nils-herrmann/clean_scirepeval huggingface dataset:
Comparing textual descriptions to process models - The automatic detection of inconsistencies

Many organizations maintain textual process descriptions alongside graphical process models. The purpose is to make process information accessible to various stakeholders, including those who are not familiar with reading and interpreting the complex execution logic of process models. Despite this merit, there is a clear risk that model and text become misaligned when changes are not applied to both descriptions consistently. For organizations with hundreds of di↵erent processes, the e↵ort required to identify and clear up such conflicts is considerable. To support organizations in keeping their process descriptions consistent, we present an approach to automatically identify inconsistencies between a process model and a corresponding textual description. Our approach detects cases where the two process representations describe activities in di↵erent orders and detect process model activities not contained in the textual description. A quantitative evaluation with 53 real-life model-text pairs demonstrates that our approach accurately identifies inconsistencies between model and text.
A kinetic framework for computational geometry

We present a new framework for geometric computing in which planar curves are formed by the motions of objects that have both position and orientation. Informally, consider the motion of a car: at each instant the car is positioned at some point in the plane, and oriented with its hood facing in some direction. Together, the position and the orientation specify a state. In the new framework, curves and polygons are paths in this state space satisfying the constraint that wherever the tangent of the position component of the curve is defined, the orientation must be either the same as that tangent, or the opposite. In other words, the car may move either forwards or backwards, but it may not skid sideways. To distinguish these structures from classical curves and polygons, we call them tracings. Several important concepts can be defined fOf tracings, some familiar from differential topology, but others apparently new. These include the notions of winding number, degree, and sweep number. A general construction, known as fiber product, is used to define various operations on tracings, including convolution and multiplication. Each fiber product involves forming all pairs of states, one from each tracing, that satisfy a certain constraint Using these ideas, it is possible to recast large parts ofcomputational geometry, such as convexity, in a new and advantageous light This applies to proofs of theorems, as well as to descriptions of algorithms. Besides allowing us to recast old material, this kinetic approach has also led to a number of new algorithms that improve on the previously known bounds. For example, one algorithm computes the distance between two convex polygons in logarithmic time, while another tests if two convex polygons can disjointIy fit into a third one in linear time (no rotations allowed). A final attraction of the kinetic approach is that, after appropriately extending the plane into a new manifold, the two-sided plane, we can define a formal duality between points and lines that maintains the sense of left and right Informal notions of such a duality had previously been used in computational geometry, but now for the first time we can say in a precise sense that, for example, an algorithm for computing the convex hull of n points is also an algorithm for computing the intersection of n half-planes. It may be of interest to note that the origins of …
Predictive coding under the free-energy principle.

This paper considers prediction and perceptual categorization as an inference problem that is solved by the brain. We assume that the brain models the world as a hierarchy or cascade of dynamical systems that encode causal structure in the sensorium. Perception is equated with the optimization or inversion of these internal models, to explain sensory data. Given a model of how sensory data are generated, we can invoke a generic approach to model inversion, based on a free energy bound on the model's evidence. The ensuing free-energy formulation furnishes equations that prescribe the process of recognition, i.e. the dynamics of neuronal activity that represent the causes of sensory input. Here, we focus on a very general model, whose hierarchical and dynamical structure enables simulated brains to recognize and predict trajectories or sequences of sensory states. We first review hierarchical dynamical models and their inversion. We then show that the brain has the necessary infrastructure to implement this inversion and illustrate this point using synthetic birds that can recognize and categorize birdsongs.
Design , Analysis and Performance of a Rotary Wing MAV

An initial design concept for a micro-coaxial rotorcraft using custom manufacturing techniques and commercial off-the-shelf components is discussed in this paper. Issues associated with the feasibility of achieving hover and fully functional flight control at small scale for a coaxial rotor configuration are addressed. Results from this initial feasibility study suggest that it is possible to develop a small scale coaxial micro rotorcraft weighing approximately 100 grams, and that available moments are appropriate for roll, yaw and lateral control. A prototype vehicle was built and its rotors were tested in a custom hover stand used to measure Thrust and power. The radio controlled vehicle was flown untethered with its own power source and exhibited good flight stability and control dynamics. The best achievable rotor performance was measured to be 42%.
Emotion, plasticity, context, and regulation: perspectives from affective neuroscience.

