Miracle-dz/UniFAR-FTUs huggingface dataset:
Binding time: Investigations on the integration of visual stimulus duration

The perception of and reaction to objects creates bindings of (object) features and responses, also called event files. In this context, time is a so far understudied feature. We conducted four experiments to investigate whether the duration of visual stimuli is also integrated into such event files. Experiments 1, 2, and 4 used a simple colour classification task and in Experiment 3 the location of a stimulus had to be classified. In all Experiments, the presentation duration of the stimuli (coloured circles) was either short (20 ms) or long (300 ms). We expected partial repetition costs as an indicator of binding. That is, performance should be better when both colour (Experiment 3: location) and duration repeat or alternate relative to partial repetitions. Results showed no partial repetition costs in Experiments 1 and 3, indicating no integration of duration into visual event files. Experiments 2 and 4 revealed partial repetition costs. Performance was better when Colour and Duration repeated compared with a partial repetition. What distinguishes the latter two experiments from the former is that the coloured stimuli could change their presentation location. The results of all four experiments show a pattern that duration can be integrated into visual event files depending on two criteria: The experimental context holds the possibility of a location change of the target stimulus (Experiments 2 and 4) and the location itself is not response relevant (Experiment 3). The role of location changes for the integration of temporal stimulus features into visual event files is discussed.
Accuracy of Xpert® MTB/RIF Ultra test for posterior oropharyngeal saliva for the diagnosis of paucibacillary pulmonary tuberculosis: a prospective multicenter study

ABSTRACT Background: Posterior oropharyngeal saliva (POS) is increasingly recognized as an alternative specimen for detecting respiratory pathogens. The accuracy of Xpert® MTB/RIF Ultra (X-Ultra), when performed on POS obtained from patients with paucibacillary pulmonary tuberculosis (TB) is unclear. Methods: We consecutively recruited adults with symptoms suggestive of pulmonary TB who were negative by both smear microscopy and Xpert MTB/RIF (X-Classic). Each participant was required to provide one bronchoalveolar lavage fluid (BALF) and one POS specimen, respectively. Diagnostic performances of X-Ultra and X-Classic on POS were compared against clinical and mycobacterial reference standards. Findings: 686 participants meeting inclusion criteria were consecutively enrolled into the study. The overall diagnostic sensitivities of X-Ultra and X-Classic on POS samples were 78.9% [95% confidence interval (CI): 72.8–83.8] and 56.4% (95% CI: 49.7–62.9), respectively; the specificities were 96.6% (95% CI: 94.3–98.1) for X-Ultra and 97.6 (95CI: 95.5–98.8) for X-Classic in POS specimens. Notably, the sensitivity of X-Ultra on POS was as sensitive as X-Classic on BALF against microbiological reference standard (78.9% VS 73.1%). Against clinical diagnosis as a reference standard, the sensitivities of X-Ultra and X-Classic on POS were 55.9% (95% CI: 50.5–61.2; 193/345) and 40.0% (95% CI: 34.8–45.4; 138/345), respectively. The risk of negative results with POS was dramatically increased with decreasing bacterial loads. Conclusions: The testing of POS using X-Ultra shows promise as a tool to identify patients with paucibacillary TB. Considering that bronchoscopy is a semi-invasive procedure, POS testing ahead of bronchoscopy, may decrease the need for bronchoscopic procedures, and the cost of care.
ON THE ORIGIN AND PHYSICS OF GAMMA FLARES IN CRAB NEBULA

