Miracle-dz/UniFAR-FTUs huggingface dataset:
Compressed sensing image reconstruction for the LOFAR Radio Telescope

The LOFAR Radio Telescope is a radio interferometer with multiple antennas placed throughout Europe. A radio interferometer samples the image of the sky in the Fourier domain; recovering the image from these samples is an inverse problem. In radio astronomy the CLEAN method has been used for many years to find a solution. Recent papers have established a link between radio interferometry and compressed sensing, which supports sparse recovery methods to reconstruct an image from interferometric data. The goal of this paper is to study sparse recovery methods on LOFAR data by comparing the accuracy of CLEAN and compressed sensing when applied to simulated LOFAR observations.
Model‐based inference for categorical survey data subject to non‐ignorable non‐response

We consider non‐response models for a single categorical response with categorical covariates whose values are always observed. We present Bayesian methods for ignorable models and a particular non‐ignorable model, and we argue that standard methods of model comparison are inappropriate for comparing ignorable and non‐ignorable models. Uncertainty about ignorability of non‐response is incorporated by introducing parameters describing the extent of non‐ignorability into a pattern mixture specification and integrating over the prior uncertainty associated with these parameters. Our approach is illustrated using polling data from the 1992 British general election panel survey. We suggest sample size adjustments for surveys when non‐ignorable non‐response is expected.
Linear colloidal crystal arrays by electrohydrodynamic printing

We use electrohydrodynamic jets of colloidal suspensions to produce arrays of colloidal crystalline stripes on surfaces. A critical factor in maintaining a stable jet is the distance of separation between the nozzle and the surface. Colloidal crystalline stripes are produced as two wetting lines of the deployed suspension merge during drying. To ensure that the two wetting lines merge, the “deployed-line-width” to “particle size” ratio is kept below a critical value so that the capillary forces overcome the frictional forces between the particles and the substrate.
Generalized Balancing Weights via Deep Neural Networks

Estimating causal effects from observational data is a central problem in many domains. A general approach is to balance covariates with weights such that the distribution of the data mimics randomization. We present generalized balancing weights, Neural Balancing Weights (NBW), to estimate the causal effects of an arbitrary mixture of discrete and continuous interventions. The weights were obtained through direct estimation of the density ratio between the source and balanced distributions by optimizing the variational representation of $f$-divergence. For this, we selected $\alpha$-divergence as it presents efficient optimization because it has an estimator whose sample complexity is independent of its ground truth value and unbiased mini-batch gradients; moreover, it is advantageous for the vanishing-gradient problem. In addition, we provide the following two methods for estimating the balancing weights: improving the generalization performance of the balancing weights and checking the balance of the distribution changed by the weights. Finally, we discuss the sample size requirements for the weights as a general problem of a curse of dimensionality when balancing multidimensional data. Our study provides a basic approach for estimating the balancing weights of multidimensional data using variational $f$-divergences.
A Combinatorial Semi-Bandit Approach to Charging Station Selection for Electric Vehicles

In this work, we address the problem of long-distance navigation for battery electric vehicles (BEVs), where one or more charging sessions are required to reach the intended destination. We consider the availability and performance of the charging stations to be unknown and stochastic, and develop a combinatorial semi-bandit framework for exploring the road network to learn the parameters of the queue time and charging power distributions. Within this framework, we first outline a pre-processing for the road network graph to handle the constrained combinatorial optimization problem in an efficient way. Then, for the pre-processed graph, we use a Bayesian approach to model the stochastic edge weights, utilizing conjugate priors for the one-parameter exponential and two-parameter gamma distributions, the latter of which is novel to multi-armed bandit literature. Finally, we apply combinatorial versions of Thompson Sampling, BayesUCB and Epsilon-greedy to the problem. We demonstrate the performance of our framework on long-distance navigation problem instances in country-sized road networks, with simulation experiments in Norway, Sweden and Finland.
An Assessment of Omega Dropwindsonde Data in Track Forecasts of Hurricane Debby (1982)

Abstract Omega dropwindsondes (ODWs) were released from two NOAA WP-3D aircraft to measure the environmental wind field in the middle and lower troposphere within 1000 km of the center of Hurricane Debby on 15 and 16 September 1982. The observations were coded in standard formats and transmitted from the aircraft to the National Hurricane Center (NHC) and the National Meteorological Center (NMC) before operational forecast deadlines. The ODW winds clearly indicated the location and strength of a midtropospheric trough in the westerlies that was the major synoptic-scale feature affecting Debby's motion. On 16 September, the dropwindsondes also identified a smaller scale cutoff low in the northern part of the trough. The cutoff low that was centered about 500 km to the north northwest of Debby affected the hurricane's motion from midday on the 16th to midday on the 17th. The ODWs provided NHC with timely information that was used subjectively in determining the official forecasts of Debby's track. The poten.
Dark Coincidences: Small-Scale Solutions with Refracted Gravity and MOND

