julilili42/papers huggingface dataset:
Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks
2022-02-28

The problem of adversarial robustness has been studied extensively for neural networks. However, for boosted decision trees and decision stumps there are almost no results, even though they are widely used in practice (e.g. XGBoost) due to their accuracy, interpretability, and efficiency. We show in this paper that for boosted decision stumps the \\textit{exact} min-max robust loss and test error for an -attack can be computed in time per input, where is the number of decision stumps and the optimal update step of the ensemble can be done in , where is the number of data points. For boosted trees we show how to efficiently calculate and optimize an upper bound on the robust loss, which leads to state-of-the-art robust test error for boosted trees on MNIST (12.5% for ), FMNIST (23.2% for ), and CIFAR-10 (74.7% for ). Moreover, the robust test error rates we achieve are competitive to the ones of provably robust convolutional networks. The code of all our experiments is available at http://github.com/max-andr/provably-robust-boosting
Convergence Guarantees for Adaptive Bayesian Quadrature Methods
2022-02-28

Adaptive Bayesian quadrature (ABQ) is a powerful approach to numerical integration that empirically compares favorably with Monte Carlo integration on problems of medium dimensionality (where non-adaptive quadrature is not competitive). Its key ingredient is an acquisition function that changes as a function of previously collected values of the integrand. While this adaptivity appears to be empirically powerful, it complicates analysis. Consequently, there are no theoretical guarantees so far for this class of methods. In this work, for a broad class of adaptive Bayesian quadrature methods, we prove consistency, deriving non-tight but informative convergence rates. To do so we introduce a new concept we call weak adaptivity. Our results identify a large and flexible class of adaptive Bayesian quadrature rules as consistent, within which practitioners can develop empirically efficient methods.
The X-ray Polarization Probe mission concept
2022-02-28

The X-ray Polarization Probe (XPP) is a second generation X-ray polarimeter following up on the Imaging X-ray Polarimetry Explorer (IXPE). The XPP will offer true broadband polarimetery over the wide 0.2-60 keV bandpass in addition to imaging polarimetry from 2-8 keV. The extended energy bandpass and improvements in sensitivity will enable the simultaneous measurement of the polarization of several emission components. These measurements will give qualitatively new information about how compact objects work, and will probe fundamental physics, i.e. strong-field quantum electrodynamics and strong gravity.
Comparison-Based Framework for Psychophysics: Lab versus Crowdsourcing
2022-02-28

Traditionally, psychophysical experiments are conducted by repeated measurements on a few well-trained participants under well-controlled conditions, often resulting in, if done properly, high quality data. In recent years, however, crowdsourcing platforms are becoming increasingly popular means of data collection, measuring many participants at the potential cost of obtaining data of worse quality. In this paper we study whether the use of comparison-based (ordinal) data, combined with machine learning algorithms, can boost the reliability of crowdsourcing studies for psychophysics, such that they can achieve performance close to a lab experiment. To this end, we compare three setups: simulations, a psychophysics lab experiment, and the same experiment on Amazon Mechanical Turk. All these experiments are conducted in a comparison-based setting where participants have to answer triplet questions of the form "is object x closer to y or to z?". We then use machine learning to solve the triplet prediction problem: given a subset of triplet questions with corresponding answers, we predict the answer to the remaining questions. Considering the limitations and noise on MTurk, we find that the accuracy of triplet prediction is surprisingly close---but not equal---to our lab study.
Long-term spectral study of the black hole Cygnus X-1 using INTEGRAL
2022-02-28

We utilize the joint capabilities of IBIS and SPI to perform a state-resolved 20--2000\\,keV analysis of the microquasar Cygnus X-1. In both LHS and HSS, the spectral analysis reveals the presence of a high-energy tail above 400\\,keV in addition to a standard, Compton-like continuum in the 20--400\\,keV range. We study the polarisation properties of the hard X-ray radiation, as well as of the polarisation of this high-energy tail. Polarisation is detected in the LHS in agreement with previous work. We find potential variations of the LHS polarisation with time and no polarisation detection in the HSS.
Monotonic Gaussian Process Flow
2022-02-28

