nils-herrmann/clean_scirepeval huggingface dataset:
Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data

The current version of the Human Disease Ontology (DO) (http://www.disease-ontology.org) database expands the utility of the ontology for the examination and comparison of genetic variation, phenotype, protein, drug and epitope data through the lens of human disease. DO is a biomedical resource of standardized common and rare disease concepts with stable identifiers organized by disease etiology. The content of DO has had 192 revisions since 2012, including the addition of 760 terms. Thirty-two percent of all terms now include definitions. DO has expanded the number and diversity of research communities and community members by 50+ during the past two years. These community members actively submit term requests, coordinate biomedical resource disease representation and provide expert curation guidance. Since the DO 2012 NAR paper, there have been hundreds of term requests and a steady increase in the number of DO listserv members, twitter followers and DO website usage. DO is moving to a multi-editor model utilizing Protégé to curate DO in web ontology language. This will enable closer collaboration with the Human Phenotype Ontology, EBI's Ontology Working Group, Mouse Genome Informatics and the Monarch Initiative among others, and enhance DO's current asserted view and multiple inferred views through reasoning.
RSSI-based relative localisation for mobile robots

In this work, we develop an anchor-less relative localisation algorithm aimed to be used in multi-robot teams. The localisation is performed based on the Received Signal Strength Indicator (RSSI) readings collected from the messages exchanged between nodes. We use the RSSI as a rough estimate of the inverse of distance between any pair of communicating nodes, and we claim that such estimates provide a coarse information of the nodes relative localisation that is still suitable to support several coordination tasks. In addition, we introduce a relative velocity estimation framework based on the RSSI measurements. This framework uses consecutive distance measurements and position estimates to provide the relative velocity vectors for all the nodes in the network. To accomplish this, we propose using a Kalman filter and the Floyd–Warshall algorithm to generate smooth RSSI pairwise signal distance for all nodes. Then we use Multidimensional Scaling to obtain relative positions from the pairwise distances. Finally, due to anchor unavailability, relative positions are adjusted to reflect the continuous mobility by using geometric transformations, thus obtaining smoother trajectories for mobile nodes. This allows us to estimate velocity and to establish a correspondence between orientation in the physical world and in the relative coordinates system. Additionally, we study the impact of several parameters in calculating the network topology, namely different approaches to provide a symmetric distances matrix, the period of the matrix dissemination, the use of synchronisation of the transmissions, and the filtering of the RSSI data. Experimental results, with a set of MicaZ motes, show that the period of matrix dissemination is the most relevant of the parameters, specifically with larger periods providing the best results, however, shorter periods are shown to be possible as long as the transmissions are synchronised.
Table detection in document images using header and trailer patterns

This paper presents a new approach to detect tabular structures present in document images and in low resolution video images. The algorithm for table detection is based on identifying the unique table start pattern and table trailer pattern. We have formulated perceptual attributes to characterize the patterns. The performance of our table detection system is tested on a set of document images picked from UW-III (University of Washington) dataset, UNLV dataset, video images of NPTEL videos, and our own dataset. Our approach demonstrates improved detection for different types of table layouts, with or without ruling lines. We have obtained correct table localization on pages with multiple tables aligned side-by-side.
Supporting social presence through lightweight photo sharing on and off the desktop

Lightweight photo sharing, particularly via mobile devices, is fast becoming a common communication medium used for maintaining a presence in the lives of friends and family. How should such systems be designed to maximize this social presence while maintaining simplicity? An experimental photo sharing system was developed and tested that, compared to current systems, offers highly simplified, group-centric sharing, automatic and persistent people-centric organization, and tightly integrated desktop and mobile sharing and viewing. In an experimental field study, the photo sharing behaviors of groups of family or friends were studied using their normal photo sharing methods and with the prototype sharing system. Results showed that users found photo sharing easier and more fun, shared more photos, and had an enhanced sense of social presence when sharing with the experimental system. Results are discussed in the context of design principles for the rapidly increasing number of lightweight photo sharing systems.
Paraphrase acquisition via crowdsourcing and machine learning

