nils-herrmann/clean_scirepeval huggingface dataset:
Running head: Working memory in L2 input processing The role of working memory in processing L2 input: Insights from eye-tracking

Our study investigated how attention paid to a target syntactic construction causative had is related the storage capacity and attention regulation function of working memory (WM) and how these WM abilities moderate the change of knowledge of the target construction in different input conditions. 80 Sri Lankan learners of English were exposed to examples of the target construction in explicit and implicit learning conditions and their eye movements were tracked as they read the input. Correlational and multiple regression analyses indicated a very strong relationship between WM abilities and gains in the knowledge of the target construction. WM scores were closely associated with gains in receptive knowledge in all input conditions, but they had a weaker link to the improvement of productive knowledge in the implicit learning conditions. The amount of attention paid to input was also strongly related to WM abilities.
Finding Alternative Translations in a Large Corpus of Movie Subtitle

provides a large collection of user contributed subtitles in various languages for movies and TV programs. Subtitle translations are valuable resources for cross-lingual studies and machine translation research. A less explored feature of the collection is the inclusion of alternative translations, which can be very useful for training paraphrase systems or collecting multi-reference test suites for machine translation. However, differences in translation may also be due to misspellings, incomplete or corrupt data files, or wrongly aligned subtitles. This paper reports our efforts in recognising and classifying alternative subtitle translations with language independent techniques. We use time-based alignment with lexical re-synchronisation techniques and BLEU score filters and sort alternative translations into categories using edit distance metrics and heuristic rules. Our approach produces large numbers of sentence-aligned translation alternatives for over 50 languages provided via the OPUS corpus collection.
A survey of robot learning from demonstration

We present a comprehensive survey of robot Learning from Demonstration (LfD), a technique that develops policies from example state to action mappings. We introduce the LfD design choices in terms of demonstrator, problem space, policy derivation and performance, and contribute the foundations for a structure in which to categorize LfD research. Specifically, we analyze and categorize the multiple ways in which examples are gathered, ranging from teleoperation to imitation, as well as the various techniques for policy derivation, including matching functions, dynamics models and plans. To conclude we discuss LfD limitations and related promising areas for future research.
Geodesic weighted Bayesian model for salient object detection

In recent years, a variety of salient object detection methods under Bayesian framework have been proposed and many achieved state of the art. However, those ignore spatial relationships and thus background regions similar to the objects are also highlighted. In this paper, we propose a novel geodesic weighted Bayesian model to address this issue. We consider spatial relationships by attaching more importance to regions which are more likely to be parts of a salient object, thus suppressing background regions. First, we learn a combined similarity via multiple features to measure similarity of adjacent regions. Then, we apply the combined similarity as edge weight to construct an undirected weighted graph and compute geodesic distance. Last, we utilize the geodesic distance to weight the observation likelihood to infer a more precise saliency map. Experiments on several benchmark datasets demonstrate the effectiveness of our model.
The particle swarm - explosion, stability, and convergence in a multidimensional complex space

The particle swarm is an algorithm for finding optimal regions of complex search spaces through the interaction of individuals in a population of particles. Even though the algorithm, which is based on a metaphor of social interaction, has been shown to perform well, researchers have not adequately explained how it works. Further, traditional versions of the algorithm have had some undesirable dynamical properties, notably the particles’ velocities needed to be limited in order to control their trajectories. The present paper analyzes a particle’s trajectory as it moves in discrete time (the algebraic view), then progresses to the view of it in continuous time (the analytical view). A five-dimensional depiction is developed, which describes the system completely. These analyses lead to a generalized model of the algorithm, containing a set of coefficients to control the system’s convergence tendencies. Some results of the particle swarm optimizer, implementing modifications derived from the analysis, suggest methods for altering the original algorithm in ways that eliminate problems and increase the ability of the particle swarm to find optima of some well-studied test functions.
Mixtures of hierarchical topics with Pachinko allocation

The four-level pachinko allocation model (PAM) (Li & McCallum, 2006) represents correlations among topics using a DAG structure. It does not, however, represent a nested hierarchy of topics, with some topical word distributions representing the vocabulary that is shared among several more specific topics. This paper presents hierarchical PAM---an enhancement that explicitly represents a topic hierarchy. This model can be seen as combining the advantages of hLDA's topical hierarchy representation with PAM's ability to mix multiple leaves of the topic hierarchy. Experimental results show improvements in likelihood of held-out documents, as well as mutual information between automatically-discovered topics and humangenerated categories such as journals.
Signal Space CoSaMP for Sparse Recovery With Redundant Dictionaries

