Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating the effect of material and weave design on comfort properties of bilayer-woven fabrics

Abstract The paper focuses on the development of a bilayer-woven fabric and investigating the effect of weave design and material type on its comfort properties. Face layer was plain woven with cotton yarn, while two different weave designs (2/2 and 3/1 twill) and four different materials (cotton, polyester, micropolyester and nylon) were used for the back layer. The comfort properties of fabric, including air permeability (AP), thermal resistance, water vapour resistance and overall moisture management capacity, were determined. It was found that both the layers of fabric as a whole contribute to the comfort properties of bilayer fabric. The highest AP was exhibited by fabrics having both layers of cotton, while 3/1 twill samples have a lower value of thermal resistance as compared to the 2/2 twill samples. The results further showed that micro polyester woven in 3/1 twill weave exhibits better comfort properties.
Determination of blood constituents reference values in broilers.

Eight hundred broilers were examined to define the reference values of nine blood constituents (total proteins, albumin, total and free cholesterol, triglycerides, aspartate aminotransferase (AST), alkaline phosphatase (ALP), calcium, and phosphorus). The broilers, coming from 20 different commercial poultry operations, were equally divided into different groups according to age (21 and 45 days), strain (Arbor Acres and Hybro), sex, and sampling season (summer or winter). Age and sampling season were the variation sources that most influenced the values of the hematochemical variables examined. The interaction of age with strain influenced total proteins total and free cholesterol, triglycerides, and ALP. The interaction of age with sampling season influenced all hematochemical constituents except triglycerides. Reference limits defined by . 975 and . 025 fractiles were computed for each blood constituent according to the significance of different variation sources and their combinations. Reference limits defined herein could be used as indicators of metabolic and health conditions of a poultry farm.
Integration of Absolute Orientation Measurements in the KinectFusion Reconstruction Pipeline

In this paper, we show how absolute orientation measurements provided by low-cost but high-fidelity IMU sensors can be integrated into the KinectFusion pipeline. We show that integration improves both runtime, robustness and quality of the 3D reconstruction. In particular, we use this orientation data to seed and regularize the ICP registration technique. We also present a technique to filter the pairs of 3D matched points based on the distribution of their distances. This filter is implemented efficiently on the GPU. Estimating the distribution of the distances helps control the number of iterations necessary for the convergence of the ICP algorithm. Finally, we show experimental results that highlight improvements in robustness, a speed-up of almost 12%, and a gain in tracking quality of 53% for the ATE metric on the Freiburg benchmark.
How Monitoring Influences Trust: A Tale of Two Faces

Monitoring changes the behavior of those who are monitored and those who monitor others. We studied behavior under different monitoring regimes in repeated trust games. We found that trustees behaved opportunistically when they anticipated monitoring—they were compliant when they knew in advance that they would be monitored, but exploited trustors when they knew in advance that they would not be monitored. Interestingly, trustors failed to anticipate how strategically their counterparts would behave. Trustors misattributed the strategic, compliant behavior they observed as signals of trustees’ trustworthiness. As a result, trustors misplaced their trust when they were unable to monitor their counterparts. We discuss the managerial implications of our results for designing and implementing monitoring systems. This paper was accepted by Yuval Rottenstreich, judgment and decision making.
Morphology Analysis and Optimization: Crucial Factor Determining the Performance of Perovskite Solar Cells

This review presents an overall discussion on the morphology analysis and optimization for perovskite (PVSK) solar cells. Surface morphology and energy alignment have been proven to play a dominant role in determining the device performance. The effect of the key parameters such as solution condition and preparation atmosphere on the crystallization of PVSK, the characterization of surface morphology and interface distribution in the perovskite layer is discussed in detail. Furthermore, the analysis of interface energy level alignment by using X-ray photoelectron spectroscopy and ultraviolet photoelectron spectroscopy is presented to reveals the correlation between morphology and charge generation and collection within the perovskite layer, and its influence on the device performance. The techniques including architecture modification, solvent annealing, etc. were reviewed as an efficient approach to improve the morphology of PVSK. It is expected that further progress will be achieved with more efforts devoted to the insight of the mechanism of surface engineering in the field of PVSK solar cells.
Are inspectors’ assessments reliable? Ratings of NHS acute hospital trust services in England

