Miracle-dz/UniFAR-FTUs huggingface dataset:
Thermoelectric and magneto-transport characteristics of interconnected networks of ferromagnetic nanowires and nanotubes

Macroscopic-scale nanostructures, situated at the interface of nanostructures and bulk materials, hold significant promise in the realm of thermoelectric materials. Nanostructuring presents a compelling avenue for enhancing material thermoelectric performance as well as unlocking intriguing nanoscale phenomena, including spin-dependent thermoelectric effects. This is achieved while preserving high power output capabilities and ease of measurements related to the overall macroscopic dimensions. Within this framework, the recently developed three-dimensional interconnected nanowire and nanotube networks, integrated into a flexible polymer membrane, emerge as promising candidates for macroscopic nanostructures. The flexibility of these composites also paves the way for advances in the burgeoning field of flexible thermoelectrics. In this study, we demonstrate that the three-dimensional nanowire networks made of ferromagnetic metals maintain the intrinsic bulk thermoelectric power of their bulk constituent even for a diameter reduced to approximately 23 nm. Furthermore, we showcase the pioneering magneto-thermoelectric measurements of three-dimensional interconnected nickel nanotube networks. These macroscopic materials, comprising interconnected nanotubes, enable the development of large-area devices that exhibit efficient thermoelectric performance, while their nanoscale tubular structures provide distinctive magneto-transport properties. This research represents a significant step toward harnessing the potential of macroscopic nanostructured materials in the field of thermoelectrics.
Probabilistic Coastal Storm Surge Analyses using Synthesized Tracks Based on Historical Typhoon Parameters

ABSTRACT Kim, H.J. and Suh, S.W. , 2016. Probabilistic coastal storm surge analyses using synthesized tracks based on historical typhoon parameters. In: Vila-Concejo, A. ; Bruce, E. ; Kennedy, D.M. , and McCarroll, R.J. (eds.), Proceedings of the 14th International Coastal Symposium (Sydney, Australia). Journal of Coastal Research, Special Issue, No. 75, pp. 1132 - 1136. Coconut Creek (Florida), ISSN 0749-0208. Natural hazards driven by storm surge increasingly threaten low-lying coastal areas and infrastructures. Although several attempts have been made to minimize coastal hazards by incorporating probabilistic, deterministic, and hybrid analyses, the nonlinear interactions between the tide and surge makes difficulties in storm surge analyses. In this study, an efficient approach to assess the vulnerability to storms surges is developed by applying a random generation scheme called the Topical Cyclone Risk Model (TCRM). The maximum radii and wind speed of synthetic storms were adjusted with central pressure based on historical track data to represent the most appropriate probability. A total of 177,244 storms were synthesized by a fully automated procedure using a Perl script; these acted as forcing in ADvanced CIRCulation (ADCIRC) model incorporating random tidal motion. By comparing the results of the hypothetical typhoon surges with observed data showed that the suggested synthetic storms based on historical tracks satisfactorily reproduce probabilistic surge elevations and show almost similar characteristics in real situation. Moreover, the simulation results showed typhoons heading to the west coast could cause higher surge heights than those landing on the south coast because of superposition of high tidal fluctuations in a macro-tidal environment. Thus, this method can be applied to coastal hazards mitigation measures against return periods and future climate change. However, parameters such as moving speed and shape of the typhoon vortex would be important in surge simulations; hence, more appropriate treatment of these characteristics should be studied in future research.
Digitalization of Free-Radical Polymerization

Current paper presents the first attempt of the digitalization of a complex polymerization process representing free-radical polymerization of vinyl monomers in the presence of small additives of stable radicals TEMPO and C60 fullerene. Chain-reaction essence of the chemical process; quantum-chemical concept of elementary reactions; digital-twin presentation of their participants; virtual thermodynamic and kinetic descriptors of the latters; digital polymeryzation passports of individually composed reaction solutions lay the foundation of the digitalization carried out. Polymerization-passport predictions made for a number of solutions with different vinyl monomers and/or different initiating free radicals are fully consistent with chemical reality.
Roots of polynomials under repeated differentiation and repeated applications of fractional differential operators