We consider parametric generation of electrostatic waves in the magnetosphere of the pulsar PSR0531. The suggested mechanism allows us to convert the pulsar rotational energy into the energy of Langmuir waves. The maximum growth rate is achieved in the “superluminal” area, where the phase velocity of perturbations exceeds the speed of light. Therefore, electromagnetic waves do not damp on particles. Instead, they create plasmon condensate, which is carried out outside of the pulsar magnetosphere and reaches the Crab Nebula. It is shown that the transfer of the energy of the plasmon condensate from the light cylinder to the active region of the nebula happens practically without losses. Unlike the plasma of the magnetosphere, the one of the nebula contains ions, i.e., it may sustain modulation instability, that leads to the collapse of the Langmuir condensate. Langmuir wave collapse, in turn, leads to the acceleration of the distribution function particles. Furthermore, the processes that lead to self-trapping of the synchrotron radiation are discussed. The self-trapping results in the growth of the radiation intensity, which manifests itself observationally as a flare. The condition for the self-trapping onset is derived, showing that if the phenomenon takes place at 100 MeV, then it does not happen at lower (or higher) energies. This specific kind of higher-/lower-energy cutoff could explain why when we observe the flare at 100 MeV that no enhanced emission is observed at lower/higher energies!
GERLUMPH DATA RELEASE 1: HIGH-RESOLUTION COSMOLOGICAL MICROLENSING MAGNIFICATION MAPS AND eResearch TOOLS

As synoptic all-sky surveys begin to discover new multiply lensed quasars, the flow of data will enable statistical cosmological microlensing studies of sufficient size to constrain quasar accretion disk and supermassive black hole properties. In preparation for this new era, we are undertaking the GPU-Enabled, High Resolution cosmological MicroLensing parameter survey (GERLUMPH). We present here the GERLUMPH Data Release 1, which consists of 12,342 high resolution cosmological microlensing magnification maps and provides the first uniform coverage of the convergence, shear, and smooth matter fraction parameter space. We use these maps to perform a comprehensive numerical investigation of the mass-sheet degeneracy, finding excellent agreement with its predictions. We study the effect of smooth matter on microlensing induced magnification fluctuations. In particular, in the minima and saddle-point regions, fluctuations are enhanced only along the critical line, while in the maxima region they are always enhanced for high smooth matter fractions (≈0.9). We describe our approach to data management, including the use of an SQL database with a Web interface for data access and online analysis, obviating the need for individuals to download large volumes of data. In combination with existing observational databases and online applications, the GERLUMPH archive represents a fundamental component of a new microlensing eResearch cloud. Our maps and tools are publicly available at http://gerlumph.swin. edu.au/.
Robust picosecond writing of a layered antiferromagnet by staggered spin-orbit fields

Ultrafast electrical switching by current-induced staggered spin-orbit fields, with minimal risk of overshoot is shown in layered easy-plane antiferromagnets with basal-plane anisotropies. Reliable switching is due to the fieldlike torque, relaxing stringent requirements with respect to precision in the duration of the excitation pulse. Focus is put on a system with weak planar biaxial anisotropy. We investigate the switching as a function of the spin-orbit field strength, pulse duration, rise and fall times, and damping using atomistic spin dynamics simulations and an effective equation for the antiferromagnetic order parameter. The critical spin-orbit field strength required for switching a biaxial system is determined, and we show that writing is possible at feasible current magnitudes. Finally, we discuss switching of systems exhibiting a dominant uniaxial basal-plane anisotropy.
Epidemiology of Extended-spectrum beta-lactamase-producing Escherichia coli at the human-animal-environment interface in Wakiso district, Uganda

