Miracle-dz/UniFAR-FTUs huggingface dataset:
Control Flow in Active Inference Systems—Part I: Classical and Quantum Formulations of Active Inference

Living systems face both environmental complexity and limited access to free-energy resources. Survival under these conditions requires a control system that can activate, or deploy, available perception and action resources in a context specific way. In this Part I, we introduce the free-energy principle (FEP) and the idea of active inference as Bayesian prediction-error minimization, and show how the control problem arises in active inference systems. We then review classical and quantum formulations of the FEP, with the former being the classical limit of the latter. In the accompanying Part II, we show that when systems are described as executing active inference driven by the FEP, their control flow systems can always be represented as tensor networks (TNs). We show how TNs as control systems can be implemented within the general framework of quantum topological neural networks, and discuss the implications of these results for modeling biological systems at multiple scales.
DeepfakeMAE: Facial Part Consistency Aware Masked Autoencoder for Deepfake Video Detection

Deepfake techniques have been used maliciously, resulting in strong research interests in developing Deepfake detection methods. Deepfake often manipulates the video content by tampering with some facial parts. However, this manipulation usually breaks the consistency among facial parts, e.g., Deepfake may change smiling lips to upset, but the eyes are still smiling. Existing works propose to spot inconsistency on some speciﬁc facial parts (e.g., lips), but they may perform poorly if new Deepfake techniques focus on the speciﬁc facial parts used by the detector. Thus, this paper proposes a new Deepfake detection model, Deepfake-MAE , which can utilize the consistencies among all facial parts. Speciﬁcally, given a real face image, we ﬁrst pre-train a masked autoencoder to learn facial part consistency by randomly masking some facial parts and reconstructing missing areas based on the remaining facial parts. Furthermore, to maximize the discrepancy between real and fake videos, we propose a novel model with dual networks that utilize the pretrained encoder and decoder, respectively. 1) The pretrained encoder is ﬁnetuned for capturing the over-all information of the given video. 2) The pretrained de-coder is utilized for distinguishing real and fake videos based on the motivation that DeepfakeMAE ’s reconstruction should be more similar to a real face image than a fake one. Our extensive experiments on standard benchmarks demonstrate that DeepfakeMAE is highly effective and especially outperforms the previous state-of-the-art method by 3.1% AUC on average in cross-dataset detection.
Quantum Engineering for Energy Applications

Quantum engineering seeks to create novel technologies based on the exploitation of distinctly nonclassical behaviors such as quantum superposition. The vast majority of currently pursued applications fall into the domain of quantum information science, with quantum computing as the most visible subdomain. However, other applications of quantum engineering are fast emerging. Here, we review the deployment of quantum engineering principles in the fields of solar energy, batteries, and nuclear energy. We identify commonalities across quantum engineering approaches in those apparently disparate fields and draw direct parallels to quantum information science. We find that a shared knowledge base is forming, which de facto corresponds to a new domain that we refer to as 'quantum energy science'. Quantum energy science bears the promise of substantial performance improvements across energy technologies such as organic solar cells, quantum batteries, and nuclear fusion. The recognition of this emerging domain may be of great relevance to actors concerned with energy innovation. It may also benefit active researchers in this domain by increasing visibility and motivating the deployment of resources and institutional support.
Near-field frequency-domain theory for propeller noise

L'auteur developpe, a partir de sa theorie generale de l'aerodynamique de surfaces helicoidales citee en reference, une theorie du bruit de champ proche d'une helice, dans le domaine frequentiel. Il prend en consideration l'epaisseur, la charge permanente et des sources quadrupolaires. A part l'approximation des pales minces et l'absence de termes representatifs de sources radiales, les equations developpees sont exactes. Breve comparaison de la theorie a des donnees experimentales
New spatial models for integrating standardized detection-nondetection and opportunistic presence-only data: application to estimating risk factors associated to powerline-induced death of birds

The constant increase in energy consumption has created the necessity of extending the energy transmission and distribution network. Placement of powerlines represent a risk for bird population. Hence, better understanding of deaths induced by powerlines, and the factors behind them are of paramount importance to reduce the impact of powerlines. To address this concern, professional surveys and citizen science data are available. While the former data type is observed in small portions of the space by experts through expensive standardized sampling protocols, the latter is opportunistically collected by citizen scientists. We set up full Bayesian spatial models that 1) fusion both professional surveys and citizen science data and 2) explicitly account for preferential sampling that affects professional surveys data and for factors that affect the quality of citizen science data. The proposed models are part of the family of latent Gaussian models as both data types are interpreted as thinned spatial point patterns and modeled as log-Gaussian Cox processes. The specification of these models assume the existence of a common latent spatial process underlying the observation of both data types. The proposed models are used both on simulated data and on real-data of powerline-induced death of birds in the Trondelag in Norway. The simulation studies clearly show increased accuracy in parameter estimates when both data types are fusioned and factors that bias their collection processes are properly accounted for. The study of powerline-induced deaths shows a clear association between the density of the powerline network and the risk that powerlines represent for bird populations. The choice of model is relevant for the conclusions from this case study as different models estimated the association between risk of powerline-induced deaths and the amount of exposed birds differently.
Development of Design Spectra for Actively Controlled Wall‐Frame Buildings

