Miracle-dz/UniFAR-FTUs huggingface dataset:
Making Meaning: Semiotics Within Predictive Knowledge Architectures

Within Reinforcement Learning, there is a fledgling approach to conceptualizing the environment in terms of predictions. Central to this predictive approach is the assertion that it is possible to construct ontologies in terms of predictions about sensation, behaviour, and time---to categorize the world into entities which express all aspects of the world using only predictions. This construction of ontologies is integral to predictive approaches to machine knowledge where objects are described exclusively in terms of how they are perceived. In this paper, we ground the Pericean model of semiotics in terms of Reinforcement Learning Methods, describing Peirce's Three Categories in the notation of General Value Functions. Using the Peircean model of semiotics, we demonstrate that predictions alone are insufficient to construct an ontology; however, we identify predictions as being integral to the meaning-making process. Moreover, we discuss how predictive knowledge provides a particularly stable foundation for semiosis\textemdash the process of making meaning\textemdash and suggest a possible avenue of research to design algorithmic methods which construct semantics and meaning using predictions.
Efficacy and Safety of Ticagrelor versus Clopidogrel in Dialysis Patients with Coronary Syndromes: A Systematic Review and Meta-Analysis

(1) Background: The optimal antiplatelet therapy for end-stage kidney disease (ESKD) patients on chronic dialysis presenting with acute or chronic coronary syndromes (ACS or CCS) remains uncertain. This meta-analysis aimed to compare the efficacy and safety endpoints of ticagrelor and clopidogrel in ESKD patients requiring dialysis and presenting with ACS or CCS. (2) Methods: Studies were included comparing ticagrelor and clopidogrel in ESKD patients on chronic dialysis with ACS or CCS. The primary composite efficacy outcome was a combination of all-cause and cardiovascular mortality, recurrent myocardial infarction or coronary revascularization, and ischemic or hemorrhagic stroke. The primary safety outcome was major and non-major bleeding events. (3) Results: Five observational studies met the eligibility criteria. The pooled analysis showed no significant difference in the primary composite efficacy outcome between ticagrelor and clopidogrel (p = 0.40). Similarly, the 2 groups had no significant differences in all-cause mortality (p = 0.82) or cardiovascular mortality (p = 0.79). Ticagrelor did not show a significantly different risk of coronary revascularization (p = 0.35) or recurrent myocardial infarction (p = 0.41) compared to clopidogrel. Also, the risk of stroke was similar (p = 0.21). The 2 groups had no significant difference in the primary composite safety outcome (p = 0.22) or major bleeding events (p = 0.27). (4) Conclusions: In ESKD patients on chronic dialysis with ACS or CCS, there was no significant difference in efficacy or safety outcomes between ticagrelor and clopidogrel. Further randomized controlled trials are needed to establish the optimal antiplatelet therapy in this population.
The ensemble Kalman filter for combined state and parameter estimation: MONTE CARLO TECHNIQUES FOR DATA ASSIMILATION IN LARGE SYSTEMS

he ensemble Kalman filter (EnKF) [1] is a sequential Monte Carlo method that provides an alternative to the traditional Kalman filter (KF) [2], [3] and adjoint or four-dimensional variational (4DVAR) methods [4]-[6] to better handle large state spaces and nonlinear error evolution. EnKF provides a simple conceptual formulation and ease of implementation, since.
Painting with Data: Visually based open-source tool for geo-computing

