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The magnetic textures generated by a perpendicularly applied magnetic field at the ferromagnetic layer of $Co/Al_{2}O_{3}/Nb$ thin film heterostructures are investigated using magneto-optical imaging and micromagnetic simulations. It is observed that the stray field caused by flux avalanches in the superconducting layer prints out a non-trivial in-plane texture in the cobalt layer, which remains stable up to temperatures much above the Nb critical temperature. These textures mimic quite closely the dendritic thermomagnetic flux avalanches that penetrate the Nb layer from its edges. For low cobalt thickness, the filamentary magnetic textures occur in pairs with opposite polarity. The previous in-plane magnetization determines the relative location of the magnetic filaments with respect to the avalanche position. Micromagnetic simulations solving the Landau-Lifshitz-Gilbert equation confirm the interpretation given for the experimental findings., Comment: 13 paginas, 5 figuras, supplementary material

Generative Flow Networks (GFlowNets) are amortized inference models designed to sample from unnormalized distributions over composable objects, with applications in generative modeling for tasks in fields such as causal discovery, NLP, and drug discovery. Traditionally, the training procedure for GFlowNets seeks to minimize the expected log-squared difference between a proposal (forward policy) and a target (backward policy) distribution, which enforces certain flow-matching conditions. While this training procedure is closely related to variational inference (VI), directly attempting standard Kullback-Leibler (KL) divergence minimization can lead to proven biased and potentially high-variance estimators. Therefore, we first review four divergence measures, namely, Renyi-$\alpha$'s, Tsallis-$\alpha$'s, reverse and forward KL's, and design statistically efficient estimators for their stochastic gradients in the context of training GFlowNets. Then, we verify that properly minimizing these divergences yields a provably correct and empirically effective training scheme, often leading to significantly faster convergence than previously proposed optimization. To achieve this, we design control variates based on the REINFORCE leave-one-out and score-matching estimators to reduce the variance of the learning objectives' gradients. Our work contributes by narrowing the gap between GFlowNets training and generalized variational approximations, paving the way for algorithmic ideas informed by the divergence minimization viewpoint., Comment: Accepted at NeurIPS 2024, https://openreview.net/forum?id=N5H4z0Pzvn

In this paper we develop an analogue of the Berkovich analytification for non-necessarily algebraic complex spaces. We apply this theory to generalize to arbitrary compact K\"ahler manifolds a result of Chi Li, proving that a stronger version of K-stability implies the existence of a unique constant scalar curvature K\"ahler metric., Comment: 61 pages

GFlowNets are a promising alternative to MCMC sampling for discrete compositional random variables. Training GFlowNets requires repeated evaluations of the unnormalized target distribution or reward function. However, for large-scale posterior sampling, this may be prohibitive since it incurs traversing the data several times. Moreover, if the data are distributed across clients, employing standard GFlowNets leads to intensive client-server communication. To alleviate both these issues, we propose embarrassingly parallel GFlowNet (EP-GFlowNet). EP-GFlowNet is a provably correct divide-and-conquer method to sample from product distributions of the form $R(\cdot) \propto R_1(\cdot) ... R_N(\cdot)$ -- e.g., in parallel or federated Bayes, where each $R_n$ is a local posterior defined on a data partition. First, in parallel, we train a local GFlowNet targeting each $R_n$ and send the resulting models to the server. Then, the server learns a global GFlowNet by enforcing our newly proposed \emph{aggregating balance} condition, requiring a single communication step. Importantly, EP-GFlowNets can also be applied to multi-objective optimization and model reuse. Our experiments illustrate the EP-GFlowNets's effectiveness on many tasks, including parallel Bayesian phylogenetics, multi-objective multiset, sequence generation, and federated Bayesian structure learning., Comment: Accepted to ICML 2024

