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Remote sensing foundation models largely break away from the traditional paradigm of designing task-specific models, offering greater scalability across multiple tasks. However, they face challenges such as low computational efficiency and limited interpretability, especially when dealing with large-scale remote sensing images. To overcome these, we draw inspiration from heat conduction, a physical process modeling local heat diffusion. Building on this idea, we are the first to explore the potential of using the parallel computing model of heat conduction to simulate the local region correlations in high-resolution remote sensing images, and introduce RS-vHeat, an efficient multi-modal remote sensing foundation model. Specifically, RS-vHeat 1) applies the Heat Conduction Operator (HCO) with a complexity of $O(N^{1.5})$ and a global receptive field, reducing computational overhead while capturing remote sensing object structure information to guide heat diffusion; 2) learns the frequency distribution representations of various scenes through a self-supervised strategy based on frequency domain hierarchical masking and multi-domain reconstruction; 3) significantly improves efficiency and performance over state-of-the-art techniques across 4 tasks and 10 datasets. Compared to attention-based remote sensing foundation models, we reduce memory usage by 84\%, FLOPs by 24\% and improves throughput by 2.7 times. The code will be made publicly available., Comment: 19 pages, 8 figures and 10 tables

Multi-user multiple-input multiple-output (MU-MIMO) systems allow multiple users to share the same wireless spectrum. Each user transmits one symbol drawn from an M-ary quadrature amplitude modulation (M-QAM) constellation set, and the resulting multi-user interference (MUI) is cancelled at the receiver. End-to-End (E2E) learning has recently been proposed to jointly design the constellation set for a modulator and symbol detector for a single user under an additive white Gaussian noise (AWGN) channel by using deep neural networks (DNN) with a symbol-error-rate (SER) performance, exceeding that of Maximum likelihood (ML) M-QAM detectors. In this paper, we extend the E2E concept to the MU-MIMO systems, where a DNN-based modulator that generates learned M constellation points and graph expectation propagation network (GEPNet) detector that cancels MUI are jointly optimised with respect to SER performance loss. Simulation results demonstrate that the proposed E2E with learned constellation outperforms GEPNet with 16-QAM by around 5 dB in terms of SER in a high MUI environment and even surpasses ML with 16-QAM in a low MUI condition, both with no additional computational complexity., Comment: submitted to a conference

We investigate the host galaxy properties of eight little red dots (LRDs) selected from the JWST UNCOVER survey, applying a new technique ({\tt\string GalfitS}) to simultaneously fit the morphology and spectral energy distribution using multi-band NIRCam images covering $\sim 1-4\,\mu {\rm m}$. We detect the host galaxy in only one LRD, MSAID38108 at $z = 4.96$, which has a stellar mass of $\log (M_*/M_{\odot}) = 8.66^{+0.24}_{-0.23}$, an effective radius $R_e=0.66^{+0.08}_{-0.05}$ kpc, and a S\'ersic index $n=0.71^{+0.07}_{-0.08}$. No host emission centered on the central point source is found in the other seven LRDs. We derive stringent upper limits for the stellar mass of a hypothetical host galaxy by conducting realistic mock simulations that place high-redshift galaxy images under the LRDs. Based on the black hole masses estimated from the broad H$\alpha$ emission line, the derived stellar mass limits are at least a factor of 10 lower than expected from the $z \approx 0$ scaling relation between black hole mass and host galaxy stellar mass. Intriguingly, four of the LRDs (50\% of the sample) show extended, off-centered emission, which is particularly prominent in the bluer bands. The asymmetric emission of two sources can be modeled as stellar emission, but the nature of the other two is unclear., Comment: 30 pages, 15 figures, accepted for publication in ApJ

Recent developments in adversarial attacks on deep learning leave many mission-critical natural language processing (NLP) systems at risk of exploitation. To address the lack of computationally efficient adversarial defense methods, this paper reports a novel, universal technique that drastically improves the robustness of Bidirectional Encoder Representations from Transformers (BERT) by combining the unitary weights with the multi-margin loss. We discover that the marriage of these two simple ideas amplifies the protection against malicious interference. Our model, the unitary multi-margin BERT (UniBERT), boosts post-attack classification accuracies significantly by 5.3% to 73.8% while maintaining competitive pre-attack accuracies. Furthermore, the pre-attack and post-attack accuracy tradeoff can be adjusted via a single scalar parameter to best fit the design requirements for the target applications.

