Miracle-dz/UniFAR-FTUs huggingface dataset:
Analytic Post-Newtonian Astrometric and Spectroscopic Models of Orbits around Black Holes

Observations of the S stars, the cluster of young stars in the inner 0.1 pc of the Galactic center, have been crucial in providing conclusive evidence for a supermassive black hole at the center of our galaxy. Since some of the stars have orbits less than that of a typical human lifetime, it is possible to observe multiple orbits and test the weak-field regime of general relativity. Current calculations of orbits require relatively slow and expensive computations in order to perform numerical integrations for the position and momentum of each star at each observing time. In this paper, we present a computationally efficient, first-order post-Newtonian model for the astrometric and spectroscopic data gathered for the S stars. We find that future, 30 m class telescopes—and potentially even current large telescopes with very high spectroscopic resolution—may be able to detect the Shapiro effect for an S star in the next decade or so.
Robot Learning from Demonstration Using Elastic Maps

Learning from Demonstration (LfD) is a popular method of reproducing and generalizing robot skills from human-provided demonstrations. In this paper, we propose a novel optimization-based LfD method that encodes demon-strations as elastic maps. An elastic map is a graph of nodes connected through a mesh of springs. We build a skill model by fitting an elastic map to the set of demonstrations. The formulated optimization problem in our approach includes three objectives with natural and physical interpretations. The main term rewards the mean squared error in the Cartesian coordinate. The second term penalizes the non-equidistant distribution of points resulting in the optimum total length of the trajectory. The third term rewards smoothness while pe-nalizing nonlinearity. These quadratic objectives form a convex problem that can be solved efficiently with local optimizers. We examine nine methods for constructing and weighting the elastic maps and study their performance in robotic tasks. We also evaluate the proposed method in several simulated and real-world experiments using a UR5e manipulator arm, and compare it to other LfD approaches to demonstrate its benefits and flexibility across a variety of metrics.
Sodium chloride inhibits effective bubbly drag reduction in turbulent bubbly Taylor–Couette flows

Using the Taylor–Couette geometry we experimentally investigate the effect of salt on drag reduction caused by bubbles present in the flow. We combine torque measurements with optical high-speed imaging to relate the bubble size to the drag experienced by the flow. Previous studies have shown that a small percentage of air (4%) can lead to dramatic drag reduction (40%). In contrast to previous laboratory experiments, which mainly used fresh water, we will vary the salinity from that of fresh water to the average salinity of ocean water. We find that the drag reduction is increasingly more inhibited for increasing salt concentrations, going from 40% for fresh water to just 15% for sea water. Salts present in the working fluid inhibit coalescence events, resulting in smaller bubbles in the flow and, with that, a decrease in the drag reduction. Above a critical salinity, increasing the salinity has no further effect on the size of bubbles in the flow and thus the drag experienced by the flow. Our new findings demonstrate the importance of sodium chloride on the bubbly drag reduction mechanism, and will further challenge naval architects to implement promising air lubrication systems on marine vessels. This article is part of the theme issue ‘Taylor–Couette and related flows on the centennial of Taylor’s seminal Philosophical Transactions paper (part 1)’.
Subject-Specific Lesion Generation and Pseudo-Healthy Synthesis for Multiple Sclerosis Brain Images

Understanding the intensity characteristics of brain lesions is key for defining image-based biomarkers in neurological studies and for predicting disease burden and outcome. In this work, we present a novel foreground-based generative method for modelling the local lesion characteristics that can both generate synthetic lesions on healthy images and synthesize subject-specific pseudo-healthy images from pathological images. Furthermore, the proposed method can be used as a data augmentation module to generate synthetic images for training brain image segmentation networks. Experiments on multiple sclerosis (MS) brain images acquired on magnetic resonance imaging (MRI) demonstrate that the proposed method can generate highly realistic pseudo-healthy and pseudo-pathological brain images. Data augmentation using the synthetic images improves the brain image segmentation performance compared to traditional data augmentation methods as well as a recent lesion-aware data augmentation technique, CarveMix. The code will be released at https://github.com/dogabasaran/lesion-synthesis.
Beyond Galaxy Bimodality: The Complex Interplay between Kinematic Morphology and Star Formation in the Local Universe

