Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of Methods for Texture Assessment of Concrete Surfaces

This article describes how the bond strength of the interface between concrete layers cast at different times is important to ensure the monolithic behavior of reinforced concrete (RC) composite members. The roughness of the substrate surface has a significant influence in this scope. Current design codes use a qualitative approach based on visual inspection to assess roughness. This procedure is highly dependent on the designer and, therefore, can lead to inaccurate results. Previous studies conducted by the authors proved that it is possible to use a quantitative criterion to classify roughness. This paper describes an experimental study conducted to compare four roughness quantification methods: the processing of the digital image (PDI) and the two-dimensional (2D) laser roughness analyzer (2D-LRA) methods, both developed by the authors; an upgrade of these, using a three-dimensional (3D) laser scanner; and the sand patch test (SPT), a simple and widespread method. The 2D-LRA proved to be the best from the four methods considered because it gives a quantitative assessment of the roughness with adequate accuracy, is nondestructive, easy and fast to use, and is cost effective.
From Fingerprint to Footprint: Cold-start Location Recommendation by Learning User Interest from App Data

ZHEN TU, Department of Electrical Engineering, Tsinghua University, China YALI FAN, Sun Yat-Sen University, China YONG LI, Department of Electrical Engineering, Tsinghua University, China XIANG CHEN, Sun Yat-Sen University, China LI SU, Tsinghua University, China DEPENG JIN, Beijing National Research Center for Information Science and Technology(BNRist), Department of Electrical Engineering, Tsinghua University, China
Shrinkage reduction of thermoset fibre composites during hardening using microwaves irradiation for curing

Composites made from vinylester resins by Fibre Composite Design and Development (FCDD) group, University of Southern Queensland (USQ) suffer considerable shrinkage during hardening. This shrinkage is particularly serious if the fibre composite components are large. It can be more than ten percent, which is much higher than claimed by some researchers and resins’ manufacturers (Clarke, 1996; Matthews and Rawlings, 1994). The main drawbacks of this shrinkage in a composite component are the stresses set up internally. These stresses are usually tensile in the core of the component and compressive on the surface (Ossward and Menges, 1995). When these stresses act together with the applied loads during service they may cause premature failure of the composite components. Currently, FCDD group solves the shrinkage problem by breaking a large composite component into smaller composite parts because smaller parts tend to have less shrinkage. These smaller parts are then joined together to form the overall structure. This is shown in Figure 1. The individual item of the component shown in Figure 1 is manufactured by casting liquid form, uncured 44 percent by volume or 33% by weight flyash particulate reinforced vinlyesters [VE/FLYASH (33%)] into moulds. By doing this, the manufacturing lead-time and costs of a composite component is significantly increased. Now, industrial microwave technology for processing polymers and polymer based composites is increasingly being applied because of its deep penetration into the material without overheating the surface (Metaxas and Meredith, 1983; Siores, 1994; Ku et al., 1997). This project attempts to reduce the shrinkage of the components by shortening the curing time of the resins using penetrating microwave energy. In addition, some of the shrinkage will be compensated for by low profile additives, which are dissolved in a solvent and fused into the composites by microwave irradiation. The amount of initiator used to initiate polymerisation, the input power of microwave energy and the amount of the low profile additives are varied to obtain an optimum combination of these parameters for minimum shrinkage of the composite components. If the project is successful, a large composite component can be made in one part with minimum shrinkage and hence minimum internal stresses. Quality of the composite component is thus improved. On top of it, building a larger microwave cavity for larger components has been considered. Its design and detailed calculations are clearly described in a book (Fuller, 1990).
Use of an Indoor Navigation System by Sighted and Blind Travelers: Performance Similarities across Visual Status and Age

NICHOLAS A. GIUDICE, Virtual Environments and Multimodal Interaction (VEMI) Laboratory, Spatial Informatics Program: School of Computing and Information Science, and Department of Psychology, The University of Maine, Orono, ME, USA BENJAMIN A. GUENTHER, Department of Psychology, The University of Maine, Orono, ME, USA TONI M. KAPLAN, Virtual Environments and Multimodal Interaction (VEMI) Laboratory, The University of Maine, Orono, ME, USA SHANE M. ANDERSON and ROBERT J. KNUESEL, Koronis Biomedical Technologies, Maple Grove, MN, USA JOSEPH F. CIOFFI, ClickAndGo Wayfinding Maps LLC, Minneapolis, MN, USA
Trade and credit reallocation: How banks help shape comparative advantage

Innovation and trade are major sources of structural change. Innovative firms with better products expand on world markets, while less productive firms shrink. Capital and labor must flow from declining toward expanding firms. Reallocation is especially important in innovative sectors with short product cycles and high turnover. But this process is, by no means, without frictions. Whenever capital and labor are locked into current uses, a country cannot fully exploit its comparative advantage in reallocationintensive, innovative sectors. Received: 23 November 2020 | Revised: 4 May 2021 | Accepted: 1 June 2021 DOI: 10.1111/roie.12564
The African Journal of Information Systems The African