The Gamma-ray Transients Monitor (GTM) on board the Formosat-8B (FS-8B) satellite is designed to detect and localize Gamma-Ray Bursts (GRBs). By utilizing 2+2 CITIROC chips to manipulate 4+4 detectors, which are composed of GAGG(Ce) scintillators coupled with Silicon Photomultipliers (SiPMs) and oriented in various directions to achieve all-sky coverage, the GRB saturation fluences of GTM in the 50 keV to 1 MeV range for Short GRBs (SGRBs) and Long GRBs (LGRBs) were estimated to be about $3.1 \times 10^{-4}$ and $5.0 \times 10^{-3}\ {\rm erg/cm^2}$, respectively, based on simulations. To precisely interpret the GTM readout signal in terms of energy, several measurements for isotope and gain calibration were conducted. Despite encountering issues with crosstalk and SiPM saturation effect in the data, the energy spectrum can still be recovered by appropriately discarding channel noise and mapping with the correct ADC-to-energy relation. This paper summarizes the energy resolution of GTM and the linear variations in the relationship between photon energy and readout signal. At 662 keV, the energy resolution is about 16 %. Also, it demonstrates that greater gain is achieved by increasing voltage or decreasing temperature.

This paper is devoted to the study of the shifted Yangian $Y_n(\sigma)$ associated to the general linear Lie algebra $\mathfrak{gl}_n$ over a field of positive characteristic. We obtain an explicit description of the center $Z(Y_n(\sigma))$ of $Y_n(\sigma)$ in terms of parabolic generators, showing that it is generated by its Harish-Chandra center and its $p$-center., Comment: 27 pages, comments are welcome. arXiv admin note: text overlap with arXiv:1710.04739 by other authors

Inter-carrier interference (ICI) caused by mobile reflectors significantly degrades the conventional orthogonal frequency division multiplexing (OFDM) performance in high-mobility environments. The orthogonal time frequency space (OTFS) modulation system effectively represents ICI in the delay-Doppler domain, thus significantly outperforming OFDM. Existing iterative and neural network (NN) based OTFS detectors suffer from high complex matrix operations and performance degradation in untrained environments, where the real wireless channel does not match the one used in the training, which often happens in real wireless networks. In this paper, we propose to embed the prior knowledge of interference extracted from the estimated channel state information (CSI) as a directed graph into a decoder untrained neural network (DUNN), namely graph-based DUNN (GDUNN). We then combine it with Bayesian parallel interference cancellation (BPIC) for OTFS symbol detection, resulting in GDUNN-BPIC. Simulation results show that the proposed GDUNN-BPIC outperforms state-of-the-art OTFS detectors under imperfect CSI.

Let $Y_2$ be the Yangian associated to the general linear Lie algebra $\mathfrak{gl}_2$, defined over an algebraically closed field $\mathbbm{k}$ of characteristic $p > 0$. In this paper, we study the representation theory of the restricted Yangian $Y^{[p]}_2$. This leads to a description of the representations of $\mathfrak{gl}_{2n}$, whose $p$-character is nilpotent with Jordan type given by a two-row partition $(n, n)$., Comment: 23 pages, final version, to appear in JLMS

In this note, we investigate Jacobian conjecture through investigation of automorphisms of polynomial rings in characteristic $p$. Making use of the technique of inverse limits, we show that under Jacobian condition for a given homomorphism $\varphi$ of the polynomial ring $\mathbf{k}[x_1,\ldots,x_n]$, if $\varphi$ preserves the maximal ideals, then $\varphi$ is an automorphism., Comment: 8 pages, to appcar in JAA

We propose a future mission concept, the MeV Astrophysical Spectroscopic Surveyor (MASS), which is a large area Compton telescope using 3D position sensitive cadmium zinc telluride (CZT) detectors optimized for emission line detection. The payload consists of two layers of CZT detectors in a misaligned chessboard layout, with a total geometric area of 4096 cm$^2$ for on-axis observations. The detectors can be operated at room-temperature with an energy resolution of 0.6\% at 0.662 MeV. The in-orbit background is estimated with a mass model. At energies around 1 MeV, a line sensitivity of about $10^{-5}$ photons cm$^{-2}$ s$^{-1}$ can be obtained with a 1 Ms observation. The main science objectives of MASS include nucleosynthesis in astrophysics and high energy astrophysics related to compact objects and transient sources. The payload CZT detectors weigh roughly 40 kg, suggesting that it can be integrated into a micro- or mini-satellite. We have constructed a pathfinder, named as MASS-Cube, to have a direct test of the technique with 4 detector units in space in the near future., Comment: accepted for publication in Experimental Astronomy

