nils-herrmann/clean_scirepeval huggingface dataset:
Imaging mass spectrometry in drug development and toxicology
Venue: Archives of Toxicology

During the last decades, imaging mass spectrometry has gained significant relevance in biomedical research. Recent advances in imaging mass spectrometry have paved the way for in situ studies on drug development, metabolism and toxicology. In contrast to whole body autoradiography that images the localization of radiolabeled compounds, imaging mass spectrometry provides the possibility to simultaneously determine the discrete tissue distribution of the parent compound and its metabolites. In addition, imaging mass spectrometry features high molecular specificity and allows comprehensive, multiplexed detection and localization of hundreds of proteins, peptides and lipids directly in tissues. Toxicologists traditionally screen for adverse findings by histopathological examination. However, studies of the molecular and cellular processes underpinning toxicological and pathologic findings induced by candidate drugs or toxins are important to reach a mechanistic understanding and an effective risk assessment strategy. One of IMS strengths is the ability to directly overlay the molecular information from the mass spectrometric analysis with the tissue section and allow correlative comparisons of molecular and histologic information. Imaging mass spectrometry could therefore be a powerful tool for omics profiling of pharmacological/toxicological effects of drug candidates and toxicants in discrete tissue regions. The aim of the present review is to provide an overview of imaging mass spectrometry, with particular focus on MALDI imaging mass spectrometry, and its use in drug development and toxicology in general.
Recognizing unseen actions in a domain adapted embedding space
Venue: 2016 IEEE International Conference on Image Processing (ICIP)

With the sustaining bloom of multimedia data, Zero shot Learning (ZSL) techniques have attracted much attention in recent years for its ability to train learning models that can handle "unseen" categories. Existing ZSL algorithms mainly take advantages of attribute based semantic space and only focus on static image data. Besides, most ZSL studies merely consider the semantic embedded labels and fail to address domain shift problem. In this paper, we purpose a deep two output model for video ZSL and action recognition tasks by computing both spatial and temporal features from video contents through distinct Convolutional Neural Networks (CNNs) and training a Multi layer Perceptron (MLP) upon extracted features to map videos to semantic embedding word vectors. Moreover, we introduce a domain adaptation strategy named "ConSSEV" by combining outputs from two distinct output layers of our MLP to improve the results of zero shot learning. Our experiments on UCF101 dataset demonstrate the purposed model has more advantages associated with more complex video embedding schemes, and outperforms the state of the art zero shot learning techniques.
Improved Deep Metric Learning with Multi class N pair Loss Objective
Venue: NIPS

Deep metric learning has gained much popularity in recent years, following the success of deep learning. However, existing frameworks of deep metric learning based on contrastive loss and triplet loss often suffer from slow convergence, partially because they employ only one negative example while not interacting with the other negative classes in each update. In this paper, we propose to address this problem with a new metric learning objective called multi class N pair loss. The proposed objective function firstly generalizes triplet loss by allowing joint comparison among more than one negative examples more specifically, N 1 negative examples and secondly reduces the computational burden of evaluating deep embedding vectors via an efficient batch construction strategy using only N pairs of examples, instead of (N+1) x N. We demonstrate the superiority of our proposed loss to the triplet loss as well as other competing loss functions for a variety of tasks on several visual recognition benchmark, including fine grained object recognition and verification, image clustering and retrieval, and face verification and identification.
A Comparative Exploration of Community Pharmacists' Views on the Nature and Management of Over the Counter (OTC) and Prescription Codeine Misuse in Three Regulatory Regimes: Ireland, South Africa and the United Kingdom
Venue: International Journal of Mental Health and Addiction

Misuse of codeine containing preparations is a public health concern given the potential for associated harms and dependence. This study explores the perspectives of community pharmacists in three regulatory regimes on issues of customer misuse of over the counter (OTC) and prescribed codeine. A qualitative design comprising six focus groups (n 45) was conducted in Ireland, United Kingdom, South Africa. Transcripts were analysed using the constant comparative method of content analysis. Pharmacists described popular codeine containing products and the need for improved medicine information and warning labels. Issues around legitimate availability of codeine and regulatory status; presence of therapeutic need; difficulties in customer pharmacist communication; business environments and retail focus were raised. Participants also discussed how they identified customers potentially misusing codeine and difficulties in relationships between pharmacists and prescribers. A number of recommendations were put forward as ways to manage the issues. The study highlights the difficulties encountered by community pharmacists operating under various regulatory regimes when supplying codeine containing preparations in negotiating patient awareness and compliance and potential ways to deal with misuse and dependence.
Medicine reclassification processes and regulations for proper use of over the counter self care medicines in Japan
Venue: Risk management and healthcare policy

