Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling nonstationary temperature maxima based on extremal dependence changing with event magnitude

The modeling of spatio-temporal trends in temperature extremes can help better understand the structure and frequency of heatwaves in a changing climate. Here, we study annual temperature maxima over Southern Europe using a century-spanning dataset observed at 44 monitoring stations. Extending the spectral representation of max-stable processes, our modeling framework relies on a novel construction of max-infinitely divisible processes, which include covariates to capture spatio-temporal non-stationarities. Our new model keeps a popular max-stable process on the boundary of the parameter space, while flexibly capturing weakening extremal dependence at increasing quantile levels and asymptotic independence. This is achieved by linking the overall magnitude of a spatial event to its spatial correlation range, in such a way that more extreme events become less spatially dependent, thus more localized. Our model reveals salient features of the spatio-temporal variability of European temperature extremes, and it clearly outperforms natural alternative models. Results show that the spatial extent of heatwaves is smaller for more severe events at higher altitudes, and that recent heatwaves are moderately wider. Our probabilistic assessment of the 2019 annual maxima confirms the severity of the 2019 heatwaves both spatially and at individual sites, especially when compared to climatic conditions prevailing in 1950-1975.
Integrating Egocentric Localization for More Realistic Point-Goal Navigation Agents

Recent work has presented embodied agents that can navigate to point-goal targets in novel indoor environments with near-perfect accuracy. However, these agents are equipped with idealized sensors for localization and take deterministic actions. This setting is practically sterile by comparison to the dirty reality of noisy sensors and actuations in the real world -- wheels can slip, motion sensors have error, actuations can rebound. In this work, we take a step towards this noisy reality, developing point-goal navigation agents that rely on visual estimates of egomotion under noisy action dynamics. We find these agents outperform naive adaptions of current point-goal agents to this setting as well as those incorporating classic localization baselines. Further, our model conceptually divides learning agent dynamics or odometry (where am I? ) from task-specific navigation policy (where do I want to go? ). This enables a seamless adaption to changing dynamics (a different robot or floor type) by simply re-calibrating the visual odometry model -- circumventing the expense of re-training of the navigation policy. Our agent was the runner-up in the PointNav track of CVPR 2020 Habitat Challenge.
UV spectroscopy confirms SU Lyn to be a symbiotic star

SU Lyn, a star that ostensibly appears to be an unremarkable late M type giant, has recently been proposed to be a symbiotic star largely based on its hard X-ray properties. The star does not display, in low-resolution optical spectra, the high excitation lines typically seen in the spectra of symbiotic stars. In the present work, UV, optical, and near-infrared observations are presented, aimed at exploring and strengthening the proposed symbiotic classification for this star. Our Far-UV 1300-1800$A$ spectrum of SU Lyn, obtained with the ASTROSAT mission's UVIT payload, shows emission lines of Si IV, C IV, OIII and N III in a spectrum typical of symbiotic stars. The UV spectrum robustly confirms SU Lyn's symbiotic nature. The detection of high excitation lines in a high-resolution optical spectrum further consolidates its symbiotic nature. As is being recognized, the potential existence of other similar symbiotic systems could significantly impact the census of symbiotic stars in the galaxy.
Bootstrap p-values reduce type 1 error of the robust rank-order test of difference in medians

The robust rank-order test (Fligner and Policello, 1981) was designed as an improvement of the non-parametric Wilcoxon-Mann-Whitney U-test to be more appropriate when the samples being compared have unequal variance. However, it tends to be excessively liberal when the samples are asymmetric. This is likely because the test statistic is assumed to have a standard normal distribution for sample sizes > 12. This work proposes an on-the-fly method to obtain the distribution of the test statistic from which the critical/p-value may be computed directly. The method of likelihood maximization is used to estimate the parameters of the parent distributions of the samples being compared. Using these estimated populations, the null distribution of the test statistic is obtained by the Monte-Carlo method. Simulations are performed to compare the proposed method with that of standard normal approximation of the test statistic. For small sample sizes (<= 20), the Monte-Carlo method outperforms the normal approximation method. This is especially true for low values of significance levels (< 5%). Additionally, when the smaller sample has the larger standard deviation, the Monte-Carlo method outperforms the normal approximation method even for large sample sizes (= 40/60). The two methods do not differ in power. Finally, a Monte-Carlo sample size of 10^4 is found to be sufficient to obtain the aforementioned relative improvements in performance. Thus, the results of this study pave the way for development of a toolbox to perform the robust rank-order test in a distribution-free manner.
Bringing Physical Physics Classroom Online – Challenges of Online Teaching in the New Normal

