Miracle-dz/UniFAR-FTUs huggingface dataset:
Repairing Regressors for Fair Binary Classification at Any Decision Threshold

We study the problem of post-processing a supervised machine-learned regressor to maximize fair binary classification at all decision thresholds. By decreasing the statistical distance between each group's score distributions, we show that we can increase fair performance across all thresholds at once, and that we can do so without a large decrease in accuracy. To this end, we introduce a formal measure of Distributional Parity, which captures the degree of similarity in the distributions of classifications for different protected groups. Our main result is to put forward a novel post-processing algorithm based on optimal transport, which provably maximizes Distributional Parity, thereby attaining common notions of group fairness like Equalized Odds or Equal Opportunity at all thresholds. We demonstrate on two fairness benchmarks that our technique works well empirically, while also outperforming and generalizing similar techniques from related work.
Semiclassical dynamics of a superconducting circuit: chaotic dynamics and fractal attractors

We study here the semiclassical dynamics of a superconducting circuit constituted by two Josephson junctions in series, in the presence of a voltage bias. We derive the equations of motion for the circuit through a Hamiltonian description of the problem, considering the voltage sources as semi-holonomic constraints. We find that the dynamics of the system corresponds to that of a planar rotor with an oscillating pivot. We show that the system exhibits a rich dynamical behaviour with chaotic properties and we present a topological classification of the cyclic solutions, providing insight into the fractal nature of the dynamical attractors.
Deep Learning-Assisted Localisation of Nanoparticles in synthetically generated two-photon microscopy images

Tracking single molecules is instrumental for quantifying the transport of molecules and nanoparticles in biological samples, e.g., in brain drug delivery studies. Existing intensity-based localisation methods are not developed for imaging with a scanning microscope, typically used for in vivo imaging. Low signal-to-noise ratios, movement of molecules out-of-focus, and high motion blur on images recorded with scanning two-photon microscopy (2PM) in vivo pose a challenge to the accurate localisation of molecules. Using data-driven models is challenging due to low data volumes, typical for in vivo experiments. We developed a 2PM image simulator to supplement scarce training data. The simulator mimics realistic motion blur, background fluorescence, and shot noise observed in vivo imaging. Training a data-driven model with simulated data improves localisation quality in simulated images and shows why intensity-based methods fail.
Option pricing using a skew random walk pricing tree

Motivated by the Corns-Satchell, continuous time, option pricing model, we develop a binary tree pricing model with underlying asset price dynamics following It\^o-Mckean skew Brownian motion. While the Corns-Satchell market model is incomplete, our discrete time market model is defined in the natural world; extended to the risk neutral world under the no-arbitrage condition where derivatives are priced under uniquely determined risk-neutral probabilities; and is complete. The skewness introduced in the natural world is preserved in the risk neutral world. Furthermore, we show that the model preserves skewness under the continuous-time limit. We provide numerical applications of our model to the valuation of European put and call options on exchange-traded funds tracking the S&P Global 1200 index.
Comparisons between different methods of solution of the Boltzmann equation adapted to the calculation of swarm parameters in a weakly ionised medium

Two numerical solutions of the Boltzmann equation are developed and applied to the calculations of swarm time-of-flight parameters in molecular model gases. It is shown that the two methods not only give near similar results but are in good agreement with calculations carried out by some other authors for the same situations. Errors already pointed out in some other works are confirmed and the need of using high-order methods of solution of the Boltzmann equation is stressed.
De-coupling and De-positioning Dense Self-supervised Learning

Dense Self-Supervised Learning (SSL) methods address the limitations of using image-level feature representations when handling images with multiple objects. Although the dense features extracted by employing segmentation maps and bounding boxes allow networks to perform SSL for each object, we show that they suffer from coupling and positional bias, which arise from the receptive field increasing with layer depth and zero-padding. We address this by introducing three data augmentation strategies, and leveraging them in (i) a decoupling module that aims to robustify the network to variations in the object's surroundings, and (ii) a de-positioning module that encourages the network to discard positional object information. We demonstrate the benefits of our method on COCO and on a new challenging benchmark, OpenImage-MINI, for object classification, semantic segmentation, and object detection. Our extensive experiments evidence the better generalization of our method compared to the SOTA dense SSL methods
A data-assisted two-stage method for the inverse random source problem