Central Bank Digital Currency (CBDC) can be defined as a virtual currency based on node network and digital encryption algorithm issued by a country which has a legal credit protection. CBDCs are supported by Distributed Ledger Technologies (DLTs), and they may allow a universal means of payments for the digital era. There are many ways to proceed, they all require central banks to develop technological expertise. Considering these points, it is important to understand the new IT governance in the financial markets due to CBDC and digital economy. Information Technology is an essential driver that will allow the new financial industry design. This paper has the objective to answer two questions through an updated Systematic Literature Review (SLR). The first question is What IT resources and tools have been considered or applied to set the governance of CBDC adoption? The second; Identify IT governance models in the financial market due to CBDC adoption. Bank for International Settlements (BIS) publications, Scopus and Web of Science were considered as sources of studies. After the strings and including criteria were applied, fourteen papers were analyzed. This paper finds many IT resources used in the CBDC adoption and some preliminary IT design related to the IT governance of CBDC, in the results and discussion section the findings are more detailed. Finally, limitations and future work are considered. Keywords: Blockchain, Central Bank Digital Currency (CBDC), Digital Economy, Distributed Ledger Technology (DLT), Information Technology (IT), IT governance., Comment: Keywords: Blockchain, Central Bank Digital Currency (CBDC), Digital Economy, Distributed Ledger Technology (DLT), Information Technology (IT), IT governance

The Transfer Matrix Method (TMM) has become a prominent tool for the optical simulation of thin$-$film solar cells, particularly among researchers specializing in organic semiconductors and perovskite materials. As the commercial viability of these solar cells continues to advance, driven by rapid developments in materials and production processes, the importance of optical simulation has grown significantly. By leveraging optical simulation, researchers can gain profound insights into photovoltaic phenomena, empowering the implementation of device optimization strategies to achieve enhanced performance. However, existing TMM$-$based packages exhibit limitations, such as requiring programming expertise, licensing fees, or lack of support for bilayer device simulation. In response to these gaps and challenges, we present the TMM Simulator (TMM$-$Sim), an intuitive and user$-$friendly tool to calculate essential photovoltaic parameters, including the optical electric field profile, exciton generation profile, fraction of light absorbed per layer, photocurrent, external quantum efficiency, internal quantum efficiency, and parasitic losses. An additional advantage of TMM$-$Sim lies in its capacity to generate outcomes suitable as input parameters for electro$-$optical device simulations. In this work, we offer a comprehensive guide, outlining a step$-$by$-$step process to use TMM$-$Sim, and provide a thorough analysis of the results. TMM$-$Sim is freely available, accessible through our web server (nanocalc.org), or downloadable from the TMM$-$Sim repository (for \textit{Unix}, \textit{Windows}, and \textit{macOS}) on \textit{GitHub}. With its user$-$friendly interface and powerful capabilities, TMM$-$Sim aims to facilitate and accelerate research in thin$-$film solar cells, fostering advancements in renewable energy technologies., Comment: 9 pages, 8 figures

The proliferation of textual data on the Internet presents a unique opportunity for institutions and companies to monitor public opinion about their services and products. Given the rapid generation of such data, the text stream mining setting, which handles sequentially arriving, potentially infinite text streams, is often more suitable than traditional batch learning. While pre-trained language models are commonly employed for their high-quality text vectorization capabilities in streaming contexts, they face challenges adapting to concept drift - the phenomenon where the data distribution changes over time, adversely affecting model performance. Addressing the issue of concept drift, this study explores the efficacy of seven text sampling methods designed to selectively fine-tune language models, thereby mitigating performance degradation. We precisely assess the impact of these methods on fine-tuning the SBERT model using four different loss functions. Our evaluation, focused on Macro F1-score and elapsed time, employs two text stream datasets and an incremental SVM classifier to benchmark performance. Our findings indicate that Softmax loss and Batch All Triplets loss are particularly effective for text stream classification, demonstrating that larger sample sizes generally correlate with improved macro F1-scores. Notably, our proposed WordPieceToken ratio sampling method significantly enhances performance with the identified loss functions, surpassing baseline results., Comment: Accepted for presentation at the 27th International Conference on Pattern Recognition (ICPR) 2024

