Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Surrogate for Direct Time Fluid Dynamics

The ubiquity of fluids in the physical world explains the need to accurately simulate their dynamics for many scientific and engineering applications. Traditionally, well established but resource intensive CFD solvers provide such simulations. The recent years have seen a surge of deep learning surrogate models substituting these solvers to alleviate the simulation process. Some approaches to build data-driven surrogates mimic the solver iterative process. They infer the next state of the fluid given its previous one. Others directly infer the state from time input. Approaches also differ in their management of the spatial information. Graph Neural Networks (GNN) can address the specificity of the irregular meshes commonly used in CFD simulations. In this article, we present our ongoing work to design a novel direct time GNN architecture for irregular meshes. It consists of a succession of graphs of increasing size connected by spline convolutions. We test our architecture on the Von K{\'a}rm{\'a}n's vortex street benchmark. It achieves small generalization errors while mitigating error accumulation along the trajectory.
Random walks in cones revisited

In this paper we continue our study of a multidimensional random walk with zero mean and finite variance killed on leaving a cone. We suggest a new approach that allows one to construct a positive harmonic function in Lipschitz cones under minimal moment conditions. This approach allows one also to obtain more accurate information about the behaviour of the harmonic function not far from the boundary of the cone. We also prove limit theorems under new moment conditions.
Incompatibility in restricted operational theories: connecting contextuality and steering

We investigate the connection between steering and contextuality in general probabilistic theories. We show that for a class of bipartite states the steerability of the state by given set of measurements is equivalent to non-existence of preparation noncontextual hidden variable model for certain restricted theory constructed from the given state and measurements. The connection between steering and contextuality is provided by the concept of incompatibility in restricted theories, which we also investigate.
Quantum Lagrangian of the Hořava theory and its nonlocalities

We perform the BFV quantization of the 2 + 1 projectable and the 3 + 1 nonprojectable versions of the Hořava theory. This is a Hamiltonian formalism, and noncanonical gauges can be used with it. In the projectable case, we show that the integration on canonical momenta reproduces the quantum Lagrangian known from the proof of renormalization of Barvinsky et al. This quantum Lagrangian is nonlocal, its nonlocality originally arose as a consequence of getting regular propagators. The matching of the BFV quantization with the quantum Lagrangian reinforces the program of quantization of the Hořava theory. We introduce a local gauge-fixing condition, hence a local Hamiltonian, that leads to the nonlocality of the Lagrangian after the integration. For the case of the nonprojectable theory, this procedure allows us to obtain the complete (nonlocal) quantum Lagrangian that takes into account the second-class contraints. We compare with the integration in general relativity, making clear the relationship between the underlying anisotropic symmetry of the Hořava theory and the nonlocality of its quantum Lagrangian.
Primordial black holes and scalar induced gravitational waves from the $E$ model with a Gauss-Bonnet term

We study an inflationary $E$ model with the Gauss-Bonnet coupling, which can enhance the curvature perturbation at small scales and thus produce a significant abundance of primordial black holes (PBHs) and detectable scalar induced gravitational waves (SIGWs). PBHs from the $E$ model with mass $30M_{\odot}$, $10^{-5}M_{\odot}$, and $10^{-12}M_{\odot}$ can explain the LIGO-Virgo events, the ultrashort-timescale microlensing events in the OGLE data, and all dark matter, respectively. SIGWs produced by the $E$ model can account for the recent NANOGrav signal. We also compute the primordial non-Gaussianity and discuss its impact on PBHs and SIGWs. The probability distribution of density contrast is modified to be right-tailed, which we find prompts the formation of PBHs, so that the abundance of PBHs is underestimated with Gaussian approximation. On the contrary, the fractional energy density of SIGWs is hardly affected.
An evaluation framework for measuring supply chain performance in the public healthcare sector: evidence from the Italian NHS

