Miracle-dz/UniFAR-FTUs huggingface dataset:
The Prompt Emission of GRB 130518A and the Study of Its Outflow through Hybrid Jet Models

The nature of the prompt emission mechanism in gamma-ray bursts (GRBs) remains uncertain to date. This question is also tied to the composition of the jet: thermal, Poynting flux dominated or hybrid with both types of components. In this work, we aim to study these questions in the context of GRB 130518A. By analysing its prompt emission data from the Fermi Gamma-ray Burst Monitor (GBM) and Large Area Telescope (LAT), we find that the time-integrated spectrum has a nonthermal component along with a subdominant blackbody component. We use these results to study the properties of the jet in various scenarios through the frameworks developed by Hascoët et al. and Gao & Zhang. Both frameworks exclude the pure fireball model for this GRB. In all other cases, the initial magnetic fraction is greater than the thermal fraction. For small launching radii of the jet, the favored nonthermal emission process is internal shocks. Magnetic reconnection seems likely only for very large initial radii.
Phenomenological aspects of CP violation

We present a pedagogical review of the phenomenology of CP violation, with emphasis on B decays. Main topics include the phenomenology of neutral meson systems, CP violation in the Standard Model of electroweak interactions, and B decays. We stress the importance of the reciprocal basis, sign conventions, rephasing invariance, general definitions of the CP transformation and the spurious phases they bring about, CP violation as originating from the clash of two contributions, the rho-eta plane, the four phases of a generalized CKM matrix, and the impact of discrete ambiguities. Specific B decays are included in order to illustrate some general techniques used in extracting information from B physics experiments. We include a series of simple exercises. The style is informal.
Quasistatic dynamical systems

We introduce the notion of a quasistatic dynamical system, which generalizes that of an ordinary dynamical system. Quasistatic dynamical systems are inspired by the namesake processes in thermodynamics, which are idealized processes where the observed system transforms (infinitesimally) slowly due to external influence, tracing out a continuous path of thermodynamic equilibria over an (infinitely) long time span. Time evolution of states under a quasistatic dynamical system is entirely deterministic, but choosing the initial state randomly renders the process a stochastic one. In the prototypical setting where the time evolution is specified by strongly chaotic maps on the circle, we obtain a description of the statistical behavior as a stochastic diffusion process, under surprisingly mild conditions on the initial distribution, by solving a well-posed martingale problem. We also consider various admissible ways of centering the process, with the curious conclusion that the ‘obvious’ centering suggested by the initial distribution sometimes fails to yield the expected diffusion.
Oﬀ-Policy Correction for Actor-Critic Methods without Importance Sampling

Compared to on-policy policy gradient techniques, oﬀ-policy model-free deep reinforcement learning (RL) that uses previously gathered data can improve sampling eﬃciency. However, oﬀ-policy learning becomes challenging when the discrepancy between the distributions of the policy of interest and the policies that collected the data increases. Although the well-studied importance sampling and oﬀ-policy policy gradient techniques were proposed to compensate for this discrepancy, they usually require a collection of long trajectories that increases the computational complexity and induce additional problems such as vanishing/exploding gradients or discarding many useful experiences. Moreover, their generalization to continuous action domains is strictly limited as they require action probabilities, which is unsuitable for deterministic policies. To overcome these limitations, we introduce a novel policy similarity measure to mitigate the eﬀects of such discrepancy. Our method oﬀers an adequate single-step oﬀ-policy correction without any probability estimates, and theoretical results show that it can achieve a contraction mapping with a ﬁxed unique point, which allows “safe” oﬀ-policy learning. An extensive set of empirical results indicate that our algorithm substantially improves the state-of-the-art and attains higher returns in fewer steps than the competing methods by eﬃciently scheduling the learning rate in Q-learning and policy optimization.
Policy-Guided Lazy Search with Feedback for Task and Motion Planning

PDDLStream solvers have recently emerged as viable solutions for Task and Motion Planning (TAMP) problems, extending PDDL to problems with continuous action spaces. Prior work has shown how PDDLStream problems can be reduced to a sequence of PDDL planning problems, which can then be solved using off-the-shelf planners. However, this approach can suffer from long runtimes. In this paper we propose LAZY, a solver for PDDLStream problems that maintains a single integrated search over action skeletons, which gets progressively more geometrically informed, as samples of possible motions are lazily drawn during motion planning. We explore how learned models of goal-directed policies and current motion sampling data can be incorporated in LAZY to adaptively guide the task planner. We show that this leads to significant speed-ups in the search for a feasible solution evaluated over unseen test environments of varying numbers of objects, goals, and initial conditions. We evaluate our TAMP approach by comparing to existing solvers for PDDLStream problems on a range of simulated 7DoF rearrangement/manipulation problems. Code can be found at https://rvl.cs. toronto.edu/learning-based-tamp.
Stellar age determination in the Mass-Luminosity plane