Systems and individuals produce data continuously. On the Internet, people share their knowledge, sentiments, and opinions, provide reviews about services and products, and so on. Automatically learning from these textual data can provide insights to organizations and institutions, thus preventing financial impacts, for example. To learn from textual data over time, the machine learning system must account for concept drift. Concept drift is a frequent phenomenon in real-world datasets and corresponds to changes in data distribution over time. For instance, a concept drift occurs when sentiments change or a word's meaning is adjusted over time. Although concept drift is frequent in real-world applications, benchmark datasets with labeled drifts are rare in the literature. To bridge this gap, this paper provides four textual drift generation methods to ease the production of datasets with labeled drifts. These methods were applied to Yelp and Airbnb datasets and tested using incremental classifiers respecting the stream mining paradigm to evaluate their ability to recover from the drifts. Results show that all methods have their performance degraded right after the drifts, and the incremental SVM is the fastest to run and recover the previous performance levels regarding accuracy and Macro F1-Score.

Deep neural networks are notoriously miscalibrated, i.e., their outputs do not reflect the true probability of the event we aim to predict. While networks for tabular or image data are usually overconfident, recent works have shown that graph neural networks (GNNs) show the opposite behavior for node-level classification. But what happens when we are predicting links? We show that, in this case, GNNs often exhibit a mixed behavior. More specifically, they may be overconfident in negative predictions while being underconfident in positive ones. Based on this observation, we propose IN-N-OUT, the first-ever method to calibrate GNNs for link prediction. IN-N-OUT is based on two simple intuitions: i) attributing true/false labels to an edge while respecting a GNNs prediction should cause but small fluctuations in that edge's embedding; and, conversely, ii) if we label that same edge contradicting our GNN, embeddings should change more substantially. An extensive experimental campaign shows that IN-N-OUT significantly improves the calibration of GNNs in link prediction, consistently outperforming the baselines available -- which are not designed for this specific task., Comment: 18 pages, 4 figures, 8 tables

The search for double Higgs production in $b\bar{b}W^+W^-$, where both $W$ bosons decay to leptons, has been rehabilitated as a good option to look for that key process to the Standard Model scalar sector study in the LHC. The missing neutrinos, however, hinder the reconstruction of useful information like the Higgs pair mass, which is very sensitive to the trilinear Higgs self-coupling. We present a solution to that problem using a Variational Autoencoder for Regression (VAER) to reconstruct the Higgs and top pairs decays $hh,t\bar{t}\to b\bar{b}W^+W^-\to b\bar{b}\ell^+\ell^{\prime -}\nu_\ell\bar{\nu}_{\ell^\prime}$. The algorithm predicts the invariant mass of non-resonant $hh$ irrespective of the trilinear coupling, even for events whose Higgs self-couplings were never presented to it. VAER is also able to identify a new Higgs resonance in an unsupervised way, showing generalization power for events not presented in its training phase. Finally, we demonstrate that VAER prediction is as useful to statistical inference as ground truth simulated distributions by computing a $\chi^2$ between trilinear coupling hypotheses based on binned invariant mass distributions of $b\bar{b}\ell^+\ell^{\prime -}\nu_\ell\bar{\nu}_{\ell^\prime}$., Comment: 29 pages, 10 figures, 2 tables

A $r$-role assignment of a simple graph $G$ is an assignment of $r$ distinct roles to the vertices of $G$, such that two vertices with the same role have the same set of roles assigned to related vertices. Furthermore, a specific $r$-role assignment defines a role graph, in which the vertices are the distinct $r$ roles, and there is an edge between two roles whenever there are two related vertices in the graph $G$ that correspond to these roles. We consider complementary prisms, which are graphs formed from the disjoint union of the graph with its respective complement, adding the edges of a perfect matching between their corresponding vertices. In this work, we characterize the complementary prisms that do not admit a $3$-role assignment. We highlight that all of them are complementary prisms of disconnected bipartite graphs. Moreover, using our findings, we show that the problem of deciding whether a complementary prism has a $3$-role assignment can be solved in polynomial time., Comment: 20 pages, 4 figures