Compressive sensing (CS) has recently emerged as a powerful framework for acquiring sparse signals. The bulk of the CS literature has focused on the case where the acquired signal has a sparse or compressible representation in an orthonormal basis. In practice, however, there are many signals that cannot be sparsely represented or approximated using an orthonormal basis, but that do have sparse representations in a redundant dictionary. Standard results in CS can sometimes be extended to handle this case provided that the dictionary is sufficiently incoherent or well conditioned, but these approaches fail to address the case of a truly redundant or overcomplete dictionary. In this paper, we describe a variant of the iterative recovery algorithm CoSaMP for this more challenging setting. We utilize the \mbi D-RIP, a condition on the sensing matrix analogous to the well-known restricted isometry property. In contrast to prior work, the method and analysis are “signal-focused”; that is, they are oriented around recovering the signal rather than its dictionary coefficients. Under the assumption that we have a near-optimal scheme for projecting vectors in signal space onto the model family of candidate sparse signals, we provide provable recovery guarantees. Developing a practical algorithm that can provably compute the required near-optimal projections remains a significant open problem, but we include simulation results using various heuristics that empirically exhibit superior performance to traditional recovery algorithms.
Neurogenesis in the dentate gyrus of the adult rat: age-related decrease of neuronal progenitor proliferation.

The hippocampus is one of the few areas of the rodent brain that continues to produce neurons postnatally. Neurogenesis reportedly persists in rats up to 11 months of age. Using bromodeoxyuridine (BrdU) labeling, the present study confirms that in the adult rat brain, neuronal progenitor cells divide at the border between the hilus and the granule cell layer (GCL). In adult rats, the progeny of these cells migrate into the GCL and express the neuronal markers NeuN and calbindin-D28k. However, neurogenesis was drastically reduced in aged rats. Six-to 27-month-old Fischer rats were injected intraperitoneally with BrdU to detect newborn cells in vivo and to follow their fate in the dentate gyrus. When killed 4-6 weeks after BrdU labeling, 12- to 27-month-old rats exhibited a significant decline in the density of BrdU-positive cells in the granule cell layer compared with 6-month-old controls. Decreased neurogenesis in aging rats was accompanied by reduced immunoreactivity for poly-sialylated neural cell adhesion molecule, a molecule that is involved in migration and process elongation of developing neurons. When animals were killed immediately (12 hr) after BrdU injection, significantly fewer labeled cells were observed in the GCL and adjacent subgranular zone of aged rats, indicative of a decrease in mitotic activity of neuronal precursor cells. The reduced proliferation was not attributable to a general aged-related metabolic impairment, because the density of BrdU-positive cells was not altered in other brain regions with known mitotic activity (e.g., hilus and lateral ventricle wall). The decline in neurogenesis that occurs throughout the lifespan of an animal can thus be related to a decreasing proliferation of granule cell precursors.
Towards a Machine-Learning Approach for Sickness Prediction in 360° Stereoscopic Videos

Virtual reality systems are widely believed to be the next major computing platform. There are, however, some barriers to adoption that must be addressed, such as that of motion sickness — which can lead to undesirable symptoms including postural instability, headaches, and nausea. Motion sickness in virtual reality occurs as a result of moving visual stimuli that cause users to perceive self-motion while they remain stationary in the real world. There are several contributing factors to both this perception of motion and the subsequent onset of sickness, including field of view, motion velocity, and stimulus depth. We verify first that differences in vection due to relative stimulus depth remain correlated with sickness. Then, we build a dataset of stereoscopic 3D videos and their corresponding sickness ratings in order to quantify their nauseogenicity, which we make available for future use. Using this dataset, we train a machine learning algorithm on hand-crafted features (quantifying speed, direction, and depth as functions of time) from each video, learning the contributions of these various features to the sickness ratings. Our predictor generally outperforms a naïve estimate, but is ultimately limited by the size of the dataset. However, our result is promising and opens the door to future work with more extensive datasets. This and further advances in this space have the potential to alleviate developer and end user concerns about motion sickness in the increasingly commonplace virtual world.
Model-based whitebox fuzzing for program binaries

Many real-world programs take highly structured and very complex inputs. The automated testing of such programs is non-trivial. If the test input does not adhere to a specific file format, the program returns a parser error. For symbolic execution-based whitebox fuzzing the corresponding error handling code becomes a significant time sink. Too much time is spent in the parser exploring too many paths leading to trivial parser errors. Naturally, the time is better spent exploring the functional part of the program where failure with valid input exposes deep and real bugs in the program. In this paper, we suggest to leverage information about the file format and the data chunks of existing, valid files to swiftly carry the exploration beyond the parser code. We call our approach Model-based Whitebox Fuzzing (MoWF) because the file format input model of blackbox fuzzers can be exploited as a constraint on the vast input space to rule out most invalid inputs during path exploration in symbolic execution. We evaluate on 13 vulnerabilities in 8 large program binaries with 6 separate file formats and found that MoWF exposes all vulnerabilities while both, traditional whitebox fuzzing and model-based blackbox fuzzing, expose only less than half, respectively. Our experiments also demonstrate that MoWF exposes 70% vulnerabilities without any seed inputs.
The effects of a 6-week plyometric training program on agility.

