Miracle-dz/UniFAR-FTUs huggingface dataset:
Beware of the Simulated DAG! Varsortability in Additive Noise Models

Additive noise models are a class of causal models in which each variable is de ned as a function of its causes plus independent noise. In such models, the ordering of variables by marginal variances may be indicative of the causal order. We introduce varsortability as a measure of agreement between the ordering by marginal variance and the causal order. We show how var-sortability dominates the performance of continuous structure learning algorithms on synthetic data. On real-world data, varsortability is an implausible and untestable assumption and we nd no indication of high varsortability. We aim to raise awareness that varsortability easily occurs in simulated additive noise models. We provide a baseline method that explicitly exploits var-sortability and advocate reporting varsortability in benchmarking data.
A Behavioral Input-Output Parametrization of Control Policies with Suboptimality Guarantees

Recent work in data-driven control has revived behavioral theory to perform a variety of complex control tasks, by directly plugging libraries of past input-output trajectories into optimal control problems. Despite recent advances, a key aspect remains unclear: how and to what extent do noise-corrupted data impact control performance? In this work, we provide a quantitative answer to this question based on the model-mismatch level incurred during a preliminary system identification phase. We formulate a Behavioral version of the Input-Output Parametrization (BIOP) for the optimal predictive control of unknown systems using output-feedback dynamic control policies. The main advantages of the proposed framework are that 1) the state-space parameters and the initial state need not be specified for controller synthesis, 2) it can be used in combination with state-of-the-art impulse response estimators, and 3) it allows to recover suboptimality results on learning the Linear Quadratic Gaussian (LQG) controller, therefore revealing how the model-mismatch level may affect the performance. Specifically, it is shown that the performance degrades linearly with the model-mismatch incurred by either classical or behavioral-based system identification.
Elemental analysis of polymetallic nodules from the Central Indian Basin: a study using EDXRF

Polymetallic nodule samples from the Central Indian Basin were analysed using energy-dispersive x-ray fluorescence employing the fundamental parameter approach. Photons of 5.96 and 22.6 keV, from radioactive sources of 55Fe and 109Cd, respectively, were used for exciting the samples. Fifteen elements, namely Si, Cl, K, Ca, Ti, Mn, Fe, Ni, Cu, Zn, Sr, Y, Zr, Mo and Pb, were quantified. The samples were diluted in cellulose nitrate powder to reduce the enhancement and absorption effects due to the unknown matrix. The experimental procedures adopted for elemental analysis were checked by analysing a NIST certified reference material (SRM 2710) and a polymetallic nodule standard reference material (NIO-2388). The measured concentrations of various elements present in the nodule samples were compared with those reported earlier using ICP-AES and ICP-MS. © 1998 John Wiley & Sons, Ltd.
A method for determining the parameters in a rheological model for viscoelastic materials by minimizing Tikhonov functionals

Mathematical models describing the behavior of viscoelastic materials are often based on evolution equations that measure the change in stress depending on its material parameters such as stiffness, viscosity or relaxation time. In this article, we introduce a Maxwell-based rheological model, define the associated forward operator and the inverse problem in order to determine the number of Maxwell elements and the material parameters of the underlying viscoelastic material. We perform a relaxation experiment by applying a strain to the material and measure the generated stress. Since the measured data varies with the number of Maxwell elements, the forward operator of the underlying inverse problem depends on parts of the solution. By introducing assumptions on the relaxation times, we propose a clustering algorithm to resolve this problem. We provide the calculations that are necessary for the minimization process and conclude with numerical results by investigating unperturbed as well as noisy data. We present different reconstruction approaches based on minimizing a least squares functional. Furthermore, we look at individual stress components to analyze different displacement rates. Finally, we study reconstructions with shortened data sets to obtain assertions on how long experiments have to be performed to identify conclusive material parameters.
Jost function formalism with complex potential

The Jost function formalism is extended with use of the complex potential in this paper. We derive the Jost function by taking into account the dual state which is defined by the complex conjugate the complex Hamiltonian. By using the unitarity of the S-matrix which is defined by the Jost function, the optical theorem with the complex potential is also derived. The role of the imaginary part of the complex potential for both the bound states and the scattering states is figured out. The numerical calculation is performed by using the complex Woods-Saxon potential, and some numerical results are demonstrated to confirmed the properties of extended Jost function formalism.
An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization

A popular assumption for out-of-distribution generalization is that the training data comprises sub-datasets, each drawn from a distinct distribution; the goal is then to"interpolate"these distributions and"extrapolate"beyond them -- this objective is broadly known as domain generalization. A common belief is that ERM can interpolate but not extrapolate and that the latter is considerably more difficult, but these claims are vague and lack formal justification. In this work, we recast generalization over sub-groups as an online game between a player minimizing risk and an adversary presenting new test distributions. Under an existing notion of inter- and extrapolation based on reweighting of sub-group likelihoods, we rigorously demonstrate that extrapolation is computationally much harder than interpolation, though their statistical complexity is not significantly different. Furthermore, we show that ERM -- or a noisy variant -- is provably minimax-optimal for both tasks. Our framework presents a new avenue for the formal analysis of domain generalization algorithms which may be of independent interest.
Improving Robustness of Learning-based Autonomous Steering Using Adversarial Images

For safety of autonomous driving, vehicles need to be able to drive under various lighting, weather, and visibility conditions in different environments. These external and environmental factors, along with internal factors associated with sensors, can pose significant challenges to perceptual data processing, hence affecting the decision-making and control of the vehicle. In this work, we address this critical issue by introducing a framework for analyzing robustness of the learning algorithm w.r. t varying quality in the image input for autonomous driving. Using the results of sensitivity analysis, we further propose an algorithm to improve the overall performance of the task of"learning to steer". The results show that our approach is able to enhance the learning outcomes up to 48%. A comparative study drawn between our approach and other related techniques, such as data augmentation and adversarial training, confirms the effectiveness of our algorithm as a way to improve the robustness and generalization of neural network training for autonomous driving.
Is Covid-19 Severity Associated With ACE2 Degradation?

Covid-19 is particularly mild with children, and its severity escalates with age. Several theories have been proposed to explain these facts. In particular, it was proposed that the lower expression of the viral receptor ACE2 in children protects them from severe Covid-19. However, other works suggested an inverse relationship between ACE2 expression and disease severity. Here we review the seemingly contradicting observations on ACE2 expression at the levels of mRNA, membrane protein and serum protein in humans and rodents and try to reconcile them at the light of the Renin-Angiotensin system (RAS) and bradykinin system, which constitute an integrated inflammatory system connected by common peptidases and interacting receptors. We find that ACE2 level is not monotonically related with age but it reaches a maximum at a young age that depends on the cell type and then decreases, consistently with almost all existing data. The increase with age of the protease Tumor necrosis factor alpha (TNF-α) converting enzyme (TACE), also known as ADAM17 (a disintegrin and metalloproteinase 17) that sheds ACE2 from the cell membrane to the serum predicts that the decrease occurs before and is steeper for ACE2 cell protein than for its mRNA. This negative relation between ACE2 level and Covid-19 severity at old age is not paradoxical but it is consistent with a mathematical model that predicts that higher viral receptor does not necessarily favour virus propagation and it can even slow it down. More importantly, the angiotensin-bradykinin system is characterized by a powerful positive feedback loop that enhances inflammation through the Angiotensin and Bradykinin receptors that upregulate ADAM17, which in turn downregulates ACE2 and upregulates TNF-α and the pro-inflammatory receptor of the cytokine interleukin 6 (IL6). Here we propose that ACE2 contributes essentially to reverse this inflammatory state by downregulating the pro-inflammatory peptides of the angiotensin-bradykinin system, and that failure to do this, possibly induced by the degradation of ACE2 by SARS-COV-2, may underlie both severe CoViD-19 infection and its many post-infection manifestations, including the multi-inflammatory syndrome of children (MIS-C). Within this view, lower severity in children despite lower ACE2 expression may be consistent with their higher expression of the alternative angiotensin II receptor ATR2 and in general of the anti-inflammatory arm of the RAS at young age.
Exploring the planetary-mass population in the Upper Scorpius association

We aim at identifying very low-mass isolated planetary-mass member candidates in the nearest OB association to the Sun, Upper Scorpius (USco) (145 pc; 5–10 Myr), to constrain the form and shape of the luminosity function and mass spectrum in this regime. We conducted a deep multiband (Y = 21.2, J = 20.5, Z = 22.0 mag) photometric survey of 6 deg2 in the central region of USco. We extend the current sequence of astrometric and spectroscopic members by about two magnitudes in Y and one magnitude in J, reaching potentially T-type free-floating members in the association with predicted masses below 5 Jupiter masses, well into the planetary-mass regime. We extracted a sample of 57 candidates in this area and present infrared spectroscopy confirming two of them as young L-type members with characteristic spectral features of 10-Myr-old brown dwarfs. Among the 57 candidates, we highlight 10 new candidates fainter than the coolest members previously confirmed spectroscopically. We do not see any obvious sign of decrease in the mass spectrum of the association, suggesting that star processes can form substellar objects with masses down to 4–5 Jupiter masses.
Crystal Structure and Diffusion Path in the Fast Lithium-Ion Conductor La0.62Li0.16TiO3