Background: Extended-spectrum beta-lactamase-producing Escherichia coli (ESBL-PE) represents a significant global public health concern. Much as humans, animals and environments harbor ESBL-PE, its epidemiology in Uganda is still not well understood. This study explains the epidemiology of ESBL-PE using the one health approach in selected farming households in Wakiso district, central Uganda. Methodology: Environmental, human, and animal samples were collected from 104 households. Additional data were obtained using observation checklists and through interviews with household members using a semi-structured questionnaire. Surface swabs, soil, water, human and animal fecal samples were introduced onto ESBL chromogenic agar. The isolates were identified using biochemical tests and double-disk synergy tests. To assess associations, prevalence ratios (PRs) were computed using a generalized linear model (GLM) analysis with modified Poisson and a log link with robust standard errors in R software. Results: A total of 82.7% (86/104) households had at least one positive ESBL-PE isolate. The overall prevalence of ESBL-PE at the human-animal-environment interface was approximately 25.0% (95% CI: 22.7-28.3). Specifically, animals, environment and humans had an ESBL-PE prevalence of 35.4%, 5.8%, and 45.4% respectively. Having visitors (adj PR= 1.19, 95% CI: 1.04-1.36), utilizing veterinary services (adj PR= 1.39, 95% CI: 1.20-1.61) and using animal waste for gardening (adj PR= 1.29, 95% CI: 1.05-1.60) were positively associated with ESBL-PE contamination. However, covering the drinking water container with a lid (adj PR= 0.84 95% CI: 0.73-0.96) was associated with absence of ESBL-PE. Conclusion: There is wider dissemination of ESBL-PE in the environment, humans, and animals, indicating poor infection prevention and control (IPC) measures in the area. Improved collaborative one health mitigation strategies such as safe water chain, farm biosecurity, household and facility-based IPC measures are recommended to reduce the burden of antimicrobial resistance at community level.
Lowest Landau level on a cone and zeta determinants

We consider the integer QH state on Riemann surfaces with conical singularities, with the main objective of detecting the effect of the gravitational anomaly directly from the form of the wave function on a singular geometry. We suggest the formula expressing the normalisation factor of the holomorphic state in terms of the regularized zeta determinant on conical surfaces and check this relation for some model geometries. We also comment on possible extensions of this result to the fractional QH states.
Distributed Stochastic Bandit Learning with Delayed Context Observation

We consider the problem where M agents collaboratively interact with an instance of a stochastic K-armed contextual bandit, where K$\gg$M. The goal of the agents is to simultaneously minimize the cumulative regret over all the agents over a time horizon T. We consider a setting where the exact context is observed after a delay and at the time of choosing the action the agents are unaware of the context and only a distribution on the set of contexts is available. Such a situation arises in different applications where at the time of the decision the context needs to be predicted (e.g., weather forecasting or stock market prediction), and the context can be estimated once the reward is obtained. We propose an Upper Confidence Bound-(UCB) based distributed algorithm and prove regret and communications bounds for linearly parametrized reward functions. We validated the performance of our algorithm via numerical simulations on synthetic data and real-world Movielens data.
Joint Angle Estimation Error Analysis and 3-D Positioning Algorithm Design for mmWave Positioning System

This article presents a comprehensive framework for jointly analyzing the angle estimation error and designing a 3-D positioning algorithm for an Internet of Things (IoT) millimeter wave (mmWave) positioning system. Initially, the azimuth and elevation Angles of Arrival (AoA) at the anchors are estimated by applying the 2-D discrete Fourier transform (2D-DFT) algorithm. The angle estimation error is then analyzed in terms of probability density functions (PDFs) by utilizing the properties of the 2D-DFT algorithm and employing challenging derivations and linear approximations. The analysis reveals that the resulting angle estimation error is non-Gaussian, distinguishing it from previous studies. Next, the complex expression of the PDF for the AoA estimation error is simplified using the first-order linear approximation of triangle functions. Subsequently, a complex expression for the variance is derived based on the obtained PDF. Specifically, the variance for the azimuth estimation error is integrated separately according to the different nonzero intervals of the obtained PDF. Additionally, the closed-form expressions of the variances are formulated using generalized hypergeometric series. Finally, the two-stage weighted least square (TSWLS) algorithm is employed to estimate the 3-D position of the mobile user (MU) using the estimated AoA and the obtained non-Gaussian variance. Extensive simulation results confirm the non-Gaussian nature of the derived angle estimation error and demonstrate the superiority of the proposed framework.
Pragmatics in Grounded Language Learning: Phenomena, Tasks, and Modeling Approaches