Japan has actively reclassified substances ranging from prescription drugs to over the counter (OTC) drugs in recent years. The sale of most OTC drugs was deregulated several times and pharmacists' supervision was deemed no longer mandatory. Japan established a new OTC evaluation system in 2015 to hear opinions from various stakeholders regarding medicine types to be reclassified. This study aimed to examine the new framework to identify candidate substances for reclassification. Moreover, we examined how to manage the safe, self care use of OTC drugs in Japan.  The necessary regulatory information on OTC approvals as of January 2015 was collected using an Internet search and relevant databases. To highlight the characteristics of OTC drugs in Japan, the UK was selected as a comparison country because it too was actively promoting the reclassification of medicines from prescription to nonprescription status, and because of economic similarity.  Japan and the UK have a risk based classification for nonprescription medicines. Japan has made OTC drugs available with mandatory pharmacists' supervision, face to face with pharmacists, or online instruction, which is similar to the "pharmacy medicine" practiced in the UK. Japan recently reformed the reclassification process to involve physicians and the public in the process; some interactions were back to "prescription only medicine" in the UK.  It is expected that the opinion of marketers, medical professionals, and the public will improve the discussion that will greatly contribute to the safe use of drugs. Monitoring the new system will be noteworthy to ensure that OTC drug users are managing their self care properly and visiting a doctor only when necessary. The supply methods are similar in Japan and the UK; however, the expected growth in the Japanese OTC market by the Cabinet and the industry is still uncertain.
Changes in Prescription and Over the Counter Medication and Dietary Supplement Use Among Older Adults in the United States, 2005 vs 2011.
Venue: JAMA internal medicine

Prescription and over the counter medicines and dietary supplements are commonly used, alone and together, among older adults. However, the effect of recent regulatory and market forces on these patterns is not known.  To characterize changes in the prevalence of medication use, including concurrent use of prescription and over the counter medications and dietary supplements, and to quantify the frequency and types of potential major drug drug interactions.  Descriptive analyses of a longitudinal, nationally representative sample of community dwelling older adults 62 to 85 years old. In home interviews with direct medication inspection were conducted in 2005 2006 and again in 2010 2011. The dates of the analysis were March to November 2015. We defined medication use as the use of at least 1 prescription or over the counter medication or dietary supplement at least daily or weekly and defined concurrent use as the regular use of at least 2 medications. We used Micromedex to identify potential major drug drug interactions.  Population estimates of the prevalence of medication use (in aggregate and by therapeutic class) concurrent use, and major drug drug interactions.  The study cohort comprised 2351 participants in 2005 2006 and 2206 in 2010 2011. Their mean age was 70.9 years in 2005 2006 and 71.4 years in 2010 2011. Fifty three percent of participants were female in 2005 2006, and 51.6% were female in 2010 2011. The use of at least 1 prescription medication slightly increased from 84.1% in 2005 2006 to 87.7% in 2010 2011 (P .003) Concurrent use of at least 5 prescription medications increased from 30.6% to 35.8% (P .02) While the use of over the counter medications declined from 44.4% to 37.9% the use of dietary supplements increased from 51.8% to 63.7% (P .001 for both) There were clinically significant increases in the use of statins (33.8% to 46.2% antiplatelets (32.8% to 43.0% and omega 3 fish oils (4.7% to 18.6% (P .05 for all) In 2010 2011, approximately 15.1% of older adults were at risk for a potential major drug drug interaction compared with an estimated 8.4% in 2005 2006 (P .001) Most of these interacting regimens involved medications and dietary supplements increasingly used in 2010 2011.  In this study, the use of prescription medications and dietary supplements, and concurrent use of interacting medications, has increased since 2005, with 15% of older adults potentially at risk for a major drug drug interaction. Improving safety with the use of multiple medications has the potential to reduce preventable adverse drug events associated with medications commonly used among older adults.
Child Sexual Abuse and the Impact of Rurality on Foster Care Outcomes: An Exploratory Analysis

Child sexual abuse remains a problem in society, often resulting in the long term placement of children in the foster care system. In Kentucky, children placed in foster care due to sexual abuse spent more time in care when compared with children removed for any other reason (AFCARS, 2013) Given the cost of long term foster care placement in both human and economic terms, few studies have specifically explored if any factors help to predict why this vulnerable population spends significantly more time in foster care. The overarching goal of this exploratory study was to use binary logistic regression to investigate whether any child demographic or environmental characteristics predicted the discharge of a child placed in Kentucky's foster care system for child sexual abuse. Results indicated that children in the most rural areas of the state were over 10 times more likely to be discharged from foster care during the federal fiscal year than those residing in the most urban areas. Given this stark reality, a focus must be allocated in understanding this phenomenon. Future research must examine whether the results speak to the necessity of systematic improvement in urban areas or if they are illustrating a unique strength found in rural areas.
Victimization and Poly Victimization in Adolescent Outpatients from Mental Health Centers: A Case Control Study
Venue: Journal of Family Violence