We report the results of a neutron powder diffraction study of the La0.62Li0. 16TiO3 perovskite that determined the diffusion path of lithium cations at room temperature. At 77 K, the Li cations are located at the 2c site (Wycoff notation of the Cmmm space group) on the (002) La deficient layer, while, at room temperature, they are spread over a wide area and migrate following the 2c−4f−2c or 2c−2d−2c tie line on the (002) layer. The probability density of Li cations has a minimum between the 2c and 4f or between the 2c and 2d positions on the diffusion path in contrast to the previous reports where the bottleneck has been thought to be located at the 2c, 2d, and 4f positions. On the basis of the present structural model, the Li-cation conductivity is discussed in terms of a two-dimensional bond-percolation model for Li-cation diffusion. It was found that the vacancy at the La site is essential for the Li-cation conduction.
Magnetic Helicity Flux across Solar Active Region Photospheres. II. Association of Hemispheric Sign Preference with Flaring Activity during Solar Cycle 24

In our earlier study (Paper I) of this series, we examined the hemispheric sign preference (HSP) of magnetic helicity flux dH/dt across photospheric surfaces of 4802 samples of 1105 unique active regions (ARs) observed during solar cycle 24. Here, we investigate any association of the HSP, expressed as a degree of compliance, with flaring activity, analyzing the same set of dH/dt estimates as used in Paper I. The AR samples under investigation are assigned to heliographic regions (HRs) defined in the Carrington longitude–latitude plane with a grid spacing of 45° in longitude and 15° in latitude. For AR samples in each of the defined HRs, we calculate the degree of HSP compliance and the average soft X-ray flare index. The strongest flaring activity is found to be in one distinctive HR with an extremely low-HSP compliance of 41% as compared to the mean and standard deviation of 62% and 7%, respectively, over all HRs. This sole HR shows an anti-HSP (i.e., <50%) and includes the highly flare-productive AR NOAA 12673, however this AR is not uniquely responsible for the HR’s low HSP. We also find that all HRs with the highest flaring activity are located in the southern hemisphere, and they tend to have lower degrees of HSP compliance. These findings point to the presence of localized regions of the convection zone with enhanced turbulence, imparting a greater magnetic complexity and a higher flaring rate to some rising magnetic flux tubes.
IoTMalware: Android IoT Malware Detection based on Deep Neural Network and Blockchain Technology

—The Internet of Things (IoT) has been revolutionizing this world by introducing exciting applications almost in all walks of daily life, such as healthcare, smart cities, smart environments, safety, remote sensing, and many more. The signiﬁcance of Android IoT devices is bespoke due to ﬂexibility and hardware support features. However, it is quite challenging to deal with increasing malware attacks on android devices with every passing day. This challenge is magniﬁed further in the case of android IoT devices due to their limited resources such as memory and power. Therefore, this paper proposes a new framework based on the blockchain and deep learning model to provide more security for Android IoT devices. Moreover, our framework is capable to ﬁnd the malware activities in a real-time environment. The proposed deep learning model analyzes various static and dynamic features extracted from thousands of feature of malware and benign apps that are already stored in blockchain distributed ledger. The multi-layer deep learning model makes decisions by analyzing the previous data and follow some steps. Firstly, it divides the malware feature into multiple level clusters. Secondly, it chooses a unique deep learning model for each malware feature set or cluster. Finally, it produces the decision by combining the results generated from all cluster levels. Furthermore, the decisions and multiple-level clustering data are stored in a blockchain that can be further used to train every specialized cluster for unique data distribution. Also, a customized smart contract is designed to detect deceptive applications through the blockchain framework. The smart contract veriﬁes the malicious application both during the uploading and downloading process of Android apps on the network. Consequently, the proposed framework provides ﬂexibility to features for run-time security regarding malware detection on heterogeneous IoT devices. Finally, the smart contract helps to approve or deny to uploading and downloading harmful Android applications.
An organized review of key factors for fake news detection

Fake news in social media has quickly become one of the most discussed topics in today's society. With false information proliferating and causing a significant impact in the political, economical, and social domains, research efforts to analyze and automatically identify this type of content have being conducted in the past few years. In this paper, we attempt to summarize the principal findings on the topic of fake news in social media, highlighting the main research path taken and giving a particular focus on the detection of fake news and bot accounts.
MDA for random forests: inconsistency, and a practical solution via the Sobol-MDA

