Miracle-dz/UniFAR-FTUs huggingface dataset:
Origins of Martian valley networks based on a comparative statistical analysis of Martian and terrestrial basin shape functions

Despite more than 35 years of study since the discovery of the Martian valley networks, questions still exist as to the fluvial process responsible for their origin. As part of the investigation on the relative importance of surface runoff versus groundwater sapping in the formation of the Martian valley networks, morphometric methods provide an objective and quantitative approach. In this study, four shape indices (circularity, elongation, lemniscate index and lemniscate ratio), traditionally used to quantify the planimetric shape of terrestrial drainage basins, are adapted into shape functions to determine the evolution of basin shape with elevation. The shape functions of 39 terrestrial basins of known origins (surface runoff, groundwater sapping, or meltwater) are analyzed to determine their ability to distinguish between different formation processes. Through hierarchical clustering analysis, it is determined that regional slope exerts a stronger control on a basin's internal shape than formation process. Once basins are divided into separate groups based on their regional slope (steep vs. flat), hierarchical clustering analysis is successful at clustering basins with respect to their formation process based on their shape functions. The shape functions are then used to characterize 23 Martian valley networks. Using self-organizing maps and k-nearest neighbour, the shape functions of the Martian basins are classified as most similar to the groundwater sapping basins of Chile's Atacama Desert. However, reasons for this similarity are not limited to groundwater sapping and could also be attributed to, among other possibilities, a combination of intermittent and scarce precipitation, the effects of impact cratering, or differences in the erosional mechanics that formed the Martian valley networks.
Management of virulence in Pseudomonas aeruginosa and Serratia marcescens using environmentally-friendly titanium dioxide nanoparticles

Antimicrobial resistance (AMR), a condition in which the efficacy of antimicrobial drugs in fighting microorganisms is reduced, has become a global challenge. Multidrug resistance (MDR) has been developing in microorganisms, where they can resist multiple medications. In particular, there has been a rise in MDR as well as extensively drug-resistant (XDR) strains of Pseudomonas aeruginosa in some regions, with prevalence rates ranging from 15% to 30%. The application of nanotechnology ranges from diagnostics to drug-delivery systems, revolutionizing healthcare, and improving disease treatment. We aimed to investigate the efficacy of titanium dioxide nanoparticles (TiO2-NPs) against various virulent traits of P. aeruginosa and S. marcescens. More than 50% reduction in the production of virulent pigments of P. aeruginosa was recorded following the treatment of TiO2-NPs. Additionally, elastases and exoproteases were inhibited by 58.21 and 74.36%, respectively. A similar result was observed against the rhamnolipid production and swimming motility of P. aeruginosa. The effect of TiO2-NPs was also validated against another opportunistic pathogen, S. marcescens, where the production of prodigiosin was reduced by 64.78%. Also, a roughly 75% attenuation of proteolytic activity and more than 50% reduction in swarming motility were found. In the control group, the cell surface hydrophobicity was 77.72%, which decreased to 24.67% with the addition of 64 μg ml−1 TiO2-NPs in culture media. The hydrophobicity index of microorganisms is crucial for their initial attachment and the formation of biofilms. In conclusion, TiO2-NPs demonstrated potential in a multi-target approach against P. aeruginosa and S. marcescens, suggesting their advantages in the prevention and treatment of infections. These nanomaterials could have vital importance in the development of novel antibacterial agents to combat drug-resistant bacteria.
High Expression of H 3 K 27 me 3 in Human Hepatocellular Carcinomas Correlates Closely with Vascular Invasion and Predicts Worse Prognosis in Patients