The authors present an overview of the neural bases of emotion. They underscore the role of the prefrontal cortex (PFC) and amygdala in 2 broad approach- and withdrawal-related emotion systems. Components and measures of affective style are identified. Emphasis is given to affective chronometry and a role for the PFC in this process is proposed. Plasticity in the central circuitry of emotion is considered, and implications of data showing experience-induced changes in the hippocampus for understanding psychopathology and stress-related symptoms are discussed. Two key forms of affective plasticity are described--context and regulation. A role for the hippocampus in context-dependent normal and dysfunctional emotional responding is proposed. Finally, implications of these data for understanding the impact on neural circuitry of interventions to promote positive affect and on mechanisms that govern health and disease are considered.
Evaluating Visualizations: Do Expert Reviews Work?

Visualization research generates beautiful images and impressive interactive systems. Such developments make fascinating demos, but how do we know if they are actually useful for real people doing real tasks? If the interaction is awkward or we have not carefully considered users’ needs, even the most well intentioned and technically developed visual display will be ineffective. Emphasis on evaluating visualizations is growing. User studies of perceptual phenomena related to visualization and comparisons of visualization tools are becoming hot topics in the visualization literature [7][13]. But along the way, researchers are discovering that user study design is rarely straightforward.
Tuned Models of Peer Assessment in MOOCs

In massive open-access online courses (MOOCs), peer grading serves as a critical tool for scaling the grading of complex, open-ended assignments to courses with tens or hundreds of thousands of students. But despite promising initial trials, it does not always deliver accurate results compared to human experts. In this paper, we develop algorithms for estimating and correcting for grader biases and reliabilities, showing significant improvement in peer grading accuracy on real data with 63,199 peer grades from Coursera’s HCI course offerings — the largest peer grading networks analysed to date. We relate grader biases and reliabilities to other student factors such as engagement, performance as well as commenting style. We also show that our model can lead to more intelligent assignment of graders to gradees.
Who's Afraid of Itten: Using the Art Theory of Color Combination to Analyze Emotions in Abstract Paintings

Color plays an essential role in everyday life and is one of the most important visual cues in human perception. In abstract art, color is one of the essential means to convey the artist's intention and to affect the viewer emotionally. However, colors are rarely experienced in isolation, rather, they are usually presented together with other colors. In fact, the expressive properties of two-color combinations have been extensively studied by artists. It is intriguing to try to understand how color combinations in abstract paintings might affect the viewer emotionally, and to investigate if a computer algorithm can learn this mechanism.
 In this work, we propose a novel computational approach able to analyze the color combinations in abstract paintings and use this information to infer whether a painting will evoke positive or negative emotions in an observer. We exploit art theory concepts to design our features and the learning algorithm. To make use of the color-group information, we propose inferring the emotions elicited by paintings based on the sparse group lasso approach. Our results show that a relative improvement of between 6% and 8% can be achieved in this way. Finally, as an application, we employ our method to generate Mondrian-like paintings and do a prospective user study to evaluate the ability of our method as an automatic tool for generating abstract paintings able to elicit positive and negative emotional responses in people.
Novel Video Prediction for Large-scale Scene using Optical Flow

Making predictions of future frames is a critical challenge in autonomous driving research. Most of the existing methods for video prediction attempt to generate future frames in simple and fixed scenes. In this paper, we propose a novel and effective optical flow conditioned method for the task of video prediction with an application to complex urban scenes. In contrast with previous work, the prediction model only requires video sequences and optical flow sequences for training and testing. Our method uses the rich spatial-temporal features in video sequences. The method takes advantage of the motion information extracting from optical flow maps between neighbor images as well as previous images. Empirical evaluations on the KITTI dataset and the Cityscapes dataset demonstrate the effectiveness of our method.
Intuition, Insight, Imagination and Creativity

Can computers have intuition and insights, and be creative? Neurocognitive models inspired by the putative processes in the brain show that these mysterious features are a consequence of information processing in complex networks. Intuition is manifested in categorization based on evaluation of similarity, when decision borders are too complex to be reduced to logical rules. It is also manifested in heuristic reasoning based on partial observations, where network activity selects only those paths that may lead to solution, excluding all bad moves. Insight results from reasoning at the higher, non-verbal level of abstraction that comes from involvement of the right hemisphere networks forming large "linguistic receptive fields." Three factors are essential for creativity in invention of novel words: knowledge of word morphology captured in network connections, imagination constrained by this knowledge, and filtering of results that selects the most interesting novel words. These principles have been implemented using a simple correlation-based algorithm for auto-associative memory. Results are surprisingly similar to those created by humans.
Design and Experimental Results of a 300-kHz Synthetic Aperture Sonar Optimized for Shallow-Water Operations

