Miracle-dz/UniFAR-FTUs huggingface dataset:
DAD characterization in electromechanical cardiac models

We investigate the possibility of modeling the delayed afterdepolarization (DAD) occurrence in the framework of the classical FitzHugh-Nagumo (FN) dynamical system, as well as in more recent electromechanically-coupled cardiac models. Within the FN model, we identify the domain in the constitutive parameters' space for which orbits exist which exhibit a sufficiently strong secondary impulse. We then address the question whether a locally-induced secondary pulse succeeds or not in originating a self-propagating traveling impulse. Our results evidence that, in the range where secondary impulses exceed the physiological threshold for DAD onset, a local impulse almost certainly causes a traveling impulse (mechanism known as all-or-none). We then consider a recently proposed electromechanically-coupled generalization of the FN model, and show that the mechanical coupling stabilizes the system, in the sense that the more strong the coupling, the less likely is DAD to occur.
Adaptive unsaturated stochastic resonance in parallel array and its application to acoustic emission signals

Among various signal processing approaches, stochastic resonance (SR) has been widely employed for weak signal detection and mechanical fault diagnosis. Various advancements have been focused on identifying useful information from the frequency domain by optimizing parameters in a post-processing environment to activate SR. Yet, these methods often require detailed information about the original signal a priori, which is challenging from measurements that are already overwhelmed by noise. Furthermore, classical bistable SR has often been employed for weak signal detection, which exhibits an inherent signal distortion due to output saturation that reduces the signal recovery performance. To address these concerns and advance the state of the art, we propose a novel signal denoising method that exploits unsaturated SR in a parallel array of piecewise continuous bistable systems. The original noise-contaminated signal is adaptively scaled by an optimal gain value that is determined from a non-dimensional model based on the attendant noise level, which is one of the few parameters that can be reliably identified from practical noise-contaminated signals. As a result, the proposed approach can operate without any post-processing optimization and parameter selection. Numerical investigations are performed with a simulated acoustic emission signal (amplitude modulated sine pulse) with various amplitudes and attendant noise levels to illustrate the operation principle and the effectiveness of the proposed approach. The results exemplify the promising potential of implementing the proposed approach for enhancing online signal denoising in practice.
Effect of CSN3 Gene Polymorphism on the Formation of Milk Gels Induced by Physical, Chemical, and Biotechnological Factors

During the last decade, research into genetic markers in the casein gene cluster has been actively introduced in cattle breeding programs. A special interest has been paid to the polymorphism of the CSN3 gene, responsible for the expression of the k-casein, playing a key role in protein coagulation, interaction with whey proteins, stabilization, and aggregation of casein micelles. This paper aimed to determine the effect of CSN3 genetic polymorphism on acid; rennet; acid–rennet; heat- and acid-induced as well as heat- and calcium-induced coagulation in skimmed milk; and protein-standardized milk systems (UF, NF, RO, VE). The influence of polymorphic variants of the CSN3 gene on the coagulation ability of milk proteins was assessed by the particle size of casein micelles, protein retention factor in the clot, and coagulation ability (duration of induction period, mass coagulation period, dynamic viscosity in gel point). The correlation between CSN3 gene polymorphism and protein coagulation was revealed. Milk systems obtained from CSN3 BB milk were found to have the shortest duration of coagulation, formation of better gel strength values, and increased yield compared to CSN3 AA. This study will improve the efficiency of milk processing and optimize the technology of dairy product production.
Ultimate thickness to bandwidth ratio of radar absorbers

Analytic properties of the reflection coefficient of a multilayer metal-backed slab are considered. The result is a new form of the dispersion relationship, which characterizes the integral of the reflectance over wavelength in terms of the total thickness and averaged static permeability of the slab. The relation may be transformed to an inequality, which produces the least thickness to bandwidth ratio achievable for a physically realizable radar absorber. The particular cases of broad-band and narrow-band absorbers are discussed. The least thickness of a 10-dB broad-band dielectric radar absorber is shown to be 1/17 of the largest operating wavelength. The discussion also involves the results of a numerical study.
Study the changes in wetland landscape patterns using remote sensing images – A case study of Jiulongjiangkou, Fujian, China