We propose a data-assisted two-stage method for solving an inverse random source problem of the Helmholtz equation. In the first stage, the regularized Kaczmarz method is employed to generate initial approximations of the mean and variance based on the mild solution of the stochastic Helmholtz equation. A dataset is then obtained by sampling the approximate and corresponding true profiles from a certain a-priori criterion. The second stage is formulated as an image-to-image translation problem, and several data-assisted approaches are utilized to handle the dataset and obtain enhanced reconstructions. Numerical experiments demonstrate that the data-assisted two-stage method provides satisfactory reconstruction for both homogeneous and inhomogeneous media with fewer realizations.
HMES: A Scalable Human Mobility and Epidemic Simulation System with Fast Intervention Modeling

Recently, the world has witnessed the most severe pandemic (COVID-19) in this century. Studies on epidemic prediction and simulation have received increasing attention. However, the current methods suffer from three issues. First, most of the current studies focus on epidemic prediction, which can not provide adequate support for intervention policy making. Second, most of the current interventions are based on population groups rather than fine-grained individuals, which can not make the measures towards the infected people and may cause waste of medical resources. Third, current simulations are not efficient and flexible enough for large-scale complex systems.In this paper, we propose a new epidemic simulation framework called HMES to address above three challenges. The proposed framework covers a full pipeline of epidemic simulation and enables comprehensive fine-grained control in large scale. In addition, we conduct experiments on real COVID-19 data. HMES demonstrates more accurate modeling of disease transmission up to 300 million people and up to 3 times acceleration compared to the state-of-the-art methods.
Enhanced Dictionary based Sparse Representation Fusion for Multi-temporal Remote Sensing Images

Abstract In remote sensing, image fusion is the process of blending two images to obtain finer details of the fused image. In this paper, an enhanced dictionary-based sparse representation (EDSR) is proposed for multitemporal image fusion. Multitemporal remote satellite images acquired on the same geographical area at different acquisition dates are merged to obtain a fused image for further analysis. Sparse representation of the image is employed in the approximation and representation of the target image. In order to improve the performance of the fusion process, a locally adaptive dictionary is created such that the dictionary contains patches extracted from both source images. The reconstruction of the image is performed using maximum absolute coefficients through the learned dictionary. The proposed EDSR technique has been compared quantitatively and qualitatively with the existing techniques, such as PCA, DWT, SWT, Ehlers, and sparse representation (SR), to evaluate its performance. The EDSR performs well in mutual information (MI) with 3.4742 and feature mutual information (FMI) with 0.4790 and provides better results in the case of degree of distortion, UIQI, and ERGAS for dataset 1 than the existing fusion methods. Experimental results on LANDSAT images revealed that the proposed technique is more effective in terms of preservation of spectral information, errors, color, and visual quality of the fused product.
Reading Strategies for Graph Visualizations that Wrap Around in Torus Topology

We investigate reading strategies for node-link diagrams that wrap around the boundaries in a flattened torus topology by examining eye tracking data recorded in a previous controlled study. Prior work showed that torus drawing affords greater flexibility in clutter reduction than traditional node-link representations, but impedes link-and-path exploration tasks, while repeating tiles around boundaries aids comprehension. However, it remains unclear what strategies users apply in different wrapping settings. This is important for design implications for future work on more effective wrapped visualizations for network applications, and cyclic data that could benefit from wrapping. We perform visual-exploratory data analysis of gaze data, and conduct statistical tests derived from the patterns identified. Results show distinguishable gaze behaviors, with more visual glances and transitions between areas of interest in the non-replicated layout. Full-context has more successful visual searches than partial-context, but the gaze allocation indicates that the layout could be more space-efficient.
Procter: Pronunciation-Aware Contextual Adapter For Personalized Speech Recognition In Neural Transducers

End-to-End (E2E) automatic speech recognition (ASR) systems used in voice assistants often have difficulties recognizing infrequent words personalized to the user, such as names and places. Rare words often have non-trivial pronunciations, and in such cases, human knowledge in the form of a pronunciation lexicon can be useful. We propose a PROnunCiation-aware conTextual adaptER (PROCTER) that dynamically injects lexicon knowledge into an RNN-T model by adding a phonemic embedding along with a textual embedding. The experimental results show that the proposed PROCTER architecture outperforms the baseline RNN-T model by improving the word error rate (WER) by 44% and 57% when measured on personalized entities and personalized rare entities, respectively, while increasing the model size (number of trainable parameters) by only 1%. Furthermore, when evaluated in a zero-shot setting to recognize personalized device names, we observe 7% WER improvement with PROCTER, as compared to only 1% WER improvement with text-only contextual attention.
Degenerate diffusion with Preisach hysteresis

Fluid diffusion in unsaturated porous media manifests strong hysteresis effects due to surface tension on the liquid-gas interface. We describe hysteresis in the pressure-saturation relation by means of the Preisach operator, which makes the resulting evolutionary PDE strongly degenerate. We prove the existence and uniqueness of a strong global solution in arbitrary space dimension using a special weak convexity concept.
Reference frames in general relativity and the galactic rotation curves

