Miracle-dz/UniFAR-FTUs huggingface dataset:
Increasing prevalence of non-clade B HIV-1 strains in heterosexual men and women, as monitored by analysis of reverse transcriptase and protease sequences.

Objective: We evaluated the prevalence of HIV-1 non-clade B over time in a formerly clade B-restricted area. Protease and reverse transcriptase regions of the pot gene were used for phylogenetic and recombination analysis and for clade assignment to HIV-1 A-D, F-H, J, and K strains of the M group. Methods: The pot gene of 349 HIV-1 patients belonging to the Italian Cohort Naive for Antiretrovirals (ICONA) were genotypically analyzed to study the prevalence of antiretroviral-associated resistance mutations. All HIV-1 pot sequences and 32 HIV reference strains were analyzed, including the reference strains for the major HIV-1 subtypes. The non-clade B sequences according to the HIV-1 Subtyping Tool program were further studied by a bootscan analysis (SimPlot) to investigate the likelihood of recombination between subtypes. Results: Phylogenetic analysis detected 19 of 349 (5.4%) non-clade B subtypes. The proportions of patients carrying non-clade B virus before and after 1997 were 1.9% and 8.4%, respectively (p = .008). Among whites, heterosexual infection and female gender were significantly associated with the presence of non-clade B subtypes (p = .001 and .005, respectively). Non-clade B HIV-1 was harbored by 14.5% of the heterosexuals who were found to be HIV-1 positive after 1997, 60% of whom were women. Bootscan analysis identified four strains as F, two as A, one as C, one as G, and 11 (57.9 %) as non-clade B recombinant subtypes. Conclusion: Detection of HIV-1 subtypes and intersubtype recombinants in a previously clade B-homogeneous area indicates that the HIV-1 epidemic is evolving in Italy and that heterosexuals and women are at increased risk of infection with non-clade B HIV-1 subtypes. Sequences inferred from the pot gene yield to establish the subtype of circulating HIV-1 strains. As a consequence, genotyping of pot gene for testing resistance to antiretrovirals warrants concomitant surveillance of non-clade B subtypes.
The Placental Microbiome

It has been commonly accepted that the intrauterine environment during pregnancy is relatively sterile and that the introduction of bacteria could lead to the development of chorioamnionitis. However, several recent studies have found both gram-positive and gram-negative bacteria — including taxa not associated with the urogenital tract — in …
TrIIPRMAL BURNMS IN MAN. IX. tJRINARY AMINO ACID PATTERNS1

It is a well known phenomenon that burned patients lose considerable amounts of nitrogenous materials following the initial trauma. They continue to stay in negative nitrogen balance for several days or even a month or more despite efforts to supply quantities of the necessary amino acids for metabolism and repair. Even though much protein material is lost in exudates (1), considerable amounts of catabolic nitrogenous substances appear in the urine. Besides the urea, creatinine, uric acid, and ammonia, relatively large amounts of amino acids and amino acid derivatives are excreted. This increase in the loss of amino acids is of special interest since it represents, for the most part, a wastage of materials needed for replacement and repair of the injured tissue. In a recent paper Nardi (2) has studied the excretion of the free amino acids in urine of the burned patient by qualitative paper chromatographic techniques. In the study reported here, the quantitative determination of the amounts of 16 amino acids excreted in the urine following burn trauma has been studied in 9 patients to define with more exactness the qualitative and the quantitative aspects of the amino acid loss. In order to determine with greater clarity the amino acid patterns in urines of these burned patients, the amount of "free" amino acid and the "total" amount of each amino acid were determined. The amount of each amino acid in the "bound" form was determined by the difference between the two values found.
Outcome of infants delivered between 24 and 28 weeks' gestation in women with severe pre-eclampsia.

