Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical reconstruction for 3D nonlinear SAR imaging via a version of the convexification method

This work extends the applicability of our recent convexiﬁcation-based algorithm for constructing images of the dielectric constant of buried or occluded target. We are orientated towards the detection of explosive-like targets such as antipersonnel land mines and improvised explosive devices in the non-invasive inspections of buildings. In our previous work, the method is posed in the perspective that we use multiple source locations running along a line of source to get a 2D image of the dielectric function. Mathematically, we solve a 1D coeﬃcient inverse problem for a hyperbolic equation for each source location. Diﬀerent from any conventional Born approximation-based technique for synthetic-aperture radar, this method does not need any linearization. In this paper, we attempt to verify the method using several 3D numerical tests with simulated data. We revisit the global convergence of the gradient descent method of our computational approach.
Triple‐port, two‐mode based two element cylindrical dielectric resonator antenna for MIMO applications

In this letter, a triple‐port multiple‐input–multiple‐output (MIMO) cylindrical dielectric resonator based antenna (cDRA) supporting 2 simultaneous operating modes is presented. The cDRAs are excited by 3 ports supporting MIMO operation. Mode‐1 is an array mode achieved by exciting the 2 element cDRA array via a simple combiner at port‐1. Ports‐ 2 and −3 support individual element excitation via coaxial probes for mode‐2 yielding individual element MIMO operation. The isolation between ports‐1, 2, and 3 is improved by generating orthogonal modes in the cDRA via proper excitation. The isolation between ports‐2 and 3 is improved by etching 2 C‐shaped defected ground structure slits. For all the cases, the isolation between the ports exceeds 20 dB over the operating band. The presented dual‐mode 3‐port MIMO antenna covers the bands between 5.15 and 6.0 GHz.
Spatial Distribution of Dark Matter in and Around Galaxy Clusters Traced by Galaxies, Gas, and Intracluster Stars in a Simulated Universe

To understand how well galaxies, gas, and intracluster stars trace dark matter in and around galaxy clusters, we use the IllustrisTNG cosmological hydrodynamical simulation and compare the spatial distribution of dark matter with that of baryonic components in clusters. To quantify the global morphology of the density distribution of each component in clusters, we fit an ellipse to the density contour of each component and derive shape parameters at different radii. We find that the ellipticity of dark matter is better correlated with that of galaxy mass-weighted number density, rather than with that of galaxy number density or galaxy velocity dispersion. We thus use the galaxy mass-weighted number density map as representative of the galaxy maps. Among three different density maps from galaxies, gas, and intracluster stars, the ellipticity of dark matter is best reproduced by that of the galaxy map over the entire radii. The virialized galaxy clusters show a better correlation of spatial distribution between dark matter and other components than the unvirialized clusters, suggesting that it requires some time for each component to follow the spatial distribution of dark matter after merging events. Our results demonstrate that galaxies are still good tracers of dark matter distribution even in the nonlinear regime corresponding to the scales in and around galaxy clusters, being consistent with the case where galaxies trace well the matter distribution on cosmologically large scales.
Robustness and Real Options for Vehicle Design and Investment Decisions Under Gas Price and Regulatory Uncertainties

Manufacturers must decide when to invest and launch a new vehicle segment or how to redesign vehicles existing segment under market uncertainties. We present an optimization framework for redesigning or investing in future vehicles using real options to address uncertainty in gas price and regulatory standards like the U.S. Corporate Average Fuel Economy (CAFE) standard. In a specific study involving a product of gasoline, hybrid electric, and electric vehicles (EV), we examine the relationship between gas price and CAFE uncertainties to support decisions by manufacturers on product mix and by policy makers on proposing standards. A real options model is used for the time delay on investment, redesign, and pricing, integrated with a robust design formulation to optimize expected net present value (ENPV) and net present value (NPV) robustness. Results for nine different scenarios suggest that policy makers should consider gas price when setting CAFE standards; and manufacturers should consider the trade-off between ENPV and robust NPVs. Results also suggest that change of product mix rather than vehicle redesign better addresses CAFE standards inflation.
Sparse Representations of Dynamical Networks: A Coprime Factorization Approach

We study a class of dynamical networks modeled by linear and time-invariant systems which are described by state-space realizations. For these networks, we investigate the relations between various types of factorizations which preserve the structure of their component subsystems' interconnection. In doing so, we provide tractable means of shifting between different types of sparsity-preserving representations and we show how to employ these factorizations to obtain distributed implementations for stabilizing and possibly stable controllers. By formulating all these results for both discrete- and continuous-time systems, we develop specialized distributed implementations that, up to this point, were only available for networks modeled as discrete-time systems.
IID Sampling from Posterior Dirichlet Process Mixtures

