Miracle-dz/UniFAR-FTUs huggingface dataset:
Large rainbow matchings in edge-colored graphs

There has been much research on the topic of ﬁnding a large rainbow matching (with no two edges having the same color) in a properly edge-colored graph, where a proper edge coloring is a coloring of the edge set such that no same-colored edges are incident. Recently, Gao, Ramadurai, Wanless, and Wormald proved that in every proper edge coloring of a graph with q colors where each color appears at least q + o ( q ) times, there is always a rainbow matching using every color. We strengthen this result by simultaneously relaxing two conditions: (i) we lift the condition on the number of colors and allow any ﬁnite number of colors and instead, put a weaker condition requiring the maximum degree of the graph to be at most q , and (ii) we also relax the proper coloring condition and require that the graph induced by each of the colors have bounded degree. This strengthening resolves a natural question inspired by the remarks made by Gao, Ramadurai, Wanless, and Wormald. As an application of this result, we show that for every proper edge coloring of a graph with 2 q + o ( q ) colors where each color appears at least q times, there is always a rainbow matching of size q . This can be seen as an asymptotic version of a conjecture of Bar´at, Gy´arf´as, and S´ark¨ozy restricted on simple graphs. We also provide a construction showing that having q + 1 colors is not enough, disproving a conjecture of Aharoni and Berger. As a by-product of our techniques, we obtain a new asymptotic version of the Brualdi–Ryser–Stein Conjecture, which is one of the central open questions in combinatorics.
Central limit theorem for the integrated squared error of the empirical second-order product density and goodness-of-fit tests for stationary point processes

Abstract Spatial point processes are mathematical models for irregular or random point patterns in the d-dimensional space, where usually d = 2 or d = 3 in applications. The second-order product density and its isotropic analogue, the pair correlation function, are important tools for analyzing stationary point processes. In the present work we derive central limit theorems for the integrated squared error (ISE) of the empirical second-order product density and for the ISE of the empirical pair correlation function when the observation window expands unboundedly. The proof techniques are based on higher-order cumulant measures and the Brillinger-mixing property of the underlying point processes. The obtained Gaussian limits are used to construct asymptotic goodness-of-fit tests for checking point process hypotheses even in the non-Poissonian case.
Label-free segmentation from cardiac ultrasound using self-supervised learning

Segmentation and measurement of cardiac chambers is critical in cardiac ultrasound but is laborious and poorly reproducible. Neural networks can assist, but supervised approaches require the same laborious manual annotations. We built a pipeline for self-supervised (no manual labels) segmentation combining computer vision, clinical domain knowledge, and deep learning. We trained on 450 echocardiograms (93,000 images) and tested on 8,393 echocardiograms (4,476,266 images; mean 61 years, 51% female), using the resulting segmentations to calculate biometrics. We also tested against external images from an additional 10,030 patients with available manual tracings of the left ventricle. r2 between clinically measured and pipeline-predicted measurements were similar to reported inter-clinician variation and comparable to supervised learning across several different measurements (r2 0.56-0.84). Average accuracy for detecting abnormal chamber size and function was 0.85 (range 0.71-0.97) compared to clinical measurements. A subset of test echocardiograms (n=553) had corresponding cardiac MRIs, where MRI is the gold standard. Correlation between pipeline and MRI measurements was similar to that between clinical echocardiogram and MRI. Finally, the pipeline accurately segments the left ventricle with an average Dice score of 0.89 (95% CI [0.89]) in the external, manually labeled dataset. Our results demonstrate a manual-label free, clinically valid, and highly scalable method for segmentation from ultrasound, a noisy but globally important imaging modality.
X-ray emission from high-redshift miniquasars: self-regulating the population of massive black holes through global warming

