nils-herrmann/clean_scirepeval huggingface dataset:
Real time pothole detection using Android smartphones with accelerometers

The importance of the road infrastructure for the society could be compared with importance of blood vessels for humans. To ensure road surface quality it should be monitored continuously and repaired as necessary. The optimal distribution of resources for road repairs is possible providing the availability of comprehensive and objective real time data about the state of the roads. Participatory sensing is a promising approach for such data collection. The paper is describing a mobile sensing system for road irregularity detection using Android OS based smart-phones. Selected data processing algorithms are discussed and their evaluation presented with true positive rate as high as 90% using real world data. The optimal parameters for the algorithms are determined as well as recommendations for their application.
A Survey of Hyperspectral Image Classification in Remote Sensing

Hyperspectral image processing has been a very dynamic area in remote sensing and other applications in recent years. Hyperspectral images provide ample spectral information to identify and distinguish spectrally similar materials for more accurate and detailed information extraction. Wide range of advanced classification techniques are available based on spectral information and spatial information. To improve classification accuracy it is essential to identify and reduce uncertainties in image processing chain. This paper presents the current practices, problems and prospects of hyperspectral image classification. In addition, some important issues affecting classification performance are discussed.
Sleep during Ramadan intermittent fasting.

During the month of Ramadan intermittent fasting, Muslims eat exclusively between sunset and sunrise, which may affect nocturnal sleep. The effects of Ramadan on sleep and rectal temperature (Tre) were examined in eight healthy young male subjects who reported at the laboratory on four occasions: (i) baseline 15 days before Ramadan (BL); (ii) on the eleventh day of Ramadan (beginning of Ramadan, BR); (iii) on the twenty-fifth day of Ramadan (end of Ramadan, ER); and (iv) 2 weeks after Ramadan (AR). Although each session was preceded by an adaptation night, data from the first night were discarded. Polysomnography was taken on ambulatory 8-channel Oxford Medilog MR-9000 II recorders. Standard electroencephalogram (EEG), electro-oculogram (EOG) and electromyogram (EMG) recordings were scored visually with the PhiTools ERA. The main finding of the study was that during Ramadan sleep latency is increased and sleep architecture modified. Sleep period time and total sleep time decreased in BR and ER. The proportion of non-rapid eye movement (NREM) sleep increased during Ramadan and its structure changed, with an increase in stage 2 proportion and a decrease in slow wave sleep (SWS) duration. Rapid eye movement (REM) sleep duration and proportion decreased during Ramadan. These changes in sleep parameters were associated with a delay in the occurrence of the acrophase of Tre and an increase in nocturnal Tre during Ramadan. However, the 24-h mean value (mesor) of Tre did not vary. The nocturnal elevation of Tre was related to a 2-3-h delay in the acrophase of the circadian rhythm. The amplitude of the circadian rhythm of Tre was decreased during Ramadan. The effects of Ramadan fasting on nocturnal sleep, with an increase in sleep latency and a decrease in SWS and REM sleep, and changes in Tre, were attributed to the inversion of drinking and meal schedule, rather than to an altered energy intake which was preserved in this study.
Low power current mode ADC for CMOS sensor IC

Current-mode algorithmic pipelined analog-to-digital converters (ADC) are suitable for sensor applications due to their area and power advantage at low resolutions. In applications of distributed sensing using redundant sensors, the speed and the resolution of the ADC is less important than the energy per bit conversion. Such performance was achieved by using the current-mode technique with transistors operating in the sub-threshold region. Both sub-threshold and current mode techniques allow for low-voltage and low-power operation. An improved current-mode, 6-bit, 125 kHz algorithmic ADC was designed for an integrated sensor in 0.18 /spl mu/m technology. The power consumption of the ADC is under 6 /spl mu/W and less than 8 pJ per bit.
Exploiting deep semantics and compositionality of natural language for Human-Robot-Interaction

We are developing a natural language interface for human robot interaction that implements reasoning about deep semantics in natural language. To realize the required deep analysis, we employ methods from cognitive linguistics, namely the modular and compositional framework of Embodied Construction Grammar (ECG) . Using ECG, robots are able to solve fine-grained reference resolution problems and other issues related to deep semantics and compositionality of natural language. This also includes verbal interaction with humans to clarify commands and queries that are too ambiguous to be executed safely. We implement our NLU framework as a ROS package and present proof-of-concept scenarios with different robots, as well as a survey on the state of the art in knowledge-based language HRI.
Targeting interleukin-1 in heart disease.

Targeting Interleukin-1 in Heart Disease   Dallas, TX 75231 Circulation doi: 10.1161/CIRCULATIONAHA.113.003199 2013;128:1910-1923 Circulation. http://circ.ahajournals.org/content/128/17/1910 World Wide Web at: The online version of this article, along with updated information and services, is located on the
Association of park size, distance, and features with physical activity in neighborhood parks.