We propose a new framework for imposing monotonicity constraints in a Bayesian nonparametric setting based on numerical solutions of stochastic differential equations. We derive a nonparametric model of monotonic functions that allows for interpretable priors and principled quantification of hierarchical uncertainty. We demonstrate the efficacy of the proposed model by providing competitive results to other probabilistic monotonic models on a number of benchmark functions. In addition, we consider the utility of a monotonic random process as a part of a hierarchical probabilistic model; we examine the task of temporal alignment of time-series data where it is beneficial to use a monotonic random process in order to preserve the uncertainty in the temporal warpings.
Uncertainty Estimates for Ordinal Embeddings
2022-02-28

To investigate objects without a describable notion of distance, one can gather ordinal information by asking triplet comparisons of the form "Is object closer to or is closer to ?" In order to learn from such data, the objects are typically embedded in a Euclidean space while satisfying as many triplet comparisons as possible. In this paper, we introduce empirical uncertainty estimates for standard embedding algorithms when few noisy triplets are available, using a bootstrap and a Bayesian approach. In particular, simulations show that these estimates are well calibrated and can serve to select embedding parameters or to quantify uncertainty in scientific applications.
Provable robustness against all adversarial $l_p$-perturbations for $p\\geq 1$
2022-02-28

In recent years several adversarial attacks and defenses have been proposed. Often seemingly robust models turn out to be non-robust when more sophisticated attacks are used. One way out of this dilemma are provable robustness guarantees. While provably robust models for specific -perturbation models have been developed, we show that they do not come with any guarantee against other -perturbations. We propose a new regularization scheme, MMR-Universal, for ReLU networks which enforces robustness wrt - and -perturbations and show how that leads to the first provably robust models wrt any -norm for .
Unlocking the Capabilities of Future High-Resolution X-ray Spectroscopy Missions Through Laboratory Astrophysics
2022-02-28

Thanks to high-resolution and non-dispersive spectrometers onboard future X-ray missions such as XRISM and Athena, we are finally poised to answer important questions about the formation and evolution of galaxies and large-scale structure. However, we currently lack an adequate understanding of many atomic processes behind the spectral features we will soon observe. Large error bars on parameters as critical as transition energies and atomic cross sections can lead to unacceptable uncertainties in the calculations of e.g., elemental abundance, velocity, and temperature. Unless we address these issues, we risk limiting the full scientific potential of these missions. Laboratory astrophysics, which comprises theoretical and experimental studies of the underlying physics behind observable astrophysical processes, is therefore central to the success of these missions.
Non-Parametric Calibration for Classification
2022-02-28

Many applications of classification methods not only require high accuracy but also reliable estimation of predictive uncertainty. However, while many current classification frameworks, in particular deep neural networks, achieve high accuracy, they tend to incorrectly estimate uncertainty. In this paper, we propose a method that adjusts the confidence estimates of a general classifier such that they approach the probability of classifying correctly. In contrast to existing approaches, our calibration method employs a non-parametric representation using a latent Gaussian process, and is specifically designed for multi-class classification. It can be applied to any classifier that outputs confidence estimates and is not limited to neural networks. We also provide a theoretical analysis regarding the over- and underconfidence of a classifier and its relationship to calibration, as well as an empirical outlook for calibrated active learning. In experiments we show the universally strong performance of our method across different classifiers and benchmark data sets, in particular for state-of-the art neural network architectures.
Semi-dynamical systems generated by autonomous Caputo fractional differential equations
2022-02-28

An autonomous Caputo fractional differential equation of order in whose vector field satisfies a global Lipschitz condition is shown to generate a semi-dynamical system in the function space of continuous functions with the topology uniform convergence on compact subsets. This contrasts with a recent result of Cong \\&amp; Tuan \\cite{cong}, which showed that such equations do not, in general, generate a dynamical system on the space .
Predicative proof theory of PDL and basic applications
2022-02-28

