Miracle-dz/UniFAR-FTUs huggingface dataset:
Sea Level Rise and Coastal Erosion

IPCC projections indicate that the rate of sea level rise (SLR) during the 21st century may be about an order of magnitude greater than the 20th century rate of 1–2 mm/year. This accelerated SLR will in turn result in much faster coastline retreat with particularly severe impacts on low-lying areas. The socioeconomic impact of such accelerated coastline retreat could be massive due to the rapid growth of coastal communities and infrastructure over the past five or six decades. The method most commonly used to estimate coastline retreat due to SLR is the simple two-dimensional mass conservation principle known as the Bruun rule. However, in view of the high level of predictive accuracy that is clearly needed to facilitate informed planning decisions for the future, can we continue to depend on the Bruun rule? This chapter discusses the evidence for and against the Bruun rule and suggests alternative methods that may be more suitable for the 21st century.
Explaining Graph Neural Networks Using Interpretable Local Surrogates

We propose an interpretable local surrogate (ILS) method for understanding the predictions of black-box graph models. Explainability methods are commonly employed to gain insights into black-box models and, given the widespread adoption of GNNs in diverse applications, understanding the underlying reasoning behind their decision-making processes becomes crucial. Our ILS method approximates the behavior of a black-box graph model by fitting a simple surrogate model in the local neighborhood of a given input example. Leveraging the interpretability of the surrogate, ILS is able to identify the most relevant nodes contributing to a specific prediction. To efficiently identify these nodes, we utilize group sparse linear models as local surrogates. Through empirical evaluations on explainability benchmarks, our method consistently outperforms state-of-the-art graph explainability methods. This demonstrates the effectiveness of our approach in providing enhanced interpretability for GNN predictions.
Effects of green tea polyphenols on inflammation and iron status

Abstract Inflammation is an underlying problem for many disease states and has been implicated in iron deficiency (ID). This study aimed to determine whether iron status is improved by epigallocatechin-3-gallate (EGCG) through reducing inflammation. Thirty-two male Sprague–Dawley rats were fed an iron-deficient diet for 2 weeks and then randomly divided into four groups (n 8 each): positive controls, negative controls, lipopolysaccharide (LPS, 0⋅5 mg/kg body weight), and LPS + EGCG (LPS plus 600 mg EGCG/kg diet) for 3 additional weeks. The study involved testing two control groups, both treated with saline. One group (positive control) was fed a regular diet containing standard iron, while the negative control was fed an iron-deficient diet. Additionally, two treatment groups were tested. The first group was given LPS, while the second group was administered LPS and fed an EGCG diet. Iron status, hepcidin, C-reactive protein (CRP), serum amyloid A (SAA), and interleukin-6 (IL-6) were measured. There were no differences in treatment groups compared with control in CRP, hepcidin, and liver iron concentrations. Serum iron concentrations were significantly lower in the LPS (P = 0⋅02) and the LPS + EGCG (P = 0⋅01) than in the positive control group. Compared to the positive control group, spleen iron concentrations were significantly lower in the negative control (P < 0⋅001) but not with both LPS groups. SAA concentrations were significantly lower in the LPS + EGCG group compared to LPS alone group. EGCG reduced SAA concentrations but did not affect hepcidin or improve serum iron concentration or other iron markers.
On the Maximization of Long-Run Reward CVaR for Markov Decision Processes

This paper studies the optimization of Markov decision processes (MDPs) from a risk-seeking perspective, where the risk is measured by conditional value-at-risk (CVaR). The objective is to find a policy that maximizes the long-run CVaR of instantaneous rewards over an infinite horizon across all history-dependent randomized policies. By establishing two optimality inequalities of opposing directions, we prove that the maximum of long-run CVaR of MDPs over the set of history-dependent randomized policies can be found within the class of stationary randomized policies. In contrast to classical MDPs, we find that there may not exist an optimal stationary deterministic policy for maximizing CVaR. Instead, we prove the existence of an optimal stationary randomized policy that requires randomizing over at most two actions. Via a convex optimization representation of CVaR, we convert the long-run CVaR maximization MDP into a minimax problem, where we prove the interchangeability of minimum and maximum and the related existence of saddle point solutions. Furthermore, we propose an algorithm that finds the saddle point solution by solving two linear programs. These results are then extended to objectives that involve maximizing some combination of mean and CVaR of rewards simultaneously. Finally, we conduct numerical experiments to demonstrate the main results.
Learning Efficient Unsupervised Satellite Image-based Building Damage Detection

