nils-herrmann/clean_scirepeval huggingface dataset:
A New Approach to Animacy Detection

Animacy is a necessary property for a referent to be an agent, and thus animacy detection is useful for a variety of natural language processing tasks, including word sense disambiguation, co-reference resolution, semantic role labeling, and others. Prior work treated animacy as a word-level property, and has developed statistical classifiers to classify words as either animate or inanimate. We discuss why this approach to the problem is ill-posed, and present a new approach based on classifying the animacy of co-reference chains. We show that simple voting approaches to inferring the animacy of a chain from its constituent words perform relatively poorly, and then present a hybrid system merging supervised machine learning (ML) and a small number of handbuilt rules to compute the animacy of referring expressions and co-reference chains. This method achieves state of the art performance. The supervised ML component leverages features such as word embeddings over referring expressions, parts of speech, and grammatical and semantic roles. The rules take into consideration parts of speech and the hypernymy structure encoded in WordNet. The system achieves an F1 of 0.88 for classifying the animacy of referring expressions, which is comparable to state of the art results for classifying the animacy of words, and achieves an F1 of 0.75 for classifying the animacy of coreference chains themselves. We release our training and test dataset, which includes 142 texts (all narratives) comprising 156,154 words, 34,698 referring expressions, and 10,941 co-reference chains. We test the method on a subset of the OntoNotes dataset, showing using manual sampling that animacy classification is 90%±2% accurate for coreference chains, and 92%±1% for referring expressions. The data also contains 46 folktales, which present an interesting challenge because they often involve characters who are members of traditionally inanimate classes (e.g., stoves that walk, trees that talk). We show that our system is able to detect the animacy of these unusual referents with an F1 of 0.95.
Congenitally missing teeth (hypodontia): A review of the literature concerning the etiology, prevalence, risk factors, patterns and treatment

Congenitally missing teeth (CMT), or as usually called hypodontia, is a highly prevalent and costly dental anomaly. Besides an unfavorable appearance, patients with missing teeth may suffer from malocclusion, periodontal damage, insufficient alveolar bone growth, reduced chewing ability, inarticulate pronunciation and other problems. Treatment might be usually expensive and multidisciplinary. This highly frequent and yet expensive anomaly is of interest to numerous clinical, basic science and public health fields such as orthodontics, pediatric dentistry, prosthodontics, periodontics, maxillofacial surgery, anatomy, anthropology and even the insurance industry. This essay reviews the findings on the etiology, prevalence, risk factors, occurrence patterns, skeletal changes and treatments of congenitally missing teeth. It seems that CMT usually appears in females and in the permanent dentition. It is not conclusive whether it tends to occur more in the maxilla or mandible and also in the anterior versus posterior segments. It can accompany various complications and should be attended by expert teams as soon as possible.
Learning scene gist with convolutional neural networks to improve object recognition

Advancements in convolutional neural networks (CNNs) have made significant strides toward achieving high performance levels on multiple object recognition tasks. While some approaches utilize information from the entire scene to propose regions of interest, the task of interpreting a particular region or object is still performed independently of other objects and features in the image. Here we demonstrate that a scene's ‘gist’ can significantly contribute to how well humans can recognize objects. These findings are consistent with the notion that humans foveate on an object and incorporate information from the periphery to aid in recognition. We use a biologically inspired two-part convolutional neural network ('GistNet') that models the fovea and periphery to provide a proof-of-principle demonstration that computational object recognition can significantly benefit from the gist of the scene as contextual information. Our model yields accuracy improvements of up to 50% in certain object categories when incorporating contextual gist, while only increasing the original model size by 5%. This proposed model mirrors our intuition about how the human visual system recognizes objects, suggesting specific biologically plausible constraints to improve machine vision and building initial steps towards the challenge of scene understanding.
Bib2vec: An Embedding-based Search System for Bibliographic Information

We propose a novel embedding model that represents relationships among several elements in bibliographic information with high representation ability and flexibility. Based on this model, we present a novel search system that shows the relationships among the elements in the ACL Anthology Reference Corpus. The evaluation results show that our model can achieve a high prediction ability and produce reasonable search results. The demonstration is available at http://tti-coin.jp/demo/bib2vec/
Strength training improves performance and pedaling characteristics in elite cyclists.

