Miracle-dz/UniFAR-FTUs huggingface dataset:
AMANet: Advancing SAR Ship Detection with Adaptive Multi-Hierarchical Attention Network

Recently, methods based on deep learning have been successfully applied to ship detection for synthetic aperture radar (SAR) images. Despite the development of numerous ship detection methodologies, detecting small and coastal ships remains a significant challenge due to the limited features and clutter in coastal environments. For that, a novel adaptive multi-hierarchical attention module (AMAM) is proposed to learn multi-scale features and adaptively aggregate salient features from various feature layers, even in complex environments. Specifically, we first fuse information from adjacent feature layers to enhance the detection of smaller targets, thereby achieving multi-scale feature enhancement. Then, to filter out the adverse effects of complex backgrounds, we dissect the previously fused multi-level features on the channel, individually excavate the salient regions, and adaptively amalgamate features originating from different channels. Thirdly, we present a novel adaptive multi-hierarchical attention network (AMANet) by embedding the AMAM between the backbone network and the feature pyramid network (FPN). Besides, the AMAM can be readily inserted between different frameworks to improve object detection. Lastly, extensive experiments on two large-scale SAR ship detection datasets demonstrate that our AMANet method is superior to state-of-the-art methods.
State Estimation for Continuum Multi-Robot Systems on SE(3)

In contrast to conventional robots, accurately modeling the kinematics and statics of continuum robots is challenging due to partially unknown material properties, parasitic effects, or unknown forces acting on the continuous body. Consequentially, state estimation approaches that utilize additional sensor information to predict the shape of continuum robots have garnered significant interest. This paper presents a novel approach to state estimation for systems with multiple coupled continuum robots, which allows estimating the shape and strain variables of multiple continuum robots in an arbitrary coupled topology. Simulations and experiments demonstrate the capabilities and versatility of the proposed method, while achieving accurate and continuous estimates for the state of such systems, resulting in average end-effector errors of 3.3 mm and 5.02{\deg} depending on the sensor setup. It is further shown, that the approach offers fast computation times of below 10 ms, enabling its utilization in quasi-static real-time scenarios with average update rates of 100-200 Hz. An open-source C++ implementation of the proposed state estimation method is made publicly available to the community.
Silting interval reduction and 0-Auslander extriangulated categories

We give a reduction theorem for silting intervals in extriangulated categories, which we call"silting interval reduction". In exact categories, it provides a redcution technique for tilting subcategories. When the extriangulated categories are 0-Auslander extriangulated categories, there are bijections between the silting objects and the support $\tau$-tilting modules over certain finite dimensional algebras. We show that silting interval reduction is compatible with $\tau$-tilting reduction under the bijections. Using this reduction, we provide an alternative proof of mutation theory in 0-Auslander extriangulated categories. We give a new construction for 0-Auslander extriangulated categories using silting mutations. Finally, we prove that $d$-Auslander extriangulated categories are related to a certain sequence of silting mutations.
Spatial variations of magmatic crustal accretion during the opening of the Tyrrhenian back‐arc from wide‐angle seismic velocity models and seismic reflection images

The structural complexity of back‐arc basins is related to the evolution of the associated subduction system. Here, we present an integrated geophysical and geological study that constrains the 3D spatial variability of magmatic activity along the Tyrrhenian back‐arc basin. We use wide‐angle seismic and gravity data, acquired in 2010 within the MEDOC experiment along a ~300 km‐long NW‐SE transect that extends from SE Sardinia Island to the NW Sicily continental margin, across the Cornaglia Terrace. The geophysical transect is coincident with a seismic reflection line from the Italian CROP experiment that we have re‐processed. The geophysical results, together with available basement dredges, support a basement along the profile fundamentally composed of continental‐type rocks, locally affected by subduction‐related magmatism. The continental nature of this region contrasts with the nature of the basement inferred along two geophysical cross‐sections located to the north of the Cornaglia Terrace in which seismic velocity of the lower crust supports significant magmatic crustal accretion. The comparison of these three cross‐sections supports that the highest magmatic activity occurred in the central and most extended region of the basin, whereas it was less important in the North and practically nonexistent in the South. These observations indicate abrupt variations of magmatism during the basin formation. As in other back‐arcs, the temperature, water content and composition of the mantle might have played an important role in such variation, but they fail to explain the abruptness of it. We propose that the interaction of the overriding continental lithospheres of Adria and Africa with the Apenninic‐Calabrian subduction system caused changes in slab rollback and trench retreat dynamics, which in turn resulted in variations of back‐arc stretching and magmatism. Based on our observations, we suggest that the Cornaglia Terrace formation process might share some similarities with the formation of oceanic crust in the Red Sea.
The coastal tract (Part 1): A conceptual approach to aggregated modelling of low-order coastal change