We start with a random polynomial $P^{N}$ of degree $N$ with independent coefficients and consider a new polynomial $P_{t}^{N}$ obtained by repeated applications of a fraction differential operator of the form $z^{a}(d/dz)^{b},$ where $a$ and $b$ are real numbers. When $b>0,$ we compute the limiting root distribution $\mu_{t}$ of $P_{t}^{N}$ as $N\rightarrow\infty. $ We show that $\mu_{t}$ is the push-forward of the limiting root distribution of $P^{N}$ under a transport map $T_{t}$. The map $T_{t}$ is defined by flowing along the characteristic curves of the PDE satisfied by the log potential of $\mu_{t}. $ In the special case of repeated differentiation, our results may be interpreted as saying that the roots evolve radially with constant speed until they hit the origin, at which point, they cease to exist. As an application, we obtain a push-forward characterization of the free self-convolution semigroup $\oplus$ of isotropic measures on $\mathbb C$. We also consider the case $b<0,$ which includes the case of repeated integration. More complicated behavior of the roots can occur in this case.
Context Enhanced Transformer for Single Image Object Detection

With the increasing importance of video data in real-world applications, there is a rising need for efficient object detection methods that utilize temporal information. While existing video object detection (VOD) techniques employ various strategies to address this challenge, they typically depend on locally adjacent frames or randomly sampled images within a clip. Although recent Transformer-based VOD methods have shown promising results, their reliance on multiple inputs and additional network complexity to incorporate temporal information limits their practical applicability. In this paper, we propose a novel approach to single image object detection, called Context Enhanced TRansformer (CETR), by incorporating temporal context into DETR using a newly designed memory module. To efficiently store temporal information, we construct a class-wise memory that collects contextual information across data. Additionally, we present a classification-based sampling technique to selectively utilize the relevant memory for the current image. In the testing, We introduce a test-time memory adaptation method that updates individual memory functions by considering the test distribution. Experiments with CityCam and ImageNet VID datasets exhibit the efficiency of the framework on various video systems. The project page and code will be made available at: https://ku-cvlab.github. io/CETR.
Navigating the Concurrency Landscape: A Survey of Race Condition Vulnerability Detectors

As technology continues to advance and we usher in the era of Industry 5.0, there has been a profound paradigm shift in operating systems, file systems, web, and network applications. The conventional utilization of multiprocessing and multicore systems has made concurrent programming increasingly pervasive. However, this transformation has brought about a new set of issues known as concurrency bugs, which, due to their wide prevalence in concurrent programs, have led to severe failures and potential security exploits. Over the past two decades, numerous researchers have dedicated their efforts to unveiling, detecting, mitigating, and preventing these bugs, with the last decade witnessing a surge in research within this domain. Among the spectrum of concurrency bugs, data races or race condition vulnerabilities stand out as the most prevalent, accounting for a staggering 80\% of all concurrency bugs. This survey paper is focused on the realm of race condition bug detectors. We systematically categorize these detectors based on the diverse methodologies they employ. Additionally, we delve into the techniques and algorithms associated with race detection, tracing the evolution of this field over time. Furthermore, we shed light on the application of fuzzing techniques in the detection of race condition vulnerabilities. By reviewing these detectors and their static analyses, we draw conclusions and outline potential future research directions, including enhancing accuracy, performance, applicability, and comprehensiveness in race condition vulnerability detection.
Toward Generalizable Machine Learning Models in Speech, Language, and Hearing Sciences: Sample Size Estimation and Reducing Overfitting

