nils-herrmann/clean_scirepeval huggingface dataset:
Table structure recognition based on robust block segmentation

This paper presents an efficient approach to identify tabular structures within either electronic or paper documents. The resulting T—Recs system takes word bounding box information as input, and outputs the corresponding logical text block units (e.g. the cells within a table environment). Starting with an arbitrary word as block seed the algorithm recursively expands this block to all words that interleave with their vertical (north and south) neighbors. Since even smallest gaps of table columns prevent their words from mutual interleaving, this initial segmentation is able to identify and isolate such columns. In order to deal with some inherent segmentation errors caused by isolated lines (e.g. headers), overhanging words, or cells spawning more than one column, a series of postprocessing steps is added. These steps benefit from a very simple distinction between type 1 and type 2 blocks: type 1 blocks are those of at most one word per line, all others are of type 2. This distinction allows the selective application of heuristics to each group of blocks. The conjoint decomposition of column blocks into subsets of table cells leads to the final block segmentation of a homogeneous abstraction level. These segments serve the final layout analysis which identifies table environments and cells that are stretching over several rows and/or columns.
Automatic projector calibration with embedded light sensors

Projection technology typically places several constraints on the geometric relationship between the projector and the projection surface to obtain an undistorted, properly sized image. In this paper we describe a simple, robust, fast, and low-cost method for automatic projector calibration that eliminates many of these constraints. We embed light sensors in the target surface, project Gray-coded binary patterns to discover the sensor locations, and then prewarp the image to accurately fit the physical features of the projection surface. This technique can be expanded to automatically stitch multiple projectors, calibrate onto non-planar surfaces for object decoration, and provide a method for simple geometry acquisition.
Predictable Low-Latency Event Detection With Parallel Complex Event Processing

The tremendous number of sensors and smart objects being deployed in the Internet of Things (IoT) pose the potential for IT systems to detect and react to live-situations. For using this hidden potential, complex event processing (CEP) systems offer means to efficiently detect event patterns (complex events) in the sensor streams and therefore, help in realizing a “distributed intelligence” in the IoT. With the increasing number of data sources and the increasing volume at which data is produced, parallelization of event detection is crucial to limit the time events need to be buffered before they actually can be processed. In this paper, we propose a pattern-sensitive partitioning model for data streams that is capable of achieving a high degree of parallelism in detecting event patterns, which formerly could only consistently be detected in a sequential manner or at a low parallelization degree. Moreover, we propose methods to dynamically adapt the parallelization degree to limit the buffering imposed on event detection in the presence of dynamic changes to the workload. Extensive evaluations of the system behavior show that the proposed partitioning model allows for a high degree of parallelism and that the proposed adaptation methods are able to meet a buffering limit for event detection under high and dynamic workloads.
Simulation as an engine of physical scene understanding.

In a glance, we can perceive whether a stack of dishes will topple, a branch will support a child's weight, a grocery bag is poorly packed and liable to tear or crush its contents, or a tool is firmly attached to a table or free to be lifted. Such rapid physical inferences are central to how people interact with the world and with each other, yet their computational underpinnings are poorly understood. We propose a model based on an "intuitive physics engine," a cognitive mechanism similar to computer engines that simulate rich physics in video games and graphics, but that uses approximate, probabilistic simulations to make robust and fast inferences in complex natural scenes where crucial information is unobserved. This single model fits data from five distinct psychophysical tasks, captures several illusions and biases, and explains core aspects of human mental models and common-sense reasoning that are instrumental to how humans understand their everyday world.
Stochastic Variance Reduction for Nonconvex Optimization

We study nonconvex finite-sum problems and analyze stochastic variance reduced gradient (SVRG) methods for them. SVRG and related methods have recently surged into prominence for convex optimization given their edge over stochastic gradient descent (SGD); but their theoretical analysis almost exclusively assumes convexity. In contrast, we obtain non-asymptotic rates of convergence of SVRG for nonconvex optimization, showing that it is provably faster than SGD and gradient descent. We also analyze a subclass of nonconvex problems on which SVRG attains linear convergence to the global optimum. We extend our analysis to mini-batch variants, showing (theoretical) linear speedup due to minibatching in parallel settings.
To excel or not to excel: Strong evidence on the adverse effect of smartphone addiction on academic performance