To paraphrase means to rewrite content while preserving the original meaning. Paraphrasing is important in fields such as text reuse in journalism, anonymizing work, and improving the quality of customer-written reviews. This article contributes to paraphrase acquisition and focuses on two aspects that are not addressed by current research: (1) acquisition via crowdsourcing, and (2) acquisition of passage-level samples. The challenge of the first aspect is automatic quality assurance; without such a means the crowdsourcing paradigm is not effective, and without crowdsourcing the creation of test corpora is unacceptably expensive for realistic order of magnitudes. The second aspect addresses the deficit that most of the previous work in generating and evaluating paraphrases has been conducted using sentence-level paraphrases or shorter; these short-sample analyses are limited in terms of application to plagiarism detection, for example. We present the Webis Crowd Paraphrase Corpus 2011 (Webis-CPC-11), which recently formed part of the PAN 2010 international plagiarism detection competition. This corpus comprises passage-level paraphrases with 4067 positive samples and 3792 negative samples that failed our criteria, using Amazon's Mechanical Turk for crowdsourcing. In this article, we review the lessons learned at PAN 2010, and explain in detail the method used to construct the corpus. The empirical contributions include machine learning experiments to explore if passage-level paraphrases can be identified in a two-class classification problem using paraphrase similarity features, and we find that a k-nearest-neighbor classifier can correctly distinguish between paraphrased and nonparaphrased samples with 0.980 precision at 0.523 recall. This result implies that just under half of our samples must be discarded (remaining 0.477 fraction), but our cost analysis shows that the automation we introduce results in a 18% financial saving and over 100 hours of time returned to the researchers when repeating a similar corpus design. On the other hand, when building an unrelated corpus requiring, say, 25% training data for the automated component, we show that the financial outcome is cost neutral, while still returning over 70 hours of time to the researchers. The work presented here is the first to join the paraphrasing and plagiarism communities.
Microscopy cell counting with fully convolutional regression networks

This paper concerns automated cell counting in microscopy images. The approach we take is to use Convolutional Neural Networks (CNNs) to regress a cell spatial density across the image. This is applicable to situations where traditional single-cell segmentation based methods do not work well due to cell clumping or overlap. We make the following contributions: (i) we develop and compare architectures for two Fully Convolutional Regression Networks (FCRNs) for this task; (ii) since the networks are fully convolutional, they can predict a density map for an input image of arbitrary size, and we exploit this to improve efficiency at training time by training end-to-end on image patches; and (iii) we show that FCRNs trained entirely on synthetic data are able to give excellent predictions on real microscopy images without fine-tuning, and that the performance can be further improved by finetuning on the real images. We set a new state-of-the-art performance for cell counting on standard synthetic image benchmarks and, as a side benefit, show the potential of the FCRNs for providing cell detections for overlapping cells.
Deep Dynamic Network Embedding for Link Prediction

Network embedding task aims at learning low-dimension latent representations of vertices while preserving the structure of a network simultaneously. Most existing network embedding methods mainly focus on static networks, which extract and condense the network information without temporal information. However, in the real world, networks keep evolving, where the linkage states between the same vertex pairs at consequential timestamps have very close correlations. In this paper, we propose to study the network embedding problem and focus on modeling the linkage evolution in the dynamic network setting. To address this problem, we propose a deep dynamic network embedding method. More specifically, the method utilizes the historical information obtained from the network snapshots at past timestamps to learn latent representations of the future network. In the proposed embedding method, the objective function is carefully designed to incorporate both the network internal and network dynamic transition structures. Extensive empirical experiments prove the effectiveness of the proposed model on various categories of real-world networks, including a human contact network, a bibliographic network, and e-mail networks. Furthermore, the experimental results also demonstrate the significant advantages of the method compared with both the state-of-the-art embedding techniques and several existing baseline methods.
BranchyNet: Fast inference via early exiting from deep neural networks

Deep neural networks are state of the art methods for many learning tasks due to their ability to extract increasingly better features at each network layer. However, the improved performance of additional layers in a deep network comes at the cost of added latency and energy usage in feedforward inference. As networks continue to get deeper and larger, these costs become more prohibitive for real-time and energy-sensitive applications. To address this issue, we present BranchyNet, a novel deep network architecture that is augmented with additional side branch classifiers. The architecture allows prediction results for a large portion of test samples to exit the network early via these branches when samples can already be inferred with high confidence. BranchyNet exploits the observation that features learned at an early layer of a network may often be sufficient for the classification of many data points. For more difficult samples, which are expected less frequently, BranchyNet will use further or all network layers to provide the best likelihood of correct prediction. We study the BranchyNet architecture using several well-known networks (LeNet, AlexNet, ResNet) and datasets (MNIST, CIFAR10) and show that it can both improve accuracy and significantly reduce the inference time of the network.
Effects of low-intensity laser therapy on the orthodontic movement velocity of human teeth: a preliminary study.

Low-intensity laser therapy (LILT) has been studied in many fields of dentistry, but to our knowledge, this is the first time that its effects on orthodontic movement velocity in humans are investigated.



