Miracle-dz/UniFAR-FTUs huggingface dataset:
Recalibration of Aleatoric and Epistemic Regression Uncertainty in Medical Imaging

The consideration of predictive uncertainty in medical imaging with deep learning is of utmost importance. We apply estimation of both aleatoric and epistemic uncertainty by variational Bayesian inference with Monte Carlo dropout to regression tasks and show that predictive uncertainty is systematically underestimated. We apply sigma scaling with a single scalar value; a simple, yet effective calibration method for both types of uncertainty. The performance of our approach is evaluated on a variety of common medical regression data sets using different state-of-the-art convolutional network architectures. In our experiments, sigma scaling is able to reliably recalibrate predictive uncertainty. It is easy to implement and maintains the accuracy. Well-calibrated uncertainty in regression allows robust rejection of unreliable predictions or detection of out-of-distribution samples. Our source code is available at: https://github.com/mlaves/well-calibrated-regression-uncertainty
Explainable Artificial Intelligence Reveals Novel Insight into Tumor Microenvironment Conditions Linked with Better Prognosis in Patients with Breast Cancer

Simple Summary Over the past decade, there has been a significant increase in the number of omics datasets that provide unprecedented opportunities to systematically characterize the underlying biological mechanisms involved in cancer evolution and to understand how the tumor microenvironment contributes to this evolution. Novel techniques in artificial intelligence (AI) can help determine areas of therapeutic need, enhance clinical trial interpretation, identify novel targets, and generate accurate predictions that are impossible with traditional statistical techniques. However, a major criticism of incorporating the highly accurate and nonlinear AI models into medical fields is the notion that AI is essentially a “black box”. We resolved this overarching problem with explainable artificial intelligence (XAI) to determine prognoses in patients with breast cancer and reveal valuable information about conditions in the tumor microenvironment that are associated with enhanced prognosis and patient survival. The benefits of using XAI in the development of new targeted therapies would be significant. Abstract We investigated the data-driven relationship between immune cell composition in the tumor microenvironment (TME) and the ≥5-year survival rates of breast cancer patients using explainable artificial intelligence (XAI) models. We acquired TCGA breast invasive carcinoma data from the cbioPortal and retrieved immune cell composition estimates from bulk RNA sequencing data from TIMER2.0 based on EPIC, CIBERSORT, TIMER, and xCell computational methods. Novel insights derived from our XAI model showed that B cells, CD8+ T cells, M0 macrophages, and NK T cells are the most critical TME features for enhanced prognosis of breast cancer patients. Our XAI model also revealed the inflection points of these critical TME features, above or below which ≥5-year survival rates improve. Subsequently, we ascertained the conditional probabilities of ≥5-year survival under specific conditions inferred from the inflection points. In particular, the XAI models revealed that the B cell fraction (relative to all cells in a sample) exceeding 0.025, M0 macrophage fraction (relative to the total immune cell content) below 0.05, and NK T cell and CD8+ T cell fractions (based on cancer type-specific arbitrary units) above 0.075 and 0.25, respectively, in the TME could enhance the ≥5-year survival in breast cancer patients. The findings could lead to accurate clinical predictions and enhanced immunotherapies, and to the design of innovative strategies to reprogram the breast TME.
Transporting treatment effects with incomplete attributes