This study aimed to verify whether achieving a dist inctive academic performance is unlikely for students at high risk of smartphone addiction. Additionally, it verified whether this phenomenon was equally applicable to male and femal e students. After implementing systematic random sampling, 293 university students participated by completing an online survey questionnaire posted on the university’s stu dent information system. The survey questionnaire collected demographic information and responses to the Smartphone Addiction Scale-Short Version (SAS-SV) items. The results sho wed that male and female university students were equally susceptible to smartphone add iction. Additionally, male and female university students were equal in achieving cumulat ive GPAs with distinction or higher within the same levels of smartphone addiction. Fur thermore, undergraduate students who were at a high risk of smartphone addiction were le ss likely to achieve cumulative GPAs of distinction or higher.
Price Fluctuations and the Use of Bitcoin: An Empirical Inquiry

Over recent years, interest has been growing in Bitcoin, an innovation which has the potential to play an important role in e-commerce and beyond. The aim of our paper is to provide a comprehensive empirical study of the payment and investment features of Bitcoin and their implications for the conduct of ecommerce. Since network externality theory suggests that the value of a network and its take-up are interlinked, we investigate both adoption and price formation. We discover that Bitcoin returns are driven primarily by its popularity, the sentiment expressed in newspaper reports on the cryptocurrency, and total number of transactions. The paper also reports on the first global survey of merchants who have adopted this technology and model the share of sales paid for with this alternative currency, using both ordinary and Tobit regressions. Our analysis examines how country, customer and company-specific characteristics interact with the proportion of sales attributed to Bitcoin. We find that company features, use of other payment methods, customers’ knowledge about Bitcoin, as well as the size of both the official and unofficial economy are significant determinants. The results presented allow a better understanding of the practical and theoretical ramifications of this innovation.
Mechatronic Design and Characterization of the Index Finger Module of a Hand Exoskeleton for Post-Stroke Rehabilitation

This paper presents HANDEXOS, a novel wearable multiphalanges device for post-stroke rehabilitation. It was designed in order to allow for a functional and safe interaction with the user's hand by means of an anthropomorphic kinematics and the minimization of the human/exoskeleton rotational axes misalignment. This paper describes the mechatronic design of the exoskeleton's index finger module, simulation, modeling, and development of the actuation unit and sensory system. Experimental results on the validation of the dynamic model and experimental characterization of the index finger module with healthy subjects are reported, showing promising results that encourage further clinical trials.
Recurrent convolutional neural networks for object-class segmentation of RGB-D video

Object-class segmentation is a computer vision task which requires labeling each pixel of an image with the class of the object it belongs to. Deep convolutional neural networks (DNN) are able to learn and exploit local spatial correlations required for this task. They are, however, restricted by their small, fixed-sized filters, which limits their ability to learn long-range dependencies. Recurrent Neural Networks (RNN), on the other hand, do not suffer from this restriction. Their iterative interpretation allows them to model long-range dependencies by propagating activity. This property might be especially useful when labeling video sequences, where both spatial and temporal long-range dependencies occur. In this work, we propose novel RNN architectures for object-class segmentation. We investigate three ways to consider past and future context in the prediction process by comparing networks that process the frames one by one with networks that have access to the whole sequence. We evaluate our models on the challenging NYU Depth v2 dataset for object-class segmentation and obtain competitive results.
Shortcut-Stacked Sentence Encoders for Multi-Domain Inference

We present a simple sequential sentence encoder for multi-domain natural language inference. Our encoder is based on stacked bidirectional LSTM-RNNs with shortcut connections and fine-tuning of word embeddings. The overall supervised model uses the above encoder to encode two input sentences into two vectors, and then uses a classifier over the vector combination to label the relationship between these two sentences as that of entailment, contradiction, or neural. Our ShortcutStacked sentence encoders achieve strong improvements over existing encoders on matched and mismatched multi-domain natural language inference (top singlemodel result in the EMNLP RepEval 2017 Shared Task (Nangia et al., 2017)). Moreover, they achieve the new state-of-theart encoding result on the original SNLI dataset (Bowman et al., 2015).
Hop Reservation Multiple Access (HRMA) for Ad-Hoc Networks

A new multichannel MAC protocol called Hop-Reservation Multiple Access (HRMA) for wireless ad-hoc networks (multi-hop packet radio networks) is introduced, specified and analyzed. HRMA is based on simple half-duplex, very-slow frequency-hopping spread spectrum (FHSS) radios and takes advantage of the time synchronization necessary for frequency hopping. HRMA allows a pair of communicating nodes to reserve a frequency hop using a reservation and handshake mechanism that guarantee collision-free data transmission in the presence of hidden terminals. We analyze the throughput achieved in HRMA for the case of a hypercube network topology assuming variable-length packets, and compare it against the multichannel slotted ALOHA protocol, which represents the current practice of MAC protocols in commercial ad-hoc networks based on spread spectrum radios, such as Metricom’s Ricochet system. The numerical results show that HRMA can achieve much higher throughput than multichannel slotted ALOHA within the traffic-load ranges of interest, especially when the average packet length is large compared to the duration of a dwell time in the frequency hopping sequence, in which case the maximum throughput of HRMA is close to the maximum possible value.
Scene Flow Estimation: A Survey

