nils-herrmann/clean_scirepeval huggingface dataset:
The Pattern Glare Test: a review and determination of normative values.

Pattern glare is characterised by symptoms of visual perceptual distortions and visual stress on viewing striped patterns. People with migraine or Meares-Irlen syndrome (visual stress) are especially prone to pattern glare. The literature on pattern glare is reviewed, and the goal of this study was to develop clinical norms for the Wilkins and Evans Pattern Glare Test. This comprises three test plates of square wave patterns of spatial frequency 0.5, 3 and 12 cycles per degree (cpd). Patients are shown the 0.5 cpd grating and the number of distortions that are reported in response to a list of questions is recorded. This is repeated for the other patterns. People who are prone to pattern glare experience visual perceptual distortions on viewing the 3 cpd grating, and pattern glare can be quantified as either the sum of distortions reported with the 3 cpd pattern or as the difference between the number of distortions with the 3 and 12 cpd gratings, the '3-12 cpd difference'. In study 1, 100 patients consulting an optometrist performed the Pattern Glare Test and the 95th percentile of responses was calculated as the limit of the normal range. The normal range for the number of distortions was found to be <4 on the 3 cpd grating and <2 for the 3-12 cpd difference. Pattern glare was similar in both genders but decreased with age. In study 2, 30 additional participants were given the test in the reverse of the usual testing order and were compared with a sub-group from study 1, matched for age and gender. Participants experienced more distortions with the 12 cpd grating if it was presented after the 3 cpd grating. However, the order did not influence the two key measures of pattern glare. In study 3, 30 further participants who reported a medical diagnosis of migraine were compared with a sub-group of the participants in study 1 who did not report migraine or frequent headaches, matched for age and gender. The migraine group reported more symptoms on viewing all gratings, particularly the 3 cpd grating. The only variable to be significantly different between the groups was the 3-12 cpd difference. In conclusion, people have an abnormal degree of pattern glare if they have a Pattern Glare Test score of >3 on the 3 cpd grating or a score of >1 on the 3-12 cpd difference. The literature suggests that these people are likely to have visual stress in everyday life and may therefore benefit from interventions designed to alleviate visual stress, such as precision tinted lenses.
Autonomous Visual Mapping and Exploration With a Micro Aerial Vehicle

Cameras are a natural fit for micro aerial vehicles (MAVs) due to their low weight, low power consumption, and two-dimensional field of view. However, computationally-intensive algorithms are required to infer the 3D structure of the environment from 2D image data. This requirement is made more difficult with the MAV’s limited payload which only allows for one CPU board. Hence, we have to design efficient algorithms for state estimation, mapping, planning, and exploration. We implement a set of algorithms on two different vision-based MAV systems such that these algorithms enable the MAVs to map and explore unknown environments. By using both self-built and off-the-shelf systems, we show that our algorithms can be used on different platforms. All algorithms necessary for autonomous mapping and exploration run on-board the MAV. Using a front-looking stereo camera as the main sensor, we maintain a tiled octree-based 3D occupancy map. The MAV uses this map for local navigation and frontier-based exploration. In addition, we use a wall-following algorithm as an alternative exploration algorithm in open areas where frontier-based exploration underperforms. During the exploration, data is transmitted to the ground station which runs ∗http://people.inf.ethz.ch/hengli large-scale visual SLAM. We estimate the MAV’s state with inertial data from an IMU together with metric velocity measurements from a custom-built optical flow sensor and pose estimates from visual odometry. We verify our approaches with experimental results, which to the best of our knowledge, demonstrate our MAVs to be the first vision-based MAVs to autonomously explore both indoor and outdoor environments.
Bangla Sentence Correction Using Deep Neural Network Based Sequence to Sequence Learning

Development in deep neural networks in particular to natural language processing has motivated researchers to apply these techniques in solving challenging problems like machine translation, automatic grammar checking, etc. In this paper, we address the problem of Bangla sentence correction and auto completion using decoder-encoder based sequence-to-sequence recurrent neural network with long short term memory cells. For this purpose, we have constructed a standard benchmark dataset incorporating mis-arrangement of words, missing words and sentence completion tasks. Based on the dataset we have trained our model and achieved 79% accuracy on the test dataset. We have made all our methods and datasets available for future use of the other researchers from: . An online tool have also been developed based on our methods and readily available to use from: http://brl.uiu.ac.bd/s2s.
Robust initialization of monocular visual-inertial estimation on aerial robots

In this paper, we propose a robust on-the-fly estimator initialization algorithm to provide high-quality initial states for monocular visual-inertial systems (VINS). Due to the non-linearity of VINS, a poor initialization can severely impact the performance of either filtering-based or graph-based methods. Our approach starts with a vision-only structure from motion (SfM) to build the up-to-scale structure of camera poses and feature positions. By loosely aligning this structure with pre-integrated IMU measurements, our approach recovers the metric scale, velocity, gravity vector, and gyroscope bias, which are treated as initial values to bootstrap the nonlinear tightly-coupled optimization framework. We highlight that our approach can perform on-the-fly initialization in various scenarios without using any prior information about system states and movement. The performance of the proposed approach is verified through the public UAV dataset and real-time onboard experiment. We make our implementation open source, which is the initialization part integrated in the VINS-Mono1.
Prevention and management of postpartum hemorrhage.

