Miracle-dz/UniFAR-FTUs huggingface dataset:
Weak formulation and scaling properties of energy fluxes in three-dimensional numerical turbulent Rayleigh–Bénard convection

We apply the weak formalism on the Boussinesq equations to characterize scaling properties of the mean and the standard deviation of the potential, kinetic and viscous energy fluxes in very well-resolved numerical simulations. The local Bolgiano–Oboukhov (BO) length is investigated and it is found that its value may change by an order of magnitude through the domain, in agreement with previous results. We then investigate the scale-by-scale averaged terms of the weak equations, which are a generalization of the Kármán–Howarth–Monin and Yaglom equations. We have not found the classical BO picture, but evidence of a mixture of BO and Kolmogorov scalings. In particular, all the energy fluxes are compatible with a BO local Hölder exponent for the temperature and a Kolmogorov 41 for the velocity. This behaviour may be related to anisotropy and to the strong heterogeneity of the convective flow, reflected in the wide distribution of BO local scales. The scale-by-scale analysis allows us also to compare the theoretical BO length computed from its definition with that empirically extracted through scalings obtained from weak analysis. Scalings are observed, but over a limited range. The key result of the work is to show that the analysis of local weak formulation of the problem is powerful to characterize the fluctuation properties.
Contact Inertial Odometry: Collisions are your Friend

Autonomous exploration of unknown environments with aerial vehicles remains a challenge, especially in perceptually degraded conditions. Dust, fog, or a lack of visual or LiDAR-based features results in severe difficulties for state estimation algorithms, which failure can be catastrophic. In this work, we show that it is indeed possible to navigate in such conditions without any exteroceptive sensing by exploiting collisions instead of treating them as constraints. To this end, we present a novel contact-based inertial odometry (CIO) algorithm: it uses estimated external forces with the environment to detect collisions and generate pseudo-measurements of the robot velocity, enabling autonomous flight. To fully exploit this method, we first perform modeling of a hybrid ground and aerial vehicle which can withstand collisions at moderate speeds, for which we develop an external wrench estimation algorithm. Then, we present our CIO algorithm and develop a reactive planner and control law which encourage exploration by bouncing off obstacles. All components of this framework are validated in hardware experiments and we demonstrate that a quadrotor can traverse a cluttered environment using an IMU only. This work can be used on drones to recover from visual inertial odometry failure or on micro-drones that do not have the payload capacity to carry cameras, LiDARs or powerful computers.
New Stick Number Bounds from Random Sampling of Confined Polygons

Abstract The stick number of a knot is the minimum number of segments needed to build a polygonal version of the knot. Despite its elementary definition and relevance to physical knots, the stick number is poorly understood: for most knots we only know bounds on the stick number. We adopt a Monte Carlo approach to finding better bounds, producing very large ensembles of random polygons in tight confinement to look for new examples of knots constructed from few segments. We generated a total of 220 billion random polygons, yielding either the exact stick number or an improved upper bound for more than 40% of the knots with 10 or fewer crossings for which the stick number was not previously known. We summarize the current state of the art in Appendix A, which gives the best known bounds on stick number for all knots up to 10 crossings.
Combined Task and Motion Planning for a Dual-arm Robot to Use a Suction Cup Tool

This paper proposes a combined task and motion planner for a dual-arm robot to use a suction cup tool. The planner consists of three sub-planners - A suction pose sub-planner and two regrasp and motion sub-planners. The suction pose sub-planner finds all the available poses for a suction cup tool to suck on the object, using the models of the tool and the object. The regrasp and motion sub-planner builds the regrasp graph that represents all possible grasp sequences to reorient and move the suction cup tool from an initial pose to a goal pose. Two regrasp graphs are used to plan for a single suction cup and the complex of the suction cup and an object respectively. The output of the proposed planner is a sequence of robot motion that uses a suction cup tool to manipulate objects following human instructions. The planner is examined and analyzed by both simulation experiments and real-world executions using several real-world tasks. The results show that the planner is efficient, robust, and can generate sequential transit and transfer robot motion to finish complicated combined task and motion planning tasks in a few seconds.
Explicit Cross-lingual Pre-training for Unsupervised Machine Translation

