Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of nonlocal data-driven methods for subgrid-scale stress modeling in large eddy simulation

A nonlocal subgrid-scale stress (SGS) model is developed based on the convolution neural network (CNN), which is a powerful supervised data-driven method and also an ideal approach to naturally consider spatial information due to its wide receptive field. The CNN-based models used in this study take primitive flow variables as input only, and then, the flow features are automatically extracted without any a priori guidance. The nonlocal models trained by direct numerical simulation (DNS) data of a turbulent channel flow at Re τ = 178 are accessed in both the a priori and a posteriori tests, providing reasonable flow statistics (such as mean velocity and velocity fluctuations) close to the DNS results even when extrapolating to a higher Reynolds number Re τ = 600. It is identified that the nonlocal models outperform local data-driven models, such as the artificial neural network, and some typical SGS models (e.g., the dynamic Smagorinsky model) in large eddy simulation (LES). The model is also robust with stable numerical simulation since the solutions can be well obtained when examining the grid resolution from one-half to double of the spatial resolution used in training. We also investigate the influence of receptive fields and propose using the two-point correlation analysis as a quantitative method to guide the design of nonlocal physical models. The present study provides effective data-driven nonlocal methods for SGS modeling in LES of complex anisotropic turbulent flows.
Statistical Estimation and Inference via Local SGD in Federated Learning

Federated Learning (FL) makes a large amount of edge computing devices (e.g., mobile phones) jointly learn a global model without data sharing. In FL, data are generated in a decentralized manner with high heterogeneity. This paper studies how to perform statistical estimation and inference in the federated setting. We analyze the so-called Local SGD, a multi-round estimation procedure that uses intermittent communication to improve communication efficiency. We first establish a {\it functional central limit theorem} that shows the averaged iterates of Local SGD weakly converge to a rescaled Brownian motion. We next provide two iterative inference methods: the {\it plug-in} and the {\it random scaling}. Random scaling constructs an asymptotically pivotal statistic for inference by using the information along the whole Local SGD path. Both the methods are communication efficient and applicable to online data. Our theoretical and empirical results show that Local SGD simultaneously achieves both statistical efficiency and communication efficiency.
DeepTracks: Geopositioning Maritime Vehicles in Video Acquired from a Moving Platform

Geopositioning and tracking a moving boat at sea is a very challenging problem, requiring boat detection, matching and estimating its GPS location from imagery with no common features. The problem can be stated as follows: given imagery from a camera mounted on a moving platform with known GPS location as the only valid sensor, we predict the geoposition of a target boat visible in images. Our solution uses recent ML algorithms, the camera-scene geometry and Bayesian filtering. The proposed pipeline first detects and tracks the target boat’s location in the image with the strategy of tracking by detection. This image location is then converted to geoposition to the local sea coordinates referenced to the camera GPS location using plane projective geometry. Finally, target boat local coordinates are transformed to global GPS coordinates to estimate the geoposition. To achieve a smooth geotrajectory, we apply unscented Kalman filter (UKF) which implicitly overcomes small detection errors in the early stages of the pipeline. We tested the performance of our approach using GPS ground truth and show the accuracy and speed of the estimated geopositions. The source code will be made public once the paper is accepted.
Derived universal Massey products

We define an obstruction to the formality of a differential graded algebra over a graded operad defined over a commutative ground ring. This obstruction lives in the derived operadic cohomology of the algebra. Moreover, it determines all operadic Massey products induced on the homology algebra, hence the name of derived universal Massey product.
Dive into Layers: Neural Network Capacity Bounding using Algebraic Geometry

The empirical results suggest that the learnability of a neural network is directly related to its size. To mathematically prove this, we borrow a tool in topological algebra: Betti numbers to measure the topological geometric complexity of input data and the neural network. By characterizing the expressive capacity of a neural network with its topological complexity, we conduct a thorough analysis and show that the network's expressive capacity is limited by the scale of its layers. Further, we derive the upper bounds of the Betti numbers on each layer within the network. As a result, the problem of architecture selection of a neural network is transformed to determining the scale of the network that can represent the input data complexity. With the presented results, the architecture selection of a fully connected network boils down to choosing a suitable size of the network such that it equips the Betti numbers that are not smaller than the Betti numbers of the input data. We perform the experiments on a real-world dataset MNIST and the results verify our analysis and conclusion. The code is publicly available.
Forest formulas of discrete Green's functions