Innovations in supply chain (SC) management are rapidly spreading in public healthcare sectors across developed countries. In particular, healthcare managers and policymakers view SC innovation as a way to lower costs and improve quality. However, although the related rhetoric is strong, the literature provides little evidence of the benefits and performance of such SC initiatives. Above all, the literature lacks a sound conceptual framework for appraising SC management in public healthcare organisations. We argue that the public nature of healthcare and the specificities of the healthcare context require a specific theoretical approach to identifying the appropriate dimensions of SC initiatives to evaluate. Drawing from an extensive literature review and a longitudinal analysis of an inductive case study – a public hospital network developed in Tuscany (ESTAV), Italy – this work proposes a framework for assessing supply chain performance in the public healthcare sector. In addition, an empirical analysis was conducted to test the soundness and robustness of the framework, and this article presents conclusions regarding its applicability and managerial implications.
Can uncertainty boost the reliability of AI-based diagnostic methods in digital pathology?

Deep learning (DL) has shown great potential in digital pathology applications. The robustness of a diagnostic DL-based solution is essential for safe clinical deployment. In this work we evaluate if adding uncertainty estimates for DL predictions in digital pathology could result in increased value for the clinical applications, by boosting the general predictive performance or by detecting mispredictions. We compare the eﬀectiveness of model-integrated methods (MC dropout and Deep ensembles) with a model-agnostic approach (Test time augmentation, TTA). Moreover, four uncertainty metrics are compared. Our experiments focus on two domain shift scenarios: a shift to a diﬀerent medical center and to an underrepresented subtype of cancer. Our results show that uncertainty estimates can add some reliability and reduce sensitivity to classiﬁcation threshold selection. While advanced metrics and deep ensembles perform best in our comparison, the added value over simpler metrics and TTA is small. Importantly, the beneﬁt of all evaluated uncertainty estimation methods is diminished by domain shift.
Index of the Transversally Elliptic Complex from N = 2 Localization in Four Dimensions

In this note we present a formula for the equivariant index of the cohomological complex obtained from localization of N = 2 SYM on simply-connected compact fourmanifolds with a T 2-action. When the theory is topologically twisted, the complex is elliptic and its index can be computed in a standard way using the Atiyah-Bott localization formula. Recently, a framework for more general types of twisting, so-called cohomological twisting, was introduced for which the complex turns out only to be transversally elliptic. While the index of such a complex was previously computed for specific manifolds and a systematic procedure for its computation was provided for some cases where the manifold can be lifted to a Sasakian S1-fibration in five dimensions, a general four-dimensional treatment was still lacking. We provide a formal, purely four-dimensional treatment of the cohomological complex, showing that the Laplacian part can be globally split off while the remaining part can be trivialized uniquely in the group-direction. This ultimately produces a simple formula for the index applicable for any compact simply-connected four-manifold, from which one can compute the perturbative partition function. ar X iv :2 11 2. 10 65 8v 2 [ he pth ] 1 M ar 2 02 2
O'TRAIN: A robust and flexible 'real or bogus' classifier for the 
study of the optical transient sky

The scientific interest in studying high-energy transient phenomena in the Universe has largely grown for the last decade. Now, multiple ground-based survey projects have emerged to continuously monitor the optical (and multi-messenger) transient sky at higher image cadences and cover always larger portions of the sky every night. These novel approaches lead to a huge increase of the global alert rates which need to be handled with care especially by keeping the false alarms as low as possible. Therefore, the standard transient detection pipelines previously designed for narrow field of view instruments must now integrate more sophisticated tools to deal with the growing number and diversity of alerts and false alarms. Deep machine learning algorithms have now proven their efficiency in recognizing patterns in images. We explore this method to provide a robust and flexible algorithm that could be included in any kind of transient detection pipeline. We built a Convolutional Neural Network (CNN) algorithm in order to perform a real/bogus classification task on transient candidate cutouts (subtraction residuals) provided by different kinds of optical telescopes. The training involved human-supervised labeling of the cutouts, which had been split in two balanced data sets with \textit{True} and \textit{False} point-like source candidates. We tested our CNN model on the candidates produced by two different transient detection pipelines. We show that our CNN algorithm can be successfully trained on a large diversity of images having very different pixel scales. Tested on optical images from four different telescopes and utilising two different transient detection pipelines, our CNN model provides robust real/bogus classification performance accuracy from 93% up to 98% of well classified candidates.
Support varieties and stable categories for algebraic groups