This paper reports on a study that uses a combination of techniques to formally characterize and determine the critical success factors influencing the effective usage of enterprise resource planning (ERP) systems, with special reference to higher education institutions. The thirty-seven ERP success factors identified from the literature are classified into: Critical, Active, Reactive and Inert categories. The classification of decision factors can generally support organizations to explore their current challenges and to adequately prepare decisions in a more participatory way for future endeavors. This study suggests a possible alternative that decision makers should take when a factor or a set of factors dominates during the implementation of ERP systems.
Modeling Human Inference Process for Textual Entailment Recognition

To prepare an evaluation dataset for textual entailment (TE) recognition, human annotators label rich linguistic phenomena on text and hypothesis expressions. These phenomena illustrate implicit human inference process to determine the relations of given text-hypothesis pairs. This paper aims at understanding what human think in TE recognition process and modeling their thinking process to deal with this problem. At first, we analyze a labelled RTE-5 test set which has been annotated with 39 linguistic phenomena of 5 aspects by Mark Sammons et al., and find that the negative entailment phenomena are very effective features for TE recognition. Then, a rule-based method and a machine learning method are proposed to extract this kind of phenomena from text-hypothesis pairs automatically. Though the systems with the machine-extracted knowledge cannot be comparable to the systems with human-labelled knowledge, they provide a new direction to think TE problems. We further annotate the negative entailment phenomena on Chinese text-hypothesis pairs in NTCIR-9 RITE-1 task, and conclude the same findings as that on the English RTE-5 datasets.
Answering Why-Not Questions on Top-K Queries

After decades of effort working on database performance, the quality and the usability of database systems have received more attention in recent years. In particular, the feature of explaining missing tuples in a query result, or the so-called “why-not” questions, has recently become an active topic. In this paper, we study the problem of answering why-not questions on top-k queries. Our motivation is that we know many users love to pose those kinds of queries when they are making multi-criteria decisions. However, they would also want to know why if their expected answers do not show up in the query results. In this paper, we develop algorithms to answer such why-not questions efficiently. Case studies and experimental results show that our algorithms are able to return high quality explanations efficiently.
OPT2020: 12th Annual Workshop on Optimization for Machine Learning SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation

Stochastic Gradient Descent (SGD) is being used routinely for optimizing non-convex functions. Yet, the standard convergence theory for SGD in the smooth non-convex setting gives a slow sublinear convergence to a stationary point. In this work, we provide several convergence theorems for SGD showing convergence to a global minimum for non-convex problems satisfying some extra structural assumptions. In particular, we focus on two large classes of structured non-convex functions: (i) Quasar (Strongly) Convex functions (a generalization of convex functions) and (ii) functions satisfying the Polyak-Lojasiewicz condition (a generalization of strongly-convex functions). Our analysis relies on an Expected Residual condition which we show is a strictly weaker assumption than previously used growth conditions, expected smoothness or bounded variance assumptions. We provide theoretical guarantees for the convergence of SGD for different step-size selections including constant, decreasing and the recently proposed stochastic Polyak step-size. In addition, all of our analysis holds for the arbitrary sampling paradigm, and as such, we give insights into the complexity of minibatching and determine an optimal minibatch size. Finally, we show that for models that interpolate the training data, we can dispense of our Expected Residual condition and give state-of-the-art results in this setting.
Magma source transition of lunar mare volcanism at 2.3 Ga

Mare basalts provide insights into the composition and thermal history of the lunar mantle. The ages of mare basalts suggest a first peak of magma activity at 3.2–3.8 Ga and a second peak at ~2 Ga. In this study, we reassess the correlation between the titanium contents and the eruption ages of mare basalt units using the compositional and chronological data updated by SELENE (Kaguya). Using morphological and geological criteria, we calculated the titanium content of 261 mare units across a representative area of each mare unit. In the Procellarum KREEP Terrane, where the latest eruptions are located, an increase in the mean titanium content is observed during the Eratosthenian period, as reported by previous studies. We found that the increase in the mean titanium content occurred within a relatively short period near approximately 2.3 Ga, suggesting that the magma source of the mare basalts changed at this particular age. Moreover, the high‐titanium basaltic eruptions are correlated with a second peak in volcanic activity near ~2 Ga. The high‐titanium basaltic eruptions occurring during the last volcanic activity period can be explained by the three possible scenarios (1) the ilmenite‐bearing cumulate rich layer in the core‐mantle boundary formed after the mantle overturn, (2) the basaltic material layers beneath the lunar crust formed through upwelling magmas, and (3) ilmenite‐bearing cumulate blocks remained in the upper mantle after the mantle overturn.
10Be in Australasian microtektites compared to tektites: Size and geographic controls