This paper is the first to review the scene flow estimation field to the best of our knowledge, which analyzes and compares methods, technical challenges, evaluation methodologies and performance of scene flow estimation. Existing algorithms are categorized in terms of scene representation, data source, and calculation scheme, and the pros and cons in each category are compared briefly. The datasets and evaluation protocols are enumerated, and the performance of the most representative methods is presented. A future vision is illustrated with few questions arisen for discussion. This survey presents a general introduction and analysis of scene flow estimation.
Emotional responses to a romantic partner's imaginary rejection: the roles of attachment anxiety, covert narcissism, and self-evaluation.

These studies tested the associations between responses to an induced imaginary romantic rejection and individual differences on dimensions of attachment and covert narcissism. In Study 1 (N=125), we examined the associations between attachment dimensions and emotional responses to a vignette depicting a scenario of romantic rejection, as measured by self-reported negative mood states, expressions of anger, somatic symptoms, and self-evaluation. Higher scores on attachment anxiety, but not on attachment avoidance, were associated with stronger reactions to the induced rejection. Moreover, decreased self-evaluation scores (self-esteem and pride) were found to mediate these associations. In Study 2 (N=88), the relative contributions of covert narcissism and attachment anxiety to the emotional responses to romantic rejection were explored. Higher scores on covert narcissism were associated with stronger reactions to the induced rejection. Moreover, covert narcissism seemed to constitute a specific aspect of attachment anxiety.
Accumulation and fragmentation of plastic debris in global environments.

One of the most ubiquitous and long-lasting recent changes to the surface of our planet is the accumulation and fragmentation of plastics. Within just a few decades since mass production of plastic products commenced in the 1950s, plastic debris has accumulated in terrestrial environments, in the open ocean, on shorelines of even the most remote islands and in the deep sea. Annual clean-up operations, costing millions of pounds sterling, are now organized in many countries and on every continent. Here we document global plastics production and the accumulation of plastic waste. While plastics typically constitute approximately 10 per cent of discarded waste, they represent a much greater proportion of the debris accumulating on shorelines. Mega- and macro-plastics have accumulated in the highest densities in the Northern Hemisphere, adjacent to urban centres, in enclosed seas and at water convergences (fronts). We report lower densities on remote island shores, on the continental shelf seabed and the lowest densities (but still a documented presence) in the deep sea and Southern Ocean. The longevity of plastic is estimated to be hundreds to thousands of years, but is likely to be far longer in deep sea and non-surface polar environments. Plastic debris poses considerable threat by choking and starving wildlife, distributing non-native and potentially harmful organisms, absorbing toxic chemicals and degrading to micro-plastics that may subsequently be ingested. Well-established annual surveys on coasts and at sea have shown that trends in mega- and macro-plastic accumulation rates are no longer uniformly increasing: rather stable, increasing and decreasing trends have all been reported. The average size of plastic particles in the environment seems to be decreasing, and the abundance and global distribution of micro-plastic fragments have increased over the last few decades. However, the environmental consequences of such microscopic debris are still poorly understood.
Teacher beliefs and technology integration practices: A critical relationship

Early studies indicated that teachersâ€TM enacted beliefs, particularly in terms of classroom technology practices, often did not align with their espoused beliefs. Researchers concluded this was due, at least in part, to a variety of external barriers that prevented teachers from using technology in ways that aligned more closely with their beliefs. However, many of these barriers (access, support, etc.) have since been eliminated in the majority of schools. This multiple case-study research was designed to revisit the question, â€œHow do the pedagogical beliefs and classroom technology practices of teachers, recognized for their technology uses, align?â€​ Twelve K-12 classroom teachers were purposefully selected based on their awardwinning technology practices, supported by evidence from personal and/or classroom websites. Follow-up interviews were conducted to examine the correspondence between teachersâ€TM classroom practices and their pedagogical beliefs. Results a c Purchase Export
Authorship Attribution with Topic Models

