Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of tumor necrosis factor-α in lipopolysaccharide-induced pathologic alterations

Tumor necrosis factor-alpha (TNF) has been implicated strongly as a principal mediator in the pathogenesis of septic shock. The authors investigated the in vivo production of TNF in CBA/J and CD-1 mice that had been primed by an intraperitoneal injection of complete Freund's adjuvant followed 2 weeks later by an intraperitoneal injection of lipopolysaccharide (LPS). TNF bioactivity peaked in both the ascites and plasma one hour after challenge, and TNF mRNA expression in the ascites cells peaked 30 minutes after LPS. After the induction of bioactivity, an interstitial pulmonary neutrophilic infiltrate occurred that was quantitated both morphometrically and by a myeloperoxidase (MPO) assay. Peripheral blood neutrophilia and lymphopenia developed after the LPS injection (PMNs: control, 46 +/- 2%; LPS, 65 +/- 3%; Lymphs control, 53 +/- 2%; LPS, 37 +/- 3%). Treatment with dexamethasone (Dex) completely inhibited the pulmonary neutrophilic infiltrate as measured by the (MPO) assay. Because Dex will inhibit the production of several cytokines, anti-TNF antiserum was given to mice at the same time as the LPS challenge to assess specifically the role of TNF in inducing these changes. This antiserum partially blocked the pulmonary neutrophil infiltrate, and completely blocked the peripheral blood changes at one hour after LPS. These data demonstrate that TNF plays an important role in the early pathophysiologic alterations that occur after systemic exposure to LPS.
On the complexity of modulo-q arguments and the chevalley-warning theorem

We study the search problem class PPAq defined as a modulo-q analog of the well-known polynomial parity argument class PPA introduced by Papadimitriou (JCSS 1994). Our first result shows that this class can be characterized in terms of PPAp for prime p. Our main result is to establish that an explicit version of a search problem associated to the Chevalley-Warning theorem is complete for PPAp for prime p. This problem is natural in that it does not explicitly involve circuits as part of the input. It is the first such complete problem for PPAp when p ≥ 3. Finally we discuss connections between Chevalley-Warning theorem and the well-studied short integer solution problem and survey the structural properties of PPAq.
Algebraic and Euclidean Lattices: Optimal Lattice Reduction and Beyond

We introduce a framework generalizing lattice reduction algorithms to module lattices in order to practically and efficiently solve the $\gamma$-Hermite Module-SVP problem over arbitrary cyclotomic fields. The core idea is to exploit the structure of the subfields for designing a doubly-recursive strategy of reduction: both recursive in the rank of the module and in the field we are working in. Besides, we demonstrate how to leverage the inherent symplectic geometry existing in the tower of fields to provide a significant speed-up of the reduction for rank two modules. The recursive strategy over the rank can also be applied to the reduction of Euclidean lattices, and we can perform a reduction in asymptotically almost the same time as matrix multiplication. As a byproduct of the design of these fast reductions, we also generalize to all cyclotomic fields and provide speedups for many previous number theoretical algorithms. Quantitatively, we show that a module of rank 2 over a cyclotomic field of degree $n$ can be heuristically reduced within approximation factor $2^{\tilde{O}(n)}$ in time $\tilde{O}(n^2B)$, where $B$ is the bitlength of the entries. For $B$ large enough, this complexity shrinks to $\tilde{O}(n^{\log_2 3}B)$. This last result is particularly striking as it goes below the estimate of $n^2B$ swaps given by the classical analysis of the LLL algorithm using the so-called potential.
Active-learning-based efficient prediction of ab-initio atomic energy: a case study on a Fe random grain boundary model with millions of atoms