Propositional dynamic logic (PDL) is presented in Schütte-style mode as one-sided semiformal tree-like sequent calculus Seq with standard cut rule and the omega-rule with principal formulas . The omega-rule-free derivations in Seq are finite (trees) and sequents deducible by these finite derivations are valid in PDL. Moreover the cut-elimination theorem for Seq is provable in Peano Arithmetic (PA)extended by transfinite induction up to Veblen's ordinal . Hence (by the cutfree subformula property) such predicative extension of PA proves that any given -free sequent is valid in PDL iff it is deducible in Seq by a finite cut- and omega-rule-free derivation, while PDL-validity of arbitrary star-free sequents is decidable in polynomial space. The former also implies standard Herbrand-style conclusions, which eventually leads to PSPACE-decidability of PDL-validity of , provided that is atomic and is in a suitable \\emph{basic conjunctive normal form}. Furthermore we consider star-free formulas in dual \\emph{basic disjunctive normal form}, and corresponding expansions whose PDL-validity problem is known to be EXPTIME-complete. We show that cutfree-derivability in Seq (hence PDL-validity) of such \\ is equivalent to plain validity of a suitable "transparent" quantified boolean formula . The whole proof can be formalized in PA extended by transfinite induction along -- actually in the corresponding primitive recursive weakening, .
Morphing Contact Representations of Graphs
2022-02-28

We consider the problem of morphing between contact representations of a plane graph. In an -contact representation of a plane graph , vertices are realized by internally disjoint elements from a family of connected geometric objects. Two such elements touch if and only if their corresponding vertices are adjacent. These touchings also induce the same embedding as in . In a morph between two -contact representations we insist that at each time step (continuously throughout the morph) we have an -contact representation. We focus on the case when is the family of triangles in that are the lower-right half of axis-parallel rectangles. Such RT-representations exist for every plane graph and right triangles are one of the simplest families of shapes supporting this property. Thus, they provide a natural case to study regarding morphs of contact representations of plane graphs. We study piecewise linear morphs, where each step is a linear morph moving the endpoints of each triangle at constant speed along straight-line trajectories. We provide a polynomial-time algorithm that decides whether there is a piecewise linear morph between two RT-representations of an -vertex plane triangulation, and, if so, computes a morph with linear morphs. As a direct consequence, we obtain that for -connected plane triangulations there is a morph between every pair of RT-representations where the ``top-most'' triangle in both representations corresponds to the same vertex. This shows that the realization space of such RT-representations of any -connected plane triangulation forms a connected set.
Semi-simple groups of compact 16-dimensional planes
2022-02-28

The automorphism group of a compact topological projective plane with a -dimensional point space is a locally compact group. If the dimension of is at least , then is known to be a Lie group. For the connected component of the condition suffices. Depending on the structure of and the configuration of the fixed elements of sharper bounds are obtained here. Example: If is semi-simple and fixes exactly one line and possibly several points on this line, then is a Lie group if . Any semi-simple group which satisfies is a Lie group.
The role of liver fat in cardiometabolic diseases is highlighted by genome-wide association study of MRI-derived measures of body composition
2022-02-28