General relativity and its Newtonian weak field limit are not sufficient to explain the observed phenomenology in the Universe, from the formation of large-scale structures to the dynamics of galaxies, with the only presence of baryonic matter. The most investigated cosmological model, the ΛCDM, accounts for the majority of observations by introducing two dark components, dark energy and dark matter, which represent ∼95% of the mass-energy budget of the Universe. Nevertheless, the ΛCDM model faces important challenges on the scale of galaxies. For example, some very tight relations between the properties of dark and baryonic matters in disk galaxies, such as the baryonic Tully–Fisher relation (BTFR), the mass discrepancy–acceleration relation (MDAR), and the radial acceleration relation (RAR), which see the emergence of the acceleration scale a0≃1.2×10−10 m s−2, cannot be intuitively explained by the CDM paradigm, where cosmic structures form through a stochastic merging process. An even more outstanding coincidence is due to the fact that the acceleration scale a0, emerging from galaxy dynamics, also seems to be related to the cosmological constant Λ. Another challenge is provided by dwarf galaxies, which are darker than what is expected in their innermost regions. These pieces of evidence can be more naturally explained, or sometimes even predicted, by modified theories of gravity, that do not introduce any dark fluid. I illustrate possible solutions to these problems with the modified theory of gravity MOND, which departs from Newtonian gravity for accelerations smaller than a0, and with Refracted Gravity, a novel classical theory of gravity introduced in 2016, where the modification of the law of gravity is instead regulated by a density scale.
Real-Time Viewport-Aware Optical Flow Estimation in 360-degree Videos for Visually-Induced Motion Sickness Mitigation

Visually-induced motion sickness (VIMS), a side effect of perceived motion caused by visual stimulation, is a major obstacle to the widespread use of Virtual Reality (VR). Along with scene object information, visual stimulation can be primarily indicated by optical flow, which characterizes the motion pattern, such as the intensity and direction of the moving image. We estimated the real time optical flow in 360-degree videos targeted at immersive user interactive visualization based on the user’s current viewport. The proposed method allows the estimation of customized visual flow for each experience of dynamic 360-degree videos and is an improvement over previous methods that consider a single optical flow value for the entire equirectangular frame. We applied our method to modulate the opacity of granulated rest frames (GRFs), a technique consisting of visual noise-like randomly distributed visual references that are stable to the user’s body during immersive pre-recorded 360-degree video experience. We report the results of a pilot one-session between-subject study with 18 participants, where users watched a 2-minute high-intensity 360-degree video. The results show that our proposed method successfully estimates optical flow, with pilot data showing that GRFs combined with real-time optical flow estimation may improve user comfort when watching 360-degree videos. However, more data are needed for statistically significant results.
Quantized Surface States of a Narrow Gap Semiconductor

Quantized surface states in an inversion layer of a narrow-gap semiconductor (NGS) with the zinc-blend (ZB) structure are investigated theoretically by the effective mass approximation (EMA) for nearly degenerate bands. When the band gap is small, and the spin-orbit interaction is large, the surface potential causes large k-linear term in the two dimensional dispersion relation, which removes the spin degeneracy. And the g-factor becomes so large that the inversion of the order of Landau levels is expected. The theory is applied to the analysis of the Schubnikov-de Haas (SdH) measurements in an inversion layer of Hg0.79 Cd0.21 Te (εG=68 meV, m0*=0.006 m). According to this analysis, the splitting of the spin degeneracy is as much as several meV, and the inversion of the order of Landau levels seems to be realized. The surface potential of the sample in this experiment is described in the parametrized form, and values of the parameters are estimated by using the experimental data.
Human-Timescale Adaptation in an Open-Ended Task Space

Foundation models have shown impressive adaptation and scalability in supervised and self-supervised learning problems, but so far these successes have not fully translated to reinforcement learning (RL). In this work, we demonstrate that training an RL agent at scale leads to a general in-context learning algorithm that can adapt to open-ended novel embodied 3D problems as quickly as humans. In a vast space of held-out environment dynamics, our adaptive agent (AdA) displays on-the-fly hypothesis-driven exploration, efficient exploitation of acquired knowledge, and can successfully be prompted with first-person demonstrations. Adaptation emerges from three ingredients: (1) meta-reinforcement learning across a vast, smooth and diverse task distribution, (2) a policy parameterised as a large-scale attention-based memory architecture, and (3) an effective automated curriculum that prioritises tasks at the frontier of an agent's capabilities. We demonstrate characteristic scaling laws with respect to network size, memory length, and richness of the training task distribution. We believe our results lay the foundation for increasingly general and adaptive RL agents that perform well across ever-larger open-ended domains.
QButterfly: Lightweight Survey Extension for Online User Interaction Studies for Non-Tech-Savvy Researchers