INTRODUCTION Hepatocellular carcinoma (HCC), with a high prevalence in Southeast Asia and Africa, is a leading lethal malignancy worldwide, and the incidence of HCC has been steadily increasing in Europe and America (1,2). Unfortunately, the long-term prognosis of patients with HCC remains poor despite recent advances in surgical techniques and medical treatment (3). This poor prognosis is in part related to the high incidence of intrahepatic metastasis and/or vascular invasion after initial treatment (4). Identification of biomarkers that can be used to define the vascular invasion and metastatic potential of HCC may facilitate the development of appropriate therapeutic strategies earlier in the course of this cancer. Thus, a substantial amount of research on HCC has focused on the discovery of specific molecular markers that are responsible for the vascular invasion and/or progression of this malignancy. To date, however, the search and identification of promising molecular and/or genetic alterations in HCC cells that have clinical/prognostic significance has remained substantially limited (5–7). It was reported that epigenetic changes are involved in silencing of various tumor-suppressor genes, facilitating tumorigenesis and/or progression of human cancers (8–10). Histone methylation has been found to play an important role in regulating gene expression and chromatin function (9). Trimethylation of lysine 27 on histone H3 (H3K27me3), a transcription-suppressive histone mark, is methylated by enhancer of zeste homolog 2 (EZH2) (11). EZH2, the catalytic subunit of Polycomb repressive complex 2 (PRC2), was found to contribute to the maintenance of cell identity, cell cycle regulation and oncoHigh Expression of H3K27me3 in Human Hepatocellular Carcinomas Correlates Closely with Vascular Invasion and Predicts Worse Prognosis in Patients
Relightful Harmonization: Lighting-aware Portrait Background Replacement

Portrait harmonization aims to composite a subject into a new background, adjusting its lighting and color to ensure harmony with the background scene. Existing harmonization techniques often only focus on adjusting the global color and brightness of the foreground and ignore crucial illumination cues from the background such as apparent lighting direction, leading to unrealistic compositions. We introduce Relightful Harmonization, a lighting-aware diffusion model designed to seamlessly harmonize sophisticated lighting effect for the foreground portrait using any background image. Our approach unfolds in three stages. First, we introduce a lighting representation module that allows our diffusion model to encode lighting information from target image background. Second, we introduce an alignment network that aligns lighting features learned from image background with lighting features learned from panorama environment maps, which is a complete representation for scene illumination. Last, to further boost the photorealism of the proposed method, we introduce a novel data simulation pipeline that generates synthetic training pairs from a diverse range of natural images, which are used to refine the model. Our method outperforms existing benchmarks in visual fidelity and lighting coherence, showing superior generalization in real-world testing scenarios, highlighting its versatility and practicality.
Innovations in Surface Modification Techniques: Advancing Hydrophilic \textit{LiYF$_{4}$:Yb, Er, Tm} Upconversion Nanoparticles and Their Applications

The development and application of upconversion nanoparticles (UCNPs) have garnered significant attention due to their unique optical properties and potential uses in bioimaging, drug delivery, and solar cells. However, the hydrophobic nature of UCNPs presents challenges in their synthesis and application, particularly in aqueous environments. We provide an overview of UCNPs, their synthesis challenges, and the importance of surface modification. Furthermore, we discuss the properties of \textit{LiYF_{4}:Yb, Er, Tm} UCNPs synthesized using novel 2,2-[ethylenebis(oxy)] bisacetic acid (EBAA) method and their versatile applications. Notably, the first Dynamic Light Scattering measurement on 05/22/2022 showed a size of 11.39 nm, and after 348 days on 04/05/2023, the same batch maintained a size of 13.8 nm, indicating excellent stability and no particle agglomeration over this extended period. This remarkable stability underscores the potential of UCNPs synthesized with the EBAA method for long-term applications. Finally, we compare the EBAA method with other surface modification techniques, exploring challenges and future perspectives for the use of hydrophilic UCNPs in various applications. This review aims to emphasize the significance of the EBAA method in advancing the field of upconversion nanoparticles and broadening their potential integration into diverse applications.
The hidden landscape