We have developed a method that can analyze large random grain boundary (GB) models with the accuracy of density functional theory (DFT) calculations using active learning. It is assumed that the atomic energy is represented by the linear regression of the atomic structural descriptor. The atomic energy is obtained through DFT calculations using a small cell extracted from a huge GB model, called replica DFT atomic energy. The uncertainty reduction (UR) approach in active learning is used to efficiently collect the training data for the atomic energy. In this approach, atomic energy is not required to search for candidate points; therefore, sequential DFT calculations are not required. This approach is suitable for massively parallel computers that can execute a large number of jobs simultaneously. In this study, we demonstrate the prediction of the atomic energy of a Fe random GB model containing one million atoms using the UR approach and show that the prediction error decreases more rapidly compared with random sampling. We conclude that the UR approach with replica DFT atomic energy is useful for modeling huge GBs and will be essential for modeling other structural defects.
Comprehensive Soccer Video Understanding: Towards Human-comparable Video Understanding System in Constrained Environment

Comprehensive video understanding, a challenging task in computer vision to understand videos like humans, has been explored in ways including object detection and tracking, action classification. However, most works for video understanding mainly focus on isolated aspects of video analysis, yet ignore the inner correlation among those tasks. Sports games videos can serve as a perfect research object with restrictive conditions, while complex and challenging enough to study the core problems in computer vision comprehensively. In this paper, we propose a new soccer video database named SoccerDB with the benchmark of object detection, action recognition, temporal action detection, and highlight detection. We further survey a collection of strong baselines on SoccerDB, which have demonstrated state-of-the-art performance on each independent task in recent years. We believe that the release of SoccerDB will tremendously advance researches of combining different tasks in closed form around the comprehensive video understanding problem.
Towards Latent Space Optimality for Auto-Encoder Based Generative Models

The field of neural generative models is dominated by the highly successful Generative Adversarial Networks (GANs) despite their challenges, such as training instability and mode collapse. Auto-Encoders (AE) with regularized latent space provides an alternative framework for generative models, albeit their performance levels have not reached that of GANs. In this work, we identify one of the causes for the under-performance of AE-based models and propose a remedial measure. Specifically, we hypothesize that the dimensionality of the AE model's latent space has a critical effect on the quality of the generated data. Under the assumption that nature generates data by sampling from a "true" generative latent space followed by a deterministic non-linearity, we show that the optimal performance is obtained when the dimensionality of the latent space of the AE-model matches with that of the "true" generative latent space. Further, we propose an algorithm called the Latent Masked Generative Auto-Encoder (LMGAE), in which the dimensionality of the model's latent space is brought closer to that of the "true" generative latent space, via a novel procedure to mask the spurious latent dimensions. We demonstrate through experiments on synthetic and several real-world datasets that the proposed formulation yields generation quality that is better than the state-of-the-art AE-based generative models and is comparable to that of GANs.
Adaptive Dynamic Model Averaging with an Application to House Price Forecasting

Dynamic model averaging (DMA) combines the forecasts of a large number of dynamic linear models (DLMs) to predict the future value of a time series. The performance of DMA critically depends on the appropriate choice of two forgetting factors. The first of these controls the speed of adaptation of the coefficient vector of each DLM, while the second enables time variation in the model averaging stage. In this paper we develop a novel, adaptive dynamic model averaging (ADMA) methodology. The proposed methodology employs a stochastic optimisation algorithm that sequentially updates the forgetting factor of each DLM, and uses a state-of-the-art non-parametric model combination algorithm from the prediction with expert advice literature, which offers finite-time performance guarantees. An empirical application to quarterly UK house price data suggests that ADMA produces more accurate forecasts than the benchmark autoregressive model, as well as competing DMA specifications.
Solvability and optimization for a class of mixed variational problems

ABSTRACT We consider an abstract mixed variational problem governed by a nonlinear operator A and a bifunctional J, in a real reflexive Banach space X. The operator A is assumed to be continuous, Lipschitz continuous on each bounded subset of X, and generalized monotone. First, we pay attention to the unique solvability of the problem. Next, we prove a continuous dependence result of the solution with respect to the data. Based on this result, we prove the existence of at least one solution for an associated optimization problem. Finally, we apply our abstract results to the well-posedness and the optimization of an antiplane frictional contact model for nonlinearly elastic materials of Hencky type.
Accurate Entrance Position Detection Based on Wi-Fi and GPS Signals Using Machine Learning