Eleven patients were recruited for this 2-month study. One half of the upper arcade was considered control group (CG) and received mechanical activation of the canine teeth every 30 days. The opposite half received the same mechanical activation and was also irradiated with a diode laser emitting light at 780 nm, during 10 seconds at 20 mW, 5 J/cm2, on 4 days of each month. Data of the biometrical progress of both groups were statistically compared.



All patients showed significant higher acceleration of the retraction of canines on the side treated with LILT when compared to the control.



Our findings suggest that LILT does accelerate human teeth movement and could therefore considerably shorten the whole treatment duration.
Cannabidiol for neurodegenerative disorders: important new clinical applications for this phytocannabinoid?

Cannabidiol (CBD) is a phytocannabinoid with therapeutic properties for numerous disorders exerted through molecular mechanisms that are yet to be completely identified. CBD acts in some experimental models as an anti-inflammatory, anticonvulsant, anti-oxidant, anti-emetic, anxiolytic and antipsychotic agent, and is therefore a potential medicine for the treatment of neuroinflammation, epilepsy, oxidative injury, vomiting and nausea, anxiety and schizophrenia, respectively. The neuroprotective potential of CBD, based on the combination of its anti-inflammatory and anti-oxidant properties, is of particular interest and is presently under intense preclinical research in numerous neurodegenerative disorders. In fact, CBD combined with Δ(9)-tetrahydrocannabinol is already under clinical evaluation in patients with Huntington's disease to determine its potential as a disease-modifying therapy. The neuroprotective properties of CBD do not appear to be exerted by the activation of key targets within the endocannabinoid system for plant-derived cannabinoids like Δ(9)-tetrahydrocannabinol, i.e. CB(1) and CB(2) receptors, as CBD has negligible activity at these cannabinoid receptors, although certain activity at the CB(2) receptor has been documented in specific pathological conditions (i.e. damage of immature brain). Within the endocannabinoid system, CBD has been shown to have an inhibitory effect on the inactivation of endocannabinoids (i.e. inhibition of FAAH enzyme), thereby enhancing the action of these endogenous molecules on cannabinoid receptors, which is also noted in certain pathological conditions. CBD acts not only through the endocannabinoid system, but also causes direct or indirect activation of metabotropic receptors for serotonin or adenosine, and can target nuclear receptors of the PPAR family and also ion channels.
Facial Expression Recognition Based on Facial Components Detection and HOG Features

In this paper, an effective method is proposed to handle the facial expression recognition problem. The system detects the face and facial components including eyes, brows and mouths. Since facial expressions result from facial muscle movements or deformations, and Histogram of Oriented Gradients (HOG) is very sensitive to the object deformations, we apply the HOG to encode these facial components as features. A linear SVM is then trained to perform the facial expression classification. We evaluate our proposed method on the JAFFE dataset and an extended Cohn-Kanade dataset. The average classification rate on the two datasets reaches 94.3% and 88.7%, respectively. Experimental results demonstrate the competitive classification accuracy of our proposed method. facial expression recognition, HOG features, facial component detection, SVM
Algorithm Selection using Reinforcement Learning

Many computational problems can be solved by multiple algorithms, with different algorithms fastest for different problem sizes, input distributions, and hardware characteristics. We consider the problem ofalgorithm selection: dynamically choose an algorithm to attack an instance of a problem with the goal of minimizing the overall execution time. We formulate the problem as a kind of Markov decision process (MDP), and use ideas from reinforcement learning to solve it. This paper introduces a kind of MDP that models the algorithm selection problem by allowing multiple state transitions. The well known Q-learning algorithm is adapted for this case in a way that combines both Monte-Carlo and Temporal Difference methods. Also, this work uses, and extends in a way to control problems, the Least-Squares Temporal Difference algorithm (LSTD ) of Boyan. The experimental study focuses on the classic problems of order statistic selection and sorting. The encouraging results reveal the potential of applying learning methods to traditional computational problems.
Improving class probability estimates for imbalanced data

Obtaining good probability estimates is imperative for many applications. The increased uncertainty and typically asymmetric costs surrounding rare events increase this need. Experts (and classification systems) often rely on probabilities to inform decisions. However, we demonstrate that class probability estimates obtained via supervised learning in imbalanced scenarios systematically underestimate the probabilities for minority class instances, despite ostensibly good overall calibration. To our knowledge, this problem has not previously been explored. We propose a new metric, the stratified Brier score, to capture class-specific calibration, analogous to the per-class metrics widely used to assess the discriminative performance of classifiers in imbalanced scenarios. We propose a simple, effective method to mitigate the bias of probability estimates for imbalanced data that bags estimators independently calibrated over balanced bootstrap samples. This approach drastically improves performance on the minority instances without greatly affecting overall calibration. We extend our previous work in this direction by providing ample additional empirical evidence for the utility of this strategy, using both support vector machines and boosted decision trees as base learners. Finally, we show that additional uncertainty can be exploited via a Bayesian approach by considering posterior distributions over bagged probability estimates.
Mitochondrial DNA Sequence Analysis - Validation and Use for Forensic Casework.