We studied whether park size, number of features in the park, and distance to a park from participants' homes were related to a park being used for physical activity.



We collected observational data on 28 specific features from 33 parks. Adult residents in surrounding areas (n=380) completed 7-day physical activity logs that included the location of their activities. We used logistic regression to examine the relative importance of park size, features, and distance to participants' homes in predicting whether a park was used for physical activity, with control for perceived neighborhood safety and aesthetics.



Parks with more features were more likely to be used for physical activity; size and distance were not significant predictors. Park facilities were more important than were park amenities. Of the park facilities, trails had the strongest relationship with park use for physical activity.



Specific park features may have significant implications for park-based physical activity. Future research should explore these factors in diverse neighborhoods and diverse parks among both younger and older populations.
A Compact 12-Way Slotted Waveguide Power Combiner for Ka-Band Applications

A power divider/combiner based on a double sided slotted waveguide geometry suitable for Ka-band applications is proposed. This structure allows up to 50% reduction of the total device length compared to previous designs of this type without compromising manufacturing complexity or combining efficiency. Efficient design guidelines based on an equivalent circuit technique are provided and the performance is demonstrated by means of a 12-way divider/combiner prototype operating in the range 29-31 GHz. Numerical simulations show that back to back insertion loss of 1.19 dB can be achieved, corresponding to a combining efficiency of 87%. The design is validated by means of manufacturing and testing an experimental prototype with measured back-to-back insertion loss of 1.83 dB with a 3 dB bandwidth of 20.8%, corresponding to a combining efficiency of 81%.
Unconstrained Multimodal Multi-Label Learning

Multimodal learning has been mostly studied by assuming that multiple label assignments are independent of each other and all the modalities are available. In this paper, we consider a more general problem where the labels contain dependency relationships and some modalities are likely to be missing. To this end, we propose a multi-label conditional restricted Boltzmann machine (ML-CRBM), which handles modality completion , fusion, and multi-label prediction in a unified framework. The proposed model is able to generate missing modalities based on observed ones, by explicitly modelling and sampling their conditional distributions. After that, it can discriminatively fuse multiple modalities to obtain shared representations under the supervision of class labels. To consider the co-occurrence of the labels, the proposed model formulates the multi-label prediction as a max-margin-based multi-task learning problem. Model parameters can be jointly learned by seeking a balance between being generative for modality generation and being discriminative for label prediction. We perform a series of experiments in terms of classification, visualization, and retrieval, and the experimental results clearly demonstrate the effectiveness of our method.
Elimination of alignment stage in feature extraction for a Slepian-Wolf fingerprint biometric system

In this paper, a simple method for extracting fingerprint features is presented. Feature extraction is carried out with regard to system requirement, in order to safely save the biometrics. In the feature extraction stage of previous methods, fingerprint images should always be aligned in the beginning. In our method the features are extracted in such a way that rotation and translation have no effect on the data obtained, and hence we don't need the alignment stage, which leads to easier implementation of biometric systems.
Analyzing and Improving Representations with the Soft Nearest Neighbor Loss

We explore and expand the Soft Nearest Neighbor Loss to measure the entanglement of class manifolds in representation space: i.e., how close pairs of points from the same class are relative to pairs of points from different classes. We demonstrate several use cases of the loss. As an analytical tool, it provides insights into the evolution of class similarity structures during learning. Surprisingly, we find that maximizing the entanglement of representations of different classes in the hidden layers is beneficial for discrimination in the final layer, possibly because it encourages representations to identify class-independent similarity structures. Maximizing the soft nearest neighbor loss in the hidden layers leads not only to improved generalization but also to better-calibrated estimates of uncertainty on outlier data. Data that is not from the training distribution can be recognized by observing that in the hidden layers, it has fewer than the normal number of neighbors from the predicted class.
Parsing Pose of People with Interaction

We propose an end-to-end multi-person pose estimation model that learns to predict keypoint locations for each person in the scene, regardless of the complexity of their social interactions. While recent multi-person pose estimation algorithms achieve high performance on scenes where people do not overlap, these algorithms produce undesired outcomes, e.g. merging two people or swapping similar parts of different people, when the people in the scene are heavily occluded. To attack this issue, we have curated a subset of COCO [20] containing such scenes and call it COCO-crowd. We formulate multi-person pose estimation as a sequential prediction problem that first generates heatmaps of the potential part locations and then assembles the parts into separate instances, each representing a single person, using convolutional LSTMs. Despite using a small-scale dataset (relative to all of COCO), we achieved comparable performance to state-of-the-art methods trained on the full COCO dataset. We also evaluate our method on the Immediacy dataset [7], which consists of images with diverse social interactions, e.g. standing shoulder to shoulder, or hugging, and achieve state-of-the-art results.
A Case Study on Improving Capacity Delivery of Battery Packs via Reconfiguration