In this article we present, the conceptual and technical background of a tool for mapping and geo-computing, Painting with Data (PWD). PWD was inspired by early computer mapping algorithms that focused on drawing as an intuitive interface between spatial data and architectural practice. PWD embraces the potential of such techniques, coupled with alternative computational representations, modes of interaction, and computational interfaces, to encourage public participation in planning and design. Essential to this task, is PWD’s integration of open-source, collaborative, interactive, and web-based technologies to create an online software with a visually based approach to spatial analysis and mapping that dramatically reduces the steep learning curve required for GIS software. As a high-level graphical interface, PWD allows users to iterate by intuitively creating spatial models on-the-fly based on their subjective understanding of information. In PWD, we deploy voxels, a data structure that organizes information as 3D pixels, which allows users to compute with spatial information visually, to potentially inform the ways in which users build quantitative models. In PWD, multiple layers of information can be visualized concurrently, and visual correlations can be made instantly. Users build spatial models by directly manipulating the map itself instead of manipulating a database that then produces a map. PWD’s high-level interaction is made possible by custom data structures that leverage GPU processing, which makes them significantly faster than traditional topological data structures. Computationally, user interactions generate visualization specifications and declarative queries that are compiled and executed by the platform. Lastly, PWD introduces a visual programming language, which enables intuitive geospatial modeling and visualization. Practical work has shown the value of PWD’s approach to design students, planning agencies and community non-profits. Throughout the process, PWD’s open-source spatial models generated a user community with more than 3000 users, including designers, students, and educators.
A Decoupled Chirp Scaling Algorithm for High-Squint SAR Data Imaging

The capability to work on the high-squint mode brings great advantages to the synthetic aperture radar (SAR) system. However, the efficient imaging of the high-squint SAR data has not been well settled. Due to the severe range-azimuth coupling (RAC), the conventional imaging algorithms fail to work properly in high-squint cases. In this article, a decoupled chirp scaling algorithm (DCSA) is proposed for high-squint SAR data imaging, whose key step is a range-azimuth decoupling (RAD) preprocessing in the 2-D frequency domain. After RAD, the majority of the RAC is eliminated, where the high-squint SAR data are simplified into the quasi-broadside mode. The data can be then processed via the chirp-scaling-typed scheme, which is summarized as follows. The data after RAD are first transformed into the range-Doppler (RD) domain, in which a nonlinear chirp scaling (NLCS) is performed in the range direction to eliminate the chirp rate variations caused by the residual RAC. The chirp scaling processing is simultaneously performed to equalize the range cell migrations (RCMs) for the full scene data. The range compression, bulk RCM correction, and compensation for the cubic and quartic phases introduced by the NLCS are then accomplished in the 2-D frequency domain. Finally, the azimuth compensation is accomplished in the RD domain to obtain the final focused SAR image. Different from the linear range walk correction (LRWC) accomplished in the azimuth time domain, the RAD method proposed here does not destroy the azimuth-invariant property of the SAR echoes. Therefore, the DCSA can adapt to the wide swath SAR data imaging. Simulation shows that, for the airborne SAR with 1-m resolution in both range and azimuth directions, and a squint angle of 45°, the DCSA can achieve the bulk focusing of the full-scene data with a range swath of 10 km, whereas the azimuth swath is not limited. Compared with the classical CSA, the DCSA requires only one more range fast Fourier transform (FFT)/inverse FFT (IFFT) and one more complex matrix multiplication, which is, therefore, timely and very efficient.
Potential Implication of Azole Persistence in the Treatment Failure of Two Haematological Patients Infected with Aspergillus fumigatus

Invasive aspergillosis (IA) is a major cause of morbidity and mortality in patients receiving allogeneic haematopoieticcell transplantation. The deep immunosuppression and a variety of potential additional complications developed in these patients result in IA reaching mortality rates of around 50–60%. This mortality is even higher when the patients are infected with azole-resistant isolates, demonstrating that, despite the complexity of management, adequate azole treatment can have a beneficial effect. It is therefore paramount to understand the reasons why antifungal treatment of IA infections caused by azole-susceptible isolates is often unsuccessful. In this respect, there are already various factors known to be important for treatment efficacy, for instance the drug concentrations achieved in the blood, which are thus often monitored. We hypothesize that antifungal persistence may be another important factor to consider. In this study we present two case reports of haematological patients who developed proven IA and suffered treatment failure, despite having been infected with susceptible isolates, receiving correct antifungal treatment and reaching therapeutic levels of the azole. Microbiological analysis of the recovered infective isolates showed that the patients were infected with multiple strains, several of which were persisters to voriconazole and/or isavuconazole. Therefore, we propose that azole persistence may have contributed to therapeutic failure in these patients and that this phenomenon should be considered in future studies.
Antioxidant Activity of a Sicilian Almond Skin Extract Using In Vitro and In Vivo Models