The onset of the COVID-19 pandemic in 2020 has greatly impacted all forms of social activities globally, including traditional classroom activities across all levels of instruction (kindergarten to universities). While many countries have opted for suspension of lessons, this cannot continue indefinitely and alternative means to continue lessons must be developed. While online and blended learning (including MOOCs) have been an active subject of research and discourse during the pre-pandemic days, onset of the pandemic has suddenly created an immediacy to such means of course delivery, better than any administrator or teaching committee could have done. This creates both a gap and tension in terms of successful and engaging content delivery, where traditional modes of synchronous content delivery is now forced to be brought online. Yet, the situation provides educators with an opportunity to explore the merits and weaknesses of online learning. Thus, this article seeks to outline the challenges and paradigm shifts involved in such synchronous online learning as a replacement for traditional classroom learning, following our experience at SUTD of conducting a full 13-week online physics course between May to August 2020. At the same time, we reflect on the merits brought about by the availability of such technologies that can potentially be translated to the physical physics classrooms.
Universal Layout Emulation for Long-Term Database Archival

Research on alternate media technologies, like film, synthetic DNA, and glass, for long-term data archival has received a lot of attention recently due to the media obsolescence issues faced by contemporary storage media like tape, Hard Disk Drives (HDD), and Solid State Disks (SSD). While researchers have developed novel layout and encoding techniques for archiving databases on these new media types, one key question remains unaddressed: How do we ensure that the decoders developed today will be available and executable by a user who is restoring an archived database several decades later in the future, on a computing platform that potentially does not even exist today? In this paper, we make the case for Universal Layout Emulation (ULE), a new approach for future-proof, long-term database archival that advocates archiving decoders together with the data to ensure successful recovery. In order to do so, ULE brings together concepts from Data Management and Digital Preservation communities by using emulation for archiving decoders. In order to show that ULE can be implemented in practice, we present the design and evaluation of Micr'Olonys, an end-to-end long-term database archival system that can be used to archive databases using visual analog media like film, microform, and archival paper.
Faster Motif Counting via Succinct Color Coding and Adaptive Sampling

We address the problem of computing the distribution of induced connected subgraphs, aka graphlets or motifs, in large graphs. The current state-of-the-art algorithms estimate the motif counts via uniform sampling by leveraging the color coding technique by Alon, Yuster, and Zwick. In this work, we extend the applicability of this approach by introducing a set of algorithmic optimizations and techniques that reduce the running time and space usage of color coding and improve the accuracy of the counts. To this end, we first show how to optimize color coding to efficiently build a compact table of a representative subsample of all graphlets in the input graph. For 8-node motifs, we can build such a table in one hour for a graph with 65M nodes and 1.8B edges, which is times larger than the state of the art. We then introduce a novel adaptive sampling scheme that breaks the “additive error barrier” of uniform sampling, guaranteeing multiplicative approximations instead of just additive ones. This allows us to count not only the most frequent motifs, but also extremely rare ones. For instance, on one graph we accurately count nearly 10.000 distinct 8-node motifs whose relative frequency is so small that uniform sampling would literally take centuries to find them. Our results show that color coding is still the most promising approach to scalable motif counting.
Analysis and Design of Strongly Stabilizing PID Controllers for Time-Delay Systems

This paper presents the analysis of the stability properties of PID controllers for dynamical systems with multiple state delays, focusing on the mathematical characterization of the potential sensitivity of stability with respect to infinitesimal parametric perturbations. These perturbations originate for instance from neglecting feedback delay, a finite difference approximation of the derivative action, or neglecting fast dynamics. The analysis of these potential sensitivity problems leads us to the introduction of a `robustified' notion of stability called \emph{strong stability}, inspired by the corresponding notion for neutral functional differential equations. We prove that strong stability can be achieved by adding a low-pass filter with a sufficiently large cut-off frequency to the control loop, on the condition that the filter itself does not destabilize the nominal closed-loop system. Throughout the paper, the theoretical results are illustrated by examples that can be analyzed analytically, including, among others, a third-order unstable system where both proportional and derivative control action are necessary for achieving stability, while the regions in the gain parameter-space for stability and strong stability are not identical. Besides the analysis of strong stability, a computational procedure is provided for designing strongly stabilizing PID controllers. Computational case-studies illustrating this design procedure complete the presentation.
Bayesian shared-parameter models for analysing sardine fishing in the Mediterranean Sea

European sardine is experiencing an overfishing around the world. The dynamics of the industrial and artisanal fishing in the Mediterranean Sea from 1970 to 2014 by country was assessed by means of Bayesian joint longitudinal modelling that uses the random effects to generate an association structure between both longitudinal measures. Model selection was based on Bayes factors approximated through the harmonic mean.
A Relativistic Orbit Model for Temporal Properties of AGN

