Miracle-dz/UniFAR-FTUs huggingface dataset:
Ozone and childhood respiratory health: A primer for US pediatric providers and a call for a more protective standard

Ground level ozone is a potent respiratory toxicant with decades of accumulated data demonstrating respiratory harms to children. Despite the ubiquity of ozone in the United States, impacting both urban and rural communities, the associated harms of exposure to this important air pollutant are often infrequently or inadequately covered during medical training including pulmonary specialization. Thus, many providers caring for children's respiratory health may have limited knowledge of the harms which may result in reduced discussion of ozone pollution during clinical encounters. Further, the current US air quality standard for ozone does not adequately protect children. In this nonsystematic review, we present basic background information for healthcare providers caring for children's respiratory health, review the US process for setting air quality standards, discuss the respiratory harms of ozone for healthy children and those with underlying respiratory disease, highlight the urgent need for a more protective ozone standard to adequately protect children's respiratory health, review impacts of climate change on ozone levels, and provide information for discussion in clinical encounters.
High inhaler resistance does not limit successful inspiratory maneuver among patients with asthma or COPD

ABSTRACT Introduction There has been an active discussion on the sustainability of inhaler therapy in respiratory diseases, and it has cast a shadow on pMDIs which rely on propellant with high global warming potential (GWP). DPIs offer a lower GWP and effective alternative, but there has been concern whether all patients can generate sufficient inspiratory effort to disperse the drug. This review focuses on airflow resistance of DPIs and its clinical relevance. Areas covered For this narrative review, we searched the literature for studies comparing flow patterns with different devices. We also included a section on clinical trials comparing reliever administration with DPI, pMDI with spacer, and nebulizer during exacerbation. Expert opinion The evidence supports the efficacy of DPIs irrespective of respiratory condition or age of the patient even during acute exacerbations. Air flow resistance does not limit the use of DPIs and the patients were able to generate sufficient inspiratory flow rate with almost any device studied. None of 16 identified clinical trials comparing reliever administration via DPIs to other types of devices during exacerbation or bronchial challenge showed statistically significant difference between the device types in FEV1 recovery. DPIs performed as well as other types of inhaler devices even during asthma or COPD exacerbation.
Processing of interlaced images in 4–10 MeV dual energy customs system for material recognition

The aim of this article is to demonstrate the practical value of radioscopic differentiation of materials in the 1\char21{}10 MeV energy range to the work of customs services. The proposed method for achieving singling out and identifying four basic groups of materials according to an atomic number is complex. Atomic numbers are identified using high- and low-energy profiles obtained through the irradiation of materials on an alternate pulse-by-pulse basis. This is done using a bremsstrahlung beam with $8\text{ }\mathrm{MeV}/4\text{ }\mathrm{MeV}$ dual boundary energies and by using scintillating crystals coupled with silicon photodiodes as detecting elements. An image segmentation technique is then used to discern the distribution of an atomic number on any given image. The color visualization of integral absorption and a material's atomic composition is carried out according to the intensity hue saturation (IHS) colorization scheme. The experiments were carried out on a full-scale prototype of an 8 MeV customs inspection system developed by the Efremov Research Institute.
Hybrid metrology: from the lab into the fab

Abstract. The accelerated pace of the semiconductor industry in recent years is putting a strain on existing dimensional metrology equipment (such as critical dimension-secondary electron microscopy, atomic force microscopy, scatterometry) to keep up with ever-increasing metrology challenges. However, a revolution appears to be forming with the recent advent of hybrid metrology (HM). We highlight some of the challenges and lessons learned when setting up a standard HM solution and describe the first-in-industry implementation of HM within a high-volume manufacturing environment.
PIFON-EPT: MR-Based Electrical Property Tomography Using Physics-Informed Fourier Networks

We propose Physics-Informed Fourier Networks for Electrical Properties (EP) Tomography (PIFON-EPT), a novel deep learning-based method for EP reconstruction using noisy and/or incomplete magnetic resonance (MR) measurements. Our approach leverages the Helmholtz equation to constrain two networks, responsible for the denoising and completion of the transmit fields, and the estimation of the object's EP, respectively. We embed a random Fourier features mapping into our networks to enable efficient learning of high-frequency details encoded in the transmit fields. We demonstrated the efficacy of PIFON-EPT through several simulated experiments at 3 and 7 T (T) MR imaging, and showed that our method can reconstruct physically consistent EP and transmit fields. Specifically, when only 20% of the noisy measured fields were used as inputs, PIFON-EPT reconstructed the EP of a phantom with $\leq 5\%$ error, and denoised and completed the measurements with $\leq 1\%$ error. Additionally, we adapted PIFON-EPT to solve the generalized Helmholtz equation that accounts for gradients of EP between inhomogeneities. This yielded improved results at interfaces between different materials without explicit knowledge of boundary conditions. PIFON-EPT is the first method that can simultaneously reconstruct EP and transmit fields from incomplete noisy MR measurements, providing new opportunities for EPT research.
Feature Partition Aggregation: A Fast Certified Defense Against a Union of Sparse Adversarial Attacks