The discrete Green's functions are the pseudoinverse (or the inverse) of the Laplacian (or its variations) of a graph. In this article, we will give combinatorial interpretations of Green's functions in terms of enumerating trees and forests in a graph that will be used to derive further formulas for several graph invariants. For example, we show that the trace of the Green's function G ${\bf{G}}$ associated with the combinatorial Laplacian of a connected simple graph Γ ${\rm{\Gamma }}$ on n $n$ vertices satisfies Tr ( G ) = ∑ λ i ≠ 0 1 λ i = 1 n τ F 2 * , $\,\text{Tr}\,({\bf{G}})=\sum _{{\lambda }_{i}\ne 0}\frac{1}{{\lambda }_{i}}=\frac{1}{n\tau }\left|{{\mathbb{F}}}_{2}^{* }\right|,$ where λ i ${\lambda }_{i}$ denotes the eigenvalues of the combinatorial Laplacian, τ $\tau $ denotes the number of spanning trees, and F 2 * ${{\mathbb{F}}}_{2}^{* }$ denotes the set of rooted spanning 2‐forests in Γ ${\rm{\Gamma }}$ . We will prove forest formulas for discrete Green's functions for directed and weighted graphs and apply them to study random walks on graphs and digraphs. We derive a forest expression of the hitting time for digraphs, which gives combinatorial proofs to old and new results about hitting times, traces of discrete Green's functions, and other related quantities.
Misdiagnosing Bank Capital Problems

Banks' reluctance to repair their balance sheets, combined with deposit insurance and regulatory forbearance in recognizing greater risks and losses, can lead to solvency problems that look like liquidity (bank-run) crises. Regulatory forbearance incentivizes banks to both retain risky loans and reject new good opportunities. With sufficient regulatory forbearance, partially-insured banks act exactly as if they are fully insured. Stress tests certify that uninsured creditors will be paid, not solvency, and have ambiguous effects on the efficiency of investment.
Multi-model Machine Learning Inference Serving with GPU Spatial Partitioning

As machine learning techniques are applied to a widening range of applications, high throughput machine learning (ML) inference servers have become critical for online service applications. Such ML inference servers pose two challenges: first, they must provide a bounded latency for each request to support consistent service-level objective (SLO), and second, they can serve multiple heterogeneous ML models in a system as certain tasks involve invocation of multiple models and consolidating multiple models can improve system utilization. To address the two requirements of ML inference servers, this paper proposes a new ML inference scheduling framework for multi-model ML inference servers. The paper first shows that with SLO constraints, current GPUs are not fully utilized for ML inference tasks. To maximize the resource efficiency of inference servers, a key mechanism proposed in this paper is to exploit hardware support for spatial partitioning of GPU resources. With the partitioning mechanism, a new abstraction layer of GPU resources is created with configurable GPU resources. The scheduler assigns requests to virtual GPUs, called gpu-lets, with the most effective amount of resources. The paper also investigates a remedy for potential interference effects when two ML tasks are running concurrently in a GPU. Our prototype implementation proves that spatial partitioning enhances throughput by 102.6% on average while satisfying SLOs.
Minidisk Accretion onto Spinning Black Hole Binaries: Quasi-periodicities and Outflows

We perform a full 3D general relativistic magnetohydrodynamical (GRMHD) simulation of an equal-mass, spinning, binary black hole approaching merger, surrounded by a circumbinary disk and with a minidisk around each black hole. For this purpose, we evolve the ideal GRMHD equations on top of an approximated spacetime for the binary that is valid in every position of space, including the black hole horizons, during the inspiral regime. We use relaxed initial data for the circumbinary disk from a previous long-term simulation, where the accretion is dominated by a m = 1 overdensity called the lump. We compare our new spinning simulation with a previous non-spinning run, studying how spin influences the minidisk properties. We analyze the accretion from the inner edge of the lump to the black hole, focusing on the angular momentum budget of the fluid around the minidisks. We find that minidisks in the spinning case have more mass over a cycle than the non-spinning case. However, in both cases we find that most of the mass received by the black holes is delivered by the direct plunging of material from the lump. We also analyze the morphology and variability of the electromagnetic fluxes, and we find they share the same periodicities of the accretion rate. In the spinning case, we find that the outflows are stronger than the non-spinning case. Our results will be useful to understand and produce realistic synthetic light curves and spectra, which can be used in future observations.
A Comparative Study of Nonlinear MPC and Differential-Flatness-Based Control for Quadrotor Agile Flight

Accurate trajectory-tracking control for quadrotors is essential for safe navigation in cluttered environments. However, this is challenging in agile flights due to nonlinear dynamics, complex aerodynamic effects, and actuation constraints. In this article, we empirically compare two state-of-the-art control frameworks: the nonlinear-model-predictive controller (NMPC) and the differential-flatness-based controller (DFBC), by tracking a wide variety of agile trajectories at speeds up to 20 m/s (i.e., 72 km/h). The comparisons are performed in both simulation and real-world environments to systematically evaluate both methods from the aspect of tracking accuracy, robustness, and computational efficiency. We show the superiority of the NMPC in tracking dynamically infeasible trajectories, at the cost of higher computation time and risk of numerical convergence issues. For both methods, we also quantitatively study the effect of adding an inner loop controller using the incremental nonlinear dynamic inversion method, and the effect of adding an aerodynamic drag model. Our real-world experiments, performed in one of the world’s largest motion capture systems, demonstrate more than 78% tracking error reduction of both NMPC and DFBC, indicating the necessity of using an inner loop controller and aerodynamic drag model for agile trajectory tracking.
Quantum algorithm for gravitational-wave matched filtering