We consider rational representations of a connected linear algebraic group $\mathbb {G}$ over a field $k$ of positive characteristic $p > 0$. We introduce a natural extension $M \mapsto \Pi (\mathbb {G})_M$ to $\mathbb {G}$-modules of the $\pi$-point support theory for modules $M$ for a finite group scheme $G$ and show that this theory is essentially equivalent to the more ‘intrinsic’ and ‘explicit’ theory $M \mapsto \mathbb {P}\mathfrak{C}(\mathbb {G})_M$ of supports for an algebraic group of exponential type, a theory which uses $1$-parameter subgroups $\mathbb {G}_a \to \mathbb {G}$. We extend our support theory to bounded complexes of $\mathbb {G}$-modules, $C^\bullet \mapsto \Pi (\mathbb {G})_{C^\bullet }$. We introduce the tensor triangulated category $\mathit {StMod}(\mathbb {G})$, the Verdier quotient of the bounded derived category $D^b(\mathit {Mod}(\mathbb {G}))$ by the thick subcategory of mock injective modules. Our support theory satisfies all the ‘standard properties’ for a theory of supports for $\mathit {StMod}(\mathbb {G})$. As an application, we employ $C^\bullet \mapsto \Pi (\mathbb {G})_{C^\bullet }$ to establish the classification of $(r)$-complete, thick tensor ideals of $\mathit {stmod}(\mathbb {G})$ in terms of locally $\mathit {stmod}(\mathbb {G})$-realizable subsets of $\Pi (\mathbb {G})$ and the classification of $(r)$-complete, localizing subcategories of $\mathit {StMod}(\mathbb {G})$ in terms of locally $\mathit {StMod}(\mathbb {G})$-realizable subsets of $\Pi (\mathbb {G})$.
Edge differentially private estimation in the $\beta$-model via jittering and method of moments

A standing challenge in data privacy is the trade-off between the level of privacy and the efficiency of statistical inference. Here we conduct an in-depth study of this trade-off for parameter estimation in the β-model (Chatterjee, Diaconis and Sly, 2011) for edge differentially private network data released via jittering (Karwa, Krivitsky and Slavković, 2017). Unlike most previous approaches based on maximum likelihood estimation for this network model, we proceed via method of moments. This choice facilitates our exploration of a substantially broader range of privacy levels – corresponding to stricter privacy – than has been to date. Over this new range we discover our proposed estimator for the parameters exhibits an interesting phase transition, with both its convergence rate and asymptotic variance following one of three different regimes of behavior depending on the level of privacy. Because identification of the operable regime is difficult to impossible in practice, we devise a novel adaptive bootstrap procedure to construct uniform inference across different phases. In fact, leveraging this bootstrap we are able to provide for simultaneous inference of all parameters in the β-model (i.e., equal to the number of vertices), which would appear to be the first result of its kind. Numerical experiments confirm the competitive and reliable finite sample performance of the proposed inference methods, next to a comparable maximum likelihood method, as well as significant advantages in terms of computational speed and memory.
On the weighted Bojanov-Chebyshev problem and the sum of translates method of Fenton

Minimax and maximin problems are investigated for a special class of functions on the interval $[0,1]$. These functions are sums of translates of positive multiples of one kernel function and a very general external field function. Due to our very general setting the minimax, equioscillation and characterization results obtained extend those of Bojanov, Fenton, Hardin, Kendall, Saff, Ambrus, Ball and Erdélyi. Moreover, we discover a surprising intertwining phenomenon of interval maxima, which provides new information even in the most classical extremal problem of Chebyshev. Bibliography: 25 titles.
Android-COCO: Android Malware Detection with Graph Neural Network for Byte- and Native-Code

