Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of the Therapeutic Effect of Treatment with Antibiotics or Nutraceuticals on Clinical Activity and the Fecal Microbiome of Dogs with Acute Diarrhea

Simple Summary Acute diarrhea in dogs is one of the most common reasons for veterinary visits. Although this disorder is generally self-limiting, antibiotics are still frequently used as treatment for acute diarrhea in clinical practice. Antimicrobial resistance represents a major challenge for public health and requires immediate and drastic solutions. To date, the emergence and spread of antimicrobial resistance has been attributed to the misuse or indiscriminate use of antibiotics. The aim of this study is to compare the effects on clinical activity and fecal microbiota of the administration of an antibiotic combination in comparison to a nutraceutical product in dogs with acute non-hemorrhagic diarrhea. The results of the present study suggest that this nutraceutical treatment had a similar clinical effect compared to the antibiotic formulation and may represent an alternative to commonly used antimicrobial therapy. Abstract Dogs with acute diarrhea are often presented to clinical practice and, although this generally represents a self-limiting condition, antibiotics are still frequently used as treatment. The aim of this study was to evaluate the effects in dogs with acute non-hemorrhagic diarrhea of the administration of an antibiotic combination in comparison to a nutraceutical product. Thirty dogs were enrolled and randomly assigned to two groups: 15 dogs (group A) received a nutraceutical commercial product while 15 dogs (group B) received an antimicrobial combination of metronidazole and spiramycin. For each dog, the Canine Acute Diarrhea Severity Index, the fecal microbiota and the Dysbiosis Index were assessed. Both stool consistency and frequency decreased on day 2 in the dogs of group A compared to baseline, while in group B, these parameters significantly decreased at days 3 and 4. The global concern for rising antibiotic resistance associated with indiscriminate use of antimicrobials, in both humans and animals, suggests the necessity of avoiding empirical and injudicious use of these molecules in diarrheic dogs. These results suggest that the nutraceutical treatment had a similar clinical effect compared to the antibiotic formulation, representing a valid antibiotic-sparing therapeutic approach in canine acute diarrhea.
Short-Circuited Turn Fault Diagnosis in Transformers by Using Vibration Signals, Statistical Time Features, and Support Vector Machines on FPGA

One of the most critical devices in an electrical system is the transformer. It is continuously under different electrical and mechanical stresses that can produce failures in its components and other electrical network devices. The short-circuited turns (SCTs) are a common winding failure. This type of fault has been widely studied in literature employing the vibration signals produced in the transformer. Although promising results have been obtained, it is not a trivial task if different severity levels and a common high-level noise are considered. This paper presents a methodology based on statistical time features (STFs) and support vector machines (SVM) to diagnose a transformer under several SCTs conditions. As STFs, 19 indicators from the transformer vibration signals are computed; then, the most discriminant features are selected using the Fisher score analysis, and the linear discriminant analysis is used for dimension reduction. Finally, a support vector machine classifier is employed to carry out the diagnosis in an automatic way. Once the methodology has been developed, it is implemented on a field-programmable gate array (FPGA) to provide a system-on-a-chip solution. A modified transformer capable of emulating different SCTs severities is employed to validate and test the methodology and its FPGA implementation. Results demonstrate the effectiveness of the proposal for diagnosing the transformer condition as an accuracy of 96.82% is obtained.
Multi-Spectral Fusion and Denoising of Color and Near-Infrared Images Using Multi-Scale Wavelet Analysis †

We formulate multi-spectral fusion and denoising for the luminance channel as a maximum a posteriori estimation problem in the wavelet domain. To deal with the discrepancy between RGB and near infrared (NIR) data in fusion, we build a discrepancy model and introduce the wavelet scale map. The scale map adjusts the wavelet coefficients of NIR data to have the same distribution as the RGB data. We use the priors of the wavelet scale map and its gradient as the contrast preservation term and gradient denoising term, respectively. Specifically, we utilize the local contrast and visibility measurements in the contrast preservation term to transfer the selected NIR data to the fusion result. We also use the gradient of NIR wavelet coefficients as the weight for the gradient denoising term in the wavelet scale map. Based on the wavelet scale map, we perform fusion of the RGB and NIR wavelet coefficients in the base and detail layers. To remove noise, we model the prior of the fused wavelet coefficients using NIR-guided Laplacian distributions. In the chrominance channels, we remove noise guided by the fused luminance channel. Based on the luminance variation after fusion, we further enhance the color of the fused image. Our experimental results demonstrated that the proposed method successfully performed the fusion of RGB and NIR images with noise reduction, detail preservation, and color enhancement.
Design and Research of Inductive Oil Pollutant Detection Sensor Based on High Gradient Magnetic Field Structure