It is generally assumed that galaxies are a bimodal population in both star formation and structure; star-forming galaxies are disks, while passive galaxies host large bulges or are entirely spheroidal. Here we test this scenario by presenting a full census of the kinematic morphologies of a volume-limited sample of galaxies in the local universe extracted from the MaNGA galaxy survey. We measure the integrated stellar line-of-sight velocity to velocity dispersion ratio (V/σ) for 4574 galaxies in the stellar mass range 9.75<logM⋆[M⊙]<11.75 . We show that at fixed stellar mass, the distribution of V/σ is not bimodal, and that a simple separation between fast and slow rotators is oversimplistic. Fast rotators are a mixture of at least two populations, referred to here as dynamically cold disks and intermediate systems, with disks dominating in both total stellar mass and number. When considering star-forming and passive galaxies separately, the star-forming population is almost entirely made up of disks, while the passive population is mixed, implying an array of quenching mechanisms. Passive disks represent ∼30% (both in number and mass) of passive galaxies, nearly a factor of two higher than that of slow rotators, reiterating that these are an important population for understanding galaxy quenching. These results paint a picture of a local universe dominated by disky galaxies, most of which become somewhat less rotation-supported upon or after quenching. While spheroids are present to a degree, they are certainly not the evolutionary end point for the majority of galaxies.
API Usage Recommendation Via Multi-View Heterogeneous Graph Representation Learning

Developers often need to decide which APIs to use for the functions being implemented. With the ever-growing number of APIs and libraries, it becomes increasingly difficult for developers to find appropriate APIs, indicating the necessity of automatic API usage recommendation. Previous studies adopt statistical models or collaborative filtering methods to mine the implicit API usage patterns for recommendation. However, they rely on the occurrence frequencies of APIs for mining usage patterns, thus prone to fail for the low-frequency APIs. Besides, prior studies generally regard the API call interaction graph as homogeneous graph, ignoring the rich information (e.g., edge types) in the structure graph. In this work, we propose a novel method named MEGA for improving the recommendation accuracy especially for the low-frequency APIs. Specifically, besides call interaction graph, MEGA considers another two new heterogeneous graphs: global API co-occurrence graph enriched with the API frequency information and hierarchical structure graph enriched with the project component information. With the three multi-view heterogeneous graphs, MEGA can capture the API usage patterns more accurately. Experiments on three Java benchmark datasets demonstrate that MEGA significantly outperforms the baseline models by at least 19% with respect to the Success Rate@1 metric. Especially, for the low-frequency APIs, MEGA also increases the baselines by at least 55% regarding the Success Rate@1 score.
Nanomechanical testing of silica nanospheres for levitated optomechanics experiments

Optically-levitated dielectric particles can serve as ultra-sensitive detectors of feeble forces and torques, as tools for use in quantum information science, and as a testbed for quantum coherence in macroscopic systems. Knowledge of the structural and optical properties of the particles is important for calibrating the sensitivity of such experiments. Here we report the results of nanomechanical testing of silica nanospheres and investigate an annealing approach which can produce closer to bulk-like behavior in the samples in terms of their elastic moduli. These results, combined with our experimental investigations of optical trap lifetimes in high vacuum at high trapping-laser intensity for both annealed and as-grown nanospheres, were used to provide a theoretical analysis of the effects of porosity and non-sphericity in the samples, identifying possible mechanisms of trapping instabilities for nanospheres with non-bulk-silica-like properties.
High resolution X-ray spectroscopy of V4641 Sgr during its 2020 outburst