Cell imbalance in large battery packs degrades their capacity delivery, especially for cells connected in series where the weakest cell dominates their overall capacity. In this article, we present a case study of exploiting system reconfigurations to mitigate the cell imbalance in battery packs. Specifically, instead of using all the cells in a battery pack to support the load, selectively skipping cells to be discharged may actually enhance the pack’s capacity delivery. Based on this observation, we propose CSR, a Cell Skipping-assisted Reconfiguration algorithm that identifies the system configuration with (near)-optimal capacity delivery. We evaluate CSR using large-scale emulation based on empirically collected discharge traces of 40 lithium-ion cells. CSR achieves close-to-optimal capacity delivery when the cell imbalance in the battery pack is low and improves the capacity delivery by about 20% and up to 1x in the case of a high imbalance.
On the Effectiveness of Least Squares Generative Adversarial Networks

Unsupervised learning with generative adversarial networks (GANs) has proven hugely successful. Regular GANs hypothesize the discriminator as a classifier with the sigmoid cross entropy loss function. However, we found that this loss function may lead to the vanishing gradients problem during the learning process. To overcome such a problem, we propose in this paper the Least Squares Generative Adversarial Networks (LSGANs) which adopt the least squares loss function for the discriminator. We show that minimizing the objective function of LSGAN yields minimizing the Pearson $x^{2}$ divergence. There are two benefits of LSGANs over regular GANs. First, LSGANs are able to generate higher quality images than regular GANs. Second, LSGANs perform more stable during the learning process. We train LSGANs on several datasets, and the experimental results show that the images generated by LSGANs are of better quality than regular GANs. Furthermore, we evaluate the stability of LSGANs in two groups. One is to compare between LSGANs and regular GANs without gradient penalty. The other one is to compare between LSGANs with gradient penalty and WGANs with gradient penalty. We conduct four experiments to illustrate the stability of LSGANs. The other one is to compare between LSGANs with gradient penalty (LSGANs-GP) and WGANs with gradient penalty (WGANs-GP). The experimental results show that LSGANs-GP succeed in training for all the difficult architectures used in WGANs-GP, including 101-layer ResNet.
Dependency-based Pre-ordering for Chinese-English Machine Translation

In statistical machine translation (SMT), syntax-based pre-ordering of the source language is an effective method for dealing with language pairs where there are great differences in their respective word orders. This paper introduces a novel pre-ordering approach based on dependency parsing for Chinese-English SMT. We present a set of dependency-based preordering rules which improved the BLEU score by 1.61 on the NIST 2006 evaluation data. We also investigate the accuracy of the rule set by conducting human evaluations.
A Hybrid Sensor System for Indoor Air Quality Monitoring

Indoor air quality is important. It influences human productivity and health. Personal pollution exposure can be measured using stationary or mobile sensor networks, but each of these approaches has drawbacks. Stationary sensor network accuracy suffers because it is difficult to place a sensor in every location people might visit. In mobile sensor networks, accuracy and drift resistance are generally sacrificed for the sake of mobility and economy. We propose a hybrid sensor network architecture, which contains both stationary sensors (for accurate readings and calibration) and mobile sensors (for coverage). Our technique uses indoor pollutant concentration prediction models to determine the structure of the hybrid sensor network. In this work, we have (1) developed a predictive model for pollutant concentration that minimizes prediction error; (2) developed algorithms for hybrid sensor network construction; and (3) deployed a sensor network to gather data on the airflow in a building, which are later used to evaluate the prediction model and hybrid sensor network synthesis algorithm. Our modeling technique reduces sensor network error by 40.4% on average relative to a technique that does not explicitly consider the inaccuracies of individual sensors. Our hybrid sensor network synthesis technique improves personal exposure measurement accuracy by 35.8% on average compared with a stationary sensor network architecture.
Extremal Polynomials for Obtaining Bounds for Spherical Codes and Designs

We investigate two extremal problems for polynomials giving upper bounds for spherical codes and for polynomials giving lower bounds for spherical designs, respectively. We consider two basic properties of the solutions of these problems. Namely, we estimate from below the number of double zeros and find zero Gegenbauer coefficients of extremal polynomials. Our results allow us to search effectively for such solutions using a computer. The best polynomials we have obtained give substantial improvements in some cases on the previously known bounds for spherical codes and designs. Some examples are given in Section 6.
Predicting the Next Best View for 3D Mesh Refinement