Evolution of coastal morphology over centuries to millennia (low-order coastal change) is relevant to chronic problems in coastal management (e.g., systematic shoreline erosion). This type of coastal change involves parts of the coast normally ignored in predictions required for management of coastal morphology: i.e., shoreline evolution linked to behavior of the continental shelf and coastal plain. We therefore introduce a meta-morphology, the coastal tract, defined as the morphological composite comprising the lower shoreface, upper shoreface and backbarrier (where present). It is the first order-system within a cascade hierarchy that provides a framework for aggregation of processes in modeling low-order coastal change. We use this framework in defining boundary conditions and internal dynamics to separate low-order from higher-order coastal behavior for site-specific cases. This procedure involves preparation of a data-model by templating site data into a structure that complies with scale-specific properties of any given predictive model. Each level of the coastal-tract cascade is distinguished as a system that shares sediments internally. This sediment sharing constrains morphological responses of the system on a given scale. The internal dynamics of these responses involve morphological coupling of the upper shoreface to the backbarrier and to the lower shoreface. The coupling mechanisms govern systematic lateral displacements of the shoreface, and therefore determine trends in shoreline advance and retreat. These changes manifest as the most fundamental modes of coastal evolution upon which higher-order (shorter-term) changes are superimposed. We illustrate the principles in a companion paper (The Coastal Tract: Part 2).
Late-time tails for scale-invariant wave equations with a potential and the near-horizon geometry of null infinity

We provide a definitive treatment, including sharp decay and the precise late-time asymptotic profile, for generic solutions of linear wave equations with a (singular) inverse-square potential in (3+1)-dimensional Minkowski spacetime. Such equations are scale-invariant and we show their solutions decay in time at a rate determined by the coefficient in the inverse-square potential. We present a novel, geometric, physical-space approach for determining late-time asymptotics, based around embedding Minkowski spacetime conformally into the spacetime $AdS_2 \times \mathbb{S}^2$ (with $AdS_2$ the two-dimensional anti de-Sitter spacetime) to turn a global late-time asymptotics problem into a local existence problem for the wave equation in $AdS_2 \times \mathbb{S}^2$. Our approach is inspired by the treatment of the near-horizon geometry of extremal black holes in the physics literature. We moreover apply our method to another scale-invariant model: the (complex-valued) charged wave equation on Minkowski spacetime in the presence of a static electric field, which can be viewed as a simplification of the charged Maxwell-Klein-Gordon equations on a black hole spacetime.
Analyzing COVID-19 Vaccination Sentiments in Nigerian Cyberspace: Insights from a Manually Annotated Twitter Dataset

Numerous successes have been achieved in combating the COVID-19 pandemic, initially using various precautionary measures like lockdowns, social distancing, and the use of face masks. More recently, various vaccinations have been developed to aid in the prevention or reduction of the severity of the COVID-19 infection. Despite the effectiveness of the precautionary measures and the vaccines, there are several controversies that are massively shared on social media platforms like Twitter. In this paper, we explore the use of state-of-the-art transformer-based language models to study people's acceptance of vaccines in Nigeria. We developed a novel dataset by crawling multi-lingual tweets using relevant hashtags and keywords. Our analysis and visualizations revealed that most tweets expressed neutral sentiments about COVID-19 vaccines, with some individuals expressing positive views, and there was no strong preference for specific vaccine types, although Moderna received slightly more positive sentiment. We also found out that fine-tuning a pre-trained LLM with an appropriate dataset can yield competitive results, even if the LLM was not initially pre-trained on the specific language of that dataset.
IMPACT OF REGIONAL VARIATION ON LUPEOL CONTENT IN CARISSA CARANDAS LINN. FRUITS: EVALUATION USING VALIDATED HIGH PERFORMANCE THIN LAYER CHROMATOGRAPHY

The present work is an attempt to evaluate the impact of regional variation on lupeol content in Carissa carandas Linn. fruits collected from different geographical regions of India using a validated HPTLC method. Chromatographic separation was achieved on TLC plates pre-coated with silica gel 60 F254 using toluene: methanol (8:1, v/v) as a mobile phase. Detection of lupeol was carried out by derivatizing the plates with 10 % methanolic sulphuric acid reagent followed by its densitometric scanning using CAMAG TLC scanner 4 at 366 nm. The method was validated as per ICH guidelines. Statistical analysis reveals that, the content of lupeol in different geographical region varied significantly.
Penrose process in Reissner-Nordström-AdS black hole spacetimes: Black hole energy factories and black hole bombs