We observed the Galactic black hole X-ray binary V4641 Sgr with the high resolution transmission gratings on Chandra during the source’s 2020 outburst. Over two epochs of Chandra gratings observations, we see numerous highly ionized metal lines, superimposed on a hot, disc-dominated X-ray continuum. The measured inner disc temperatures and luminosities imply an unfeasibly small inner disc radius, such that we suggest that the central engine of V4641 Sgr is obscured, and we are viewing scattered X-rays. We find that the emission lines in the Chandra spectra cannot be constrained by a single photoionized model, instead finding that two separate photoionized model components are required, one to reproduce the iron lines and a second for the other metals. We compare the observed X-ray spectra of V4641 Sgr to optical studies during previous outbursts of the source, suggesting that the lines originate in an accretion disc wind, potentially with a spherical geometry.
Flow Annealed Importance Sampling Bootstrap

Normalizing flows are tractable density models that can approximate complicated target distributions, e.g. Boltzmann distributions of physical systems. However, current methods for training flows either suffer from mode-seeking behavior, use samples from the target generated beforehand by expensive MCMC methods, or use stochastic losses that have high variance. To avoid these problems, we augment flows with annealed importance sampling (AIS) and minimize the mass-covering $\alpha$-divergence with $\alpha=2$, which minimizes importance weight variance. Our method, Flow AIS Bootstrap (FAB), uses AIS to generate samples in regions where the flow is a poor approximation of the target, facilitating the discovery of new modes. We apply FAB to multimodal targets and show that we can approximate them very accurately where previous methods fail. To the best of our knowledge, we are the first to learn the Boltzmann distribution of the alanine dipeptide molecule using only the unnormalized target density, without access to samples generated via Molecular Dynamics (MD) simulations: FAB produces better results than training via maximum likelihood on MD samples while using 100 times fewer target evaluations. After reweighting the samples, we obtain unbiased histograms of dihedral angles that are almost identical to the ground truth.
'Labelling the Gaps': A Weakly Supervised Automatic Eye Gaze Estimation

Over the past few years, there has been an increasing interest to interpret gaze direction in an unconstrained environment with limited supervision. Owing to data curation and annotation issues, replicating gaze estimation method to other platforms, such as unconstrained outdoor or AR/VR, might lead to significant drop in performance due to insufficient availability of accurately annotated data for model training. In this paper, we explore an interesting yet challenging problem of gaze estimation method with a limited amount of labelled data. The proposed method distills knowledge from the labelled subset with visual features; including identity-specific appearance, gaze trajectory consistency and motion features. Given a gaze trajectory, the method utilizes label information of only the start and the end frames of a gaze sequence. An extension of the proposed method further reduces the requirement of labelled frames to only the start frame with a minor drop in the generated label's quality. We evaluate the proposed method on four benchmark datasets (CAVE, TabletGaze, MPII and Gaze360) as well as web-crawled YouTube videos. Our proposed method reduces the annotation effort to as low as 2.67%, with minimal impact on performance; indicating the potential of our model enabling gaze estimation 'in-the-wild' setup.
Enhancing spin squeezing using soft-core interactions

We propose a new protocol for preparing spin squeezed states in controllable atomic, molecular, and optical systems, with particular relevance to emerging optical clock platforms compatible with Rydberg interactions. By combining a short-ranged, soft-core potential with an external drive, we can transform naturally emerging Ising interactions into an XX spin model while opening a many-body gap. The gap helps maintain the system within a collective manifold of states where metrologically useful spin squeezing can be generated at a level comparable to the spin squeezing generated in systems with genuine all-to-all interactions. We examine the robustness of our protocol to experimentally-relevant decoherence and show favorable performance over typical protocols lacking gap protection.
On Hyers–Ulam Stability of Cauchy and Wilson Equations

Abstract We study the Hyers–Ulam stability problem for the Cauchy and Wilson integral equations where 𝐺 is a topological group, 𝑓, 𝑔 : 𝐺 → ℂ are continuous functions, μ is a complex measure with compact support and σ is a continuous involution of 𝐺. The result obtained in this paper are natural extensions of the previous works concerning the Hyers–Ulam stability of the Cauchy and Wilson functional equations done in the particular case of μ=δe : The Dirac measure concentrated at the identity element of 𝐺.
TAG: Boosting Text-VQA via Text-aware Visual Question-answer Generation