3D reconstruction is a core task in many applications such as robot navigation or sites inspections. Finding the best poses to capture part of the scene is one of the most challenging topic that goes under the name of Next Best View. Recently, many volumetric methods have been proposed; they choose the Next Best View by reasoning over a 3D voxelized space and by finding which pose minimizes the uncertainty decoded into the voxels. Such methods are effective, but they do not scale well since the underlaying representation requires a huge amount of memory. In this paper we propose a novel mesh-based approach which focuses on the worst reconstructed region of the environment mesh. We define a photo-consistent index to evaluate the 3D mesh accuracy, and an energy function over the worst regions of the mesh which takes into account the mutual parallax with respect to the previous cameras, the angle of incidence of the viewing ray to the surface and the visibility of the region. We test our approach over a well known dataset and achieve state-of-the-art results.
Bayesian indoor positioning systems

In this paper, we introduce a new approach to location estimation where, instead of locating a single client, we simultaneously locate a set of wireless clients. We present a Bayesian hierarchical model for indoor location estimation in wireless networks. We demonstrate that our model achieves accuracy that is similar to other published models and algorithms. By harnessing prior knowledge, our model eliminates the requirement for training data as compared with existing approaches, thereby introducing the notion of a fully adaptive zero profiling approach to location estimation.
Microperforated Hymen Presenting Spontaneous Pregnancy with Cesarean Delivery and Hymenotomy Surgery: A Case Report

Female genital tract anomalies including imperforate hymen affect sexual life and fertility.



In the present case, we describe a pregnant woman diagnosed with imperforate hymen which never had penetrative vaginal sex. A 27-year-old married patient with 2 months of amenorrhea presented in a clinic without any other complications. Her history of difficult intercourse and prolonged menstrual flow were reported, and subsequent vaginal examination confirmed the diagnosis of imperforate hymen even though she claims to made pinhole surgery in hymen during puberty. Her urine pregnancy test was positive, and an ultrasound examination revealed 8.3 weeks pregnant. The pregnancy was followed up to 39.5 weeks when she entered in cesarean delivery in urgency. Due to perioperative complications in our study, a concomitant hymenotomy was successfully performed. The patient was discharged with the baby, and vaginal anatomy was restored.



This case study suggests that even though as microperforated hymen surgery in puberty can permit pregnancy and intervention with cesarean section and hymenotomy is a good option to reduce the resulting perioperative complications which indirectly affect the increase of the fertilisation and improvement of later sexual life.
[Depression and anxiety in menopausal women: associated factors].

to determine the prevalence of depression and anxiety in climacteric women and the probable factors responsible for its occurrence.



a transversal study that has selected 93 women attended at a climacteric outpatient clinic, from May 2006 to August 2007. Inclusion criteria were: women from 40 to 65 years old who agreed with participating in the project.



patients in hormonal therapy, hormone-therapy by implant, DIUs and depo injections in the preceding six months, endocrinopathies leading to menstrual irregularities, hepatopathies, thrombopathies, use of drugs which interfere in the menstrual cycle, anxiolytics and antidepressants (as their use indicates previous diagnosis of mood disorders), hysterectomy, oophorectomy, cancer or psychiatric disease, and patients who had been submitted to radio or chemotherapy. During the interview, four questionnaires were applied: Anamnesis, containing socio-demographic, clinical and living habits data; Blatt-Kupperman's Menopausal Index for climacteric syndrome diagnosis; Anxiety sub-scale of the Hospital Anxiety and Depression scale (HADS-A) for anxiety diagnosis; and Beck's Depression Inventory for the diagnosis of depression. Descriptive and correlation analysis among the variables, chi2 and Hosmer-Lemeshow tests were performed using the Statistica Software program, version 6.



the average depression prevalence among the patients was 36.8%, while that of anxiety was 53.7%. There was no significant difference between the prevalence of depression and anxiety in the three phases of climacterium. There was a significant relationship between the presence of moderate climacteric symptoms and the presence of mood alterations (p<0.001). Depression was more frequent in women with anxiety (OR=4.2) and insomnia (OR=4.9), having a job being a protection factor (OR=0.2). Risk factors related to anxiety were the presence of depression (OR=6.1) and antecedents of pre-menstrual tension (OR=7.0).



the prevalence of depression and anxiety is high in climacterium, being possible to detect risk factors related to their occurrence.
Bridging the gap between tracking and detecting changes in XML

There are two main approaches to manage changes in XML documents, change-tracking and diff. Changetracking tools, which record edit actions while they are performed on the source document, are able to capture the exact editing process. That is much more difficult for diff algorithms, which have to reconstruct it by comparing two different versions. Interestingly, these algorithms process both text-centric and datacentric XML documents the same way. In this paper, we show that more accurate, clear, and human-readable results can be achieved on text-centric resources, by employing specific models and algorithms. We describe and discuss a specialized diff algorithm for such a class of documents. We also compare a Java implementation of the algorithm, named JNDiff, with other general-purpose or data-oriented diff tools, focusing on the quality of their output.