The Penrose process for the decay of electrically charged particles in a Reissner-Nordstr\"om-anti-de Sitter black hole spacetime is studied. To extract large quantities of energy one needs to mount a recursive Penrose process where particles are confined and can bounce back to suffer ever again a decaying process in the black hole electric ergoregion. In an asymptotically anti-de Sitter (AdS) spacetime, two situations of confinement are possible. One situation uses a reflecting mirror at some radius, which obliges the energetic outgoing particles to return to the decaying point. The other situation uses the natural AdS property that sends back at some intrinsic returning radius those outgoing energetic particles. In addition, besides the conservation laws the decaying process must obey, one has to set conditions at the decaying point for the particles debris. These conditions restrain the possible scenarios, but there are still a great number of available scenarios for the decays. Within these, we choose two scenarios, scenario 1 and scenario 2, that pertain to the masses and electric charges of the final particles. Thus, in the mirror situation we find that scenario 1 leads to a black hole energy factory, and scenario 2 ends in a black hole bomb. In the no mirror situation, i.e., pure Reissner-Nordstr\"om-AdS, scenario 1 leads again to a black hole energy factory, but scenario 2 yields no bomb. This happens because the volume in which the particles are confined increases to infinity along the chain of decays, leading to a zero value of the extracted energy per unit volume and the bomb is demined. The whole treatment performed here involves no backreaction on the black hole mass and electric charge, nevertheless we speculate that the end state of the recursive process is a Reissner-Nordstr\"om-AdS black hole with very short hair, i.e., with one particle at rest at some definite radius.
Effects of Type Ia Supernovae Absolute Magnitude Priors on the Hubble Constant Value

We systematically explore the influence of the prior of the peak absolute magnitude (M) of Type Ia supernovae (SNe Ia) on the measurement of the Hubble constant (H 0) from SNe Ia observations. We consider five different data-motivated M priors, representing varying levels of dispersion, and assume the spatially flat ΛCDM cosmological model. Different M priors lead to relative changes in the mean values of H 0 from 2% to 7%. Loose priors on M yield H 0 estimates consistent with both the Planck 2018 result and the SH0ES result at the 68% confidence level. We also examine the potential impact of peculiar velocity subtraction on the value of H 0 and show that it is insignificant for the SNe Ia observations with redshift z > 0.01 used in our analyses. We also repeat the analysis in the cosmography model and find very similar results. This suggests that our results are robust and model independent.
Dual-Domain Coarse-to-Fine Progressive Estimation Network for Simultaneous Denoising, Limited-View Reconstruction, and Attenuation Correction of Cardiac SPECT

Single-Photon Emission Computed Tomography (SPECT) is widely applied for the diagnosis of coronary artery diseases. Low-dose (LD) SPECT aims to minimize radiation exposure but leads to increased image noise. Limited-view (LV) SPECT, such as the latest GE MyoSPECT ES system, enables accelerated scanning and reduces hardware expenses but degrades reconstruction accuracy. Additionally, Computed Tomography (CT) is commonly used to derive attenuation maps ($\mu$-maps) for attenuation correction (AC) of cardiac SPECT, but it will introduce additional radiation exposure and SPECT-CT misalignments. Although various methods have been developed to solely focus on LD denoising, LV reconstruction, or CT-free AC in SPECT, the solution for simultaneously addressing these tasks remains challenging and under-explored. Furthermore, it is essential to explore the potential of fusing cross-domain and cross-modality information across these interrelated tasks to further enhance the accuracy of each task. Thus, we propose a Dual-Domain Coarse-to-Fine Progressive Network (DuDoCFNet), a multi-task learning method for simultaneous LD denoising, LV reconstruction, and CT-free $\mu$-map generation of cardiac SPECT. Paired dual-domain networks in DuDoCFNet are cascaded using a multi-layer fusion mechanism for cross-domain and cross-modality feature fusion. Two-stage progressive learning strategies are applied in both projection and image domains to achieve coarse-to-fine estimations of SPECT projections and CT-derived $\mu$-maps. Our experiments demonstrate DuDoCFNet's superior accuracy in estimating projections, generating $\mu$-maps, and AC reconstructions compared to existing single- or multi-task learning methods, under various iterations and LD levels. The source code of this work is available at https://github.com/XiongchaoChen/DuDoCFNet-MultiTask.
CNN architecture extraction on edge GPU

