A novel model is here introduced for the SOC change index defined as the normalized difference between the actual Soil Organic Carbon and the value assumed at an initial reference year. It is tailored on the RothC carbon model dynamics and assumes as baseline the value of the SOC equilibrium under constant environmental conditions. A sensitivity analysis is performed to evaluate the response of the model to changes of temperature, Net Primary Production (NPP), and land use soil class (forest, grassland, arable). A non-standard monthly time-stepping procedure has been proposed to approximate the SOC change index in the Alta Murgia National Park, a protected area in the Italian Apulia region, selected as test site. In the case of arable class, the SOC change index exhibits a negative trend which can be inverted by a suitable organic fertilization program here proposed.
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Since the World Health Organization announced the COVID-19 pandemic in March 2020, curbing the spread of the virus has become an international priority. It has greatly affected people's lifestyles. In this article, we observe and analyze the impact of the pandemic on people's lives using changes in smartphone application usage. First, through observing the daily usage change trends of all users during the pandemic, we can understand and analyze the effects of restrictive measures and policies during the pandemic on people's lives. In addition, it is also helpful for the government and health departments to take more appropriate restrictive measures in the case of future pandemics. Second, we defined the usage change features and found 9 different usage change patterns during the pandemic according to clusters of users and show the diversity of daily usage changes. It helps to understand and analyze the different impacts of the pandemic and restrictive measures on different types of people in more detail. Finally, according to prediction models, we discover the main related factors of each usage change type from user preferences and demographic information. It helps to predict changes in smartphone activity during future pandemics or when other restrictive measures are implemented, which may become a new indicator to judge and manage the risks of measures or events.
Unlike its intercept, a linear classifier's weight vector cannot be tuned by a simple grid search. Hence, this paper proposes weight vector tuning of a generic binary linear classifier through the parameterization of a decomposition of the discriminant by a scalar which controls the trade-off between conflicting informative and noisy terms. By varying this parameter, the original weight vector is modified in a meaningful way. Applying this method to a number of linear classifiers under a variety of data dimensionality and sample size settings reveals that the classification performance loss due to non-optimal native hyperparameters can be compensated for by weight vector tuning. This yields computational savings as the proposed tuning method reduces to tuning a scalar compared to tuning the native hyperparameter, which may involve repeated weight vector generation along with its burden of optimization, dimensionality reduction, etc., depending on the classifier. It is also found that weight vector tuning significantly improves the performance of Linear Discriminant Analysis (LDA) under high estimation noise. Proceeding from this second finding, an asymptotic study of the misclassification probability of the parameterized LDA classifier in the growth regime where the data dimensionality and sample size are comparable is conducted. Using random matrix theory, the misclassification probability is shown to converge to a quantity that is a function of the true statistics of the data. Additionally, an estimator of the misclassification probability is derived. Finally, computationally efficient tuning of the parameter using this estimator is demonstrated on real data.
Empirical observations often consist of anomalies (or outliers) that contaminate the data. Accurate identification of anomalous samples is crucial for the success of downstream data analysis tasks. To automatically identify anomalies, we propose a new type of autoencoder (AE) which we term Probabilistic Robust autoencoder (PRAE). PRAE is designed to simultaneously remove outliers and identify a low-dimensional representation for the inlier samples. We first describe Robust AE (RAE) as a model that aims to split the data to inlier samples from which a low dimensional representation is learned via an AE, and anomalous (outlier) samples that are excluded as they do not fit the low dimensional representation. Robust AE minimizes the reconstruction of the AE while attempting to incorporate as many observations as possible. This could be realized by subtracting from the reconstruction term an $\ell_0$ norm counting the number of selected observations. Since the $\ell_0$ norm is not differentiable, we propose two probabilistic relaxations for the RAE approach and demonstrate that they can effectively identify anomalies. We prove that the solution to PRAE is equivalent to the solution of RAE and demonstrate using extensive simulations that PRAE is at par with state-of-the-art methods for anomaly detection.
Generative models trained using Differential Privacy (DP) are increasingly used to produce and share synthetic data in a privacy-friendly manner. In this paper, we set out to analyze the impact of DP on these models vis-a-vis underrepresented classes and subgroups of data. We do so from two angles: 1) the size of classes and subgroups in the synthetic data, and 2) classification accuracy on them. We also evaluate the effect of various levels of imbalance and privacy budgets. Our experiments, conducted using three state-of-the-art DP models (PrivBayes, DP-WGAN, and PATE-GAN), show that DP results in opposite size distributions in the generated synthetic data. More precisely, it affects the gap between the majority and minority classes and subgroups, either reducing it (a "Robin Hood" effect) or increasing it ("Matthew" effect). However, both of these size shifts lead to similar disparate impacts on a classifier's accuracy, affecting disproportionately more the underrepresented subparts of the data. As a result, we call for caution when analyzing or training a model on synthetic data, or risk treating different subpopulations unevenly, which might also lead to unreliable conclusions.