The credibility of a regulator could be threatened if stakeholders perceive that assessments of performance made by its inspectors are unreliable. Yet there is little published research on the reliability of inspectors’ assessments of health care organizations’ services. Objectives We investigated the inter-rater reliability of assessments made by inspectors inspecting acute hospitals in England during the piloting of a new regulatory model implemented by the Care Quality Commission (CQC) during 2013 and 2014. Multi-professional teams of inspectors rated service provision on a four-point scale for each of five domains: safety; effectiveness; caring; responsiveness; and leadership. Methods In an online survey, we asked individual inspectors to assign a domain and a rating to each of 10 vignettes of service information extracted from CQC inspection reports. We used these data to simulate the ratings that might be produced by teams of inspectors. We also observed inspection teams in action, and interviewed inspectors and staff from hospitals that had been inspected. Results Levels of agreement varied substantially from vignette to vignette. Characteristics such as professional background explained only a very small part of the variation. Overall, agreement was higher on ratings than on domains, and for groups of inspectors compared with individual inspectors. A number of potential causes of disagreement were identified, such as differences regarding the weight that should be given to contextual factors and general uncertainty about interpreting the rating and domain categories. Conclusion Groups of inspectors produced more reliable assessments than individual inspectors, and there is evidence to support the utility of appropriate discussions between inspectors in improving reliability. The reliability of domain allocations was lower than for ratings. It is important to define categories and rating levels clearly, and to train inspectors in their use. Further research is needed to replicate these results now that the model has been fully implemented, and to understand better the impact that inspector uncertainty and disagreement may have on published CQC ratings.
Augmented film narrative by use of non-photorealistic rendering

Our research is about the effects of non-photorealistic rendering in the perception of stereoscopic cinema. We conducted a study with 27 participants, using eye-tracker to evaluate ocular behavior during the free viewing of a stereoscopic film. We obtained ocular fixation data that were correlated with visual entropy. Gaze data leads us to believe that facing a highly abstract non-photorealistic scene the gaze seems to react under the effect of a perceptive anxiety. This ocular response seems to spontaneously assign greater sensorimotor resources to the analysis of the character's behavior, transforming his face into the main key to analyse the information and sense of the global scene. This behavior appears to us as an adaptive response to a visual environment endowed with abstraction and cognitive uncertainty. Our results could be useful for conceiving new non-photorealistic rendering tools for 3D films and cinematic virtual reality.
Periostin antisense oligonucleotide suppresses bleomycin‐induced formation of a lung premetastatic niche for melanoma

Metastasis is the leading cause of cancer death. A tumor‐supportive microenvironment, or premetastatic niche, at potential secondary tumor sites plays an important role in metastasis, especially in tumor cell colonization. Although a fibrotic milieu is known to promote tumorigenesis and metastasis, the underlying molecular contributors to this effect have remained unclear. Here we show that periostin, a component of the extracellular matrix that functions in tissue remodeling, has a key role in formation of a fibrotic environment that promotes tumor metastatic colonization. We found that periostin was widely expressed in fibrotic lesions of mice with bleomycin‐induced lung fibrosis, and that up‐regulation of periostin expression coincided with activation of myofibroblasts positive for α‐smooth muscle actin. We established a lung metastasis model for B16 murine melanoma cells and showed that metastatic colonization of the lung by these cells was markedly promoted by bleomycin‐induced lung fibrosis. Inhibition of periostin expression by giving an intratracheal antisense oligonucleotide targeting periostin mRNA was found to suppress bleomycin‐induced lung fibrosis and thereby to attenuate metastatic colonization of the lung by melanoma cells. Our results indicate that periostin is a key player in the development of bleomycin‐induced fibrosis and consequent enhancement of tumor cell colonization in the lung. Our results therefore implicate periostin as a potential target for prevention or treatment of lung metastasis.
Influence of Deleterious Phases Precipitation in the Corrosion Resistance Measured by Double Loop Electrochemical Polarization Reactivation Test in Superduplex Stainless Steel