With the popularity of Android growing exponentially, the amount of malware has significantly exploded. It is arguably one of the most viral problems on mobile platforms. Recently, various approaches have been introduced to detect Android malware, the majority of these are either based on the Manifest File features or the structural information, such as control flow graph and API calls. Among those methods, nearly all of them only consider the Java byte-code as the target to detect malicious behaviors. However, Recent research and our own statistics show that native payloads are commonly used in both benign and malicious apps. Current state-of-the-art Android static analysis tools avoid handling native method invocation. None of those tools have the capability to capture the inter-language behaviors. In this work, we explore an ensemble mechanism, which presents how the combination of byte-code and native-code analysis of Android applications can be efficiently used to cope with the advanced sophistication of Android malware. We, therefore, present a multi-layer approach that utilizes deep learning, natural language processing (NLP), as well as graph embedding techniques to handle the threats of Android malware, both from the Java byte-code and native code. After that, we design an ensemble algorithm to get the final result of malware detection system. To be specific, the first layer of our detection approach operates on the byte-code of application and the native code level, whereas the second layer focuses on the ensemble algorithm. Large-scale experiments on 100,113 samples (35,113 malware and 65,000 benign) show that only byte-code sub-system yields 99.8% accuracy and native-code sub-system yields an accuracy of 96.6%, whereas the Android-COCO method attains an accuracy of 99.86% which outperforms various related works.
MoCaNet: Motion Retargeting in-the-wild via Canonicalization Networks

We present a novel framework that brings the 3D motion retargeting task from controlled environments to in-the-wild scenarios. In particular, our method is capable of retargeting body motion from a character in a 2D monocular video to a 3D character without using any motion capture system or 3D reconstruction procedure. It is designed to leverage massive online videos for unsupervised training, needless of 3D annotations or motion-body pairing information. The proposed method is built upon two novel canonicalization operations, structure canonicalization and view canonicalization. Trained with the canonicalization operations and the derived regularizations, our method learns to factorize a skeleton sequence into three independent semantic subspaces, i.e., motion, structure, and view angle. The disentangled representation enables motion retargeting from 2D to 3D with high precision. Our method achieves superior performance on motion transfer benchmarks with large body variations and challenging actions. Notably, the canonicalized skeleton sequence could serve as a disentangled and interpretable representation of human motion that benefits action analysis and motion retrieval.
Control of a Hexapod Robot Considering Terrain Interaction

Bio-inspired walking hexapod robots are a relatively young branch in robotics in both state of the art and applications. Despite their high degree of flexibility and adaptability derived by their redundant design, the research field that compliments their abilities is still very lacking. In this paper will be proposed state-of-the-art hexapod robot specific control architecture that allows for full control over robot speed, body orientation and walk gait type to employ. Furthermore terrain interaction will be deeply investigated, leading to the development of a terrain-adapting control algorithm that will allow the robot to react swiftly to terrain shape and asperities such as non-linearities and non-continuity within the workspace. It will be presented a dynamic model derived from the interpretation of the hexapod movement to be comparable to these of the base-platform PKM machines, and said model will be validated through Matlab SimMechanicsTM physics simulation. A feed-back control system able to recognize leg-terrain touch and react accordingly to assure movement stability will then be developed. Finally results coming from an experimental campaign based of the PhantomX AX Metal Hexapod Mark II robotic platform by Trossen RoboticsTM is reported.
Multi-agent Communication with Graph Information Bottleneck under Limited Bandwidth