The aims of the present study were to establish interpersonal victimization rates in a clinical sample and to analyze this sample's risk of victimization relative to the general population. The sample was composed of 472 adolescents (12 17 years of age) 118 outpatients from public mental health centers and 354 students who were matched by age and sex. Following previous studies, this research defined poly victimization as four or more victimization types occurring during the previous year. The clinical group was more likely to report sexual victimization (OR 9.540) conventional crime (OR 3.120) caregiver victimization (OR 3.469) witnessing and indirect victimization (OR 3.466) electronic victimization (OR 2.809) and poly victimization (OR 4.319) compared with the control group. Clinical samples present an increased risk of interpersonal poly victimization compared with the general population. The influence of poly victimization on mental health should be considered in the evaluation and treatment of adolescent outpatients.
DeeperCut: A Deeper, Stronger, and Faster Multi person Pose Estimation Model
Venue: ECCV

The goal of this paper is to advance the state of the art of articulated pose estimation in scenes with multiple people. To that end we contribute on three fronts. We propose (1) improved body part detectors that generate effective bottom up proposals for body parts; (2) novel image conditioned pairwise terms that allow to assemble the proposals into a variable number of consistent body part configurations; and (3) an incremental optimization strategy that explores the search space more efficiently thus leading both to better performance and significant speed up factors. Evaluation is done on two single person and two multi person pose estimation benchmarks. The proposed approach significantly outperforms best known multi person pose estimation results while demonstrating competitive performance on the task of single person pose estimation (Models and code available at http:/pose.mpi inf.mpg.de)
Deep learning for tactile understanding from visual and haptic data
Venue: 2016 IEEE International Conference on Robotics and Automation (ICRA)

Robots which interact with the physical world will benefit from a fine grained tactile understanding of objects and surfaces. Additionally, for certain tasks, robots may need to know the haptic properties of an object before touching it. To enable better tactile understanding for robots, we propose a method of classifying surfaces with haptic adjectives (e.g. compressible or smooth) from both visual and physical interaction data. Humans typically combine visual predictions and feedback from physical interactions to accurately predict haptic properties and interact with the world. Inspired by this cognitive pattern, we propose and explore a purely visual haptic prediction model. Purely visual models enable a robot to "feel" without physical interaction. Furthermore, we demonstrate that using both visual and physical interaction signals together yields more accurate haptic classification. Our models take advantage of recent advances in deep neural networks by employing a unified approach to learning features for physical interaction and visual observations. Even though we employ little domain specific knowledge, our model still achieves better results than methods based on hand designed features.
Using Word Embedding to Evaluate the Coherence of Topics from Twitter Data
Venue: SIGIR

Scholars often seek to understand topics discussed on Twitter using topic modelling approaches. Several coherence metrics have been proposed for evaluating the coherence of the topics generated by these approaches, including the pre calculated Pointwise Mutual Information (PMI) of word pairs and the Latent Semantic Analysis (LSA) word representation vectors. As Twitter data contains abbreviations and a number of peculiarities (e.g. hashtags) it can be challenging to train effective PMI data or LSA word representation. Recently, Word Embedding (WE) has emerged as a particularly effective approach for capturing the similarity among words. Hence, in this paper, we propose new Word Embedding based topic coherence metrics. To determine the usefulness of these new metrics, we compare them with the previous PMI/LSA based metrics. We also conduct a large scale crowdsourced user study to determine whether the new Word Embedding based metrics better align with human preferences. Using two Twitter datasets, our results show that the WE based metrics can capture the coherence of topics in tweets more robustly and efficiently than the PMI/LSA based ones.
DWT Based LCP Features for the Classification of Steel Surface Defects in SEM Images with KNN Classifier

This paper presents a Texture based approach for detecting and characterizing defects on steel surfaces, through which the quality inspection of steel products is performed. Our objective is to detect the defects in the surface of steel products. Major defects like corrosion, crack, scratch and fracture are considered for testing and training process. The Discrete Wavelet Transform (DWT) based Local Configuration Pattern (LCP) features are given as input to the KNN classifier. In order to explore multichannel discriminative information of both the microscopic configuration and local structures, DWT based LCP which is a combination of Local Binary Pattern (LBP) and Microscopic Configuration modeling (MiC) method is proposed. LCP and Defect Severity Index values are calculated for the sub images obtained after applying DWT. The overall accuracy of DWT based LCP method is 96.7% The results show that the proposed method produces better classification accuracy when compared to the classical methods.
Assessing the Ability of LSTMs to Learn Syntax Sensitive Dependencies
Venue: TACL