We present a unified model for X-ray quasi-periodic oscillations (QPOs) seen in Narrow-line Seyfert 1 (NLSy1) galaxies, $\gamma$-ray and optical band QPOs that are seen in Blazars. The origin of these QPOs is attributed to the plasma motion in corona or jets of these AGN. In the case of X-ray QPOs, we applied the general relativistic precession model for the two simultaneous QPOs seen in NLSy1 1H 0707-945 and deduce orbital parameters, such the radius of the emission region, and spin parameter $a$ for a circular orbit; we obtained the Carter's constant $Q$, $a$, and the radius in the case of a spherical orbit solution. In other cases where only one X-ray QPO is seen, we localized the orbital parameters for NLSy1 galaxies REJ 1034+396, 2XMM J123103.2+110648, MS 2254.9-3712, Mrk 766, and MCG-06-30-15. By applying the lighthouse model, we found that a kinematic origin of the jet based $\gamma$-ray and optical QPOs, in a relativistic MHD framework, is possible. Based on the inbuilt Hamiltonian formulation with a power-law distribution in the orbital energy of the plasma consisting of only circular or spherical trajectories, we show that the resulting Fourier power spectral density (PSD) has a break corresponding to the energy at ISCO. Further, we derive connection formulae between the slopes in the PSD and that of the energy distribution. Overall, given the preliminary but promising results of these relativistic orbit models to match the observed QPO frequencies and PSD at diverse scales in the inner corona and the jet, it motivates us to build detailed models, including a transfer function for the energy spectrum in the corona and relativistic MHD jet models for plasma flow and its polarization properties.
PAC Reinforcement Learning Algorithm for General-Sum Markov Games

This article presents a theoretical framework for probably approximately correct (PAC) multi-agent reinforcement learning (MARL) algorithms for Markov games. Using the idea of delayed Q-learning, this article extends the well-known Nash Q-learning algorithm to build a new PAC MARL algorithm for general-sum Markov games. In addition to guiding the design of a provably PAC MARL algorithm, the framework enables checking whether an arbitrary MARL algorithm is PAC. Comparative numerical results demonstrate the algorithm's performance and robustness.
Sub-linear Regret Bounds for Bayesian Optimisation in Unknown Search Spaces

Bayesian optimisation is a popular method for efficient optimisation of expensive black-box functions. Traditionally, BO assumes that the search space is known. However, in many problems, this assumption does not hold. To this end, we propose a novel BO algorithm which expands (and shifts) the search space over iterations based on controlling the expansion rate thought a hyperharmonic series. Further, we propose another variant of our algorithm that scales to high dimensions. We show theoretically that for both our algorithms, the cumulative regret grows at sub-linear rates. Our experiments with synthetic and real-world optimisation tasks demonstrate the superiority of our algorithms over the current state-of-the-art methods for Bayesian optimisation in unknown search space.
Sensing versus adaptation in bacterial chemotaxis

We show how the competition between sensing and adaptation can result in a performance peak in E.coli chemotaxis using extensive numerical simulations in a detailed theoretical model. Receptor clustering amplifies the input signal coming from ligand binding which enhances chemotactic efficiency. However, large clusters also induce large fluctuations in the activity which causes adaptation to take over. The activity, and hence the run-tumble motility now gets controlled by methylation levels which are part of adaptation module, rather than ligand binding. This reduces chemotactic efficiency.
Learning From Multiple Datasets With Heterogeneous and Partial Labels for Universal Lesion Detection in CT

Large-scale datasets with high-quality labels are desired for training accurate deep learning models. However, due to the annotation cost, datasets in medical imaging are often either partially-labeled or small. For example, DeepLesion is such a large-scale CT image dataset with lesions of various types, but it also has many unlabeled lesions (missing annotations). When training a lesion detector on a partially-labeled dataset, the missing annotations will generate incorrect negative signals and degrade the performance. Besides DeepLesion, there are several small single-type datasets, such as LUNA for lung nodules and LiTS for liver tumors. These datasets have heterogeneous label scopes, i.e., different lesion types are labeled in different datasets with other types ignored. In this work, we aim to develop a universal lesion detection algorithm to detect a variety of lesions. The problem of heterogeneous and partial labels is tackled. First, we build a simple yet effective lesion detection framework named Lesion ENSemble (LENS). LENS can efficiently learn from multiple heterogeneous lesion datasets in a multi-task fashion and leverage their synergy by proposal fusion. Next, we propose strategies to mine missing annotations from partially-labeled datasets by exploiting clinical prior knowledge and cross-dataset knowledge transfer. Finally, we train our framework on four public lesion datasets and evaluate it on 800 manually-labeled sub-volumes in DeepLesion. Our method brings a relative improvement of 49% compared to the current state-of-the-art approach in the metric of average sensitivity. We have publicly released our manual 3D annotations of DeepLesion online.11https://github.com/viggin/DeepLesion_manual_test_set
On the Origin of a Rotating Metal-poor Stellar Population in the Milky Way Nuclear Cluster