Existing Building Damage Detection (BDD) methods always require labour-intensive pixel-level annotations of buildings and their conditions, hence largely limiting their applications. In this paper, we investigate a challenging yet practical scenario of BDD, Unsupervised Building Damage Detection (U-BDD), where only unlabelled pre- and post-disaster satellite image pairs are provided. As a pilot study, we have first proposed an advanced U-BDD baseline that leverages pre-trained vision-language foundation models to address the U-BDD task. However, the apparent domain gap between satellite and generic images causes low confidence in the foundation models used to identify buildings and their damages. In response, we further present a novel self-supervised framework, U-BDD++, which improves upon the U-BDD baseline by addressing domain-specific issues associated with satellite imagery. Extensive experiments on the widely used building damage assessment benchmark demonstrate the effectiveness of the proposed method for unsupervised building damage detection. The presented annotation-free and foundation model-based paradigm ensures an efficient learning phase. This study opens a new direction for real-world BDD and sets a strong baseline for future research.
Moderate Dimension Reduction for k-Center Clustering

The Johnson-Lindenstrauss (JL) Lemma introduced the concept of dimension reduction via a random linear map, which has become a fundamental technique in many computational settings. For a set of $n$ points in $\mathbb{R}^d$ and any fixed $\epsilon>0$, it reduces the dimension $d$ to $O(\log n)$ while preserving, with high probability, all the pairwise Euclidean distances within factor $1+\epsilon$. Perhaps surprisingly, the target dimension can be lower if one only wishes to preserve the optimal value of a certain problem, e.g., max-cut or $k$-means. However, for some notorious problems, like diameter (aka furthest pair), dimension reduction via the JL map to below $O(\log n)$ does not preserve the optimal value within factor $1+\epsilon$. We propose to focus on another regime, of \emph{moderate dimension reduction}, where a problem's value is preserved within factor $\alpha=O(1)$ (or even larger) using target dimension $\log n / \mathrm{poly}(\alpha)$. We establish the viability of this approach and show that the famous $k$-center problem is $\alpha$-approximated when reducing to dimension $O(\tfrac{\log n}{\alpha^2}+\log k)$. Along the way, we address the diameter problem via the special case $k=1$. Our result extends to several important variants of $k$-center (with outliers, capacities, or fairness constraints), and the bound improves further with the input's doubling dimension. While our $poly(\alpha)$-factor improvement in the dimension may seem small, it actually has significant implications for streaming algorithms, and easily yields an algorithm for $k$-center in dynamic geometric streams, that achieves $O(\alpha)$-approximation using space $\mathrm{poly}(kdn^{1/\alpha^2})$. This is the first algorithm to beat $O(n)$ space in high dimension $d$, as all previous algorithms require space at least $\exp(d)$. Furthermore, it extends to the $k$-center variants mentioned above.
Simple Lévy α-Stable Model Analysis of Elastic pp and pp¯ Low-|t| Data from SPS to LHC Energies

A simple Lévy α-stable (SL) model is used to describe the data on elastic pp and pp¯ scattering at low-|t| from SPS energies up to LHC energies. The SL model is demonstrated to describe the data with a strong non-exponential feature in a statistically acceptable manner. The energy dependence of the parameters of the model is determined and analyzed. The Lévy α parameter of the model has an energy-independent value of 1.959 ± 0.002 following from the strong non-exponential behavior of the data. We strengthen the conclusion that the discrepancy between TOTEM and ATLAS elastic pp differential cross section measurements arises only in the normalization and not in the shape of the distribution of the data as a function of t. We find that the slope parameter has different values for pp and pp¯ elastic scattering at LHC energies. This may be the effect of the odderon exchange or the jump in the energy dependence of the slope parameter in the energy interval 3 GeV ≲s≲ 4 GeV.
SAGE: Bridging Semantic and Actionable Parts for GEneralizable Articulated-Object Manipulation under Language Instructions

