Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing Scientific Data Transfer on Globus with Error-Bounded Lossy Compression

The increasing volume and velocity of science data necessitate the frequent movement of enormous data volumes as part of routine research activities. As a result, limited wide-area bandwidth often leads to bottlenecks in research progress. However, in many cases, consuming applications (e.g., for analysis, visualization, and machine learning) can achieve acceptable performance on reduced-precision data, and thus researchers may wish to compromise on data precision to reduce transfer and storage costs. Error-bounded lossy compression presents a promising approach as it can significantly reduce data volumes while preserving data integrity based on user-specified error bounds. In this paper, we propose a novel data transfer framework called Ocelot that integrates error-bounded lossy compression into the Globus data transfer infrastructure. We note four key contributions: (1) Ocelot is the first integration of lossy compression in Globus to significantly improve scientific data transfer performance over wide area network (WAN). (2) We propose an effective machine-learning based lossy compression quality estimation model that can predict the quality of error-bounded lossy compressors, which is fundamental to ensure that transferred data are acceptable to users. (3) We develop optimized strategies to reduce the compression time overhead, counter the compute-node waiting time, and improve transfer speed for compressed files. (4) We perform evaluations using many real-world scientific applications across different domains and distributed Globus endpoints. Our experiments show that Ocelot can improve dataset transfer performance substantially, and the quality of lossy compression (time, ratio and data distortion) can be predicted accurately for the purpose of quality assurance.
MAP- and MLE-Based Teaching

Imagine a learner L who tries to infer a hidden concept from a collection of observations. Building on the work [4] of Ferri et al., we assume the learner to be parameterized by priors P(c) and by c-conditional likelihoods P(z|c) where c ranges over all concepts in a given class C and z ranges over all observations in an observation set Z. L is called a MAP-learner (resp. an MLE-learner) if it thinks of a collection S of observations as a random sample and returns the concept with the maximum a-posteriori probability (resp. the concept which maximizes the c-conditional likelihood of S). Depending on whether L assumes that S is obtained from ordered or unordered sampling resp. from sampling with or without replacement, we can distinguish four different sampling modes. Given a target concept c in C, a teacher for a MAP-learner L aims at finding a smallest collection of observations that causes L to return c. This approach leads in a natural manner to various notions of a MAP- or MLE-teaching dimension of a concept class C. Our main results are: We show that this teaching model has some desirable monotonicity properties. We clarify how the four sampling modes are related to each other. As for the (important! ) special case, where concepts are subsets of a domain and observations are 0,1-labeled examples, we obtain some additional results. First of all, we characterize the MAP- and MLE-teaching dimension associated with an optimally parameterized MAP-learner graph-theoretically. From this central result, some other ones are easy to derive. It is shown, for instance, that the MLE-teaching dimension is either equal to the MAP-teaching dimension or exceeds the latter by 1. It is shown furthermore that these dimensions can be bounded from above by the so-called antichain number, the VC-dimension and related combinatorial parameters. Moreover they can be computed in polynomial time.
Temporal network compression via network hashing

Pairwise temporal interactions between entities can be represented as temporal networks, which code the propagation of processes such as epidemic spreading or information cascades, evolving on top of them. The largest outcome of these processes is directly linked to the structure of the underlying network. Indeed, a node of a network at a given time cannot affect more nodes in the future than it can reach via time-respecting paths. This set of nodes reachable from a source defines an out-component, which identification is costly. In this paper, we propose an efficient matrix algorithm to tackle this issue and show that it outperforms other state-of-the-art methods. Secondly, we propose a hashing framework to coarsen large temporal networks into smaller proxies on which out-components are more easily estimated, and then recombined to obtain the initial components. Our graph hashing solution has implications in privacy respecting representation of temporal networks.
A new Machine Learning-based method for identification of time-correlated events at tagged photon facilities

We present a new Machine Learning-based multivariate analysis method for the selection of time-correlated hits in the tagging system and devices used to detect particles in the final state at the bremsstrahlung-based tagged photon facilities. This method can be applied instead of the widely used sampling and subtraction of the time-uncorrelated background, in particular at experiments aiming for high precision, where the subtraction of the time-uncorrelated background leads to increased uncertainties. Moreover, the identification of events with Machine Learning algorithms allows to preserve the information about correlations of kinematic variables in the final state, which can be advantageous for further phenomenological analyses of the experimental results.
Self-Diagnosis and Large Language Models: A New Front for Medical Misinformation