Abstract Background &amp; Aims Obesity and associated morbidities, metabolic associated liver disease (MAFLD) included, constitute some of the largest public health threats worldwide. Body composition and related risk factors are known to be heritable and identification of their genetic determinants may aid in the development of better prevention and treatment strategies. Recently, large-scale whole-body MRI data has become available, providing more specific measures of body composition than anthropometrics such as body mass index. Here, we aimed to elucidate the genetic architecture of body composition, by conducting the first genome-wide association study (GWAS) of these MRI-derived measures. Methods We ran both univariate and multivariate GWAS on fourteen MRI-derived measurements of adipose and muscle tissue distribution, derived from scans from 34,036 White European UK Biobank participants (mean age of 64.5 years, 51.5% female). Results Through multivariate analysis, we discovered 108 loci with distributed effects across the body composition measures and 256 significant genes primarily involved in immune system functioning. Liver fat stood out, with a highly discoverable and oligogenic architecture and the strongest genetic associations. Comparison with 21 common cardiometabolic traits revealed both shared and specific genetic influences, with higher mean heritability for the MRI measures (h 2 =.25 vs. .16, p=1.4×10 −6 ). We found substantial genetic correlations between the body composition measures and a range of cardiometabolic diseases, with the strongest correlation between liver fat and type 2 diabetes (r g =.48, p=1.6×10 −22 ). Conclusions These findings show that MRI-derived body composition measures complement conventional body anthropometrics and other biomarkers of cardiometabolic health, highlighting the central role of liver fat, and improving our knowledge of the genetic architecture of body composition and related diseases.
In vivo antiviral efficacy and immune dampening effect of zapnometinib emphasize a dual therapeutic potential against COVID-19
2022-02-28

<title>Abstract</title> Coronaviruses, in particular the current pandemic strains of SARS-CoV-2, represent an ever-evolving threat to human life. Currently available antiviral drugs are not suitable for severely infected patients in later hyperinflammatory stages of the disease. New therapeutics that inhibit virus propagation and target inflammation would be ideal. We demonstrate that the host targeted MEK inhibitor zapnometinib displays such a dual effect. The drug efficiently reduced virus titers of different SARS-CoV-2 variants and other highly pathogenic human coronaviruses such as SARS-CoV-1 and MERS-CoV in vitro. In a Syrian hamster infection model, zapnometinib reduced SARS-CoV-2 viral load and alleviated lung pathology. Drug safety biomarkers, body weight change and clinical observations, indicated that zapnometinib was well tolerated and showed no toxicity. Moreover, the immune dampening effect could be confirmed in an acute lung injury mouse model and in vitro experiments in primary human blood cells that demonstrated the reduction of disease related cytokines and chemokines. Thus, in contrast to direct-acting antivirals, zapnometinib displays a dual therapeutic effect highlighting its potential in the treatment of severe COVID-19 and other acute viral infections leading to hyperinflammation.
Middle Miocene climate and stable oxygen isotopes in Europe
2022-02-28

v1. Three ECHAM5-wiso model experiments for European region. For the first experiment (Europe_PI.nc), boundary conditions are set as pre-industrial and are identical to those in Botsyun et al., 2020. We also conducted two experiments with Middle Miocene boundary conditions reflecting two <em>p</em>CO<sub>2 </sub>settings (278 ppm and 450 ppm; Europe_Mio_278.nc and Europe_Mio_450.nc experiments) within current estimates of the Middle Miocene <em>p</em>CO<sub>2.</sub> v2. Global ECHAM5-wiso model experiments. geosp_simulations.nc contains surface geopotential for PI and Miocene experiments with modified Alpine topography and paleogeography.<br>
Middle Miocene climate and stable oxygen isotopes in Europe
2022-02-28

Three ECHAM5-wiso model experiments for European region. For the first experiment (Europe_PI.nc), boundary conditions are set as pre-industrial and are identical to those in Botsyun et al., 2020. We also conducted two experiments with Middle Miocene boundary conditions reflecting two <em>p</em>CO<sub>2 </sub>settings (278 ppm and 450 ppm; Europe_Mio_278.nc and Europe_Mio_450.nc experiments) within current estimates of the Middle Miocene <em>p</em>CO<sub>2.</sub>
C reactive protein flare predicts response to checkpoint inhibitor treatment in non-small cell lung cancer
2022-03-01