Authorship attribution deals with identifying the authors of anonymous texts. Traditionally, research in this field has focused on formal texts, such as essays and novels, but recently more attention has been given to texts generated by on-line users, such as e-mails and blogs. Authorship attribution of such on-line texts is a more challenging task than traditional authorship attribution, because such texts tend to be short, and the number of candidate authors is often larger than in traditional settings. We address this challenge by using topic models to obtain author representations. In addition to exploring novel ways of applying two popular topic models to this task, we test our new model that projects authors and documents to two disjoint topic spaces. Utilizing our model in authorship attribution yields state-of-the-art performance on several data sets, containing either formal texts written by a few authors or informal texts generated by tens to thousands of on-line users. We also present experimental results that demonstrate the applicability of topical author representations to two other problems: inferring the sentiment polarity of texts, and predicting the ratings that users would give to items such as movies.
Adversarial Symmetric Variational Autoencoder

A new form of variational autoencoder (VAE) is developed, in which the joint distribution of data and codes is considered in two (symmetric) forms: (i) from observed data fed through the encoder to yield codes, and (ii) from latent codes drawn from a simple prior and propagated through the decoder to manifest data. Lower bounds are learned for marginal log-likelihood fits observed data and latent codes. When learning with the variational bound, one seeks to minimize the symmetric Kullback-Leibler divergence of joint density functions from (i) and (ii), while simultaneously seeking to maximize the two marginal log-likelihoods. To facilitate learning, a new form of adversarial training is developed. An extensive set of experiments is performed, in which we demonstrate state-of-the-art data reconstruction and generation on several image benchmark datasets.
High performance lithographic hotspot detection using hierarchically refined machine learning

Under real and continuously improving manufacturing conditions, lithography hotspot detection faces several key challenges. First, real hotspots become less but harder to fix at post-layout stages; second, false alarm rate must be kept low to avoid excessive and expensive post-processing hotspot removal; third, full chip physical verification and optimization require fast turn-around time. To address these issues, we propose a high performance lithographic hotspot detection flow with ultra-fast speed and high fidelity. It consists of a novel set of hotspot signature definitions and a hierarchically refined detection flow with powerful machine learning kernels, ANN (artificial neural network) and SVM (support vector machine). We have implemented our algorithm with industry-strength engine under real manufacturing conditions in 45nm process, and showed that it significantly out-performs previous state-of-the-art algorithms in hotspot detection false alarm rate (2.4X to 2300X reduction) and simulation run-time (5X to 237X reduction), meanwhile archiving similar or slightly better hotspot detection accuracies. Such high performance lithographic hotspot detection under real manufacturing conditions is especially suitable for guiding lithography friendly physical design.
Fine-Tuning Deep Neural Networks in Continuous Learning Scenarios

The revival of deep neural networks and the availability of ImageNet laid the foundation for recent success in highly complex recognition tasks. However, ImageNet does not cover all visual concepts of all possible application scenarios. Hence, application experts still record new data constantly and expect the data to be used upon its availability. In this paper, we follow this observation and apply the classical concept of fine-tuning deep neural networks to scenarios where data from known or completely new classes is continuously added. Besides a straightforward realization of continuous fine-tuning, we empirically analyze how computational burdens of training can be further reduced. Finally, we visualize how the network’s attention maps evolve over time which allows for visually investigating what the network learned during continuous fine-tuning.
Functional Gradient Motion Planning in Reproducing Kernel Hilbert Spaces

We introduce a functional gradient descent trajectory optimization algorithm for robot motion planning in Reproducing Kernel Hilbert Spaces (RKHSs). Functional gradient algorithms are a popular choice for motion planning in complex many-degree-of-freedom robots, since they (in theory) work by directly optimizing within a space of continuous trajectories to avoid obstacles while maintaining geometric properties such as smoothness. However, in practice, implementations such as CHOMP and TrajOpt typically commit to a fixed, finite parametrization of trajectories, often as a sequence of waypoints. Such a parameterization can lose much of the benefit of reasoning in a continuous trajectory space: e.g., it can require taking an inconveniently small step size and large number of iterations to maintain smoothness. Our work generalizes functional gradient trajectory optimization by formulating it as minimization of a cost functional in an RKHS. This generalization lets us represent trajectories as linear combinations of kernel functions. As a result, we are able to take larger steps and achieve a locally optimal trajectory in just a few iterations. Depending on the selection of kernel, we can directly optimize in spaces of trajectories that are inherently smooth in velocity, jerk, curvature, etc., and that have a low-dimensional, adaptively chosen parameterization. Our experiments illustrate the effectiveness of the planner for different kernels, including Gaussian RBFs with independent and coupled interactions among robot joints, Laplacian RBFs, and B-splines, as compared to the standard discretized waypoint representation.