Observations of high-redshift quasars at z � 6 imply that supermassive black holes (SMBHs) with masses M � 10 9 M⊙ were in place less than 1 Gyr after the Big Bang. If these SMBHs assembled from “seed” BHs left behind by the first stars, then they must have accreted gas at close to the Eddington limit during a large fraction ( � 50%) of the time. A generic problem with this scenario, however, is that the mass density in M � 10 6 M⊙ SMBHs at z � 6 already exceeds the locally observed SMBH mass density by several orders of magnitude; in order to avoid this overproduction, BH seed formation and growth must become significantly less efficientin less massive protogalaxies through some form of feedback, while proceeding unabated in the most massive galaxies that formed first. Using Monte-Carlo realizations of the merger and growth history of BHs, we show that X-rays from the earliest accreting BHs can provide such a feedback mechanism, on a global scale. Our calculations paint a self-consistent picture of black-hole-made climate change, in which the first miniquasars—among them the ancestors of the z � 6 quasar SMBHs—globally warm the intergalactic medium and suppress the formation and growth of subsequent generations of BHs. We present two specific models with global miniquasar feedback that provide excellent agreement with recent estimates of the z = 6 SMBH mass function. For each of these models, we estimate the rate of BH mergers at z > 6 that could be detected by the proposed gravitational-wave observatory eLISA/NGO.
Gravitational cubic interactions for a simple mixed-symmetry gauge field in AdS and flat backgrounds

Cubic interactions between the simplest mixed-symmetry gauge field and gravity are constructed in anti-de Sitter (AdS) and flat backgrounds. Non-Abelian cubic interactions are obtained in AdS following various perturbative methods including the Fradkin–Vasiliev construction, with and without Stückelberg fields. The action that features the maximal number of Stückelberg fields can be considered in the flat limit without loss of physical degrees of freedom. The resulting interactions in flat space are compared with a classification of vertices obtained via the antifield cohomological perturbative method. It is shown that the gauge algebra becomes Abelian in the flat limit, in contrast to what happens for totally symmetric gauge fields in AdS.
CLIP-Fields: Weakly Supervised Semantic Fields for Robotic Memory

We propose CLIP-Fields, an implicit scene model that can be used for a variety of tasks, such as segmentation, instance identification, semantic search over space, and view localization. CLIP-Fields learns a mapping from spatial locations to semantic embedding vectors. Importantly, we show that this mapping can be trained with supervision coming only from web-image and web-text trained models such as CLIP, Detic, and Sentence-BERT; and thus uses no direct human supervision. When compared to baselines like Mask-RCNN, our method outperforms on few-shot instance identification or semantic segmentation on the HM3D dataset with only a fraction of the examples. Finally, we show that using CLIP-Fields as a scene memory, robots can perform semantic navigation in real-world environments. Our code and demonstration videos are available here: https://mahis.life/clip-fields
RoHNAS: A Neural Architecture Search Framework With Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule Networks

Neural Architecture Search (NAS) algorithms aim at finding efficient Deep Neural Network (DNN) architectures for a given application under given system constraints. DNNs are computationally-complex as well as vulnerable to adversarial attacks. In order to address multiple design objectives, we propose RoHNAS, a novel NAS framework that jointly optimizes for adversarial-robustness and hardware-efficiency of DNNs executed on specialized hardware accelerators. Besides the traditional convolutional DNNs, RoHNAS additionally accounts for complex types of DNNs such as Capsule Networks. For reducing the exploration time, RoHNAS analyzes and selects appropriate values of adversarial perturbation for each dataset to employ in the NAS flow. Extensive evaluations on multi - Graphics Processing Unit (GPU) - High Performance Computing (HPC) nodes provide a set of Pareto-optimal solutions, leveraging the tradeoff between the above-discussed design objectives. For example, a Pareto-optimal DNN for the CIFAR-10 dataset exhibits 86.07% accuracy, while having an energy of 38.63 mJ, a memory footprint of 11.85 MiB, and a latency of 4.47 ms.
HUBBLE PARAMETER MEASUREMENT CONSTRAINTS ON THE COSMOLOGICAL DECELERATION–ACCELERATION TRANSITION REDSHIFT

