Miracle-dz/UniFAR-FTUs huggingface dataset:
War, Inflation, Monetary Reforms and the Art Market .The Belgian Art market (1944 – 1951)

During World War II, the art market experienced a massive boom in occupied countries. The discretion, the inflation proof character, the absence of market intervention and the possibility to resell artworks abroad have been suggested to explain why investing in artworks was one of the most interesting opportunities under the German boot. On basis of an original database of close to 4000 artworks sold between 1944 and 1951 at Giroux, one of the most important Art Gallery in Brussels, this paper analyzes, the price movements on the Belgian art market following the liberation. Market reactions following the war are used to understand which motivations played the most important role in investors’ decisions. Prices on the art market experienced a massive drop. This huge price decline is attributed to two elements: fear of prosecution for war profits and the monetary reforms set into place in October 1944.
The discovery of radio halos in the frontier fields clusters Abell S1063 and Abell 370

Context. Massive merging galaxy clusters often host diffuse megaparsec-scale radio synchrotron emission. This emission originates from relativistic electrons in the ionized intracluster medium. An important question is how these synchrotron emitting relativistic electrons are accelerated. Aims. Our aim is to search for diffuse emission in the Frontier Fields clusters Abell S1063 and Abell 370 and characterize its properties. While these clusters are very massive and well studied at some other wavelengths, no diffuse emission has been reported for these clusters so far. Methods. We obtained 325 MHz Giant Metrewave Radio Telescope (GMRT) and 1–4 GHz Jansky Very Large Array (VLA) observations of Abell S1063 and Abell 370. We complement these data with Chandra and XMM-Newton X-ray observations. Results. In our sensitive images, we discover radio halos in both clusters. In Abell S1063, a giant radio halo is found with a size of ∼1.2 Mpc. The integrated spectral index between 325 MHz and 1.5 GHz is −0.94 ± 0.08 and it steepens to −1.77 ± 0.20 between 1.5 and 3.0 GHz. This spectral steepening provides support for the turbulent reacceleration model for radio halo formation. Abell 370 hosts a faint radio halo mostly centered on the southern part of this binary merging cluster, with a size of ∼500−700 kpc. The spectral index between 325 MHz and 1.5 GHz is −1.10 ± 0.09. Both radio halos follow the known scaling relation between the cluster mass proxy Y500 and radio power, which is consistent with the idea that they are related to ongoing cluster merger events.
Efficient Debiased Variational Bayes by Multilevel Monte Carlo Methods

Variational Bayes is a method to find a good approximation of the posterior probability distribution of latent variables from a parametric family of distributions. The evidence lower bound (ELBO), which is nothing but the model evidence minus the Kullback-Leibler divergence, has been commonly used as a quality measure in the optimization process. However, the model evidence itself has been considered computationally intractable since it is expressed as a nested expectation with an outer expectation with respect to the training dataset and an inner conditional expectation with respect to latent variables. Similarly, if the Kullback-Leibler divergence is replaced with another divergence metric, the corresponding lower bound on the model evidence is often given by such a nested expectation. The standard (nested) Monte Carlo method can be used to estimate such quantities, whereas the resulting estimate is biased and the variance is often quite large. Recently the authors provided an unbiased estimator of the model evidence with small variance by applying the idea from multilevel Monte Carlo (MLMC) methods. In this article, we give more examples involving nested expectations in the context of variational Bayes where MLMC methods can help construct low-variance unbiased estimators, and provide numerical results which demonstrate the effectiveness of our proposed estimators.
Deep-Learning Image Reconstruction for Real-Time Photoacoustic System

Recent advances in photoacoustic (PA) imaging have enabled detailed images of microvascular structure and quantitative measurement of blood oxygenation or perfusion. Standard reconstruction methods for PA imaging are based on solving an inverse problem using appropriate signal and system models. For handheld scanners, however, the ill-posed conditions of limited detection view and bandwidth yield low image contrast and severe structure loss in most instances. In this paper, we propose a practical reconstruction method based on a deep convolutional neural network (CNN) to overcome those problems. It is designed for real-time clinical applications and trained by large-scale synthetic data mimicking typical microvessel networks. Experimental results using synthetic and real datasets confirm that the deep-learning approach provides superior reconstructions compared to conventional methods.
Two Channel Audio Zooming System For Smartphone