The physical interpretation of the exact solutions of the Einstein field equations is, in general, a challenging task, part of the difficulties lying in the significance of the coordinate system. We discuss the extension of the International Astronomical Union (IAU) reference system to the exact theory. It is seen that such an extension, retaining some of its crucial properties, can be achieved in a special class of spacetimes, admitting nonshearing congruences of observers which, at infinity, have zero vorticity and acceleration. As applications, we consider the Friedmann-Lema\^itre-Robertson-Walker (FLRW), Kerr and Newman-Unti-Tamburino (NUT) spacetimes, the van Stockum rotating dust cylinder, spinning cosmic strings and, finally, we debunk the so-called Balasin-Grumiller (BG) model, and the claims that the galaxies' rotation curves can be explained through gravitomagnetic effects without the need for dark matter. The BG spacetime is shown to be completely inappropriate as a galactic model: its dust is actually static with respect to the asymptotic inertial frame, its gravitomagnetic effects arise from unphysical singularities along the axis (a pair of NUT rods, combined with a spinning cosmic string), and the rotation curves obtained are merely down to an invalid choice of reference frame -- the congruence of zero angular momentum observers, which are being dragged by the singularities.
Steerable adaptive first‐ and second‐order differential microphones

Directional microphone arrays are spatial filters that can be designed to improve the sound quality of speech in audio telecommunication by removing acoustic signals arriving from undesired directions. Recently, there has been a large interest in adaptive microphone systems (also sometimes referred to as ‘‘smart microphones’’), since these systems automatically self‐adjust their spatial response to minimize acoustic interference and thereby increase the desired signal‐to‐noise ratio. The talk will present adaptive first‐ and second‐order differential microphone arrays that have fixed look directions dependent on the array geometry. A generalized version of the adaptive first‐order system will then be presented where the look direction can be arbitrarily oriented (independent of the array spatial orientation). The commensurate problem of acoustic echo cancellation with adaptive microphones will also be discussed and some potential solutions given.
Dimension reduction for the conditional mean in regressions with categorical predictors

Consider the regression of a response Y on a vector of quantitative predictors X and a categorical predictor W. In this article we describe a first method for reducing the dimension of X without loss of information on the conditional mean E(Y|X, W) and without requiring a prespecified parametric model. The method, which allows for, but does not require, parametric versions of the subpopulation mean functions E(Y|X, W = w), includes a procedure for inference about the dimension of X after reduction. This work integrates previous studies on dimension reduction for the conditional mean E(Y|X) in the absence of categorical predictors and dimension reduction for the full conditional distribution of Y|(X, W). The methodology we describe may be particularly useful for constructing low-dimensional plots to aid in model-building at the outset of an analysis. Our proposals provide an often parsimonious alternative to the standard technique of modeling with interaction terms to adapt a mean function for different subpopulations determined by the levels of W. Examples illustrating this and other aspects of the development are presented.
Semantic Image Translation for Repairing the Texture Defects of Building Models

The accurate representation of 3-D building models in urban environments is significantly hindered by challenges such as texture occlusion, blurring, and missing details, which are difficult to mitigate through standard photogrammetric texture mapping pipelines. Current image completion methods often struggle to produce structured results and effectively handle the intricate nature of highly structured façade textures with diverse architectural styles. Furthermore, existing image synthesis methods encounter difficulties in preserving high-frequency details and artificial regular structures, which are essential for achieving realistic façade texture synthesis. To address these challenges, we introduce a novel approach for synthesizing façade texture images that authentically reflect the architectural style from a structured label map, guided by a ground-truth façade image. In order to preserve fine details and regular structures, we propose a regularity-aware multidomain method that capitalizes on frequency information and corner maps. We also incorporate semantic region-adaptive normalization (SEAN) blocks into our generator to enable versatile style transfer. To generate plausible structured images without undesirable regions, we employ image completion techniques to remove occlusions according to semantics prior to image inference. Our proposed method is also capable of synthesizing texture images with specific styles for façades that lack preexisting textures, using manually annotated labels. Experimental results on publicly available façade image and 3-D model datasets demonstrate that our method yields superior results and effectively addresses issues associated with flawed textures.
Mole Recruitment: Poisoning of Image Classifiers via Selective Batch Sampling