With the discovery of the polymerase chain reaction (PCR) in the mid-1980's, the last in a series of critical molecular biology techniques (to include the isolation of DNA from human and non-human biological material, and primary sequence analysis of DNA) had been developed to rapidly analyze minute quantities of mitochondrial DNA (mtDNA). This was especially true for mtDNA isolated from challenged sources, such as ancient or aged skeletal material and hair shafts. One of the beneficiaries of this work has been the forensic community. Over the last decade, a significant amount of research has been conducted to develop PCR-based sequencing assays for the mtDNA control region (CR), which have subsequently been used to further characterize the CR. As a result, the reliability of these assays has been investigated, the limitations of the procedures have been determined, and critical aspects of the analysis process have been identified, so that careful control and monitoring will provide the basis for reliable testing. With the application of these assays to forensic identification casework, mtDNA sequence analysis has been properly validated, and is a reliable procedure for the examination of biological evidence encountered in forensic criminalistic cases.
An Improved Approach for RSSI-Based only Calibration-Free Real-Time Indoor Localization on IEEE 802.11 and 802.15.4 Wireless Networks

Assuming a reliable and responsive spatial contextualization service is a must-have in IEEE 802.11 and 802.15.4 wireless networks, a suitable approach consists of the implementation of localization capabilities, as an additional application layer to the communication protocol stack. Considering the applicative scenario where satellite-based positioning applications are denied, such as indoor environments, and excluding data packet arrivals time measurements due to lack of time resolution, received signal strength indicator (RSSI) measurements, obtained according to IEEE 802.11 and 802.15.4 data access technologies, are the unique data sources suitable for indoor geo-referencing using COTS devices. In the existing literature, many RSSI based localization systems are introduced and experimentally validated, nevertheless they require periodic calibrations and significant information fusion from different sensors that dramatically decrease overall systems reliability and their effective availability. This motivates the work presented in this paper, which introduces an approach for an RSSI-based calibration-free and real-time indoor localization. While switched-beam array-based hardware (compliant with IEEE 802.15.4 router functionality) has already been presented by the author, the focus of this paper is the creation of an algorithmic layer for use with the pre-existing hardware capable to enable full localization and data contextualization over a standard 802.15.4 wireless sensor network using only RSSI information without the need of lengthy offline calibration phase. System validation reports the localization results in a typical indoor site, where the system has shown high accuracy, leading to a sub-metrical overall mean error and an almost 100% site coverage within 1 m localization error.
Robust Road Detection and Tracking in Challenging Scenarios Based on Markov Random Fields With Unsupervised Learning

This paper presents a robust stereo-vision-based drivable road detection and tracking system that was designed to navigate an intelligent vehicle through challenging traffic scenarios and increment road safety in such scenarios with advanced driver-assistance systems (ADAS). This system is based on a formulation of stereo with homography as a maximum a posteriori (MAP) problem in a Markov random held (MRF). Under this formulation, we develop an alternating optimization algorithm that alternates between computing the binary labeling for road/nonroad classification and learning the optimal parameters from the current input stereo pair itself. Furthermore, online extrinsic camera parameter reestimation and automatic MRF parameter tuning are performed to enhance the robustness and accuracy of the proposed system. In the experiments, the system was tested on our experimental intelligent vehicles under various real challenging scenarios. The results have substantiated the effectiveness and the robustness of the proposed system with respect to various challenging road scenarios such as heterogeneous road materials/textures, heavy shadows, changing illumination and weather conditions, and dynamic vehicle movements.
Implications of location and touch for on-body projected interfaces

Very recently, there has been a perfect storm of technical advances that has culminated in the emergence of a new interaction modality: on-body interfaces. Such systems enable the wearer to use their body as an input and output platform with interactive graphics. Projects such as PALMbit and Skinput sought to answer the initial and fundamental question: whether or not on-body interfaces were technologically possible. Although considerable technical work remains, we believe it is important to begin shifting the question away from how and what, and towards where, and ultimately why. These are the class of questions that inform the design of next generation systems. To better understand and explore this expansive space, we employed a mixed-methods research process involving more than two thousand individuals. This started with high-resolution, but low-detail crowdsourced data. We then combined this with rich, expert interviews, exploring aspects ranging from aesthetics to kinesthetics. The results of this complimentary, structured exploration, point the way towards more comfortable, efficacious, and enjoyable on-body user experiences.