nils-herrmann/clean_scirepeval huggingface dataset:
Maritime situation analysis framework: Vessel interaction classification and anomaly detection

Maritime domain awareness is critical for protecting sea lanes, ports, harbors, offshore structures like oil and gas rigs and other types of critical infrastructure against common threats and illegal activities. Typical examples range from smuggling of drugs and weapons, human trafficking and piracy all the way to terror attacks. Limited surveillance resources constrain maritime domain awareness and compromise full security coverage at all times. This situation calls for innovative intelligent systems for interactive situation analysis to assist marine authorities and security personal in their routine surveillance operations. In this article, we propose a novel situation analysis approach to analyze marine traffic data and differentiate various scenarios of vessel engagement for the purpose of detecting anomalies of interest for marine vessels that operate over some period of time in relative proximity to each other. We consider such scenarios as probabilistic processes and analyze complex vessel trajectories using machine learning to model common patterns. Specifically, we represent patterns as left-to-right Hidden Markov Models and classify them using Support Vector Machines. To differentiate suspicious activities from unobjectionable behavior, we explore fusion of data and information, including kinematic features, geospatial features, contextual information and maritime domain knowledge. Our experimental evaluation shows the effectiveness of the proposed approach using comprehensive real-world vessel tracking data from coastal waters of North America.
Pharmacologic management of neuropathic pain: Evidence-based recommendations

Patients with neuropathic pain (NP) are challenging to manage and evidence-based clinical recommendations for pharmacologic management are needed. Systematic literature reviews, randomized clinical trials, and existing guidelines were evaluated at a consensus meeting. Medications were considered for recommendation if their efficacy was supported by at least one methodologically-sound, randomized clinical trial (RCT) demonstrating superiority to placebo or a relevant comparison treatment. Recommendations were based on the amount and consistency of evidence, degree of efficacy, safety, and clinical experience of the authors. Available RCTs typically evaluated chronic NP of moderate to severe intensity. Recommended first-line treatments include certain antidepressants (i.e., tricyclic antidepressants and dual reuptake inhibitors of both serotonin and norepinephrine), calcium channel alpha2-delta ligands (i.e., gabapentin and pregabalin), and topical lidocaine. Opioid analgesics and tramadol are recommended as generally second-line treatments that can be considered for first-line use in select clinical circumstances. Other medications that would generally be used as third-line treatments but that could also be used as second-line treatments in some circumstances include certain antiepileptic and antidepressant medications, mexiletine, N-methyl-D-aspartate receptor antagonists, and topical capsaicin. Medication selection should be individualized, considering side effects, potential beneficial or deleterious effects on comorbidities, and whether prompt onset of pain relief is necessary. To date, no medications have demonstrated efficacy in lumbosacral radiculopathy, which is probably the most common type of NP. Long-term studies, head-to-head comparisons between medications, studies involving combinations of medications, and RCTs examining treatment of central NP are lacking and should be a priority for future research.
Visual Attention Models for Scene Text Recognition

In this paper we propose an approach to lexicon-free recognition of text in scene images. Our approach relies on a LSTM-based soft visual attention model learned from convolutional features. A set of feature vectors are derived from an intermediate convolutional layer corresponding to different areas of the image. This permits encoding of spatial information into the image representation. In this way, the framework is able to learn how to selectively focus on different parts of the image. At every time step the recognizer emits one character using a weighted combination of the convolutional feature vectors according to the learned attention model. Training can be done end-to-end using only word level annotations. In addition, we show that modifying the beam search algorithm by integrating an explicit language model leads to significantly better recognition results. We validate the performance of our approach on standard SVT and ICDAR'03 scene text datasets, showing state-of-the-art performance in unconstrained text recognition.
A compact on-interposer passive equalizer for chip-to-chip high-speed data transmission

In this paper, a new compact on-interposer passive equalizer was proposed for chip-to-chip high-speed data transmission on the silicon-based on-interposer channel. The proposed equalizer uses the parasitic resistance and inductance of the coil-shaped on-interposer shunt metal line structure to produce the high-pass filter for loss compensation. This results in wide-band channel equalization and low power-consumption. Moreover, the compact coil-shaped structure of the proposed equalizer allows for wide I/O and high adjustability. The remarkable performance of the proposed compact on-interposer passive equalizer is successfully demonstrated by a frequency-and time-domain simulation of up to 10 Gbps.
The microbiome of the upper airways: focus on chronic rhinosinusitis

Upper airway diseases including allergic rhinitis, chronic rhinosinusitis with or without polyps, and cystic fibrosis are characterized by substantially different inflammatory profiles. Traditionally, studies on the association of specific bacterial patterns with inflammatory profiles of diseases had been dependent on bacterial culturing. In the past 30 years, molecular biology methods have allowed bacterial culture free studies of microbial communities, revealing microbiota much more diverse than previously recognized including those found in the upper airway. At presence, the study of the pathophysiology of upper airway diseases is necessary to establish the relationship between the microbiome and inflammatory patterns to find their clinical reflections and also their possible causal relationships. Such investigations may elucidate the path to therapeutic approaches in correcting an imbalanced microbiome. In the review we summarized techniques used and the current knowledge on the microbiome of upper airway diseases, the limitations and pitfalls, and identified areas of interest for further research.
Exosome secreted by MSC reduces myocardial ischemia/reperfusion injury.