The success of long short term memory (LSTM) neural networks in language processing is typically attributed to their ability to capture long distance statistical regularities. Linguistic regularities are often sensitive to syntactic structure; can such dependencies be captured by LSTMs, which do not have explicit structural representations? We begin addressing this question using number agreement in English subject verb dependencies. We probe the architecture's grammatical competence both using training objectives with an explicit grammatical target (number prediction, grammaticality judgments) and using language models. In the strongly supervised settings, the LSTM achieved very high overall accuracy (less than 1% errors) but errors increased when sequential and structural information conflicted. The frequency of such errors rose sharply in the language modeling setting. We conclude that LSTMs can capture a non trivial amount of grammatical structure given targeted supervision, but stronger architectures may be required to further reduce errors; furthermore, the language modeling signal is insufficient for capturing syntax sensitive dependencies, and should be supplemented with more direct supervision if such dependencies need to be captured.
Document Image Quality Assessment Using Discriminative Sparse Representation
Venue: 2016 12th IAPR Workshop on Document Analysis Systems (DAS)

The goal of document image quality assessment (DIQA) is to build a computational model which can predict the degree of degradation for document images. Based on the estimated quality scores, the immediate feedback can be provided by document processing and analysis systems, which helps to maintain, organize, recognize and retrieve the information from document images. Recently, the bag of visual words (BoV) based approaches have gained increasing attention from researchers to fulfill the task of quality assessment, but how to use BoV to represent images more accurately is still a challenging problem. In this paper, we propose to utilize a sparse representation based method to estimate document image's quality with respect to the OCR capability. Unlike the conventional sparse representation approaches, we introduce the target quality scores into the training phase of sparse representation. The proposed method improves the discriminability of the system and ensures the obtained codebook is more suitable for our assessment task. The experimental results on a public dataset show that the proposed method outperforms other hand crafted and BoV based DIQA approaches.
Document Image Quality Assessment Based on Texture Similarity Index
Venue: 2016 12th IAPR Workshop on Document Analysis Systems (DAS)

In this paper, a full reference document image quality assessment (FR DIQA) method using texture features is proposed. Local binary patterns (LBP) as texture features are extracted at the local and global levels for each image. For each extracted LBP feature set, a similarity measure called the LBP similarity index (LBPSI) is computed. A weighting strategy is further proposed to improve the LBPSI obtained based on local LBP features. The LBPSIs computed for both local and global features are then combined to get the final LBPSI, which also provides the best performance for DIQA. To evaluate the proposed method, two different datasets were used. The first dataset is composed of document images, whereas the second one includes natural scene images. The mean human opinion scores (MHOS) were considered as ground truth for performance evaluation. The results obtained from the proposed LBPSI method indicate a significant improvement in automatically/accurately predicting image quality, especially on the document image based dataset.
Optimizing coflow completion times with utility max min fairness
Venue: IEEE INFOCOM 2016 The 35th Annual IEEE International Conference on Computer Communications

In data parallel frameworks such as MapReduce and Spark, a coflow represents a set of network flows used to transfer intermediate data between successive computation stages for a job. The completion time of a job is then determined by the collective behavior of such a coflow, rather than any individual flow within, and influenced by the amount of network bandwidth allocated to it. Different jobs in a shared cluster have different degrees of sensitivity to their completion times, modeled by their respective utility functions. In this paper, we focus on the design and implementation of a new utility optimal scheduler across competing coflows, in order to provide differential treatment to coflows with different degrees of sensitivity, yet still satisfying max min fairness across these coflows. Though this objective can be formulated as a lexicographical maximization problem, it is challenging to solve in practice due to its inherent multi objective and discrete nature. To address this challenge, we first divide the problem into iterative steps of single objective subproblems; and in each of these steps, we then perform a series of transformations to obtain an equivalent linear programming (LP) problem, which can be efficiently solved in practice. To demonstrate that our solutions are practically feasible, we have implemented it as a real world coflow scheduler based on the Varys open source framework to evaluate its effectiveness.
Learning Image Matching by Simply Watching Video
Venue: ECCV

This work presents an unsupervised learning based approach to the ubiquitous computer vision problem of image matching. We start from the insight that the problem of frame interpolation implicitly solves for inter frame correspondences. This permits the application of analysis by synthesis: we first train and apply a Convolutional Neural Network for frame interpolation, then obtain correspondences by inverting the learned CNN. The key benefit behind this strategy is that the CNN for frame interpolation can be trained in an unsupervised manner by exploiting the temporal coherence that is naturally contained in real world video sequences. The present model therefore learns image matching by simply "watching videos" Besides a promise to be more generally applicable, the presented approach achieves surprising performance comparable to traditional empirically designed methods.