Text-VQA aims at answering questions that require understanding the textual cues in an image. Despite the great progress of existing Text-VQA methods, their performance suffers from insufficient human-labeled question-answer (QA) pairs. However, we observe that, in general, the scene text is not fully exploited in the existing datasets -- only a small portion of the text in each image participates in the annotated QA activities. This results in a huge waste of useful information. To address this deficiency, we develop a new method to generate high-quality and diverse QA pairs by explicitly utilizing the existing rich text available in the scene context of each image. Specifically, we propose, TAG, a text-aware visual question-answer generation architecture that learns to produce meaningful, and accurate QA samples using a multimodal transformer. The architecture exploits underexplored scene text information and enhances scene understanding of Text-VQA models by combining the generated QA pairs with the initial training data. Extensive experimental results on two well-known Text-VQA benchmarks (TextVQA and ST-VQA) demonstrate that our proposed TAG effectively enlarges the training data that helps improve the Text-VQA performance without extra labeling effort. Moreover, our model outperforms state-of-the-art approaches that are pre-trained with extra large-scale data. Code is available at https://github.com/HenryJunW/TAG.
A Study of Modeling Rising Intonation in Cantonese Neural Speech Synthesis

In human speech, the attitude of a speaker cannot be fully expressed only by the textual content. It has to come along with the intonation. Declarative questions are commonly used in daily Cantonese conversations, and they are usually uttered with rising intonation. Vanilla neural text-to-speech (TTS) systems are not capable of synthesizing rising intonation for these sentences due to the loss of semantic information. Though it has become more common to complement the systems with extra language models, their performance in modeling rising intonation is not well studied. In this paper, we propose to complement the Cantonese TTS model with a BERT-based statement/question classifier. We design different training strategies and compare their performance. We conduct our experiments on a Cantonese corpus named CanTTS. Empirical results show that the separate training approach obtains the best generalization performance and feasibility.
Intermittent chaos in Hamiltonian dynamical systems

Diese Dissertation beschaftigt sich mit der statistischen Charakterisierung von chaotischen Trajektorien in Hamiltonschen dynamischen Systemen mit gemischtem Phasenraum. Chaotische Trajektorien verweilen in der Nahe regularer Gebiete und fuhren zu einem intermittierendem Verhalten. Dies wird quantifiziert durch das Potenzgesetz P(T)~T^{a-1} in der Statistik der Verweilzeiten T und bestimmt z. B. die Transporteigenschaften und den Zerfall der Korrelation. Fur Systeme mit Ein-Parameter-Familien von marginal-instabilen periodischen Bahnen wird der Exponent a = 2 analytisch berechnet und gilt fur chaotische Billards mit parallelen Wanden, Kreis-Billards (z.B. ringformige und Pilz-Billiards) und stuckweise lineare Abbildungen. Im Allgemeinen hat zusatzliches weises Rauschen zwei Auswirkungen auf P(T): mittlere T werden wahrscheinlicher (a=0.5), da die Trajektorien in die regularen Gebiete eintreten, wahrend P(T) fur grosere T exponentiell zerfallt. In einem Modell von N gekoppelten zweidimensionalen Abbildungen wird ein zusatzliches asymptotisches Potenzgesetz beobachtet, aufgrund der Stickiness (eng. Klebrigkeit) von hochdimensionalen KAM-tori. Dieser Exponent wachst mit N. Dies bietet eine neue Erklarung fur das Auftreten von starkem Chaos in hochdimensionalen Hamiltonschen Systemen. Zusammengenommen stutzen alle Ergebnisse dieser Arbeit eine neuartige Interpretation der chaotischen Hamiltonschen Dynamik, in der die Stickiness eine zentrale Rolle spielt.
Differentially Private Vertical Federated Clustering

