Miracle-dz/UniFAR-FTUs huggingface dataset:
A Large-Scale Study on the Prevalence and Usage of TEE-based Features on Android

In the realm of mobile security, where OS-based protections have proven insufficient against robust attackers, Trusted Execution Environments (TEEs) have emerged as a hardware-based security technology. Despite the industry's persistence in advancing TEE technology, the impact on end users and developers remains largely unexplored. This study addresses this gap by conducting a large-scale analysis of TEE utilization in Android applications, focusing on the key areas of cryptography, digital rights management, biometric authentication, and secure dialogs. To facilitate our extensive analysis, we introduce Mobsec Analytika, a framework tailored for large-scale app examinations, which we make available to the research community. Through the analysis of 170,550 popular Android apps, our analysis illuminates the implementation of TEE-related features and their contextual usage. Our findings reveal that TEE features are predominantly utilized indirectly through third-party libraries, with only 6.7% of apps directly invoking the APIs. Moreover, the study reveals the underutilization of the recent TEE-based UI feature Protected Confirmation.
Video-based Sequential Bayesian Homography Estimation for Soccer Field Registration

A novel Bayesian framework is proposed, which explicitly relates the homography of one video frame to the next through an affine transformation while explicitly modelling keypoint uncertainty. The literature has previously used differential homography between subsequent frames, but not in a Bayesian setting. In cases where Bayesian methods have been applied, camera motion is not adequately modelled, and keypoints are treated as deterministic. The proposed method, Bayesian Homography Inference from Tracked Keypoints (BHITK), employs a two-stage Kalman filter and significantly improves existing methods. Existing keypoint detection methods may be easily augmented with BHITK. It enables less sophisticated and less computationally expensive methods to outperform the state-of-the-art approaches in most homography evaluation metrics. Furthermore, the homography annotations of the WorldCup and TS-WorldCup datasets have been refined using a custom homography annotation tool released for public use. The refined datasets are consolidated and released as the consolidated and refined WorldCup (CARWC) dataset.
MIFA: Metadata, Incentives, Formats, and Accessibility guidelines to improve the reuse of AI datasets for bioimage analysis

Artificial Intelligence methods are powerful tools for biological image analysis and processing. High-quality annotated images are key to training and developing new methods, but access to such data is often hindered by the lack of standards for sharing datasets. We brought together community experts in a workshop to develop guidelines to improve the reuse of bioimages and annotations for AI applications. These include standards on data formats, metadata, data presentation and sharing, and incentives to generate new datasets. We are positive that the MIFA (Metadata, Incentives, Formats, and Accessibility) recommendations will accelerate the development of AI tools for bioimage analysis by facilitating access to high quality training data.
Normal Forms and Tyurin Degenerations of K3 Surfaces Polarised by a Rank 18 Lattice

We study projective Type II degenerations of K3 surfaces polarised by a certain rank 18 lattice, where the central fibre consists of a pair of rational surfaces glued along a smooth elliptic curve. Given such a degeneration, one may construct other degenerations of the same kind by flopping curves on the central fibre, but the degenerations obtained from this process are not usually projective. We construct a series of examples which are all projective and which are all related by flopping single curves from one component of the central fibre to the other. The components of the central fibres obtained include weak del Pezzo surfaces of all possible degrees. This shows that projectivity need not impose any meaningful constraints on the surfaces that can arise as components in Type II degenerations.
RONAALP: Reduced-Order Nonlinear Approximation with Active Learning Procedure

Many engineering applications rely on the evaluation of expensive, non-linear high-dimensional functions. In this paper, we propose the RONAALP algorithm (Reduced Order Nonlinear Approximation with Active Learning Procedure) to incrementally learn a fast and accurate reduced-order surrogate model of a target function on-the-fly as the application progresses. First, the combination of nonlinear auto-encoder, community clustering and radial basis function networks allows to learn an efficient and compact surrogate model with limited training data. Secondly, the active learning procedure overcome any extrapolation issue when evaluating the surrogate model outside of its initial training range during the online stage. This results in generalizable, fast and accurate reduced-order models of high-dimensional functions. The method is demonstrated on three direct numerical simulations of hypersonic flows in chemical nonequilibrium. Accurate simulations of these flows rely on detailed thermochemical gas models that dramatically increase the cost of such calculations. Using RONAALP to learn a reduced-order thermodynamic model surrogate on-the-fly, the cost of such simulation was reduced by up to 75% while maintaining an error of less than 10% on relevant quantities of interest.
Beyond gender: The intersectional impact of community demographics on the continuation rates of male and female students into high school physics