Deep networks are susceptible to numerous types of adversarial attacks. Certiﬁed defenses provide guarantees on a model’s robustness, but most of these defenses are restricted to a single attack type. In contrast, this paper proposes feature partition aggregation (FPA) – a certiﬁed defense against a union of attack types, namely evasion, backdoor, and poisoning attacks. We speciﬁcally consider an (cid:96) 0 or sparse attacker that arbitrarily controls an unknown subset of the training and test features – even across all instances. FPA generates robustness guarantees via an ensemble whose submodels are trained on disjoint feature sets. Following existing certiﬁed sparse defenses, we generalize FPA’s guarantees to top-k predictions. FPA signiﬁcantly outperforms state-of-the-art sparse defenses providing larger and stronger robustness guarantees, while simultaneously being up to 5 , 000 × faster.
xURLLC-Aware Service Provisioning in Vehicular Networks: A Semantic Communication Perspective

Semantic communication (SemCom), as an emerging paradigm focusing on meaning delivery, has recently been considered a promising solution for the inevitable crisis of scarce communication resources. This trend stimulates us to explore the potential of applying SemCom to wireless vehicular networks, which normally consume a tremendous amount of resources to meet stringent reliability and latency requirements. Unfortunately, the unique background knowledge matching mechanism in SemCom makes it challenging to simultaneously realize efficient service provisioning for multiple users in vehicle-to-vehicle networks. To this end, this paper identifies and jointly addresses two fundamental problems of knowledge base construction (KBC) and vehicle service pairing (VSP) inherently existing in SemCom-enabled vehicular networks in alignment with the next-generation ultra-reliable and low-latency communication (xURLLC) requirements. Concretely, we first derive the knowledge matching based queuing latency specific for semantic data packets, and then formulate a latency-minimization problem subject to several KBC and VSP related reliability constraints. Afterward, a SemCom-empowered Service Supplying Solution (S$^{\text{4}}$) is proposed along with the theoretical analysis of its optimality guarantee and computational complexity. Numerical results demonstrate the superiority of S$^{\text{4}}$ in terms of average queuing latency, semantic data packet throughput, user knowledge matching degree and knowledge preference satisfaction compared with two benchmarks.
Detection of Epilepsy Seizure using Different Dimensionality Reduction Techniques and Machine Learning on Transform Domain

: An Electroencephalogram (EEG) is a non-invasive exam that records electrical activity of the brain. This exam is used to help diagnose conditions such as different brain problems. EEG signals are taken for the purpose of epilepsy detection and with Discrete Wavelet Transform (DWT) and machine learning classifier, they perform epilepsy detection. In Epilepsy seizure detection, mainly machine learning classifiers and statistical features are used. The hidden information in the EEG signal is useful for detecting diseases affecting the brain. Sometimes it is very difficult to identify the minimum changes in the EEG in time and frequency domain’s purpose. The DWT can give good decomposition of the signals in different frequency bands and feature extraction. We use the tri-dimensionality reduction algorithm. ; Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Linear Discriminant Analysis (LDA). Finally, features are selected by using a fusion rule and at the last step three different classifiers Support Vector Machine (SVM), Naive Bayes (NB) and K-Nearest-Neighbor (KNN) has been used for the classification. The proposed framework is tested on the Bonn dataset and the simulation results provide the maximum accuracy for the combination of LDA and NB for 10-fold cross validation technique. It shows the maximum average sensitivity, specificity, accuracy, Precision and Recall of 100%, 100%, 100%, 100% and 100%. The results prove the effectiveness of this model .
Least $H^2$ Norm Updating Quadratic Interpolation Model Function for Derivative-free Trust-region Algorithms

Derivative-free optimization methods are numerical methods for optimization problems in which no derivative information is used. Such optimization problems are widely seen in many real applications. One particular class of derivative-free optimization algorithms is trust-region algorithms based on quadratic models given by under-determined interpolation. Different techniques in updating the quadratic model from iteration to iteration will give different interpolation models. In this paper, we propose a new way to update the quadratic model by minimizing the $H^2$ norm of the difference between neighbouring quadratic models. Motivation for applying the $H^2$ norm is given, and theoretical properties of our new updating technique are also presented. Projection in the sense of $H^2$ norm and interpolation error analysis of our model function are proposed. We obtain the coefficients of the quadratic model function by using the KKT conditions. Numerical results show advantages of our model, and the derivative-free algorithms based on our least $H^2$ norm updating quadratic model functions can solve test problems with fewer function evaluations than algorithms based on least Frobenius norm updating.
Grounding Graph Network Simulators using Physical Sensor Observations