In this paper, two microphone based systems for audio zooming is proposed for the first time. The audio zooming application allows sound capture and enhancement from the front direction while attenuating interfering sources from all other directions. The complete audio zooming system utilizes beamforming based target extraction. In particular, Minimum Power Distortionless Response (MPDR) beamformer and Griffith Jim Beamformer (GJBF) are explored. This is followed by block thresholding for residual noise and interference suppression, and zooming effect creation. A number of simulation and real life experiments using Samsung smartphone (Samsung Galaxy A5) were conducted. Objective and subjective measures confirm the rich user experience.
Bayesian Quantile and Expectile Optimisation

Bayesian optimisation is widely used to optimise stochastic black box functions. While most strategies are focused on optimising conditional expectations, a large variety of applications require risk-averse decisions and alternative criteria accounting for the distribution tails need to be considered. In this paper, we propose new variational models for Bayesian quantile and expectile regression that are well-suited for heteroscedastic settings. Our models consist of two latent Gaussian processes accounting respectively for the conditional quantile (or expectile) and variance that are chained through asymmetric likelihood functions. Furthermore, we propose two Bayesian optimisation strategies, either derived from a GP-UCB or Thompson sampling, that are tailored to such models and that can accommodate large batches of points. As illustrated in the experimental section, the proposed approach clearly outperforms the state of the art.
On Equivalence and Cores for Incomplete Databases in Open and Closed Worlds

Data exchange heavily relies on the notion of incomplete database instances. Several semantics for such instances have been proposed and include open (OWA), closed (CWA), and open-closed (OCWA) world. For all these semantics important questions are: whether one incomplete instance semantically implies another; when two are semantically equivalent; and whether a smaller or smallest semantically equivalent instance exists. For OWA and CWA these questions are fully answered. For several variants of OCWA, however, they remain open. In this work we adress these questions for Closed Powerset semantics and the OCWA semantics of Libkin and Sirangelo, 2011. We define a new OCWA semantics, called OCWA*, in terms of homomorphic covers that subsumes both semantics, and characterize semantic implication and equivalence in terms of such covers. This characterization yields a guess-and-check algorithm to decide equivalence, and shows that the problem is NP-complete. For the minimization problem we show that for several common notions of minimality there is in general no unique minimal equivalent instance for Closed Powerset semantics, and consequently not for the more expressive OCWA* either. However, for Closed Powerset semantics we show that one can find, for any incomplete database, a unique finite set of its subinstances which are subinstances (up to renaming of nulls) of all instances semantically equivalent to the original incomplete one. We study properties of this set, and extend the analysis to OCWA*.
Explosive Force Production in Older Adults Following a Traditional Strength Training Program

Purpose: Age‐related loss and atrophy of fast‐twitch muscle fibers was associated with decline in explosive force production (EFP). Strength training with explosive and rapid muscle contractions has shown promise for increasing EFP in older adults. Current exercise recommendations for untrained older adults from the American College of Sports Medicine and Surgeon General do not include such activities. Using current strength training guidelines for older adults, we examined the effect of a 12‐week strength training program on EFP. Methods: Subjects were 22 men and women (67.7±5.5 years) who participated in a 12‐week strength training program and 5 men and women who served as controls (67.4±7.3 years). Maximal isometric strength (MVC) of the quadriceps and EFP (time to reach to 30, 60, and 90% of MVC) were recorded before and after training. Results: A significant 13% increase in strength was observed in the exercise group with no change in the control group. Explosive force production was unchanged in either group. Conclusions: Strength training consistent with current recommendations for older adults is an efficacious means of increasing strength in healthy, community‐dwelling older adults but does not improve their ability to generate force quickly.
Towards Mixed-Initiative Human–Robot Interaction: Assessment of Discriminative Physiological and Behavioral Features for Performance Prediction

The design of human–robot interactions is a key challenge to optimize operational performance. A promising approach is to consider mixed-initiative interactions in which the tasks and authority of each human and artificial agents are dynamically defined according to their current abilities. An important issue for the implementation of mixed-initiative systems is to monitor human performance to dynamically drive task allocation between human and artificial agents (i.e., robots). We, therefore, designed an experimental scenario involving missions whereby participants had to cooperate with a robot to fight fires while facing hazards. Two levels of robot automation (manual vs. autonomous) were randomly manipulated to assess their impact on the participants’ performance across missions. Cardiac activity, eye-tracking, and participants’ actions on the user interface were collected. The participants performed differently to an extent that we could identify high and low score mission groups that also exhibited different behavioral, cardiac and ocular patterns. More specifically, our findings indicated that the higher level of automation could be beneficial to low-scoring participants but detrimental to high-scoring ones, and vice versa. In addition, inter-subject single-trial classification results showed that the studied behavioral and physiological features were relevant to predict mission performance. The highest average balanced accuracy (74%) was reached using the features extracted from all input devices. These results suggest that an adaptive HRI driving system, that would aim at maximizing performance, would be capable of analyzing such physiological and behavior markers online to further change the level of automation when it is relevant for the mission purpose.
Symmetry in Self-Similarity in Space and Time - Short Time Transients and Power-Law Spatial Asymptotes