People rely heavily on context to enrich meaning beyond what is literally said, enabling concise but effective communication. To interact successfully and naturally with people, user-facing artiﬁcial intelligence systems will require similar skills in pragmatics : relying on various types of context—from shared linguistic goals and conventions, to the visual and embodied world—to use language effectively. We survey existing grounded settings and pragmatic modeling approaches and analyze how the task goals, environmental contexts, and communicative affordances in each work enrich linguistic meaning. We present recommendations for future grounded task design to naturally elicit pragmatic phenomena, and suggest directions that focus on a broader range of communicative contexts and affordances.
Optimal exploration strategies for finite horizon regret minimization in some adaptive control problems

In this work, we consider the problem of regret minimization in adaptive minimum variance and linear quadratic control problems. Regret minimization has been extensively studied in the literature for both types of adaptive control problems. Most of these works give results of the optimal rate of the regret in the asymptotic regime. In the minimum variance case, the optimal asymptotic rate for the regret is $\log(T)$ which can be reached without any additional external excitation. On the contrary, for most adaptive linear quadratic problems, it is necessary to add an external excitation in order to get the optimal asymptotic rate of $\sqrt{T}$. In this paper, we will actually show from an a theoretical study, as well as, in simulations that when the control horizon is pre-specified a lower regret can be obtained with either no external excitation or a new exploration type termed immediate.
Optimizing Polymatroid Functions

We consider a class of optimization problems that involve determining the maximum value that a function in a particular class can attain subject to a collection of difference constraints. We show that a particular linear programming technique, based on duality and projections, can be used to rederive some structural results that were previously established using more ad hoc methods. We then show that this technique can be used to obtain a polynomial-time algorithm for a certain type of simple difference constraints. Finally we give lower bound results that show that certain possible extensions of these results are probably not feasible.
Stellar Parameters for a Sample of Stars with Planets

Abstract The study of chemical abundances in stars with planets is an important ingredient for the models of formation and evolution of planetary systems. In order to determine accurate abundances, it is crucial to have a reliable set of atmospheric parameters. In this work, we describe the homogeneous determination of effective temperatures, surface gravities and iron abundances for a large sample of stars with planets as well as a control sample of stars without giant planets. Our results indicate that the metallicity distribution of the stars with planets is more metal rich by ~0.13 dex than the control sample stars.
Drone-based Volume Estimation in Indoor Environments

Volume estimation in large indoor spaces is an important challenge in robotic inspection of industrial warehouses. We propose an approach for volume estimation for autonomous systems using visual features for indoor localization and surface reconstruction from 2D-LiDAR measurements. A Gaussian Process-based model incorporates information collected from measurements given statistical prior information about the terrain, from which the volume estimate is computed. Our algorithm finds feasible trajectories which minimize the uncertainty of the volume estimate. We show results in simulation for the surface reconstruction and volume estimate of topographic data.
Attacking and Defending Deep-Learning-Based Off-Device Wireless Positioning Systems

Localization services for wireless devices play an increasingly important role in our daily lives and a plethora of emerging services and applications already rely on precise position information. Widely used on-device positioning methods, such as the global positioning system, enable accurate outdoor positioning and provide the users with full control over what services and applications are allowed to access their location information. In order to provide accurate positioning indoors or in cluttered urban scenarios without line-of-sight satellite connectivity, powerful off-device positioning systems, which process channel state information (CSI) measured at the infrastructure base stations or access points with deep neural networks, have emerged recently. Such off-device wireless positioning systems inherently link a user's data transmission with its localization, since accurate CSI measurements are necessary for reliable wireless communication -- this not only prevents the users from controlling who can access this information but also enables virtually everyone in the device's range to estimate its location, resulting in serious privacy and security concerns. We therefore propose on-device attacks against off-device wireless positioning systems in multi-antenna orthogonal frequency-division multiplexing systems while remaining standard compliant and minimizing the impact on quality-of-service, and we demonstrate their efficacy using real-world measured datasets for cellular outdoor and wireless LAN indoor scenarios. We also investigate defenses to counter such attack mechanisms, and we discuss the limitations and implications on protecting location privacy in existing and future wireless communication systems.
Regularized Stein Variational Gradient Flow

