Miracle-dz/UniFAR-FTUs huggingface dataset:
Process Variation-Aware Compact Model of Strip Waveguides for Photonic Circuit Simulation

We report a novel process variation-aware compact model of strip waveguides that is suitable for circuit-level simulation of waveguide-based process design kit (PDK) elements. The model is shown to describe both loss and—using a novel expression for the thermo-optic effect in high index contrast materials—the thermo-optic behavior of strip waveguides. A novel group extraction method enables modeling the effective index's (<inline-formula><tex-math notation="LaTeX">$n_{\text{eff}}$</tex-math></inline-formula>) sensitivity to local process variations without the presumption of variation source. Use of Euler-bend Mach-Zehnder interferometers (MZIs) fabricated in a 300 mm wafer run allow model parameter extraction at widths up to 2.5 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>m (highly multi-mode) with strong suppression of higher-order mode excitation. Experimental results prove the reported model can self-consistently describe waveguide phase, loss, and thermo-optic behavior across all measured devices over an unprecedented range of optical bandwidth, waveguide widths, and temperatures.
Standard and nonstandard Turing patterns and waves in the CIMA reaction

We describe experimental evidence of stable triangular and hexagon-band mixed mode nonstandard patterns, in a three-dimensional chemical reaction-diffusion system with steep gradients of chemical constraints. These gradients confine the structures in a more or less thick stratum of the system. At onset, patterns develop in monolayers which approximate two-dimensional systems; but beyond onset, three-dimensional aspects have to be considered. We show that the nonstandard pattern symmetries result from the coupling of standard hexagonal and striped pattern modes which develop at adjacent positions, due to the differences in parameter values along the direction of the gradients. We evidence a Turing-Hopf codimension-2 point and show that some mixed mode chaotic dynamics, reminiscent of spatio-temporal intermittency combining the Turing and the Hopf modes, are also a consequence of the three-dimensional aspect of the structure. The relations between these observations and the theoretical studies performed in genuine two-dimensional systems are still open to discussion.
Technology Trends for Massive MIMO towards 6G

At the dawn of the next-generation wireless systems and networks, massive multiple-input multiple-output (MIMO) in combination with leading-edge technologies, methodologies, and architectures are poised to be a cornerstone technology. Capitalizing on its successful integration and scalability within 5G and beyond, massive MIMO has proven its merits and adaptability. Notably, a series of evolutionary advancements and revolutionary trends have begun to materialize in recent years, envisioned to redefine the landscape of future 6G wireless systems and networks. In particular, the capabilities and performance of future massive MIMO systems will be amplified through the incorporation of cutting-edge technologies, structures, and strategies. These include intelligent omni-surfaces (IOSs)/intelligent reflecting surfaces (IRSs), artificial intelligence (AI), Terahertz (THz) communications, and cell-free architectures. In addition, an array of diverse applications built on the foundation of massive MIMO will continue to proliferate and thrive. These encompass wireless localization and sensing, vehicular communications, non-terrestrial communications, remote sensing, and inter-planetary communications, among others.
COVID-Net USPro: An Open-Source Explainable Few-Shot Deep Prototypical Network to Monitor and Detect COVID-19 Infection from Point-of-Care Ultrasound Images

As the Coronavirus Disease 2019 (COVID-19) continues to impact many aspects of life and the global healthcare systems, the adoption of rapid and effective screening methods to prevent further spread of the virus and lessen the burden on healthcare providers is a necessity. As a cheap and widely accessible medical image modality, point-of-care ultrasound (POCUS) imaging allows radiologists to identify symptoms and assess severity through visual inspection of the chest ultrasound images. Combined with the recent advancements in computer science, applications of deep learning techniques in medical image analysis have shown promising results, demonstrating that artificial intelligence-based solutions can accelerate the diagnosis of COVID-19 and lower the burden on healthcare professionals. However, the lack of a huge amount of well-annotated data poses a challenge in building effective deep neural networks in the case of novel diseases and pandemics. Motivated by this, we present COVID-Net USPro, an explainable few-shot deep prototypical network, that monitors and detects COVID-19 positive cases with high precision and recall from minimal ultrasound images. COVID-Net USPro achieves 99.65% overall accuracy, 99.7% recall and 99.67% precision for COVID-19 positive cases when trained with only 5 shots. The analytic pipeline and results were verified by our contributing clinician with extensive experience in POCUS interpretation, ensuring that the network makes decisions based on actual patterns.
Multi-Task Learning with Prior Information