The inﬂuence of Dirichlet process mixture is ubiquitous in the Bayesian nonpara-metrics literature. But sampling from its posterior distribution remains a challenge, despite the advent of various Markov chain Monte Carlo methods. The primary challenge is the inﬁnite-dimensional setup, and even if the inﬁnite-dimensional random measure is integrated out, high-dimensionality and discreteness still remain diﬃcult issues to deal with. In this article, exploiting the key ideas proposed in Bhattacharya (2021b), we propose a novel methodology for drawing iid realizations from posteriors of Dirichlet process mixtures. We focus in particular on the more general and ﬂexible model of Bhattacharya (2008), so that the methods developed here are simply applicable to the traditional Dirichlet process mixture. We illustrate our ideas on the well-known enzyme, acidity and the galaxy datasets, which are usually considered benchmark datasets for mixture applications. Generating 10 , 000 iid realizations from the Dirichlet process mixture posterior of Bhattacharya (2008) given these datasets took 19 minutes, 8 minutes and 5 minutes, respectively, in our parallel implementation.
Remark on a paper by Wu

In this short note, we give an aﬃrmative answer to Wu’s conjecture on practical numbers, which was posed in [X. -H. Wu, Special forms and the distribution of practical numbers , Acta Math. Hungar. , 160 (2020), 405-411].
Scaling up Kernels in 3D CNNs

Recent advances in 2D CNNs and vision transformers (ViTs) reveal that large kernels are essential for enough receptive ﬁelds and high performance. Inspired by this literature, we examine the feasibility and challenges of 3D large-kernel designs. We demonstrate that applying large convolutional kernels in 3D CNNs has more difﬁculties in both performance and efﬁciency. Existing techniques that work well in 2D CNNs are ineffective in 3D networks, including the popular depth-wise convolutions. To overcome these obstacles, we present the spatial-wise group convolution and its large-kernel module (SW-LK block). It avoids the optimization and efﬁciency issues of naive 3D large kernels. Our large-kernel 3D CNN network, i.e. , LargeKernel3D, yields non-trivial improvements on various 3D tasks, including semantic segmentation and object detection. Notably, it achieves 73.9% mIoU on the ScanNetv2 semantic segmentation and 72.8% NDS nuScenes object detection benchmarks, ranking 1 st on the nuScenes LIDAR leaderboard. It is further boosted to 74.2% NDS with a simple multi-modal fusion. LargeKernel3D attains comparable or superior results than its CNN and transformer counterparts. For the ﬁrst time, we show that large kernels are feasible and essential for 3D networks. Code and models will be available at github.com/dvlab-research/LargeKernel3D.
Pisces: efficient federated learning via guided asynchronous training

Federated learning (FL) is typically performed in a synchronous parallel manner, and the involvement of a slow client delays the training progress. Current FL systems employ a participant selection strategy to select fast clients with quality data in each iteration. However, this is not always possible in practice, and the selection strategy has to navigate a knotty tradeoff between the speed and the data quality. This paper makes a case for asynchronous FL by presenting Pisces, a new FL system with intelligent participant selection and model aggregation for accelerated training despite slow clients. To avoid incurring excessive resource cost and stale training computation, Pisces uses a novel scoring mechanism to identify suitable clients to participate in each training iteration. It also adapts the aggregation pace dynamically to bound the progress gap between the participating clients and the server, with a provable convergence guarantee in a smooth non-convex setting. We have implemented Pisces in an open-source FL platform, Plato, and evaluated its performance in large-scale experiments with popular vision and language models. Pisces outperforms the state-of-the-art synchronous and asynchronous alternatives, reducing the time-to-accuracy by up to 2.0X and 1.9X, respectively.
University of Birmingham Generating atmospheric turbulence using passive grids in an expansion test section of a wind tunnel

Generating atmospheric turbulence in wind tunnels is an important issue in the study of 7 wind turbine aerodynamics. A turbulent inlet is usually generated using passive grids. However, to 8 obtain an atmospheric-like flow field relatively large length scales ( L~30 cm) and high turbulence 9 intensities ( I~15 %) need to be reproduced. In this work, the passive grid technique has been used in 10 combination with a downstream expansion test section in order to investigate the generation of 11 atmospheric like turbulence, with the possibility of varying both the turbulence intensity and the 12 integral length scale of the flow field independently. Four passive grids with different mesh and bar 13 sizes were used with four wind velocities and five downstream measurement positions. It was found 14 that the flow field is isotropic and homogeneous for distances less than what is recommended in 15 literature ( x/M~5 ). The effect of the expansion on the turbulence characteristics is also investigated in 16 detail for the first time. The study confirms that by adding an expansion test section it is possible to 17 increase both turbulence intensity and integral length scale downstream from the grid with limited 18 impact on the overall flow quality in terms of anisotropy and energy spectra.
Semi-Supervised Change Detection Of Small Water Bodies Using Rgb And Multispectral Images In Peruvian Rainforests