Improving healthcare quality and access remains a critical concern for countries worldwide. Consequently, the rise of large language models (LLMs) has erupted a wealth of discussion around healthcare applications among researchers and consumers alike. While the ability of these models to pass medical exams has been used to argue in favour of their use in medical training and diagnosis, the impact of their inevitable use as a self-diagnostic tool and their role in spreading healthcare misinformation has not been evaluated. In this work, we critically evaluate LLMs' capabilities from the lens of a general user self-diagnosing, as well as the means through which LLMs may aid in the spread of medical misinformation. To accomplish this, we develop a testing methodology which can be used to evaluate responses to open-ended questions mimicking real-world use cases. In doing so, we reveal that a) these models perform worse than previously known, and b) they exhibit peculiar behaviours, including overconfidence when stating incorrect recommendations, which increases the risk of spreading medical misinformation.
SAR-NeRF: Neural Radiance Fields for Synthetic Aperture Radar Multi-View Representation

SAR images are highly sensitive to observation configurations, and they exhibit significant variations across different viewing angles, making it challenging to represent and learn their anisotropic features. As a result, deep learning methods often generalize poorly across different view angles. Inspired by the concept of neural radiance fields (NeRF), this study combines SAR imaging mechanisms with neural networks to propose a novel NeRF model for SAR image generation. Following the mapping and projection pinciples, a set of SAR images is modeled implicitly as a function of attenuation coefficients and scattering intensities in the 3D imaging space through a differentiable rendering equation. SAR-NeRF is then constructed to learn the distribution of attenuation coefficients and scattering intensities of voxels, where the vectorized form of 3D voxel SAR rendering equation and the sampling relationship between the 3D space voxels and the 2D view ray grids are analytically derived. Through quantitative experiments on various datasets, we thoroughly assess the multi-view representation and generalization capabilities of SAR-NeRF. Additionally, it is found that SAR-NeRF augumented dataset can significantly improve SAR target classification performance under few-shot learning setup, where a 10-type classification accuracy of 91.6\% can be achieved by using only 12 images per class.
Using BOLD-fMRI to Compute the Respiration Volume per Time (RTV) and Respiration Variation (RV) with Convolutional Neural Networks (CNN) in the Human Connectome Development Cohort

In many fMRI studies, respiratory signals are unavailable or do not have acceptable quality. Consequently, the direct removal of low-frequency respiratory variations from BOLD signals is not possible. This study proposes a one-dimensional CNN model for reconstruction of two respiratory measures, RV and RVT. Results show that a CNN can capture informative features from resting BOLD signals and reconstruct realistic RV and RVT timeseries. It is expected that application of the proposed method will lower the cost of fMRI studies, reduce complexity, and decrease the burden on participants as they will not be required to wear a respiratory bellows.
Solution of Fractional Heat-Like and Fractional Wave-Like Equation by Using Modern Strategy

Abstract This paper introduces a novel form of the Adomian decomposition (ADM) method for solving fractional-order heat-like and wave-like equations with starting and boundary value problems. The derivations are provided in the sense of Caputo. In order to help understanding, the generalised formulation of the current approach is provided. Several numerical examples of fractional-order diffusion-wave equations (FDWEs) are solved using the suggested method in this context. In addition to examining the applicability of the suggested method to the solving of fractional-order heat-like and wave-like equations, a graphical depiction of the solutions to three instructive cases was constructed. Solution graphs were arrived at for integer and fractional-order problems. The derived and exact solutions to integer-order problems were found to be in excellent agreement. The subject of the present research endeavour is the convergence of fractional-order solutions. This strategy is considered to be the most successful way of addressing fractional-order initial-boundary value issues in science and engineering. This strategy is presented here.
Wear properties of graphene-reinforced aluminium metal matrix composite: A review

Abstract The recent advancement in graphene-reinforced aluminium matrix composites improves wear behaviour in the production of lightweight and high-performance nanocomposites. Considerable works have been devoted to using graphene nanoparticles as solid self-lubricants to increase wear resistance, minimise friction coefficients, improve service efficiency, and extend the lifespan of related sliding components. In general, wear behaviour often depends on the homogeneous distribution of graphene in the aluminium matrix. The non-uniform distribution of reinforcement due to the tendency of graphene to agglomerate in aluminium matrix and its poor wettability becomes a challenge in developing optimum functional of composites. The wettability of graphene can be enhanced by proper processing methods and sufficient addition of magnesium that can improve the wear and frictional properties of the produced composites. Hence, this review article provides recent findings and the influence of graphene as reinforcement materials in composites, including the effects on wear behaviour and friction properties. This article also discusses new advancements in the effect of graphene in self-lubricating aluminium matrix composites and the impact of reinforcement on the wear mechanisms of the composites. The future direction of the wear properties of MMCs is also covered at the end of the review.
Statistical causality and quasimartingales