Human ESC-derived mesenchymal stem cell (MSC)-conditioned medium (CM) was previously shown to mediate cardioprotection during myocardial ischemia/reperfusion injury through large complexes of 50-100 nm. Here we show that these MSCs secreted 50- to 100-nm particles. These particles could be visualized by electron microscopy and were shown to be phospholipid vesicles consisting of cholesterol, sphingomyelin, and phosphatidylcholine. They contained coimmunoprecipitating exosome-associated proteins, e.g., CD81, CD9, and Alix. These particles were purified as a homogeneous population of particles with a hydrodynamic radius of 55-65 nm by size-exclusion fractionation on a HPLC. Together these observations indicated that these particles are exosomes. These purified exosomes reduced infarct size in a mouse model of myocardial ischemia/reperfusion injury. Therefore, MSC mediated its cardioprotective paracrine effect by secreting exosomes. This novel role of exosomes highlights a new perspective into intercellular mediation of tissue injury and repair, and engenders novel approaches to the development of biologics for tissue repair.
How To Extract Fashion Trends From Social Media? A Robust Object Detector With Support For Unsupervised Learning

With the proliferation of social media, fashion inspired from celebrities, reputed designers as well as fashion influencers has shortned the cycle of fashion design and manufacturing. However, with the explosion of fashion related content and large number of user generated fashion photos, it is an arduous task for fashion designers to wade through social media photos and create a digest of trending fashion. Designers do not just wish to have fashion related photos at one place but seek search functionalities that can let them search photos with natural language queries such as ‘red dress’, ’vintage handbags’, etc in order to spot the trends. This necessitates deep parsing of fashion photos on social media to localize and classify multiple fashion items from a given fashion photo. While object detection competitions such as MSCOCO have thousands of samples for each of the object categories, it is quite difficult to get large labeled datasets for fast fashion items. Moreover, state-of-the-art object detectors [2, 7, 9] do not have any functionality to ingest large amount of unlabeled data available on social media in order to fine tune object detectors with labeled datasets. In this work, we show application of a generic object detector [11], that can be pretrained in an unsupervised manner, on 24 categories from recently released Open Images V4 dataset. We first train the base architecture of the object detector using unsupervisd learning on 60K unlabeled photos from 24 categories gathered from social media, and then subsequently fine tune it on 8.2K labeled photos from Open Images V4 dataset. On 300 × 300 image inputs, we achieve 72.7% mAP on a test dataset of 2.4K photos while performing 11% to 17% better as compared to the state-of-the-art object detectors. We show that this improvement is due to our choice of architecture that lets us do unsupervised learning and that performs significantly better in identifying small objects.
ARCH-COMP18 Category Report: Continuous and Hybrid Systems with Linear Continuous Dynamics

This report presents the results of a friendly competition for formal verification of continuous and hybrid systems with linear continuous dynamics. The friendly competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in 2018. In its second edition, 9 tools have been applied to solve six different benchmark problems in the category for linear continuous dynamics (in alphabetical order): CORA, CORA/SX, C2E2, Flow*, HyDRA, Hylaa, Hylaa-Continuous, JuliaReach, SpaceEx, and XSpeed. This report is a snapshot of the current landscape of tools and the types of benchmarks they are particularly suited for. Due to the diversity of problems, we are not ranking tools, yet the presented results probably provide the most complete assessment of tools for the safety verification of continuous and hybrid systems with linear continuous dynamics up to this date. G. Frehse (ed.), ARCH18 (EPiC Series in Computing, vol. 54), pp. 23–52 ARCH-COMP18 Linear Dynamics Althoff et al.
Location optimization for data concentrator unit in IEEE 802.15.4 smart grid networks

In the smart grid system, an advanced meter infrastructure (AMI) is an integral subsystem mainly used to collect monthly consumption and load profile. Hence, a large amount of information will be exchanged within these systems. Data concentrator unit (DCU) is used to collect the information from smart meters before forwarding to meter data management system. In order to meet the AMI's QoS such as throughput and delay, the optimal placement for DCU has to be thoroughly investigated. This paper aims at developing an optimal location algorithm for the DCU placement in a non-beacon-mode IEEE 802.15.4 smart grid network. The optimization algorithm preliminarily computes the DCU position based on a minimum hop count metric. Nevertheless, it is possible that multiple positions achieving the minimum hop count may be found; therefore, the additional performance metric, i.e. the averaged throughput and delay, will be used to select the ultimately optimal location. In this paper, the maximum throughput with the acceptable averaged delay constraint is proposed by considering the behavior of the AMI meters which is almost stationary in the network. From the simulation results, it is obvious that the proposed methodology is significantly effective.
Keratosis Pilaris and its Subtypes: Associations, New Molecular and Pharmacologic Etiologies, and Therapeutic Options.