Pre-training has proven to be effective in unsupervised machine translation due to its ability to model deep context information in cross-lingual scenarios. However, the cross-lingual information obtained from shared BPE spaces is inexplicit and limited. In this paper, we propose a novel cross-lingual pre-training method for unsupervised machine translation by incorporating explicit cross-lingual training signals. Specifically, we first calculate cross-lingual n-gram embeddings and infer an n-gram translation table from them. With those n-gram translation pairs, we propose a new pre-training model called Cross-lingual Masked Language Model (CMLM), which randomly chooses source n-grams in the input text stream and predicts their translation candidates at each time step. Experiments show that our method can incorporate beneficial cross-lingual information into pre-trained models. Taking pre-trained CMLM models as the encoder and decoder, we significantly improve the performance of unsupervised machine translation.
Fast computation of kernel statistics using genotype value decomposition

Because of the recent advances of genome sequences, a large number of human genome sequences are available for the study of human genetics. Genome-wide association studies typically focus on associations between single-nucleotide polymorphisms and traits such as major human diseases. However, the statistical power of classical single-marker association analysis for rare variants is limited. To address the challenge, rare and low-frequency variants are often grouped into a gene or pathway level, and the effects of multiple variants evaluated based on collapsing methods. The sequential kernel association test (SKAT) is one of the most effective collapsing methods. SKAT utilizes the kernel matrix. The size of the kernel matrix is O(n^2), where the sample size is n, so that the calculation of the data using the kernel method requires a long time. As the sample sizes of human genetic studies increase, the computational time is getting more and more problematic. In the present paper, the genotype value decomposition method is proposed for the handling the sequential kernel in a short period of time. The method can be referred to as genotype value decomposition. In the present paper, it is shown that the genetic relationship matrix and Identity by State (IBS) matrix can be obtained using the genotype value vectors. By using this method, the SKAT can be conducted with time complexity O(n). The proposed method enables to conduct SKAT for samples of human genetics.
Nonlinear dynamics of weakly dissipative optomechanical systems

Optomechanical systems attract a lot of attention because they provide a novel platform for quantum measurements, transduction, hybrid systems, and fundamental studies of quantum physics. Their classical nonlinear dynamics is surprisingly rich and so far remains underexplored. Works devoted to this subject have typically focussed on dissipation constants which are substantially larger than those encountered in current experiments, such that the nonlinear dynamics of weakly dissipative optomechanical systems is almost uncharted waters. In this work, we fill this gap and investigate the regular and chaotic dynamics in this important regime. To analyze the dynamical attractors, we have extended the ‘generalized alignment index’ method to dissipative systems. We show that, even when chaotic motion is absent, the dynamics in the weakly dissipative regime is extremely sensitive to initial conditions. We argue that reducing dissipation allows chaotic dynamics to appear at a substantially smaller driving strength and enables various routes to chaos. We identify three generic features in weakly dissipative classical optomechanical nonlinear dynamics: the Neimark–Sacker bifurcation between limit cycles and limit tori (leading to a comb of sidebands in the spectrum), the quasiperiodic route to chaos, and the existence of transient chaos.
Algorithms for Optimal Diverse Matching

Bipartite b-matching, where agents on one side of a market are matched to one or more agents or items on the other, is a classical model that is used in myriad application areas such as healthcare, advertising, education, and general resource allocation. Traditionally, the primary goal of such models is to maximize a linear function of the constituent matches (e.g., linear social welfare maximization) subject to some constraints. Recent work has studied a new goal of balancing whole-match diversity and economic efficiency, where the objective is instead a monotone submodular function over the matching. These more general models are largely NP-hard. In this work, we develop a combinatorial algorithm that constructs provably-optimal diverse b-matchings in pseudo-polynomial time. Then, we show how to extend our algorithm to solve new variations of the diverse b-matching problem. We then compare directly, on real-world datasets, against the state-of-the-art, quadratic-programming-based approach to solving diverse b-matching problems and show that our method outperforms it in both speed and (anytime) solution quality.
Extending tamely ramified strict 1-motives into két log 1-motives

