Miracle-dz/UniFAR-FTUs huggingface dataset:
Imputation for High-Dimensional Linear Regression

We study high-dimensional regression with missing entries in the covariates. A common strategy in practice is to \emph{impute} the missing entries with an appropriate substitute and then implement a standard statistical procedure acting as if the covariates were fully observed. Recent literature on this subject proposes instead to design a specific, often complicated or non-convex, algorithm tailored to the case of missing covariates. We investigate a simpler approach where we fill-in the missing entries with their conditional mean given the observed covariates. We show that this imputation scheme coupled with standard off-the-shelf procedures such as the LASSO and square-root LASSO retains the minimax estimation rate in the random-design setting where the covariates are i.i. d. \ sub-Gaussian. We further show that the square-root LASSO remains \emph{pivotal} in this setting. It is often the case that the conditional expectation cannot be computed exactly and must be approximated from data. We study two cases where the covariates either follow an autoregressive (AR) process, or are jointly Gaussian with sparse precision matrix. We propose tractable estimators for the conditional expectation and then perform linear regression via LASSO, and show similar estimation rates in both cases. We complement our theoretical results with simulations on synthetic and semi-synthetic examples, illustrating not only the sharpness of our bounds, but also the broader utility of this strategy beyond our theoretical assumptions.
Handling noise in image deblurring via joint learning

Currently, many blind deblurring methods assume blurred images are noise-free and perform unsatisfactorily on the blurry images with noise. Unfortunately, noise is quite common in real scenes. A straightforward solution is to denoise images before deblurring them. However, even state-of-the-art denoisers cannot guarantee to remove noise entirely. Slight residual noise in the denoised images could cause significant artifacts in the deblurring stage. To tackle this problem, we propose a cascaded framework consisting of a denoiser subnetwork and a deblurring subnetwork. In contrast to previous methods, we train the two subnetworks jointly. Joint learning reduces the effect of the residual noise after denoising on deblurring, hence improves the robustness of deblurring to heavy noise. Moreover, our method is also helpful for blur kernel estimation. Experiments on the CelebA dataset and the GOPRO dataset show that our method performs favorably against several state-of-the-art methods.
Algorithmic Fairness

An increasing number of decisions regarding the daily lives of human beings are being controlled by artificial intelligence (AI) algorithms in spheres ranging from healthcare, transportation, and education to college admissions, recruitment, provision of loans and many more realms. Since they now touch on many aspects of our lives, it is crucial to develop AI algorithms that are not only accurate but also objective and fair. Recent studies have shown that algorithmic decision-making may be inherently prone to unfairness, even when there is no intention for it. This paper presents an overview of the main concepts of identifying, measuring and improving algorithmic fairness when using AI algorithms. The paper begins by discussing the causes of algorithmic bias and unfairness and the common definitions and measures for fairness. Fairness-enhancing mechanisms are then reviewed and divided into pre-process, in-process and post-process mechanisms. A comprehensive comparison of the mechanisms is then conducted, towards a better understanding of which mechanisms should be used in different scenarios. The paper then describes the most commonly used fairness-related datasets in this field. Finally, the paper ends by reviewing several emerging research sub-fields of algorithmic fairness.
Towards a framework for understanding societal and ethical implications of Artificial Intelligence

Artificial Intelligence (AI) is one of the most discussed technologies today. There are many innovative applications such as the diagnosis and treatment of cancer, customer experience, new business, education, contagious diseases propagation and optimization of the management of humanitarian catastrophes. However, with all those opportunities also comes great responsibility to ensure good and fair practice of AI. The objective of this paper is to identify the main societal and ethical challenges implied by a massive uptake of AI. We have surveyed the literature for the most common challenges and classified them in seven groups: 1) Non-desired effects, 2) Liability, 3) Unknown consequences, 4) Relation people-robots, 5) Concentration of power and wealth, 6) Intentional bad uses, and 7) AI for weapons and warfare. The challenges should be dealt with in different ways depending on their origin; some have technological solutions, while others require ethical, societal, or political answers. Depending on the origin, different stakeholders might need to act. Whatever the identified stakeholder, not treating those issues will lead to uncertainty and unforeseen consequences with potentially large negative societal impact, hurting especially the most vulnerable groups of societies. Technology is helping to take better decisions, and AI is promoting data-driven decisions in addition to experience- and intuition-based discussion, with many improvements happening. However, the negative side effects of this technology need to be well understood and acted upon before we launch them massively into the world.
Boosted and Differentially Private Ensembles of Decision Trees