PURPOSE Many studies using machine learning (ML) in speech, language, and hearing sciences rely upon cross-validations with single data splitting. This study's first purpose is to provide quantitative evidence that would incentivize researchers to instead use the more robust data splitting method of nested k-fold cross-validation. The second purpose is to present methods and MATLAB code to perform power analysis for ML-based analysis during the design of a study. METHOD First, the significant impact of different cross-validations on ML outcomes was demonstrated using real-world clinical data. Then, Monte Carlo simulations were used to quantify the interactions among the employed cross-validation method, the discriminative power of features, the dimensionality of the feature space, the dimensionality of the model, and the sample size. Four different cross-validation methods (single holdout, 10-fold, train-validation-test, and nested 10-fold) were compared based on the statistical power and confidence of the resulting ML models. Distributions of the null and alternative hypotheses were used to determine the minimum required sample size for obtaining a statistically significant outcome (5% significance) with 80% power. Statistical confidence of the model was defined as the probability of correct features being selected for inclusion in the final model. RESULTS ML models generated based on the single holdout method had very low statistical power and confidence, leading to overestimation of classification accuracy. Conversely, the nested 10-fold cross-validation method resulted in the highest statistical confidence and power while also providing an unbiased estimate of accuracy. The required sample size using the single holdout method could be 50% higher than what would be needed if nested k-fold cross-validation were used. Statistical confidence in the model based on nested k-fold cross-validation was as much as four times higher than the confidence obtained with the single holdout-based model. A computational model, MATLAB code, and lookup tables are provided to assist researchers with estimating the minimum sample size needed during study design. CONCLUSION The adoption of nested k-fold cross-validation is critical for unbiased and robust ML studies in the speech, language, and hearing sciences. SUPPLEMENTAL MATERIAL https://doi.org/10.23641/asha.25237045.
DRStageNet: Deep Learning for Diabetic Retinopathy Staging from Fundus Images

Diabetic retinopathy (DR) is a prevalent complication of diabetes associated with a significant risk of vision loss. Timely identification is critical to curb vision impairment. Algorithms for DR staging from digital fundus images (DFIs) have been recently proposed. However, models often fail to generalize due to distribution shifts between the source domain on which the model was trained and the target domain where it is deployed. A common and particularly challenging shift is often encountered when the source- and target-domain supports do not fully overlap. In this research, we introduce DRStageNet, a deep learning model designed to mitigate this challenge. We used seven publicly available datasets, comprising a total of 93,534 DFIs that cover a variety of patient demographics, ethnicities, geographic origins and comorbidities. We fine-tune DINOv2, a pretrained model of self-supervised vision transformer, and implement a multi-source domain fine-tuning strategy to enhance generalization performance. We benchmark and demonstrate the superiority of our method to two state-of-the-art benchmarks, including a recently published foundation model. We adapted the grad-rollout method to our regression task in order to provide high-resolution explainability heatmaps. The error analysis showed that 59\% of the main errors had incorrect reference labels. DRStageNet is accessible at URL [upon acceptance of the manuscript].
Optimizing Heat Alert Issuance with Reinforcement Learning

A key strategy in societal adaptation to climate change is the use of alert systems to reduce the adverse health impacts of extreme heat events by prompting preventative action. In this work, we investigate reinforcement learning (RL) as a tool to optimize the effectiveness of such systems. Our contributions are threefold. First, we introduce a novel RL environment enabling the evaluation of the effectiveness of heat alert policies to reduce heat-related hospitalizations. The rewards model is trained from a comprehensive dataset of historical weather, Medicare health records, and socioeconomic/geographic features. We use variational Bayesian techniques to address low-signal effects and spatial heterogeneity, which are commonly encountered in climate&health settings. The transition model incorporates real historical weather patterns enriched by a data augmentation mechanism based on climate region similarity. Second, we use this environment to evaluate standard RL algorithms in the context of heat alert issuance. Our analysis shows that policy constraints are needed to improve the initially poor performance of RL. Lastly, a post hoc contrastive analysis provides insight into scenarios where our modified heat alert-RL policies yield significant gains/losses over the current National Weather Service alert policy in the United States.
Disorder-induced non-linear growth of viscously-unstable immiscible two-phase flow fingers in porous media