This paper aims at detecting an accurate position of the main entrance of the buildings. The proposed approach relies on the fact that the GPS signals drop significantly when the user enters a building. Moreover, as most of the public buildings provide Wi-Fi services, the Wi-Fi received signal strength (RSS) can be utilized in order to detect the entrance of the buildings. The rationale behind this paper is that the GPS signals decrease as the user gets close to the main entrance and the Wi-Fi signal increases as the user approaches the main entrance. Several real experiments have been conducted in order to guarantee the feasibility of the proposed approach. The experiment results have shown an interesting result and the accuracy of the whole system was one meter
Arithmetic addition of two integers by deep image classification networks: experiments to quantify their autonomous reasoning ability

The unprecedented performance achieved by deep convolutional neural networks for image classification is linked primarily to their ability of capturing rich structural features at various layers within networks. Here we design a series of experiments, inspired by children's learning of the arithmetic addition of two integers, to showcase that such deep networks can go beyond the structural features to learn deeper knowledge. In our experiments, a set of images is constructed, each image containing an arithmetic addition $n+m$ in its central area, and several classification networks are then trained over a subset of images, using the sum as the label. Tests on the excluded images show that, as the image set gets larger, the networks have well learnt the law of arithmetic additions so as to build up their autonomous reasoning ability strongly. For instance, networks trained over a small percentage of images can classify a big majority of the remaining images correctly, and many arithmetic additions involving some integers that have never been seen during the training can also be solved correctly by the trained networks.
Inception Architecture and Residual Connections in Classification of Breast Cancer Histology Images

This paper presents results of applying Inception v4 deep convolutional neural network to ICIAR-2018 Breast Cancer Classification Grand Challenge, part a. The Challenge task is to classify breast cancer biopsy results, presented in form of hematoxylin and eosin stained images. Breast cancer classification is of primary interest to the medical practitioners and thus binary classification of breast cancer images have been under investigation by many researchers, but multi-class categorization of histology breast images have been challenging due to the subtle differences among the categories. In this work extensive data augmentation is conducted to reduce overfitting and effectiveness of committee of several Inception v4 networks is studied. We report 89% accuracy on 4 class classification task and 93.7% on carcinoma/non-carcinoma two class classification task using our test set of 80 images.
On Certifying Robust Models by Polyhedral Envelope

Certifying neural networks enables one to offer guarantees on a model’s robustness. In this work, we use linear approximation to obtain an upper and lower bound of the model’s output when the input data is perturbed within a predefined adversarial budget. This allows us to bound the adversary-free region in the data neighborhood by a polyhedral envelope, and calculate robustness guarantees based on this geometric approximation. Compared with existing methods, our approach gives a finer-grain quantitative evaluation of a model’s robustness. Therefore, the certification method can not only obtain better certified bounds than the state-of-the-art techniques given the same adversarial budget but also derives a faster search scheme for optimal adversarial budget. Furthermore, we introduce a simple regularization scheme based on our method that enables us to effectively train robust models.
Cosmology and Dark Matter

Cosmology and astroparticle physics give strongest possible evidence for the incompleteness of the Standard Model of particle physics. Leaving aside misterious dark energy, which may or may not be just the cosmological constant, two properties of the Universe cannot be explained by the Standard Model: dark matter and matter-antimatter asymmtery. Dark matter particles may well be discovered in foreseeable future; this issue is under intense experimental investigation. Theoretical hypotheses on the nature of the dark matter particles are numerous, so we concentrate on several well motivated candidates, such as WIMPs, axions and sterile neutrinos, and also give examples of less motivated and more elusive candidates such as fuzzy dark matter. This gives an idea of the spectrum of conceivable dark matter candidates, while certainly not exhausting it. We then consider the matter-antimatter asymmetry and discuss whether it may result from physics at 100~GeV -- TeV scale. Finally, we turn to the earliest epoch of the cosmological evolution. Although the latter topic does not appear immediately related to contemporary particle physics, it is of great interest due to its fundamental nature. We emphasize that the cosmological data, notably, on CMB anisotropies, unequivocally show that the well understood hot stage was not the earliest one. The best guess for the earlier stage is inflation, which is consistent with everything known to date; however, there are alternative scenarios. We discuss the ways to study the earliest epoch, with emphasis on future cosmological observations.
$\widehat{\mathfrak{sl}}(n)_N$ WZW conformal blocks from $SU(N)$ instanton partition functions on ${\mathbb {C}}^2/{\mathbb {Z}}_n$

