nils-herrmann/clean_scirepeval huggingface dataset:
Action Schema Networks: Generalised Policies With Deep Learning

In this paper, we introduce the Action Schema Network (ASNet): a neural network architecture for learning generalised policies for probabilistic planning problems. By mimicking the relational structure of planning problems, ASNets are able to adopt a weight sharing scheme which allows the network to be applied to any problem from a given planning domain. This allows the cost of training the network to be amortised over all problems in that domain. Further, we propose a training method which balances exploration and supervised training on small problems to produce a policy which remains robust when evaluated on larger problems. In experiments, we show that ASNet’s learning capability allows it to significantly outperform traditional non-learning planners in several challenging domains.
The subunit principle in nasal reconstruction.

The nasal surface is made up of several concave and convex surfaces separated from one another by ridges and valleys. Gonzalez-Ulloa has designated the nose an aesthetic unit of the face. These smaller parts (tip, dorsum, sidewalls, alar lobules, and soft triangles) may be called topographic subunits. When a large part of a subunit has been lost, replacing the entire subunit rather than simply patching the defect often gives a superior result. This subunit approach to nasal reconstruction causes unsatisfactory border scars of flaps to mimic the normal shadowed valleys and lighted ridges of the nasal surface. Furthermore, as trapdoor contraction occurs, the entire reconstructed subunit bulges in a way that simulates the normal contour of a nasal tip, dorsal hump, or alar lobule. Photographs show five patients in whom this principle was followed and one in whom it was not.
Single-Channel Multi-Speaker Separation using Deep Clustering

Deep clustering is a recently introduced deep learning architecture that uses discriminatively trained embeddings as the basis for clustering. It was recently applied to spectrogram segmentation, resulting in impressive results on speaker-independent multi-speaker separation. In this paper we extend the baseline system with an end-to-end signal approximation objective that greatly improves performance on a challenging speech separation. We first significantly improve upon the baseline system performance by incorporating better regularization, larger temporal context, and a deeper architecture, culminating in an overall improvement in signal to distortion ratio (SDR) of 10.3 dB compared to the baseline of 6.0 dB for two-speaker separation, as well as a 7.1 dB SDR improvement for three-speaker separation. We then extend the model to incorporate an enhancement layer to refine the signal estimates, and perform end-to-end training through both the clustering and enhancement stages to maximize signal fidelity. We evaluate the results using automatic speech recognition. The new signal approximation objective, combined with end-to-end training, produces unprecedented performance, reducing the word error rate (WER) from 89.1% down to 30.8%. This represents a major advancement towards solving the cocktail party problem.
Affect in *text and speech

As technology and human-computer interaction advances, there is an increased interest in affective computing. One of the current challenges in computational speech and text processing is addressing affective and expressive meaning, an area that has received fairly sparse attention in linguistics. Linguistic investigation in this area is motivated both by the need for scientific study of subjective language phenomena, and by useful applications such as expressive text-to-speech synthesis. The study makes contributions to the study of affect and language, by describing a novel data resource, outlining models and challenges for exploring affect in language, applying computational methods toward this problem with included empirical results, and suggesting paths for further research. After the introduction, followed by a survey of several areas of related work in Chapter 2, Chapter 3 presents a newly developed sentence-annotated corpus resource divided into three parts for large-scale exploration of affect in texts (specifically tales). Besides covering annotation and data set description, the chapter includes a hierarchical affect model and a qualitative-interpretive examination suggesting characteristics of a subset of the data marked by high agreement in affective label assignments. Chapter 4 is devoted to experimental work on automatic affect prediction in text. Different computational methods are explored based on the labeled data set and affect hierarchy outlined in the previous chapter, with an emphasis on supervised machine learning whose results seem particularly interesting when including true affect history in the feature set. Moreover, besides contrasting classification accuracy of methods in isolation, methods’ predictions are combined with weighting approaches into a joint prediction. In addition, classification with the high agreement data is specifically explored, and the impact of access to knowledge about previous affect history is contrasted empirically. Chapter 5 moves on to discuss emotion in speech. It applies interactive evolutionary computation to evolve fundamental parameters of emotional prosody in perceptual experiments with human listeners, indicating both emotion-specific trends and types of variations, and implications at the local word-level. Chapter 6 provides suggestions for continued work in related and novel areas. A concluding chapter summarizes the dissertation and its contributions.
Animated movie activity characterization by image and text information fusion

In the context of animated movie characterization, we present an information fusion approach mixing very different types of data related to the activity within a movie. These data are the features extracted from images, words extracted from the synopses and expert knowledge. The difficulty of this fusion is due to the very different semantic level of these data. The aim of this work is to get a movie activity characterization in order to help the constitution of automatic summary, content based video retrieval system, etc. Two strategies are proposed : a first one aiming at giving a global description of the activity within the movie, and a second one providing a local description of activity. Tests and results are proposed on animated movies from the Annecy International Animation Film Festival.
Sports video classification using HMMS