The thesis investigates and highlights the role of fine scale green structure in the urban landscape with regards to the regulating ecosystem services of runoff mitigation, wind speed regulation and modification to mean radiant temperatures. The analysis was based on case studies in southern Sweden, projecting from a flooding incident in the Hojea river catchment in 2007 and the current seafront development of Lomma Harbour and similar schemes in the Oresund region. The aim has been to explore the potential of how seemingly fine scale green structure may contribute to regulating ecosystem services and thereby play an important role to SuDS (sustainable drainage systems), the urban microclimate and climate responsive design. Part of the aim has been to retrieve this information through quantitative indices and computational modelling, assuming that a numerical approach can produce comparative, rigorous and perceptible outcomes that act as efficient communication tools for e.g. city officials, planners, landscape architects, etc. The work combined a historical approach as well as computational modelling of numerical and tangible indices to shed some light on the hidden processes of regulating ecosystem services of fine-scale green structure. The SCS-CN approach was used for estimating surface runoff and ENVI-met for green structure influences on microclimate. It was found that within the green infrastructure network, individual configurations of green structure elements often influenced regulating ecosystem services beyond their proportionate size. Their influence depended in turn on place-specificity and contextual characteristics of e.g. geographical location, time of year, site spatial configuration and architectural composition. This is discussed with regard to urban densification (spatial), climate change and sustainable development and how the two tools used here can help in green infrastructure planning. The results are also discussed from a qualitative angle, introducing the question of how the collective configuration of dispersed green structure can contribute to a resilient green infrastructure in the urban landscape.
Frozen Bardeen-Dirac stars and light ball

In this paper, we study solutions of a static spherically symmetric system, which is composed of the coupling with the Bardeen action and two Dirac fields. For the case where only the Bardeen action is present, the magnetic charge $q$ can be infinite, then when the magnetic charge is greater than a certain value $q_b$, there exists a black hole solution, which is called the Bardeen black hole (BBH). However, if the Dirac field is introduced, we find that the magnetic charge can only be smaller than the critical value $q_b$, in which there is no black hole solution. Moreover, in the region $q<q_b$, we find that if the magnetic charge exceeds another critical value $q_c$ (i.e., $q_c<q<q_b$), the frequency of the Dirac field can approach zero, and the solution where a critical horizon appears is similar to an extremal black hole outside the critical horizon but has a nonsingular interior. The Dirac fields are also almost concentrated within it. In fact, this is a frozen star solution, we call such solutions frozen Bardeen-Dirac stars (FBDSs). We analyze the light rings of FBDSs and find that there exists a ``true"light ring outside the critical horizon, but inside it, the velocity of photons is very close to zero, which leads to the formation of a ``light ball"inside the critical horizon.
On the proximal point algorithms for solving the monotone inclusion problem

We consider finding a zero point of the maximally monotone operator $T$. First, instead of using the proximal point algorithm (PPA) for this purpose, we employ PPA to solve its Yosida regularization $T_{\lambda}$. Then, based on an $O(a_{k+1})$ ($a_{k+1}\geq \varepsilon>0$) resolvent index of $T$, it turns out that we can establish a convergence rate of $O (1/{\sqrt{\sum_{i=0}^{k}a_{i+1}^2}})$ for both the $\|T_{\lambda}(\cdot)\|$ and the gap function $\mathtt{Gap}(\cdot)$ in the non-ergodic sense, and $O(1/\sum_{i=0}^{k}a_{i+1})$ for $\mathtt{Gap}(\cdot)$ in the ergodic sense. Second, to enhance the convergence rate of the newly-proposed PPA, we introduce an accelerated variant called the Contracting PPA. By utilizing a resolvent index of $T$ bounded by $O(a_{k+1})$ ($a_{k+1}\geq \varepsilon>0$), we establish a convergence rate of $O(1/\sum_{i=0}^{k}a_{i+1})$ for both $\|T_{\lambda}(\cdot)\|$ and $\mathtt {Gap}(\cdot)$, considering the non-ergodic sense. Third, to mitigate the limitation that the Contracting PPA lacks a convergence guarantee, we propose two additional versions of the algorithm. These novel approaches not only ensure guaranteed convergence but also provide sublinear and linear convergence rates for both $\|T_{\lambda}(\cdot)\|$ and $\mathtt {Gap}(\cdot)$, respectively, in the non-ergodic sense.
Engineering of high-$Q$ states via collective mode coupling in chains of Mie resonators