We explore the origin of a population of stars recently detected in the inner parsec of the Milky Way (MW) nuclear cluster, which exhibits subsolar metallicity and a higher rotation compared to the dominant population. Using state-of-the-art N-body simulations, we model the infall of massive stellar systems into the Galactic center, both of Galactic and extragalactic origin. We show that the newly discovered population can either be the remnant of a massive star cluster formed a few kiloparsecs away from the Galactic center (Galactic scenario) or be accreted from a dwarf galaxy originally located at 10–100 kpc (extragalactic scenario) and that reached the Galactic center 3–5 Gyr ago. A comparison between our models and characteristic Galactocentric distance and metallicity distributions of MW satellites and globular clusters favors the Galactic scenario. A comparison with clusters associated with the Enceladus-Sausage, Sequoia, Sagittarius, and Canis Major structures suggests that the progenitor of the observed metal-poor substructure formed in situ rather than being accreted.
ASCOT simulations of W7-X neutron rates: effect of magnetic configuration

Demonstrating improved confinement of energetic ions is one of the key goals of the Wendelstein 7-X (W7-X) stellarator. In the past campaigns, measuring confined fast ions has proven to be challenging. Future deuterium campaigns would open up the option of using fusion-produced neutrons to indirectly observe confined fast ions. There are two neutron populations: 2.45 MeV neutrons from thermonuclear and beam-target fusion, and 14.1 MeV neutrons from DT reactions between tritium fusion products and bulk deuterium. The 14.1 MeV neutron signal can be measured using a scintillating fiber neutron detector, whereas the overall neutron rate is monitored by common radiation safety detectors, for instance fission chambers. The fusion rates are dependent on the slowing-down distribution of the deuterium and tritium ions, which in turn depend on the magnetic configuration via fast ion orbits. In this work, we investigate the effect of magnetic configuration on neutron production rates in W7-X. The neutral beam injection, beam and triton slowing-down distributions, and the fusion reactivity are simulated with the ASCOT suite of codes. The results indicate that the magnetic configuration has only a small effect on the production of 2.45 MeV neutrons from DD fusion and, particularly, on the 14.1 MeV neutron production rates. Despite triton losses of up to 50 %, the amount of 14.1 MeV neutrons produced might be sufficient for a time-resolved detection using a scintillating fiber detector, although only in high-performance discharges.
The Resolution Matrix for Visualizing Functional Network Connectivity

The resolution matrix is a mathematical tool for analyzing inverse problems such as computational imaging systems. When treating network connectivity estimation as an inverse problem, the resolution matrix describes the degree to which network nodes and edges can be resolved. This is useful both for quantifying robustness of the network estimate, as well as identifying correlated activity. In this report we analyze the resolution matrix for functional MRI data from the Human Connectome project. We find that common metrics of the resolution metric can be used to identify networked activity, though with a new twist on the relationship between default mode network and the frontoparietal attention network.
A comparison of oscillatory characteristics in covert speech and speech perception

Covert speech, the silent production of words in the mind, has been studied increasingly to understand and decode thoughts. This task has often been compared to speech perception as it brings about similar topographical activation patterns in common brain areas. In studies of speech comprehension, neural oscillations are thought to play a key role in the sampling of speech at varying temporal scales. However, very little is known about the role of oscillations in covert speech. In this study, we aimed to determine to what extent each oscillatory frequency band is used to process words in covert speech and speech perception tasks. Secondly, we asked whether the {\theta} and {\gamma} activity in the two tasks are related through phase-amplitude coupling (PAC). First, continuous wavelet transform was performed on epoched signals and subsequently two-tailed t-tests between two classes were conducted to determine statistical distinctions in frequency and time. While the perception task dynamically uses all frequencies with more prominent {\theta} and {\gamma} activity, the covert task favoured higher frequencies with significantly higher {\gamma} activity than perception. Moreover, the perception condition produced significant {\theta}-{\gamma} PAC suggesting a linkage of syllabic and phonological sampling. Although this was found to be suppressed in the covert condition, we found significant pseudo-coupling between perception {\theta} and covert speech {\gamma}. We report that covert speech processing is largely conducted by higher frequencies, and that the {\gamma}- and {\theta}-bands may function similarly and differently across tasks, respectively. This study is the first to characterize covert speech in terms of neural oscillatory engagement. Future studies are directed to explore oscillatory characteristics and inter-task relationships with a more diverse vocabulary.