Generalizable manipulation of articulated objects remains a challenging problem in many real-world scenarios, given the diverse object structures, functionalities, and goals. In these tasks, both semantic interpretations and physical plausibilities are crucial for a policy to succeed. To address this problem, we propose SAGE, a novel framework that bridges the understanding of semantic and actionable parts of articulated objects to achieve generalizable manipulation under language instructions. Given a manipulation goal specified by natural language, an instruction interpreter with Large Language Models (LLMs) first translates them into programmatic actions on the object's semantic parts. This process also involves a scene context parser for understanding the visual inputs, which is designed to generate scene descriptions with both rich information and accurate interaction-related facts by joining the forces of generalist Visual-Language Models (VLMs) and domain-specialist part perception models. To further convert the action programs into executable policies, a part grounding module then maps the object semantic parts suggested by the instruction interpreter into so-called Generalizable Actionable Parts (GAParts). Finally, an interactive feedback module is incorporated to respond to failures, which greatly increases the robustness of the overall framework. Experiments both in simulation environments and on real robots show that our framework can handle a large variety of articulated objects with diverse language-instructed goals. We also provide a new benchmark for language-guided articulated-object manipulation in realistic scenarios.
Potentials on the conformally compactified Minkowski spacetime and their application to quark deconfinement

We study a class of conformal metric deformations in the quasi-radial coordinate parameterizing the 3-sphere in the conformally compactified Minkowski spacetime $S^1\times S^3$. After reduction of the associated Laplace-Beltrami operators to a Schr\"odinger form, a corresponding class of exactly solvable potentials (each one containing a scalar and a gradient term) is found. In particular, the scalar piece of these potentials can be exactly or quasi-exactly solvable, and among them we find the finite range confining trigonometric potentials of P\"oschl-Teller, Scarf and Rosen-Morse. As an application of the results developed in the paper, the large compactification radius limit of the interaction described by some of these potentials is studied, and this regime is shown to be relevant to a quantum mechanical quark deconfinement mechanism.
Finding and counting small tournaments in large tournaments

We present new algorithms for counting and detecting small tournaments in a given tournament. In particular, it is proved that every tournament on four vertices (there are four) can be detected in $O(n^2)$ time and counted in $O(n^\omega)$ time where $\omega<2.373$ is the matrix multiplication exponent. It is also proved that any tournament on five vertices (there are $12$) can be counted in $O(n^{\omega+1})$ time. As for lower-bounds, we prove that for almost all $k$-vertex tournaments, the complexity of the detection problem is not easier than the complexity of the corresponding well-studied counting problem for {\em undirected cliques} of order $k-O(\log k)$.
Efficiency of Turbulent Reacceleration by Solenoidal Turbulence and Its Application to the Origin of Radio Megahalos in Cluster Outskirts

Recent radio observations with the Low Frequency Array (LOFAR) discovered diffuse emission extending beyond the scale of classical radio halos. The presence of such megahalos indicates that the amplification of the magnetic field and acceleration of relativistic particles are working in the cluster outskirts, presumably due to the combination of shocks and turbulence that dissipate energy in these regions. Cosmological magnetohydrodynamical (MHD) simulations of galaxy clusters suggest that solenoidal turbulence has a significant energy budget in the outskirts of galaxy clusters. In this paper, we explore the possibility that this turbulence contributes to the emission observed in megahalos through second-order Fermi acceleration of relativistic particles and magnetic field amplification by the dynamo. We focus on the case of A2255 and find that this scenario can explain the basic properties of the diffuse emission component that is observed under assumptions that are used in previous literature. More specifically, we conduct a numerical follow-up, solving the Fokker–Planck equation by using a snapshot of an MHD simulation and deducing the synchrotron brightness integrated along the lines of sight. We find that a volume-filling emission, ranging between 30% and almost 100% of the projected area, depending on our assumptions on the particle diffusion and transport, can be detected at LOFAR sensitivities. Assuming a magnetic field B ∼ 0.2 μG, as derived from a dynamo model applied to the emitting region, we find that the observed brightness can be matched when ∼1% of the solenoidal turbulent energy flux is channeled into particle acceleration.
Computing Repairs Under Functional and Inclusion Dependencies via Argumentation