Almond skins are known for their antioxidative and anti-inflammatory properties, which are mainly due to the presence of polyphenols. The aim of the present study was to evaluate the antioxidant and anti-inflammatory effects of almond skin extract (ASE) obtained from the Sicilian cultivar “Fascionello” and to evaluate the possible mechanisms of action using an in vitro model of human monocytic U937 cells as well as an in vivo model of carrageenan (CAR)-induced paw edema. The in vitro studies demonstrated that pretreatment with ASE inhibited the formation of ROS and apoptosis. The in vivo studies showed that ASE restored the CAR-induced tissue changes; restored the activity of endogenous antioxidant enzymes, such as superoxide dismutase, catalase, and glutathione; and decreased neutrophil infiltration, lipid peroxidation, and the release of proinflammatory mediators. The anti-inflammatory and antioxidant effects of ASE could be associated with the inhibition of the pro-inflammatory nuclear NF-κB and the activation of the nuclear factor-erythroid 2-related factor 2 (Nrf2) antioxidant pathways. In conclusion, almond skin could reduce the levels of inflammation and oxidative stress and could be beneficial in the treatment of several disorders.
The “Unruly” Snowflake: (Re)imagining School Readiness for Two-Year-Old Children

Engaging with Posthuman theories this article puts to work concepts of affect, bodies, and voice to reimagine an ordinary classroom event of making cards for families and carers. The article will outline how Human Capital Theory and neoliberal logic can reveal how specific ways of demonstrating learning are privileged in practice. To challenge the dominant discourse, and the binary of ready/not ready, this article makes a call for an intra-active posthuman pedagogy, so that time in space can be slowed down to follow the agentic rustles of material voices and bodies to (re)imagine school readiness.
Asymptotic theory of the estimated partial likelihood

A semi-parametric method, Estimated Paxtial Likelihood(EPL) method, is proposed for the estimation of the relative risk parameters when auxiliaxy covariates are present. The EPL method makes full use of the avaiable covariates and auxiliary measurement without imposing the parametric structure on the missing procedures. The EPL function reduces to the partial likelihood function of Cox(1972) when the auxiliary is perfect measurement about true covariates. Since the EPL takes a non-parametric appraoch with respect to the underline missing process, therfore avoides some strong assumptions, e.g. the rare disease assumption(Prentice 1982, Pepe , Self and Prentice 1991). The asymptotic distribution theory is estabilished for the proposed estimator for the case in which the surrogate or mismeasured covariates are categorical. The proposed EPL estimator is found to be consistant and asymptoticaly normally distributed. A consistent variance estimator is provided. An extension of the EPL is presented when the validation sample is a stratified random sample.
Plant-Based Dairy Alternatives Contribute to a Healthy and Sustainable Diet

Plant-based foods are increasing in popularity as more and more people are concerned about personal and planetary health. The consumption of plant-based dairy alternatives (PBDAs) has assumed a more significant dietary role in populations shifting to more sustainable eating habits. Plant-based drinks (PBDs) made from soya and other legumes have ample protein levels. PBDs that are appropriately fortified have adequate levels of important vitamins and minerals comparable to dairy milk. For the PBDs examined, the greenhouse gas emissions were diminished by 59–71% per 250 mL, and the land use and eutrophication impact was markedly less than the levels displayed by dairy milk. The water usage for the oat and soya drinks, but not rice drinks, was substantially lower compared to dairy milk. When one substitutes the 250 mL serving of dairy milk allowed within the EAT Lancet Planetary Health Diet for a fortified plant-based drink, we found that the nutritional status is not compromised but the environmental footprint is reduced. Combining a nutrient density score with an environmental index can easily lead to a misclassification of food when the full nutrition profile is not utilized or only a selection of environmental factors is used. Many PBDAs have been categorized as ultra-processed foods (UPFs). Such a classification, with the implied adverse nutritional and health associations, is inconsistent with current findings regarding the nutritional quality of such products and may discourage people from transitioning to a plant-based diet with its health and environmental advantages.
Stochastic approximation for efficient LSTD and least squares regression