Remote sensing images play an important role in landscape pattern analysis. Medium/low-resolution remote sensing images are widely used because they are freely available. However, sometimes the results are not precise enough due to the data quality. Therefore, this study utilized the 30m medium-resolution remote sensing images acquired by the Landsat-8 satellite, which is associated with super-resolution technology of deep learning to upsample the spatial resolution from 30 * 30 m2 to about 7.5 * 7.5 m2 and perform artificial interpretation on the upsampling images. Several indexes of the landscape pattern were used to analyze the landscape composition and landscape pattern evolution trend of the Jiulongjiangkou wetland in Fujian Province from 2013 to 2021. The results showed that: (1) The total wetland area decreased from 101.26 km2 to 92.65 km2. (2) The silt beach has the most dramatic change; its area is decreased by 6.53 km2. (3) The speed of urban expansion is obvious. The industrial and mining lands increased from 9.15 km2 to 22.15 km2, and the traffic land increased from 1.9 km2 to 3.46 km2. The study found that wind speed, temperature, radiation index, storm runoff, and human activities are the five main driving factors affecting wetland change.
Effects of land use change on the ecosystem service value – A case study of Jiulongjiangkou, Fujian, China, from 2013 to 2021

In this study, we utilized the dynamic changes in land use and ecosystem service value to provide ecological security protection and realize the coordinated development of ecosystem service functions for the Jiulongjiangkou Wetland Reserve. We counted the land use changes and calculated the ecological service value of the Jiulongjiangkou by using eCognition 9.0, ArcMap 10.2, Fragstats 4.2, and other software based on the remote sensing image from 2013 to 2021 with five phases, which were captured by Landsat-8 satellite. The results showed that the land use type of Jiulongjiangkou City changed significantly and was affected by human activities, in which the wetland area decreased by 8.61 km2, a decrease of 8.5%. In the past ten years, the value of ecosystem service has not increased, which decreased by 1.3*108 RMB in 2021 compared with 2013. In the future, we should continue to increase the protection of forest lands and waters, maintain the stability of the ecosystem of protected areas, rationally plan the land for construction, actively develop green agriculture, and steadily enhance the value of ecosystem service.
Application and comparison of multiple machine learning models for the prediction of the laminar burning velocity for CH3OH/H2/air mixtures

Recent investigations have indicated that co-firing CH3OH with H2 is a promising approach to develop a carbon-neutral energy system. However, accurate measurements of laminar burning velocities over a wide range of equivalence ratios, H2 mole fractions, pressures and temperatures are complicated and may not available. Hence, this research deeply investigates the application of several machine learning models in predicting the laminar burning velocities of CH3OH/H2 blended fuels. Results denoted that Random Forest Regressor is the most persuasive model based on a thorough comparison, as indicated by the correlation coefficient of 0.99707.
Mixtures of Gaussian process experts based on kernel stick-breaking processes

Mixtures of Gaussian process experts is a class of models that can simultaneously address two of the key limitations inherent in standard Gaussian processes: scalability and predictive performance. In particular, models that use Dirichlet processes as gating functions permit straightforward interpretation and automatic selection of the number of experts in a mixture. While the existing models are intuitive and capable of capturing non-stationarity, multi-modality and heteroskedasticity, the simplicity of their gating functions may limit the predictive performance when applied to complex data-generating processes. Capitalising on the recent advancement in the dependent Dirichlet processes literature, we propose a new mixture model of Gaussian process experts based on kernel stick-breaking processes. Our model maintains the intuitive appeal yet improve the performance of the existing models. To make it practical, we design a sampler for posterior computation based on the slice sampling. The model behaviour and improved predictive performance are demonstrated in experiments using six datasets.
Constraining mass transfer and common-envelope physics with post-supernova companion monitoring

