Nonparametric Density Estimation under Distribution Drift

We study nonparametric density estimation in non-stationary drift settings. Given a sequence of independent samples taken from a distribution that gradually changes in time, the goal is to compute the best estimate for the current distribution. We prove tight minimax risk bounds for both discrete and continuous smooth densities, where the minimum is over all possible estimates and the maximum is over all possible distributions that satisfy the drift constraints. Our technique handles a broad class of drift models, and generalizes previous results on agnostic learning under drift.Comment: Camera Ready versio

An Adaptive Algorithm for Learning with Unknown Distribution Drift

We develop and analyze a general technique for learning with an unknown distribution drift. Given a sequence of independent observations from the last $T$ steps of a drifting distribution, our algorithm agnostically learns a family of functions with respect to the current distribution at time $T$. Unlike previous work, our technique does not require prior knowledge about the magnitude of the drift. Instead, the algorithm adapts to the sample data. Without explicitly estimating the drift, the algorithm learns a family of functions with almost the same error as a learning algorithm that knows the magnitude of the drift in advance. Furthermore, since our algorithm adapts to the data, it can guarantee a better learning error than an algorithm that relies on loose bounds on the drift.Comment: Fixed typos and references. Updated conclusio

Accurate MapReduce Algorithms for k-Median and k-Means in General Metric Spaces

Center-based clustering is a fundamental primitive for data analysis and becomes very challenging for large datasets. In this paper, we focus on the popular k-median and k-means variants which, given a set P of points from a metric space and a parameter k<|P|, require to identify a set S of k centers minimizing, respectively, the sum of the distances and of the squared distances of all points in P from their closest centers. Our specific focus is on general metric spaces, for which it is reasonable to require that the centers belong to the input set (i.e., S subseteq P). We present coreset-based 3-round distributed approximation algorithms for the above problems using the MapReduce computational model. The algorithms are rather simple and obliviously adapt to the intrinsic complexity of the dataset, captured by the doubling dimension D of the metric space. Remarkably, the algorithms attain approximation ratios that can be made arbitrarily close to those achievable by the best known polynomial-time sequential approximations, and they are very space efficient for small D, requiring local memory sizes substantially sublinear in the input size. To the best of our knowledge, no previous distributed approaches were able to attain similar quality-performance guarantees in general metric spaces

Tight Lower Bounds on Worst-Case Guarantees for Zero-Shot Learning with Attributes

We develop a rigorous mathematical analysis of zero-shot learning with attributes. In this setting, the goal is to label novel classes with no training data, only detectors for attributes and a description of how those attributes are correlated with the target classes, called the class-attribute matrix. We develop the first non-trivial lower bound on the worst-case error of the best map from attributes to classes for this setting, even with perfect attribute detectors. The lower bound characterizes the theoretical intrinsic difficulty of the zero-shot problem based on the available information -- the class-attribute matrix -- and the bound is practically computable from it. Our lower bound is tight, as we show that we can always find a randomized map from attributes to classes whose expected error is upper bounded by the value of the lower bound. We show that our analysis can be predictive of how standard zero-shot methods behave in practice, including which classes will likely be confused with others

Heterogeneity of Early Host Response to Infection with Four Low-Pathogenic H7 Viruses with a Different Evolutionary History in the Field

Once low-pathogenic avian influenza viruses (LPAIVs) of the H5 and H7 subtypes from wild birds enter into poultry species, there is the possibility of them mutating into highly pathogenic avian influenza viruses (HPAIVs), resulting in severe epizootics with up to 100% mortality. This mutation from a LPAIV to HPAIV strain is the main cause of an AIV's major economic impact on poultry production. Although AIVs are inextricably linked to their hosts in their evolutionary history, the contribution of host-related factors in the emergence of HPAI viruses has only been marginally explored so far. In this study, transcriptomic sequencing of tracheal tissue from chickens infected with four distinct LP H7 viruses, characterized by a different history of pathogenicity evolution in the field, was implemented. Despite the inoculation of a normalized infectious dose of viruses belonging to the same subtype (H7) and pathotype (LPAI), the use of animals of the same age, sex and species as well as the identification of a comparable viral load in the target samples, the analyses revealed a heterogeneity in the gene expression profile in response to infection with each of the H7 viruses administered

Protective Efficacy of H9N2 Avian Influenza Vaccines Inactivated by Ionizing Radiation Methods Administered by the Parenteral or Mucosal Routes

H9N2 viruses have become, over the last 20 years, one of the most diffused poultry pathogens and have reached a level of endemicity in several countries. Attempts to control the spread and reduce the circulation of H9N2 have relied mainly on vaccination in endemic countries. However, the high level of adaptation to poultry, testified by low minimum infectious doses, replication to high titers, and high transmissibility, has severely hampered the results of vaccination campaigns. Commercially available vaccines have demonstrated high efficacy in protecting against clinical disease, but variable results have also been observed in reducing the level of replication and viral shedding in domestic poultry species. Antigenic drift and increased chances of zoonotic infections are the results of incomplete protection offered by the currently available vaccines, of which the vast majority are based on formalin-inactivated whole virus antigens. In our work, we evaluated experimental vaccines based on an H9N2 virus, inactivated by irradiation treatment, in reducing viral shedding upon different challenge doses and compared their efficacy with formalin-inactivated vaccines. Moreover, we evaluated mucosal delivery of inactivated antigens as an alternative route to subcutaneous and intramuscular vaccination. The results showed complete protection and prevention of replication in subcutaneously vaccinated Specific Pathogen Free White Leghorn chickens at low-to-intermediate challenge doses but a limited reduction of shedding at a high challenge dose. Mucosally vaccinated chickens showed a more variable response to experimental infection at all tested challenge doses and the main effect of vaccination attained the reduction of infected birds in the early phase of infection. Concerning mucosal vaccination, the irradiated vaccine was the only one affording complete protection from infection at the lowest challenge dose. Vaccine formulations based on H9N2 inactivated by irradiation demonstrated a potential for better performances than vaccines based on the formalin-inactivated antigen in terms of reduction of shedding and prevention of infection