We propose stochastic approximation based methods with randomization of samples in two different settings - one for policy evaluation using the least squares temporal difference (LSTD) algorithm and the other for solving the least squares problem. We consider a “big data” regime where both the dimension, d, of the data and the number, T, of training samples are large. Through finite time analyses we provide performance bounds for these methods both in high probability and in expectation. In particular, we show that, with probability 1 , an -approximation of the (LSTD or least squares regression) solution can be computed in O(dln(1= )= 2 ) complexity, irrespective of the number of samplesT . We demonstrate the practicality of our solution scheme for LSTD empirically by combining it with the LSPI algorithm in a traffic signal control application. Several machine learning problems involve either finding the fixed point of an operator or minimizing a loss function from a given set of training data. In particular, in this paper we consider the problems of policy evaluation in reinforcement learning (RL) and least squares regression for a classic parameter estimation setting. Given a fixed training data set, current methods for these problems are computationally expensive. Instead we propose randomized stochastic approximation (SA) algorithms that draw data samples from a uniform distribution on the training set. In the RL context, our algorithm can be seen as a fixed point iteration, while in the least squares context it is a gradient descent rule. From the finite time analyses of these methods that we provide, we observe that both our algorithms converge at the optimal rate, in high probability as well as in expectation. This coupled with the significant decrease in the computational cost of our algorithms, make them appealing in the canonical big data settings. First, we describe the reinforcement learning setting. The problem considered is to estimate the value function V of a given policy . Temporal difference (TD) methods are well-known in this context, and they are known to converge to the fixed point X = T (X ), whereT is the Bellman operator, is the orthogonal projection onto the space within which we approximate the value function, andX is the feature matrix that characterizes this space. LSTD Bradtke and Barto [1996] is one popular temporal difference algorithm. It estimates the fixed point of T using empirical dataf(xi;ri;x 0);i = 1;:::;T )g obtained by simulating the Markov decision process (MDP) with the underlying policy . This estimate is given as the solution to: ^ T = A 1 T bT;
Integrating technology in teaching and learning practices: students’ competencies

Technology usage in teaching and learning is not a new pedagogical practice and the benefits of its adoption are noted in the literature. However, an area that is often neglected when integrating technology into teaching and learning is the competency level of the would-be learners or students. This study investigated student competency levels based on factors such as their prior exposure to computers and the availability of facilitating conditions such as human or technical support. The study adopted a descriptive approach and was quantitative in nature. Data was collected from 368 students by means of a questionnaire. Descriptive statistics were obtained through quantitative analysis and the computer-based assessment acceptance model (CBAAM) was adopted. The results showed that the provisioning of facilitating conditions in a technology-integrated academic environment positively influences student competency in the use of technology. Furthermore, results showed that prior exposure to computers significantly impacts student competency levels in such an environment.
Uncertainty quantification for wavefield reconstruction inversion using a PDE-free semidefinite Hessian and randomize-then-optimize method