Generalizations of the AGT correspondence between 4D $\mathcal{N}=2$ $SU(2)$ supersymmetric gauge theory on ${\mathbb {C}}^2$ with $\Omega$-deformation and 2D Liouville conformal field theory include a correspondence between 4D $\mathcal{N}=2$ $SU(N)$ supersymmetric gauge theories, $N = 2, 3, \ldots$, on ${\mathbb {C}}^2/{\mathbb {Z}}_n$, $n = 2, 3, \ldots$, with $\Omega$-deformation and 2D conformal field theories with $\mathcal{W}^{\ para}_{N, n}$ ($n$-th parafermion $\mathcal{W}_N$) symmetry and $\widehat{\mathfrak{sl}}(n)_N$ symmetry. In this work, we trivialize the factor with $\mathcal{W}^{\ para}_{N, n}$ symmetry in the 4D $SU(N)$ instanton partition functions on ${\mathbb {C}}^2/{\mathbb {Z}}_n$ (by using specific choices of parameters and imposing specific conditions on the $N$-tuples of Young diagrams that label the states), and extract the 2D $\widehat{\mathfrak{sl}}(n)_N$ WZW conformal blocks, $n = 2, 3, \ldots$, $N = 1, 2, \ldots\, . $
Landau Levels of Topologically-Protected Surface States Probed by Dual-Gated Quantum Capacitance.

Spectroscopy of discrete Landau levels (LLs) in bulk-insulating three-dimensional topological insulators (3D TIs) in perpendicular magnetic field characterizes the Dirac nature of their surface states. Despite a number of studies demonstrating the quantum Hall effect (QHE) of topological surface states, quantitative evaluation of the LL energies, which serve as fundamental electronic quantities for study of the quantum states, is still limited. In this work, we explore the density of states of LLs by measuring quantum capacitance (CQ) in a truly bulk insulating 3D TI via a van der Waals heterostructure configuration. By applying dual gate voltages, we access the individual surface states' LLs and extract their chemical potentials to quantify the LL spacings of each surface. We evaluate the LLs' energies at two distinguished QH states, namely dissipationless (ν= ±1) and dissipative (ν= 0) states in the 3D TI.
libmolgrid: GPU Accelerated Molecular Gridding for Deep Learning Applications

There are many ways to represent a molecule as input to a machine learning model and each is associated with loss and retention of certain kinds of information. In the interest of preserving three-dimensional spatial information, including bond angles and torsions, we have developed libmolgrid, a general-purpose library for representing three-dimensional molecules using multidimensional arrays. This library also provides functionality for composing batches of data suited to machine learning workflows, including data augmentation, class balancing, and example stratification according to a regression variable or data subgroup, and it further supports temporal and spatial recurrences over that data to facilitate work with recurrent neural networks, dynamical data, and size extensive modeling. It was designed for seamless integration with popular deep learning frameworks, including Caffe, PyTorch, and Keras, providing good performance by leveraging graphical processing units (GPUs) for computationally-intensive tasks and efficient memory usage through the use of memory views over preallocated buffers. libmolgrid is a free and open source project that is actively supported, serving the growing need in the molecular modeling community for tools that streamline the process of data ingestion, representation construction, and principled machine learning model development.
On algebraic Stein operators for Gaussian polynomials