This work investigates the influence of deleterious phases precipitation on the corrosion resistance of two wrought superduplex stainless steels UNS S32750 with similar composition, but different grain sizes. Isothermal treatments were carried out to introduce different amounts of deleterious phases, such as chi (χ), sigma (σ) and secondary austenite (γ2). The specimens were tested by double loop electrochemical potentiodynamic reactivation test (DL-EPR). The kinetics of precipitation in the two steels was different due to the difference of the grain sizes. The results show a correlation between the sensitization degrees, measured by DL-EPR, with the amount of deleterious phases precipitated.
Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights

Shotgun metagenomic analysis of the human associated microbiome provides a rich set of microbial features for prediction and biomarker discovery in the context of human diseases and health conditions. However, the use of such high-resolution microbial features presents new challenges, and validated computational tools for learning tasks are lacking. Moreover, classification rules have scarcely been validated in independent studies, posing questions about the generality and generalization of disease-predictive models across cohorts. In this paper, we comprehensively assess approaches to metagenomics-based prediction tasks and for quantitative assessment of the strength of potential microbiome-phenotype associations. We develop a computational framework for prediction tasks using quantitative microbiome profiles, including species-level relative abundances and presence of strain-specific markers. A comprehensive meta-analysis, with particular emphasis on generalization across cohorts, was performed in a collection of 2424 publicly available metagenomic samples from eight large-scale studies. Cross-validation revealed good disease-prediction capabilities, which were in general improved by feature selection and use of strain-specific markers instead of species-level taxonomic abundance. In cross-study analysis, models transferred between studies were in some cases less accurate than models tested by within-study cross-validation. Interestingly, the addition of healthy (control) samples from other studies to training sets improved disease prediction capabilities. Some microbial species (most notably Streptococcus anginosus) seem to characterize general dysbiotic states of the microbiome rather than connections with a specific disease. Our results in modelling features of the “healthy” microbiome can be considered a first step toward defining general microbial dysbiosis. The software framework, microbiome profiles, and metadata for thousands of samples are publicly available at http://segatalab.cibio. unitn.it/tools/metaml.
Dual Heuristic Programming Based Neurocontroller for Vibration Isolation Control

The dual heuristic programming (DHP) approach has a superior capability to solve approximate dynamic programming problems in the family of adaptive critic designs (ACDs). In this paper, a DHP based controller is developed for vibration isolation applications. In the specific case of an active-passive isolator, a multilayer feedforward neural network is pre- trained as a differentiable model of the plant for the adaptation of the critic and action networks. In addition, in order to avoid plunging in the local minima during the training process, pseudorandom signals, which represent the vibrations of the base, are applied to the vibration isolation system. This technique greatly improves the robustness of the DHP controller against unknown disturbances. Moreover, the "shadow critic" training strategy is adopted to improve the convergence rate of the training. Simulation results have shown that the developed DHP controller alleviates vibration disturbances more effectively and have better control performance in comparison with the passive isolator. Additionally, as compared with the one adapted on-line, the pre-trained model network leads the training to be more efficient. Therefore, it further demonstrates the effectiveness of the DHP controller designed with the pre-trained model network even in the presence of unmodeled uncertainties.
Distributed Computing with Heterogeneous Communication Constraints: The Worst-Case Computation Load and Proof by Contradiction

We consider a distributed computing framework where the distributed nodes have different communication capabilities, motivated by the heterogeneous networks in data centers and mobile edge computing systems. Following the structure of MapReduce, this framework consists of Map computation phase, Shuffle phase, and Reduce computation phase. The Shuffle phase allows distributed nodes to exchange intermediate values, in the presence of heterogeneous communication bottlenecks for different nodes (heterogeneous communication load constraints). Focusing on two-node and three-node (K=2, 3) distributed computing systems with heterogeneous communication load constraints, in this work we characterize the minimum total computation load, as well as the minimum worst-case computation load for some cases. The worst-case computation load depends on the computation load of a node with the heaviest job. Therefore, by minimizing the worst-case computation load it could potentially minimize the system latency. We show an interesting insight that, for some cases, there is a tradeoff between the minimum total computation load and the minimum worst-case computation load, in the sense that both cannot be achieved at the same time. The achievability schemes are proposed with careful design on the file assignment and data shuffling. Finally, beyond the cut-set bound, a novel converse is proposed using the proof by contradiction.
Sequential Search with Refinement: Model and Application with Click-Stream Data