Efficient trapping of light in nanostructures is essential for the development of optical devices that are based on the interaction between light and matter. In this work, we show theoretically and experimentally that one-dimensional arrays of subwavelength dielectric Mie-resonant particles can support collective resonances with increased $Q$-factors. We demonstrate that the increase of the $Q$-factor can be explained by interaction between the collective electric and magnetic dipole modes of the chain resulting in appearance of the inflection point at the band edge. The considered effect is studied experimentally in the chain of high-index ceramic cylinders in the microwave spectral range.
CLIMATE CHANGE SIMULATIONS IN TERMS OF SOLAR RADIATION AND CLOUD FRACTION BASED ON PRECIS OVER MALAYSIA REGION

Climate change is a significant change of weather patterns over a long period of time. Malaysia can be vulnerable to such impact, since economics of this region rely strongly on agriculture and natural sources. This paper simulated the solar radiation and total cloud fraction for Malaysia by the end of the 21 st century based on the A2 and the B2 scenarios by utilizing the latest generation of the Hadley Centre regional climate modeling system, PRECIS (Providing Regional Climates for Impact Studies). Relative to the baseline scenario, the average increase in solar radiation was 5 - 12W under the A2 scenario and 3 - 10W under the B2 scenario. The changes were significant at 95% confidence level across most of the land area. The average total fraction over Malaysia was projected to reduce by 0.14 to 0.032 under the A2 scenario and 0.11 to 0.0057 under the B2 scenario compared to the baseline scenario. Significant changes were observed over the whole domain of investigation during December-January-February under both scenarios. Study results suggest that a significant change in climate variability may potentially increase climate- related risks such as air quality impact and vulnerability in the region.
ReRoGCRL: Representation-based Robustness in Goal-Conditioned Reinforcement Learning

While Goal-Conditioned Reinforcement Learning (GCRL) has gained attention, its algorithmic robustness against adversarial perturbations remains unexplored. The attacks and robust representation training methods that are designed for traditional RL become less effective when applied to GCRL. To address this challenge, we first propose the Semi-Contrastive Representation attack, a novel approach inspired by the adversarial contrastive attack. Unlike existing attacks in RL, it only necessitates information from the policy function and can be seamlessly implemented during deployment. Then, to mitigate the vulnerability of existing GCRL algorithms, we introduce Adversarial Representation Tactics, which combines Semi-Contrastive Adversarial Augmentation with Sensitivity-Aware Regularizer to improve the adversarial robustness of the underlying RL agent against various types of perturbations. Extensive experiments validate the superior performance of our attack and defence methods across multiple state-of-the-art GCRL algorithms. Our code is available at https://github.com/TrustAI/ReRoGCRL.
Context-Aware Iteration Policy Network for Efficient Optical Flow Estimation

Existing recurrent optical flow estimation networks are computationally expensive since they use a fixed large number of iterations to update the flow field for each sample. An efficient network should skip iterations when the flow improvement is limited. In this paper, we develop a Context-Aware Iteration Policy Network for efficient optical flow estimation, which determines the optimal number of iterations per sample. The policy network achieves this by learning contextual information to realize whether flow improvement is bottlenecked or minimal. On the one hand, we use iteration embedding and historical hidden cell, which include previous iterations information, to convey how flow has changed from previous iterations. On the other hand, we use the incremental loss to make the policy network implicitly perceive the magnitude of optical flow improvement in the subsequent iteration. Furthermore, the computational complexity in our dynamic network is controllable, allowing us to satisfy various resource preferences with a single trained model. Our policy network can be easily integrated into state-of-the-art optical flow networks. Extensive experiments show that our method maintains performance while reducing FLOPs by about 40%/20% for the Sintel/KITTI datasets.
Dynamics Harmonic Analysis of Robotic Systems: Application in Data-Driven Koopman Modelling