The design of a shallow-water synthetic aperture sonar (SAS) requires an understanding of key system and environmental issues. The main factors that limit SAS performance are as follows: micronavigation accuracy, where micronavigation is defined as the problem of estimating the acoustic path lengths to allow the focusing of the aperture; multipath effects; and target view angle changes. All of them can degrade shadow classification performance. Micronavigation accuracy is successfully addressed by the gyrostabilized displaced phase center antenna technique, which combines data-driven motion estimates with external attitude sensors. Multipath effects in shallow water are effectively countered by narrow vertical beams. Shadow blur induced by view angle changes is mitigated by increasing the center frequency, to reduce the SAS integration length while still maintaining the desired resolution and by designing the system with a minimum grazing angle of about 6deg to reduce shadow length. The combination of these factors led to the choice of a 300-kHz center frequency and of a multipath mitigation scheme that uses multiple vertical beams. Experimental results obtained with a sonar incorporating these features have produced SAS images with 1.6 cm times 5 cm resolution in range times cross-range and shadow contrast in excess of 5 dB, at ranges of up to 170 m in 20 m of water.
Millimeter-Wave MIMO Prototype: Measurements and Experimental Results

Millimeter-wave MIMO systems are one of the candidate schemes for 5G wireless standardization efforts. In this context, the main contributions of this article are three-fold. First, we describe parallel sets of measurements at identical transmit-receive location pairs with 2.9, 29 and 61 GHz carrier frequencies in indoor office, shopping mall, and outdoor settings. These measurements provide insights on propagation, blockage and material penetration losses, and the key elements necessary in system design to make mm-Wave systems viable in practice. Second, one of these elements is hybrid beamforming necessary for better link margins by reaping the array gain with large antenna dimensions. From the class of fully-flexible hybrid beamformers, we describe a robust class of directional beamformers toward meeting the high data-rate requirements of mm-Wave systems. Third, leveraging these design insights, we then describe an experimental prototype system at 28 GHz that realizes high data rates on both the downlink and uplink and robustly maintains these rates in outdoor and indoor mobility scenarios. In addition to maintaining large signal constellation sizes in spite of radio frequency challenges, this prototype leverages the directional nature of the mm-Wave channel to perform seamless beam switching and handover across mm-Wave base stations, thereby overcoming the path losses in non-line-of-sight links and blockages encountered at mm-Wave frequencies.
A Penalty-Function Approach for Pruning Feedforward Neural Networks

This article proposes the use of a penalty function for pruning feedforward neural network by weight elimination. The penalty function proposed consists of two terms. The first term is to discourage the use of unnecessary connections, and the second term is to prevent the weights of the connections from taking excessively large values. Simple criteria for eliminating weights from the network are also given. The effectiveness of this penalty function is tested on three well-known problems: the contiguity problem, the parity problems, and the monks problems. The resulting pruned networks obtained for many of these problems have fewer connections than previously reported in the literature.
Exploiting geographic dependencies for real estate appraisal: a mutual perspective of ranking and clustering

It is traditionally a challenge for home buyers to understand, compare and contrast the investment values of real estates. While a number of estate appraisal methods have been developed to value real property, the performances of these methods have been limited by the traditional data sources for estate appraisal. However, with the development of new ways of collecting estate-related mobile data, there is a potential to leverage geographic dependencies of estates for enhancing estate appraisal. Indeed, the geographic dependencies of the value of an estate can be from the characteristics of its own neighborhood (individual), the values of its nearby estates (peer), and the prosperity of the affiliated latent business area (zone). To this end, in this paper, we propose a geographic method, named ClusRanking, for estate appraisal by leveraging the mutual enforcement of ranking and clustering power. ClusRanking is able to exploit geographic individual, peer, and zone dependencies in a probabilistic ranking model. Specifically, we first extract the geographic utility of estates from geography data, estimate the neighborhood popularity of estates by mining taxicab trajectory data, and model the influence of latent business areas via ClusRanking. Also, we use a linear model to fuse these three influential factors and predict estate investment values. Moreover, we simultaneously consider individual, peer and zone dependencies, and derive an estate-specific ranking likelihood as the objective function. Finally, we conduct a comprehensive evaluation with real-world estate related data, and the experimental results demonstrate the effectiveness of our method.
Combining routing and traffic data for detection of IP forwarding anomalies