In this paper we address the problem of sports video classification Using Hidden Markov Models (HMMs). For each sports genre, we construct two HMMs representing motion and color features respectively. The observation sequences generated from the principal motion direction and the principal color of each frame are fed to a motion and a color HMM respectively. The outputs are integrated to make a final decision. We tested our scheme on 220 minutes of sports video with four genre types: Ice hockey, basketball, football, and soccer, and achieved an overall classification accuracy of 93%.
UUV Simulator: A Gazebo-based package for underwater intervention and multi-robot simulation

This paper describes the Unmanned Underwater Vehicle (UUV) Simulator, an extension of the open-source robotics simulator Gazebo to underwater scenarios, that can simulate multiple underwater robots and intervention tasks using robotic manipulators. This is achieved mainly through a set of newly implemented plugins that model underwater hydrostatic and hydrodynamic effects, thrusters, sensors, and external disturbances. In contrast to existing solutions, it reuses and extends a general-purpose robotics simulation platform to underwater environments.
Online Survival Analysis Software to Assess the Prognostic Value of Biomarkers Using Transcriptomic Data in Non-Small-Cell Lung Cancer

In the last decade, optimized treatment for non-small cell lung cancer had lead to improved prognosis, but the overall survival is still very short. To further understand the molecular basis of the disease we have to identify biomarkers related to survival. Here we present the development of an online tool suitable for the real-time meta-analysis of published lung cancer microarray datasets to identify biomarkers related to survival. We searched the caBIG, GEO and TCGA repositories to identify samples with published gene expression data and survival information. Univariate and multivariate Cox regression analysis, Kaplan-Meier survival plot with hazard ratio and logrank P value are calculated and plotted in R. The complete analysis tool can be accessed online at: www.kmplot.com/lung. All together 1,715 samples of ten independent datasets were integrated into the system. As a demonstration, we used the tool to validate 21 previously published survival associated biomarkers. Of these, survival was best predicted by CDK1 (p<1E-16), CD24 (p<1E-16) and CADM1 (p = 7E-12) in adenocarcinomas and by CCNE1 (p = 2.3E-09) and VEGF (p = 3.3E-10) in all NSCLC patients. Additional genes significantly correlated to survival include RAD51, CDKN2A, OPN, EZH2, ANXA3, ADAM28 and ERCC1. In summary, we established an integrated database and an online tool capable of uni- and multivariate analysis for in silico validation of new biomarker candidates in non-small cell lung cancer.
Viargo - A generic virtual reality interaction library

Traditionally, interaction techniques for virtual reality applications are implemented in a proprietary way on specific target platforms, e. g., requiring specific hardware, physics or rendering libraries, which withholds reusability and portability. Though hardware abstraction layers for numerous devices are provided by multiple virtual reality libraries, they are usually tightly bound to a particular rendering environment. In this paper we introduce Viargo - a generic virtual reality interaction library, which serves as additional software layer that is independent from the application and its linked libraries, i. e., a once developed interaction technique, such as walking with a head-mounted display or multi-touch interaction, can be ported to different hard- or software environments with minimal code adaptation. We describe the underlying concepts and present examples on how to integrate Viargo in different graphics engines, thus extending proprietary graphics libraries with a few lines of code to easy-to-use virtual reality engines.
THE RELATIONSHIP BETWEEN TEACHER IMMEDIACY BEHAVIORS AND LEARNERS ’ PERCEPTIONS OF SOCIAL PRESENCE AND SATISFACTION IN OPEN AND DISTANCE EDUCATION : THE CASE OF ANADOLU UNIVERSITY OPEN EDUCATION

A significant number of studies in the literature stress the important role of teacher immediacy behaviors on learners’ perceptions of social presence and satisfaction in open and distance learning environments. Yet, those studies were conducted in different open and distance education institutions than the current example of which unique characteristics and applications are commonly recognized in the field. Unlike others, the current study examined the effects of both verbal and nonverbal instructor immediacy behaviors on learners’ perceptions of social presence and satisfaction in face-to-face academic tutoring services provided in open and distance learning environments. Results indicated a moderate and positive relationship between the control variable and outcome variables
Interventions targeting attention in young children with autism.

The ability to focus and sustain one's attention is critical for learning. Children with autism demonstrate unusual characteristics of attention from infancy. It is reasonable to assume that early anomalies in attention influence a child's developmental trajectories. Therapeutic interventions for autism often focus on core features of autism such as communication and socialization, while very few interventions specifically address attention. The purpose of this article is to provide clinicians a description of attention characteristics in children with autism and discuss interventions thought to improve attention.