The Stein Variational Gradient Descent (SVGD) algorithm is an deterministic particle method for sampling. However, a mean-field analysis reveals that the gradient flow corresponding to the SVGD algorithm (i.e., the Stein Variational Gradient Flow) only provides a constant-order approximation to the Wasserstein Gradient Flow corresponding to the KL-divergence minimization. In this work, we propose the Regularized Stein Variational Gradient Flow which interpolates between the Stein Variational Gradient Flow and the Wasserstein Gradient Flow. We establish various theoretical properties of the Regularized Stein Variational Gradient Flow (and its time-discretization) including convergence to equilibrium, existence and uniqueness of weak solutions, and stability of the solutions. We provide preliminary numerical evidence of the improved performance offered by the regularization.
Multi-Label Quantification

Quantification, variously called supervised prevalence estimation or learning to quantify, is the supervised learning task of generating predictors of the relative frequencies (a.k.a. prevalence values) of the classes of interest in unlabelled data samples. While many quantification methods have been proposed in the past for binary problems and, to a lesser extent, single-label multiclass problems, the multi-label setting (i.e., the scenario in which the classes of interest are not mutually exclusive) remains by and large unexplored. A straightforward solution to the multi-label quantification problem could simply consist of recasting the problem as a set of independent binary quantification problems. Such a solution is simple but naïve, since the independence assumption upon which it rests is, in most cases, not satisfied. In these cases, knowing the relative frequency of one class could be of help in determining the prevalence of other related classes. We propose the first truly multi-label quantification methods, i.e., methods for inferring estimators of class prevalence values that strive to leverage the stochastic dependencies among the classes of interest in order to predict their relative frequencies more accurately. We show empirical evidence that natively multi-label solutions outperform the naïve approaches by a large margin. The code to reproduce all our experiments is available online.
AutoTemplate: A Simple Recipe for Lexically Constrained Text Generation

Lexically constrained text generation is one of the constrained text generation tasks, which aims to generate text that covers all the given constraint lexicons. While the existing approaches tackle this problem using a lexically constrained beam search algorithm or dedicated model using non-autoregressive decoding, there is a trade-off between the generated text quality and the hard constraint satisfaction. We introduce AutoTemplate, a simple yet effective lexically constrained text generation framework divided into template generation and lexicalization tasks. The template generation is to generate the text with the placeholders, and lexicalization replaces them into the constraint lexicons to perform lexically constrained text generation. We conducted the experiments on two tasks: keywords-to-sentence generations and entity-guided summarization. Experimental results show that the AutoTemplate outperforms the competitive baselines on both tasks while satisfying the hard lexical constraints.
Topological Superfluid Responses of Graphene-Superconductor Platforms

We provide a rigorous and transparent explanation for the puzzling theoretical findings of a quantized superfluid stiffness of superconducting graphene in the Dirac regime. We prove that the contribution of a single Dirac cone to the superfluid stiffness is proportional to its topological charge, and can be controlled in experiments using a Zeeman field. Further, we show that the quantization persists upon effecting strain and is resilient against weak disorder. By employing an adiabatic approach for superfluid responses, we unify the superfluid stiffness and quantum capacitance in Dirac superconductors. Based on this unified picture we predict a topological quantum capacitance for a Josephson junction consisting of a strained graphene hybrid and a conventional superconductor.
Emergence of oscillations in quantum entanglement during rescattering

We study the time evolution of entanglement in a model system consisting of an electron and its parent ion during the quantum mechanical process of rescattering due to a strong few-cycle laser pulse. We characterize the entanglement by calculating both the linear entropy and the Neumann entropy for several values of the pulse intensity. We find that the emerging oscillations in the entropy exhibit local maxima at the zero crossings of the electric field of the laser pulse. The time-dependent Wigner function, which is the best tool in connecting the classical and the quantum features of a process, is also calculated and analysed in this model.