Abstract We define két abelian schemes, két 1-motives and két log 1-motives and formulate duality theory for these objects. Then we show that tamely ramified strict 1-motives over a discrete valuation field can be extended uniquely to két log 1-motives over the corresponding discrete valuation ring. As an application, we present a proof to a result of Kato stated in [12, §4.3] without proof. To a tamely ramified strict 1-motive over a discrete valuation field, we associate a monodromy pairing and compare it with Raynaud’s geometric monodromy.
Object Viewpoint Classification Based 3D Bounding Box Estimation for Autonomous Vehicles

3D object detection is one of the most important tasks for the perception systems of autonomous vehicles. With the significant success in the field of 2D object detection, several monocular image based 3D object detection algorithms have been proposed based on advanced 2D object detectors and the geometric constraints between the 2D and 3D bounding boxes. In this paper, we propose a novel method for determining the configuration of the 2D-3D geometric constraints which is based on the well-known 2D-3D two stage object detection framework. First, we discrete viewpoints in which the camera shots the object into 16 categories with respect to the observation relationship between camera and objects. Second, we design a viewpoint classifier by integrated a new sub-branch into the existing multi-branches CNN. Then, the configuration of geometric constraint between the 2D and 3D bounding boxes can be determined according to the output of this classifier. Extensive experiments on the KITTI dataset show that, our method not only improves the computational efficiency, but also increases the overall precision of the model, especially to the orientation angle estimation.
Deep weakly-supervised learning methods for classification and localization in histology images: a survey

Using state-of-the-art deep learning (DL) models to diagnose cancer from histology data presents several challenges related to the nature and availability of labeled histology images, including image size, stain variations, and label ambiguity. In addition, cancer grading and the localization of regions of interest (ROIs) in such images normally rely on both image- and pixel-level labels, with the latter requiring a costly annotation process. Deep weakly-supervised object localization (WSOL) methods provide different strategies for low-cost training of DL models. Given only image-class annotations, these methods can be trained to simultaneously classify an image, and yield class activation maps (CAMs) for ROI localization. This paper provides a review of deep WSOL methods to identify and locate diseases in histology images, without the need for pixel-level annotations. We propose a taxonomy in which these methods are divided into bottom-up and top-down methods according to the information flow in models. Although the latter have seen only limited progress, recent bottom-up methods are currently driving a lot of progress with the use of deep WSOL methods. Early works focused on designing different spatial pooling functions. However, those methods quickly peaked in term of localization accuracy and revealed a major limitation, namely, – the under-activation of CAMs, which leads to high false negative localization. Subsequent works aimed to alleviate this shortcoming and recover the complete object from the background, using different techniques such as perturbation, self-attention, shallow features, pseudo-annotation, and task decoupling.In the present paper, representative deep WSOL methods from our taxonomy are also evaluated and compared in terms of classification and localization accuracy using two challenging public histology datasets – one for colon cancer (GlaS), and a second, for breast cancer (CAMELYON16). Overall, the results indicate poor localization performance, particularly for generic methods that were initially designed to process natural images. Methods designed to address the challenges posed by histology data often use priors such as ROI size, or additional pixel-wise supervision estimated from a pre-trained classifier, allowing them to achieve better results. However, all the methods suffer from high false positive/negative localization. Classification performance is mainly affected by the model selection process, which uses either the classification or the localization metric. Finally, four key challenges are identified in the application of deep WSOL methods in histology, namely, – under-/over-activation of CAMs, sensitivity to thresholding, and model selection – and research avenues are provided to mitigate them. Our code is publicly available at https://github.com/jeromerony/survey_wsl_histology
3DSiameseNet to Analyze Brain MRI

Prediction of the cognitive evolution of a person susceptible to develop a neurodegenerative disorder is crucial to provide an appropriate treatment as soon as possible. In this paper we propose a 3D siamese network designed to extract features from whole-brain 3D MRI images. We show that it is possible to extract meaningful features using convolution layers, reducing the need of classical image processing operations such as segmentation or pre-computing features such as cortical thickness. To lead this study we used the Alzheimer's Disease Neuroimaging Initiative (ADNI), a public data base of 3D MRI brain images. A set of 247 subjects has been extracted, all of the subjects having 2 images in a range of 12 months. In order to measure the evolution of the patients states we have compared these 2 images. Our work has been inspired at the beginning by an article of Bhagwat et al. in 2018, who have proposed a siamese network to predict the status of patients but without any convolutional layers and reducing the MRI images to a vector of features extracted from predefined ROIs. We show that our network achieves an accuracy of 90\% in the classification of cognitively declining VS stable patients. This result has been obtained without the help of a cognitive score and with a small number of patients comparing to the current datasets size claimed in deep learning domain.
Standards der Rehabilitation von entzündlich-rheumatischen und vergesellschafteten Erkrankungen