This study examines the complex interplay of gender and other demographics on continuation rates in high school physics. Using a diverse dataset that combines demographics from the Canadian Census and eleven years of gendered enrolment data from the Ontario Ministry of Education, we track student cohorts as they transition from mandatory science to elective physics courses. We then employ hierarchical linear modelling to quantify the interaction effects between gender and other demographics, providing a detailed perspective on the on continuation in physics. Our results indicate the racial demographics of a school's neighbourhood have a limited impact on continuation once controlling for other factors such as socioeconomic status, though neighbourhoods with a higher Black population were a notable exception, consistently exhibiting significantly lower continuation rates for both male and female students. A potential role model effect related to parental education was also found as the proportion of parents with STEM degrees correlates positively with increased continuation, whereas an increase in non-STEM degrees corresponds with a reduced SCR. The most pronounced effects are school-level factors. Continuation rates in physics are very strongly correlated with continuation in chemistry or calculus - effects which are much stronger for male than female students. Conversely, continuation in biology positively correlates with the continuation of female students in physics, with little to no effect found for male students. Nevertheless, the effect sizes observed for chemistry and calculus markedly outweigh that for biology. This is further evidence that considering STEM as a homogeneous subject when examining gender disparities is misguided. These insights can guide future education policies and initiatives to increase continuation rates and foster greater gender equity in physics education.
Quantum phases of hardcore bosons with repulsive dipolar density-density interactions on two-dimensional lattices

We analyse the ground-state quantum phase diagram of hardcore Bosons interacting with repulsive dipolar potentials. The bosons dynamics is described by the extended-Bose-Hubbard Hamiltonian on a two-dimensional lattice. The ground state results from the interplay between the lattice geometry and the long-range interactions, which we account for by means of a classical spin mean-field approach limited by the size of the considered unit cells. This extended classical spin mean-field theory accounts for the long-range density-density interaction without truncation. We consider three different lattice geometries: square, honeycomb, and triangular. In the limit of zero hopping the ground state is always a devil's staircase of solid (gapped) phases. Such crystalline phases with broken translational symmetry are robust with respect to finite hopping amplitudes. At intermediate hopping amplitudes, these gapped phases melt, giving rise to various lattice supersolid phases, which can have exotic features with multiple sublattice densities. At sufficiently large hoppings the ground state is a superfluid. The stability of phases predicted by our approach is gauged by comparison to the known quantum phase diagrams of the Bose-Hubbard model with nearest-neighbour interactions as well as quantum Monte Carlo simulations for the dipolar case on the square lattice. Our results are of immediate relevance for experimental realisations of self-organised crystalline ordering patterns in analogue quantum simulators, e.g., with ultracold dipolar atoms in an optical lattice.
From principle to practice Embedding sustainability in clothing supply chain strategies

The fashion clothing industry is particularly subject to strong external pressure for sustainable behaviour and the increased outsourcing of manufacturing has created long, globally fragmented supply chains. Supply chain management (SCM) has come to the fore in this industry, as the way a firm designs and manages its supply chain can provide competitive advantage in a market focused on cost, speed and availability. It also provides a relational view of the supply chain that can be used to evaluate and address the environmental, social and economic impacts of products, processes and practices. Building on sustainability theory and supply chain strategy, this chapter attempts to provide an integrated understanding of how supply chains can be managed in practice to effectively address the dimensions of sustainability. It analyses all stages within the clothing supply chain and assesses the different environmental and social impacts that can occur throughout the product life-cycle. It considers the value of long-term, collaborative relationships in providing a coordinated supply chain strategy which addresses these serious, increasingly high profile issues, and in turn generates competitive advantage. The chapter will address the following research questions:
A striated, far travelled clast of rhyolitic tuff from Thames river deposits at Ardleigh, Essex, England: evidence for early Middle Pleistocene glaciation in the Thames catchment

Abstract This paper reports the discovery of an in-situ striated, far-travelled, oversized clast in the Ardleigh Gravels of the Kesgrave Sands and Gravels of the River Thames at Ardleigh, east of Colchester in Essex, eastern England. The morphology, petrography and geochemistry of the clast, and the sedimentology of the host deposit are described. The striations are interpreted, on the basis of their sub-parallelism and the shape and sub-roundedness of the clast, as glacial and the clast is provenanced to Ordovician rocks of the Llŷn and Snowdonia regions of North Wales. On the basis of clast frequency within the Colchester Formation gravels of the Kesgrave Sands and Gravels it is inferred that glaciers reached the Cotswold region of the Thames catchment. Floe-ice transport during spring flood is invoked for movement from the glaciated region to eastern England. The paper discusses the possible age of the glaciation and recognises that it is difficult to be more precise than a cold stage in the early Middle Pleistocene (MIS 18, 16 or 14). Attention is drawn to the possibility of glaciation associated with a diamicton in the region of the Cotswold Hills known as the Bruern Till, but stresses the need for new work on this deposit.
Population analyses reveal heterogenous encoding in the medial prefrontal cortex during naturalistic foraging