SARS-CoV-2 infection and replication in human gastric organoids

COVID-19 typically manifests as a respiratory illness, but several clinical reports have described gastrointestinal symptoms. This is particularly true in children in whom gastrointestinal symptoms are frequent and viral shedding outlasts viral clearance from the respiratory system. These observations raise the question of whether the virus can replicate within the stomach. Here we generate gastric organoids from fetal, pediatric, and adult biopsies as in vitro models of SARS-CoV-2 infection. To facilitate infection, we induce reverse polarity in the gastric organoids. We find that the pediatric and late fetal gastric organoids are susceptible to infection with SARS-CoV-2, while viral replication is significantly lower in undifferentiated organoids of early fetal and adult origin. We demonstrate that adult gastric organoids are more susceptible to infection following differentiation. We perform transcriptomic analysis to reveal a moderate innate antiviral response and a lack of differentially expressed genes belonging to the interferon family. Collectively, we show that the virus can efficiently infect the gastric epithelium, suggesting that the stomach might have an active role in fecal-oral SARS-CoV-2 transmission.Several clinical reports have described gastrointestinal symptoms for COVID-19, though whether the virus can replicate within the stomach remains unclear. Here the authors generate gastric organoids from human biopsies and show that the virus can efficiently infect gastric epithelium, suggesting that the stomach might have an active role in fecal-oral transmission

Distributed Clustering in General Metrics via Coresets

Center-based clustering is a fundamental primitive for data analysis and is very challenging for large datasets. We developed coreset based space/round-efficient MapReduce algorithms to solve the k-center, k-median, and k-means variants in general metrics. Remarkably, the algorithms obliviously adapt to the doubling dimension of the metric space, and attain approximation ratios that can be made arbitrarily close to those achievable by the best known polynomial-time sequential approximations

A systematic review of human coronaviruses survival on environmental surfaces

The current pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has led people to implement preventive measures, including surface disinfection and use of alcohol-based hand gel, in order to avoid viral transmission via fomites. However, the role of surface transmission is still debated. The present systematic review aims to summarize all the evidence on surface survival of coronaviruses infecting humans. The analysis of 18 studies showed the longest coronavirus survival time is 28&nbsp;days at room temperature (RT) on different surfaces: polymer banknotes, vinyl, steel, glass, and paper banknotes. Concerning SARS-CoV-2 human infection from contaminated surfaces, dangerous viral load on surfaces for up to 21&nbsp;days was determined on polymer banknotes, steel, glass and paper banknotes. For viruses other than SARS-CoV-2, the longest period of survival was 14&nbsp;days, recorded on glass. Environmental conditions can affect virus survival, and indeed, low temperatures and low humidity support prolonged survival of viruses on contaminated surfaces independently of surface type. Furthermore, it has been shown that exposure to sunlight significantly reduces the risk of surface transmission. Although studies are increasingly investigating the topic of coronavirus survival, it is difficult to compare them, given the methodology differences. For this reason, it is advisable to define a reference working protocol for virus survival trials, but, as an immediate measure, there is also a need for further investigations of coronavirus survival on surfaces

Prevalence of SARS-CoV-2 RNA on inanimate surfaces: a systematic review and meta-analysis

Coronavirus disease (COVID-19) is a respiratory disease affecting many people and able to be transmitted through direct and perhaps indirect contact. Direct contact transmission, mediated by aerosols or droplets, is widely demonstrated, whereas indirect transmission is only supported by collateral evidence such as virus persistence on inanimate surfaces and data from other similar viruses. The present systematic review aims to estimate SARS-CoV-2 prevalence on inanimate surfaces, identifying risk levels according to surface characteristics. Data were obtained from studies in published papers collected from two databases (PubMed and Embase) with the last search on 1 September 2020. Included studies had to be papers in English, had to deal with coronavirus and had to consider inanimate surfaces in real settings. Studies were coded according to our assessment of the risk that the investigated surfaces could be contaminated by SARS-CoV-2. A meta-analysis and a metaregression were carried out to quantify virus RNA prevalence and to identify important factors driving differences among studies. Thirty-nine out of forty retrieved paper reported studies carried out in healthcare settings on the prevalence of virus RNA, five studies carry out also analyses through cell culture and six tested the viability of isolated viruses. Overall prevalences of SARS-CoV-2 RNA on high-, medium- and low-risk surfaces were 0.22 (CI95 [0.152-0.296]), 0.04 (CI95 [0.007-0.090]), and 0.00 (CI95 [0.00-0.019]), respectively. The duration surfaces were exposed to virus sources (patients) was the main factor explaining differences in prevalence