The simultaneous availability of experimental and observational data to estimate a treatment effect is both an opportunity and a statistical challenge: Combining the information gathered from both data is a promising avenue to build upon the internal validity of randomized controlled trials (RCTs) and a greater external validity of observational data, but it raises methodological issues, especially due to different sampling designs inducing distributional shifts. We focus on the aim of transporting a causal effect estimated on an RCT onto a target population described by a set of covariates. Available methods such as inverse propensity weighting are not designed to handle missing values, which are however common in both data. In addition to coupling the assumptions for causal identifiability and for the missing values mechanism and to defining appropriate strategies, one has to consider the specific structure of the data with two sources and treatment and outcome only available in the RCT. We study different approaches and their underlying assumptions on the data generating processes and distribution of missing values and suggest several adapted methods, in particular multiple imputation strategies. These methods are assessed in an extensive simulation study and practical guidelines are provided for different scenarios. This work is motivated by the analysis of a large registry of over 20,000 major trauma patients and a multi-centered RCT studying the effect of tranexamic acid administration on mortality. The analysis illustrates how the missing values handling can impact the conclusion about the effect transported from the RCT to the target population.
Low-rank Tensor Estimation via Riemannian Gauss-Newton: Statistical Optimality and Second-Order Convergence

In this paper, we consider the estimation of a low Tucker rank tensor from a number of noisy linear measurements. The general problem covers many specific examples arising from applications, including tensor regression, tensor completion, and tensor PCA/SVD. We consider an efficient Riemannian Gauss-Newton (RGN) method for low Tucker rank tensor estimation. Different from the generic (super)linear convergence guarantee of RGN in the literature, we prove the first local quadratic convergence guarantee of RGN for low-rank tensor estimation in the noisy setting under some regularity conditions and provide the corresponding estimation error upper bounds. A deterministic estimation error lower bound, which matches the upper bound, is provided that demonstrates the statistical optimality of RGN. The merit of RGN is illustrated through two machine learning applications: tensor regression and tensor SVD. Finally, we provide the simulation results to corroborate our theoretical findings.
Identifying Actions for Sound Event Classification

In Psychology, actions are paramount for humans to identify sound events. In Machine Learning (ML), action recognition achieves high accuracy; however, it has not been asked whether identifying actions can benefit Sound Event Classification (SEC), as opposed to mapping the audio directly to a sound event. Therefore, we propose a new Psychology-inspired approach for SEC that includes identification of actions via human listeners. To achieve this goal, we used crowdsourcing to have listeners identify 20 actions that in isolation or in combination may have produced any of the 50 sound events in the well-studied dataset ESC-50. The resulting annotations for each audio recording relate actions to a database of sound events for the first time. The annotations were used to create semantic representations called Action Vectors (AVs). We evaluated SEC by comparing the AVs with two types of audio features - log-mel spectrograms and state-of-the-art audio embeddings. Because audio features and AVs capture different abstractions of the acoustic content, we combined them and achieved one of the highest reported accuracies (88%).
Exponential decay of correlations for Gibbs measures on attractors of Axiom A flows

In this paper we study the decay of correlations for Gibbs measures associated to codimension one Axiom A attractors for flows. We prove that a codimension one Axiom A attractors whose strong stable foliation is C either have exponential decay of correlations with respect to all Gibbs measures associated to Hölder continuous potentials or their stable and unstable bundles are jointly integrable. As a consequence, there exist C-open sets of C-vector fields generating Axiom A flows having attractors so that: (i) mix exponentially with respect to equilibrium states associated with Hölder continuous potentials, (ii) their time-1 maps satisfy an almost sure invariance principle, and (iii) the growth of the number of closed orbits of length T is described by the topological entropy of the attractor.
Ensembles of GANs for synthetic training data generation

Insufficient training data is a major bottleneck for most deep learning practices, not least in medical imaging where data is difficult to collect and publicly available datasets are scarce due to ethics and privacy. This work investigates the use of synthetic images, created by generative adversarial networks (GANs), as the only source of training data. We demonstrate that for this application, it is of great importance to make use of multiple GANs to improve the diversity of the generated data, i.e. to sufficiently cover the data distribution. While a single GAN can generate seemingly diverse image content, training on this data in most cases lead to severe over-fitting. We test the impact of ensembled GANs on synthetic 2D data as well as common image datasets (SVHN and CIFAR-10), and using both DCGANs and progressively growing GANs. As a specific use case, we focus on synthesizing digital pathology patches to provide anonymized training data.
The Effects of Air Quality on the Spread of the COVID-19. An Artificial Intelligence Approach