Boosted ensemble of decision tree (DT) classifiers are extremely popular in international competitions, yet to our knowledge nothing is formally known on how to make them \textit{also} differential private (DP), up to the point that random forests currently reign supreme in the DP stage. Our paper starts with the proof that the privacy vs boosting picture for DT involves a notable and general technical tradeoff: the sensitivity tends to increase with the boosting rate of the loss, for any proper loss. DT induction algorithms being fundamentally iterative, our finding implies non-trivial choices to select or tune the loss to balance noise against utility to split nodes. To address this, we craft a new parametererized proper loss, called the M$\alpha$-loss, which, as we show, allows to finely tune the tradeoff in the complete spectrum of sensitivity vs boosting guarantees. We then introduce \textit{objective calibration} as a method to adaptively tune the tradeoff during DT induction to limit the privacy budget spent while formally being able to keep boosting-compliant convergence on limited-depth nodes with high probability. Extensive experiments on 19 UCI domains reveal that objective calibration is highly competitive, even in the DP-free setting. Our approach tends to very significantly beat random forests, in particular on high DP regimes ($\varepsilon \leq 0.1$) and even with boosted ensembles containing ten times less trees, which could be crucial to keep a key feature of DT models under differential privacy: interpretability.
Parallel Microbial Ecology of Pasteuria and Nematode Species in Scottish Soils

Pasteuria spp. are endospore forming bacteria which act as natural antagonists to many of the most economically significant plant parasitic nematodes (PPNs). Highly species-specific nematode suppression may be observed in soils containing a sufficiently high density of Pasteuria spp. spores. This suppression is enacted by the bacteria via inhibition of root invasion and sterilization of the nematode host. Molecular methods for the detection of Pasteuria spp. from environmental DNA (eDNA) have been described; however, these methods are limited in both scale and in depth. We report the use of small subunit rRNA gene metabarcoding to profile Pasteuria spp. and nematode communities in parallel. We have investigated Pasteuria spp. population structure in Scottish soils using eDNA from two sources: soil extracted DNA from the second National Soil Inventory of Scotland (NSIS2); and nematode extracted DNA collected from farms in the East Scotland Farm Network (ESFN). We compared the Pasteuria spp. community culture to both nematode community structure and the physiochemical properties of soils. Our results indicate that Pasteuria spp. populations in Scottish soils are broadly dominated by two sequence variants. The first of these aligns with high identity to Pasteuria hartismeri, a species first described parasitizing Meloidogyne ardenensis, a nematode parasite of woody and perennial plants in northern Europe. The second aligns with a Pasteuria-like sequence which was first recovered from a farm near Edinburgh which was found to contain bacterial feeding nematodes and Pratylenchus spp. encumbered by Pasteuria spp. endospores. Further, soil carbon, moisture, bulk density, and pH showed a strong correlation with the Pasteuria spp. community composition. These results indicate that metabarcoding is appropriate for the sensitive, specific, and semi-quantitative profiling of Pasteuria species from eDNA.
The Influence of Recombinational Processes to Induce Dormancy in Trypanosoma cruzi

The protozoan Trypanosoma cruzi is the causative agent of Chagas disease, a neglected tropical disease that affects around 8 million people worldwide. Chagas disease can be divided into two stages: an acute stage with high parasitemia followed by a low parasitemia chronic stage. Recently, the importance of dormancy concerning drug resistance in T. cruzi amastigotes has been shown. Here, we quantify the percentage of dormant parasites from different T. cruzi DTUs during their replicative epimastigote and amastigote stages. For this study, cells of T. cruzi CL Brener (DTU TcVI); Bug (DTU TcV); Y (DTU TcII); and Dm28c (DTU TcI) were used. In order to determine the proliferation rate and percentage of dormancy in epimastigotes, fluorescent-labeled cells were collected every 24 h for flow cytometer analysis, and cells showing maximum fluorescence after 144 h of growth were considered dormant. For the quantification of dormant amastigotes, fluorescent-labeled trypomastigotes were used for infection of LLC-MK2 cells. The number of amastigotes per infected LLC-MK2 cell was determined, and those parasites that presented fluorescent staining after 96 h of infection were considered dormant. A higher number of dormant cells was observed in hybrid strains when compared to non-hybrid strains for both epimastigote and amastigote forms. In order to investigate, the involvement of homologous recombination in the determination of dormancy in T. cruzi, we treated CL Brener cells with gamma radiation, which generates DNA lesions repaired by this process. Interestingly, the dormancy percentage was increased in gamma-irradiated cells. Since, we have previously shown that naturally-occurring hybrid T. cruzi strains present higher transcription of RAD51—a key gene in recombination process —we also measured the percentage of dormant cells from T. cruzi clone CL Brener harboring single knockout for RAD51. Our results showed a significative reduction of dormant cells in this T. cruzi CL Brener RAD51 mutant, evidencing a role of homologous recombination in the process of dormancy in this parasite. Altogether, our data suggest the existence of an adaptive difference between T. cruzi strains to generate dormant cells, and that homologous recombination may be important for dormancy in this parasite.
Editorial: BCR Signaling and B Cell Activation