Postpartum hemorrhage, the loss of more than 500 mL of blood after delivery, occurs in up to 18 percent of births and is the most common maternal morbidity in developed countries. Although risk factors and preventive strategies are dearly documented, not all cases are expected or avoidable. Uterine atony is responsible for most cases and can be managed with uterine massage in conjunction with oxytocin, prostaglandins, and ergot alkaloids. Retained placenta is a less common cause and requires examination of the placenta, exploration of the uterine cavity, and manual removal of retained tissue. Rarely, an invasive placenta causes postpartum hemorrhage and may require surgical management. Traumatic causes include lacerations, uterine rupture, and uterine inversion. Coagulopathies require dotting factor replacement for the identified deficiency. Early recognition, systematic evaluation and treatment, and prompt fluid resuscitation minimize the potentially serious outcomes associated with postpartum hemorrhage.
Determination of normal values for navicular drop during walking: a new model correcting for foot length and gender

The navicular drop test is a measure to evaluate the function of the medial longitudinal arch, which is important for examination of patients with overuse injuries. Conflicting results have been found with regard to differences in navicular drop between healthy and injured participants. Normal values have not yet been established as foot length, age, gender, and Body Mass Index (BMI) may influence the navicular drop. The purpose of the study was to investigate the influence of foot length, age, gender, and BMI on the navicular drop during walking.



Navicular drop was measured with a novel technique (Video Sequence Analysis, VSA) using 2D video. Flat reflective markers were placed on the medial side of the calcaneus, the navicular tuberosity, and the head of the first metatarsal bone. The navicular drop was calculated as the perpendicular distance between the marker on the navicular tuberosity and the line between the markers on calcaneus and first metatarsal head. The distance between the floor and the line in standing position between the markers on calcaneus and first metatarsal were added afterwards.



280 randomly selected participants without any foot problems were analysed during treadmill walking (144 men, 136 women). Foot length had a significant influence on the navicular drop in both men (p < 0.001) and women (p = 0.015), whereas no significant effect was found of age (p = 0.27) or BMI (p = 0.88). Per 10 mm increase in foot length, the navicular drop increased by 0.40 mm for males and 0.31 mm for females. Linear models were created to calculate the navicular drop relative to foot length.



The study demonstrated that the dynamic navicular drop is influenced by foot length and gender. Lack of adjustment for these factors may explain, at least to some extent, the disagreement between previous studies on navicular drop. Future studies should account for differences in these parameters.
Evolution and social anxiety. The role of attraction, social competition, and social hierarchies.

If human social anxiety is not predominately about the fear of physical injury or attack, as it is in other animals, then, to understand human social anxiety (i.e., fear of evaluation), it is necessary to consider why certain types of relationships are so important. Why do humans need to court the good feelings of others and fear not doing so? And why, when people wish to appear attractive to others (e.g., to make friends, date a desired sexual partner, or give a good presentation), do some people become so overwhelmed with anxiety that they behave submissively and fearfully (which can be seen as unattractive) or are avoidant? This article has suggested that humans have evolved to compete for attractiveness to make good impressions because these are related to eliciting important social resources and investments from others. These, in turn, have been linked to inclusive fitness and have physiological regulating effects. Being allocated a low social rank or ostracized carries many negative consequences for controlling social resources and physiological regulation. Social anxiety, like shame, can be adaptive to the extent that it helps people to "stay on track" with what is socially acceptable and what is not and could result in social sanction and exclusion. However, dysfunctional social anxiety is the result of activation of basic defensive mechanisms (and modules for) for threat detection and response (e.g., inhibition, eye-gaze avoidance, flight, or submission) that can be recruited rapidly for dealing with immediate threats, override conscious wishes, and interfere with being seen as a "useful associate." Second, this article has suggested that socially anxious people are highly attuned to the competitive dynamics of trying to elicit approval and investment from others but that they perceive themselves to start from an inferior (i.e., low-rank) position and, because of this, activate submissive defensives when attempting to present themselves as confident, able, and attractive to others. These submissive defenses (which evolved to inhibit animals in low-rank positions from making claims on resources or up-rank bids) interfere with confident performance, leading to a failure cycle. While psychological therapies may target specific modules, cognitions, and behaviors (e.g., damage limitation behaviors, eyes gaze avoidance, theory of mind beliefs) that underpin social anxiety, drugs may work by having a more generalized effect on the threat-safety balance such that there is a different "weighting" given to various social threats and opportunities. If social anxiety (and disorders associated with it) are increasing in the modern age, one reason may be invigorated competition for social prestige, attractiveness, and resources.
Investigating Esperanto's Statistical Proportions Relative to other Languages using Neural Networks and Zipf's Law