We compile a list of 28 independent measurements of the Hubble parameter between redshifts 0.07 ⩽ z ⩽ 2.3 and use this to place constraints on model parameters of constant and time-evolving dark energy cosmologies. These H(z) measurements by themselves require a currently accelerating cosmological expansion at about, or better than, 3σ confidence. The mean and standard deviation of the six best-fit model deceleration–acceleration transition redshifts (for the three cosmological models and two Hubble constant priors we consider) are zda = 0.74 ± 0.05, in good agreement with the recent Busca et al. determination of zda = 0.82 ± 0.08 based on 11 H(z) measurements between redshifts 0.2 ⩽ z ⩽ 2.3, almost entirely from baryon-acoustic-oscillation-like data.
Quasi-Taylor Samplers for Diffusion Generative Models based on Ideal Derivatives

Diffusion generative models have emerged as a new challenger to popular deep neural generative models such as GANs, but have the drawback that they often require a huge number of neural function evaluations (NFEs) during synthesis unless some sophisticated sampling strategies are employed. This paper proposes new efficient samplers based on the numerical schemes derived by the familiar Taylor expansion, which directly solves the ODE/SDE of interest. In general, it is not easy to compute the derivatives that are required in higher-order Taylor schemes, but in the case of diffusion models, this difficulty is alleviated by the trick that the authors call ``ideal derivative substitution,'' in which the higher-order derivatives are replaced by tractable ones. To derive ideal derivatives, the authors argue the ``single point approximation,'' in which the true score function is approximated by a conditional one, holds in many cases, and considered the derivatives of this approximation. Applying thus obtained new quasi-Taylor samplers to image generation tasks, the authors experimentally confirmed that the proposed samplers could synthesize plausible images in small number of NFEs, and that the performance was better or at the same level as DDIM and Runge-Kutta methods. The paper also argues the relevance of the proposed samplers to the existing ones mentioned above.
Generalization Analysis on Learning with a Concurrent Verifier

Machine learning technologies have been used in a wide range of practical systems. In practical situations, it is natural to expect the input-output pairs of a machine learning model to satisfy some requirements. However, it is difficult to obtain a model that satisfies requirements by just learning from examples. A simple solution is to add a module that checks whether the input-output pairs meet the requirements and then modifies the model's outputs. Such a module, which we call a {\em concurrent verifier} (CV), can give a certification, although how the generalizability of the machine learning model changes using a CV is unclear. This paper gives a generalization analysis of learning with a CV. We analyze how the learnability of a machine learning model changes with a CV and show a condition where we can obtain a guaranteed hypothesis using a verifier only in the inference time. We also show that typical error bounds based on Rademacher complexity will be no larger than that of the original model when using a CV in multi-class classification and structured prediction settings.
Like a bilingual baby: The advantage of visually grounding a bilingual language model

Unlike most neural language models, humans learn language in a rich, multi-sensory and, often, multi-lingual environment. Current language models typically fail to fully capture the complexities of multilingual language use. We train an LSTM language model on images and captions in English and Spanish from MS-COCO-ES. We find that the visual grounding improves the model's understanding of semantic similarity both within and across languages and improves perplexity. However, we find no significant advantage of visual grounding for abstract words. Our results provide additional evidence of the advantages of visually grounded language models and point to the need for more naturalistic language data from multilingual speakers and multilingual datasets with perceptual grounding.
Stellar cluster formation in a Milky Way-sized galaxy at z > 4 – I. The proto-globular cluster population and the imposter amongst us

The formation history of globular clusters (GCs) at redshift z > 4 remains an unsolved problem. In this work, we use the cosmological, N-body hydrodynamical ‘zoom-in’ simulation GigaEris to study the properties and formation environment of proto-GC candidates in the region surrounding the progenitor of a Milky Way-sized galaxy. The simulation employs a modern implementation of smoothed-particle hydrodynamics, including metal-line cooling and metal and thermal diffusion. We define proto-GC candidate systems as gravitationally bound stellar systems with baryonic mass fraction Fb ≥ 0.75 and stellar velocity dispersion σ⋆ < 20 km s−1. At z = 4.4, we identify nine systems that satisfy our criteria, all of which form between 10 and 30 kpc from the centre of the main host. Their baryonic masses are in the range 105–107 M⊙. By the end of the simulation, they still have a relatively low stellar mass (M⋆ ∼ 104–105 M⊙) and a metallicity (−1.8 ≲ [Fe/H] ≲ −0.8) similar to the blue Galactic GCs. All of the identified systems except one appear to be associated with gas filaments accreting onto the main galaxy in the circum-galactic region and formed at z = 5–4. The exception is the oldest object, which appears to be a stripped compact dwarf galaxy that has interacted with the main halo between z = 5.8 and z = 5.2 and has lost its entire dark matter content due to tidal mass loss.
A deep XMM-Newton survey of M 31