We present an analytical model that describes the response of companion stars after being impacted by a supernova in a close binary system. This model captures key properties of the luminosity evolution obtained from 1D stellar evolution calculations fairly well: a high-luminosity plateau phase and a decaying tail phase. It can be used to constrain the pre-supernova binary properties from the observed photometry of the companion star several years after the explosion in a relatively simple manner. The derived binary parameters are useful in constraining the evolutionary scenario for the progenitors and the physics of binary interactions. We apply our model to some known stripped-envelope supernova companions (SN1993J, SN2001ig, SN2006jc, SN2011dh, and SN2013ge). Combined with other observational constraints such as the pre-supernova progenitor photometry, we find that SN1993J and SN2011dh likely had relatively massive companions on wide orbits, while SN2006jc may have had a relatively low-mass companion on a tight orbit. This trend suggests that Type IIb supernova progenitors evolved from stable mass transfer channels and Type Ibc progenitors may have formed from common-envelope channels. The constraints on orbital separation helps us probe the highly uncertain common-envelope physics for massive stars, especially with multiple epochs of companion observations. We also highlight possible limitations of our model due to the assumptions made in the underlying 1D models.
Sodium enhancement in evolved CVs

We present follow-up spectroscopy of 21 cataclysmic variables (CVs) with evolved secondaries and ongoing or recentlyterminated mass transfer. Evolutionary models predict that the secondaries should have anomalous surface abundances owing to nuclear burning in their cores during their main-sequence evolution and subsequent envelope stripping by their companion white dwarfs. To test these models, we measure sodium (Na) abundances of the donors from the Fraunhofer "D" doublet. Accounting for interstellar absorption, we find that all objects in our sample have enhanced Na abundances. We measure 0.3 . [Na/H] . 1.5 dex across the sample, with a median [Na/H] = 0.956 dex, i.e., about an order of magnitude enhancement over solar values. To interpret these values, we run MESA binary evolution models of CVs in which mass transfer begins just as the donor leaves the main sequence. These generically predict Na enhancement in donors with initial donor masses & 1M , consistent with our observations. In the models, Na enrichment occurs in the donors’ cores via the NeNa cycle near the end of their main-sequence evolution. Na-enhanced material is exposed when the binaries reach orbital periods of a few hours. Donors with higher initial masses are predicted to have higher Na abundances at fixed orbital period owing to their higher core temperatures during main-sequence evolution. The observed [Na/H] values are on average ≈0.3 dex higher than predicted by the models. Surface abundances of evolved CV donors provide a unique opportunity to study nuclear burning products in the cores of intermediate-mass stars.
Unified Sequence-to-Sequence Learning for Single- and Multi-Modal Visual Object Tracking

In this paper, we introduce a new sequence-to-sequence learning framework for RGB-based and multi-modal object tracking. First, we present SeqTrack for RGB-based tracking. It casts visual tracking as a sequence generation task, forecasting object bounding boxes in an autoregressive manner. This differs from previous trackers, which depend on the design of intricate head networks, such as classification and regression heads. SeqTrack employs a basic encoder-decoder transformer architecture. The encoder utilizes a bidirectional transformer for feature extraction, while the decoder generates bounding box sequences autoregressively using a causal transformer. The loss function is a plain cross-entropy. Second, we introduce SeqTrackv2, a unified sequence-to-sequence framework for multi-modal tracking tasks. Expanding upon SeqTrack, SeqTrackv2 integrates a unified interface for auxiliary modalities and a set of task-prompt tokens to specify the task. This enables it to manage multi-modal tracking tasks using a unified model and parameter set. This sequence learning paradigm not only simplifies the tracking framework, but also showcases superior performance across 14 challenging benchmarks spanning five single- and multi-modal tracking tasks. The code and models are available at https://github.com/chenxin-dlut/SeqTrackv2.
Optimization-Inspired Cross-Attention Transformer for Compressive Sensing