The COVID-19 pandemic considerably affects public health systems around the world. The lack of knowledge about the virus, the extension of this phenomenon, and the speed of the evolution of the infection are all factors that highlight the necessity of employing new approaches to study these events. Artiﬁcial intelligence techniques may be useful in analyzing data related to areas affected by the virus. The aim of this work is to investigate any possible relationships be-tween air quality and conﬁrmed cases of COVID-19 in Italian districts. Speciﬁcally, we report an analysis of the correlation between daily COVID-19 cases and environmental factors, such as temperature, relative humidity, and atmospheric pollutants. Our analysis conﬁrms a signiﬁcant association of some environmental parameters with the spread of the virus. This suggests that machine learning models trained on the environmental parameters to predict the number of future infected cases may be accurate. Predictive models may be useful for helping institutions in making decisions for protecting the population and contrasting the pandemic.
Prandtl number dependence of the small-scale properties in turbulent Rayleigh-Bénard convection

We analyze the Prandtl number (Pr) dependence of spectra and fluxes of kinetic energy, as well as the energy injection rates and dissipation rates, of turbulent thermal convection using numerical data. As expected, for a flow with Pr / 1, the inertial-range kinetic energy flux is constant, and the kinetic energy spectrum is Kolmogorov-like (k−5/3). More importantly, we show that the amplitudes of the kinetic energy fluxes and spectra and those of structure functions increase with the decrease of Pr, thus exhibiting stronger nonlinearity for flows with small Prandtl numbers. Consistent with these observations, the kinetic energy injection rates and the dissipation rates too increase with the decrease of Pr. Our results are in agreement with earlier studies that report the Reynolds number to be a decreasing function of Prandtl number in turbulent convection. On the other hand, the tail of the probability distributions of the local heat flux grows with the increase of Pr, indicating increased fluctuations in the local heat flux with Pr.
RelTransformer: Balancing the Visual Relationship Detection from Local Context, Scene and Memory

Visual relationship recognition (VRR) is a fundamental scene understanding task. The structure that VRR provides is essential to improve the AI interpretability in downstream tasks such as image captioning and visual question answering. Several recent studies showed that the long-tail problem in VRR is even more critical than that in object recognition due to the compositional complexity and structure. To overcome this limitation, we propose a novel transformer-based framework, dubbed as RelTransformer, which performs relationship prediction using rich semantic features from multiple image levels. We assume that more abundant contextual features can generate more accurate and discriminative relationships, which can be useful when sufﬁ-cient training data are lacking. The key feature of our model is its ability to aggregate three different-level features (local context, scene, and dataset-level) to compositionally predict the visual relationship. We evaluate our model on the visual genome and two “long-tail” VRR datasets, GQA-LT and VG8k-LT. Extensive experiments demonstrate that our Rel-Transformer could improve over the state-of-the-art base-lines on all the datasets. In addition, our model signiﬁcantly improves the accuracy of GQA-LT by 27 . 4% upon the best baselines on tail-relationship prediction. Our code is available in https://github.com/Vision-CAIR/RelTransformer.
Non-Parametric Few-Shot Learning for Word Sense Disambiguation

Word sense disambiguation (WSD) is a long-standing problem in natural language processing. One significant challenge in supervised all-words WSD is to classify among senses for a majority of words that lie in the long-tail distribution. For instance, 84% of the annotated words have less than 10 examples in the SemCor training data. This issue is more pronounced as the imbalance occurs in both word and sense distributions. In this work, we propose MetricWSD, a non-parametric few-shot learning approach to mitigate this data imbalance issue. By learning to compute distances among the senses of a given word through episodic training, MetricWSD transfers knowledge (a learned metric space) from high-frequency words to infrequent ones. MetricWSD constructs the training episodes tailored to word frequencies and explicitly addresses the problem of the skewed distribution, as opposed to mixing all the words trained with parametric models in previous work. Without resorting to any lexical resources, MetricWSD obtains strong performance against parametric alternatives, achieving a 75.1 F1 score on the unified WSD evaluation benchmark (Raganato et al., 2017b). Our analysis further validates that infrequent words and senses enjoy significant improvement.
Powered Dirichlet Process for Controlling the Importance of "Rich-Get-Richer" Prior Assumptions in Bayesian Clustering