Biomarkers for predicting response to anti-programmed death-1 (PD-1) immune checkpoint blockade (ICB) in non-small cell lung cancer (NSCLC) remain in demand. Since anti-tumor immune activation is a process, early dynamic changes of the acute-phase reactant C reactive protein (CRP) may serve as a predictive on-treatment biomarker. In a retrospective (N=105) and prospective (N=108) ICB-treated NSCLC cohort, early CRP kinetics were stratified after the start of immunotherapy until weeks 4, 6, and 12 as follows: an early doubling of baseline CRP followed by a drop below baseline (CRP flare-responder), a drop of at least 30% below baseline without prior flare (CRP responders), or those who remained as CRP non-responders. In our study, we observed characteristic longitudinal changes of serum CRP concentration after the initiation of ICB. In the prospective cohort, N=40 patients were defined as CRP non-responders, N=39 as CRP responders, and N=29 as CRP flare-responders with a median progression-free survival (PFS) of 2.4, 8.1, and 14.3 months, respectively, and overall survival (OS) of 6.6, 18.6, and 32.9 months (both log-rank p&lt;0.001). Of note, CRP flare-responses, characterized by a sharp on-treatment CRP increase in the first weeks after therapy initiation, followed by a decrease of CRP serum level below baseline, predict ICB response as early as 4 weeks after therapy initiation. Of note, early CRP kinetics showed no predictive value for chemoimmunotherapy or when steroids were administered concurrently. On-treatment CRP kinetics had a predictive value for both major histological NSCLC subtypes, adenocarcinoma and squamous cell carcinoma. The results were verified in an independent retrospective cohort of 105 patients. In conclusion, CRP flare predicted anti-PD-1 monotherapy response and survival in two independent cohorts including a total of 213 patients with NSCLC, regardless of histology. Due to its wide clinical availability, early CRP kinetics could become an easily determined, cost-efficient, and non-invasive biomarker to predict response to checkpoint inhibitors in NSCLC within the first month.
Digging deeper into colonial palaeontological practices in modern day Mexico and Brazil
2022-03-01

Scientific practices stemming from colonialism, whereby middle- and low-income countries supply data for high-income countries and the contributions of local expertise are devalued, are still prevalent today in the field of palaeontology. In response to these unjust practices, countries such as Mexico and Brazil adopted protective laws and regulations during the twentieth century to preserve their palaeontological heritage. However, scientific colonialism is still reflected in many publications describing fossil specimens recovered from these countries. Here, we present examples of ‘palaeontological colonialism’ from publications on Jurassic–Cretaceous fossils from NE Mexico and NE Brazil spanning the last three decades. Common issues that we identified in these publications are the absence of both fieldwork and export permit declarations and the lack of local experts among authorships. In Mexico, access to many fossil specimens is restricted on account of these specimens being housed in private collections, whereas a high number of studies on Brazilian fossils are based on specimens illegally reposited in foreign collections, particularly in Germany and Japan. Finally, we outline and discuss the wider academic and social impacts of these research practices, and propose exhaustive recommendations to scientists, journals, museums, research institutions and government and funding agencies in order to overcome these practices.
Excessive Invariance Causes Adversarial Vulnerability
2022-03-01

Despite their impressive performance, deep neural networks exhibit striking failures on out-of-distribution inputs. One core idea of adversarial example research is to reveal neural network errors under such distribution shifts. We decompose these errors into two complementary sources: sensitivity and invariance. We show deep networks are not only too sensitive to task-irrelevant changes of their input, as is well-known from epsilon-adversarial examples, but are also too invariant to a wide range of task-relevant changes, thus making vast regions in input space vulnerable to adversarial attacks. We show such excessive invariance occurs across various tasks and architecture types. On MNIST and ImageNet one can manipulate the class-specific content of almost any image without changing the hidden activations. We identify an insufficiency of the standard cross-entropy loss as a reason for these failures. Further, we extend this objective based on an information-theoretic analysis so it encourages the model to consider all task-dependent features in its decision. This provides the first approach tailored explicitly to overcome excessive invariance and resulting vulnerabilities.