The self-similarity in space and time (hereafter self-similarity), either deterministic or statistical, is characterized by similarity exponents and a function of scaled variable, called the scaling function. In the present paper, we address mainly the self-similarity in the limit of early stage, as opposed to the latter one, and also consider the scaling functions that decay or grow algebraically, as opposed to the rapidly decaying functions such as Gaussian or error function. In particular, in the case of simple diffusion, our symmetry analysis shows a mathematical mechanism by which the rapidly decaying scaling functions are generated by other polynomial scaling functions. While the former is adapted to the self-similarity in the late-stage processes, the latter is adapted to the early stages. This paper sheds some light on the internal structure of the family of self-similarities generated by a simple diffusion equation. Then, we present an example of self-similarity for the late stage whose scaling function has power-law tail, and also several cases of self-similarity for the early stages. These examples show the utility of self-similarity to a wider range of phenomena other than the late stage behaviors with rapidly decaying scaling functions.
Turbulent scalar flux in inclined jets in crossflow: counter gradient transport and deep learning modelling

Abstract A cylindrical and inclined jet in crossflow is studied under two distinct velocity ratios, $r=1$ and $r=2$, using highly resolved large eddy simulations. First, an investigation of turbulent scalar mixing sheds light onto the previously observed but unexplained phenomenon of negative turbulent diffusivity. We identify two distinct types of counter gradient transport, prevalent in different regions: the first, throughout the windward shear layer, is caused by cross-gradient transport; the second, close to the wall right after injection, is caused by non-local effects. Then, we propose a deep learning approach for modelling the turbulent scalar flux by adapting the tensor basis neural network previously developed to model Reynolds stresses (Ling et al., J. Fluid Mech., vol. 807, 2016a, pp. 155–166). This approach uses a deep neural network with embedded coordinate frame invariance to predict a tensorial turbulent diffusivity that is not explicitly available in the high-fidelity data used for training. After ensuring analytically that the matrix diffusivity leads to a stable solution for the advection diffusion equation, we apply this approach in the inclined jets in crossflow under study. The results show significant improvement compared to a simple model, particularly where cross-gradient effects play an important role in turbulent mixing. The model proposed herein is not limited to jets in crossflow; it can be used in any turbulent flow where the Reynolds averaged transport of a scalar is considered.
Shades of Perception- User Factors in Identifying Password Strength

The purpose of this study was to measure whether participant education, profession, and technical skill level exhibited a relationship with identification of password strength. Participants reviewed 50 passwords and labeled each as weak or strong. A Chi-square test of independence was used to measure relationships between education, profession, technical skill level relative to the frequency of weak and strong password identification. The results demonstrate significant relationships across all variable combinations except for technical skill and strong passwords which demonstrated no relationship. This research has three limitations. Data collection was dependent upon participant self-reporting and has limited externalized power. Further, the instrument was constructed under the assumption that all participants could read English and understood the concept of password strength. Finally, we did not control for external tool use (i.e., password strength meter). The results build upon existing literature insofar as the outcomes add to the collective understanding of user perception of passwords in specific and authentication in general. Whereas prior research has explored similar areas, such work has done so by having participants create passwords. This work measures perception of pre-generated passwords. The results demonstrate a need for further investigation into why users continue to rely on weak passwords. The originality of this work rests in soliciting a broad spectrum of participants and measuring potential correlations between participant education, profession, and technical skill level.
Critical properties of the many-particle (interacting) Aubry-Andr\'e model ground-state localization-delocalization transition

As opposed to random disorder, which localizes single-particle wave-functions in 1D at arbi-trarily small disorder strengths, there is a localization-delocalization transition for quasi-periodic disorder in the 1D Aubry-Andr´e model at a ﬁnite disorder strength. On the single-particle level, many properties of the ground-state critical behavior have been revealed by applying a real-space renormalization-group scheme; the critical properties are determined solely by the continued fraction expansion of the incommensurate frequency of the disorder. Here, we investigate the many-particle localization-delocalization transition in the Aubry-Andr´e model with and without interactions. In contrast to the single-particle case, we ﬁnd that the critical exponents depend on a Diophantine equation relating the incommensurate frequency of the disorder and the ﬁlling fraction which gen-eralizes the dependence, in the single-particle spectrum, on the continued fraction expansion of the incommensurate frequency. This equation can be viewed as a generalization of the resonance condition in the commensurate case. When interactions are included, numerical evidence suggests that interactions may be irrelevant at at least some of these critical points, meaning that the critical exponent relations obtained from the Diophantine equation may actually survive in the interacting case. be accurate and less prone to ﬁnite-size eﬀects than χ F . However, ﬁnite-size eﬀects are the largest source of error.
Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual Features