We provide a user-friendly, flexible, and lightweight open-source HCI toolkit (github.com/QButterfly) that allows non-tech-savvy researchers to conduct online user interaction studies using the widespread Qualtrics and LimeSurvey platforms. These platforms already provide rich functionality (e.g., for experiments or usability tests) and therefore lend themselves to an extension to display stimulus web pages and record clickstreams. The toolkit consists of a survey template with embedded JavaScript, a JavaScript library embedded in the HTML web pages, and scripts to analyze the collected data. No special programming skills are required to set up a study or match survey data and user interaction data after data collection. We empirically validated the software in a laboratory and a field study. We conclude that this extension, even in its preliminary version, has the potential to make online user interaction studies (e.g., with crowdsourced participants) accessible to a broader range of researchers.
Hydrogen column density variability in a sample of local Compton-thin AGN

We present the analysis of multiepoch observations of a set of 12 variable, Compton-thin, local (z<0.1) active galactic nuclei (AGN) selected from the 100-month BAT catalog. We analyze all available X-ray data from \chandra, \xmm, and \nustar, adding up to a total of 53 individual observations. This corresponds to between 3 and 7 observations per source, probing variability timescales between a few days and $\sim 20$~yr. All sources have at least one \nustar observation, ensuring high-energy coverage, which allows us to disentangle the line-of-sight and reflection components in the X-ray spectra. For each source, we model all available spectra simultaneously, using the physical torus models \myt, \bor, and \uxc. The simultaneous fitting, along with the high-energy coverage, allows us to place tight constraints on torus parameters such as the torus covering factor, inclination angle, and average column density. We also estimate the line-of-sight column density ($N_{\rm H}$) for each individual observation. Within the 12 sources, we detect clear line-of-sight $N_{\rm H}$ variability in 5, non-variability in 5, and for 2 of them it is not possible to fully disentangle intrinsic-luminosity and $N_{\rm H}$ variability. We observe large differences between the average values of line-of-sight $N_{\rm H}$ (or $N_{\rm H}$ of the obscurer) and the average $N_{\rm H}$ of the torus (or $N_{\rm H}$ of the reflector), for each source, by a factor between $\sim2$ to $>100$. This behavior, which suggests a physical disconnect between the absorber and the reflector, is more extreme in sources that present $N_{\rm H}$ variability. $N_{\rm H}$-variable AGN also tend to present larger obscuration and broader cloud distributions than their non-variable counterparts. We observe that large changes in obscuration only occur at long timescales, and use this to place tentative lower limits on torus cloud sizes.
Robust Knowledge Adaptation for Federated Unsupervised Person ReID

Person Re-identification (ReID) has been extensively studied in recent years due to the increasing demand in public security. However, collecting and dealing with sensitive personal data raises privacy concerns. Therefore, federated learning has been explored for Person ReID, which aims to share minimal sensitive data between different parties (clients). However, existing federated learning based person ReID methods generally rely on laborious and time-consuming data annotations and it is difficult to guarantee cross-domain consistency. Thus, in this work, a federated unsupervised cluster-contrastive (FedUCC) learning method is proposed for Person ReID. FedUCC introduces a three-stage modelling strategy following a coarse-to-fine manner. In detail, generic knowledge, specialized knowledge and patch knowledge are discovered using a deep neural network. This enables the sharing of mutual knowledge among clients while retaining local domain-specific knowledge based on the kinds of network layers and their parameters. Comprehensive experiments on 8 public benchmark datasets demonstrate the state-of-the-art performance of our proposed method.
Significance of anonymity and privacy in improving inclusivity and diversity in Higher Education Learning Environments

Interactions between lecturers and students are the key to learning in the higher education environment. In this paper, the investigation pursues two different contexts to understand these interactions and the impact of anonymity and privacy in different interactions in the Computer Science (CS) department. The first context"different interaction between a lecturer and students"is investigated using phenomenological research approach by interviewing lecturer in CS ($N_a = 5$). The second context"the significance of anonymity and privacy in interactions"is investigated using a quantitative and qualitative questionnaire-based research method using an online student questionnaire ($N_b = 53$). The study finds a large gap between students' perception of preferred communication methods and the use of the same communication method. From the second context study, it is evident that"anonymity and privacy"in online surveys and module evaluations are preferred by all student participants, thus supporting diversity and inclusivity.
Targeted Image Reconstruction by Sampling Pre-trained Diffusion Model