By integrating certain optimization solvers with deep neural networks, deep unfolding network (DUN) with good interpretability and high performance has attracted growing attention in compressive sensing (CS). However, existing DUNs often improve the visual quality at the price of a large number of parameters and have the problem of feature information loss during iteration. In this paper, we propose an Optimization-inspired Cross-attention Transformer (OCT) module as an iterative process, leading to a lightweight OCT-based Unfolding Framework (OCTUF) for image CS. Specifically, we design a novel Dual Cross Attention (Dual-CA) sub-module, which consists of an Inertia-Supplied Cross Attention (ISCA) block and a Projection-Guided Cross Attention (PGCA) block. ISCA block introduces multi-channel inertia forces and increases the memory effect by a cross attention mechanism between adjacent iterations. And, PGCA block achieves an enhanced information interaction, which introduces the inertia force into the gradient descent step through a cross attention block. Extensive CS experiments manifest that our OCTUF achieves superior performance compared to state-of-the-art methods while training lower complexity. Codes are available at https://github.com/songjiechong/OCTUF.
Controlled Density Transport Using Perron Frobenius Generators

We consider the problem of the transport of a density of states from an initial state distribution to a desired final state distribution through a dynamical system with actuation. In particular, we consider the case where the control signal is a function of time, but not space; that is, the same actuation is applied at every point in the state space. This is motivated by several problems in fluid mechanics, such as mixing and manipulation of a collection of particles by a global control input such as a uniform magnetic field, as well as by more general control problems where a density function describes an uncertainty distribution or a distribution of agents in a multi-agent system. We formulate this problem using the generators of the Perron-Frobenius operator associated with the drift and control vector fields of the system. By considering finite-dimensional approximations of these operators, the density transport problem can be expressed as a control problem for a bilinear system in a high-dimensional, lifted state. With this system, we frame the density control problem as a problem of driving moments of the density function to the moments of a desired density function, where the moments of the density can be expressed as an output which is linear in the lifted state. This output tracking problem for the lifted bilinear system is then solved using differential dynamic programming, an iterative trajectory optimization scheme.
Higher partial wave contamination in finite-volume 1-to-2 transitions

In their seminal work, Lellouch and L\"uscher derived a conversion factor relating a finite-volume matrix element, calculable using numerical lattice QCD, with the infinite-volume decay amplitude for $K \to \pi \pi$. The conversion factor depends on the $\pi \pi \to \pi \pi$ scattering amplitude with the same total isospin (either zero or two) as the $\pi \pi$ decay channel. Although an infinite tower of $\pi \pi \to \pi \pi$ partial-wave components affect the conversion factor, the $S$-wave ($\ell=0$) component is expected to dominate, and only this contribution is included in the well-known Lellouch-L\"uscher factor, with other $\pi \pi \to \pi \pi$ partial-wave amplitudes formally set to zero. However, as the precision of lattice calculations increases, it may become important to assess the systematic uncertainty arising from this approximation. With this motivation, we compare the $S$-wave-only results with those truncated at the next contaminating partial wave: the $G$-wave ($\ell=4$) for zero total momentum in the finite-volume frame and the $D$-wave ($\ell=2$) otherwise. Using the general framework for $1 \overset{\mathcal J}{\to} 2$ transitions, we quantify the effect of higher partial waves for systems with zero and non-zero total momentum as well as with anti-periodic boundary conditions, presenting both generic numerical examples and results for realistic $\pi \pi$ amplitudes taken from chiral perturbation theory and dispersive analysis. We also consider the accidental degeneracy occurring in the 8$^{\text {th}}$ excited state of the zero-momentum system. This exhibits qualitatively new features at $\ell=4$, not seen in the $\ell=0$ truncation.
S$^2$MAT: Simultaneous and Self-Reinforced Mapping and Tracking in Dynamic Urban Scenariosorcing Framework for Simultaneous Mapping and Tracking in Unbounded Urban Environments