The refractive index of an optical medium is essential for studying a variety of physical phenomena. One useful method for determining the refractive index of scalar materials (i.e, materials which are characterized by a scalar dielectric function) is to employ the Kramers-Kronig (K-K) relations. The K-K method is particularly useful in cases where ellipsometric measurements are unavailable, a situation that frequently occurs in many laboratories. Although some packages can perform this calculation, they usually lack a graphical interface and are complex to implement and use. Those deficiencies inhibits their utilization by a plethora of researchers unfamiliar with programming languages. To address the aforementioned gap, we have developed the Refractive Index Calculator (RI-Calc) program that provides an intuitive and user-friendly interface. The RI-Calc program allows users to input the absorption coefficient spectrum and then easily calculate the complex refractive index and the complex relative permittivity of a broad range of thin films, including of molecules, polymers, blends, and perovskites. The program has been thoroughly tested, taking into account the Lorentz oscillator model and experimental data from a materials' refractive index database, demonstrating consistent outcomes. It is compatible with Windows, Unix, and macOS operating systems. You can download the RI-Calc binaries from our GitHub repository or conveniently access the program through our dedicated web server at nanocalc.org., Comment: 8 pages, 7 figures

This paper is devoted to bifurcations of periodic solutions of nonlinear measure differential equations with a parameter. Main tools are nonlinear generalized differential equations (in the sense of Kurzweil) and the Kurzweil gauge type generalized integral. We continue the research started by the first author under the supervision of the second one., Comment: Second version of the paper. Mainly some misprints have been corrected

Epidemiology and Public Health have increasingly relied on structured and unstructured data, collected inside and outside of typical health systems, to study, identify, and mitigate diseases at the population level. Focusing on infectious disease, we review how Digital Epidemiology (DE) was at the beginning of 2020 and how it was changed by the COVID-19 pandemic, in both nature and breadth. We argue that DE will become a progressively useful tool as long as its potential is recognized and its risks are minimized. Therefore, we expand on the current views and present a new definition of DE that, by highlighting the statistical nature of the datasets, helps in identifying possible biases. We offer some recommendations to reduce inequity and threats to privacy and argue in favour of complex multidisciplinary approaches to tackling infectious diseases., Comment: 18 pages, 2 figures and a supplement material file

Due to the advent and increase in the popularity of the Internet, people have been producing and disseminating textual data in several ways, such as reviews, social media posts, and news articles. As a result, numerous researchers have been working on discovering patterns in textual data, especially because social media posts function as social sensors, indicating peoples' opinions, interests, etc. However, most tasks regarding natural language processing are addressed using traditional machine learning methods and static datasets. This setting can lead to several problems, such as an outdated dataset, which may not correspond to reality, and an outdated model, which has its performance degrading over time. Concept drift is another aspect that emphasizes these issues, which corresponds to data distribution and pattern changes. In a text stream scenario, it is even more challenging due to its characteristics, such as the high speed and data arriving sequentially. In addition, models for this type of scenario must adhere to the constraints mentioned above while learning from the stream by storing texts for a limited time and consuming low memory. In this study, we performed a systematic literature review regarding concept drift adaptation in text stream scenarios. Considering well-defined criteria, we selected 40 papers to unravel aspects such as text drift categories, types of text drift detection, model update mechanism, the addressed stream mining tasks, types of text representations, and text representation update mechanism. In addition, we discussed drift visualization and simulation and listed real-world datasets used in the selected papers. Therefore, this paper comprehensively reviews the concept drift adaptation in text stream mining scenarios., Comment: 49 pages