Miracle-dz/UniFAR-FTUs huggingface dataset:
Sintra Grows Healthy: development and implementation of a food literacy curriculum for primary schools

Abstract Objective: Describe the process of development and implementation of Health at the Table – a food literacy curriculum for primary school aged children. Design: Through a community-based research process, Health at the Table development and implementation took place in four stages: exploratory study, production, implementation and monitoring. Setting: Primary schools of Sintra’s municipality, Portugal. Participants: Children (6–10 years), teachers, school staff and children’s legal guardians of three primary schools during the pilot project and eight primary schools in the second year. Results: During the needs assessment phase, 99·1 % (n 341) of the children’s legal guardians, 100 % (n 34) of the teachers and 100 % (n 19) of the school staff considered that the school plays an important or very important role in children’s food literacy (stage 1). During the pilot project, a manual with sixty session plans was developed (stage 2). In the second year, Health at the Table was implemented by seventy-two trained teachers during one school year (stage 3). Most of the teachers agreed that the curriculum was appropriate (69·2 %) and that children developed health, wellness/well-being and environmental skills (83·1 %). Most of the children said they had learned about healthy eating (86·3 %) and claimed to eat healthier since the Health at the Table implementation (58·9 %) (stage 4). Conclusions: Health at the Table is a food literacy curriculum that can be reproduced in similar contexts in a sustainable way. The need to combine educational strategies with a healthy school food environment is reinforced to increase effectiveness in tackling childhood obesity.
A Comprehensive Study on Occlusion Invariant Face Recognition under Face Mask Occlusion

The face mask is an essential sanitaryware in daily lives growing during the pandemic period and is a big threat to current face recognition systems. The masks destroy a lot of details in a large area of face, and it makes it difficult to recognize them even for humans. The evaluation report shows the difficulty well when recognizing masked faces. Rapid development and breakthrough of deep learning in the recent past have witnessed most promising results from face recognition algorithms. But they fail to perform far from satisfactory levels in the unconstrained environment during the challenges such as varying lighting conditions, low resolution, facial expressions, pose variation and occlusions. Facial occlusions are considered one of the most intractable problems. Especially when the occlusion occupies a large region of the face because it destroys lots of official features.
A REFINEMENT SEQUENCE OF PARTIAL LOOP ERASURE

We introduce partial loop erasing operators. We show that by applying a refinement sequence of partial loop erasing operators to a finite Markov chain, we get a process equivalent to the standard loop erased Markov chain. As an application, we construct loop erased random paths on bounded domains of resistance spaces as the weak limit of the loop erasure of the Markov chains on a sequence of finite sets approximating the space. The limit is independent of the approximating sequences, and the random paths we constructed are simple paths almost surely. Finally, we show that the scaling limit of the loop erased random walks on the Sierpiński carpet graphs exists, and is equivalent to the loop erased random paths on the Sierpińksi carpet.
On the Reducedness of Quiver Schemes

In this paper we prove that a quiver scheme in characteristic zero is reduced if the moment map is flat. We use the reducedness result to show that the equivariant integration formula computes the K-theoretic Nekrasov partition function of five dimensional N = 2 quiver gauge theories when the moment map is flat. We also give an explicit characterization of flatness of moment map for finite and affine type A Dynkin quivers with framings. As an application, we give a refinement of a theorem of Ivan Losev on the relation between quantized Nakajima quiver variety in type A and parabolic finite W-algebra in type A.
Effect of Prostaglandin E2 Agonist Omidenepag on the Expression of Matrix Metalloproteinase in Trabecular Meshwork Cells

Purpose To investigate the effects of prostaglandin E2 agonist omidenepag (OMD) on the expression of matrix metalloproteinase (MMP) in human trabecular meshwork (TM) cells. Methods Primarily cultured human TM cells were exposed to 0, 1, 10, or 40 μmol/L OMD for 3 days. The permeability through the TM cell monolayer was assessed using carboxyfluorescein. Expressions of messenger ribonucleic acid and protein levels of MMP-1, MMP-3, and MMP-9 were measured by reverse transcription polymerase chain reaction and Western blotting, respectively. Also, the permeability, expression of messenger ribonucleic acid, and protein levels of MMPs were measured after exposure to 1 μmol/L latanoprost free acid (LAT). Results OMD and LAT did not affect the cellular survival (all p > 0.05). Each concentration of OMD and LAT did not affect the permeability of carboxyfluorescein significantly (all p > 0.05). LAT increased the level of MMP-1 protein but did not increase the levels of MMP-3 and MMP-9 proteins. Each concentration of OMD did not affect the levels of MMP-1, MMP-3, and MMP-9 proteins (all p > 0.05) Conclusions In TM cells, prostaglandin E2 agonist OMD did not increase the permeability through the TM cell monolayer, and the protein levels of MMPs. These suggest that the direct effect on the trabecular outflow by OMD may be limited.
Small-Signal Stability Analysis of Numerical Integration Methods