P . b . b . G Z 0 2 Z 0 3 1 1 0 8 M , V e r l a g s p o s t a m t : 3 0 0 2 P u r k e r s d o r f , E r s c h e i n u n g s o r t : 3 0 0 3 G a b l i t z Indexed in SCOPUS/EMBASE/Excerpta Medica www.kup. at/mineralstoffwechsel Österreichische Gesellschaft für Orthopädie und Orthopädische Chirurgie Österreichische Gesellschaft für Rheumatologie Offizielles Organ der Österreichischen Gesellschaft zur Erforschung des Knochens und Mineralstoffwechsels Member of the Standards der Rehabilitation von
Leray turbulence: what can we learn from acceleration compared to velocity

In a recent paper we presented evidence for the occurence of Leray-like singularities with positive Sedov-Taylor exponent $\alpha$ in turbulent flows recorded in Modane's wind tunnel, by looking at simultaneous acceleration and velocity records. Here we use another tool which allows to get other informations on the dynamics of turbulent bursts. We compare the structure functions for velocity and acceleration in the same turbulent flows. This shows the possible contribution of other types of self-similar solutions because this new study shows that statistics is seemingly dominated by singularities with small positive or even negative values of the exponent $\alpha$, that corresponds to "weakly singular" solutions with singular acceleration, and regular velocity. We present several reasons explaining that the exponent $\alpha$ derived from the structure functions curves, may look to be negative.
HeteSpaceyWalk: A Heterogeneous Spacey Random Walk for Heterogeneous Information Network Embedding

Heterogeneous information network (HIN) embedding has gained increasing interests recently. However, the current way of random-walk based HIN embedding methods have paid few attention to the higher-order Markov chain nature of meta-path guided random walks, especially to the stationarity issue. In this paper, we systematically formalize the meta-path guided random walk as a higher-order Markov chain process,and present a heterogeneous personalized spacey random walk to efficiently and effectively attain the expected stationary distribution among nodes. Then we propose a generalized scalable framework to leverage the heterogeneous personalized spacey random walk to learn embeddings for multiple types of nodes in an HIN guided by a meta-path, a meta-graph, and a meta-schema respectively. We conduct extensive experiments in several heterogeneous networks and demonstrate that our methods substantially outperform the existing state-of-the-art network embedding algorithms.
Dynamic Programming for Sequential Deterministic Quantization of Discrete Memoryless Channels

In this article, under a general cost function <inline-formula> <tex-math notation="LaTeX">$C$ </tex-math></inline-formula>, we present a dynamic programming (DP) method to obtain an optimal sequential deterministic quantizer (SDQ) for <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula>-ary input discrete memoryless channel (DMC). The DP method has complexity <inline-formula> <tex-math notation="LaTeX">$O(q (N-M)^{2}\,\,M)$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> are the alphabet sizes of the DMC output and quantizer output, respectively. Then, starting from the quadrangle inequality, two techniques are applied to reduce the DP method’s complexity. One technique makes use of the Shor-Moran-Aggarwal-Wilber-Klawe (SMAWK) algorithm and achieves complexity <inline-formula> <tex-math notation="LaTeX">$O(q (N-M) M)$ </tex-math></inline-formula>. The other technique is much easier to be implemented and achieves complexity <inline-formula> <tex-math notation="LaTeX">$O(q (N^{2} - M^{2}))$ </tex-math></inline-formula>. We further derive a sufficient condition under which the optimal SDQ is optimal among all quantizers and the two techniques are applicable. This generalizes the results in the literature for binary-input DMC. Next, we show that the cost function of <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-mutual information (<inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-MI)-maximizing quantizer belongs to the category of <inline-formula> <tex-math notation="LaTeX">$C$ </tex-math></inline-formula>. We further prove that under a weaker condition than the sufficient condition we derived, the aforementioned two techniques are applicable to the design of <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-MI-maximizing quantizer. Finally, we illustrate the particular application of our design method to practical pulse-amplitude modulation systems.