Objective: To determine whether there are differences in neonatal outcome between infants born to mothers with severe pre-eclampsia and those born to normotensive mothers with preterm labor and intact membranes between 24 and 28 weeks' gestation. Materials and methods: Over a 4-year period between 1991 and 1995, neonates of women with severe pre-eclampsia delivering between 24 and 28 weeks were matched for maternal age, antenatally assigned gestational age and mode of delivery to normotensive women delivering during the same period. Results: Fifty-eight women with severe pre-eclampsia were matched to 58 normotensive controls who delivered as a result of preterm labor. Antenatal steroids were used more often in pre-eclamptic women (75% vs. 47%, p < 0.01). The mean birth weight of pre-eclamptic neonates was significantly lower than that of controls, 767 g vs. 989 g, respectively. Other neonatal complications were similar for both groups. Neonates of pre-eclamptics required longer ventilator support (21 vs. .
The Economics of Overtime Working: List of tables

Numerous individuals throughout international labour markets work hours in excess of their standard contractual hours. Overtime working is a vital consideration in the employment and wage decisions of many households and firms. From a policy perspective, overtime is at the centre of interest in the work sharing/unemployment trade off. Robert Hart presents the first comprehensive economic evaluation of this phenomenon, examining theoretical, empirical and policy aspects of overtime hours and pay. In a comparative assessment of labour supply, labour demand and compensating differential models of overtime behaviour, he utilises detailed international evidence drawn from the United States, Western Europe and Japan. Policy initiatives linked to job creation, work sharing, absenteeism and payroll taxation are critically assessed and presented in an intuitive manner. Displaying analytical rigour and empirical expertise, Robert Hart's work extends far beyond a mere summary of existing research to enliven and inform debate.
Psychotherapy trainees' experience of counterproductive events in supervision.

The authors conducted interviews with 13 psychotherapy trainees about a counterproductive event that occurred in individual supervision, which was defined as any experience that was hindering, unhelpful, or harmful in relation to the trainee's growth as a therapist. A qualitative analysis revealed that trainees typically attributed their experiences of counterproductive events to their supervisors dismissing their thoughts and feelings. All trainees experienced a negative interaction with their supervisors following the counteractive event, yet most did not believe their supervisors were aware of the event's counterproductive nature. All trainees believed the counterproductive event weakened the supervisory relationship and led to a change in the way they approached their supervisors. Although trainees typically thought the counterproductive events negatively affected their work with clients, most did not disclose their counterproductive experience with their supervisors.
Response of lake Mar Chiquita in Argentina to climate change: data analysis and application of a lake model

Lake Mar Chiquita (30°54’S-62°51’W), a closed, shallow, hypersaline-lake in Central Argentina (30°S, 62°W) has recorded hydrological changes for the 20th century in Southern South America (SESA) by sharp variations in its water-level. The lake, located at the lowermost end of an endorheic basin, has a large catchment area (37,570 km) situated in the western part of the Parana-La Plata basin. It is also a part of the ChacoPampean plain, an zone of forests and grasslands that has been extensively cleared for agricultural activities since the end of the nineteenth century. During the hydrological changes that occurred during the 70’s in SESA, the lake has undergone a significant water-level rise. Historical and instrumental data, combined with sediment core studies (sedimentological, isotopic, and diatom analysis) showed that Mar Chiquita is an ideal site for recording highand low-frequency changes in hydrological budget. Therefore, this lake can be considered as a regional and temporal integrator of its catchment water balance. A detailed hydrological study was initiated through a modelling approach in order to determine lake level variations in response to climate changes. In this paper, we present preliminary results, based on hydroclimatic records (precipitation and river discharge data) covering the last quarter century. The lake water balance model is used to determine the lake hydrological behaviour and to quantify its water balance. From available hydroclimatic data, an important overestimation of the simulated lake level was evidenced. We discuss the possible processes involved in this discrepancy.
Inapproximability of Diameter in super-linear time: Beyond the 5/3 ratio