Esperanto is a constructed natural language, which was intended to be an easy-to-learn lingua franca. Zipf's law models the statistical proportions of various phenomena in human ecology, including natural languages. Given Esperanto’s artificial origins, one wonders how “natural” it appears, relative to other natural languages, in the context of Zipf’s law. To explore this question, we collected a total of 283 books from six languages: English, French, German, Italian, Spanish, and Esperanto. We applied Zipf-based metrics on our corpus to extract distributions for word, word distance, word bigram, word trigram, and word length for each book. Statistical analyses show that Esperanto’s statistical proportions are similar to those of other languages. We then trained artificial neural networks (ANNs) to classify books according to language. The ANNs achieved high accuracy rates (86.3% to 98.6%). Subsequent analysis identified German as having the most unique proportions, followed by Esperanto, Italian, Spanish, English, and French. Analysis of misclassified patterns shows that Esperanto’s statistical proportions resemble mostly those of German and Spanish, and least those of French and Italian.
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks

Detecting test samples drawn sufficiently far away from the training distribution statistically or adversarially is a fundamental requirement for deploying a good classifier in many real-world machine learning applications. However, deep neural networks with the softmax classifier are known to produce highly overconfident posterior distributions even for such abnormal samples. In this paper, we propose a simple yet effective method for detecting any abnormal samples, which is applicable to any pre-trained softmax neural classifier. We obtain the class conditional Gaussian distributions with respect to (lowand upper-level) features of the deep models under Gaussian discriminant analysis, which result in a confidence score based on the Mahalanobis distance. While most prior methods have been evaluated for detecting either out-of-distribution or adversarial samples, but not both, the proposed method achieves the state-of-the-art performances for both cases in our experiments. Moreover, we found that our proposed method is more robust in harsh cases, e.g., when the training dataset has noisy labels or small number of samples. Finally, we show that the proposed method enjoys broader usage by applying it to class-incremental learning: whenever out-of-distribution samples are detected, our classification rule can incorporate new classes well without further training deep models.
The loss function of sensorimotor learning.

Motor learning can be defined as changing performance so as to optimize some function of the task, such as accuracy. The measure of accuracy that is optimized is called a loss function and specifies how the CNS rates the relative success or cost of a particular movement outcome. Models of pointing in sensorimotor control and learning usually assume a quadratic loss function in which the mean squared error is minimized. Here we develop a technique for measuring the loss associated with errors. Subjects were required to perform a task while we experimentally controlled the skewness of the distribution of errors they experienced. Based on the change in the subjects' average performance, we infer the loss function. We show that people use a loss function in which the cost increases approximately quadratically with error for small errors and significantly less than quadratically for large errors. The system is thus robust to outliers. This suggests that models of sensorimotor control and learning that have assumed minimizing squared error are a good approximation but tend to penalize large errors excessively.
The Impacts of Carbon Tariff on Green Supply Chain Design

With the growing awareness of global warming, many companies worldwide are improving their supply chain sustainability, under pressure from the government or their own shareholders. However, not all countries around the world equally emphasize the threat. For example, some countries still have not implemented any carbon dioxide emission regulations to address this problem. Carbon regulations in only subglobal areas may result in even higher global emissions because of carbon leakage. One possible approach to cope with the carbon leakage problem is to impose carbon tariffs on the goods from unregulated countries. In this paper, a mathematical model is built to explore the impacts of carbon tariff imposition on the supply chain network design, where the carbon tariff is imposed when the goods flow from unregulated countries to regulated countries. Moreover, our paper provides a complete experimental study by applying this model in a real case study, Company G, a major Taiwan-based multinational company in the electronic products industry. The outcomes demonstrate the conditions where the introduction of carbon tariffs forces firms from unregulated countries to take actions to reduce carbon emissions.
A Dynamic Model of Crowdfunding