Recent studies have shown that introducing communication be-tween agents can significantly improve overall performance in cooperative Multi-agent reinforcement learning (MARL). In many real-world scenarios, communication can be expensive and the band-width of the multi-agent system is subject to certain constraints. Redundant messages who occupy the communication resources can block the transmission of informative messages and thus jeop-ardize the performance. In this paper, we aim to learn the minimal sufficient communication messages. First, we initiate the communication between agents by a complete graph. Then we introduce the graph information bottleneck (GIB) principle into this complete graph and derive the optimization over graph structures. Based on the optimization, a novel multi-agent communication module, called CommGIB, is proposed, which effectively compresses the structure information and node information in the communication graph to deal with bandwidth-constrained settings. Extensive experiments in Traffic Control and StanCraft II are conducted. The results indicate that the proposed methods can achieve better performance in bandwidth-restricted settings compared with state-of-the-art algorithms, with especially large margins in large-scale multi-agent tasks.
Close range depth sensing cameras for virtual reality based hand rehabilitation

Purpose – The Leap Motion represents a new generation of depth sensing cameras designed for close range tracking of hands and fingers, operating with minimal latency and high spatial precision (0.01 mm). The purpose of this paper is to develop virtual reality (VR) simulations of three well-known hand-based rehabilitation tasks using a commercial game engine and utilising a Leap camera as the primary mode of interaction. The authors present results from an initial evaluation by professional clinicians of these VR simulations for use in their hand and finger physical therapy practice. Design/methodology/approach – A cross-disciplinary team of researchers collaborated with a local software company to create three dimension interactive simulations of three hand focused rehabilitation tasks: Cotton Balls, Stacking Blocks, and the Nine Hole Peg Test. These simulations were presented to a group of eight physiotherapists and occupational therapists (n=8) based in the Regional Acquired Brain Injury Unit, Belfast H.
On the Erdős-Purdy problem and the Zarankiewitz problem for semialgebraic graphs

Erdős and Purdy, and later Agarwal and Sharir, conjectured that any set of n points in R determine at most Cn congruent k-simplices for even d. We obtain the first significant progress towards this conjecture, showing that this number is at most Cn for k < d. As a consequence, we obtain an upper bound of Cn for the number of similar k-simplices determined by n points in R, which improves the results of Agarwal, Apfelbaum, Purdy and Sharir. This problem is motivated by the problem of exact pattern matching. We also address Zarankiewicz-type questions of finding the maximum number of edges in semi-algebraic graphs with no Ku,u. Here, we improve the previous result of Fox, Pach, Sheffer, Suk, and Zahl, and Do for d ≤ 4, as well as for any d and moderately large u. We get an improvement of their results for any d and u for unit-distance graphs, which was one of the main applications of their results. From a more general prospective, our results are proved using classical cutting techniques. In the recent years, we saw a great development of the polynomial partitioning method in incidence geometry that followed the breakthrough result by Guth and Katz. One consequence of that development is that the attention of the researchers in incidence geometry swayed in polynomial techniques. In this paper, we argue that there is a number of open problems where classical techniques work better. Carnegie Mellon University, Pittsburgh, US and Moscow Institute of Science and Technology, Russia. Email: nfrankl@andrew.cmu. edu G-SCOP, CNRS, University Grenoble-Alpes, France and Moscow Institute of Physics and Technology, Russia; Email: kupavskii@yandex.ru Research of Andrey Kupavskii is supported by the grant RSF N 21-71-10092, https://rscf.ru/project/21-71-10092/
Experimental study of the characteristics of energetic electrons outside LCFS in EXL-50 spherical torus

Significant number of confined energetic electrons have been observed outside of the LCFS (last-closed flux surface) of EXL-50’s solenoid-free electron cyclotron resonance heating (ECRH) sustained plasmas. Several measurement technologies have been applied to verify the key characteristics of energetic electrons for the first time. Experiments reveal that the presence of high-temperature, low-density electrons can carry relatively large quantities of the stored energy. The boundary between the thermal plasma and the energetic fluid is clearly separated and the distance between the two boundaries can reach tens of centimeters (around the size of the minor radius of the thermal plasma). This implies that the Grad-Shafranov equilibrium is not adequate to describe the equilibrium of EXL-50 plasma and a multi-fluid model is required. Particle simulations of full orbits show that energetic electrons can be well confined outside the LCFS. This is consistent with the experimental observations.