Keratosis pilaris is a common skin disorder comprising less common variants and rare subtypes, including keratosis pilaris rubra, erythromelanosis follicularis faciei et colli, and the spectrum of keratosis pilaris atrophicans. Data, and critical analysis of existing data, are lacking, so the etiologies, pathogeneses, disease associations, and treatments of these clinical entities are poorly understood. The present article aims to fill this knowledge gap by reviewing literature in the PubMed, EMBASE, and CINAHL databases and providing a comprehensive, analytical summary of the clinical characteristics and pathophysiology of keratosis pilaris and its subtypes through the lens of disease associations, genetics, and pharmacologic etiologies. Histopathologic, genomic, and epidemiologic evidence points to keratosis pilaris as a primary disorder of the pilosebaceous unit as a result of inherited mutations or acquired disruptions in various biomolecular pathways. Recent data highlight aberrant Ras signaling as an important contributor to the pathophysiology of keratosis pilaris and its subtypes. We also evaluate data on treatments for keratosis pilaris and its subtypes, including topical, systemic, and energy-based therapies. The effectiveness of various types of lasers in treating keratosis pilaris and its subtypes deserves wider recognition.
DL Team at SemEval-2018 Task 1: Tweet Affect Detection using Sentiment Lexicons and Embeddings

The paper describes our approach for SemEval-2018  Affect Detection in Tweets. We perform experiments with manually compelled sentiment lexicons and word embeddings. We test their performance on twitter affect detection task to determine which features produce the most informative representation of a sentence. We demonstrate that general-purpose word embeddings produces more informative sentence representation than lexicon features. However, combining lexicon features with embeddings yields higher performance than embeddings alone.
A family of OWA operators based on Faulhaber's formulas

In this paper we develop a new family of Ordered Weighted Averaging (OWA) operators. Weight vector is obtained from a desired orness of the operator. Using Faulhaber’s formulas we obtain direct and simple expressions for the weight vector without any iteration loop. With the exception of one weight, the remaining follow a straight line relation. As a result, a fast and robust algorithm is developed. The resulting weight vector is suboptimal according with the Maximum Entropy criterion, but it is very close to the optimal. Comparisons are done with other procedures.
The Strong Protective Effect of Circumcision against Cancer of the Penis

Male circumcision protects against cancer of the penis, the invasive form of which is a devastating disease confined almost exclusively to uncircumcised men. Major etiological factors are phimosis, balanitis, and high-risk types of human papillomavirus (HPV), which are more prevalent in the glans penis and coronal sulcus covered by the foreskin, as well as on the penile shaft, of uncircumcised men. Circumcised men clear HPV infections more quickly. Phimosis (a constricted foreskin opening impeding the passage of urine) is confined to uncircumcised men, in whom balanitis (affecting 10%) is more common than in circumcised men. Each is strongly associated with risk of penile cancer. These findings have led to calls for promotion of male circumcision, especially in infancy, to help reduce the global burden of penile cancer. Even more relevant globally is protection from cervical cancer, which is 10-times more common, being much higher in women with uncircumcised male partners. Male circumcision also provides indirect protection against various other infections in women, along with direct protection for men from a number of genital tract infections, including HIV. Given that adverse consequences of medical male circumcision, especially when performed in infancy, are rare, this simple prophylactic procedure should be promoted.
Thermal Conductivity of a Zirconia Thermal Barrier Coating

The conductivity of a thermal-barrier coating composed of atmospheric plasma sprayed 8 mass percent yttria partially stabilized zirconia has been measured. This coating was sprayed on a substrate of 410 stainless steel. An absolute, steady-state measurement method was used to measure thermal conductivity from 400 to 800 K. The thermal conductivity of the coating is 0.62 W/(m⋅K). This measurement has shown to be temperature independent.
Discriminative Learning of Generative Models for Sequence Classification and Motion Tracking