The paper provides a novel framework to study the accuracy and stability of numerical integration schemes when employed for the time domain simulation of power systems. A matrix pencil-based approach is adopted to evaluate the error between the dynamic modes of the power system and the modes of the approximated discrete-time system arising from the application of the numerical method. The proposed approach can provide meaningful insights on how different methods compare to each other when applied to a power system, while being general enough to be systematically utilized for, in principle, any numerical method. The framework is illustrated for a handful of well-known explicit and implicit methods, while simulation results are presented based on the WSCC 9-bus system, as well as on a 1,479-bus dynamic model of the All-Island Irish Transmission System.
Tensor Ring Parametrized Variational Quantum Circuits for Large Scale Quantum Machine Learning

Quantum Machine Learning (QML) is an emerging research area advocating the use of quantum computing for advancement in machine learning. Since the discovery of the capability of Parametrized Variational Quantum Circuits (VQC) to replace Artiﬁcial Neural Networks, they have been widely adopted to different tasks in Quantum Machine Learning. However, despite their potential to outperform neural networks, VQCs are limited to small scale applications given the challenges in scalability of quantum circuits. To address this shortcoming, we propose an algorithm that compresses the quantum state within the circuit using a tensor ring representation. Using the input qubit state in the tensor ring representation, single qubit gates maintain the tensor ring representation. However, the same is not true for two qubit gates in general, where an approximation is used to have the output as a tensor ring representation. Using this approximation, the storage and computational time increases linearly in the number of qubits and number of layers, as compared to the exponential increase with exact simulation algorithms. This approximation is used to implement the tensor ring VQC. The training of the parameters of tensor ring VQC is performed using a gradient descent based algorithm, where efﬁcient approaches for backpropagation are used. The proposed approach is evaluated on two datasets: Iris and MNIST for the classiﬁcation task to show the improved accuracy using more number of qubits. We achieve a test accuracy of 83.33% on Iris dataset and a maximum of 99.30% and 76.31% on binary and ternary classiﬁcation of MNIST dataset using various circuit architectures. The results from
ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformer

The accelerating magnetic resonance imaging (MRI) reconstruction process is a challenging ill-posed inverse problem due to the excessive under-sampling operation in <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-space. In this paper, we propose a recurrent Transformer model, namely ReconFormer, for MRI reconstruction, which can iteratively reconstruct high-fidelity magnetic resonance images from highly under-sampled <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-space data (e.g., up to <inline-formula> <tex-math notation="LaTeX">$8\times $ </tex-math></inline-formula> acceleration). In particular, the proposed architecture is built upon Recurrent Pyramid Transformer Layers (RPTLs). The core design of the proposed method is Recurrent Scale-wise Attention (RSA), which jointly exploits intrinsic multi-scale information at every architecture unit as well as the dependencies of the deep feature correlation through recurrent states. Moreover, benefiting from its recurrent nature, ReconFormer is lightweight compared to other baselines and only contains 1.1 M trainable parameters. We validate the effectiveness of ReconFormer on multiple datasets with different magnetic resonance sequences and show that it achieves significant improvements over the state-of-the-art methods with better parameter efficiency. The implementation code and pre-trained weights are available at <uri>https://github.com/guopengf/ReconFormer</uri>.
An Ensemble Model for Face Liveness Detection

In this paper, we present a passive method to detect face presentation attack a.k. a face liveness detection using an ensemble deep learning technique. Face liveness detection is one of the key steps involved in user identity verification of customers during the online onboarding/transaction processes. During identity verification, an unauthenticated user tries to bypass the verification system by several means, for example, they can capture a user photo from social media and do an imposter attack using printouts of users faces or using a digital photo from a mobile device and even create a more sophisticated attack like video replay attack. We have tried to understand the different methods of attack and created an in-house large-scale dataset covering all the kinds of attacks to train a robust deep learning model. We propose an ensemble method where multiple features of the face and background regions are learned to predict whether the user is a bonafide or an attacker.
Online Soft Conformance Checking: Any Perspective Can Indicate Deviations

Within process mining, a relevant activity is conformance checking. Such activity consists of establishing the extent to which actual executions of a process conform the expected behavior of a reference model. Current techniques focus on prescriptive models of the control-flow as references. In certain scenarios, however, a prescriptive model might not be available and, additionally, the control-flow perspective might not be ideal for this purpose. This paper tackles these two problems by suggesting a conformance approach that uses a descriptive model (i.e., a pattern of the observed behavior over a certain amount of time) which is not necessarily referring to the control-flow (e.g., it can be based on the social network of handover of work). Additionally, the entire approach can work both offline and online, thus providing feedback in real time. The approach, which is implemented in ProM, has been tested and results from 3 experiments with real world as well as synthetic data are reported.
Adversarially Guided Subgoal Generation for Hierarchical Reinforcement Learning