We discover a connection between finding subset-maximal repairs for sets of functional and inclusion dependencies, and computing extensions within argumentation frameworks (AFs). We study the complexity of the existence of a repair and deciding whether a given tuple belongs to some (or every) repair, by simulating the instances of these problems via AFs. We prove that subset-maximal repairs under functional dependencies correspond to the naive extensions, which also coincide with the preferred and stable extensions in the resulting AFs. For inclusion dependencies, one needs a pre-processing step on the resulting AFs in order for the extensions to coincide. Allowing both types of dependencies breaks this relationship between extensions, and only preferred semantics captures the repairs. Finally, we establish that the complexities of the above decision problems are NP-complete and Pi_2^P-complete, when both functional and inclusion dependencies are allowed.
MobileUtr: Revisiting the relationship between light-weight CNN and Transformer for efficient medical image segmentation

Due to the scarcity and specific imaging characteristics in medical images, light-weighting Vision Transformers (ViTs) for efficient medical image segmentation is a significant challenge, and current studies have not yet paid attention to this issue. This work revisits the relationship between CNNs and Transformers in lightweight universal networks for medical image segmentation, aiming to integrate the advantages of both worlds at the infrastructure design level. In order to leverage the inductive bias inherent in CNNs, we abstract a Transformer-like lightweight CNNs block (ConvUtr) as the patch embeddings of ViTs, feeding Transformer with denoised, non-redundant and highly condensed semantic information. Moreover, an adaptive Local-Global-Local (LGL) block is introduced to facilitate efficient local-to-global information flow exchange, maximizing Transformer's global context information extraction capabilities. Finally, we build an efficient medical image segmentation model (MobileUtr) based on CNN and Transformer. Extensive experiments on five public medical image datasets with three different modalities demonstrate the superiority of MobileUtr over the state-of-the-art methods, while boasting lighter weights and lower computational cost. Code is available at https://github.com/FengheTan9/MobileUtr.
Deep Generative Attacks and Countermeasures for Data-Driven Offline Signature Verification

While previous studies have explored attacks via random, simple, and skilled forgeries, generative attacks have received limited attention in the data-driven signature verification (DASV) process. Thus, this paper explores the impact of generative attacks on DASV and proposes practical and interpretable countermeasures. We investigate the power of two prominent Deep Generative Models (DGMs), Variational Auto-encoders (VAE) and Conditional Generative Adversarial Networks (CGAN), on their ability to generate signatures that would successfully deceive DASV. Additionally, we evaluate the quality of generated images using the Structural Similarity Index measure (SSIM) and use the same to explain the attack's success. Finally, we propose countermeasures that effectively reduce the impact of deep generative attacks on DASV. We first generated six synthetic datasets from three benchmark offline-signature datasets viz. CEDAR, BHSig260- Bengali, and BHSig260-Hindi using VAE and CGAN. Then, we built baseline DASVs using Xception, ResNet152V2, and DenseNet201. These DASVs achieved average (over the three datasets) False Accept Rates (FARs) of 2.55%, 3.17%, and 1.06%, respectively. Then, we attacked these baselines using the synthetic datasets. The VAE-generated signatures increased average FARs to 10.4%, 10.1%, and 7.5%, while CGAN-generated signatures to 32.5%, 30%, and 26.1%. The variation in the effectiveness of attack for VAE and CGAN was investigated further and explained by a strong (rho = -0.86) negative correlation between FARs and SSIMs. We created another set of synthetic datasets and used the same to retrain the DASVs. The retained baseline showed significant robustness to random, skilled, and generative attacks as the FARs shrank to less than 1% on average. The findings underscore the importance of studying generative attacks and potential countermeasures for DASV.
Deeper into Self-Supervised Monocular Indoor Depth Estimation