Foraging in the wild requires coordinated switching of critical functions, including goal-oriented navigation and context-appropriate action selection. Nevertheless, few studies have examined how different functions are represented in the brain during naturalistic foraging. To address this question, we recorded multiple single-unit activities from the medial prefrontal cortex (mPFC) of rats seeking a sucrose reward in the presence of a robotic predator (Lobsterbot) that posed periodic threats. Simultaneously recorded ensemble activities from 10-24 neurons were analyzed in reference to various behavioral indices as the animal moved freely between the nest (N) and the goal (E) across the foraging (F) area. In the E-zone, the rat initially received and gradually learned to avoid unpredictable attacks by the Lobsterbot. An artificial neural network, trained with simultaneously recorded neural activity, estimated the rat’s current distance from the Lobsterbot. The accuracy of distance estimation was the highest in the middle F-zone in which the dominant behavior was active navigation. The spatial encoding persisted in the N-zone when non-navigational behaviors such as grooming, rearing, and sniffing were excluded. In contrast, the accuracy decreased as the animal approached the E-zone, when the activity of the same neuronal ensembles was more correlated with dynamic decision-making between food procurement and Lobsterbot evasion. A population-wide analysis confirmed a highly heterogeneous encoding by the region. To further assess the decision-related activity in the E-zone, a naïve Bayesian classifier was trained to predict the success and failure of avoidance behavior. The classifier predicted the avoidance outcome as much as 6 s before the head withdrawal. In addition, two sub-populations of recorded units with distinct temporal dynamics contributed differently to the prediction. These findings suggest that the mPFC neurons may adopt at least two modes of heterogenous encoding that reflect the processing of relevant spatial context and the imminent situational challenge.
Following the p63/Keratin5 Basal Cells in the Sensory and Non-sensory Epithelia of The Vomeronasal Organ

The Vomeronasal organ (VNO) is a part of the accessory olfactory system, which is responsible for detecting pheromones, chemical factors that trigger a spectrum of sexual and social behaviors. The vomeronasal epithelium (VNE) shares several features with the epithelium of the main olfactory epithelium (MOE). However, it is a distinct neuroepithelium populated by chemosensory neurons that differ from the olfactory sensory neurons (OSNs) in cellular structure, receptor expression, and connectivity. The vomeronasal organ of rodents comprises a sensory epithelium and a thin non-sensory epithelium that morphologically resembles the respiratory epithelium. Sox2-positive cells have been previously identified as the stem cell population that gives rise to neuronal progenitors in MOE and VNE. In addition to these, the MOE also comprises p63 positive horizontal basal cells (HBCs), a second pool of quiescent stem cells that become active in response to injury. Immunolabeling against the transcription factor p63, Keratin-5 (Krt5), Krt14 and Krt5Cre tracing experiments highlighted the existence of horizontal basal cells distributed along the basal lamina of the VNO forming from progenitors along the basal lamina of the marginal zones. Moreover, these experiments revealed that the NSE of rodents is, like the respiratory epithelium, a stratified epithelium where the p63/Krt5+ basal cells self-replicate and give rise to the apical columnar cells facing the lumen of the VNO.
Accelerating protein engineering with fitness landscape modeling and reinforcement learning

Protein engineering is essential for a variety of applications, such as designing biologic drugs, optimizing enzymes, and developing novel functional molecules. Accurate protein fitness landscape modeling, such as predicting protein properties in sequence space, is critical for efficient protein engineering. Yet, due to the complexity of the landscape and high-dimensional sequence space, it remains as an unsolved problem. In this work, we present µFormer, a deep learning framework that combines a pre-trained protein language model with three scoring modules targeting protein features at multiple levels, to tackle this grand challenge. µFormer achieves state-of-the-art performance across diverse tasks, including predicting high-order mutants, modeling epistatic effects, handling insertion/deletion mutations, and generalizing to out-of-distribution scenarios. On the basis of prediction power, integrating µFormer with a reinforcement learning framework enables efficient exploration of the vast mutant space. We showcase that this integrated approach can design protein variants with up to 5-point mutations and potentially significant enhancement in activity for engineering tasks. The results highlight µFormer as a powerful and versatile tool for protein design, accelerating the development of innovative proteins tailored for specific applications.
Les peuplements benthiques lagunaires lies a la pollution en zone urbaine d'Abidjan (Cote d'Ivoire)