Concept of causality is very popular and applicable nowadays, especially when we consider the cases "what would happen if" and "what would have happened if". Here we consider the concept of causality based on the Granger’s definition of causality, introduced in Mykland (1986). Many of the systems to which it is natural to apply tests of causality take place in continuous time, so we will consider the continuous time processes. Here we consider the connection between the concept of causality and the property of being a quasimartingale. Quasimartingales were investigated by Fisk (1965), Orey and specially Rao (1969). Namely, in this paper we prove an equivalence between the given concept of causality and preservation of quasimartingale property if the filtration is getting larger. We prove the same equivalence for the stopped quasimartingale with respect to the truncated filtrations.
The Diffeomorphism Group Approach to Vorticity Model Equations

In fluid mechanics, the vorticity provides a valuable alternative perspective of the behavior of flows. Constantin-Lax-Majda [10] approached studying the 3D vorticity equation by proposing a 1D model equation with significant analytic similarities, the Constantin-Lax-Majda equation. This has been followed by a collection of model equations in both 1D and 2D whose behaviors capture many aspects of the full 3D equations. This thesis contains many new results for several of these equations. We begin by outlining the original analytic theory as well as the Euler-Arnold theory which studies these equations as geodesic equations on infinite dimensional manifolds. We build on the work of Castro-Córdoba [8] and Bauer-Kolev-Preston [4] to show that every solution to the Wunsch equation, a special case of the generalized Constantin-Lax-Majda equation, blows up in finite time. This result also applies to the Constantin-Lax-Majda equation itself. We also investigate the Euler-Weil-Petersson equation which has significant links to the Wunsch equation in the context of Teichmüller theory. Additionally, we lay the foundations for a geometric theory of the surface quasi-geostrophic equation (SQG). Originally discovered in the context of geophysical fluid mechanics (see Pedlosky [61]), SQG was proposed by Constantin-Majda-Tabak [12] as a 2D version of the 1D ConstantinLax-Majda equation. In a blog post, Tao [76] showed that SQG arises as the critical point of a functional. This discovery naturally leads to the formulation of SQG as an Euler-Arnold equation. In this thesis we show that the associated geometric space has a smooth, non-Fredholm Riemannian exponential map, and has arbitrarily large curvature of both signs. Finally we discuss the geometric setting for the Axi-symmetric Euler equations. Here we consider a 3D analogue of the 2D flows considered in Preston [63]. Surprisingly, while the 2D flows iv exhibit negative curvature, we show that the corresponding 3D flows exhibit positive curvature and a rich structure of conjugate points. Such a result may have significant ramifications for our understanding of the nature of stability in 2D and 3D fluids.
RENORMING VOLATILITIES IN A FAMILY OF GARCH MODELS

This paper studies the weak convergence of renorming volatilities in a family of GARCH(1,1) models from a functional point of view. After suitable renormalization, it is shown that the limiting distribution is a geometric Brownian motion when the associated top Lyapunov exponent γ > 0 and is an exponential functional of the maximum process of a Brownian motion when γ = 0. This indicates that the volatility of the GARCH(1,1)-type model has a completely different random structure according to the sign of γ. The obtained results further strengthen our understanding of volatilities in GARCH-type models. Simulation studies are carried out to assess our findings.
The SP-multiple-alignment concept as a generalisation of six other variants of ‘Information Compression via the Matching and Unification of Patterns’

This paper focusses on the powerful concept of SP-multiple-alignment , a key part of the SP System (SPS), meaning the SP Theory of Intelligence and its realisation in the SP Computer Model . The SPS is outlined in an appendix. More specifically, the paper shows with examples how the SP-multiple-alignment construct may function as a generalisation of six other variants of ‘Information Compression via the Matching and Unification of Patterns’ (ICMUP). Each of those six variants is described in a separate section, and in each case there is a demonstration of how that variant may be modeled via the SP-multiple-alignment construct.
On random- and systematic-scan samplers

We introduce a simple time-homogeneous Markov embedding of a class of time-inhomogeneous Markov chains widely used in the context of Monte Carlo sampling algorithms, such as systematic-scan Metropolis-within-Gibbs samplers. This allows us to establish that systematic-scan samplers involving two factors are always better than their random-scan counterparts, when asymptotic variance is the criterion of interest. We also show that this embedding sheds some light on the result of Maire et al. (2014) and discuss the scenario involving more than two factors.
Understanding Space in Proof Complexity: Separations and Trade-ofis via Substitutions (Extended Abstract)