I consider the issue of learning generative probabilistic models (e.g., Bayesian Networks) for the problems of classification and regression. As the generative models now serve as target-predicting functions, the learning problem can be treated differently from the traditional density estimation. Unlike the likelihood maximizing generative learning that fits a model to overall data, the discriminative learning is an alternative estimation method that optimizes the objectives that are much closely related with the prediction task (e.g., the conditional likelihood of target variables given input attributes). The contribution of this work is three-fold. First, for the family of general generative models, I provide a unifying parametric gradient-based optimization method for the discriminative learning. In the second part, not restricted to the classification problem with discrete targets, the method is applied to the continuous multivariate state domain, resulting in dynamical systems learned discriminatively. This is very appealing approach toward the structured state prediction problems such as motion tracking, in that the discriminative models in discrete domains (e.g., Conditional Random Fields or Maximum Entropy Markov Models) can be problematic to be extended to handle continuous targets properly. For the CMU motion capture data, I evaluate the generalization performance of the proposed methods on the 3D human pose tracking problem from the monocular videos. Despite the improved prediction performance of the discriminative learning, the parametric gradient-based optimization may have certain drawbacks such as the computational overhead and the sensitivity to the choice of the initial model. In the third part, I address these issues by introducing a novel recursive method for discriminative learning. The proposed method estimates a mixture of generative models, where the component to be added at each stage is selected in a greedy fashion, by the criterion maximizing the conditional likelihood of the new mixture. The approach is highly efficient as it reduces to the generative learning of the base generative models on weighted data. Moreover it is less sensitive to the initial model choice by enhancing the mixture model recursively. The improved classification performance of the proposed method is demonstrated in an extensive set of evaluations on time-series sequence data, including human motion classification problems.
On the modeling of error functions as high dimensional landscapes for weight initialization in learning networks

Next generation deep neural networks for classification hosted on embedded platforms will rely on fast, efficient, and accurate learning algorithms. Initialization of weights in learning networks has a great impact on the classification accuracy. In this paper we focus on deriving good initial weights by modeling the error function of a deep neural network as a high-dimensional landscape. We observe that due to the inherent complexity in its algebraic structure, such an error function may conform to general results of the statistics of large systems. To this end we apply some results from Random Matrix Theory to analyse these functions. We model the error function in terms of a Hamiltonian in N-dimensions and derive some theoretical results about its general behavior. These results are further used to make better initial guesses of weights for the learning algorithm.
Pyriform Aperture Augmentation as An Adjunct to Rhinoplasty.

Skeletal deficiency in the central midface impacts nasal aesthetics. This lack of lower face projection can be corrected by alloplastic augmentation of the pyriform aperture. Creating convexity in the deficient midface will make the nose seem less prominent. Augmentation of the pyriform aperture is, therefore, often a useful adjunct during the rhinoplasty procedure. Augmenting the skeleton in this area can alter the projection of the nasal base, the nasolabial angle, and the vertical plane of the lip. The implant design and surgical techniques described here are extensions of others' previous efforts to improve paranasal aesthetics.
Information Work in Bone Marrow Transplant: Reducing Misalignment of Perspectives

Patients are often overwhelmed in their efforts to understand their illnesses and determine what actions to take. In this paper, we want to show why care is sometimes not co-managed well between clinicians and patients, and the necessary information is often not well coordinated. Through a 2.5-year field study of an adult bone marrow transplant (BMT) clinic, we show there are different experiences of temporal ordering, or temporalities, between clinicians and patients (and their caregivers). We also show that misalignments between these temporalities can seriously affect the articulation (coordination) and information work that must go on for people to co-manage their conditions with clinicians. As one example, information flows can be misaligned, as a result of differing temporalities, causing sometimes an overwhelming amount of information to be presented and sometimes a lack of properly contextualized information. We also argue that these misalignments in temporalities, important in medicine, are a general coordination problem.
Few-layer MoS2: a promising layered semiconductor.

Due to the recent expanding interest in two-dimensional layered materials, molybdenum disulfide (MoS2) has been receiving much research attention. Having an ultrathin layered structure and an appreciable direct band gap of 1.9 eV in the monolayer regime, few-layer MoS2 has good potential applications in nanoelectronics, optoelectronics, and flexible devices. In addition, the capability of controlling spin and valley degrees of freedom makes it a promising material for spintronic and valleytronic devices. In this review, we attempt to provide an overview of the research relevant to the structural and physical properties, fabrication methods, and electronic devices of few-layer MoS2. Recent developments and advances in studying the material are highlighted.
Epigenome-wide Association Studies and the Interpretation of Disease -Omics

Epigenome-wide association studies represent one means of applying genome-wide assays to identify molecular events that could be associated with human phenotypes. The epigenome is especially intriguing as a target for study, as epigenetic regulatory processes are, by definition, heritable from parent to daughter cells and are found to have transcriptional regulatory properties. As such, the epigenome is an attractive candidate for mediating long-term responses to cellular stimuli, such as environmental effects modifying disease risk. Such epigenomic studies represent a broader category of disease -omics, which suffer from multiple problems in design and execution that severely limit their interpretability. Here we define many of the problems with current epigenomic studies and propose solutions that can be applied to allow this and other disease -omics studies to achieve their potential for generating valuable insights.