Artisanal and Small-scale Gold Mining (ASGM) is an important source of income for many households, but it can have large social and environmental effects, especially in rainforests of developing countries. The Sentinel-2 satellites collect multispectral images that can be used for the purpose of detecting changes in water extent and quality which indicates the locations of mining sites. This work focuses on the recognition of ASGM activities in Peruvian Amazon rainforests. We tested several semi-supervised classifiers based on Support Vector Machines (SVMs) to detect the changes of water bodies from 2019 to 2021 in the Madre de Dios region, which is one of the global hotspots of ASGM activities. Experiments show that SVM-based models can achieve reasonable performance for both RGB (using Cohen’s κ 0.49) and 6-channel images (using Cohen’s κ 0.71) with very limited annotations. The efficacy of incorporating Lab color space for change detection is analyzed as well.
On Generalized WKB Expansion of Monodromy Generating Function

We study symplectic properties of the monodromy map of the Schrödinger equation on a Riemann surface with a meromorphic potential having second-order poles. At first, we discuss the conditions for the base projective connection, which induces its own set of Darboux homological coordinates, to imply the Goldman Poisson structure on the character variety. Using this result, we extend the paper [Theoret. and Math. Phys. 206 (2021), 258-295, arXiv:1910.07140], by performing generalized WKB expansion of the generating function of monodromy symplectomorphism (the Yang-Yang function) and computing its first three terms.
Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting

Multivariate Time Series (MTS) forecasting plays a vital role in a wide range of applications. Recently, Spatial-Temporal Graph Neural Networks (STGNNs) have become increasingly popular MTS forecasting methods. STGNNs jointly model the spatial and temporal patterns of MTS through graph neural networks and sequential models, significantly improving the prediction accuracy. But limited by model complexity, most STGNNs only consider short-term historical MTS data, such as data over the past one hour. However, the patterns of time series and the dependencies between them (i.e., the temporal and spatial patterns) need to be analyzed based on long-term historical MTS data. To address this issue, we propose a novel framework, in which STGNN is Enhanced by a scalable time series Pre-training model (STEP). Specifically, we design a pre-training model to efficiently learn temporal patterns from very long-term history time series (e.g., the past two weeks) and generate segment-level representations. These representations provide contextual information for short-term time series input to STGNNs and facilitate modeling dependencies between time series. Experiments on three public real-world datasets demonstrate that our framework is capable of significantly enhancing downstream STGNNs, and our pre-training model aptly captures temporal patterns.
Efficient Pricing and Calibration of High-Dimensional Basket Options

This paper studies equity basket options -- i.e., multi-dimensional derivatives whose payoffs depend on the value of a weighted sum of the underlying stocks -- and develops a new and innovative approach to ensure consistency between options on individual stocks and on the index comprising them. Specifically, we show how to resolve a well-known problem that when individual constituent distributions of an equity index are inferred from the single-stock option markets and combined in a multi-dimensional local/stochastic volatility model, the resulting basket option prices will not generate a skew matching that of the options on the equity index corresponding to the basket. To address this ``insufficient skewness'', we proceed in two steps. First, we propose an ``effective'' local volatility model by mapping the general multi-dimensional basket onto a collection of marginal distributions. Second, we build a multivariate dependence structure between all the marginal distributions assuming a jump-diffusion model for the effective projection parameters, and show how to calibrate the basket to the index smile. Numerical tests and calibration exercises demonstrate an excellent fit for a basket of as many as 30 stocks with fast calculation time.
Event-Case Correlation for Process Mining using Probabilistic Optimization

Process mining supports the analysis of the actual behavior and performance of business processes using event logs. % such as, e.g., sales transactions recorded by an ERP system. An essential requirement is that every event in the log must be associated with a unique case identifier (e.g., the order ID of an order-to-cash process). In reality, however, this case identifier may not always be present, especially when logs are acquired from different systems or extracted from non-process-aware information systems. In such settings, the event log needs to be pre-processed by grouping events into cases -- an operation known as event correlation. Existing techniques for correlating events have worked with assumptions to make the problem tractable: some assume the generative processes to be acyclic, while others require heuristic information or user input. Moreover, %these techniques' primary assumption is that they abstract the log to activities and timestamps, and miss the opportunity to use data attributes. % In this paper, we lift these assumptions and propose a new technique called EC-SA-Data based on probabilistic optimization. The technique takes as inputs a sequence of timestamped events (the log without case IDs), a process model describing the underlying business process, and constraints over the event attributes. Our approach returns an event log in which every event is associated with a case identifier. The technique allows users to incorporate rules on process knowledge and data constraints flexibly. The approach minimizes the misalignment between the generated log and the input process model, maximizes the support of the given data constraints over the correlated log, and the variance between activity durations across cases. Our experiments with various real-life datasets show the advantages of our approach over the state of the art.
Delay-aware Multiple Access Design for Intelligent Reflecting Surface Aided Uplink Transmission