The purpose was to investigate the effect of 25 weeks heavy strength training in young elite cyclists. Nine cyclists performed endurance training and heavy strength training (ES) while seven cyclists performed endurance training only (E). ES, but not E, resulted in increases in isometric half squat performance, lean lower body mass, peak power output during Wingate test, peak aerobic power output (W(max)), power output at 4 mmol L(-1)[la(-)], mean power output during 40-min all-out trial, and earlier occurrence of peak torque during the pedal stroke (P < 0.05). ES achieved superior improvements in W(max) and mean power output during 40-min all-out trial compared with E (P < 0.05). The improvement in 40-min all-out performance was associated with the change toward achieving peak torque earlier in the pedal stroke (r = 0.66, P < 0.01). Neither of the groups displayed alterations in VO2max or cycling economy. In conclusion, heavy strength training leads to improved cycling performance in elite cyclists as evidenced by a superior effect size of ES training vs E training on relative improvements in power output at 4 mmol L(-1)[la(-)], peak power output during 30-s Wingate test, W(max), and mean power output during 40-min all-out trial.
Multi-robot visual SLAM using a Rao-Blackwellized particle filter

This paper describes an approach to solve the Simultaneous Localization and Mapping (SLAM) problem with a team of cooperative autonomous vehicles. We consider that each robot is equipped with a stereo camera and is able to observe visual landmarks in the environment. The SLAM approach presented here is feature-based, thus the map is represented by a set of three dimensional landmarks each one defined by a global position in space and a visual descriptor. The robots move independently along different trajectories and make relative measurements to landmarks in the environment in order to jointly build a common map using a Rao-Blackwellized particle filter. We show results obtained in a simulated environment that validate the SLAM approach. The process of observing a visual landmark is simulated in the following way: first, the relative measurement obtained by the robot is corrupted with gaussian noise, using a noise model for a standard stereo camera. Second, the visual description of the landmark is altered by noise, simulating the changes in the descriptor which may occur when the robot observes the same landmark under different scales and viewpoints. In addition, the noise in the odometry of the robots also takes values obtained from real robots. We propose an approach to manage data associations in the context of visual features. Different experiments have been performed, with variations in the path followed by the robots and the parameters in the particle filter. Finally, the results obtained in simulation demonstrate that the approach is suitable for small robot teams.
Human detection by Neural Networks using a low-cost short-range Doppler radar sensor

In this paper, we propose the human detection technique using Neural Networks to effectively classify the Doppler signals caused by human walking along with the background noise sources. The frequency or phase feature vectors converted from the given input signal are directly used as the input of Neural Networks. In addition, Gaussian noise is added in the input nodes of Neural Network in order to prevent the overfitting problem. We developed the low-cost & short-range K-band Doppler radar for the experiment. The proposed technique was examined with human walking data accompanied with the background noises caused by the fan, rain, snow, and other outdoor environmental factors. The trained Neural Network detection technique can detect human walking with 95.2% of the true positive rate and it has 4.6% of the false positive rate.
Psychological health monitoring for pilots and astronauts by tracking sleep-stress-emotion changes

The effects of stress on human behavior and performance have been well recognized. However, little has been investigated on the impact of psychological stress on pilots and astronauts. Living a life of constantly altered sleeping hours, pilots or astronauts are experiencing sleep stage fragments or long working hours with little or no sleep. The relationship between sleep and stress is reciprocal. Studies have shown that even in low-stress conditions, sleep-deprived participants demonstrate anger and anxiety a lot more frequently than participants with normal sleep quality and hours. In this paper, we introduce a new psychological health-monitoring framework. The prognostics sensors include Actiwatch for sleep monitoring, BioPatch from Zephyr to collect electrocardiogram (ECG) for heart rate and heart rate variability (HRV) for emotion changing monitoring. In this paper, a new Sleep-HRV-Emotion analytical model is proposed to assess the risk of emotion changes in real time. Prognostics analysis and prediction of psychological health is performed based on the proposed new model. Based on the output of the prognostics analysis, several stress intervention strategies are suggested both for the general public as well as for pilots and astronauts in particular. These interventions include the time to rest/sleep, hours of sleep, ultrasound pulses, and Yoga breathing. As such, these intervention strategies help recover, preserve, or improve pilot and astronaut performance, especially during long-duration space flights, and, ultimately, human settlement on other planetary bodies.
Audio Visual Scene-Aware Dialog (AVSD) Challenge at DSTC7

Scene-aware dialog systems will be able to have conversations with users about the objects and events around them. Progress on such systems can be made by integrating state-of-the-art technologies from multiple research areas including end-to-end dialog systems visual dialog,and video description. We introduce the Audio Visual SceneAware Dialog (AVSD) challenge and dataset. In this challenge, which is one track of the 7th Dialog System Technology Challenges (DSTC7) workshop1, the task is to build a system that generates responses in a dialog about an input video.
CONTRAST ENHANCEMENT OF COLOR IMAGES USING IMPROVED RETINEX METHOD