Multi-task learning aims to boost the generalization performance of multiple related tasks simultaneously by leveraging information contained in those tasks. In this paper, we propose a multi-task learning framework, where we utilize prior knowledge about the relations between features. We also impose a penalty on the coefficients changing for each specific feature to ensure related tasks have similar coefficients on common features shared among them. In addition, we capture a common set of features via group sparsity. The objective is formulated as a non-smooth convex optimization problem, which can be solved with various methods, including gradient descent method with fixed stepsize, iterative shrinkage-thresholding algorithm (ISTA) with back-tracking, and its variation -- fast iterative shrinkage-thresholding algorithm (FISTA). In light of the sub-linear convergence rate of the methods aforementioned, we propose an asymptotically linear convergent algorithm with theoretical guarantee. Empirical experiments on both regression and classification tasks with real-world datasets demonstrate that our proposed algorithms are capable of improving the generalization performance of multiple related tasks.
Informed Down-Sampled Lexicase Selection: Identifying productive training cases for efficient problem solving

Genetic Programming (GP) often uses large training sets and requires all individuals to be evaluated on all training cases during selection. Random down-sampled lexicase selection evaluates individuals on only a random subset of the training cases allowing for more individuals to be explored with the same amount of program executions. However, sampling randomly can exclude important cases from the down-sample for a number of generations, while cases that measure the same behavior (synonymous cases) may be overused. In this work, we introduce Informed Down-Sampled Lexicase Selection. This method leverages population statistics to build down-samples that contain more distinct and therefore informative training cases. Through an empirical investigation across two different GP systems (PushGP and Grammar-Guided GP), we find that informed down-sampling significantly outperforms random down-sampling on a set of contemporary program synthesis benchmark problems. Through an analysis of the created down-samples, we find that important training cases are included in the down-sample consistently across independent evolutionary runs and systems. We hypothesize that this improvement can be attributed to the ability of Informed Down-Sampled Lexicase Selection to maintain more specialist individuals over the course of evolution, while still benefiting from reduced per-evaluation costs.
A Survey on Deep Industrial Transfer Learning in Fault Prognostics

Due to its probabilistic nature, fault prognostics is a prime example of a use case for deep learning utilizing big data. However, the low availability of such data sets combined with the high effort of fitting, parameterizing and evaluating complex learning algorithms to the heterogenous and dynamic settings typical for industrial applications oftentimes prevents the practical application of this approach. Automatic adaptation to new or dynamically changing fault prognostics scenarios can be achieved using transfer learning or continual learning methods. In this paper, a first survey of such approaches is carried out, aiming at establishing best practices for future research in this field. It is shown that the field is lacking common benchmarks to robustly compare results and facilitate scientific progress. Therefore, the data sets utilized in these publications are surveyed as well in order to identify suitable candidates for such benchmark scenarios.
Beckman Defense

Optimal transport (OT) based distributional robust optimisation (DRO) has received some traction in the recent past. However, it is at a nascent stage but has a sound potential in robustifying the deep learning models. Interestingly, OT barycenters demonstrate a good robustness against adversarial attacks. Owing to the computationally expensive nature of OT barycenters, they have not been investigated under DRO framework. In this work, we propose a new barycenter, namely Beckman barycenter, which can be computed efficiently and used for training the network to defend against adversarial attacks in conjunction with adversarial training. We propose a novel formulation of Beckman barycenter and analytically obtain the barycenter using the marginals of the input image. We show that the Beckman barycenter can be used to train adversarially trained networks to improve the robustness. Our training is extremely efficient as it requires only a single epoch of training. Elaborate experiments on CIFAR-10, CIFAR-100 and Tiny ImageNet demonstrate that training an adversarially robust network with Beckman barycenter can significantly increase the performance. Under auto attack, we get a a maximum boost of 10\% in CIFAR-10, 8.34\% in CIFAR-100 and 11.51\% in Tiny ImageNet. Our code is available at https://github.com/Visual-Conception-Group/test-barycentric-defense.
Federated Learning for Data Streams

Federated learning (FL) is an effective solution to train machine learning models on the increasing amount of data generated by IoT devices and smartphones while keeping such data localized. Most previous work on federated learning assumes that clients operate on static datasets collected before training starts. This approach may be inefficient because 1) it ignores new samples clients collect during training, and 2) it may require a potentially long preparatory phase for clients to collect enough data. Moreover, learning on static datasets may be simply impossible in scenarios with small aggregate storage across devices. It is, therefore, necessary to design federated algorithms able to learn from data streams. In this work, we formulate and study the problem of \emph{federated learning for data streams}. We propose a general FL algorithm to learn from data streams through an opportune weighted empirical risk minimization. Our theoretical analysis provides insights to configure such an algorithm, and we evaluate its performance on a wide range of machine learning tasks.
Landscape classification through coupling method