This paper develops a novel multiple access (MA) protocol for an intelligent reflecting (IRS) aided uplink transmission network by incorporating the IRS-aided time-division MA (I-TDMA) protocol and the IRS-aided non-orthogonal MA protocol (I-NOMA) protocol as special cases. Under the proposed IRS-aided hybrid MA (I-HMA) framework, a sum transmission delay minimization problem is formulated by jointly optimizing the device’s rearranged order, time and power allocation, as well as dynamic IRS beamforming patterns over time subject to the transmit power constraints. An analytical comparison for the minimum sum delay of the proposed I-HMA protocol, I-TDMA as well as I-NOMA is provided, and a computationally efficient algorithm is proposed to obtain the high-quality solution to the optimization problem. Simulation results reveal that the proposed protocol can significantly reduce the sum delay by combining the additional gain of dynamic IRS beamforming with the high spectral efficiency of NOMA, which indicates the effectiveness of integrating IRS into the proposed HMA protocol.
Polarized x-rays constrain the disk-jet geometry in the black hole x-ray binary Cygnus X-1

A black hole x-ray binary (XRB) system forms when gas is stripped from a normal star and accretes onto a black hole, which heats the gas sufficiently to emit x-rays. We report a polarimetric observation of the XRB Cygnus X-1 using the Imaging X-ray Polarimetry Explorer. The electric field position angle aligns with the outflowing jet, indicating that the jet is launched from the inner x-ray–emitting region. The polarization degree is 4.01 ± 0.20% at 2 to 8 kiloelectronvolts, implying that the accretion disk is viewed closer to edge-on than the binary orbit. These observations reveal that hot x-ray–emitting plasma is spatially extended in a plane perpendicular to, not parallel to, the jet axis. Description x-ray polarization of Cygnus X-1 A black hole in a binary system can rip material off of its companion star, which heats up and forms an accretion disk. The disc emits light in the optical and x-ray bands, forming an x-ray binary (XRB) system. Some XRBs also launch a jet of fast-moving material that is visible at radio wavelengths. Krawczynski et al. observed the x-ray polarization of Cygnus X-1, a black hole XRB with a radio jet. By comparing the measured polarization properties with several competing XRB models, they eliminated some hypothesized geometries and determined that the x-ray–emitting region extends parallel to the accretion disc. —KTS x-ray polarization measurements determine the geometric arrangement of hot material accreting onto a black hole.
SJ-HD^2R: Selective Joint High Dynamic Range and Denoising Imaging for Dynamic Scenes

Ghosting artifacts, motion blur, and low fidelity in highlight are the main challenges in High Dynamic Range (HDR) imaging from multiple Low Dynamic Range (LDR) images. These issues come from using the medium-exposed image as the reference frame in previous methods. To deal with them, we propose to use the under-exposed image as the reference to avoid these issues. However, the heavy noise in dark regions of the under-exposed image becomes a new problem. Therefore, we propose a joint HDR and denoising pipeline, containing two sub-networks: (i) a pre-denoising network (PreDNNet) to adaptively denoise input LDRs by exploiting exposure priors; (ii) a pyramid cascading fusion network (PCFNet), introducing an attention mechanism and cascading structure in a multi-scale manner. To further leverage these two paradigms, we propose a selective and joint HDR and denoising (SJ-HD$^2$R) imaging framework, utilizing scenario-specific priors to conduct the path selection with an accuracy of more than 93.3$\%$. We create the first joint HDR and denoising benchmark dataset, which contains a variety of challenging HDR and denoising scenes and supports the switching of the reference image. Extensive experiment results show that our method achieves superior performance to previous methods.
On Function of Evolution of Distribution for Time Homogeneous Markov Processes

A study of time homogeneous, real valued Markov processes with a special property and a non-atomic initial distribution is provided. The new notion of a function of evolution of distribution which determines the dependency between one dimensional distributions of a process is introduced. This, along with the notion of bridge operators which determine the backward structure, as opposed to the forward structure determined by the usual semi-group operators, paves a way to the new approach for dealing with ﬁnite-dimensional distributions of Markov processes. This, in particular, produces explicit formulas which eﬀec-tively simplify the computations of ﬁnite-dimensional distributions, giving an alternative to the standard approach based on computations using the chain rule of transition densities. Various examples illustrating the new approach are presented.