Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Neural Architecture Search and Quantization

Designing neural architectures is a fundamental step in deep learning applications. As a partner technique, model compression on neural networks has been widely investigated to gear the needs that the deep learning algorithms could be run with the limited computation resources on mobile devices. Currently, both the tasks of architecture design and model compression require expertise tricks and tedious trials. In this paper, we integrate these two tasks into one unified framework, which enables the joint architecture search with quantization (compression) policies for neural networks. This method is named JASQ. Here our goal is to automatically find a compact neural network model with high performance that is suitable for mobile devices. Technically, a multi-objective evolutionary search algorithm is introduced to search the models under the balance between model size and performance accuracy. In experiments, we find that our approach outperforms the methods that search only for architectures or only for quantization policies. 1) Specifically, given existing networks, our approach can provide them with learning-based quantization policies, and outperforms their 2 bits, 4 bits, 8 bits, and 16 bits counterparts. It can yield higher accuracies than the float models, for example, over 1.02% higher accuracy on MobileNet-v1. 2) What is more, under the balance between model size and performance accuracy, two models are obtained with joint search of architectures and quantization policies: a high-accuracy model and a small model, JASQNet and JASQNet-Small that achieves 2.97% error rate with 0.9 MB on CIFAR-10.
Modeling the Human-Robot Trust Phenomenon

This article presents a conceptual framework for human-robot trust which uses computational representations inspired by game theory to represent a definition of trust, derived from social psychology. This conceptual framework generates several testable hypotheses related to human-robot trust. This article examines these hypotheses and a series of experiments we have conducted which both provide support for and also conflict with our framework for trust. We also discuss the methodological challenges associated with investigating trust. The article concludes with a description of the important areas for future research on the topic of human-robot trust.
Differential diagnosis of Crohn's disease using antibodies to glycoprotein 2 and Saccharomyces cerevisiae.

BACKGROUND/AIMS Glycoprotein 2 (GP2), the major autoantigen of Crohn's disease (CD)-specific pancreatic autoantibodies, is reportedly correlated with several characteristics of CD. We investigated this serological marker in Turkish patients with CD and assessed its utility in combination with anti-Saccharomyces cerevisiae antibodies (ASCAs) for differential diagnosis of CD. MATERIALS AND METHODS A total of 60 patients with CD, 62 patients with ulcerative colitis (UC), and 46 healthy controls with a definite diagnosis who were similar in age and sex were enrolled in the study conducted from November 2011 to October 2012. ASCA and anti-GP2 levels were measured using commercially available kits. RESULTS Anti-GP2 IgA and IgG levels were higher in patients with CD (25%) than in those with UC (5%) and controls (2%). The seroprevalence of anti-GP2 IgA was markedly higher than that of IgG in patients with CD in contrast to previous studies. The specificity and positive predictive value of seropositivity for both ASCA and anti-GP2 were 100%. ASCA IgA seropositivity was correlated with a complicated disease course and a history of surgery. There was no correlation between anti-GP2 seropositivity and disease location, disease behavior, or a history of surgery. CONCLUSION The combination of ASCA and anti-GP2 may enable differentiation of CD from UC. As ASCA seropositivity is associated with a more complicated disease course, patients seropositive for ASCA at the initial diagnosis should undergo more intense therapy.
Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity

Background Prior work has demonstrated the near-perfect accuracy of a deep learning retinal image analysis system for diagnosing plus disease in retinopathy of prematurity (ROP). Here we assess the screening potential of this scoring system by determining its ability to detect all components of ROP diagnosis. Methods Clinical examination and fundus photography were performed at seven participating centres. A deep learning system was trained to detect plus disease, generating a quantitative assessment of retinal vascular abnormality (the i-ROP plus score) on a 1–9 scale. Overall ROP disease category was established using a consensus reference standard diagnosis combining clinical and image-based diagnosis. Experts then ranked ordered a second data set of 100 posterior images according to overall ROP severity. Results 4861 examinations from 870 infants were analysed. 155 examinations (3%) had a reference standard diagnosis of type 1 ROP. The i-ROP deep learning (DL) vascular severity score had an area under the receiver operating curve of 0.960 for detecting type 1 ROP. Establishing a threshold i-ROP DL score of 3 conferred 94% sensitivity, 79% specificity, 13% positive predictive value and 99.7% negative predictive value for type 1 ROP. There was strong correlation between expert rank ordering of overall ROP severity and the i-ROP DL vascular severity score (Spearman correlation coefficient=0.93; p<0.0001). Conclusion The i-ROP DL system accurately identifies diagnostic categories and overall disease severity in an automated fashion, after being trained only on posterior pole vascular morphology. These data provide proof of concept that a deep learning screening platform could improve objectivity of ROP diagnosis and accessibility of screening.
Attachment state of mind and childhood experiences of maltreatment as predictors of sensitive care from infancy through middle childhood: Results from a longitudinal study of parents involved with Child Protective Services