The purpose of the study was to determine if six weeks of plyometric training can improve an athlete's agility. Subjects were divided into two groups, a plyometric training and a control group. The plyometric training group performed in a six week plyometric training program and the control group did not perform any plyometric training techniques. All subjects participated in two agility tests: T-test and Illinois Agility Test, and a force plate test for ground reaction times both pre and post testing. Univariate ANCOVAs were conducted to analyze the change scores (post - pre) in the independent variables by group (training or control) with pre scores as covariates. The Univariate ANCOVA revealed a significant group effect F2,26 = 25.42, p=0.0000 for the T-test agility measure. For the Illinois Agility test, a significant group effect F2,26 = 27.24, p = 0.000 was also found. The plyometric training group had quicker posttest times compared to the control group for the agility tests. A significant group effect F2,26 = 7.81, p = 0.002 was found for the Force Plate test. The plyometric training group reduced time on the ground on the posttest compared to the control group. The results of this study show that plyometric training can be an effective training technique to improve an athlete's agility. Key PointsPlyometric training can enhance agility of athletes.6 weeks of plyometric training is sufficient to see agility results.Ground reaction times are decreased with plyometric training.
IGLOO: Slicing the Features Space to Represent Long Sequences

Up until recently Recurrent neural networks (RNNs) have been the standard go-to component when processing sequential data with neural networks. Issues relative to vanishing gradient have been partly addressed by Long short-term memory (LSTM) and gated recurrent unit (GRU), but in practice experiments show that very long terms dependencies (beyond 1000 time steps) are difficult to learn. We introduce IGLOO, a new neural network architecture which aims at being faster than both LSTM and GRU but also their respective CuDNN optimized versions when convergence happens and provide an alternative in the case when there is no convergence at all. IGLOO’s core idea is to use the relationships between patches sliced out of the features maps of convolution layers at different levels of granularity to build a representation for the sequence. We show that the model can deal with dependencies of more than 25,000 steps in a reasonable time frame. Beyond the well bench-marked copy-memory and addition problems which show good results, we also achieve best recorded accuracy on permuted MNIST (98.4%). IGLOO is also applied on the IMDB set and on the TUH Abnormal EEG Corpus.
Computational Intelligence, Networked Systems and Their Applications

This paper presents the metro constant illumination controller, which is designed to solve the current problem that the interior illumination in a moving metro fluctuates drastically with the changes of exterior environment. By detecting the real-time interior illumination values and adjusting the lights in a metro with PID controller based on BP neural network, the controller can keep the metro interior illumination values around the preset value. Simulations and actual test results show that the PID controller based on BP neural network has strong adaptability and robustness in a nonlinear system. It can both save energy and solve the problem of drastically fluctuating illumination in a moving metro, which cannot be achieved in conventional PID controllers.
Significant Triples : Adjective + Noun + Verb Combinations

We investigate the identification and, to some extent, the classification of collocational word groups that consist of an adjectival modifier (A), an accusative object noun (N), and a verb (V) by means of parsing a newspaper corpus with a lexicalized probabilistic grammar.1 Data triples are extracted from the resulting Viterbi parses and subsequently evaluated by means of a statistical association measure. The extraction results are then compared to predefined descriptive classes of ANV-triples. We also use a decision tree algorithm to classify part of the data obtained, on the basis of a small set of manually classified examples. Much of the candidate data is lexicographically relevant: the triples include idiomatic combinations (e.g. (sich) eine goldene Nase verdienen, ‘get rich’, lit. ‘earn oneself a golden nose’), combinations of N+V and N+A collocations (e.g. (eine) klare Absage erteilen, ‘refuse resolutely’, lit. ‘give a clear refusal’), next to cases where N+V or N+A collocations are found, in combination with other (not necessarily collocational) context partners. To extract such data from text corpora, a grammar is needed that captures verb+object relations: simple pattern matching on part-of-speech shapes is not sufficient. Statistical tools then allow to order the data in a way useful for subsequent manual selection by lexicographers. 1This work has been carried out in the context of the Transferbereich 32: Automatische Exzerption, a DFG-funded project aiming at the creation of support tools for the corpus-based updating of printed dictionaries in lexicography, carried out in cooperation with the publishers Langenscheidt KG and Duden BIFAB AG.
Comparison of data-driven analysis methods for identification of functional connectivity in fMRI