Un echantillonnage de la maerofaune benth ique effectue dans ces memes biotopes montre que ceux-ci ne sont pas azoiques. On a recolte dans la baie de Mareory Namolycastis indica, NotofrUlsrus wteTicells, LoTipes aberrons, Heteroponope africana ; dans la baie de Cocody Loanda/ia frUlcuwta, N. indico, Nereis victoriano, CrassostTea gasar, Loripes aberrans ct Heteropanope capart; ; enfin dans la baie de Bietri N. indica, GlyceTa cOllvoluta, NotOfrUlStuS loteTiceus, /phigenia de/e.f. teTti.
Use of Space-Filling Curves in Generating a National Rural Sampling Frame for HIV/AIDS Research∗

The classical geographic research problem of regionalization and resource allocation is most commonly tackled by means of location-allocation methods. This paper introduces the spatial-order method as an alternative for creating regions or clusters. The spatial-order method utilizes space-filling curves, also known as Peano curves, to determine the nearness or spatial order of areal units, such as counties. Given a capacity constraint, the areal units are grouped consecutively according to their spatial order values. We applied the method to create clusters of rural counties for a national sampling survey of HIV/AIDS patients in the United States. Using the criteria that each cluster had approximately 50 new AIDS cases in 1991–1993 and that contiguity of areal units was maximized, 226 clusters were created from the 1,853 rural counties or health districts. The rural clusters generated by this method have been adopted as the national rural sampling frame in the HIV Cost and Services Utilization Study (HCSU.
A Large Field-of-view, Single-cell-resolution Two- and Three-Photon Microscope for Deep and Wide Imaging

In vivo imaging of large-scale neuron activity plays a pivotal role in unraveling the function of the brain’s network. Multiphoton microscopy, a powerful tool for deep-tissue imaging, has received sustained interest in advancing its speed, field of view and imaging depth. However, to avoid thermal damage in scattering biological tissue, field of view decreases exponentially as imaging depth increases. We present a suite of innovations to overcome constraints on the field of view in three-photon microscopy and to perform deep imaging that is inaccessible to two-photon microscopy. These innovations enable us to image neuronal activities in a ∼3.5-mm diameter field-of-view at 4 Hz with single-cell resolution and in the deepest cortical layer of mouse brains. We further demonstrate simultaneous large field-of-view two-photon and three-photon imaging, subcortical imaging in the mouse brain, and whole-brain imaging in adult zebrafish. The demonstrated techniques can be integrated into any multiphoton microscope for large-field-of-view imaging for system-level neural circuit research.
The Impact of Corporate Governance on the Performance of Stock Broking Companies in Malaysia

This study is conducted in order to examine the practice of good corporate governance by analyzing the board composition, ownership concentration, CEO duality and the board size concentrating in stock broking companies. Developed nations has been attracting investors through fairness and transparency in business through good corporate governance in the capital market industry. Thus, improving corporate governance is highly critical since globalization leads to increasing competition for capital, and investors consider corporate governance when making investment decisions. While the weak corporate governance has been recognized as one of the major sources of East Asia’s vulnerabilities to the financial crisis, Malaysian Government realized the weaknesses of the local regulatory environment and that stronger regulations and adequate governance are required in order to protect the local capital market and to ensure market integrity is maintained in order to attract foreign and local investments. Some stock broking companies may prioritize revenue rather than adherence to good compliance and corporate governance practices. It is believed that a positive impact can contribute to the overall performance of the company as it can shield and protect itself and investors from unforeseen negative market conditions and support the longevity of the company’s business. Thus, it is necessary to examine how the corporate governance correlated to firm performance, and identifying which areas of corporate governance is crucial. This study is examining the corporate governance variables using secondary data gathered from publicly published Annual Report. The data analysis consists of descriptive and inferential statistics, correlation and regression and was performed using SPSS. The result shows that variables OC, CD, BS are correlated with ROA,
Estimation of Carbon Sequestration Potential of Different Multipurpose Tree Species Grown under Energy Plantation of Jharkhand Region of India

A b s t r a c t K e y w o r d s The present study was conducted on potential of carbon sequestration by different multipurpose tree species of energy plantation in different agroclimatic sub zones of Jharkhand state of India. The tree species studied were Acacia auriculiformis A.cum (ex Benth.), Cassia siamea lam and Eucalyptus hybrid of four years old plantation from the different location. The dry ashing method was used for estimating the total carbon content in the tree. The data for carbon estimation of above ground parts (bole, branches, leaves and bark) and below ground parts (roots) were collected. The observation of the study resulted that maximum carbon sequestration obtained by Eucalyptus hybrid (31.56 kg C/tree/year) compared to other two species whereas below ground maximum carbon sequestration obtained by Acacia auriculiformis (2.62 kg C/tree/year) followed by Cassia siamea (2.14 kg C /tree/year) whereas the minimum carbon sequestration of root obtained by Eucalyptus hybrid. Above ground biomass Below ground biomass Carbon sequestration Energy plantation Multipurpose tree