The first essential ingredient to build up Stein's method for a continuous target distribution is to identify a so-called \textit{Stein operator}, namely a linear differential operator with polynomial coefficients. In this paper, we introduce the notion of \textit{algebraic} Stein operators (see Definition \ref{def:algebraic-Stein-Operator}), and provide a novel algebraic method to find \emph{all} the algebraic Stein operators up to a given order and polynomial degree for a target random variable of the form $Y=h(X)$, where $X=(X_1,\dots, X_d)$ has i.i. d$. $ standard Gaussian components and $h\in \KK[X]$ is a polynomial with coefficients in the ring $\KK$. Our approach links the existence of an algebraic Stein operator with \textit{null controllability} of a certain linear discrete system. A \texttt{MATLAB} code checks the null controllability up to a given finite time $T$ (the order of the differential operator), and provides all \textit{null control} sequences (polynomial coefficients of the differential operator) up to a given maximum degree $m$. This is the first paper that connects Stein's method with computational algebra to find Stein operators for highly complex probability distributions, such as $H_{20}(X_1)$, where $H_p$ is the $p$-th Hermite polynomial. A number of examples of Stein operators for $H_p(X_1)$, $p=3,4,5,6,7,8,10,12$, are gathered in the extended Appendix of this arXiv version. We also introduce a widely applicable approach to proving that Stein operators characterise the target distribution, and use it to prove, amongst other examples, that the Stein operators for $H_p(X_1)$, $p=3,\ldots,8$, with minimum possible maximal polynomial degree $m$ characterise their target distribution.
Viscometric flow of dense granular materials under controlled pressure and shear stress

Abstract This study examines the flow of dense granular materials under external shear stress and pressure using discrete element method simulations. In this method, the material is allowed to strain along all periodic directions and adapt its solid volume fraction in response to an imbalance between the internal state of stress and the external applied stress. By systematically varying the external shear stress and pressure, the steady rheological response is simulated for: (1) rate-independent quasi-static flow; and (2) rate-dependent inertial flow. The simulated flow is viscometric with non-negligible first and second normal stress differences. While both normal stress differences are negative in inertial flows, the first normal stress difference switches from negative to slightly positive, and second normal stress difference tends to zero in quasi-static flows. The first normal stress difference emerges from a lack of coaxiality between a second-rank contact fabric tensor and strain rate tensor in the flow plane, while the second normal stress difference is linked to an excess of contacts in the shear plane compared with the vorticity direction. A general rheological model of second order (in terms of strain rate tensor) is proposed to describe the two types of flow, and the model is calibrated for various values of interparticle friction from simulations on nearly monodisperse spheres. The model incorporates normal stress differences in both regimes of flow and provides a complete viscometric description of steady dense granular flows.
A Study of the Blue Straggler Population of the Old Open Cluster Collinder 261

Blue stragglers (BSs) are stars located in an unexpected region of the color–magnitude diagram (CMD) of a stellar population, as they appear bluer and more luminous than the stars in the turn-off region. They are ubiquitous, since they have been found among Milky Way field stars, in open and globular clusters, and also in other galaxies of the Local Group. Here we present a study on the BS population of the old and metal-rich open cluster Collinder 261, based on Gaia DR2 data and on a multi-epoch radial velocity survey conducted with Fibre Large Array Multi Element Spectrograph (FLAMES) at the Very Large Telescope (VLT). We also analyze the radial distribution of the BS population to probe the dynamical status of the cluster. BS candidates were identified first with Gaia DR2, according to their position on the CMD, proper motions, and parallaxes. Their radial distribution was compared with those of main sequence, red giant, and red clump stars, to evaluate mass segregation. Additionally, their radial velocities (and the associated uncertainties) were compared with the mean radial velocity and velocity dispersion of the cluster. When possible, close binaries and long-period binaries were also identified, based on the radial velocity variations for the different epochs. We also looked for yellow stragglers, i.e., possible evolved BSs. We found 53 BS members of Collinder 261, six of them were already identified in previous catalogs. Among the BS candidates with radial velocity measurements, we found one long-period binary, five close-binary systems, three nonvariable stars; we also identified one yellow straggler.