We propose a structural model of consumer sequential search under uncertainty about attribute levels of products. Our identification of the search model relies on exclusion restriction variables that separate consumer utility and search cost. Because such exclusion restrictions are often available in online clickstream data, the identification and corresponding estimation strategy is generalizable for many online shopping websites where such data can be easily collected. Furthermore, one important feature of online search technology is that it gives consumers the ability to refine search results using tools such as sorting and filtering based on product attributes. The proposed model can coherently integrate consumers' decisions of search and refine. The model is instantiated using consumer clickstream data of online hotel bookings provided by a travel website. The results show that refinement tools have significant effects on consumer behavior and market structure. We find that the refinement tools encourage 34% more searches and enhance the utility of purchased products by 18%. However, most websites by default rank search results according to their qualities or relevance to consumers (e.g., Google). When consumers are unaware of such default ranking rules, they may engage in disproportionately more searches using refinement tools. Consequently, overall welfare surplus may deteriorate when search cost outweighs the enhanced utility. In contrast, if the website simply informs consumers that the default ranking already reflects product quality or relevance, consumers search less and the welfare surplus improves. We also find that refinement tools lead to a less concentrated market structure.
Profound genetic divergence and asymmetric parental genome contributions as hallmarks of hybrid speciation in polyploid toads

The evolutionary causes and consequences of allopolyploidization, an exceptional pathway to instant hybrid speciation, are poorly investigated in animals. In particular, when and why hybrid polyploids versus diploids are produced, and constraints on sources of paternal and maternal ancestors, remain underexplored. Using the Palearctic green toad radiation (including bisexually reproducing species of three ploidy levels) as model, we generate a range-wide multi-locus phylogeny of 15 taxa and present four new insights: (i) at least five (up to seven) distinct allotriploid and allotetraploid taxa have evolved in the Pleistocene; (ii) all maternal and paternal ancestors of hybrid polyploids stem from two deeply diverged nuclear clades (6 Mya, 3.1–9.6 Mya), with distinctly greater divergence than the parental species of diploid hybrids found at secondary contact zones; (iii) allotriploid taxa possess two conspecific genomes and a deeply diverged allospecific one, suggesting that genomic imbalance and divergence are causal for their partly clonal reproductive mode; (iv) maternal versus paternal genome contributions exhibit asymmetry, with the maternal nuclear (and mitochondrial) genome of polyploids always coming from the same clade, and the paternal genome from the other. We compare our findings with similar patterns in diploid/polyploid vertebrates, and suggest deep ancestral divergence as a precondition for successful allopolyploidization.
ClosNets: a Priori Sparse Topologies for Faster DNN Training

Fully-connected layers in deep neural networks (DNN) are often the throughput and power bottleneck during training. This is due to their large size and low data reuse. Pruning dense layers can significantly reduce the size of these networks, but this approach can only be applied after training. In this work we propose a novel fully-connected layer that reduces the memory requirements of DNNs without sacrificing accuracy. We replace a dense matrix with products of sparse matrices whose topologies we pick in advance. This allows us to: (1) train significantly smaller networks without a loss in accuracy, and (2) store the network weights without having to store connection indices. We therefore achieve significant training speedups due to the smaller network size, and a reduced amount of computation per epoch. We tested several sparse layer topologies and found that Clos networks perform well due to their high path diversity, shallowness, and high model accuracy. With the ClosNets, we are able to reduce dense layer sizes by as much as an order of magnitude without hurting model accuracy.
Part Annotations via Pairwise Correspondence

We explore the use of an interface to mark pairs of points on two images which are in “correspondence” with one another, as a way of collecting part annotations. The interface allows annotations of visual categories that are structurally diverse, such as chairs and buildings, where it is difficult to define a set of parts, or landmarks, that are consistent, namable or uniquely defined across all instances of the category. It allows flexibility in annotation – the landmarks can be instance specific, are not constrained by language, could be many to one, etc and requires little category specific instructions. We compare our approach to two popular methods of collecting part annotations, (1) drawing bounding boxes for a set of parts, and (2) annotating a set of landmarks, in terms of annotation setup overhead, cost, difficulty, applicability and utility, and identify scenarios where one method is better suited than the others. Preliminary experiments suggest that such annotations between a sparse set of pairs can be used to bootstrap many high level visual recognition tasks such as part discovery and semantic saliency.