MOE Key Laboratory of Protein Sciences, Center for Life Sciences, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, School of Life Sciences, Beijing Key Lab for Immunological Research on Chronic Diseases, Institute for Immunology, Tsinghua University, Beijing, China, 2 Laboratory of Immune Receptor Activation, Francis Crick Institute, London, United Kingdom, Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, United States, Department of Immunology, Sungkyunkwan University School of Medicine, Suwon, South Korea
Face/Off: The Interchangeable Side of Candida Albicans

Due to limited mobility, fungi, like most unicellular organisms, have evolved mechanisms to adapt to sudden chemical and/or physical variation in their environment. Candida albicans is recognized as a model organism to study eukaryotic responses to environmental changes, as this human commensal yeast but also opportunistic pathogen responds to numerous environmental cues through switching morphologies from yeast to hyphae growth. This mechanism is largely controlled by two major pathways: cAMP-PKA and MAPK, but each environmental signal is sensed by specific sensors. However, morphological switching is not the only response C. albicans exerts in response to environmental cues. Recently, fungal cell wall remodeling in response to host-derived environmental cues has been identified as a way for C. albicans to manipulate the innate immune system. The fungal cell wall is composed of a chitin skeleton linked to a network of β-glucan, which anchors proteins and mannans to the fungal cell surface. As localized on the cell surface, these molecules drive interactions with the environment and other cells, particularly with host immune cells. C. albicans is recognized by immune cells such as neutrophils and macrophages via pathogen recognition receptors (PRRs) that bind different components of the cell wall. While β-glucan and mannan are proinflammatory molecules, chitin can induce anti-inflammatory responses. Interestingly, C. albicans is able to regulate the exposure of these pathogen-associated molecular patterns (PAMPs) according to environmental cues resulting in a modulation of the host immune response. This review describes the mechanisms involved in C. albicans response to environmental changes and their effect on immune recognition.
Advantages and Limitations of the Neonatal Immune System

During early post-natal life, neonates must adjust to the transition from the sheltered intra-uterine environment to the microbe-laden external world, wherein they encounter a constellation of antigens and the colonization by the microbiome. At this vulnerable stage, neonatal immune responses are considered immature and present significant differences to those of adults. Pertinent to innate immunity, functional and quantitative deficiencies in antigen-presenting cells and phagocytes are often documented. Exposure to environmental antigens and microbial colonization is associated with epigenetic immune cell reprogramming and activation of effector and regulatory mechanisms that ensure age-depended immune system maturation and prevention of tissue damage. Moreover, neonatal innate immune memory has emerged as a critical mechanism providing protection against infectious agents. Still, in neonates, inexperience to antigenic exposure, along with enhancement of tissue-protective immunosuppressive mechanisms are often associated with severe immunopathological conditions, including sepsis and neurodevelopmental disorders. Despite significant advances in the field, adequate vaccination in newborns is still in its infancy due to elemental restrictions associated also with defective immune responses. In this review, we provide an overview of neonatal innate immune cells, highlighting phenotypic and functional disparities with their adult counterparts. We also discuss the effects of epigenetic modifications and microbial colonization on the regulation of neonatal immunity. A recent update on mechanisms underlying dysregulated neonatal innate immunity and linked infectious and neurodevelopmental diseases is provided. Understanding of the mechanisms that augment innate immune responsiveness in neonates may facilitate the development of improved vaccination protocols that can protect against pathogens and organ damage.
The Molecular Organization of Self-awareness: Paralimbic Dopamine-GABA Interaction

The electrophysiology of the paralimbic network (“default mode”) for self-awareness has drawn much attention in the past couple of decades. In contrast, knowledge of the molecular organization of conscious experience has only lately come into focus. We here review newer data on dopaminergic control of awareness in humans, particularly in self-awareness. These results implicate mainly dopaminergic neurotransmission and the control of GABAergic function directly in the paralimbic network. The findings are important for understanding addiction, developmental disorders, and dysfunctional consciousness.
Effects of an In-home Multicomponent Exergame Training on Physical Functions, Cognition, and Brain Volume of Older Adults: A Randomized Controlled Trial