Crowdfunding is quickly emerging as an alternative to traditional methods of funding new products. In a crowdfunding campaign, a seller solicits financial contributions from a crowd, usually in the form of pre-buying an unrealized product, and commits to producing the product if the total amount pledged is above a certain threshold. We provide a model of crowdfunding in which consumers arrive sequentially and make decisions about whether to pledge or not. Pledging is not costless, and hence consumers would prefer not to pledge if they think the campaign will not succeed. This can lead to cascades where a campaign fails to raise the required amount even though there are enough consumers who want the product. The paper introduces a novel stochastic process --- anticipating random walks --- to analyze this problem. The analysis helps explain why some campaigns fail and some do not, and provides guidelines about how sellers should design their campaigns in order to maximize their chances of success. More broadly, Anticipating Random Walks can also find application in settings where agents make decisions sequentially and these decisions are not just affected by past actions of others, but also by how they will impact the decisions of future actors as well.
Metadata Enrichment of Multi-disciplinary Digital Library: A Semantic-Based Approach

In the scientific digital libraries, some papers from different research communities can be described by community-dependent keywords even if they share a semantically similar topic. Articles that are not tagged with enough keyword variations are poorly indexed in any information retrieval system which limits potentially fruitful exchanges between scientific disciplines. In this paper, we introduce a novel experimentally designed pipeline for multi-label semantic-based tagging developed for open-access metadata digital libraries. The approach starts by learning from a standard scientific categorization and a sample of topic tagged articles to find semantically relevant articles and enrich its metadata accordingly. Our proposed pipeline aims to enable researchers reaching articles from various disciplines that tend to use different terminologies. It allows retrieving semantically relevant articles given a limited known variation of search terms. In addition to achieving an accuracy that is higher than an expanded query based method using a topic synonym set extracted from a semantic network, our experiments also show a higher computational scalability versus other comparable techniques. We created a new benchmark extracted from the open-access metadata of a scientific digital library and published it along with the experiment code to allow further research in the topic.
Arabic sentiment analysis: Lexicon-based and corpus-based

The emergence of the Web 2.0 technology generated a massive amount of raw data by enabling Internet users to post their opinions, reviews, comments on the web. Processing this raw data to extract useful information can be a very challenging task. An example of important information that can be automatically extracted from the users' posts and comments is their opinions on different issues, events, services, products, etc. This problem of Sentiment Analysis (SA) has been studied well on the English language and two main approaches have been devised: corpus-based and lexicon-based. This paper addresses both approaches to SA for the Arabic language. Since there is a limited number of publically available Arabic dataset and Arabic lexicons for SA, this paper starts by building a manually annotated dataset and then takes the reader through the detailed steps of building the lexicon. Experiments are conducted throughout the different stages of this process to observe the improvements gained on the accuracy of the system and compare them to corpus-based approach.
Reducing Energy Costs in Electric-Motor-Driven Systems: Savings Through Output Power Reduction and Energy Regeneration

Electric-motor systems convert nearly half of worldwide electric energy into the mechanical energy ultimately used in the final application or process. The integral optimization of electric-motor-driven systems (EMODSs), including the use of high-efficiency, well-sized components, is the key strategy to effectively maximize their overall efficiency. However, the largest energy savings potential in motor-driven systems is associated with the reduction of the power required by the driven equipment through speed/torque control and/or with the partial reuse of the energy stored in the system. In this article, the basic principles and some practical examples of output power reduction and energy regeneration in EMODSs are presented.
A high speed frequency divider in 0.18μm CMOS for wireless sensor networks

This paper presents a 1V low-voltage high speed frequency divider-by-2 which is fabricated in a standard 0.18μm TSMC RF CMOS process. Employing parallel current switching topology, the 2:1 frequency divider operates up to 6.5GHz while consuming 4.64mA current with test buffers at a supply voltage of 1V, and the chip area of the core circuit is 0.065×0.055mm2.
AOL's Named Entity Resolver: Solving Disambiguation via Document Strongly Connected Components and Ad-Hoc Edges Construction

Named Entity Disambiguation is the task of disambiguating named entity mentions in unstructured text and linking them to their corresponding entries in a large knowledge base such as Freebase. Practically, each text match in a given document should be mapped to the correct entity out of the corresponding entities in the knowledge base or none of them if no correct entity is found (Empty Entry). The case of an empty entry makes the problem at hand more complex, but by solving it, one can successfully cope with missing and erroneous data as well as unknown entities. In this work we present AOL's Named Entity Resolver which was designed to handle real life scenarios including empty entries. As part of the automated news analysis platform, it processes over 500K news articles a day, entities from each article are extracted and disambiguated. According to our experiments, AOL's resolver shows much better results in disambiguating entities mapped to Wikipedia or Freebase compared to industry leading products.