In many applications, multiple parties have private data regarding the same set of users but on disjoint sets of attributes, and a server wants to leverage the data to train a model. To enable model learning while protecting the privacy of the data subjects, we need vertical federated learning (VFL) techniques, where the data parties share only information for training the model, instead of the private data. However, it is challenging to ensure that the shared information maintains privacy while learning accurate models. To the best of our knowledge, the algorithm proposed in this paper is the first practical solution for differentially private vertical federated k -means clustering, where the server can obtain a set of global centers with a provable differential privacy guarantee. Our algorithm assumes an untrusted central server that aggregates differentially private local centers and membership encodings from local data parties. It builds a weighted grid as the synopsis of the global dataset based on the received information. Final centers are generated by running any k -means algorithm on the weighted grid. Our approach for grid weight estimation uses a novel, light-weight, and differentially private set intersection cardinality estimation algorithm based on the Flajolet-Martin sketch. To improve the estimation accuracy in the setting with more than two data parties, we further propose a refined version of the weights estimation algorithm and a parameter tuning strategy to reduce the final k -means loss to be close to that in the central private setting. We provide theoretical utility analysis and experimental evaluation results for the cluster centers computed by our algorithm and show that our approach performs better both theoretically and empirically than the two baselines based on existing techniques.
Development of mathematical models for quantitative OCT: A review

We review mathematical models describing how Optical Coherence Tomography works. Hereby, we focus on models based on Maxwell's equations and their simplifications. We highlight especially the effects of different modeling assumptions for the incident illumination, the medium, the light propagation, and the measurement setup and illustrate the qualitatively differing behavior in numerical simulations of the OCT data and compare them with real data from OCT measurements.
r-c on A ug 2022 Superconducting diode in a ﬁeld-free superconductor/magnetic-dot nanostructure

A strong superconducting diode eﬀect (SDE) is revealed in a thin superconducting ﬁlm periodically nanostructured with magnetic dots. The SDE is caused by the current-activated dissipation mitigated by vortex-antivortex pairs (VAPs), which periodically nucleate under the dots, move and annihilate in the superconductor - eventually driving the system to the high-resistive state. Invers-ing the polarity of the applied current destimulates the nucleation of VAPs, the system remains superconducting up to far larger currents, leading to the pronounced diodic response. Our dissipative Ginzburg-Landau simulations detail the involved processes, and provide reliable geometric and parametric ranges for the experimental realization of such a non-volatile superconducting diode, which operates in absence of any applied magnetic ﬁeld while being ﬂuxonic by design.
Two-Stream Transformer Architecture for Long Video Understanding

Pure vision transformer architectures are highly effective for short video classification and action recognition tasks. However, due to the quadratic complexity of self attention and lack of inductive bias, transformers are resource intensive and suffer from data inefficiencies. Long form video understanding tasks amplify data and memory efficiency problems in transformers making current approaches unfeasible to implement on data or memory restricted domains. This paper introduces an efficient Spatio-Temporal Attention Network (STAN) which uses a two-stream transformer architecture to model dependencies between static image features and temporal contextual features. Our proposed approach can classify videos up to two minutes in length on a single GPU, is data efficient, and achieves SOTA performance on several long video understanding tasks.
Eccentricity estimation for five binary black hole mergers with higher-order gravitational wave modes

The detection of orbital eccentricity for a binary black hole system via gravitational waves is a key signature to distinguish between the possible binary origins. The identification of eccentricity has been difficult so far due to the limited availability of eccentric gravitational waveforms over the full range of black hole masses and eccentricities. Here we evaluate the eccentricity of five black hole mergers detected by the LIGO and Virgo observatories for the first time using the TEOBResumSGeneral model. This model accounts for the full eccentricity range possible and incorporates higher-order gravitational wave modes critical to model emission from highly eccentric orbits. The binaries have been selected due to previous hints of eccentricity or due to their unusual mass and spin. While other studies found marginal evidence for eccentricity for some of these events, our analyses do not favor the incorporation of eccentricity compared to the quasi-circular case. While lacking the eccentric evidence of other analyses, we find our analyses marginally shifts the posterior in multiple parameters for several events when allowing eccentricity to be non-zero.