We introduce the use of harmonic analysis to decompose the state space of symmetric robotic systems into orthogonal isotypic subspaces. These are lower-dimensional spaces that capture distinct, symmetric, and synergistic motions. For linear dynamics, we characterize how this decomposition leads to a subdivision of the dynamics into independent linear systems on each subspace, a property we term dynamics harmonic analysis (DHA). To exploit this property, we use Koopman operator theory to propose an equivariant deep-learning architecture that leverages the properties of DHA to learn a global linear model of system dynamics. Our architecture, validated on synthetic systems and the dynamics of locomotion of a quadrupedal robot, demonstrates enhanced generalization, sample efficiency, and interpretability, with less trainable parameters and computational costs.
Variational quantum algorithm-preserving feasible space for solving the uncapacitated facility location problem

The Quantum Alternating Operator Ansatz (QAOA+) is one of the Variational Quantum Algorithm (VQA) specifically developed to tackle combinatorial optimization problems by exploring the feasible space in search of a target solution. For constrained optimization problems with unconstrained variables, which we call Unconstrained-Variables Problems (UVPs), the mixed operators in the QAOA+ circuit are applied to the constrained variables, while the single-qubit rotating gates $R_X$ operate on the unconstrained variables. The expressibility of this circuit is limited by the shortage of two-qubit gates and the parameter sharing in the $R_X$, which consequently impacts the performance of QAOA+ for solving UVPs. Therefore, it is crucial to develop a suitable ansatz for UVPs. In this paper, we propose the Variational Quantum Algorithm-Preserving Feasible Space (VQA-PFS) ansatz, exemplified by the Uncapacitated Facility Location Problem (UFLP), that applies mixed operators on constrained variables while employing Hardware-Efficient Ansatz (HEA) on unconstrained variables. The numerical results demonstrate that VQA-PFS significantly enhances the success probability and exhibits faster convergence compared to QAOA+, Quantum Approximation Optimization Algorithm (QAOA), and HEA. Furthermore, VQA-PFS reduces the circuit depth dramatically in comparison to QAOA+ and QAOA. Our algorithm is general and instructive in tackling UVPs.
Very high order treatment of embedded curved boundaries in compressible flows: ADER discontinuous Galerkin with a space-time Reconstruction for Off-site data

In this paper we present a novel approach for the design of high order general boundary conditions when approximating solutions of the Euler equations on domains with curved boundaries, using meshes which may not be boundary conformal. When dealing with curved boundaries and/or unfitted discretizations, the consistency of boundary conditions is a well-known challenge, especially in the context of high order schemes. In order to tackle such consistency problems, the so-called Reconstruction for Off-site Data (ROD) method has been recently introduced in the finite volume framework: it is based on performing a boundary polynomial reconstruction that embeds the considered boundary treatment thanks to the implementation of a constrained minimization problem. This work is devoted to the development of the ROD approach in the context of discontinuous finite elements. We use the genuine space-time nature of the local ADER predictors to reformulate the ROD as a single space-time reconstruction procedure. This allows us to avoid a new reconstruction (linear system inversion) at each sub-time node and retrieve a single space-time polynomial that embeds the considered boundary conditions for the entire space-time element. Several numerical experiments are presented proving the consistency of the new approach for all kinds of boundary conditions. Computations involving the interaction of shocks with embedded curved boundaries are made possible through an a posteriori limiting technique.
Global convergence of block proximal iteratively reweighted algorithm with extrapolation

In this paper, we propose a proximal iteratively reweighted algorithm with extrapolation based on block coordinate update aimed at solving a class of optimization problems which is the sum of a smooth possibly nonconvex loss function and a general nonconvex regularizer with a special structure. The proposed algorithm can be used to solve the $\ell_p(0<p<1)$ regularization problem by employing a updating strategy of the smoothing parameter. It is proved that there exists the nonzero extrapolation parameter such that the objective function is nonincreasing. Moreover, the global convergence and local convergence rate are obtained by using the Kurdyka-{\L}ojasiewicz (KL) property on the objective function. Numerical experiments are given to indicate the efficiency of the proposed algorithm.