Preliminary design of a structure controlled by active bracing or mass dampers to withstand seismic motion or high winds requires rapid means of analysis and estimates. A response spectrum technique applied to simplified models of complex structures can provide such means. The spectra required for preliminary evaluation of a controlled structure include, besides the usual response parameters (displacement, acceleration, base shear), the control resource parameters, such as the size and capacity of hydraulic actuators; power requirements; and energy consumption. This paper presents an approach to develop design spectra based on the analogy of behavior of wall frame structures with the behavior of shear‐flexural cantilever beams treated by the continuum approach. This approach is verified numerically and experimentally using simplified parameters representing the structures. The same approach is further used to develop typical response and resource spectra using simulated ground motions according to a curre.
Signatures of Electric Field and Layer Separation Effects on the Spin-Valley Physics of MoSe2/WSe2 Heterobilayers: From Energy Bands to Dipolar Excitons

Multilayered van der Waals heterostructures based on transition metal dichalcogenides are suitable platforms on which to study interlayer (dipolar) excitons, in which electrons and holes are localized in different layers. Interestingly, these excitonic complexes exhibit pronounced valley Zeeman signatures, but how their spin-valley physics can be further altered due to external parameters—such as electric field and interlayer separation—remains largely unexplored. Here, we perform a systematic analysis of the spin-valley physics in MoSe2/WSe2 heterobilayers under the influence of an external electric field and changes of the interlayer separation. In particular, we analyze the spin (Sz) and orbital (Lz) degrees of freedom, and the symmetry properties of the relevant band edges (at K, Q, and Γ points) of high-symmetry stackings at 0° (R-type) and 60° (H-type) angles—the important building blocks present in moiré or atomically reconstructed structures. We reveal distinct hybridization signatures on the spin and the orbital degrees of freedom of low-energy bands, due to the wave function mixing between the layers, which are stacking-dependent, and can be further modified by electric field and interlayer distance variation. We find that H-type stackings favor large changes in the g-factors as a function of the electric field, e.g., from −5 to 3 in the valence bands of the Hhh stacking, because of the opposite orientation of Sz and Lz of the individual monolayers. For the low-energy dipolar excitons (direct and indirect in k-space), we quantify the electric dipole moments and polarizabilities, reflecting the layer delocalization of the constituent bands. Furthermore, our results show that direct dipolar excitons carry a robust valley Zeeman effect nearly independent of the electric field, but tunable by the interlayer distance, which can be rendered experimentally accessible via applied external pressure. For the momentum-indirect dipolar excitons, our symmetry analysis indicates that phonon-mediated optical processes can easily take place. In particular, for the indirect excitons with conduction bands at the Q point for H-type stackings, we find marked variations of the valley Zeeman (∼4) as a function of the electric field, which notably stands out from the other dipolar exciton species. Our analysis suggests that stronger signatures of the coupled spin-valley physics are favored in H-type stackings, which can be experimentally investigated in samples with twist angle close to 60°. In summary, our study provides fundamental microscopic insights into the spin-valley physics of van der Waals heterostructures, which are relevant to understanding the valley Zeeman splitting of dipolar excitonic complexes, and also intralayer excitons.
Towards entanglement entropy of random large-N theories

A large class of strongly correlated quantum systems can be described in certain large-N limits by quadratic in field actions along with self-consistency equations that determine the two-point functions. We use the replica approach and the notion of shifted Matsubara frequency to compute von Neumann and R\'enyi entanglement entropies for generic bi-partitioning of such systems. We argue that the von Neumann entropy can be computed from equilibrium spectral functions w/o partitioning, while the R\'enyi entropy requires re-calculating the spectrum in the interacting case. We demonstrate the flexibility of the method by applying it to examples of a two-site problem in presence of decoherence, and coupled Sachdev-Ye-Kitaev models.
Feature Importance Disparities for Data Bias Investigations