A trained neural network model contains information on the training data. Given such a model, malicious parties can leverage the"knowledge"in this model and design ways to print out any usable information (known as model inversion attack). Therefore, it is valuable to explore the ways to conduct a such attack and demonstrate its severity. In this work, we proposed ways to generate a data point of the target class without prior knowledge of the exact target distribution by using a pre-trained diffusion model.
Hermitian and Unitary Almost-Companion Matrices of Polynomials on Demand

We introduce the concept of the almost-companion matrix (ACM) by relaxing the non-derogatory property of the standard companion matrix (CM). That is, we define an ACM as a matrix whose characteristic polynomial coincides with a given monic and generally complex polynomial. The greater flexibility inherent in the ACM concept, compared to CM, allows the construction of ACMs that have convenient matrix structures satisfying desired additional conditions, compatibly with specific properties of the polynomial coefficients. We demonstrate the construction of Hermitian and unitary ACMs starting from appropriate third-degree polynomials, with implications for their use in physical-mathematical problems, such as the parameterization of the Hamiltonian, density, or evolution matrix of a qutrit. We show that the ACM provides a means of identifying the properties of a given polynomial and finding its roots. For example, we describe the ACM-based solution of cubic complex algebraic equations without resorting to the use of the Cardano-Dal Ferro formulas. We also show the necessary and sufficient conditions on the coefficients of a polynomial for it to represent the characteristic polynomial of a unitary ACM. The presented approach can be generalized to complex polynomials of higher degrees.
Segregator: Global Point Cloud Registration with Semantic and Geometric Cues

This paper presents Segregator, a global point cloud registration framework that exploits both semantic information and geometric distribution to efficiently build up outlier-robust correspondences and search for inliers. Current state-of-the-art algorithms rely on point features to set up putative correspondences and refine them by employing pair-wise distance consistency checks. However, such a scheme suffers from degenerate cases, where the descriptive capability of local point features downgrades, and unconstrained cases, where length-preserving (1-TRIMs)-based checks cannot sufficiently constrain whether the current observation is consistent with others, resulting in a complexified NP-complete problem to solve. To tackle these problems, on the one hand, we propose a novel degeneracy-robust and efficient corresponding procedure consisting of both instance-level semantic clusters and geometric-level point features. On the other hand, Gaussian distribution-based translation and rotation invariant measurements (G-TRIMs) are proposed to conduct the consistency check and further constrain the problem size. We validated our proposed algorithm on extensive real-world data-based experiments. The code is available: https://github.com/Pamphlett/Segregator.
The Chance of Winning Election Impacts on Social Media Strategy

Social media has been a paramount arena for election campaigns for political actors. While many studies have been paying attention to the political campaigns related to partisanship, politicians also can conduct different campaigns according to their chances of winning. Leading candidates, for example, do not behave the same as fringe candidates in their elections, and vice versa. We, however, know little about this difference in social media political campaign strategies according to their odds in elections. We tackle this problem by analyzing candidates' tweets in terms of users, topics, and sentiment of replies. Our study finds that, as their chances of winning increase, candidates narrow the targets they communicate with, from people in general to the electrical districts and specific persons (verified accounts or accounts with many followers). Our study brings new insights into the candidates' campaign strategies through the analysis based on the novel perspective of the candidate's electoral situation.
Statistics of tidal and deformation eigenvalue fields in the primordial Gaussian matter distribution: the two-dimensional case

We study the statistical properties of the eigenvalues of the primordial tidal and deformation tensor for two-dimensional random Gaussian cosmic density fields. With the tidal and deformation tensors, Hessians of the gravitational and velocity potential, being Gaussian, the eigenvalue fields are distinctly non-Gaussian. We extend the Doroshkevich formula for the joined distribution of eigenvalues to two-dimensional fields and evaluate the two- and three-point correlation functions. In addition, we assess the number densities of singular points of the eigenvalue fields and find their corresponding two- and three-point correlation functions. The incentive for the present study is based on our interest in developing a fully analytical theory for the structure and dynamics of the cosmic web. The tidal forces and the resulting mass element deformation shape the prominent anisotropic wall-like and filamentary components of the cosmic web. Less well-known is that the weblike spatial pattern is already recognizable in the primordial tidal and deformation eigenvalue field. Against the full phase-space assessment of structure formation in the Universe, the caustic skeleton theory entails an analytical framework for the nonlinear evolution of the cosmic web. It accomplishes this by describing the folding of the dark matter sheet and quantifying the emerging caustic singularities, which are fully specified by the spatial properties of the deformation eigenvalues and eigenvectors. Finally, the eigenvalues of the primordial tidal tensor are crucial in the generation of the angular momentum of galaxies. Understanding their spatial distribution is a critical element in predicting the resulting rotation amplitude and orientation.