Hierarchical reinforcement learning (HRL) proposes to solve difﬁcult tasks by performing decision-making and control at successively higher levels of temporal abstraction. However, off-policy training in HRL often suffers from the problem of non-stationary high-level decision making since the low-level policy is constantly changing. In this paper, we propose a novel HRL approach for mitigating the non-stationarity by adversarially enforcing the high-level policy to generate subgoals compatible with the current in-stantiation of the low-level policy. In practice, the adversarial learning can be implemented by training a simple discriminator network concurrently with the high-level policy which determines the compatibility level of subgoals. Experiments with state-of-the-art algorithms show that our approach signiﬁcantly improves learning efﬁciency and overall performance of HRL in various challenging continuous control tasks.
Physical Reasoning in an Open World

Most work on physical reasoning, both in artificial intelligence and in cognitive science, has focused on closed-world reasoning, in which it is assumed that the problem specification specifies all relevant objects and substance, all their relations in an initial situation, and all exogenous events. However, in many situations, it is important to do open-world reasoning; that is, making valid conclusions from very incomplete information. We have implemented in Prolog an open-world reasoner for a toy microworld of containers that can be loaded, unloaded, sealed, unsealed, carried, and dumped.
pvCNN: Privacy-Preserving and Verifiable Convolutional Neural Network Testing

We propose a new approach for privacy-preserving and verifiable convolutional neural network (CNN) testing in a distrustful multi-stakeholder environment. The approach is aimed to enable that a CNN model developer convinces a user of the truthful CNN performance over non-public data from multiple testers, while respecting model and data privacy. To balance the security and efficiency issues, we appropriately integrate three tools with the CNN testing, including collaborative inference, homomorphic encryption (HE) and zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK). We start with strategically partitioning a CNN model into a private part kept locally by the model developer, and a public part outsourced to an outside server. Then, the private part runs over the HE-protected test data sent by a tester, and transmits its outputs to the public part for accomplishing subsequent computations of the CNN testing. Second, the correctness of the above CNN testing is enforced by generating zk-SNARK based proofs, with an emphasis on optimizing proving overhead for two-dimensional (2-D) convolution operations, since the operations dominate the performance bottleneck during generating proofs. We specifically present a new quadratic matrix program (QMP)-based arithmetic circuit with a single multiplication gate for expressing 2-D convolution operations between multiple filters and inputs in a batch manner. Third, we aggregate multiple proofs with respect to a same CNN model but different testers’ test data (i.e., different statements) into one proof, and ensure that the validity of the aggregated proof implies the validity of the original multiple proofs. Lastly, our experimental results demonstrate that our QMP-based zk-SNARK performs nearly $13.9\times $ faster than the existing quadratic arithmetic program (QAP)-based zk-SNARK in proving time, and $17.6\times $ faster in Setup time, for high-dimension matrix multiplication. Besides, the limitation on handling a bounded number of multiplications of QAP-based zk-SNARK is relieved.
Well-posedness of a Navier–Stokes–Cahn–Hilliard System for Incompressible Two-phase Flows with Surfactant

We investigate a diffuse-interface model that describes the dynamics of incompressible two-phase viscous flows with surfactant. The resulting system of partial differential equations consists of a sixth-order Cahn-Hilliard equation for the difference of local concentrations of the binary fluid mixture coupled with a fourth-order Cahn-Hilliard equation for the local concentration of the surfactant. The former has a smooth potential, while the latter has a singular potential. Both equations are coupled with a Navier-Stokes system for the (volume averaged) fluid velocity. The evolution system is endowed with suitable initial conditions, a no-slip boundary condition for the velocity field and homogeneous Neumann boundary conditions for the phase functions as well as for the chemical potentials. We first prove the existence of a global weak solution, which turns out to be unique in two dimensions. Stronger regularity assumptions on the initial data allow us to prove the existence of a unique global (resp. local) strong solution in two (resp. three) dimensions. In the two dimensional case, we can derive a continuous dependence estimate with respect to the norms controlled by the total energy. Then we establish instantaneous regularization properties of global weak solutions for $t>0$. In particular, we show that the surfactant concentration stays uniformly away from the pure states $0$ and $1$ after some positive time.
Lensing Machines: Representing Perspective in Latent Variable Models