It is widely held that one cause of downstream bias in classifiers is bias present in the training data. Rectifying such biases may involve context-dependent interventions such as training separate models on subgroups, removing features with bias in the collection process, or even conducting real-world experiments to ascertain sources of bias. Despite the need for such data bias investigations, few automated methods exist to assist practitioners in these efforts. In this paper, we present one such method that given a dataset $X$ consisting of protected and unprotected features, outcomes $y$, and a regressor $h$ that predicts $y$ given $X$, outputs a tuple $(f_j, g)$, with the following property: $g$ corresponds to a subset of the training dataset $(X, y)$, such that the $j^{th}$ feature $f_j$ has much larger (or smaller) influence in the subgroup $g$, than on the dataset overall, which we call feature importance disparity (FID). We show across $4$ datasets and $4$ common feature importance methods of broad interest to the machine learning community that we can efficiently find subgroups with large FID values even over exponentially large subgroup classes and in practice these groups correspond to subgroups with potentially serious bias issues as measured by standard fairness metrics.
An Optimization Study of Diversification Return Portfolios

The concept of Diversification Return (DR) was introduced by Booth and Fama in 1990s and it has been well studied in the finance literature mainly focusing on the various sources it may be generated. However, unlike the classical Mean-Variance (MV) model of Markowitz, DR portfolios lack optimization theory for justifying their often outstanding empirical performance. In this paper, we first explain what the DR criterion tries to achieve in terms of portfolio centrality. A consequence of this explanation is that practically imposed norm constraints in fact implicitly enforce constraints on DR. We then derive the maximum DR portfolio under given risk and obtain the efficient DR frontier. We further develop a separation theorem for this frontier and establish a relationship between the DR frontier and Markowitz MV efficient frontier. In the particular case where the variance vector is proportional to the expected return vector of the underlining assets, the two frontiers yield same efficient portfolios. The proof techniques heavily depend on recently developed geometric interpretation of the maximum DR portfolio. Finally, we use DAX30 stock data to illustrate the obtained results and demonstrate an interesting link to the maximum diversification ratio portfolio studied by Choueifaty and Coignard.
IXPE Observations of the Quintessential Wind-accreting X-Ray Pulsar Vela X-1

The radiation from accreting X-ray pulsars was expected to be highly polarized, with some estimates for the polarization degree of up to 80%. However, phase-resolved and energy-resolved polarimetry of X-ray pulsars is required in order to test different models and to shed light on the emission processes and the geometry of the emission region. Here we present the first results of the observations of the accreting X-ray pulsar Vela X-1 performed with the Imaging X-ray Polarimetry Explorer. Vela X-1 is considered to be the archetypal example of a wind-accreting, high-mass X-ray binary system, consisting of a highly magnetized neutron star accreting matter from its supergiant stellar companion. The spectropolarimetric analysis of the phase-averaged data for Vela X-1 reveals a polarization degree (PD) of 2.3% ± 0.4% at the polarization angle (PA) of −47. °3 ± 5. °4. A low PD is consistent with the results obtained for other X-ray pulsars and is likely related to the inverse temperature structure of the neutron star atmosphere. The energy-resolved analysis shows the PD above 5 keV reaching 6%–10% and a ∼90° difference in the PA compared to the data in the 2–3 keV range. The phase-resolved spectropolarimetric analysis finds a PD in the range 0%–9% with the PA varying between −80° and 40°.
Likes and Fragments: Examining Perceptions of Time Spent on TikTok

Researchers use information about the amount of time people spend on digital media for numerous purposes. While social media platforms commonly do not allow external access to measure the use time directly, a usual alternative method is to use participants' self-estimation. However, doubts were raised about the self-estimation's accuracy, posing questions regarding the cognitive factors that underline people's perceptions of the time they spend on social media. In this work, we build on prior studies and explore a novel social media platform in the context of use time: TikTok. We conduct platform-independent measurements of people's self-reported and server-logged TikTok usage (n=255) to understand how users' demographics and platform engagement influence their perceptions of the time they spend on the platform and their estimation accuracy. Our work adds to the body of work seeking to understand time estimations in different digital contexts and identifies new influential engagement factors.
Associated and non-associated visco-plasticity and plasticity in soil mechanics

The visco-plastic model is established as a realistic general description of soil behaviour as well as a computationally convenient fiction for solving any properly formulated plasticity problem. The Paper focuses attention on simple elastic-ideally plastic formulations in which both associated and non-associated forms of behaviour are assumed. In the non-associated form the usual bounding theorems are not applicable and it is of interest that in some cases collapse situations significantly different from those given by limit theorems are predicted. Also, some problems of strain-dependent plasticity are solved to demonstrate the flexibility of the viscoplastic algorithm. Le modele viscoplastique constitue une description generale et realiste du comportement du sol aussi bien qu'un modele facilement calculable pour resoudre tout probleme de plasticite convenablement formule. L'etude concentre l'attention sur des schemas (ou modeles) ou les deux types de comportement (elasticite simple et plasticite ideale).
Hierarchical Discriminative Learning Improves Visual Representations of Biomedical Microscopy