One of the most used priors in Bayesian clustering is the Dirichlet prior. It can be expressed as a Chinese Restaurant Process. This process allows nonparametric estimation of the number of clusters when partitioning datasets. Its key feature is the"rich-get-richer"property, which assumes a cluster has an a priori probability to get chosen linearly dependent on population. In this paper, we show that such prior is not always the best choice to model data. We derive the Powered Chinese Restaurant process from a modified version of the Dirichlet-Multinomial distribution to answer this problem. We then develop some of its fundamental properties (expected number of clusters, convergence). Unlike state-of-the-art efforts in this direction, this new formulation allows for direct control of the importance of the"rich-get-richer"prior.
Case Study on Using Colours in Constructing Emotions by Interactive Digital Narratives

This article addresses the possibility of supporting the construction of emotions in the participants of Interactive Digital Narratives (IDN) by means of colours. The article uses goal models for expressing protostories. The core of the article consists of the case study where two colour synesthetes were asked to choose colours for eight emotions. Thereafter the same synesthetes were asked to choose colours for the emotions that support the attainment of goals in the Cinderella narrative, which serves as an example protostory. The article also discusses the perspectives for applying the method proposed by us to using colours in constructing emotions by IDNs.
The Multiple Uses of Polypropylene/Polyethylene Terephthalate Microfibrillar Composite Structures to Support Waste Management—Composite Processing and Properties

Composite processing and subsequent characterization of microfibrillar composites (MFC) were the focus of this work. Compression molding of wound MFC filaments was used to fabricate MFC composites. The MFC composites were composed of polypropylene (PP) as matrix materials and polyethylene terephthalate (PET) as reinforcement fibers. The PP/PET blends were mixed with PET contents ranging from 22 wt% to 45 wt%. The effect of processing parameters, pressure, temperature, and holding time on the mechanical properties of the MFCs was investigated. Tensile tests were conducted to optimize the processing parameter and weight ratio of PET. Tensile strength and modulus increased with the increase in PET content. PP/45 wt% PET MFC composites properties reached the value of PP/30 wt% GF. Falling weight tests were conducted on MFC composites. The MFC composites showed the ability to absorb the impact energy compared to neat PP and PP/30 wt% GF.
Single-Step Green Synthesis of Highly Concentrated and Stable Colloidal Dispersion of Core-Shell Silver Nanoparticles and Their Antimicrobial and Ultra-High Catalytic Properties

The versatile one-pot green synthesis of a highly concentrated and stable colloidal dispersion of silver nanoparticles (Ag NPs) was carried out using the self-assembled tannic acid without using any other hazardous chemicals. Tannic acid (Plant-based polyphenol) was used as a reducing and stabilizing agent for silver nitrate in a mild alkaline condition. The synthesized Ag NPs were characterized for their concentration, capping, size distribution, and shape. The experimental results confirmed the successful synthesis of nearly spherical and highly concentrated (2281 ppm) Ag NPs, capped with poly-tannic acid (Ag NPs-PTA). The average particle size of Ag NPs-PTA was found to be 9.90 ± 1.60 nm. The colloidal dispersion of synthesized nanoparticles was observed to be stable for more than 15 months in the ambient environment (25 °C, 65% relative humidity). The synthesized AgNPs-PTA showed an effective antimicrobial activity against Staphylococcus Aureus (ZOI 3.0 mM) and Escherichia coli (ZOI 3.5 mM). Ag NPs-PTA also exhibited enhanced catalytic properties. It reduces 4-nitrophenol into 4-aminophenol in the presence of NaBH4 with a normalized rate constant (Knor = K/m) of 615.04 mL·s−1·mg−1. For comparison, bare Ag NPs show catalytic activity with a normalized rate constant of 139.78 mL·s−1·mg−1. Furthermore, AgNPs-PTA were stable for more than 15 months under ambient conditions. The ultra-high catalytic and good antimicrobial properties can be attributed to the fine size and good aqueous stability of Ag NPs-PTA. The unique core-shell structure and ease of synthesis render the synthesized nanoparticles superior to others, with potential for large-scale applications, especially in the field of catalysis and medical.
Sexual Dimorphism and Morphological Modularity in Acanthoscelides obtectus (Say, 1831) (Coleoptera: Chrysomelidae): A Geometric Morphometric Approach