High Be-10 contents in tektites reported in literature are taken as evidence of a source material, melted at the impact site, enriched in atmospheric Be-10; i.e., a soil or sediment. In 0.8 Ma Australasian tektites, Be-10 content increases with distance from the putative impact location in Indochina, with geographic averages from 69 x 10(6) atoms/g (Indochina) to 136 x 10(6) atoms/g (Australia). Here we report, for the first time, Be-10 contents in microtektites collected from Antarctica and the South China Sea. We show that microtektites are similar to 30 x 10(6) atoms/g richer in Be-10 than tektites from the same geographic areas. Antarctic microtektites, with an average Be-10 content of 184 x 10(6) atoms/g after correction for in situ production, are the richest impact glass ever measured. The simplest explanation for such systematic size and geographic trends is that the source depth of the melt within the target surface decreases with ejection velocity. Indeed, higher initial kinetic energy implies higher launch distances and higher fragmentation of the ejecta. Antarctic microtektite source depth may tentatively be restricted to the upper tens of centimeters at the impact site. Alternative models invoking a marine or loessic sediment source, or a secondary enrichment in the microtektite (either by atmospheric scavenging, selective fractionation by volatilization, or post-depositional contamination) fail to reproduce the observed relationships.
the 21st Annual Conference of the European Association for Machine Translation

Previous attempts at injecting semantic frame biases into SMT training for low resource languages failed because either (a) no semantic parser is available for the low resource input language; or (b) the output English language semantic parses excise relevant parts of the alignment space too aggressively. We present the first semantic SMT model to succeed in significantly improving translation quality across many low resource input languages for which no automatic SRL is available — consistently and across all common MT metrics. The results we report are the best by far to date for this type of approach; our analyses suggest that in general, easier approaches toward including semantics in training SMT models may be more feasible than generally assumed even for low resource languages where semantic parsers remain scarce.
DEEP NEURAL NETWORKS FOR ESTIMATION AND INFERENCE

We study deep neural networks and their use in semiparametric inference. We es- tablish novel nonasymptotic high probability bounds for deep feedforward neural nets. These deliver rates of convergence that are sufﬁciently fast (in some cases minimax op- timal) to allow us to establish valid second-step inference after ﬁrst-step estimation with deep learning, a result also new to the literature. Our nonasymptotic high probability bounds, and the subsequent semiparametric inference, treat the current standard architecture: fully connected feedforward neural networks (multilayer perceptrons), with the now-common rectiﬁed linear unit activation function, unbounded weights, and a depth explicitly diverging with the sample size. We discuss other architectures as well, including ﬁxed-width, very deep networks. We establish the nonasymptotic bounds for these deep nets for a general class of nonparametric regression-type loss functions, which includes as special cases least squares, logistic regression, and other generalized linear models. We then apply our theory to develop semiparametric inference, focus- ing on causal parameters for concreteness, and demonstrate the effectiveness of deep learning with an empirical application to direct mail marketing.
AP Research Student Samples from the 2020 Exam Administration-Sample B

Major Depressive Disorder (MDD) has become one of the most common mental disorders in the United States, illustrating the importance of antidepressant research. The Porsolt Swim Test (PST) is a rare assay intended to test experimental manipulations meant to stimulate or prevent depressive states for the purpose of evaluating antidepressant compounds. The volume of controversy over the reliability and interpretation of the test can be accredited to its basis on conditional stressors and lack of holistic consideration of behavior. But with its intentions and economical and noninvasive nature, it is a better model than many available, suggesting high potential and abilities for valuable reinstitution as a common assay. This research utilized the concept of the gut-brain axis and codependent relationship of the body with microorganisms by quantifying PST rat gut bacteria genera relevant to serotonin synthesis. The significant imbalances of relevant taxa identified in the gut compositions of the rats aligned with their behavioral phenotypes, validating the PST as a model for studying depression. This research provides support for reviving the PST as a common antidepressant research practice. Implicating many other applications, composition diversity and functions of microbial communities have potential to inform more personalized approaches to drug development strategies and augment treatment efficiency.
Ambient aerosol concentrations of sugars and sugar-alcohols at four different sites in Norway