Rain streaks bring complicated pixel intensity changes and additional gradients, greatly obstructing the extraction of image features from background. This causes serious performance degradation in feature-based applications. Thus, it is critical to remove rain streaks from a single rainy image to recover image features. Recently, many excellent image deraining methods have made remarkable progress. However, these human visual system-driven approaches mainly focus on improving image quality with pixel recovery as loss function, and neglect how to enhance image feature recovery ability. To address this issue, we propose a task-driven image deraining algorithm to strengthen image feature supply for subsequent feature-based applications. Due to the extensive use and strong practicability of Scale-Invariant Feature Transform (SIFT), we first propose two separate networks using distinct losses and modules to achieve two goals, respectively. One is difference of Gaussian (DoG) pyramid recovery network (DPRNet) for SIFT detection, and the other gradients of Gaussian images recovery network (GGIRNet) for SIFT description. Second, in the DPRNet we propose an alternative interest point loss that directly penalizes scale response extrema to recover the DoG pyramid. Third, we advance a gradient attention module in the GGIRNet to recover those gradients of Gaussian images. Finally, with the recovered DoG pyramid and gradients, we can regain SIFT key points. This divide-and-conquer scheme to set different objectives for SIFT detection and description leads to good robustness. Compared with state-of-the-art methods, experimental results demonstrate that our proposed algorithm achieves better performance in both the number of recovered SIFT key points and their accuracy., Comment: A direct DoG feature pyramid recovery from rainy pixels solution for SIFT detection, accepted by T-CSVT, 2023

We consider the quasi Hermitian limit of a non-Hermitian extended Su Schrieffer Heeger model, in which the hopping amplitudes obey a specific relation so that the system may be mapped to a corresponding Hermitian one and its energy spectrum is completely real. Analogous to the Hermitian case, one may use the modified winding number to determine the total number of edge states on the boundaries to achieve a modified bulk-boundary correspondence. Due to the skin effect in nonHermitian systems, the spectral winding numbers must be used to classify such systems further. It dictates how the edge states would be distributed over the left and right boundaries. We then naively extend the criteria to the cases that the quasi Hermitian condition is violated. For all the cases that we consider, no inconsistency has been found., Comment: 21 pages, 10 figures

It is a challenge to numerically solve nonlinear partial differential equations whose solution involves discontinuity. In the context of numerical simulators for multi-phase flow in porous media, there exists a long-standing issue known as Grid Orientation Effect (GOE), wherein different numerical solutions can be obtained when considering grids with different orientations under certain unfavorable conditions. Our perspective is that GOE arises due to numerical instability near displacement fronts, where spurious oscillations accompanied by sharp fronts, if not adequately suppressed, lead to GOE. To reduce or even eliminate GOE, we propose augmenting adaptive artificial viscosity when solving the saturation equation. It has been demonstrated that appropriate artificial viscosity can effectively reduce or even eliminate GOE. The proposed numerical method can be easily applied in practical engineering problems., Comment: 10 pages

Due to its ubiquitous and contact-free nature, the use of WiFi infrastructure for performing sensing tasks has tremendous potential. However, the channel state information (CSI) measured by a WiFi receiver suffers from errors in both its gain and phase, which can significantly hinder sensing tasks. By analyzing these errors from different WiFi receivers, a mathematical model for these gain and phase errors is developed in this work. Based on these models, several theoretically justified preprocessing algorithms for correcting such errors at a receiver and, thus, obtaining clean CSI are presented. Simulation results show that at typical system parameters, the developed algorithms for cleaning CSI can reduce noise by $40$% and $200$%, respectively, compared to baseline methods for gain correction and phase correction, without significantly impacting computational cost. The superiority of the proposed methods is also validated in a real-world test bed for respiration rate monitoring (an example sensing task), where they improve the estimation signal-to-noise ratio by $20$% compared to baseline methods., Comment: Paper is accepted to IEEE Transactions on Wireless Communications