Simple Summary The seed beetle Acanthoscelides obtectus used in this study is a worldwide pest species that inhabits storage facilities and fields of beans. Knowing that sexual dimorphism is very common among insects, we investigated the level of morphological differences between the sexes. Such an approach allowed us to look into the modular organization of this organism. As expected, the females were larger than the males. The observed two modular organization (thorax and abdomen) was sex specific, indicating that reproductive function has the central role in forming the patterns of modularity. It seems that natural selection is driving force for females, while males are influenced more by sexual selection. Abstract Sexual dimorphism and specific patterns of development contribute in a great manner to the direction and degree of the sexual differences in body size and shape in many insects. Using a landmark-based geometric morpohometrics approach, we investigated sex-specific morphological size and shape variation in the seed beetle, Acanthoscelides obtectus. We also tested the functional hypothesis of the two morphological modules—thorax and abdomen in both sexes. Female-biased sexual dimorphism in size was shown, while differences in shape were reflected in the wider thorax and abdomen and shorter abdomen in females in comparison to males. The functional hypothesis of a two-module body was confirmed only in females before correction for size, and in both sexes after the allometry correction. Our results indicate that reproductive function has the central role in forming the patterns of modularity. We hypothesize that high morphological integration of the abdomen in females results from intense stabilizing selection, while the more relaxed integration in males is driven by the higher intensity of sexual selection.
Dual Inhibition of P-gp and BCRP Improves Oral Topotecan Bioavailability in Rodents

P-glycoprotein (P-gp) inhibition has been studied to overcome multidrug resistance in cancer chemotherapy but failed in clinical trials due to low/toxic effects. Recently, a dual modulation of transporters and natural derivatives have been examined to surmount this limitation. We examined breast cancer resistance protein (BCRP) inhibition in vitro and in vivo by P-gp inhibitors derived from natural compounds in previous studies. P-gp inhibitors increased the accumulation of the anticancer drug, topotecan (TPT)—a substrate of P-gp and BCRP, albeit with higher affinity for BCRP—in BCRP-overexpressing cells, resulting in cell death. These dual inhibitors, when orally co-administered with TPT, enhanced TPT bioavailability with slightly reduced total oral clearance (Clt/F) in rats. In xenograft mice, they strengthened oral TPT-induced tumor reduction with no alterations in body weight. Moreover, we investigated the effects of an oral drug formulation (Cremophor® EL, Tween® 80, and polyethylene glycol 400) on the transporters function. The excipients increased TPT accumulation in P-gp- or BCRP-overexpressing cells. Oral TPT bioavailability was higher with the formulation than with a control, as shown by the increases in the maximum plasma concentration (Cmax) and the area under the plasma concentration–time curve from zero to infinity (AUCINF) (p < 0.01). Therefore, oral TPT bioavailability was enhanced by P-gp/BCRP dual inhibition, which resulted in a formulation-mediated increase in absorption and decrease in elimination, and a dual inhibitor-mediated decrease in elimination. These results suggest that the combination of dual inhibition by a natural derivative and the drug formulation can be a useful clinical approach.