Monocular depth estimation using Convolutional Neural Networks (CNNs) has shown impressive performance in outdoor driving scenes. However, self-supervised learning of indoor depth from monocular sequences is quite challenging for researchers because of the following two main reasons. One is the large areas of low-texture regions and the other is the complex ego-motion on indoor training datasets. In this work, our proposed method, named IndoorDepth, consists of two innovations. In particular, we first propose a novel photometric loss with improved structural similarity (SSIM) function to tackle the challenge from low-texture regions. Moreover, in order to further mitigate the issue of inaccurate ego-motion prediction, multiple photometric losses at different stages are used to train a deeper pose network with two residual pose blocks. Subsequent ablation study can validate the effectiveness of each new idea. Experiments on the NYUv2 benchmark demonstrate that our IndoorDepth outperforms the previous state-of-the-art methods by a large margin. In addition, we also validate the generalization ability of our method on ScanNet dataset. Code is availabe at https://github.com/fcntes/IndoorDepth.
RaftGP: Random Fast Graph Partitioning

Graph partitioning (GP), a.k. a. community detection, is a classic problem that divides the node set of a graph into densely-connected blocks. Following prior work on the IEEE HPEC Graph Challenge benchmark and recent advances in graph machine learning, we propose a novel RAndom FasT Graph Partitioning (RaftGP) method based on an efficient graph embedding scheme. It uses the Gaussian random projection to extract community-preserving features from classic GP objectives. These features are fed into a graph neural network (GNN) to derive low-dimensional node embeddings. Surprisingly, our experiments demonstrate that a randomly initialized GNN even without training is enough for RaftGP to derive informative community-preserving embeddings and support high-quality GP. To enable the derived embeddings to tackle GP, we introduce a hierarchical model selection algorithm that simultaneously determines the number of blocks and the corresponding GP result. We evaluate RaftGP on the Graph Challenge benchmark and compare the performance with five baselines, where our method can achieve a better trade-off between quality and efficiency. In particular, compared to the baseline algorithm [1] of the IEEE HPEC Graph Challenge, our method is 6.68x - 23.9x faster on graphs with 1E3 - 5E4 nodes and at least 64.5x faster on larger (IE5 node) graphs on which the baseline takes more than 1E4 seconds. Our method achieves better accuracy on all test cases. We also develop a new graph generator to address some limitations of the original generator in the benchmark.
POPULATION ASSESSMENT AND MANAGEMENT NEEDS OF A GREEN TURTLE, Chelonia mydas, POPULATION IN THE WESTERN CARIBBEAN

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy POPULATION ASSESSMENT AND MANAGEMENT NEEDS OF A GREEN TURTLE, Chelonia mydas, POPULATION IN THE WESTERN CARIBBEAN By Cathi Lynn Campbell May 2003 Chair: Michael P. Moulton Major Department: Wildlife Ecology and Conservation In this study I estimated survival rates of large juvenile and adult female green turtles (Chelonia mydas) from mark-recapture data from the Nicaragua foraging ground and from band recovery analysis. I used these data to estimate the long-term growth rate of a simulated population to assess the current status of the Tortuguero, Costa Rica green turtle population. I also examined potential changes to current harvest levels in Nicaragua to estimate sustained yield. Based on band recovery analysis, large juvenile and adult green turtles tagged at Nicaragua turtle fishing sites and adult females tagged on the nesting beach exhibit low mean annual survival probabilities, 0 = 0.55 (SE = 0.04) and, 0 = 0.82 (SE = 0.12), respectively. Based on a series of matrix population projections simulations using these survival rate estimates, there is evidence that the Tortuguero population is probably declining. The severity of the threat, however, depends in part on the proportion of large juveniles from this population that are exposed to the Nicaragua turtle fishery. Based on