An inductive oil pollutant detection sensor based on a high-gradient magnetic field structure is designed in this paper, which is mainly used for online detection and fault analysis of pollutants in hydraulic and lubricating oil systems. The innovation of the sensor is based on the inductance detection method. Permalloy is embedded in the sensing region of the sensor, so that the detection area generates a high gradient magnetic field to enhance the detection accuracy of the sensor. Compared with traditional inductive sensors, the sensor has a significant improvement in detection accuracy, and the addition of permalloy greatly improves the stability of the sensor’s detection unit structure. The article theoretically analyzes the working principle of the sensor, optimizes the design parameters and structure of the sensor through simulation, determines the best permalloy parameters, and establishes an experimental system for verification. Experimental results show that when a piece of permalloy is added to the sensing unit, the signal-to-noise ratio (SNR) of iron particles is increased by more than 20%, and the signal-to-noise ratio of copper particles is increased by more than 70%. When two pieces of permalloy are added, the signal-to-noise ratio for iron particles is increased by more than 70%, and the SNR for copper particles is increased several times. This method raises the lower limit of detection for ferromagnetic metal particles to 20 μm, and the lower limit for detection of non-ferromagnetic metal particles to 80 μm, which is the higher detection accuracy of the planar coil sensors. This paper provides a new and faster online method for pollutant detection in oil, which is of great significance for diagnosing and monitoring the health of oil in mechanical systems.
Data Augmentation with Unsupervised Machine Translation Improves the Structural Similarity of Cross-lingual Word Embeddings

Unsupervised cross-lingual word embedding(CLWE) methods learn a linear transformation matrix that maps two monolingual embedding spaces that are separately trained with monolingual corpora. This method relies on the assumption that the two embedding spaces are structurally similar, which does not necessarily hold true in general. In this paper, we argue that using a pseudo-parallel corpus generated by an unsupervised machine translation model facilitates the structural similarity of the two embedding spaces and improves the quality of CLWEs in the unsupervised mapping method. We show that our approach outperforms other alternative approaches given the same amount of data, and, through detailed analysis, we show that data augmentation with the pseudo data from unsupervised machine translation is especially effective for mapping-based CLWEs because (1) the pseudo data makes the source and target corpora (partially) parallel; (2) the pseudo data contains information on the original language that helps to learn similar embedding spaces between the source and target languages.
Positive spectrahedra: invariance principles and pseudorandom generators

In a recent work, O’Donnell, Servedio and Tan (STOC 2019) gave explicit pseudorandom generators (s) for arbitrary m-facet polytopes in n variables with seed length poly-logarithmic in m,n, concluding a sequence of works in the last decade, that was started by Diakonikolas, Gopalan, Jaiswal, Servedio, Viola (SICOMP 2010) and Meka, Zuckerman (SICOMP 2013) for fooling linear and polynomial threshold functions, respectively. In this work, we consider a natural extension of s for intersections of positive spectrahedra. A positive spectrahedron is a Boolean function f(x)=[x1A1+⋯ +xnAn ≼ B] where the Ais are k× k positive semidefinite matrices. We construct explicit s that δ-fool “regular” width-M positive spectrahedra (i.e., when none of the Ais are dominant) over the Boolean space with seed length (logk,logn, M, 1/δ). Our main technical contributions are the following: We first prove an invariance principle for positive spectrahedra via the well-known Lindeberg method. As far as we are aware such a generalization of the Lindeberg method was unknown. Second, we prove an upper bound on noise sensitivity and a Littlewood-Offord theorem for positive spectrahedra. Using these results, we give applications for constructing s for positive spectrahedra, learning theory, discrepancy sets for positive spectrahedra (over the Boolean cube) and s for intersections of structured polynomial threshold functions.
Marginalization of Regression-Adjusted Treatment Effects in Indirect Comparisons with Limited Patient-Level Data