We show, assuming the Strong Exponential Time Hypothesis, that for every $\varepsilon > 0$, approximating directed Diameter on $m$-arc graphs within ratio $7/4 - \varepsilon$ requires $m^{4/3 - o(1)}$ time. Our construction uses nonnegative edge weights but even holds for sparse digraphs, i.e., for which the number of vertices $n$ and the number of arcs $m$ satisfy $m = n \log^{O(1)} n$. This is the first result that conditionally rules out a near-linear time $5/3$-approximation for Diameter.
Low Tensor Train- and Low Multilinear Rank Approximations for De-speckling and Compression of 3D Optical Coherence Tomography Images

This paper proposes low tensor-train (TT) rank and low multilinear (ML) rank approximations for de-speckling and compression of 3D optical coherence tomography (OCT) images for a given compression ratio (CR). To this end, we derive the alternating direction method of multipliers based algorithms for the related problems constrained with the low TT- and low ML rank. Rank constraints are implemented through the Schatten-p (Sp) norm, p e {0, 1/2, 2/3, 1}, of unfolded matrices. We provide the proofs of global convergence towards a stationary point for both algorithms. Rank adjusted 3D OCT image tensors are finally approximated through tensor train- and Tucker alternating least squares decompositions. We comparatively validate the low TT- and low ML rank methods on twenty-two 3D OCT images with the JPEG2000 and 3D SPIHT compression methods, as well as with no compression 2D bilateral filtering (BF), 2D median filtering (MF), and enhanced low-rank plus sparse matrix decomposition (ELRpSD) methods. For the CR<10, the low Sp TT rank method with pe{0, 1/2, 2/3} yields either highest or comparable signal-to-noise ratio (SNR), and comparable or better contrast-to-noise ratio (CNR), mean segmentation errors (SEs) of retina layers and expert-based image quality score (EIQS) than original image and image compression methods. It compares favorably in terms of CNR, fairly in terms of SE and EIQS with the no image compression methods. Thus, for CR<10 the low S2/3 TT rank approximation can be considered a good choice for visual inspection based diagnostics. For 2<CR<60, the low S1 ML rank method compares favorably in terms of SE with image compression methods and with 2D BF and ELRpSD. It is slightly inferior to 2D MF. Thus, for 2<CR<60, the low S1 ML rank approximation can be considered a good choice for segmentation based diagnostics either on-site or in the remote mode of operation.
HydaLearn: Highly Dynamic Task Weighting for Multi-task Learning with Auxiliary Tasks

Multi-task learning (MTL) can improve performance on a task by sharing representations with one or more related auxiliary-tasks. Usually, MTL-networks are trained on a composite loss function formed by a constant weighted combination of the separate task losses. In practice, constant loss weights lead to poor results for two reasons: (i) the relevance of the auxiliary tasks can gradually drift throughout the learning process; (ii) for mini-batch based optimisation, the optimal task weights vary significantly from one update to the next depending on mini-batch sample composition. We introduce HydaLearn, an intelligent weighting algorithm that connects main-task gain to the individual task gradients, in order to inform dynamic loss weighting at the mini-batch level, addressing i and ii. Using HydaLearn, we report performance increases on synthetic data, as well as on two supervised learning domains.
Internality, transfer, and infinitesimal modeling of infinite processes†

A probability model is underdetermined when there is no rational reason to assign a particular infinitesimal value as the probability of single events. Pruss claims that hyperreal probabilities are underdetermined. The claim is based upon external hyperreal-valued measures. We show that internal hyperfinite measures are not underdetermined. The importance of internality stems from the fact that Robinson's transfer principle only applies to internal entities. We also evaluate the claim that transferless ordered fields (surreals, Levi-Civita field, Laurent series) may have advantages over hyperreals in probabilistic modeling. We show that probabilities developed over such fields are less expressive than hyperreal probabilities.
Buy Me That Look: An Approach for Recommending Similar Fashion Products