This paper proposes a probabilistic approach to investigate the shape of the landscape of multi-dimensional potential functions. By using an appropriate coupling scheme, two copies of the overdamped Langevin dynamics of the potential function are coupled. For unimodal and multimodal potential functions, the tail distributions of the coupling times are shown to have qualitatively diﬀerent dependency on the noise magnitude. More speciﬁcally, for a single-well potential function that is strongly convex, the exponential tail of the coupling time distribution is independent of noise and uniformly bounded away from zero by the convexity parameter; while for a multi-well potential function, the exponential tail is exponentially small with respect to the noise magnitude with the exponents determined by the essential barrier height , a quantity proposed in this paper which, in a sense, characterizes the “non-convexity” of the potential function. This provides a promising approach to detect the shape of a potential landscape through the coupling time distributions which, in certain sense, shares the similar spirit with the well-know problem “Can one hear the shape of a drum? ” proposed by Kac in his famous paper [18]. Theoretical results are numerically veriﬁed and discussed for a variety of examples in diﬀerent contexts, including the Rosenbrock function, the potential of interacting particle systems, as well as the loss functions of artiﬁcial neural networks.
Large-width asymptotics for ReLU neural networks with $\alpha$-Stable initializations

There is a recent and growing literature on large-width asymptotic properties of Gaussian neural networks (NNs), namely NNs whose weights are initialized as Gaussian distributions. Two popular problems are: i) the study of the large-width distributions of NNs, which characterizes the infinitely wide limit of a rescaled NN in terms of a Gaussian stochastic process; ii) the study of the large-width training dynamics of NNs, which characterizes the infinitely wide dynamics in terms of a deterministic kernel, referred to as the neural tangent kernel (NTK), and shows that, for a sufficiently large width, the gradient descent achieves zero training error at a linear rate. In this paper, we consider these problems for $\alpha$-Stable NNs, namely NNs whose weights are initialized as $\alpha$-Stable distributions with $\alpha\in(0,2]$. First, for $\alpha$-Stable NNs with a ReLU activation function, we show that if the NN's width goes to infinity then a rescaled NN converges weakly to an $\alpha$-Stable stochastic process. As a difference with respect to the Gaussian setting, our result shows that the choice of the activation function affects the scaling of the NN, that is: to achieve the infinitely wide $\alpha$-Stable process, the ReLU activation requires an additional logarithmic term in the scaling with respect to sub-linear activations. Then, we study the large-width training dynamics of $\alpha$-Stable ReLU-NNs, characterizing the infinitely wide dynamics in terms of a random kernel, referred to as the $\alpha$-Stable NTK, and showing that, for a sufficiently large width, the gradient descent achieves zero training error at a linear rate. The randomness of the $\alpha$-Stable NTK is a further difference with respect to the Gaussian setting, that is: within the $\alpha$-Stable setting, the randomness of the NN at initialization does not vanish in the large-width regime of the training.
$d$-wave Superconductivity, Pseudogap, and the Phase Diagram of $t$-$t'$-$J$ Model at Finite Temperature

Recently, a robust $d$-wave superconductivity has been unveiled in the ground state of the 2D $t$-$t'$-$J$ model -- with both nearest-neighbor ($t$) and next-nearest-neighbor ($t'$) hoppings -- through the density matrix renormalization group calculations in the ground state. In this study, we exploit the state-of-the-art thermal tensor network approach to accurately simulate the finite-temperature electron states of the $t$-$t'$-$J$ model on cylinders with widths up to $W=6$. Our analysis suggests that in the dome-like superconducting phase, the $d$-wave pairing susceptibility exhibits a divergent behavior with $\chi_\textrm{SC} \propto 1/T^\alpha$ below the onset temperature $T_c^*$. Near the optimal doping, $T_c^*$ reaches its highest value of about $0.05 t$ ($\equiv 0.15 J$). Above $T_c^*$ yet below a higher crossover temperature $T^*$, the magnetic susceptibility is suppressed, and the Fermi surface also exhibits node-antinode structure, resembling the pseudogap behaviors observed in cuprates. Our unbiased and accurate thermal tensor network calculations obtain the phase diagram of the $t$-$t'$-$J$ model with $t'/t>0$, shedding light on the $d$-wave superconducting and pseudogap phases in the enigmatic cuprate phase diagram.
Assessment of the uncertainty associated with systematic errors in digital instruments: an experimental study on offset errors