In dynamic spectrum access (DSA) networks, secondary users (SUs) need to opportunistically access primary users' (PUs) radio spectrum without causing significant interference. Since the interaction between the SU and the PU systems are limited, deep reinforcement learning (DRL) has been introduced to help SUs to conduct spectrum access. Specifically, deep recurrent Q network (DRQN) has been utilized in DSA networks for SUs to aggregate the information from the recent experiences to make spectrum access decisions. DRQN is notorious for its sample efficiency in the sense that it needs a rather large number of training data samples to tune its parameters which is a computationally demanding task. In our recent work, deep echo state network (DEQN) has been introduced to DSA networks to address the sample efficiency issue of DRQN. In this paper, we analytically show that DEQN comparatively requires less amount of training samples than DRQN to converge to the best policy. Furthermore, we introduce a method to determine the right hyperparameters for the DEQN providing system design guidance for DEQN-based DSA networks. Extensive performance evaluation confirms that DEQN-based DSA strategy is the superior choice with regard to computational power while outperforming DRQN-based DSA strategies.

The orthogonal time frequency space (OTFS) symbol detector design for high mobility communication scenarios has received numerous attention lately. Current state-of-the-art OTFS detectors mainly can be divided into two categories; iterative and training-based deep neural network (DNN) detectors. Many practical iterative detectors rely on minimum-mean-square-error (MMSE) denoiser to get the initial symbol estimates. However, their computational complexity increases exponentially with the number of detected symbols. Training-based DNN detectors typically suffer from dependency on the availability of large computation resources and the fidelity of synthetic datasets for the training phase, which are both costly. In this paper, we propose an untrained DNN based on the deep image prior (DIP) and decoder architecture, referred to as D-DIP that replaces the MMSE denoiser in the iterative detector. DIP is a type of DNN that requires no training, which makes it beneficial in OTFS detector design. Then we propose to combine the D-DIP denoiser with the Bayesian parallel interference cancellation (BPIC) detector to perform iterative symbol detection, referred to as D-DIP-BPIC. Our simulation results show that the symbol error rate (SER) performance of the proposed D-DIP-BPIC detector outperforms practical state-of-the-art detectors by 0.5 dB and retains low computational complexity.

We construct the four-derivative supersymmetric extension of $(1,0), 6D$ supergravity coupled to Yang-Mills and hypermultiplets. The hypermultiplet scalars are taken to parametrize the quaternionic projective space $Hp(n)=Sp(n,1)/Sp(n)\times Sp(1)_R$. The hyperscalar kinetic term is not deformed, and the quaternionic K\"ahler structure and symmetries of $Hp(n)$ are preserved. The result is a three parameter Lagrangian supersymmetric up to first order in these parameters. Considering the case of $Hp(1)$ we compare our result with that obtained from the compactification of $10D$ heterotic supergravity on four-torus, consistently truncated to $N=(1,0)$, in which the hyperscalars parametrize $SO(4,1)/SO(4)$. We find that depending on how $Sp(1) \subset Sp(1,1)$ is embedded in $SO(4)$, the results agree for a specific value of the parameter that governs the higher derivative hypermultiplet couplings., Comment: 29 pages

Within linear response theory, the absorptive part of highly anisotropic optical conductivities are analytically calculated for distinct tilts in two-dimensional (2D) tilted semi-Dirac bands (SDBs). The transverse optical conductivities always vanish. The interband longitudinal optical conductivities (LOCs) in 2D tilted SDBs differ qualitatively in the power-law scaling of $\omega$ as $\mathrm{Re}\sigma_{\perp}^{\mathrm{IB}}(\omega)\propto\sigma_0\sqrt{\omega}$ and $\mathrm{Re}\sigma_{\parallel}^{\mathrm{IB}}(\omega)\propto\sigma_0/\sqrt{\omega}$. By contrast, the intraband LOCs in 2D tilted SDBs depend on $\mu$ in the power-law scaling as $\mathrm{Re}\sigma_{\perp}^{\mathrm{D}}(\omega)\propto\sigma_0\mu \sqrt{\mu}$ and $\mathrm{Re}\sigma_{\parallel}^{\mathrm{D}}(\omega)\propto\sigma_0\mu/\sqrt{\mu}$. The tilt-dependent behaviors of LOCs could qualitatively characterize distinct impact of band tilting in 2D tilted SDBs. In particular, for arbitrary tilt $t$ satisfying $0