Have you ever looked at an Instagram model, or a model in a fashion e-commerce web-page, and thought "Wish I could get a list of fashion items similar to the ones worn by the model! ". This is what we address in this paper, where we propose a novel computer vision based technique called ShopLook to address the challenging problem of recommending similar fashion products. The proposed method has been evaluated at Myntra (www.myntra.com), a leading online fashion e-commerce platform. In particular, given a user query and the corresponding Product Display Page (PDP) against the query, the goal of our method is to recommend similar fashion products corresponding to the entire set of fashion articles worn by a model in the PDP full-shot image (the one showing the entire model from head to toe). The novelty and strength of our method lies in its capability to recommend similar articles for all the fashion items worn by the model, in addition to the primary article corresponding to the query. This is not only important to promote cross-sells for boosting revenue, but also for improving customer experience and engagement. In addition, our approach is also capable of recommending similar products for User Generated Content (UGC), eg. , fashion article images uploaded by users. Formally, our proposed method consists of the following components (in the same order): i) Human keypoint detection, ii) Pose classification, iii) Article localisation and object detection, along with active learning feedback, and iv) Triplet network based image embedding model.
Disentangled Adversarial Autoencoder for Subject-Invariant Physiological Feature Extraction

Recent developments in biosignal processing have enabled users to exploit their physiological status for manipulating devices in a reliable and safe manner. One major challenge of physiological sensing lies in the variability of biosignals across different users and tasks. To address this issue, we propose an adversarial feature extractor for transfer learning to exploit disentangled universal representations. We consider the trade-off between task-relevant features and user-discriminative information by introducing additional adversary and nuisance networks in order to manipulate the latent representations such that the learned feature extractor is applicable to unknown users and various tasks. Results on cross-subject transfer evaluations exhibit the benefits of the proposed framework, with up to $8.8\%$ improvement in average accuracy of classification, and demonstrate adaptability to a broader range of subjects.
Intracellular distribution of chromium and toxicity on growth in Chlorella pyrenoidosa

The effect of chromium on the growth response on green alga Chlorella pyrenoidosa were examined. An increase in chromium concentration caused a significant reduction in the growth of Chlorella cells, and the the EC50value was 1.6 mg/L, while lethal concentration of chromium appears to be approximately 20 mg/L. We have investigated the intracellular distribution of chromium, and the elementel composition of the treated cells. Bulk of the chromium is localized in the cell wall, while the membrane and soluble fraction contains the smaller part of the total chromium.
Geometric limits of cyclic subgroups of
SO0(1,k + 1) and SU(1,k + 1)

We study geometric limits of convex-cocompact cyclic subgroups of the rank 1 groups SO_0(1, k+1) and SU(1, k+1). We construct examples of sequences of subgroups of such groups G that converge algebraically and whose geometric limit strictly contains the algebraic limit, thus generalizing the example first described by Jorgensen for subgroups of SO_0(1,3). We also give necessary and sufficient conditions for a subgroup of SO_0(1, k+1) to arise as geometric limit of a sequence of cyclic subgroups. We then discuss generalizations of such examples to sequence of representations of free groups, and applications of our constructions in that setting.
Automatic Generation of Road Geometries to Create Challenging Scenarios for Automated Vehicles Based on the Sensor Setup

For the offline safety assessment of automated vehicles, the most challenging and critical scenarios must be identified efficiently. Therefore, we present a new approach to define challenging scenarios based on a sensor setup model of the ego-vehicle. First, a static optimal approaching path of a road user to the ego-vehicle is calculated using an A* algorithm. We consider a poor perception of the road user by the automated vehicle as optimal, because we want to define scenarios that are as critical as possible. The path is then transferred to a dynamic scenario, where the trajectory of the road user and the road layout are determined. The result is an optimal road geometry, so that the ego-vehicle can perceive an approaching object as poorly as possible. The focus of our work is on the highway as the Operational Design Domain (ODD).
Making a Case for 3D Convolutions for Object Segmentation in Videos