Aging is associated with a decline in physical functions, cognition and brain structure. Considering that human life is based on an inseparable physical-cognitive interplay, combined physical-cognitive training through exergames is a promising approach to counteract age-related impairments. The aim of this study was to assess the effects of an in-home multicomponent exergame training on [i] physical and cognitive functions and [ii] brain volume of older adults compared to a usual care control group. Thirty-seven healthy and independently living older adults aged 65 years and older were randomly assigned to an intervention (exergame training) or a control (usual care) group. Over 16 weeks, the participants of the intervention group absolved three home-based exergame sessions per week (à 30–40 min) including Tai Chi-inspired exercises, dancing and step-based cognitive games. The control participants continued with their normal daily living. Pre- and post-measurements included assessments of physical (gait parameters, functional muscle strength, balance, aerobic endurance) and cognitive (processing speed, short-term attention span, working memory, inhibition, mental flexibility) functions. T1-weighted magnetic resonance imaging was conducted to assess brain volume. Thirty-one participants (mean age = 73.9 ± 6.4 years, range = 65–90 years, 16 female) completed the study. Inhibition and working memory significantly improved post-intervention in favor of the intervention group [inhibition: F(1) = 2.537, p = 0.046, np2 = 0.11, working memory: F(1) = 5.872, p = 0.015, np2 = 0.02]. Two measures of short-term attentional span showed improvements after training in favor of the control group [F(1) = 4.309, p = 0.038, np2 = 0.03, F(1) = 8.504, p = 0.004, np2 = 0.04]. No significant training effects were evident for physical functions or brain volume. Both groups exhibited a significant decrease in gray matter volume of frontal areas and the hippocampus over time. The findings indicate a positive influence of exergame training on executive functioning. No improvements in physical functions or brain volume were evident in this study. Better adapted individualized training challenge and a longer training period are suggested. Further studies are needed that assess training-related structural brain plasticity and its effect on performance, daily life functioning and healthy aging.
Scoping Reviews, Systematic Reviews, and Meta-Analysis: Applications in Veterinary Medicine

Evidence-based decision making is a hallmark of effective veterinary clinical practice. Scoping reviews, systematic reviews, and meta-analyses all are methods intended to provide transparent and replicable ways of summarizing a body of research to address an important clinical or public health issue. As these methods increasingly are being used by researchers and read by practitioners, it is important to understand the distinction between these techniques and to understand what research questions they can, and cannot, address. This review provides an overview of scoping reviews, systematic reviews, and meta-analysis, including a discussion of the method and uses. A sample dataset and coding to conduct a simple meta-analysis in the statistical program R also are provided. Scoping reviews are a descriptive approach, designed to chart the literature around a particular topic. The approach involves an extensive literature search, following by a structured mapping, or charting, of the literature. The results of scoping reviews can help to inform future research by identifying gaps in the existing literature and also can be used to identify areas where there may be a sufficient depth of literature to warrant a systematic review. Systematic reviews are intended to address a specific question by identifying and summarizing all of the available research that has addressed the review question. Questions types that can be addressed by a systematic review include prevalence/incidence questions, and questions related to etiology, intervention efficacy, and diagnostic test accuracy. The systematic review process follows structured steps with multiple reviewers working in parallel to reduce the potential for bias. An extensive literature search is undertaken and, for each relevant study identified by the search, a formal extraction of data, including the effect size, and assessment of the risk of bias is performed. The results from multiple studies can be combined using meta-analysis. Meta-analysis provides a summary effect size, and allows heterogeneity of effect among studies to be quantified and explored. These evidence synthesis approaches can provide scientific input to evidence-based clinical decision-making for veterinarians and regulatory bodies, and also can be useful for identifying gaps in the literature to enhance the efficiency of future research in a topic area.
Current Trends in the Optical Characterization of Two-Dimensional Carbon Nanomaterials

Graphene and graphene-related materials have received great attention because of their outstanding properties like Young's modulus, chemical inertness, high electrical and thermal conductivity, or large mobility. To utilize two-dimensional (2D) materials in any practical application, an excellent characterization of the nanomaterials is needed as such dimensions, even small variations in size, or composition, are accompanied by drastic changes in the material properties. Simultaneously, it is sophisticated to perform characterizations at such small dimensions. This review highlights the wide range of different characterization methods for the 2D materials, mainly attributing carbon-based materials as they are by far the ones most often used today. The strengths as well as the limitations of the individual methods, ranging from light microscopy, scanning electron microscopy, transmission electron microscopy, scanning transmission electron microscopy, scanning tunneling microscopy (conductive), atomic force microscopy, scanning electrochemical microscopy, Raman spectroscopy, UV–vis, X-ray photoelectron spectroscopy, X-ray fluorescence spectroscopy, energy-dispersive X-ray spectroscopy, Auger electron spectroscopy, electron energy loss spectroscopy, X-ray diffraction, inductively coupled plasma atomic emission spectroscopy to dynamic light scattering, are discussed. By using these methods, the flake size and shape, the number of layers, the conductivity, the morphology, the number and type of defects, the chemical composition, and the colloidal properties of the 2D materials can be investigated.