The immiscible displacement of a fluid by another one inside a porous medium produces different types of patterns depending on the capillary number Ca and viscosity ratio M. At high Ca, viscous fingers resulting from the viscous instability between fluid-fluid interfaces are believed to exhibit the same Laplacian growth behavior as viscously-unstable fingers observed in Hele-Shaw cells by Saffman and Taylor [1], or as diffusion limited aggregates (DLA) [2]. I.e., the interface velocity depends linearly on the local gradient of the physical field that drives the growth process (for two-phase flow, the pressure field). However, steady-state two-phase flow in porous media is known to exhibit a regime for which the flow rate depends as a non-linear power law on the global pressure drop, due to the disorder in the capillary barriers at pore throats. A similar nonlinear growth regime was also evidenced experimentally for viscously-unstable drainage in two-dimensional porous media 20 years ago [3]. Here we revisit this flow regime using dynamic pore-network modeling, and explore the non-linearity in the growth properties. We characterize the previously-unstudied dependencies of the statistical finger width and nonlinear growth law's exponent on Ca, and discuss quantitatively, based on theoretical arguments, how disorder in the capillary barriers controls the growth process' non-linearity, and why the flow regime crosses over to Laplacian growth at sufficiently high Ca. In addition, the statistical properties of the fingering patterns are compared to those of Saffman-Taylor fingers, DLA growth patterns, and the results from the aforementioned previous experimental study.
High-dimensional order parameters and neural network classifiers applied to amorphous ices.

Amorphous ice phases are key constituents of water's complex structural landscape. This study investigates the polyamorphic nature of water, focusing on the complexities within low-density amorphous ice (LDA), high-density amorphous ice, and the recently discovered medium-density amorphous ice (MDA). We use rotationally invariant, high-dimensional order parameters to capture a wide spectrum of local symmetries for the characterization of local oxygen environments. We train a neural network to classify these local environments and investigate the distinctiveness of MDA within the structural landscape of amorphous ice. Our results highlight the difficulty in accurately differentiating MDA from LDA due to structural similarities. Beyond water, our methodology can be applied to investigate the structural properties and phases of disordered materials.
A City Fit for Children: Mapping and Analysis of Child Friendly Cities Initiatives

Over the two last decades, a number of States have implemented Child Friendly Cities (CFC) initiatives, designed to promote both children’s rights and children’s well-being at subnational level. However, limited research is available with respect to the global distribution of CFC initiatives, and the extent to which initiatives cover and effectively implement the nine constitutive elements of the Child Friendly Cities framework. In this paper we address these issues by:(1) mapping all registered CFC initiatives around the world to reflect their global distribution, (2) analyze whether these initiatives cover the nine constitutive elements of the Child Friendly Cities framework and, (3) provide a meta-analysis of challenges and good practices on the basis of a sample of the registered initiatives. We conclude that the global distribution of CFC initiatives is uneven and that most CFC initiatives do not fully implement the CFC framework; they cherry-pick amongst elements, rather than cover the elements comprehensively. Our research also shows that, despite the existence of some good practices, there are recurrent challenges and limitations in respect of CFC initiatives that undermine their effectiveness in practice. To tackle these shortcomings, we encourage States and cities to genuinely embrace the concept of Child Friendly Cities and its implications.
Engineered Ordinary Differential Equations as Classification Algorithm (EODECA): thorough characterization and testing