Physical simulations that accurately model reality are crucial for many engineering disciplines such as mechanical engineering and robotic motion planning. In recent years, learned Graph Network Simulators produced accurate mesh-based simulations while requiring only a fraction of the computational cost of traditional simulators. Yet, the resulting predictors are confined to learning from data generated by existing mesh-based simulators and thus cannot include real world sensory information such as point cloud data. As these predictors have to simulate complex physical systems from only an initial state, they exhibit a high error accumulation for long-term predictions. In this work, we integrate sensory information to ground Graph Network Simulators on real world observations. In particular, we predict the mesh state of deformable objects by utilizing point cloud data. The resulting model allows for accurate predictions over longer time horizons, even under uncertainties in the simulation, such as unknown material properties. Since point clouds are usually not available for every time step, especially in online settings, we employ an imputation-based model. The model can make use of such additional information only when provided, and resorts to a standard Graph Network Simulator, otherwise. We experimentally validate our approach on a suite of prediction tasks for mesh-based interactions between soft and rigid bodies. Our method results in utilization of additional point cloud information to accurately predict stable simulations where existing Graph Network Simulators fail.
Gaussian Switch Sampling: A Second-Order Approach to Active Learning

In active learning, acquisition functions define informativeness directly on the representation position within the model manifold. However, for most machine learning models (in particular neural networks) this representation is not fixed due to the training pool fluctuations in between active learning rounds. Therefore, several popular strategies are sensitive to experiment parameters (e.g., architecture) and do not consider model robustness to out-of-distribution settings. To alleviate this issue, we propose a grounded second-order definition of information content and sample importance within the context of active learning. Specifically, we define importance by how often a neural network “forgets” a sample during training artifacts of second-order representation shifts. We show that our definition produces highly accurate importance scores even when the model representations are constrained by the lack of training data. Motivated by our analysis, we develop the Gaussian switch sampling (GauSS). We show that GauSS is setup agnostic and robust to anomalous distributions with exhaustive experiments on three in-distribution benchmarks, three out-of-distribution benchmarks, and three different architectures. We report an improvement of up to 5% when compared against four popular query strategies.
Sharpness-Aware Minimization: An Implicit Regularization Perspective

Sharpness-Aware Minimization (SAM) is a recent optimization framework aiming to improve the deep neural network generalization, through obtaining ﬂatter (i.e. less sharp) solutions. As SAM has been numerically successful, recent papers have studied the theoretical aspects of the framework. In this work, we study SAM through an implicit regularization lens, and present a new theoretical explanation of why SAM generalizes well. To this end, we study the least-squares linear regression problem and show a bias-variance trade-oﬀ for SAM’s error over the course of the algorithm. We show SAM has lower bias compared to Gradient Descent (GD), while having higher variance. This shows SAM can outperform GD, specially if the algorithm is stopped early , which is often the case when training large neural networks due to the prohibitive computational cost. We extend our results to kernel regression, as well as stochastic optimization and discuss how implicit regularization of SAM can improve upon vanilla training.
The Riemannian Langevin equation and conic programs

Diffusion limits provide a framework for the asymptotic analysis of stochastic gradient descent (SGD) schemes used in machine learning. We consider an alternative framework, the Riemannian Langevin equation (RLE), that generalizes the classical paradigm of equilibration in R^n to a Riemannian manifold (M^n, g). The most subtle part of this equation is the description of Brownian motion on (M^n, g). Explicit formulas are presented for some fundamental cones.
On bounded complex Jacobi matrices and related moment problems

In this paper we study the linear functional $S$ on complex polynomials which is associated to a bounded complex Jacobi matrix $J$. The associated moment problem is considered: find a positive Borel measure $\mu$ on $\mathbb{C}$ subject to conditions $\int z^n d\mu = s_n$, where $s_n$ are prescribed complex numbers (moments). This moment problem may be viewed as an extension of the Stieltjes and Hamburger moment problems to the complex plane. Sufficient conditions for the solvability of the moment problem are provided. As a corollary, we obtain conditions for the existence of an integral representation $S(p) = \int_\mathbb{C} p(z) d\mu$, with a positive Borel measure $\mu$. An interrelation of the associated to the complex Jacobi matrix operator $A_0$, acting in $l^2$ on finite vectors, and the multiplication by z operator in $L^2_\mu$ is discussed as well.
A Symmetry and Graph Regularized Nonnegative Matrix Factorization Model for Community Detection