Characteristics of attention in children with autism are presented. Intervention studies featuring measures of attention as an outcome variable for young children with autism are reviewed to present interventions that have empirical evidence for improvements in attention. Results are synthesized by strategy, specific feature of attention targeted, and results for both habilitative goals and accommodations for attention.



Although research is not extensive, several strategies to support attention in young children with autism have been investigated. The empirical findings regarding these strategies can inform evidence-based practice.
Predicting Demographics of High-Resolution Geographies with Geotagged Tweets

In this paper, we consider the problem of predicting demographics of geographic units given geotagged Tweets that are composed within these units. Traditional survey methods that offer demographics estimates are usually limited in terms of geographic resolution, geographic boundaries, and time intervals. Thus, it would be highly useful to develop computational methods that can complement traditional survey methods by offering demographics estimates at finer geographic resolutions, with flexible geographic boundaries (i.e. not confined to administrative boundaries), and at different time intervals. While prior work has focused on predicting demographics and health statistics at relatively coarse geographic resolutions such as the county-level or state-level, we introduce an approach to predict demographics at finer geographic resolutions such as the blockgroup-level. For the task of predicting gender and race/ethnicity counts at the blockgrouplevel, an approach adapted from prior work to our problem achieves an average correlation of 0.389 (gender) and 0.569 (race) on a held-out test dataset. Our approach outperforms this prior approach with an average correlation of 0.671 (gender) and 0.692 (race).
Stereoscopic First Person View System for Drone Navigation

Ground control of unmanned aerial vehicles (UAV) is a key to the advancement of this technology for commercial purposes. The need for reliable ground control arises in scenarios where human intervention is necessary, e.g. handover situations when autonomous systems fail. Manual flights are also needed for collecting diverse datasets to train deep neural network-based control systems. This axiom is even more prominent for the case of unmanned flying robots where there is no simple solution to capture optimal navigation footage. In such scenarios, improving the ground control and developing better autonomous systems are two sides of the same coin. To improve the ground control experience, and thus the quality of the footage, we propose to upgrade onboard teleoperation systems to a fully immersive setup that provides operators with a stereoscopic first person view (FPV) through a virtual reality (VR) head-mounted display. We tested users (n = 7) by asking them to fly our drone on the field. Test flights showed that operators flying our system can take off, fly, and land successfully while wearing VR headsets. In addition, we ran two experiments with prerecorded videos of the flights and walks to a wider set of participants (n = 69 and n = 20) to compare the proposed technology to the experience provided by current drone FPV solutions that only include monoscopic vision. Our immersive stereoscopic setup enables higher accuracy depth perception, which has clear implications for achieving better teleoperation and unmanned navigation. Our studies show comprehensive data on the impact of motion and simulator sickness in case of stereoscopic setup. We present the device specifications as well as the measures that improve teleoperation experience and reduce induced simulator sickness. Our approach provides higher perception fidelity during flights, which leads to a more precise better teleoperation and ultimately translates into better flight data for training deep UAV control policies.
Recovery of water bottles plastic using tribo-electrostatic separation process

The recycling of plastic materials is considered nowadays as a major strategic issue, since several million tons of plastic are the subject of recovery and recycling. The consumption of plastics in African countries is continuously growing up and the need of low-cost recycling methods is becoming more pressing. The purpose of this paper is an experimental investigation for recycling of plastic waste from PET water bottles using the free-fall electrostatic separation device. Water bottles are manufactured with two kinds of plastic, PET (Polyethylene terephthalate) and HDPE (High-density polyethylene), are first crushed into 2-3 mm size particles. Triboelectric charging of the granules was performed using a fluidized air device build-up with a plastic located between PET and HDPE in the triboelectric series. Due to the plate-shape of the particles, the free-fall tribo-electrostatic separator is efficient in terms of recovery and purity of the separated products.
An Analysis of the Feasibility of Graph Compression Techniques for Indexing Regular Path Queries

Regular path queries (RPQs) are a fundamental part of recent graph query languages like SPARQL and PGQL. They allow the definition of recursive path structures through regular expressions in a declarative pattern matching environment. We study the use of the K2-tree graph compression technique to materialize RPQ results with low memory consumption for indexing. Compact index representations enable the efficient storage of multiple indexes for varying RPQs.
Inflammatory, immune-mediated adverse reactions related to soft tissue dermal fillers.

An increasing number of persons seek medical solutions for esthetic indications and for diverse pathological conditions, such as malformations, trauma, or cancer. Despite manufacturers' and different authors' claims that fillers are non-immunogenic or that complications are uncommon, unwanted adverse reactions do occur.



To review the literature regarding the multiple types of immune-mediated adverse reactions related to medical dermal filler injections/prosthesis.