This paper deals with the assessment of the uncertainty due to systematic errors, particularly in A/D conversion-based instruments. The problem of defining and assessing systematic errors is briefly discussed, and the conceptual scheme of gauge repeatability and reproducibility is adopted. A practical example regarding the evaluation of the uncertainty caused by the systematic offset error is presented. The experimental results, obtained under various ambient conditions, show that modelling the variability of systematic errors is more problematic than suggested by the ISO 5725 norm. Additionally, the paper demonstrates the substantial difference between the type B uncertainty evaluation, obtained via the maximum entropy principle applied to manufacturer's specifications, and the type A (experimental) uncertainty evaluation, which reflects actually observable reality. Although it is reasonable to assume a uniform distribution of the offset error, experiments demonstrate that the distribution is not centred and that a correction must be applied. In such a context, this work motivates a more pragmatic and experimental approach to uncertainty, with respect to the directions of supplement 1 of GUM.
N EURAL M ESSAGE P ASSING FOR O BJECTIVE -B ASED U NCERTAINTY Q UANTIFICATION AND O PTIMAL E XPERIMENTAL D ESIGN

Various real-world scientiﬁc applications involve the mathematical modeling of complex uncertain systems with numerous unknown parameters. Accurate parameter estimation is often practically infeasible in such systems, as the available training data may be insufﬁcient and the cost of acquiring additional data may be high. In such cases, it is desirable to represent the model uncertainty in a Bayesian paradigm, based on which we can design robust operators retaining the best overall performance across all possible models and design optimal experiments that can effectively reduce uncertainty to enhance the performance of such operators maximally. While objective-based uncertainty quantiﬁcation (objective-UQ) based on MOCU (mean objective cost of uncertainty) has been an effective means for quantifying uncertainty in complex systems, a major drawback has been the high computational cost of estimating MOCU. In this work, we propose a novel scheme to reduce the computational cost for objective-UQ via MOCU based on a data-driven approach. We adopt a neural message-passing model for surrogate modeling, incorporating a novel axiomatic constraint loss that penalizes an increase in the estimated system uncertainty. As an illustrative example, we consider the optimal experimental design (OED) problem for uncertain Kuramoto models, where the goal is to predict the experiments that can most effectively enhance robust synchronization performance through uncertainty reduction. We show that our proposed approach can accelerate MOCU-based OED by four to ﬁve orders of magnitude, virtually without any visible performance loss compared to the state-of-the-art. The proposed approach is applicable to general OED tasks, beyond the Kuramoto model. 1
An autocrine signaling circuit in hepatic stellate cells underlies advanced fibrosis in nonalcoholic steatohepatitis

Advanced hepatic fibrosis, driven by the activation of hepatic stellate cells (HSCs), affects millions worldwide and is the strongest predictor of mortality in nonalcoholic steatohepatitis (NASH); however, there are no approved antifibrotic therapies. To identify antifibrotic drug targets, we integrated progressive transcriptomic and morphological responses that accompany HSC activation in advanced disease using single-nucleus RNA sequencing and tissue clearing in a robust murine NASH model. In advanced fibrosis, we found that an autocrine HSC signaling circuit emerged that was composed of 68 receptor-ligand interactions conserved between murine and human NASH. These predicted interactions were supported by the parallel appearance of markedly increased direct stellate cell-cell contacts in murine NASH. As proof of principle, pharmacological inhibition of one such autocrine interaction, neurotrophic receptor tyrosine kinase 3–neurotrophin 3, inhibited human HSC activation in culture and reversed advanced murine NASH fibrosis. In summary, we uncovered a repertoire of antifibrotic drug targets underlying advanced fibrosis in vivo. The findings suggest a therapeutic paradigm in which stage-specific therapies could yield enhanced antifibrotic efficacy in patients with advanced hepatic fibrosis. Description A repertoire of autocrine signaling modalities in advanced hepatic fibrosis constitutes potential therapeutic targets. Targeting advanced liver fibrosis Nonalcoholic steatohepatitis (NASH) has no approved therapy and advanced, fibrotic stages of the disease are in particular difficult to target. Wang et al. show that in both human and murine NASH, a profibrotic autocrine signaling loop comprising dozens of receptor-ligand pairs emerges in hepatic stellate cells specifically during late-stage disease. Pharmacological targeting of one of these receptor-ligand interactions reduced advanced fibrosis in a NASH mouse model, establishing this interaction as an antifibrotic target and suggesting the possibility of a disease stage-specific treatment. —CAC