Population adjustment methods such as matching-adjusted indirect comparison (MAIC) are increasingly used to compare marginal treatment effects when there are cross-trial differences in effect modifiers and limited patient-level data. MAIC is based on propensity score weighting, which is sensitive to poor covariate overlap and cannot extrapolate beyond the observed covariate space. Current outcome regression-based alternatives can extrapolate but target a conditional treatment effect that is incompatible in the indirect comparison. When adjusting for covariates, one must integrate or average the conditional estimate over the population of interest to recover a compatible marginal treatment effect. We propose a marginalization method based on parametric G-computation that can be easily applied where the outcome regression is a generalized linear model or a Cox model. In addition, we introduce a novel general-purpose method based on the ideas underlying multiple imputation, which we term multiple imputation marginalization (MIM) and is applicable to a wide range of models, including parametric survival models. The approaches view the covariate adjustment regression as a nuisance model and separate its estimation from the evaluation of the marginal treatment effect of interest. Both methods can accommodate a Bayesian statistical framework, which naturally integrates the analysis into a probabilistic framework. A simulation study provides proof-of-principle for the methods and benchmarks their performance against MAIC and the conventional outcome regression. The simulations are based on scenarios with binary outcomes and continuous covariates, with the log-odds ratio as the measure of effect. The marginalized outcome regression approaches achieve more precise and more accurate estimates than MAIC, particularly when covariate overlap is poor, and yield unbiased marginal treatment effect estimates under no failures of assumptions. Furthermore, the marginalized covariate-adjusted estimates provide greater precision and accuracy than the conditional estimates produced by the conventional outcome regression, which are systematically biased because the log-odds ratio is a non-collapsible measure of effect.
"Call me sexist, but..." : Revisiting Sexism Detection Using Psychological Scales and Adversarial Samples

Research has focused on automated methods to effectively detect sexism online. Although overt sexism seems easy to spot, its subtle forms and manifold expressions are not. In this paper, we outline the different dimensions of sexism by grounding them in their implementation in psychological scales. From the scales, we derive a codebook for sexism in social media, which we use to annotate existing and novel datasets, surfacing their limitations in breadth and validity with respect to the construct of sexism. Next, we leverage the annotated datasets to generate adversarial examples, and test the reliability of sexism detection methods. Results indicate that current machine learning models pick up on a very narrow set of linguistic markers of sexism and do not generalize well to out-of-domain examples. Yet, including diverse data and adversarial examples at training time results in models that generalize better and that are more robust to artifacts of data collection. By providing a scale-based codebook and insights regarding the shortcomings of the state-of-the-art, we hope to contribute to the development of better and broader models for sexism detection, including reflections on theory-driven approaches to data collection.
Sustainability of nature walking trails: predicting walking tourists’ engagement in pro-environmental behaviors

ABSTRACT This study aimed to understand how tourists’ pro-environmental behavior (PEB) can be promoted in natural walking trails. Specifically, this study examined the links among place attachment, place-specific PEB, and general PEB in everyday life, and the effects of power on PEB. Using data collected from 273 walking tourists, the results of structural equation modeling revealed place identity, nature bonding, social bonding, and general PEB directly affecting place-specific PEB. In addition, power was found to have a direct effect on only general PEB. These results demonstrate the importance of focusing affective strategies and providing effective infrastructure to promote tourists’ environmental practices.
The impact of digital transformation on salespeople: an empirical investigation using the JD-R model

Abstract Many firms are engaging in the digital transformation (DT) of their sales forces, and this trend has accelerated during the COVID-19 pandemic. However, research on DT as a profound organizational change process, as well as salespeople’s individual psychological reactions to such initiatives, is still limited. Although DT offers salespeople more and better resources for work-related goal attainment, it increases job demands and typically generates high uncertainty, which companies must then manage. We draw on job demands-resources (JD-R) theory to account for these bright and dark side effects simultaneously. We analyze the direct, mediated, and moderated effects of uncertainty reduction initiatives (resources) and excessive workload (demands) on the perceived usefulness of DT (i.e. salespeople’s motivation to embrace it), the stress it generates, and the ultimate success of DT integration. In doing so, we shed light on the complex pattern of relationships that characterizes salespeople’s psychological reactions to DT. Using DT context-specific constructs, we test our model and hypotheses on a sample of 144 salespeople of a firm engaged in DT. Our results fill several gaps in the technology in sales, DT, and JD-R literatures, and we provide managers with several guidelines to better manage DT in sales.
Commentary: practical insights for sales force digitalization success: the scholar’s perspective

Abstract The Zoltners, Sinha, Sahay, Shastri, and Lorimer (2021) article provides an excellent macro model of how sales digitization is related to organizational success. Additionally, the authors provide recommendations for avoiding pitfalls and achieving success in sales digitization programs. The intent of this article is to facilitate future research on these linkages identified in the Zoltners et al. article by discussing how specific models and theories might inform research on these linkages, including Complexity Leadership Theory, business models and contextual boundary conditions, a resource-based view of the firm, and Transaction Cost Theory.
COVID-19’s impact on tourism: will compensatory travel intention appear?