Community is a fundamental and critical characteristic of a Large-scale Undirected Network (LUN) like a social network, making community detection a vital yet thorny issue in LUN representation learning. Owing to its good scalability and interpretability, a Symmetric and Nonnegative Matrix Factorization (SNMF) model is commonly used to tackle this issue. However, it adopts a unique Latent Factor (LF) matrix for precisely representing an LUN's symmetry, which leads to a reduced LF space that impairs its representational learning ability. Motivated by this discovery, this study proposes a Symmetry and Graph-regularized Nonnegative Matrix Factorization (SGNMF) method that adopts three-fold ideas: a) leveraging multiple LF matrices to represent an LUN, thereby enhancing its representation learning ability; b) introducing a symmetry regularization term that implies the equality constraint between its multiple LF matrices, thereby illustrating an LUN's symmetry; and c) incorporating graph regularization into its learning objective, thereby illustrating an LUN's intrinsic geometry. A theoretical proof is provided to ensure SGNMF's convergence on an LUN. Extensive experiment results on ten LUNs from real applications demonstrate that our proposed SGNMF-based community detector significantly outperforms several baseline and state-of-the-art models in achieving highly-accurate results for community detection.
Embedding Fourier for Ultra-High-Definition Low-Light Image Enhancement

Ultra-High-Definition (UHD) photo has gradually become the standard configuration in advanced imaging devices. The new standard unveils many issues in existing approaches for low-light image enhancement (LLIE), especially in dealing with the intricate issue of joint luminance enhancement and noise removal while remaining efficient. Unlike existing methods that address the problem in the spatial domain, we propose a new solution, UHDFour, that embeds Fourier transform into a cascaded network. Our approach is motivated by a few unique characteristics in the Fourier domain: 1) most luminance information concentrates on amplitudes while noise is closely related to phases, and 2) a high-resolution image and its low-resolution version share similar amplitude patterns.Through embedding Fourier into our network, the amplitude and phase of a low-light image are separately processed to avoid amplifying noise when enhancing luminance. Besides, UHDFour is scalable to UHD images by implementing amplitude and phase enhancement under the low-resolution regime and then adjusting the high-resolution scale with few computations. We also contribute the first real UHD LLIE dataset, \textbf{UHD-LL}, that contains 2,150 low-noise/normal-clear 4K image pairs with diverse darkness and noise levels captured in different scenarios. With this dataset, we systematically analyze the performance of existing LLIE methods for processing UHD images and demonstrate the advantage of our solution. We believe our new framework, coupled with the dataset, would push the frontier of LLIE towards UHD. The code and dataset are available at https://li-chongyi.github. io/UHDFour.
Spatial noise correlations beyond nearest neighbors in 
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Si-Ge spin qubits

We detect correlations in qubit-energy fluctuations of non-neighboring qubits defined in isotopically purified Si/SiGe quantum dots. At low frequencies (where the noise is strongest), the correlation coefficient reaches 10% for a next-nearest-neighbor qubit-pair separated by 200 nm. Assigning the observed noise to be of electrical origin, a simple theoretical model quantitatively reproduces the measurements and predicts a polynomial decay of correlations with interqubit distance. Our results quantify long-range correlations of noise dephasing quantum-dot spin qubits arranged in arrays, essential for scalability and fault-tolerance of such systems.
Revisiting the Gumbel-Softmax in MADDPG

MADDPG is an algorithm in multi-agent reinforcement learning (MARL) that extends the popular single-agent method, DDPG, to multi-agent scenarios. Importantly, DDPG is an algorithm designed for continuous action spaces, where the gradient of the state-action value function exists. For this algorithm to work in discrete action spaces, discrete gradient estimation must be performed. For MADDPG, the Gumbel-Softmax (GS) estimator is used -- a reparameterisation which relaxes a discrete distribution into a similar continuous one. This method, however, is statistically biased, and a recent MARL benchmarking paper suggests that this bias makes MADDPG perform poorly in grid-world situations, where the action space is discrete. Fortunately, many alternatives to the GS exist, boasting a wide range of properties. This paper explores several of these alternatives and integrates them into MADDPG for discrete grid-world scenarios. The corresponding impact on various performance metrics is then measured and analysed. It is found that one of the proposed estimators performs significantly better than the original GS in several tasks, achieving up to 55% higher returns, along with faster convergence.