating with different direction of arrivals.
Vehicle Speed Prediction by Two-Level Data Driven Models in Vehicular Networks

Vehicle speed prediction provides important information for many intelligent vehicular and transportation applications. Accurate on-road vehicle speed prediction is challenging, because an individual vehicle speed is affected by many factors, e.g., the traffic condition, vehicle type, and driver’s behavior, in either deterministic or stochastic way. This paper proposes a novel data-driven vehicle speed prediction method in the context of vehicular networks, in which the real-time traffic information is accessible and utilized for vehicle speed prediction. It first predicts the average traffic speeds of road segments by using neural network models based on historical traffic data. Hidden Markov models (HMMs) are then utilized to present the statistical relationship between individual vehicle speeds and the traffic speed. Prediction for individual vehicle speeds is realized by applying the forward–backward algorithm on HMMs. To evaluate the prediction performance, simulations are set up in the SUMO microscopic traffic simulator with the application of a real Luxembourg motorway network and traffic count data. The vehicle speed prediction result shows that our proposed method outperforms other ones in terms of prediction accuracy.
The mesorectum in rectal cancer surgery--the clue to pelvic recurrence?

Five cases are described where minute foci of adenocarcinoma have been demonstrated in the mesorectum several centimetres distal to the apparent lower edge of a rectal cancer. In 2 of these there was no other evidence of lymphatic spread of the tumour. In orthodox anterior resection much of this tissue remains in the pelvis, and its is suggested that these foci might lead to suture-line or pelvic recurrence. Total excision of the mesorectum has, therefore, been carried out as a part of over 100 consecutive anterior resections. Fifty of these, which were classified as 'curative' or 'conceivably curative' operations, have now been followed for over 2 years with no pelvic or staple-line recurrence.
Deep-Space Communications and Coding : A Marriage Made in Heaven

It is almost a quarter of a century since the launch in 1968 of NASA's Pioneer 9 spacecraft on the first mission into deep-space that relied on coding to enhance communications on the critical downlink channel. The channel code used was a binary convolutional code that was decoded with sequential decoding--we will have much to say about this code in the sequel.] The success of this channel coding system had repercussions that extended far beyond NASA's space program. It is no exaggeration to say that the Pioneer 9 mission provided communications engineers with the first incontrovertible demonstration of the practical utility of channel coding techniques and thereby paved the way for the successful application of coding to many other channels.