Despite the increasing prevalence of robots in daily life, their navigation capabilities are still limited to environments with prior knowledge, such as a global map. To fully unlock the potential of robots, it is crucial to enable them to navigate in large-scale unknown and changing unstructured scenarios. This requires the robot to construct an accurate static map in real-time as it explores, while filtering out moving objects to ensure mapping accuracy and, if possible, achieving high-quality pedestrian tracking and collision avoidance. While existing methods can achieve individual goals of spatial mapping or dynamic object detection and tracking, there has been limited research on effectively integrating these two tasks, which are actually coupled and reciprocal. In this work, we propose a solution called S$^2$MAT (Simultaneous and Self-Reinforced Mapping and Tracking) that integrates a front-end dynamic object detection and tracking module with a back-end static mapping module. S$^2$MAT leverages the close and reciprocal interplay between these two modules to efficiently and effectively solve the open problem of simultaneous tracking and mapping in highly dynamic scenarios. We conducted extensive experiments using widely-used datasets and simulations, providing both qualitative and quantitative results to demonstrate S$^2$MAT's state-of-the-art performance in dynamic object detection, tracking, and high-quality static structure mapping. Additionally, we performed long-range robotic navigation in real-world urban scenarios spanning over 7 km, which included challenging obstacles like pedestrians and other traffic agents. The successful navigation provides a comprehensive test of S$^2$MAT's robustness, scalability, efficiency, quality, and its ability to benefit autonomous robots in wild scenarios without pre-built maps.
NAP at SemEval-2023 Task 3: Is Less Really More? (Back-)Translation as Data Augmentation Strategies for Detecting Persuasion Techniques

Persuasion techniques detection in news in a multi-lingual setup is non-trivial and comes with challenges, including little training data. Our system successfully leverages (back-)translation as data augmentation strategies with multi-lingual transformer models for the task of detecting persuasion techniques. The automatic and human evaluation of our augmented data allows us to explore whether (back-)translation aid or hinder performance. Our in-depth analyses indicate that both data augmentation strategies boost performance; however, balancing human-produced and machine-generated data seems to be crucial.
Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

Machine learning methods for satellite data have a range of societally relevant applications, but labels used to train models can be difficult or impossible to acquire. Self-supervision is a natural solution in settings with limited labeled data, but current self-supervised models for satellite data fail to take advantage of the characteristics of that data, including the temporal dimension (which is critical for many applications, such as monitoring crop growth) and availability of data from many complementary sensors (which can significantly improve a model's predictive performance). We present Presto (the Pretrained Remote Sensing Transformer), a model pre-trained on remote sensing pixel-timeseries data. By designing Presto specifically for remote sensing data, we can create a significantly smaller but performant model. Presto excels at a wide variety of globally distributed remote sensing tasks and performs competitively with much larger models while requiring far less compute. Presto can be used for transfer learning or as a feature extractor for simple models, enabling efficient deployment at scale.
Computing the L1 optimal transport density: a FEM approach

The $L^1$ optimal transport density $\mu^*$ is the unique $L^\infty$ solution of the Monge-Kantorovich equations. It has been recently characterized also as the unique minimizer of the $L^1$ -transport energy functional E. In the present work we develop and we prove convergence of a numerical approxi- mation scheme for $\mu^*$ . Our approach relies upon the combination of a FEM- inspired variational approximation of E with a minimization algorithm based on a gradient flow method.
$L^2(I;H^1(\Omega))$ and $L^2(I;L^2(\Omega))$ best approximation type error estimates for Galerkin solutions of transient Stokes problems

In this paper we establish best approximation type estimates for the fully discrete Galerkin solutions of transient Stokes problem in $L^2(I;L^2(\Omega)^d)$ and $L^2(I;H^1(\Omega)^d)$ norms. These estimates fill the gap in the error analysis of the transient Stokes problems and have a number of applications. The analysis naturally extends to inhomogeneous parabolic problems. The best type $L^2(I;H^1(\Omega))$ error estimates seems to be new even for scalar parabolic problems.