Sugars and sugar-alcohols are demonstrated to be important constituents of the ambient aerosol water-soluble organic carbon fraction, and to be tracers for primary biological aerosol particles (PBAP). In the present study, levels of four sugars (fructose, glucose, sucrose, trehalose) and three sugar-alcohols (arabitol, inositol, mannitol) in ambient aerosols have been quantified using a novel HPLC/HRMS-TOF (High Performance Liquid Chromatography in combination with High Resolution Mass Spectrometry – Time of Flight) method to assess the contribution of PBAP to PM>sub>10 and PM 2.5 . Samples were collected at four sites in Norway at different times of the year in order to reflect the various contributing sources and the spatial and seasonal variation of the selected compounds. Sugars and sugar-alcohols were present at all sites investigated, underlining the ubiquity of these highly polar organic compounds. The highest concentrations were reported for sucrose, reaching a maximum concentration of 320 ng m −3 in PM 10 and 55 ng m −3 in PM 2.5 . The mean concentration of sucrose was up to 10 times higher than fructose, glucose and the dimeric sugar trehalose. The mean concentrations of the sugar-alcohols were typically lower, or equal, to that of the monomeric sugars and trehalose. Peak concentrations of arabitol and mannitol did not exceed 30 ng m −3 in PM 10 , and for PM 2.5 all concentrations were below 6 ng m −3 . Sugars and sugar-alcohols were associated primarily with coarse aerosols except during wintertime at the suburban site in Elverum, where a shift towards sub micron aerosols was observed. It is proposed that this shift was due to the intensive use of wood burning for residential heating at this site during winter, confirmed by high concurrent concentrations of levoglucosan. Elevated concentrations of sugars in PM 2.5 were observed during spring and early summer at the rural background site Birkenes. It is hypothesized that this was due to ruptured pollen.
Digital Image Computing: Techniques and Applications

Digital Image Computing: Techniques and Applications is the premier biennial conference in Australia on the topics of image processing and image analysis. This seventh edition of the proceedings has seen an unprecedented level of submission, on such diverse areas as: Image processing; Face recognition; Segmentation; Registration; Motion analysis; Medical imaging; Object recognition; Virtual environments; Graphics; Stereo-vision; and Video analysis. These two volumes contain all the 108 accepted papers and five invited talks that were presented at the conference. These two volumes provide the Australian and international imaging research community with a snapshot of current theoretical and practical developments in these areas. They are of value to any engineer, computer scientist, mathematician, statistician or student interested in these matters.
College Major Choice and the Gender Gap

This paper studies how college majors are chosen, focusing on the underlying gender gap. I collect a data set of Northwestern University sophomores that contains their subjective expectations about choice-specific c outcomes, and estimate a model where majors are chosen under uncertainty. Enjoying coursework and gaining parents’ approval are the most important determinants in the choice for both genders. However, males and females differ in their preferences in the workplace, with males caring about the pecuniary outcomes in the workplace much more than females. The gender gap is mainly due to gender differences in preferences and tastes, and not because females are underconfident about their academic ability or fear monetary discrimination. The findings in this paper make a case for policies that change attitudes toward gender roles.
Language teaching

An enduring contribution of Richards Sparks’ work has been his exploration of the relationship between a learner’s first language (L1) and a language that he or she learns subsequently (L2). The present paper considers that relationship from the perspective of learners and teachers of a ‘foreign’ language in classroom settings. The paper first sets the broader context by looking briefly at the recently developed concept of ‘translanguaging’ and the more established notion of ‘multilingual competence’. It then moves into the domain of the foreign language classroom. It outlines studies of how much, and why, foreign language teachers have been observed to use their students’ L1 (often, in the teachers’ own view, excessively). It argues that attempts to exclude the students’ L1 have been not only impracticable but also misguided. It proposes a framework within which teachers can avoid the dangers of using the L1 excessively but also build on its potential as a support for learning.
Visual and Semantic Feature Spaces for Zero-shot Image Decoding

Large image galleries are notoriously difﬁcult to categorise and navigate without a great deal of manual organisation. We present a new methodology that could enable a Brain-Computer Interface (BCI) system for image retrieval, as an alternative to the automated tagging approaches recently emerging. We propose a regression framework that allows us to effectively map EEG signals into a visuo-semantic feature space, where the stimulus image can be matched. We use a set of intermediate features ranging from low-level edge detection, to the semantic properties of an image’s depicted content, reﬂecting theoretical and empirical knowledge of how the brain processes visual input and extracts meaning from it. A real-world system would need to generalise to images not seen during training, so we use zero-shot learning to decode image features from brain activity. Using our approach under this challenging setting, we were able to decode left-out individual stimuli at a rate signiﬁcantly above chance.
Repository ISTITUZIONALE

—Traceability is a key factor for the agri-food sector. RFID technology, widely adopted for supply chain management, can be used effectively for the traceability management. In this paper, a framework for the evaluation of a traceability system for the agri-food industry is presented and the automation level in an RFID-based traceability system is analyzed and compared with respect to traditional ones. Internal and external traceability are both considered and formalized, in order to classify different environments, according to their automation level. Traceability systems used in a sample sector are experimentally analyzed, showing that by using RFID technology, agri-food enterprises increase their automation level and also their efﬁciency, in a sustainable way.