Neural networks have become popular due to their versatility and state-of-the-art results in many applications, such as image classification, natural language processing, speech recognition, forecasting, etc. These applications are also used in resource-constrained environments such as embedded devices. In this work, the susceptibility of neural network implementations to reverse engineering is explored on the NVIDIA Jetson Nano microcomputer via side-channel analysis. To this end, an architecture extraction attack is presented. In the attack, 15 popular convolutional neural network architectures (EfficientNets, MobileNets, NasNet, etc.) are implemented on the GPU of Jetson Nano and the electromagnetic radiation of the GPU is analyzed during the inference operation of the neural networks. The results of the analysis show that neural network architectures are easily distinguishable using deep learning-based side-channel analysis.
The EDGE-CALIFA Survey: An Extragalactic Database for Galaxy Evolution Studies

The EDGE-CALIFA survey provides spatially resolved optical integral-field unit and CO spectroscopy for 125 galaxies selected from the Calar Alto Legacy Integral Field Area Survey (CALIFA) Data Release 3 sample. The Extragalactic Database for Galaxy Evolution (EDGE) presents the spatially resolved products of the survey as pixel tables that reduce the oversampling in the original images and facilitate comparison of pixels from different images. By joining these pixel tables to lower-dimensional tables that provide radial profiles, integrated spectra, or global properties, it is possible to investigate the dependence of local conditions on large-scale properties. The database is freely accessible and has been utilized in several publications. We illustrate the use of this database and highlight the effects of CO upper limits on the inferred slopes of the local scaling relations between the stellar mass, star formation rate (SFR), and H2 surface densities. We find that the correlation between H2 and SFR surface density is the tightest among the three relations.
Linear Relative Pose Estimation Founded on Pose-only Imaging Geometry

How to efficiently and accurately handle image matching outliers is a critical issue in two-view relative estimation. The prevailing RANSAC method necessitates that the minimal point pairs be inliers. This paper introduces a linear relative pose estimation algorithm for n $( n \geq 6$) point pairs, which is founded on the recent pose-only imaging geometry to filter out outliers by proper reweighting. The proposed algorithm is able to handle planar degenerate scenes, and enhance robustness and accuracy in the presence of a substantial ratio of outliers. Specifically, we embed the linear global translation (LiGT) constraint into the strategies of iteratively reweighted least-squares (IRLS) and RANSAC so as to realize robust outlier removal. Simulations and real tests of the Strecha dataset show that the proposed algorithm achieves relative rotation accuracy improvement of 2 $\sim$ 10 times in face of as large as 80% outliers.
Graph-Informed Neural Networks for Sparse Grid-Based Discontinuity Detectors

In this paper, we present a novel approach for detecting the discontinuity interfaces of a discontinuous function. This approach leverages Graph-Informed Neural Networks (GINNs) and sparse grids to address discontinuity detection also in domains of dimension larger than 3. GINNs, trained to identify troubled points on sparse grids, exploit graph structures built on the grids to achieve efficient and accurate discontinuity detection performances. We also introduce a recursive algorithm for general sparse grid-based detectors, characterized by convergence properties and easy applicability. Numerical experiments on functions with dimensions n = 2 and n = 4 demonstrate the efficiency and robust generalization of GINNs in detecting discontinuity interfaces. Notably, the trained GINNs offer portability and versatility, allowing integration into various algorithms and sharing among users.
Catch-Up Mix: Catch-Up Class for Struggling Filters in CNN

Deep learning has made significant advances in computer vision, particularly in image classification tasks. Despite their high accuracy on training data, deep learning models often face challenges related to complexity and overfitting. One notable concern is that the model often relies heavily on a limited subset of filters for making predictions. This dependency can result in compromised generalization and an increased vulnerability to minor variations. While regularization techniques like weight decay, dropout, and data augmentation are commonly used to address this issue, they may not directly tackle the reliance on specific filters. Our observations reveal that the heavy reliance problem gets severe when slow-learning filters are deprived of learning opportunities due to fast-learning filters. Drawing inspiration from image augmentation research that combats over-reliance on specific image regions by removing and replacing parts of images, Our idea is to mitigate the problem of over-reliance on strong filters by substituting highly activated features. To this end, we present a novel method called Catch-up Mix, which provides learning opportunities to a wide range of filters during training, focusing on filters that may lag behind. By mixing activation maps with relatively lower norms, Catch-up Mix promotes the development of more diverse representations and reduces reliance on a small subset of filters. Experimental results demonstrate the superiority of our method in various vision classification datasets, providing enhanced robustness.
Multi-phase $k$-quadrature domains and applications to acoustic waves and magnetic fields

The primary objective of this paper is to explore the multi-phase variant of quadrature domains associated with the Helmholtz equation, commonly referred to as $k$-quadrature domains. Our investigation employs both the minimization problem approach, which delves into the segregation ground state of an energy functional, and the partial balayage procedure, drawing inspiration from the recent work by Gardiner and Sj\"odin. Furthermore, we present practical applications of these concepts in the realms of acoustic waves and magnetic fields.