In this work, we present a data poisoning attack that confounds machine learning models without any manipulation of the image or label. This is achieved by simply leveraging the most confounding natural samples found within the training data itself, in a new form of a targeted attack coined"Mole Recruitment. "We define moles as the training samples of a class that appear most similar to samples of another class, and show that simply restructuring training batches with an optimal number of moles can lead to significant degradation in the performance of the targeted class. We show the efficacy of this novel attack in an offline setting across several standard image classification datasets, and demonstrate the real-world viability of this attack in a continual learning (CL) setting. Our analysis reveals that state-of-the-art models are susceptible to Mole Recruitment, thereby exposing a previously undetected vulnerability of image classifiers.
Clusters of African countries based on the social contacts and associated socioeconomic indicators relevant to the spread of the epidemic

Introduction. It is well known that social contact patterns differ from country to country. This variation coincides with significant socioeconomic heterogeneity that complicates the design of effective non-pharmaceutical interventions. This study examined how socioeconomic heterogeneity in selected African countries might be factored in to explain better social contact mix patterns between countries. Methods. We used a standardized contact matrix for 32 African countries, estimated in [31]. We scaled the matrices using an epidemic model from [34]. We also analyzed aggregated data from the World Bank country website. The data includes 28 variables; social, economic, environmental, institutional, governance, health and well-being, education, gender inequality, and other development-related indicators describing countries. Principal components analysis was used to visualize socioeconomic similarities between countries and identify the indicators for maximum variation. The (2D)2 PC A approach was used to reduce the dimension of the synthetic contact matrices for each country to avoid the dimensionality curse. Agglomerative hierarchical clustering was then used to identify groups of countries with similar social patterns, taking into account the countrys socioeconomic performance. Results. Our model yielded four meaningful clusters, each with a few distinguishing features. Social contacts varied between groups but were generally similar within each set. The countrys socioeconomic performance influenced the clusters. Conclusions. Our results suggest that integrating socioeconomic factors into social contacts can better explain infectious disease transmission dynamics and that similar interventions can be implemented in countries within the cluster.
Demystifying Misconceptions in Social Bots Research

Research on social bots aims at advancing knowledge and providing solutions to one of the most debated forms of online manipulation. Yet, social bot research is plagued by widespread biases, hyped results, and misconceptions that set the stage for ambiguities, unrealistic expectations, and seemingly irreconcilable findings. Overcoming such issues is instrumental towards ensuring reliable solutions and reaffirming the validity of the scientific method. In this contribution, we review some recent results in social bots research, highlighting and revising factual errors as well as methodological and conceptual biases. More importantly, we demystify common misconceptions, addressing fundamental points on how social bots research is discussed. Our analysis surfaces the need to discuss research about online disinformation and manipulation in a rigorous, unbiased, and responsible way. This article bolsters such effort by identifying and refuting common fallacious arguments used by both proponents and opponents of social bots research, as well as providing directions toward sound methodologies for future research in the field.
Are There Universal Signatures of Topological Phases in High-Harmonic Generation? Probably Not.

High harmonic generation (HHG) has developed in recent years as a promising tool for ultrafast materials spectroscopy. At the forefront of these advancements, several works proposed to use HHG as an all-optical probe for topology of quantum matter by identifying its signatures in the emission spectra. However, it remains unclear if such spectral signatures are indeed a robust and general approach for probing topology. To address this point, we perform here a fully ab-initio study of HHG from prototypical two-dimensional topological insulators in the Kane-Mele quantum spin-Hall and anomalous-Hall phases. We analyze the spectra and previously proposed topological signatures by comparing HHG from the topological and trivial phases. We demonstrate and provide detailed microscopic explanations of why in these systems none of the thus far proposed observables uniquely and universally probe material topology. Specifically, we find that the: (i) HHG helicity, (ii) anomalous HHG ellipticity, (iii) HHG elliptical dichroism and (iv) temporal delays in HHG emission, are all unreliable signatures of topological phases. Our results suggest that extreme care must be taken when interpreting HHG spectra for topological signatures, and that contributions from the crystal symmetries and chemical nature might be dominant over those from topology. They hint that a truly universal topological signature in nonlinear optics is unlikely, and raise important questions regarding possible utilization and detection of topology in systems out-of-equilibrium.
Robust Missing Traffic Flow Imputation Considering Nonnegativity and Road Capacity

There are increasing concerns about missing traffic data in recent years. In this paper, a robust missing traffic flow data imputation approach based on matrix completion is proposed. In the proposed method, the similarity of traffic flow from day to day is exploited to impute missing data by the low-rank hypothesis of constructed traffic flow matrix. And the physical limitation of road capacity and nonnegativity is also considered through the optimization process, which avoids the possibility of producing negative and overcapacity values. Moreover, the proposed algorithm can impute missing data and recover outlier in a unify framework. The experiment results show that the proposed method is more accurate, stable, and reasonable.