Aims. The largest Local Group spiral galaxy, M 31, has been completely imaged for the first time, obtaining a luminosity lower limit ~10 35 erg s -1 in the 0.2–4.5 keV band. Our XMM-Newton EPIC survey combines archival observations along the major axis, from June 2000 to July 2004, with observations taken between June 2006 and February 2008 that cover the remainder of the D 25 ellipse. The main goal of the paper is to study the X-ray source population of M 31. Methods. An X-ray catalogue of 1897 sources was created, with 914 detected for the first time. Source classification and identification were based on X-ray hardness ratios, spatial extent of the sources, and cross correlation with catalogues in the X-ray, optical, infrared, and radio wavelengths. We also analysed the long-term variability of the X-ray sources and this variability allows us to distinguish between X-ray binaries and active galactic nuclei (AGN). Furthermore, supernova remnant classifications of previous studies that did not use long-term variability as a classification criterion could be validated. Including previous Chandra and ROSAT observations in the long-term variability study allowed us to detect additional transient or at least highly variable sources, which are good candidate X-ray binaries. Results. Fourteen of the 30 supersoft source (SSS) candidates represent supersoft emission of optical novae. Many of the 25 supernova remnants (SNRs) and 31 SNR candidates lie within the 10 kpc dust ring and other star-forming regions in M 31. This connection between SNRs and star-forming regions implies that most of the remnants originate in type II supernovae. The brightest sources in X-rays in M 31 belong to the class of X-ray binaries (XRBs). Ten low-mass XRBs (LMXBs) and 26 LMXB candidates were identified based on their temporal variability. In addition, 36 LMXBs and 17 LMXB candidates were identified owing to correlations with globular clusters and globular cluster candidates. From optical and X-ray colour-colour diagrams, possible high-mass XRB (HMXB) candidates were selected. Two of these candidates have an X-ray spectrum as is expected for an HMXB containing a neutron star primary. Conclusions. While our survey has greatly improved our understanding of the X-ray source populations in M 31, at this point 65% of the sources can still only be classified as “hard” sources; i.e. it is not possible to decide whether these sources are X-ray binaries or Crab-like supernova remnants in M 31 or X-ray sources in the background. Deeper observations in X-ray and at other wavelengths would help classify these sources.
DYNAMICS OF A PROMINENCE-HORN STRUCTURE DURING ITS EVAPORATION IN THE SOLAR CORONA

The physical connections among and formation mechanisms of various components of the prominence-horn cavity system remain elusive. Here we present observations of such a system, focusing on a section of the prominence that rises and separates gradually from the main body. This forms a configuration sufficiently simple to yield clues regarding the above issues. It is characterized by embedding horns, oscillations, and a gradual disappearance of the separated material. The prominence-horn structure exhibits a large-amplitude longitudinal oscillation with a period of ∼150 minutes and an amplitude of ∼30 Mm along the trajectory defined by the concave horn structure. The horns also experience a simultaneous transverse oscillation with a much smaller amplitude (∼3 Mm) and a shorter period (∼10–15 minutes), likely representative of a global mode of the large-scale magnetic structure. The gradual disappearance of the structure indicates that the horn, an observational manifestation of the field-aligned transition region separating the cool and dense prominence from the hot and tenuous corona, is formed due to the heating and diluting process of the central prominence mass; most previous studies suggested that it is the opposite process, i.e., the cooling and condensation of coronal plasmas, that formed the horn. This study also demonstrates how the prominence transports magnetic flux to the upper corona, a process essential for the gradual build-up of pre-eruption magnetic energy.
Tight Error Bounds for Nonnegative Orthogonality Constraints and Exact Penalties