Color images provide large information for human visual perception compared to grayscale images. Color image enhancement methods enhance the visual data to increase the clarity of the color image. It increases human perception of information. Different color image contrast enhancement methods are used to increase the contrast of the color images. The Retinex algorithms enhance the color images similar to the scene perceived by the human eye. Multiscale retinex with color restoration (MSRCR) is a type of retinex algorithm. The MSRCR algorithm results in graying out and halo artifacts at the edges of the images. So here the focus is on improving the MSRCR algorithm by combining it with contrast limited adaptive histogram equalization (CLAHE) using image.
Sentence Simplification with Memory-Augmented Neural Networks

Sentence simplification aims to simplify the content and structure of complex sentences, and thus make them easier to interpret for human readers, and easier to process for downstream NLP applications. Recent advances in neural machine translation have paved the way for novel approaches to the task. In this paper, we adapt an architecture with augmented memory capacities called Neural Semantic Encoders (Munkhdalai and Yu, 2017) for sentence simplification. Our experiments demonstrate the effectiveness of our approach on different simplification datasets, both in terms of automatic evaluation measures and human judgments.
A W band low-loss waveguide-to-microstrip probe transition for millimeter-wave applications

Waveguide-to-microstrip transitions are indispensable in millimeter components to combine planar integrated circuits with waveguide transmission elements for system researches and applications. In this paper, a W band low-loss broadband waveguide-to-microstrip probe transition is designed, optimized, fabricated and measured, which turns out to have very low insertion loss from 85GHz to 105GHz. This probe transition is very suitable for MIC and MMIC module applications around 94GHz.
On the Role of Design Patterns in Quality-Driven Re-engineering

Design patterns have been widely adopted and well investigated by the software engineering community over the past decade. However, their primary use is still associated with forward engineering and the design phase of the software life-cycle. In this paper, we would like to examine design patterns from a different perspective namely, their classi£cation and usage for software re-engineering and restructuring. Speci£cally, twenty three design patterns originally presented in the “Gang of Four” book are reclassi£ed for re-engineering purposes into two major categories, primitive and complex. Moreover, their relationships and their impact to speci£c re-engineering objectives are presented in terms of a layered model that is denoted by six different relations namely : uses, re£nes, con¤icts, is-similar-to, combines-with, and requires. The paper also discusses how the classi£cation scheme can be applied for the re-engineering and restructuring of object-oriented systems.
Genomic cloning, chromosomal mapping, and expression analysis of Msal-2

Mutations of SALL1 related to spalt of Drosophila have been found to cause Townes-Brocks syndrome, suggesting a function of SALL1 for the development of anus, limbs, ears, and kidneys. No function is yet known for SALL2, another human spalt-like gene. The structure of SALL2 is different from SALL1 and all other vertebrate spalt-like genes described in mouse, Xenopus, and Medaka, suggesting that SALL2-like genes might also exist in other vertebrates. Consistent with this hypothesis, we isolated and characterized a SALL2 homologous mouse gene, Msal-2. In contrast to other vertebrate spalt-like genes both SALL2 and Msal-2 encode only three double zinc finger domains, the most carboxyterminal of which only distantly resembles spalt-like zinc fingers. The evolutionary conservation of SALL2/Msal-2 suggests that two lines of sal-like genes with presumably different functions arose from an early evolutionary duplication of a common ancestor gene. Msal-2 is expressed throughout embryonic development but also in adult tissues, predominantly in brain. However, the function of SALL2/Msal-2 still needs to be determined.
Antibacterial effects of oregano (Origanum vulgare) against gram negative bacilli

The oil, aqueous infusion and decoction of oregano (Origanum vulgare), of the family Limiaceae, were asessed for antibacterial activity against 11 different genera of Gram–ve bacilli viz., Aeromonas hydrophila, Citrobacter sp., Enterobacter aerogenese, Escherichia coli, Flavobacterium sp., Klebsiella ozaenae, K. pneumoniae, Proteus mirabilis, Pseudomonas aeruginosa, Salmonella typhi, S. paratyphi B, Serratia marcescens and Shigella dysenteriae, by disc diffusion method. Oregano oil exhibited the highest activity against Citrobacter species with mean zone of inhibition of 24.0 mm ± 0.5. The aqueous infusion also showed significant inhibitory activity against Klebsiella pneumoniae (20.1 mm ± 6.1 SD), Klebsiella ozaenae (19.5 mm ± 0.5 SD) and Enterobacter aerogenes (18.0 mm). Besides, all isolates were found resistant to the aqueous decoction of oregano seeds.
#Sleepyteens: Social media use in adolescence is associated with poor sleep quality, anxiety, depression and low self-esteem.