Many datasets represent a combination of different ways of looking at the same data that lead to different generalizations. For example, a corpus with examples generated by different people may be mixtures of many perspectives and can be viewed with different perspectives by others. It isnt always possible to represent the viewpoints by a clean separation, in advance, of examples representing each viewpoint and train a separate model for each viewpoint. We introduce lensing, a mixed initiative technique to extract lenses or mappings between machine learned representations and perspectives of human experts, and to generate lensed models that afford multiple perspectives of the same dataset. We apply lensing for two classes of latent variable models: a mixed membership model, a matrix factorization model in the context of two mental health applications, and we capture and imbue the perspectives of clinical psychologists into these models. Our work shows the benefits of the machine learning practitioner formally incorporating the perspective of a knowledgeable domain expert into their models rather than estimating unlensed models themselves in isolation.
A Critical Review of Baseband Architectures for CubeSats Communication Systems

<p><u>This survey</u> <sup>1 </sup>fills in an important gap in the literature on CubeSat communication systems’ designs and architectures. The paper <sup>2 </sup>comprehensively reviews the design details in-depth and <sup>3 </sup>critically evaluates the performance and reliability of the reviewed systems by analyzing the demonstration and testing results and illustrating implicit assumptions in the reviewed works. Furthermore, the paper <sup>4 </sup>proposes an original CubeSat communication system categorization based on the design approach and <sup>5 </sup>analyzes the features and limitations of each category. Consequently, this leads to <b>identifying the limitations</b> of current CubeSat communication systems and the <b>promising future developments</b> that can boost CubeSat communications capabilities. </p>
A Novel Mix-Normalization Method for Generalizable Multi-Source Person Re-Identification

Person re-identification (Re-ID) has achieved great success in the supervised scenario. However, it is difficult to directly transfer the supervised model to arbitrary unseen domains due to the model overfitting to the seen source domains. In this paper, we aim to tackle the generalizable multi-source person Re-ID task (i.e., there are multiple available source domains, and the testing domain is unseen during training) from the data augmentation perspective, thus we put forward a novel method, termed MixNorm. It consists of domain-aware mix-normalization (DMN) and domain-aware center regularization (DCR). Different from the conventional data augmentation, the proposed domain-aware mix-normalization enhances the diversity of features during training from the normalization perspective of the neural network, which can effectively alleviate the model overfitting to the source domains, so as to boost the generalization capability of the model in the unseen domain. To further promote the efficacy of the proposed DMN, we exploit the domain-aware center regularization to better map the diversely generated features into the same space. Extensive experiments on multiple benchmark datasets validate the effectiveness of the proposed method and show that the proposed method can outperform the state-of-the-art methods. Besides, further analysis also reveals the superiority of the proposed method.
Explore the Expression: Facial Expression Generation using Auxiliary Classifier Generative Adversarial Network

Facial expressions are a form of non-verbal communication that humans perform seamlessly for meaningful transfer of information. Most of the literature addresses the facial expression recognition aspect however, with the advent of Generative Models, it has become possible to explore the affect space in addition to mere classification of a set of expressions. In this article, we propose a generative model architecture which robustly generates a set of facial expressions for multiple character identities and explores the possibilities of generating complex expressions by combining the simple ones.
Novel Tin-based Gel Polymer Electrode and Its Use in Sodium-ion Capacitor

We present a novel tin based flexible, gel polymer negative electrode for use in sodium-ion devices. The electrodes have been prepared by trapping tin micro-particles in a porous electrolyte membrane formed by immersion precipitation phase-inversion and soaking in a sodium-ion conducting liquid electrolyte. The tin particles are the active electrode materials as well as electron conductors, while the gel polymer enable efficient ion-transfer to the tin particles. The composite gel electrode and its performance in sodium-ion capacitor has been analyzed using scanning electron microscopy. x-ray diffraction, cyclic voltammetry and galvanostatic charge-discharge. The specific capacity of the capacitor shows an initial increase, up to 175 charge-discharge cycles. This increase has been explained by the increase in surface area of tin due to disintegration of the larger particles upon repeated insertion/de-insertion of sodium. A high capacitance of 210 mAh g at 1 A g has also been measured.
Accelerate Model Parallel Training by Using Efficient Graph Traversal Order in Device Placement

Modern neural networks require long training to reach decent performance on massive datasets. One common approach to speed up training is model parallelization, where large neural networks are split across multiple devices. However, different device placements of the same neural network lead to different training times. Most of the existing device placement solutions treat the problem as sequential decision-making by traversing neural network graphs and assigning their neurons to different devices. This work studies the impact of graph traversal order on device placement. In particular, we empirically study how different graph traversal order leads to different device placement, which in turn affects the training execution time. Our experiment results show that the best graph traversal order depends on the type of neural networks and their computation graphs features. In this work, we also provide recommendations on choosing graph traversal order in device placement for various neural network families to improve the training time in model parallelization.