Many systems today rely heavily on virtual private network (VPN) technology to connect networks and protect their services on the Internet. While prior studies compare the performance of different implementations, they do not consider adversarial settings. To address this gap, we evaluate the resilience of VPN implementations to flooding-based denial-of-service (DoS) attacks. We focus on a class of stateless flooding attacks, which are particularly threatening to real connections, as they can be carried out by an off-path attacker using spoofed IP addresses. We have implemented various attacks to evaluate DoS resilience for three major open-source VPN solutions, with surprising results: On high-performance hardware with a $40\,\mathrm{Gb/s}$ interface, data transfer over established WireGuard connections can be fully denied with $700\,\mathrm{Mb/s}$ of attack traffic. For strongSwan (IPsec), an adversary can block any legitimate connections from being established using only $75\,\mathrm{Mb/s}$ of attack traffic. OpenVPN can be overwhelmed with $100\,\mathrm{Mb/s}$ of flood traffic denying data transfer through the VPN connection as well as connection establishment completely. Further analysis has revealed implementation bugs and major inefficiencies in the implementations related to concurrency aspects. These findings demonstrate a need for more adversarial testing of VPN implementations with respect to DoS resilience.
Numerous studies have examined the associations between long-term exposure to fine particulate matter (PM2.5) and adverse health outcomes. Recently, many of these studies have begun to employ high-resolution predicted PM2.5 concentrations, which are subject to measurement error. Previous approaches for exposure measurement error correction have either been applied in non-causal settings or have only considered a categorical exposure. Moreover, most procedures have failed to account for uncertainty induced by error correction when fitting an exposure-response function (ERF). To remedy these deficiencies, we develop a multiple imputation framework that combines regression calibration and Bayesian techniques to estimate a causal ERF. We demonstrate how the output of the measurement error correction steps can be seamlessly integrated into a Bayesian additive regression trees (BART) estimator of the causal ERF. We also demonstrate how locally-weighted smoothing of the posterior samples from BART can be used to create a better fitting ERF. Our proposed approach also properly propagates the exposure measurement error uncertainty to yield accurate standard error estimates. We assess the robustness of our proposed approach in an extensive simulation study. We then apply our methodology to estimate the effects of PM2.5 on all-cause mortality among Medicare enrollees in the New England from 2000-2012.
This tutorial shows how to build, fit, and criticize disease transmission models in Stan, and should be useful to researchers interested in modeling the SARS-CoV-2 pandemic and other infectious diseases in a Bayesian framework. Bayesian modeling provides a principled way to quantify uncertainty and incorporate both data and prior knowledge into the model estimates. Stan is an expressive probabilistic programming language that abstracts the inference and allows users to focus on the modeling. As a result, Stan code is readable and easily extensible, which makes the modeler's work more transparent. Furthermore, Stan's main inference engine, Hamiltonian Monte Carlo sampling, is amiable to diagnostics, which means the user can verify whether the obtained inference is reliable. In this tutorial, we demonstrate how to formulate, fit, and diagnose a compartmental transmission model in Stan, first with a simple Susceptible-Infected-Recovered (SIR) model, then with a more elaborate transmission model used during the SARS-CoV-2 pandemic. We also cover advanced topics which can further help practitioners fit sophisticated models; notably, how to use simulations to probe the model and priors, and computational techniques to scale-up models based on ordinary differential equations.
As an important problem of causal inference, we discuss the estimation of treatment effects (TEs) under unobserved confounding. Representing the confounder as a latent variable, we propose Intact-VAE, a new variant of variational autoencoder (VAE), motivated by the prognostic score that is sufficient for identifying TEs. Our VAE also naturally gives representation balanced for treatment groups, using its prior. Experiments on (semi-)synthetic datasets show state-of-the-art performance under diverse settings. Based on the identifiability of our model, further theoretical developments on identification and consistent estimation are also discussed. This paves the way towards principled causal effect estimation by deep neural networks.
Markerless augmented reality (AR) has the potential to provide engaging experiences and improve outcomes across a wide variety of industries; the overlaying of virtual content, or holograms, onto a view of the real world without the need for predefined markers provides great convenience and flexibility. However, unwanted hologram movement frequently occurs in markerless smartphone AR due to challenging visual conditions or device movement, and resulting error in device pose tracking. We develop a method for measuring hologram positional errors on commercial smartphone markerless AR platforms, implement it as an open-source AR app, HoloMeasure, and use the app to conduct systematic quantitative characterizations of hologram stability across 6 different user actions, 3 different smartphone models, and over 200 different environments. Our study demonstrates significant levels of spatial instability in holograms in all but the simplest settings, and underscores the need for further enhancements to pose tracking algorithms for smartphone-based markerless AR.
In order to avoid the curse of dimensionality, frequently encountered in Big Data analysis, there was a vast development in the field of linear and nonlinear dimension reduction techniques in recent years. These techniques (sometimes referred to as manifold learning) assume that the scattered input data is lying on a lower dimensional manifold, thus the high dimensionality problem can be overcome by learning the lower dimensionality behavior. However, in real life applications, data is often very noisy. In this work, we propose a method to approximate $\mathcal{M}$ a $d$-dimensional $C^{m+1}$ smooth submanifold of $\mathbb{R}^n$ ($d \ll n$) based upon noisy scattered data points (i.e., a data cloud). We assume that the data points are located "near" the lower dimensional manifold and suggest a non-linear moving least-squares projection on an approximating $d$-dimensional manifold. Under some mild assumptions, the resulting approximant is shown to be infinitely smooth and of high approximation order (i.e., $O(h^{m+1})$, where $h$ is the fill distance and $m$ is the degree of the local polynomial approximation). The method presented here assumes no analytic knowledge of the approximated manifold and the approximation algorithm is linear in the large dimension $n$. Furthermore, the approximating manifold can serve as a framework to perform operations directly on the high dimensional data in a computationally efficient manner. This way, the preparatory step of dimension reduction, which induces distortions to the data, can be avoided altogether.
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