Abstract The current longitudinal study examined whether attachment states of mind and childhood maltreatment predict sensitive caregiving during infancy, early childhood, and middle childhood among a sample of 178 parents who were involved with Child Protective Services. Nearly all the parents had themselves experienced childhood maltreatment based on their reports on the Childhood Trauma Questionnaire—Short Form (Bernstein et al., 2003) when their children were infants. Adult Attachment Interviews (George, Kaplan, & Main, 1985) were administered to parents when their children were infants (M = 10.92 months, SD = 8.66). Parental sensitivity was rated based on observations of parent–child interactions at three time points: infancy, early childhood, and middle childhood. During infancy, dismissing states of mind of parents predicted marginally lower sensitivity scores than autonomous states of mind. In early and middle childhood, dismissing states of mind of parents predicted significantly lower sensitivity ratings than autonomous states of mind. Unresolved states of mind of parents predicted significantly lower sensitivity scores than autonomous states of mind only during early childhood. Childhood maltreatment was not significantly associated with parents’ sensitivity ratings at all three time points. Findings suggest that among parents with Child Protective Services involvement, most of whom had themselves experienced maltreatment, parents’ unresolved states of mind predict insensitive caregiving in early childhood, and parents’ dismissing states of mind predict insensitive caregiving from infancy through middle childhood.
Detection of Salmonella from animal sources in South Africa between 2007 and 2014

Retrospective laboratory-based surveillance was conducted on Salmonella serotypes isolated from various animal species from 2007 to 2014 at the Agricultural Research Council, Onderstepoort Veterinary Research Institute, South Africa. During the surveillance period, 1229 salmonellae isolations were recorded. Around 108 different serotypes were recovered from nine different food and non-food animal host species. The three most common serotypes were Salmonella enterica subspecies enterica serotype Heidelberg (n = 200), Salmonella enterica subspecies enterica serotype Enteritidis (n = 170) and Salmonella enterica subspecies enterica serotype Typhimurium (n = 146). These were followed by Salmonella enterica subspecies enterica serotype Anatum (n = 62) and Salmonella enterica subspecies enterica serotype Infantis (n = 57). Salmonella enterica subspecies enterica serotype Schwarzengrund and Salmonella enterica subspecies enterica serotype Muenchen were recovered in 50 and 48 cases, respectively. Of the total number of isolations recorded during the period under review, 871 (70.8%) occurred in poultry and other birds, 162 (13.2%) in horses, 116 (9.4%) in cattle, 26 (2.1%) in sheep and goats, 22 (1.8%) in rhinoceroses, 16 (1.3%) in pigs, 8 (0.6%) in crocodiles, 6 (0.5%) in cats and 6 (0.5%) in leopards. Food animals accounted for 83.5% of the total isolations, with cattle and poultry representing approximately 72.7%. Forty-two (3.4 %) isolates were found from non-food animals that include rhinoceroses (n = 22), crocodiles (n = 8), leopards (n = 6) and cats (n = 6). Salmonella Heidelberg was the most frequently isolated serotype, whereas S. Typhimurium had the widest zoological distribution. Clinical laboratory isolation of different Salmonella serotypes from various hosts may aid in recognising the threat to livestock, public and environmental health. Moreover, it may also highlight the potential zoonotic and food safety risk implications of the detected Salmonella serotypes.
Themed Section: Adrenoceptors—New Roles for Old Players

Stress plays a role in tumourigenesis through catecholamines acting at β-adrenoceptors including β1-, β2and β3-adrenoceptors, and the use of β-adrenoceptor antagonists seems to counteract tumour growth and progression. Preclinical evidence and metaanalysis data demonstrate that melanoma shows a positive response to β-adrenoceptor blockers and in particular to propranolol acting mainly at β1and β2-adrenoceptors. Although evidence suggesting that β3-adrenoceptors may play a role as a therapeutic target in infantile haemangiomas has been recently reviewed, a comprehensive analysis of the data available from preclinical studies supporting a possible role of β3-adrenoceptors in melanoma was not available. Here, we review data from the literature demonstrating that propranolol may be effective at counteracting melanoma growth, and we provide preclinical evidence that β3-adrenoceptors may also play a role in the pathophysiology of melanoma, thus opening the door for further clinical assays trying to explore β3-adrenoceptor blockers as novel alternatives for its treatment.
In vitro activity of ceftazidime–avibactam, ceftolozane–tazobactam, and other comparable agents against clinically important Gram-negative bacilli: results from the 2017 Surveillance of Multicenter Antimicrobial Resistance in Taiwan (SMART)