IP forwarding anomalies, triggered by equipment failures, implementation bugs, or configuration errors, can significantly disrupt and degrade network service. Robust and reliable detection of such anomalies is essential to rapid problem diagnosis, problem mitigation, and repair. We propose a simple, robust method that integrates routing and traffic data streams to reliably detect forwarding anomalies. The overall method is scalable, automated and self-training. We find this technique effectively identifies forwarding anomalies, while avoiding the high false alarms rate that would otherwise result if either stream were used unilaterally.
Investor sentiment and the cross-section of stock returns

We examine how investor sentiment affects the cross-section of stock returns. Theory predicts that a broad wave of sentiment will disproportionately affect stocks whose valuations are highly subjective and are difficult to arbitrage. We test this prediction by studying how the cross-section of subsequent stock returns varies with proxies for beginning-of-period investor sentiment. When sentiment is low, subsequent returns are relatively high on smaller stocks, high volatility stocks, unprofitable stocks, non-dividend-paying stocks, extreme-growth stocks, and distressed stocks, consistent with an initial underpricing of these stocks. When sentiment is high, on the other hand, these patterns attenuate or fully reverse. The results are consistent with predictions and appear unlikely to reflect an alternative explanation based on compensation for systematic risk. ∗ We thank Ned Elton, Xavier Gabaix, Owen Lamont, Martin Lettau, Anthony Lynch, Sheridan Titman, and Jeremy Stein for helpful comments, as well as participants of seminars at Baruch College, Chicago Quantitative Alliance, the Federal Reserve Bank of New York, Harvard University, Indiana University, NBER, Norwegian School of Economics and Business, Norwegian School of Management, University of Amsterdam, University of British Columbia, University of Illinois, University of Texas, and University of Wisconsin. We gratefully acknowledge financial support from the Q Group (Baker and Wurgler) and the Division of Research of the Harvard Business School (Baker).
Development and validation of blue ray, an optical modem for the MEDUSA class AUVs

Wireless optical communications are emerging as a viable solution for high-speed data transmission over short ranges in the ocean, complementing mainstream acoustic communication systems that operate over much longer ranges, but at lower data rates. The current drive to develop cooperative autonomous vehicular systems to carry out surveying and other complex missions in the ocean critically depends on the existence of such communication links to share sensory and coordination information. This paper presents a compact, low power consumption, cost-efficient, and lightweight optical modem designed for fast data transmission between MEDUSA underwater vehicles at ranges on the order of 10 m. The transmitter uses LEDs for ON-OFF keying, while the receiver front-end adopts a transimpedance architecture with a photodiode detector. This simplistic transmission and reception technique reduces the overall design and hardware complexities compared to, e.g., laser diodes and photomultiplier tubes. The LEDs are arranged in a circular array, with a photodiode at the centre to enable half-duplex operation. These modems are designed to achieve data rates of 20-200 kb/s over short-range, line-of-sight, configurations. A transparent casing is customised to fit the Medusa vehicles, with proper alignment for inter-vehicle communication when they move in formation.
The maternal brain and its plasticity in humans

This article is part of a Special Issue "Parental Care". Early mother-infant relationships play important roles in infants' optimal development. New mothers undergo neurobiological changes that support developing mother-infant relationships regardless of great individual differences in those relationships. In this article, we review the neural plasticity in human mothers' brains based on functional magnetic resonance imaging (fMRI) studies. First, we review the neural circuits that are involved in establishing and maintaining mother-infant relationships. Second, we discuss early postpartum factors (e.g., birth and feeding methods, hormones, and parental sensitivity) that are associated with individual differences in maternal brain neuroplasticity. Third, we discuss abnormal changes in the maternal brain related to psychopathology (i.e., postpartum depression, posttraumatic stress disorder, substance abuse) and potential brain remodeling associated with interventions. Last, we highlight potentially important future research directions to better understand normative changes in the maternal brain and risks for abnormal changes that may disrupt early mother-infant relationships.
Staying Alive: A Docking Station for Autonomous Robot Recharging

Autonomous mobile robots are constrained in their long-term functionality due to a limited on-board power supply. Typically, rechargeable batteries are utilized that may only provide a few hours of peak usage before recharging is necessary. Recharging requires a robot to be taken offline, and attached to a battery charger via human intervention. This is unacceptable in environments where long-term autonomous capabilities are necessary. We present a method to provide long-term autonomy by implementing autonomous recharging. A recharging station design is presented, consisting of a stationary docking station and a docking mechanism mounted to a Pioneer 2DX robot. The docking station and robot docking mechanism are designed to work together, providing a mechanical and electrical connection between the charging system and the robot. Algorithms are implemented to monitor the battery voltage and control the docking procedure, as well as account for any errors that may occur. Initial experiments that demonstrate the validity of the approach and design are presented.