Learning high-quality, self-supervised, visual representations is essential to advance the role of computer vision in biomedical microscopy and clinical medicine. Previous work has focused on self-supervised representation learning (SSL) methods developed for instance discrimination and applied them directly to image patches, or fields-of-view, sampled from gigapixel whole-slide images (WSIs) used for cancer diagnosis. However, this strategy is limited because it (1) assumes patches from the same patient are independent, (2) neglects the patient-slide-patch hierarchy of clinical biomedical microscopy, and (3) requires strong data augmentations that can degrade downstream performance. Importantly, sampled patches from WSIs of a patient's tumor are a diverse set of image examples that capture the same underlying cancer diagnosis. This motivated HiDisc, a data-driven method that leverages the inherent patient-slide-patch hierarchy of clinical biomedical microscopy to define a hierarchical discriminative learning task that implicitly learns features of the underlying diagnosis. HiDisc uses a self-supervised contrastive learning framework in which positive patch pairs are defined based on a common ancestry in the data hierarchy, and a unified patch, slide, and patient discriminative learning objective is used for visual SSL. We benchmark HiDisc visual representations on two vision tasks using two biomedical microscopy datasets, and demonstrate that (1) HiDisc Pre-training outperforms current state-of-the-art self-supervised Pre-training methods for cancer diagnosis and genetic mutation prediction, and (2) HiDisc learns high-quality visual representations using natural patch diversity without strong data augmentations.
Are We Really Making Much Progress? Bag-of-Words vs. Sequence vs. Graph vs. Hierarchy for Single-and Multi-Label Text Classiﬁcation

The popularity of graph neural networks has triggered a resurgence of graph-based meth-ods for single-label and multi-label text clas-siﬁcation. However, it is unclear whether these graph-based methods are beneﬁcial compared to standard machine learning meth-ods and modern pretrained language models. We compare a rich selection of bag-of-words, sequence-based, graph-based, and hierarchical methods for text classiﬁcation. We aggregate results from the literature over 5 single-label and 7 multi-label datasets and run our own experiments. Our ﬁndings un-ambiguously demonstrate that for single-label and multi-label classiﬁcation tasks, the graph-based methods fail to outperform ﬁne-tuned language models and sometimes even perform worse than standard machine learning meth-ods like multilayer perceptron (MLP) on a bag-of-words. This questions the enormous amount of effort put into the development of new graph-based methods in the last years and the promises they make for text classiﬁcation. Given our extensive experiments, we conﬁrm that pretrained language models remain state-of-the-art in text classiﬁcation despite all recent specialized advances. We argue that future work in text classiﬁcation should thoroughly test against strong baselines like MLPs to properly assess the true scientiﬁc progress. The source code is available:
Probabilistic limit theorems induced by the zeros of polynomials

Sequences of discrete random variables are studied whose probability generating functions are zero‐free in a sector of the complex plane around the positive real axis. Sharp bounds on the cumulants of all orders are stated, leading to Berry–Esseen bounds, moderate deviation results, concentration inequalities, and mod‐Gaussian convergence. In addition, an alternate proof of the cumulant bound with improved constants for a class of polynomials all of whose roots lie on the unit circle is provided. A variety of examples is discussed in detail.
Asymptotic properties of endogeneity corrections using nonlinear transformations

This paper studies the asymptotic properties of endogeneity corrections based on nonlinear transformations without external instruments, which were originally proposed by Park and Gupta (2012) and have become popular in applied research. In contrast to the original copula-based estimator, our approach is based on a nonparametric control function and does not require a conformably specified copula. Moreover, we allow for exogenous regressors, which may be (linearly) correlated with the endogenous regressor(s). We establish consistency, asymptotic normality and validity of the bootstrap for the unknown model parameters. An empirical application on wage data of the US current population survey demonstrates the usefulness of the method.
Don't fear the unlabelled: safe semi-supervised learning via debiasing

Semi-supervised learning (SSL) provides an effective means of leveraging unlabelled data to improve a model performance. Even though the domain has received a considerable amount of attention in the past years, most methods present the common drawback of lacking theoretical guarantees. Our starting point is to notice that the estimate of the risk that most discriminative SSL methods minimise is biased, even asymptotically. This bias impedes the use of standard statistical learning theory and can hurt empirical performance. We propose a simple way of removing the bias. Our debiasing approach is straightforward to implement and applicable to most deep SSL methods. We provide simple theoretical guarantees on the trustworthiness of these modified methods, without having to rely on the strong assumptions on the data distribution that SSL theory usually requires. In particular, we provide generalisation error bounds for the proposed methods. We evaluate debiased versions of different existing SSL methods, such as the Pseudo-label method and Fixmatch, and show that debiasing can compete with classic deep SSL techniques in various settings by providing better calibrated models. Additionally, we provide a theoretical explanation of the intuition of the popular SSL methods.