Data-driven analysis methods, such as independent component analysis (ICA) and clustering, have found a fruitful application in the analysis of functional magnetic resonance imaging (fMRI) data for identifying functionally connected brain networks. Unlike the traditional regression-based hypothesis-driven analysis methods, the principal advantage of data-driven methods is their applicability to experimental paradigms in the absence of a priori model of brain activity. Although ICA and clustering rely on very different assumptions on the underlying distributions, they produce surprisingly similar results for signals with large variation. The main goal of this thesis is to understand the factors that contribute to the differences in the identification of functional connectivity based on ICA and a more general version of clustering, Gaussian mixture model (GMM), and their relations. We provide a detailed empirical comparison of ICA and clustering based on GMM. We introduce a component-wise matching and comparison scheme of resulting ICA and GMM components based on their correlations. We apply this scheme to the synthetic fMRI data and investigate the influence of noise and length of time course on the performance of ICA and GMM, comparing with ground truth and with each other. For the real fMRI data, we propose a method of choosing a threshold to determine which of resulting components are meaningful to compare using the cumulative distribution function of their empirical correlations. In addition, we present an alternate method to model selection for selecting the optimal total number of components for ICA and GMM using the task-related and contrast functions. For extracting task-related components, we find that GMM outperforms ICA when the total number of components are less then ten and the performance between ICA and GMM is almost identical for larger numbers of the total components. Furthermore, we observe that about a third of the components of each model are meaningful to be compared to the components of the other.  Assistant Professor of Computer Science and Engineering
A Data Structure for Dynamic Trees

We propose a data structure to maintain a collection of vertex-disjoint trees under a sequence of two kinds of operations: a link operation that combines two trees into one by adding an edge, and a cut operation that divides one tree into two by deleting an edge. Our data structure requires O(log n) time per operation when the time is amortized over a sequence of operations. Using our data structure, we obtain new fast algorithms for the following problems:
 (1) Computing deepest common ancestors.
 (2) Solving various network flow problems including finding maximum flows, blocking flows, and acyclic flows.
 (3) Computing certain kinds of constrained minimum spanning trees.
 (4) Implementing the network simplex algorithm for the transshipment problem.
 Our most significant application is (2); we obtain an O(mn log n)-time algorithm to find a maximum flow in a network of n vertices and m edges, beating by a factor of log n the fastest algorithm previously known for sparse graphs.
Instance-Based Learning Algorithms

Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributed networks. However, no investigation has analyzed algorithms that use only specific instances to solve incremental learning tasks. In this paper, we describe a framework and methodology, called instance-based learning, that generates classification predictions using only specific instances. Instance-based learning algorithms do not maintain a set of abstractions derived from specific instances. This approach extends the nearest neighbor algorithm, which has large storage requirements. We describe how storage requirements can be significantly reduced with, at most, minor sacrifices in learning rate and classification accuracy. While the storage-reducing algorithm performs well on several real-world databases, its performance degrades rapidly with the level of attribute noise in training instances. Therefore, we extended it with a significance test to distinguish noisy instances. This extended algorithm's performance degrades gracefully with increasing noise levels and compares favorably with a noise-tolerant decision tree algorithm.
Query suggestion based on user landing pages

This poster investigates a novel query suggestion technique that selects query refinements through a combination of many users' post-query navigation patterns and the query logs of a large search engine. We compare this technique, which uses the queries that retrieve in the top-ranked search results places where searchers end up after post-query browsing (i.e., the landing pages), with an approach based on query refinements from user search sessions extracted from query logs. Our findings demonstrate the effectiveness of using landing pages for the direct generation of query suggestions, as well as the complementary nature of the suggestions it generates with regard to traditional query log based refinement methodologies.
Big five factors and facets and the prediction of behavior.

The authors compared the Big 5 factors of personality with the facets or traits of personality that constitute those factors on their ability to predict 40 behavior criteria. Both the broad factors and the narrow facets predicted substantial numbers of criteria, but the latter did noticeably better in that regard, even when the number of facet predictors was limited to the number of factor predictors. Moreover, the criterion variance accounted for by the personality facets often included large portions not predicted by the personality factors. The narrow facets, therefore, were able to substantially increase the maximum prediction achieved by the broad factors. The results of this study are interpreted as supporting a more detailed approach to personality assessment, one that goes beyond the measurement of the Big 5 factors alone.