This study examined how social media use related to sleep quality, self-esteem, anxiety and depression in 467 Scottish adolescents. We measured overall social media use, nighttime-specific social media use, emotional investment in social media, sleep quality, self-esteem and levels of anxiety and depression. Adolescents who used social media more - both overall and at night - and those who were more emotionally invested in social media experienced poorer sleep quality, lower self-esteem and higher levels of anxiety and depression. Nighttime-specific social media use predicted poorer sleep quality after controlling for anxiety, depression and self-esteem. These findings contribute to the growing body of evidence that social media use is related to various aspects of wellbeing in adolescents. In addition, our results indicate that nighttime-specific social media use and emotional investment in social media are two important factors that merit further investigation in relation to adolescent sleep and wellbeing.
Determining Optimal Police Patrol Areas with Maximal Covering and Backup Covering Location Models

This paper presents a new method for determining efficient spatial distributions of police patrol areas. This method employs a traditional maximal covering formulation and an innovative backup covering formulation to provide alternative optimal solutions to police decision makers, and to address the lack of objective quantitative methods for police area design in the literature or in practice. This research demonstrates that operations research methods can be used in police decision making, presents a new backup coverage model that is appropriate for patrol area design, and encourages the integration of geographic information systems and optimal solution procedures. The models and methods are tested with the police geography of Dallas, TX. The optimal solutions are compared with the existing police geography, showing substantial improvement in number of incidents covered as well as total distance traveled.
JSAI: a static analysis platform for JavaScript

JavaScript is used everywhere from the browser to the server, including desktops and mobile devices. However, the current state of the art in JavaScript static analysis lags far behind that of other languages such as C and Java. Our goal is to help remedy this lack. We describe JSAI, a formally specified, robust abstract interpreter for JavaScript. JSAI uses novel abstract domains to compute a reduced product of type inference, pointer analysis, control-flow analysis, string analysis, and integer and boolean constant propagation. Part of JSAI's novelty is user-configurable analysis sensitivity, i.e., context-, path-, and heap-sensitivity. JSAI is designed to be provably sound with respect to a specific concrete semantics for JavaScript, which has been extensively tested against a commercial JavaScript implementation. We provide a comprehensive evaluation of JSAI's performance and precision using an extensive benchmark suite, including real-world JavaScript applications, machine generated JavaScript code via Emscripten, and browser addons. We use JSAI's configurability to evaluate a large number of analysis sensitivities (some well-known, some novel) and observe some surprising results that go against common wisdom. These results highlight the usefulness of a configurable analysis platform such as JSAI.
Image Scale 1 Image Scale 2 Merge Image

In this work, we revisit atrous convolution, a powerful tool to explicitly adjust filter’s field-of-view as well as control the resolution of feature responses computed by Deep Convolutional Neural Networks, in the application of semantic image segmentation. To handle the problem of segmenting objects at multiple scales, we design modules which employ atrous convolution in cascade or in parallel to capture multi-scale context by adopting multiple atrous rates. Furthermore, we propose to augment our previously proposed Atrous Spatial Pyramid Pooling module, which probes convolutional features at multiple scales, with image-level features encoding global context and further boost performance. We also elaborate on implementation details and share our experience on training our system. The proposed ‘DeepLabv3’ system significantly improves over our previous DeepLab versions without DenseCRF post-processing and attains comparable performance with other state-of-art models on the PASCAL VOC 2012 semantic image segmentation benchmark.
Secondary syphilis simulating oral hairy leukoplakia.

We describe a case of secondary syphilis of the tongue in which the main clinical presentation of the disease was similar to oral hairy leukoplakia. In a man who was HIV seronegative, the first symptom was a dryness of the throat followed by a feeling of foreign body in the tongue. Lesions were painful without cutaneous manifestations of secondary syphilis. IgM-fluorescent treponemal antibody test and typical serologic parameters promptly led to the diagnosis of secondary syphilis. We initiated an appropriate antibiotic therapy using benzathine penicillin, which induced healing of the tongue lesions. The differential diagnosis of this lesion may include oral squamous carcinoma, leukoplakia, candidosis, lichen planus, and, especially, hairy oral leukoplakia. This case report emphasizes the importance of considering secondary syphilis in the differential diagnosis of hairy oral leukoplakia. Depending on the clinical picture, the possibility of syphilis should not be overlooked in the differential diagnosis of many diseases of the oral mucosa.
Braodcast Chromatic Numbers of Graphs

A function π : V → {1, . . . , k} is a broadcast coloring of order k if π(u) = π(v) implies that the distance between u and v is more than π(u). The minimum order of a broadcast coloring is called the broadcast chromatic number of G, and is denoted χb(G). In this paper we introduce this coloring and study its properties. In particular, we explore the relationship with the vertex cover and chromatic numbers. While there is a polynomial-time algorithm to determine whether χb(G) ≤ 3, we show that it is NP-hard to determine if χb(G) ≤ 4. We also determine the maximum broadcast chromatic number of a tree, and show that the broadcast chromatic number of the infinite grid is finite.