For current state-of-the-art satisflability algorithms based on the DPLL procedure and clause learn- ing, the two main bottlenecks are the amounts of time and memory used. In the fleld of proof complexity, these resources correspond to the length and space of resolution proofs for formulas in conjunctive normal form (CNF). There has been a long line of research investigating these proof complexity measures, but while strong results have been established for length, our understanding of space and how it relates to length has remained quite poor. In particular, the question whether resolution proofs can be optimized for length and space simulta- neously, or whether there are trade-ofis between these two measures, has remained essentially open apart from a few results in restricted settings. In this paper, we remedy this situation by proving a host of length-space trade-ofi results for resolution in a completely general setting. Our collection of trade-ofis cover almost the whole range of values for the space complexity of formulas, and most of the trade-ofis are superpolynomial or even exponential and essentially tight. Using similar techniques, we show that these trade-ofis in fact extend (albeit with worse parameters) to the exponentially stronger k-DNF resolution proof systems, which operate with formulas in disjunctive normal form with terms of bounded arity k. We also answer the open question whether the k-DNF resolution systems form a strict hierarchy with respect to space in the a-rmative. Our key technical contribution is the following, somewhat surprising, theorem: Any CNF formula F can be transformed by simple variable substitution into a new formula F 0 such that if F has the right properties, F 0 can be proven in essentially the same length as F, whereas on the other hand the minimal number of lines one needs to keep in memory simultaneously in any proof of F 0 is lower-bounded by the minimal number of variables needed simultaneously in any proof of F. Applying this theorem to so-called pebbling formulas deflned in terms of pebble games on directed acyclic graphs, we obtain our results.
Stock Liquidity and Cash Holdings: Evidence from Malaysia

The level of cash held by listed companies has raised academic interest. In particular, many studies have been conducted to understand the reason why companies hold so much cash. Previous studies on the determinants of cash holdings have focused on firm-specific characteristics. More recently, researchers have found that stock liquidity played an important role in explaining the cash holding behaviour. This study examines the effect of stock liquidity on cash holdings among listed companies in Malaysia. The findings indicate that stock liquidity has a positive impact on cash holdings. This suggests that companies have a higher propensity to hold cash to repurchase its own stocks, which resulted in higher stock liquidity.
The Influence of Gravity on Fluid-Structure Impact

Fluid-structure interaction is a well-known and complicated problem. Its formulation requires simpli�cations in modelling, and usually the presence of gravity is one aspect which is neglected, especially in violent impact. In this thesis we account for the in uence of gravity on two physically di�erent but mathematically similar two-dimensional ows. First (in Chapters 2{4), sloshing impact of a standing wave on the lid of a rigid tank, and second (in Chapter 5), impact of a rigid blunt body entering water which is initially at rest. Chapter 1 presents the motivation, literature, aim and structure of the thesis. In Chapter 2, gravity is neglected and model equations are solved analytically, in particular the linearised hydrodynamic problem with and without the lid using the Wagner approximation, time and coordinate stretching, and displacement potential. Chapter 3, introduces gravity into the formulation and the model is solved semi-analytically to determine its in uence on the width of the wetted region and on the pressure distribution on the lid during impact. We numerically �nd the e�ect of gravity on the moving contact points, hydrodynamic pressure, and surface elevation in Chapter 4. Chapter 5 studies the in uence of gravity on the impact of a blunt body entering vertically with a constant speed into an initially at water. All problems are formulated and solved within the Wagner model. In both problems we found visible e�ects of gravity on the positions of moving contact points after the early stage of impact. Gravity shortens the size of the wetted region. Consequently, the velocities of the contact points are decreased by gravity. The effect of gravity on the surface elevation is shown for both problems. Negative hydrodynamic pressures and forces are found during the ii sloshing impact stage when gravity is neglected. Numerically, it was found that gravity decreases the hydrodynamic pressure on the lid of the tank. Similarly the hydrodynamic pressure is found to be decreased by gravity in the water-entry problem. Also in the waterentry problem it is shown that gravity increases both the thickness and the mass ux into the spray jets. Also the total energy (potential and kinetic) of the system, and th
THE RELATIONSHIP BETWEEN ECOLOGICAL, MORPHOLOGICAL AND GENETIC FACTORS AMONG LORIS SPECIES IN SRI LANKA 1 A RELAÇÃO ENTRE FATORES ECOLÓGICOS, MORFOLÓGICOS E GENÉTICOS ENTRE AS ESPÉCIES DE LÓRIS NO SRI LANKA

- The study on Loris species diversity in Sri Lanka was considered is based on environmental, morphological, and genetic factors. The distribution of the Loris species, according to genetics and morphology, was the focal point of this work. We have observed seven groups from 25 districts in Sri Lanka in the environmental analysis. When we compare the ecological factors of the Loris species in the study of distribution patterns, the genetic and morphological characteristics are considered for the occurrence of each species, and it also align with the environmental clustering style as seven clusters. Our results suggest that segregation plays a key role in the evolution of genetic structure within the Loris species populations under conditions of free movement. Given the monophyletic arrival of the Loris species, our study challenges the view that geographical barriers are strictly needed for genetic divergence. The study further raises the interesting prospect that social forces - such as social structure, feeding habits, and the breeding of Loris - might