Quantum computational devices currently under development have the potential to accelerate data analysis techniques beyond the ability of any classical algorithm. We propose the application of a quantum algorithm for the detection of unknown signals in noisy data. We apply Grover’s algorithm to matched ﬁltering, a signal processing technique that compares data to a number of candidate signal templates. In comparison to the classical method, this provides a speedup proportional to the square root of the number of templates, which would make possible otherwise intractable searches. We demonstrate both a proof-of-principle quantum circuit implementation and a simulation of the algorithm’s application to the detection of the gravitational wave signal GW150914. We discuss the time complexity and space requirements of our algorithm as well as its implications for the currently computationally limited searches for continuous gravitational waves.
Generalized multipole moments of dynamical horizons in binary black hole mergers

In this work, we uncover new features in the source multipole moments of the dynamical horizons in binary black hole mergers using numerical relativity. We show that they encode detailed information about the dynamics of the binary black hole system are found to be strongly correlated with its dynamics. The dynamical horizons of the two black holes are found to acquire multipole moments that vanish when the horizons are isolated. Out of these the dominant moment is found to be the quadrupole moment. The dominant quadrupole multipole moment is also shown to be strongly correlated with gravitational field of the system at null infinity. Therefore the gravitational waves carried away from the system contain information about the geometrical structure of the black holes in the strong field regime. We also find that, in the post-merger phase, the multipolar structure of the outer common dynamical horizon of the system is strongly correlated with that of the individual horizons just before the merger. The outer common horizon then settles down to equilibrium as suggested by the quasi normal ringing of its multipole moments.
Interindividual Variability and Normal Ranges of Whole Blood and Plasma Thrombin Generation.

BACKGROUND Assays measuring thrombin generation (TG) in plasma increasingly gained attention in the field of thrombosis and hemostasis. Adaptation of the method enabled the measurement of TG in whole blood (WB). Despite their potential, TG assays did not reach the stage of universal clinical application, partly because of the absence of normal ranges. Our study aimed to accurately determine normal ranges and interindividual variability of TG and correlate results with coagulation factor levels, sex, and oral contraceptive usage. METHODS The study protocol was evaluated by the local medical ethical board. In total, 129 healthy volunteers gave full informed consent. Normal ranges of TG in platelet-poor plasma (PPP), platelet-rich plasma (PRP), and WB were determined according to CLSI guidelines. RESULTS Our study is the first to measure normal ranges of TG in PPP, PRP, and WB in a large healthy cohort. Significant correlations were found between TG in plasma and WB. Interindividual variability of TG in WB was comparable to that of plasma. Oral contraceptive use increased TG in PPP, PRP, and WB. The inhibitory effect of thrombomodulin on TG was significantly lower in females than in males. This effect was more pronounced upon oral contraceptive use. Primary clotting factor determinants for TG parameters depended on the tissue factor concentration, but were similar in WB and plasma. CONCLUSIONS Establishing normal ranges for TG brings us 1 step closer to clinical use. Good correlations between plasma and WB (including clotting factor determinants for TG) suggest that WB TG can be reliably used in clinic.
Access Control Using Spatially Invariant Permutation of Feature Maps for Semantic Segmentation Models

In this paper, we propose an access control method that uses the spatially invariant permutation of feature maps with a secret key for protecting semantic segmentation models. Segmentation models are trained and tested by permuting se-lected feature maps with a secret key. The proposed method allows rightful users with the correct key not only to access a model to full capacity but also to degrade the performance for unauthorized users. Conventional access control methods have focused only on image classification tasks, and these methods have never been applied to semantic segmentation tasks. In an experiment, the protected models were demonstrated to allow rightful users to obtain almost the same performance as that of non-protected models but also to be robust against access by unauthorized users without a key. In addition, a conventional method with block-wise transformations was also verified to have degraded performance under semantic segmentation models.
An Oracle and Observations for the OpenAI Gym / ALE Freeway Environment

The OpenAI Gym project contains hundreds of control problems whose goal is to provide a testbed for reinforcement learning algorithms. One such problem is Freeway-ram-v0, where the observations presented to the agent are 128 bytes of RAM. While the goals of the project are for non-expert AI agents to solve the control problems with general training, in this work, we seek to learn more about the problem, so that we can better evaluate solutions. In particular, we develop on oracle to play the game, so that we may have baselines for success. We present details of the oracle, plus optimal game-playing situations that can be used for training and testing AI agents.
J1721+8842: a gravitationally lensed binary quasar with a proximate damped Lyman-alpha absorber