For the intersection of the Stiefel manifold and the set of nonnegative matrices in R n × r , we present global and local error bounds with easily computable residual functions and explicit coeﬃcients. Moreover, we show that the error bounds cannot be improved except for the coeﬃcients, which explains why two square-root terms are necessary in the bounds when 1 < r < n for the nonnegativity and orthogonality, respectively. The error bounds are applied to penalty methods for minimizing a Lipschitz continuous function with nonnegative orthogonality constraints. Under only the Lipschitz continuity of the objective function, we prove the exactness of penalty problems that penalize the nonnegativity constraint, or the orthogonality constraint, or both constraints. Our results cover both global and local minimizers.
COVID-19-related Nepali Tweets Classification in a Low Resource Setting

Billions of people across the globe have been using social media platforms in their local languages to voice their opinions about the various topics related to the COVID-19 pandemic. Several organizations, including the World Health Organization, have developed automated social media analysis tools that classify COVID-19-related tweets to various topics. However, these tools that help combat the pandemic are limited to very few languages, making several countries unable to take their benefit. While multi-lingual or low-resource language-specific tools are being developed, there is still a need to expand their coverage, such as for the Nepali language. In this paper, we identify the eight most common COVID-19 discussion topics among the Twitter community using the Nepali language, set up an online platform to automatically gather Nepali tweets containing the COVID-19-related keywords, classify the tweets into the eight topics, and visualize the results across the period in a web-based dashboard. We compare the performance of two state-of-the-art multi-lingual language models for Nepali tweet classification, one generic (mBERT) and the other Nepali language family-specific model (MuRIL). Our results show that the models’ relative performance depends on the data size, with MuRIL doing better for a larger dataset. The annotated data, models, and the web-based dashboard are open-sourced at https://github.com/naamiinepal/covid-tweet-classification.
Two-Photon Interference from a Quantum Emitter in Hexagonal Boron Nitride

Recently discovered quantum emitters in two-dimensional (2D) materials have opened new perspectives of integrated photonic devices for quantum information. Most of these applications require the emitted photons to be indistinguishable, which has remained elusive in 2D materials. Here, we investigate two-photon interference of a quantum emitter generated in hexagonal boron nitride (hBN) using an electron beam. We measure the correlations of zero-phonon-line photons in a Hong-Ou-Mandel (HOM) interferometer under non-resonant excitation. We find that the emitted photons exhibit a partial indistinguishability of $0.44 \pm 0.11$ in a 3 ns time window, which corresponds to a corrected value of $0.56 \pm 0.11$ after accounting for imperfect emitter purity. The dependence of the HOM visibility on the width of the post-selection time window allows us to estimate the dephasing time of the emitter to be $\sim 1.5$ ns, about half the limit set by spontaneous emission. A visibility above 90 % is under reach using Purcell effect with up-to-date 2D material photonics.
Sequential Ensembling for Semantic Segmentation

Ensemble approaches for deep-learning-based semantic segmentation remain insufficiently explored despite the proliferation of competitive benchmarks and downstream applications. In this work, we explore and benchmark the popular ensembling approach of combining predictions of multiple, independently-trained, state-of-the-art models at test time on popular datasets. Furthermore, we propose a novel method inspired by boosting to sequentially ensemble networks that significantly outperforms the naive ensemble baseline. Our approach trains a cascade of models conditioned on class probabilities predicted by the previous model as an additional input. A key benefit of this approach is that it allows for dynamic computation offloading, which helps deploy models on mobile devices. Our proposed novel ADaptive modulatiON (ADON) block allows spatial feature modulation at various layers using previous-stage probabilities. Our approach does not require sophisticated sample selection strategies during training and works with multiple neural architectures. We significantly improve over the naive ensemble baseline on challenging datasets such as Cityscapes, ADE-20K, COCO-Stuff, and PASCAL-Context and set a new state-of-the-art.