ABSTRACT The coronavirus disease (COVID-19) has had a major impact on global tourism. We adopted social media data to quantify the change in Chinese consumers’ attitudes toward traveling during the pandemic, as no study has explored whether there is compensatory travel intention under the threat of infectious diseases. Using natural language processing technology and geographic information system technology, our analysis shows that Chinese consumers’ interest in tourism increased during the pandemic. Our results show that travel attitudes can be positively affected by compensatory consumption when travel is restricted by infectious diseases and has implications for tourism scholars, marketing managers, and stakeholders.
I NDU S TRY-S P E C I F I C EXCHANGE RAT E VOLAT I L I T Y AND IN T ERMED I A T E GOOD S TRADE

This study empirically analyses the impact of industry-specific real exchange rate volatility on intra-Asian trade of intermediate goods at an industry level. In contrast to the recent studies, our empirical results reveal that the exchange rate volatility has significantly negative effect only on two industries: general machinery and electrical machinery. However, no significant effects are found on the intermediate goods trade of other machinery industries. Such different impacts of the exchange rate volatility across industries reflect the growing intra-firm trade and the choice of invoice currency, which is related to the characteristics of the traded goods.
Command of Media’s Metaphors

On a June weekend in 1959, an elite group of sociologists, philosophers, editors, artists, and television producers gathered in the Poconos to discuss media. Their invitation was to “Mass Media in Modern Society,” an interdisciplinary conference hosted by the Tamiment Institute and Daedalus, the house organ of the Academy of Arts and Sciences. What constituted mass media in 1959—and who publicized media, then a new concept in the vernacular, as a topic of mass concern—were the thirty-five celebrity panelists’ unresolved and obsessive themes. Turning to the minutes from the conference as well as its two afterlives in print, this essay disambiguates the nascent media concept’s ideological, technological, and environmental implications, five years before Marshall McLuhan’s breakthrough text, Understanding Media: The Extensions of Man. Within the panelists’ responses is not a coherent theory of media but a multitude of overdetermined media metaphors—and a model for understanding media as such, then and now. The mid-twentieth-century consolidation of the media concept is where most of its greatest theorists (from Raymond Williams to John Guillory) conclude their analyses, but it’s where this study begins.
What an Image Is

This article investigates how images are understood inside and outside heritage institutions. It focuses on information specialists in libraries, archives, and museums and on a very specific yet substantial end-user group for visual heritage material: university scholars in the humanities. Based on a survey on the production and use of descriptive metadata, this study discloses that there is an ontological divide between these two groups, and that the extensive production of descriptive metadata does not match the needs and interest of researchers in the humanities, but rather other end users. An increased dialogue is needed between these two groups concerning what metadata should be attached to images. This potentially could lead to a broader and more extended scholarly use of visual heritage material.
Putative role of PV and SOM interneuron subtypes in working memory

Working memory is an essential human trait required for all cognitive activities. Our previous model from Mongillo et al. (1), Mi et al. (2) uses synaptic facilitation to store traces of working memory. Thus memories can be maintained without persistent neural activity. A critical component of this model is a central inhibition which prevents multiple item representations from being active at the same time. We know from experimental studies that multiple genetically-defined interneuron subtypes (e.g. PV, SOM) with different excitability and connectivity properties mediate inhibition in the cortex. The role of these sub-types in working memory however is not known. Here we develop a modified model with these interneuron subtypes, and propose their functional roles in working memory. We make concrete testable predictions about the roles of these groups.
Scaling principles of white matter brain connectivity

Brains come in many shapes and sizes. Nature has endowed big-brained primate species like humans with a proportionally large cerebral cortex. White matter connectivity – the brain’s infrastructure for long-range communication – might not always scale at the same pace as the cortex. We investigated the consequences of this allometric scaling for white matter brain network connectivity. Structural T1 and diffusion MRI data were collated across fourteen primate species, describing a comprehensive 350-fold range in brain volume. We report volumetric scaling relationships that point towards a restriction in macroscale connectivity in larger brains. Building on previous findings, we show cortical surface to outpace white matter volume and the corpus callosum, suggesting the emergence of a white matter ‘bottleneck’ of lower levels of connectedness through the corpus callosum in larger brains. At the network level, we find a potential consequence of this bottleneck in shaping connectivity patterns, with homologous regions in the left and right hemisphere showing more divergent connectivity in larger brains. Our findings show conserved scaling relationships of major brain components and their consequence for macroscale brain circuitry, providing a comparative framework for expected connectivity architecture in larger brains such as the human brain.
Running title: X-ray microanalyses of Cd accumulation in willow

Trees that accumulate metals are important plants for restoring contaminated soil because of their high biomass. We examined the cadmium tolerance and growth rate of six willow (Salix) species common in Japan. To characterize in detail the localization of Cd and its ligands, synchrotron radiation-based micro X-ray fluorescence analysis was used. This revealed the accumulation of cadmium at the tips of the serrations in leaves, and the phellogen and/or the phelloderm under the stem surface. μ-X-ray absorption near-edge-structure spectra of Cd in all the accumulation sites were similar to that of Cd ion coordinated by O ligands in S. gilgiana.