A comprehensive MEDLINE, PubMed, and Google Scholar electronic database search was performed (2000-January 2012). Selected articles published before 2000 referring to general concerns regarding the studied topic were also included. The search provided almost 300 articles. Finally, 235 studies were selected and included.



All known fillers present in the market have been shown to be able to provoke early- and late-onset inflammatory adverse reactions. Their true prevalence is unknown but appears to be significant. The majority of the late-onset adverse effects are inflammatory and immune-mediated in nature. Edema, granulomas, sarcoid-like disorders, and panniculitis are the findings most commonly seen. Rarely, systemic granulomatous and autoimmune diseases, and to lesser extent acute hypersensitivity reactions can be seen.



All implanted, injected, and blood-contact biomaterials trigger a wide variety of adverse reactions that may appear early or late and range from local to systemic. Most fillers act more as adjuvants than as direct T-cell activators, on a background of genetic predisposition. Their treatment has not been the subject of well-designed studies. Management of both acute and systemic reactions is often difficult and requires anti-inflammatory and occasionally immunosuppressive therapy.
Ranking the strategies for Indian medical tourism sector through the integration of SWOT analysis and TOPSIS method.

Organizations have to evaluate their internal and external environments in this highly competitive world. Strengths, weaknesses, opportunities and threats (SWOT) analysis is a very useful technique which analyzes the strengths, weaknesses, opportunities and threats of an organization for taking strategic decisions and it also provides a foundation for the formulation of strategies. But the drawback of SWOT analysis is that it does not quantify the importance of individual factors affecting the organization and the individual factors are described in brief without weighing them. Because of this reason, SWOT analysis can be integrated with any multiple attribute decision-making (MADM) technique like the technique for order preference by similarity to ideal solution (TOPSIS), analytical hierarchy process, etc., to evaluate the best alternative among the available strategic alternatives. The paper aims to discuss these issues.  In this study, SWOT analysis is integrated with a multicriteria decision-making technique called TOPSIS to rank different strategies for Indian medical tourism in order of priority.  SO strategy (providing best facilitation and care to the medical tourists at par to developed countries) is the best strategy which matches with the four elements of S, W, O and T of SWOT matrix and 35 strategic indicators.  This paper proposes a solution based on a combined SWOT analysis and TOPSIS approach to help the organizations to evaluate and select strategies.  Creating a new technology or administering a new strategy always has some degree of resistance by employees. To minimize resistance, the author has used TOPSIS as it involves group thinking, requiring every manager of the organization to analyze and evaluate different alternatives and average measure of each parameter in final decision matrix.
A Distributed Data-Gathering Protocol Using AUV in Underwater Sensor Networks

In this paper, we propose a distributed data-gathering scheme using an autonomous underwater vehicle (AUV) working as a mobile sink to gather data from a randomly distributed underwater sensor network where sensor nodes are clustered around several cluster headers. Unlike conventional data-gathering schemes where the AUV visits either every node or every cluster header, the proposed scheme allows the AUV to visit some selected nodes named path-nodes in a way that reduces the overall transmission power of the sensor nodes. Monte Carlo simulations are performed to investigate the performance of the proposed scheme compared with several preexisting techniques employing the AUV in terms of total amount of energy consumption, standard deviation of each node's energy consumption, latency to gather data at a sink, and controlling overhead. Simulation results show that the proposed scheme not only reduces the total energy consumption but also distributes the energy consumption more uniformly over the network, thereby increasing the lifetime of the network.
Performance analysis of MIMO free-space optical systems in gamma-gamma fading

Atmospheric turbulence induced fading is one of the main impairments affecting free-space optics (FSO) communications. In recent years, Gamma-Gamma fading has become the dominant fading model for FSO links because of its excellent agreement with measurement data for a wide range of turbulence conditions. However, in contrast to RF communications, the analysis techniques for FSO are not well developed and prior work has mostly resorted to simulations and numerical integration for performance evaluation in Gamma-Gamma fading. In this paper, we express the pairwise error probabilities of single-input single- output (SISO) and multiple-input multiple-output (MIMO) FSO systems with intensity modulation and direct detection (IM/DD) as generalized infinite power series with respect to the signal- to-noise ratio. For numerical evaluation these power series are truncated to a finite number of terms and an upper bound for the associated approximation error is provided. The resulting finite power series enables fast and accurate numerical evaluation of the bit error rate of IM/DD FSO with on-off keying and pulse position modulation in SISO and MIMO Gamma-Gamma fading channels. Furthermore, we extend the well-known RF concepts of diversity and combining gain to FSO and Gamma-Gamma fading. In particular, we provide simple closed-form expressions for the diversity gain and the combining gain of MIMO FSO with repetition coding across lasers at the transmitter and equal gain combining or maximal ratio combining at the receiver.