The data of full-waveform inversion often contains noise, which induces uncertainties in the inversion results. Ideally, one would like to run a number of independent inversions with different realizations of the noise and assess model-side uncertainties from the resulting models, however this is not feasible because we collect the data only once. To circumvent this restriction, various sampling schemes have been devised to generate an ensemble of models that fit the data to within the noise level. Such sampling schemes typically involve running multiple inversions or evaluating the Hessian of the cost function, both of which are computationally expensive. In this work, we propose a new method to quantify uncertainties based on a novel formulation of the full-waveform inversion problem – wavefield reconstruction inversion. Based on this formulation, we formulate a semidefinite approximation of the corresponding Hessian matrix. By precomputing certain quantities, we are able to apply this Hessian to given input vectors without additional solutions of the underlying partial differential equations. To generate a sample, we solve an auxiliary stochastic optimization problem involving this Hessian. The result is a computationally feasible method that, with little overhead, can generate as many samples as required at small additional cost. We test our method on the synthetic BG Compass model and compare the results to a direct-sampling approach. The results show the feasibility of applying our method to computing statistical quantities such as the mean and standard deviation in the context of wavefield reconstruction inversion.
Simulation of urban non-motorized traffic: A agent-based modeling approach based on big data of bike sharing and social force model

Encouraging cycling, one of the urban non-motorized transport modes, has been well recognized as an environment-friendly way that alleviates urban traffic congestions and solves the first/last mile issue. However, concerns about the efficiency and safety of urban cycling have been widely made by urban planners due to the unclear right-of-way and discontinuous non-motorized corridor for cycling. This study uses the dynamic location data of the Meituan Bike (formerly Mobike) in the Hi-tech Park area of Shenzhen to analyze the spatial-temporal variations of bikeshare use, aiming at identifying the traffic corridors of cycling. Combined with agent-based modeling technique and social force model, this research proposes a new approach of simulating the urban non-motorized traffic, and hence provides valuable insights for building bicycle lanes for cycling corridors. The results show that (1) the usage of bike sharing during weekdays is 2.5 times that on weekdays, and the cycling corridors are usually the main and secondary roads that are in the vicinity of the metro stations; (2) adding bicycle lanes can reduce the traffic density of the non-motorized volume by 6% in an overall, and save the travel time of cyclists and pedestrians by 6.4% and 3.7%, respectively.
From degrees to dollars: Exploring the link between elite education and startup funding

Many startups look for venture capital (VC) funding to grow their business. Anecdotally, in the Southeast Asia context, startup founders who attended an elite institute of higher learning have greater access to this kind of capital, but this has not been demonstrated quantitatively. We look at a dataset of nearly 800 startups in Southeast Asia who have received VC funding in the last 5 years and look for any correlation between the founder's educational background and the amount of VC funding received. We find that founders who have obtained a postgraduate degree from an elite university receive significantly more VC funding than those who graduated from other universities. Furthermore, this effect is only seen at later stages of funding, not at the seed stage. Potential reasons for this effect could be from stronger signaling of business acumen or technical skill; or better access to funding networks.
Rotating Velocimetry Based Upon Rotational Doppler Effect of Perfect Laguerre–Gaussian Light Modes

Laser Doppler rotational velocimetry has emerged as a pivotal technology for astronomical survey and remote sensing. While high-order optical vortices are commonly used for accurate rotating velocimetry, their large annular radii can pose a challenge when detecting a small cross-sectional rotator directly. Here, we overcome this limitation by exploiting perfect Laguerre–Gaussian (PLG) light modes carrying orbital angular momentum, based upon the rotational Doppler effect. In this situation, the beating frequency signals can be observed as well, regardless of the rotator being exposed to the symmetrically or asymmetrically superposed PLG light modes, thus revealing the linked rotating velocity. What is more, a direct proof-of-principle experiment is conducted to validate the performance of the velocimetry system. It shows that ±15-order modes provide the optimal sensitivity of detecting rotating velocity, corresponding to a relative uncertainty of below 0.48%. In contrast to the existing techniques, our rotating velocimetry platform not only enables small target detection, but also offers a tunable flexibility together with a higher velocimetry accuracy. Hence, this indicates the generation of a high-accuracy rotating velocimetry pathway by resorting to the perfect structured vortex light modes. Such validated findings might be useful for practical optical metrology and remote sensing scenarios.