EODECA (Engineered Ordinary Differential Equations as Classification Algorithm) is a novel approach at the intersection of machine learning and dynamical systems theory, presenting a unique framework for classification tasks [1]. This method stands out with its dynamical system structure, utilizing ordinary differential equations (ODEs) to efficiently handle complex classification challenges. The paper delves into EODECA's dynamical properties, emphasizing its resilience against random perturbations and robust performance across various classification scenarios. Notably, EODECA's design incorporates the ability to embed stable attractors in the phase space, enhancing reliability and allowing for reversible dynamics. In this paper, we carry out a comprehensive analysis by expanding on the work [1], and employing a Euler discretization scheme. In particular, we evaluate EODECA's performance across five distinct classification problems, examining its adaptability and efficiency. Significantly, we demonstrate EODECA's effectiveness on the MNIST and Fashion MNIST datasets, achieving impressive accuracies of $98.06\%$ and $88.21\%$, respectively. These results are comparable to those of a multi-layer perceptron (MLP), underscoring EODECA's potential in complex data processing tasks. We further explore the model's learning journey, assessing its evolution in both pre and post training environments and highlighting its ability to navigate towards stable attractors. The study also investigates the invertibility of EODECA, shedding light on its decision-making processes and internal workings. This paper presents a significant step towards a more transparent and robust machine learning paradigm, bridging the gap between machine learning algorithms and dynamical systems methodologies.
Hochschild cohomology of Lie-Rinehart algebras

We compute the Hochschild cohomology of universal enveloping algebras of Lie-Rinehart algebras in terms of the Poisson cohomology of the associated graded quotient algebras. Central in our approach are two cochain complexes of"nonlinear Chevalley-Eilenberg"cochains whose origins lie in Lie-Rinehart modules"up to homotopy", one on the Hochschild cochains of the base algebra, another related to the adjoint representation. The Poincare-Birkhoff-Witt isomorphism is then extended to a certain intertwiner between such modules. Finally, exploiting the twisted Calabi-Yau structure, we obtain results for the dual Hochschild and cyclic homology.
Invariant Anomaly Detection under Distribution Shifts: A Causal Perspective

Anomaly detection (AD) is the machine learning task of identifying highly discrepant abnormal samples by solely relying on the consistency of the normal training samples. Under the constraints of a distribution shift, the assumption that training samples and test samples are drawn from the same distribution breaks down. In this work, by leveraging tools from causal inference we attempt to increase the resilience of anomaly detection models to different kinds of distribution shifts. We begin by elucidating a simple yet necessary statistical property that ensures invariant representations, which is critical for robust AD under both domain and covariate shifts. From this property, we derive a regularization term which, when minimized, leads to partial distribution invariance across environments. Through extensive experimental evaluation on both synthetic and real-world tasks, covering a range of six different AD methods, we demonstrated significant improvements in out-of-distribution performance. Under both covariate and domain shift, models regularized with our proposed term showed marked increased robustness. Code is available at: https://github.com/JoaoCarv/invariant-anomaly-detection.
Balanced infinitesimal bialgebras, double Poisson gebras and pre-Calabi-Yau algebras

We consider the properad that governs the balanced infinitesimal bialgebras equipped with a coproduct of degree $1-d$. This properad naturally encodes a part of the structure of the pre-Calabi-Yau algebras of degree $d$. We compute the cobar construction of its Koszul dual coproperad and show that its gebras lie between the homotopy double Poisson gebras and the pre-Calabi-Yau algebras. Finally, we show that, if one is willing to consider their curved version, the two resulting notions of curved homotopy balanced infinitesimal bialgebra and curved homotopy double Poisson gebra are equivalent. A relationship with the homotopy odd Lie bialgebras is also discussed.
An F5 Algorithm for Tropical Gröbner Bases in the Weyl Algebras

A Gr\"obner basis computation for the Weyl algebra with respect to a tropical term order and by using a homogenization-dehomogenization technique is sufficiently sluggish. A significant number of reductions to zero occur. To improve the computation, a tropical F5 algorithm is developed for this context. As a member of the family of signature-based algorithms, this algorithm keeps track of where Weyl algebra elements come from to anticipate reductions to zero. The total order for ordering module monomials or signatures in this paper is designed as close as possible to the definition of the tropical term order. As in Vaccon et al. (2021), this total order is not compatible with the tropical term order.