Text contained in an image carries high-level semantics that can be exploited to achieve richer image understanding. In particular, the mere presence of text provides strong guiding content that should be employed to tackle a diversity of computer vision tasks such as image retrieval, fine-grained classification, and visual question answering. In this paper, we address the problem of fine-grained classification and image retrieval by leveraging textual information along with visual cues to comprehend the existing intrinsic relation between the two modalities. The novelty of the proposed model consists of the usage of a PHOC descriptor to construct a bag of textual words along with a Fisher Vector Encoding that captures the morphology of text. This approach provides a stronger multimodal representation for this task and as our experiments demonstrate, it achieves state-of-the-art results on two different tasks, fine-grained classification and image retrieval. The code of this model will be publicly available at1.
Hallucinating Statistical Moment and Subspace Descriptors from Object and Saliency Detectors for Action Recognition

In this paper, we build on a deep translational action recognition network which takes RGB frames as input to learn to predict both action concepts and auxiliary supervisory feature descriptors e.g., Optical Flow Features and/or Improved Dense Trajectory descriptors. The translation is performed by so-called hallucination streams trained to predict auxiliary cues which are simultaneously fed into classification layers, and then hallucinated for free at the testing stage to boost recognition. In this paper, we design and hallucinate two descriptors, one leveraging four popular object detectors applied to training videos, and the other leveraging image- and video-level saliency detectors. The first descriptor encodes the detector- and ImageNet-wise class prediction scores, confidence scores, and spatial locations of bounding boxes and frame indexes to capture the spatio-temporal distribution of features per video. Another descriptor encodes spatio-angular gradient distributions of saliency maps and intensity patterns. Inspired by the characteristic function of the probability distribution, we capture four statistical moments on the above intermediate descriptors. As numbers of coefficients in the mean, covariance, coskewness and cokurtotsis grow linearly, quadratically, cubically and quartically w.r. t. the dimension of feature vectors, we describe the covariance matrix by its leading n' eigenvectors (so-called subspace) and we capture skewness/kurtosis rather than costly coskewness/cokurtosis. We obtain state of the art on three popular datasets.
Optomechanical transport of cold atoms induced by structured light

Optomechanical pattern forming instabilities in a cloud of cold atoms lead to self-organized spatial structures of light and atoms. Here, we consider the optomechanical self-structuring of a cold atomic cloud in the presence of a phase structured input field, carrying orbital angular momentum. For a planar ring cavity setup, a model of coupled cavity field and atomic density equations describes a wide range of drifting modulation instabilities in the transverse plane. This leads to the formation of rotating self-organized rings of light-atom lattices. Using linear stability analysis and numerical simulations of the coupled atomic and optical dynamics, we demonstrate the presence of macroscopic atomic transport corresponding to the pattern rotation, induced by the structured pump phase profile
Dynamic Spatial-Temporal Representation Learning for Traffic Flow Prediction

As a crucial component in intelligent transportation systems, traffic flow prediction has recently attracted widespread research interest in the field of artificial intelligence (AI) with the increasing availability of massive traffic mobility data. Its key challenge lies in how to integrate diverse factors (such as temporal rules and spatial dependencies) to infer the evolution trend of traffic flow. To address this problem, we propose a unified neural network called Attentive Traffic Flow Machine (ATFM), which can effectively learn the spatial-temporal feature representations of traffic flow with an attention mechanism. In particular, our ATFM is composed of two progressive Convolutional Long Short-Term Memory (ConvLSTM [1]) units connected with a convolutional layer. Specifically, the first ConvLSTM unit takes normal traffic flow features as input and generates a hidden state at each time-step, which is further fed into the connected convolutional layer for spatial attention map inference. The second ConvLSTM unit aims at learning the dynamic spatial-temporal representations from the attentionally weighted traffic flow features. Further, we develop two deep learning frameworks based on ATFM to predict citywide short-term/long-term traffic flow by adaptively incorporating the sequential and periodic data as well as other external influences. Extensive experiments on two standard benchmarks well demonstrate the superiority of the proposed method for traffic flow prediction. Moreover, to verify the generalization of our method, we also apply the customized framework to forecast the passenger pickup/dropoff demands in traffic prediction and show its superior performance. Our code and data are available at https://github.com/liulingbo918/ATFM.