High-redshift binary quasars provide key insights into mergers and quasar activity, and are useful tools for probing the spatial kine- matics and chemistry of galaxies along the line of sight. However, only three sub-10-kpc binaries have been confirmed above z = 1. Gravitational lensing would provide a way to easily resolve such binaries, study them in higher resolution, and provide more sightlines, though the required alignment with a massive foreground galaxy is rare. Through image deconvolution of StanCam Nordic Optical Telescope (NOT) monitoring data, we reveal two further point sources in the known, z ≈ 2 . 38, quadruply lensed quasar (quad) J1721 + 8842. An ALFOSC / NOT long-slit spectrum shows that the brighter of these two sources is a quasar with z = 2 . 369 ± 0 . 007 based on the C iii ] line, while the C iii ] redshift of the quad is z = 2 . 364 ± 0 . 003. Lens modelling using point-source positions rules out a single source model, favouring an isothermal lens mass profile with two quasar sources separated by ∼ 6 . 0 kpc (0.73 ′′ ) in projection. Given the resolving ability from lensing and current lensed quasar statistics, this discovery suggests a large population of undiscovered, unlensed sub-10-kpc binaries. We also analyse spectra of two images of the quad, showing narrow Ly α emission within the trough of a proximate damped Lyman- α absorber (PDLA). An apparent mismatch between the continuum and narrow line flux ratios provides a new potential tool for simultaneously studying microlensing and the quasar host galaxy. Signs of the PDLA are also seen in the second source, but a deeper spectrum is still required to confirm this. Thanks to the multiple lines of sight from lensing and two quasar sources, this system o ff ers simultaneous subparsec- and kiloparsec-scale probes of a PDLA.
Role of Sugar and Auxin Crosstalk in Plant Growth and Development.

Under the natural environment, nutrient signals interact with phytohormones to coordinate and reprogram plant growth and survival. Sugars are important molecules that control almost all morphological and physiological processes in plants, ranging from seed germination to senescence. In addition to their functions as energy resources, osmoregulation, storage molecules, and structural components, sugars function as signaling molecules and interact with various plant signaling pathways, such as hormones, stress and light to modulate growth and development according to fluctuating environmental conditions. Auxin, being an important phytohormone, is associated with almost all stages of the plant's life cycle and also plays a vital role in response to the dynamic environment for better growth and survival. In the previous years, substantial progress has been made that showed a range of common responses mediated by sugars and auxin signaling. This review discusses how sugar signaling affects auxin at various levels from its biosynthesis to perception and downstream gene activation. On the same note, the review also highlights the role of auxin signaling in fine-tuning sugar metabolism and carbon partitioning. Furthermore, we discussed the crosstalk between the two signaling machineries in the regulation of various biological processes, such as gene expression, cell cycle, development, root system architecture and shoot growth. In conclusion, the review emphasized the role of sugar and auxin crosstalk in the regulation of several agriculturally important traits. Thus, engineering of sugar and auxin signaling pathways could potentially provide new avenues to manipulate for agricultural purposes. This article is protected by copyright. All rights reserved.
OMEGA-PSIR: An ecosystem for Building University CRIS Network in Poland

OMEGA-PSIR, developed at the Warsaw University of Technology (WUT), is an institutional Current Research Information System (CRIS). At University it can be used for various purposes, such as presenting the University research, knowledge management, internal research assessment purposes, as well as reporting to the National Authorities in Poland. Since 2015 one can observe a growing interest among the Polish universities. It is now adopted by 15 universities in Poland. In the paper we will discuss how the bottom-up initiative of the Polish universities became a successful approach for building a low-cost network of the institutional CRIS systems, cooperating with the national CRIS system.
Exploiting Synergies in Catalysis and Gas Sensing using Noble Metal‐Loaded Oxide Composites

Noble metal/metal oxide nanocomposites are very important in various fields of catalysis and play an evenly important role in gas sensing. Although there are many similarities regarding the choice of materials, the synthesis and the fundamental mechanisms, both research fields have been mostly treated independently up to now. In both fields, open questions regarding the elementary steps in the interaction of the gases with the active species and the role of the noble metal, the semiconducting metal oxide support and the interface remain. In this concept article, we first outline the importance of such composites in catalysis and gas sensing focussing on Pt/Pd, as well as CeO2 and SnO2 as transition metal oxides. Next, the state of art of both fundamental and relevant surface reactions and electronic mechanisms are described. Finally, we highlight the synergy of jointly exploring catalysis and gas sensing of noble metal/metal oxide nanocomposite materials and the benefit for both research fields if they are dealt with simultaneously using advanced characterization and operando methods, sophisticated preparation techniques, testing of the performance, and predictive theoretical modelling.