Objectives We investigated the in vitro antimicrobial susceptibilities of clinically important Gram-negative bacteria (GNB) from 16 major teaching hospitals in Taiwan in 2017. Materials and methods Escherichia coli (n=686) and Klebsiella pneumoniae bloodstream isolates (n=673), non-typhoid Salmonella (NTS; n=221) from various sources, Shigella species (n=21) from fecal samples, and Neisseria gonorrhoeae (n=129) from the genitourinary tract were collected. Antibiotic minimum inhibitory concentrations (MICs) were determined using the broth microdilution method. Alleles encoding K. pneumoniae carbapenemases (KPCs), New Delhi metallo-β-lactamases (NDMs), Verona integron-encoded metallo-β-lactamase, imipenemase, OXA-48-like, and mcr-1-5 genes were detected by molecular methods in Enterobacteriaceae isolates. Results Five (0.7%) E. coli isolates harbored mcr-1 alleles. Twenty-four (3.6%), seven (1.0%), four (0.6%), and one (0.15%) K. pneumoniae isolates contained bla KPC, bla OXA-48-like, mcr-1, and bla NDM, respectively. Three (1.4%) NTS and no Shigella isolates harbored mcr-1 genes. Seventy-one (10.5%) K. pneumoniae isolates displayed non-susceptibility (NS) to carbapenem agent(s). Phenotypically extended-spectrum β-lactamase (ESBL)-producing K. pneumoniae isolates showed significantly higher rates of ertapenem, tigecycline, and ceftolozane–tazobactam (CLZ– TAZ) NS (40.2%, 16.3%, and 71%–80%, respectively) than E. coli isolates exhibiting ESBL phenotypes (5.4%, 0.7%, and 18%–28%, respectively). All phenotypically ESBL-producing E. coli isolates were ceftazidime–avibactam (CAZ–AVB) susceptible. Two (8.3%) KPC-producing K. pneumoniae isolates showed CAZ–AVB NS. Hospital-acquired K. pneumoniae isolates were significantly less susceptible to ertapenem and CLZ–TAZ than hospital-acquired E. coli isolates. Conclusion Third-generation cephalosporins remain the optimal choice for treating NTS, Shigella, and gonococcal infections in Taiwan. Hospital-acquired and phenotypically ESBL-producing K. pneumoniae are a heavy resistance burden in Taiwan.
Combining Fact Extraction and Verification with Neural Semantic Matching Networks

The increasing concern with misinformation has stimulated research efforts on automatic fact checking. The recentlyreleased FEVER dataset introduced a benchmark factverification task in which a system is asked to verify a claim using evidential sentences from Wikipedia documents. In this paper, we present a connected system consisting of three homogeneous neural semantic matching models that conduct document retrieval, sentence selection, and claim verification jointly for fact extraction and verification. For evidence retrieval (document retrieval and sentence selection), unlike traditional vector space IR models in which queries and sources are matched in some pre-designed term vector space, we develop neural models to perform deep semantic matching from raw textual input, assuming no intermediate term representation and no access to structured external knowledge bases. We also show that Pageview frequency can also help improve the performance of evidence retrieval results, that later can be matched by using our neural semantic matching network. For claim verification, unlike previous approaches that simply feed upstream retrieved evidence and the claim to a natural language inference (NLI) model, we further enhance the NLI model by providing it with internal semantic relatedness scores (hence integrating it with the evidence retrieval modules) and ontological WordNet features. Experiments on the FEVER dataset indicate that (1) our neural semantic matching method outperforms popular TF-IDF and encoder models, by significant margins on all evidence retrieval metrics, (2) the additional relatedness score and WordNet features improve the NLI model via better semantic awareness, and (3) by formalizing all three subtasks as a similar semantic matching problem and improving on all three stages, the complete model is able to achieve the state-of-the-art results on the FEVER test set (two times greater than baseline results). 1
Utility of Glycosylated TIMP3 molecules: Inhibition of MMPs and TACE to improve cardiac function in rat myocardial infarct model

Tissue Inhibitor of Metalloproteinase 3 (TIMP3) is a secreted protein that has a great utility to inhibit elevated metalloproteinase (MMP) activity in injured tissues including infarcted cardiac tissue, inflamed vessels, and joint cartilages. An imbalance between TIMP3 and active MMP levels in the local tissue area may cause worsening of disease progression. To counter balance elevated MMP levels, exogenous administration of TIMP3 appeared to be beneficial in preclinical studies. However, the current form of WT‐TIMP3 molecule has a limitation to be a therapeutic candidate due to low production yield, short plasma half‐life, injection site retention, and difficulty in delivery, etc. We have engineered TIMP3 molecules by adding extra glycosylation sites or fusing with albumin, Fc, and antibody to improve pharmacokinetic properties. In general, the C‐terminal fusion of TIMP3 improved expression and production in mammalian cells and extended half‐lives dramatically 5‐20 folds. Of note, a site‐specific glycosylation at K22S/F34N resulted in a higher level of expression and better cardiac function compared to other fusion proteins in the context of left ventricle ejection fraction (LVEF) changes in a rat myocardial infarction model. It appeared that cardiac efficacy depends on a high ECM binding affinity, in which K22S/F34N and N‐TIMP3 showed a higher binding to the ECM compared to other engineered molecules. In conclusion, we found that the ECM binding and sustained residence of injected TIMP3 molecules are important for cardiac tissue localization and inhibition of adverse remodeling activity.
Lung inflammation and disease: A perspective on microbial homeostasis and metabolism