Variable importance measures are the main tools used to analyse the black-box mechanisms of random forests. Although the mean decrease accuracy is widely accepted as the most efficient variable importance measure for random forests, little is known about its statistical properties. In fact, the definition of mean decrease accuracy varies across the main random forest software. In this article, our objective is to rigorously analyse the behaviour of the main mean decrease accuracy implementations. Consequently, we mathematically formalize the various implemented MDA algorithms, and then establish their limits when the sample size increases. This asymptotic analysis reveals that these mean decrease accuracy versions differ as importance measures, since they converge towards different quantities. More importantly, we break down these limits into three components: the first two terms are related to Sobol indices, which are well-defined measures of a covariate contribution to the response variance, widely used in the sensitivity analysis field, as opposed to the third term, whose value increases with dependence within covariates. Thus, we theoretically demonstrate that the mean decrease accuracy does not target the right quantity to detect influential covariates in a dependent setting, a fact that has already been noticed experimentally. To address this issue, we define a new importance measure for random forests, the Sobol-mean decrease accuracy, which fixes the flaws of the original mean decrease accuracy, and consistently estimates the accuracy decrease of the forest retrained without a given covariate, but with an efficient computational cost. The Sobol-mean decrease accuracy empirically outperforms its competitors on both simulated and real data for variable selection. An open source implementation in R and C ++ is available online.
EvoSpex: An Evolutionary Algorithm for Learning Postconditions

Software reliability is a primary concern in the construction of software, and thus a fundamental component in the definition of software quality. Analyzing software reliability requires a specification of the intended behavior of the software under analysis, and at the source code level, such specifications typically take the form of assertions. Unfortunately, software many times lacks such specifications, or only provides them for scenario-specific behaviors, as assertions accompanying tests. This issue seriously diminishes the analyzability of software with respect to its reliability. In this paper, we tackle this problem by proposing a technique that, given a Java method, automatically produces a specification of the method's current behavior, in the form of postcondition assertions. This mechanism is based on generating executions of the method under analysis to obtain valid pre/post state pairs, mutating these pairs to obtain (allegedly) invalid ones, and then using a genetic algorithm to produce an assertion that is satisfied by the valid pre/post pairs, while leaving out the invalid ones. The technique, which targets in particular methods of reference-based class implementations, is assessed on a benchmark of open source Java projects, showing that our genetic algorithm is able to generate post-conditions that are stronger and more accurate, than those generated by related automated approaches, as evaluated by an automated oracle assessment tool. Moreover, our technique is also able to infer an important part of manually written rich postconditions in verified classes, and reproduce contracts for methods whose class implementations were automatically synthesized from specifications.
Near-Field Millimeter-Wave Imaging via Circular-Arc MIMO Arrays

Millimeter-wave (MMW) imaging has a wide prospect in application of concealed weapons detection. We propose a circular-arc multiple-input multiple-output (MIMO) array scheme with uniformly spaced transmit and receive antennas along the horizontal-arc direction, while scanning along the vertical direction. The antenna beams of the circular-arc MIMO array can provide more uniform coverage of the imaging scene than those of the linear or planar MIMO arrays. Further, a near-field three-dimensional (3-D) imaging algorithm, based on the spatial frequency domain processing, is presented with analysis of sampling criteria and resolutions. Numerical simulations, as well as comparisons with the back-projection (BP) algorithm, are provided to show the efficacy of the proposed approach.
NOMU: Neural Optimization-based Model Uncertainty

We study methods for estimating model uncertainty for neural networks (NNs) in regression. To isolate the effect of model uncertainty, we focus on a noiseless setting with scarce training data. We introduce five important desiderata regarding model uncertainty that any method should satisfy. However, we find that established benchmarks often fail to reliably capture some of these desiderata, even those that are required by Bayesian theory. To address this, we introduce a new approach for capturing model uncertainty for NNs, which we call Neural Optimization-based Model Uncertainty (NOMU). The main idea of NOMU is to design a network architecture consisting of two connected sub-NNs, one for model prediction and one for model uncertainty, and to train it using a carefully-designed loss function. Importantly, our design enforces that NOMU satisfies our five desiderata. Due to its modular architecture, NOMU can provide model uncertainty for any given (previously trained) NN if given access to its training data. We evaluate NOMU in various regressions tasks and noiseless Bayesian optimization (BO) with costly evaluations. In regression, NOMU performs at least as well as state-of-the-art methods. In BO, NOMU even outperforms all considered benchmarks.