The task of object segmentation in videos is usually accomplished by processing appearance and motion information separately using standard 2D convolutional networks, followed by a learned fusion of the two sources of information. On the other hand, 3D convolutional networks have been successfully applied for video classification tasks, but have not been leveraged as effectively to problems involving dense per-pixel interpretation of videos compared to their 2D convolutional counterparts and lag behind the aforementioned networks in terms of performance. In this work, we show that 3D CNNs can be effectively applied to dense video prediction tasks such as salient object segmentation. We propose a simple yet effective encoder-decoder network architecture consisting entirely of 3D convolutions that can be trained end-to-end using a standard cross-entropy loss. To this end, we leverage an efficient 3D encoder, and propose a 3D decoder architecture, that comprises novel 3D Global Convolution layers and 3D Refinement modules. Our approach outperforms existing state-of-the-arts by a large margin on the DAVIS'16 Unsupervised, FBMS and ViSal dataset benchmarks in addition to being faster, thus showing that our architecture can efficiently learn expressive spatio-temporal features and produce high quality video segmentation masks. Our code and models will be made publicly available.
Homogenization and hypocoercivity for Fokker-Planck equations driven by shear flows

We study the long-time dynamics of two-dimensional linear Fokker-Planck equations driven by a drift that can be decomposed in the sum of a large shear component and the gradient of a regular potential depending on one spatial variable. The problem can be interpreted as that of a passive scalar advected by a slightly compressible shear flow, and undergoing small diffusion. For the corresponding stochastic differential equation, we give explicit homogenization rates in terms of a family of time-scales depending on the parameter measuring the strength of the incompressible perturbation. This is achieved by exploiting an auxiliary Poisson problem, and by computing the related effective diffusivity coefficients. Regarding the long-time behaviour of the solution of the Fokker-Planck equation, we provide explicit decay rates to the unique invariant measure by employing a quantitative version of the classical hypocoercivity scheme. From a fluid mechanics perspective, this turns out to be equivalent to quantifying the phenomenon of enhanced diffusion for slightly compressible shear flows.
Stochastic thermodynamics of system with continuous space of states

We analyze the stochastic thermodynamics of systems with continuous space of states. The evolution equation, the rate of entropy production, and other results are obtained by a continuous time limit of a discrete time formulation. We point out the role of time reversal and of the dissipation part of the probability current on the production of entropy. We show that the rate of entropy production is a bilinear form in the components of the dissipation probability current with coefficients being the components of the precision matrix related to the Gaussian noise. We have also analyzed a type of noise that makes the energy function to be strictly constant along the stochastic trajectory, being appropriate to describe an isolated system. This type of noise leads to nonzero entropy production and thus to an increase of entropy in the system. This result contrasts with the invariance of the entropy predicted by the Liouville equation, which also describes an isolated system.
Untangling the complexity of market competition in consumer goods—A complex Hilbert PCA analysis

Today’s consumer goods markets are rapidly evolving with significant growth in the number of information media as well as the number of competitive products. In this environment, obtaining a quantitative grasp of heterogeneous interactions of firms and customers, which have attracted interest of management scientists and economists, requires the analysis of extremely high-dimensional data. Existing approaches in quantitative research could not handle such data without any reliable prior knowledge nor strong assumptions. Alternatively, we propose a novel method called complex Hilbert principal component analysis (CHPCA) and construct a synchronization network using Hodge decomposition. CHPCA enables us to extract significant comovements with a time lead/delay in the data, and Hodge decomposition is useful for identifying the time-structure of correlations. We apply this method to the Japanese beer market data and reveal comovement of variables related to the consumer choice process across multiple products. Furthermore, we find remarkable customer heterogeneity by calculating the coordinates of each customer in the space derived from the results of CHPCA. Lastly, we discuss the policy and managerial implications, limitations, and further development of the proposed method.
Computing Information Agreement

Agreement measures are useful to both compare different evaluations of the same diagnostic outcomes and validate new rating systems or devices. Information Agreement (IA) is an information-theoretic-based agreement measure introduced to overcome all the limitations and alleged pitfalls of Cohen's Kappa. However, it is only able to deal with agreement matrices whose values are positive natural numbers. This work extends IA admitting also 0 as a possible value for the agreement matrix cells.