It is now well appreciated that the human microbiome plays a significant role in a number of processes in the body, significantly affecting its metabolic, inflammatory, and immune homeostasis. Recent research has revealed that almost every mucosal surface in the human body is associated with a resident commensal microbiome of its own. While the gut microbiome and its role in regulation of host metabolism along with its alteration in a disease state has been well studied, there is a lacuna in understanding the resident microbiota of other mucosal surfaces. Among these, the scientific information on the role of lung microbiota in pulmonary diseases is currently severely limited. Historically, lungs have been considered to be sterile and lung diseases have only been studied in the context of bacterial pathogenesis. Recently however, studies have revealed a resilient microbiome in the upper and lower respiratory tracts and there is increased evidence on its central role in respiratory diseases. Knowledge of lung microbiome and its metabolic fallout (local and systemic) is still in its nascent stages and attracting immense interest in recent times. In this review, we will provide a perspective on lung‐associated metabolic disorders defined for lung diseases (e.g., chronic obstructive pulmonary disease, asthma, and respiratory depression due to infection) and correlate it with lung microbial perturbation. Such perturbations may be due to altered biochemical or metabolic stress as well. Finally, we will draw evidence from microbiome and classical microbiology literature to demonstrate how specific lung morbidities associate with specific metabolic characteristics of the disease, and with the role of microbiome in this context. © 2018 IUBMB Life, 71(1):152–165, 2019
Performance Consistency of AlSi10Mg Alloy Manufactured by Simulating Multi Laser Beam Selective Laser Melting (SLM): Microstructures and Mechanical Properties

Multi-laser beam selective laser melting (SLM) technology based on a powder bed has been used to manufacture AlSi10Mg samples. The AlSi10Mg alloy was used as research material to systematically study the performance consistency of both the laser overlap areas and the isolated areas of the multi-laser beam SLM manufactured parts. The microstructures and mechanical properties of all isolated and overlap processing areas were compared under optimized process parameters. It was discovered that there is a raised platform at the junction of the overlap areas and the isolated areas of the multi-laser SLM samples. The roughness is significantly reduced after two scans. However, the surface roughness of the samples is highest after four scans. As the number of laser scans increases, the relative density of the overlap areas of the samples improves, and there is no significant change in hardness. The tensile properties of the tensile samples are poor when the overlap area width is 0, 0.1, or 0.2 mm. When the widths of the overlap areas are equal to or greater than 0.3 mm, there is no significant difference in the tensile strength between the overlap and the isolated areas.
Motion effects in proton treatments of hepatocellular carcinoma—4D robustly optimised pencil beam scanning plans versus double scattering plans

Pencil beam scanning (PBS) proton therapy enables better dose conformality for complex anatomical geometries than passive proton scattering techniques, but is more susceptible to organ motion. This becomes an issue when treating moving tumours in the thorax or abdomen. Novel four-dimensional treatment planning approaches have been developed to increase the robustness of PBS plans against motion. However, their efficacy still needs to be examined by means of 4D dynamically accumulated dose (4DDD) analyses. This study investigates the potential use of 4D robust optimisation to maintain sufficient target coverage in the presence of organ motion, while sparing surrounding healthy tissue, for hepatocellular carcinoma (HCC). The liver is particularly suited to study motion interplay effects since the treatment region exhibits smaller density gradients and more homogeneous tissue than targets in the thorax, making it less prone to range errors. A facility-specific beam time model, developed and experimentally validated previously, was used for the clinical evaluation. 4DDD analyses of eleven target volumes did not show a significant improvement of the target coverage using 4D robust optimisation, but a reduction of the dose to close-by organs at risk. Interplay effects were averaged out for the applied fractionation scheme of 15 fractions. Contrary to PBS, passive double scattering (DS) plans yielded homogeneous 4DDD dose distributions in a single fraction. But, in some cases, they exceeded organ at risk dose limits, which were only satisfied in PBS. The average normal liver dose could be decreased by almost 6% compared to non-robustly optimised PBS plans and by 16% compared to DS plans when implementing 4D robust optimisation. Except for some very small tumours with large motion amplitudes, 4D robustly optimised PBS plans were found to be clinically acceptable even without supplementary motion mitigation techniques.