The ages of stars have historically relied on isochrone fitting of standardised grids of models. While these stellar models have provided key constraints on observational samples of massive stars, they inherit many systematic uncertainties, mainly in the internal mixing mechanisms applied throughout the grid, fundamentally undermining the isochrone method. In this work, we utilize the M-L plane of Higgins & Vink as a method of determining stellar age, with mixing-corrected models applying a calibrated core overshooting αov and rotation rate to fit the observational data. We provide multiple test-beds to showcase our new method, while also providing comparisons to the commonly-used isochrone method, highlighting the dominant systematic errors. We reproduce the evolution of individual O stars, and analyse the wider sample of O and B supergiants from the VLT-FLAMES Tarantula Survey, providing dedicated models with estimates for αov, Ω/Ωcrit, and ultimately stellar ages. The M-L plane highlights a large discrepancy in the spectroscopic masses of the O supergiant sample. Furthermore the M-L plane also demonstrates that the evolutionary masses of the B supergiant sample are inappropriate. Finally, we utilize detached eclipsing binaries, VFTS 642 and VFTS 500, and present their ages resulting from their precise dynamical masses, offering an opportunity to constrain their interior mixing. For the near-TAMS system, VFTS 500, we find that both components require a large amount of core overshooting (αov ≃ 0.5), implying an extended main-sequence width. We hence infer that the vast majority of B supergiants are still burning hydrogen in their cores.
Toward improved loading, cooling, and trapping of molecules in magneto-optical traps

Recent experiments have demonstrated direct cooling and trapping of diatomic and triatomic molecules in magneto-optical traps (MOTs). However, even the best molecular MOTs to date still have density 10−5 times smaller than in typical atomic MOTs. The main limiting factors are: (i) inefficiencies in slowing molecules to velocities low enough to be captured by the MOT, (ii) low MOT capture velocities, and (iii) limits on density within the MOT resulting from sub-Doppler heating (Devlin and Tarbutt 2018 Phys. Rev. A 90 063415). All of these are consequences of the need to drive ‘Type-II’ optical cycling transitions, where dark states appear in Zeeman sublevels, in order to avoid rotational branching. We present simulations demonstrating ways to mitigate each of these limitations. This should pave the way toward loading molecules into conservative traps with sufficiently high density and number to evaporatively cool them to quantum degeneracy.
The IceCube Neutrino Observatory - Contributions to ICRC 2017 Part II: Properties of the Atmospheric and Astrophysical Neutrino Flux

Papers on the properties of the atmospheric and astrophysical neutrino flux submitted to the 35th International Cosmic Ray Conference (ICRC 2017, Busan, South Korea) by the IceCube Collaboration Contents: 1 - Search for Astrophysical Tau Neutrinos in Six Years of High-Energy Starting Events in IceCube 2 - Multi-flavour PeV neutrino search with IceCube 3 - High Energy Astrophysical Neutrino Flux Measurement Using Neutrinoinduced Cascades Observed in 4 Years of IceCube Data 4 - A Measurement of the Diffuse Astrophysical Muon Neutrino Flux Using Eight Years of IceCube Data 5 - Characterizing the Flux of Atmospheric Neutrinos with IceCube-DeepCore 6 - Measurement of High Energy Neutrino – Nucleon Cross Section and Astrophysical Neutrino Flux Anisotropy Study of Cascade Channel with IceCube 7 - Observation of Astrophysical Neutrinos in Six Years of IceCube Data 8 - All-flavor Multi-Channel Analysis of the Astrophysical Neutrino Spectrum with IceCube 9 - Differential limit on an EHE neutrino flux component in the presence of astrophysical background from nine years of IceCube data 10 - Improving Future High-Energy Tau Neutrino Searches in IceCube 11 - Search for Astrophysical Tau Neutrinos with the IceCube Waveforms
Conditionally Risk-Averse Contextual Bandits

Contextual bandits with average-case statistical guarantees are inadequate in risk-averse situations because they might trade off degraded worst-case behaviour for better average performance. Designing a risk-averse contextual bandit is challenging because exploration is necessary but risk-aversion is sensitive to the entire distribution of rewards; nonetheless we exhibit the first risk-averse contextual bandit algorithm with an online regret guarantee. We conduct experiments from diverse scenarios where worst-case outcomes should be avoided, from dynamic pricing, inventory management, and self-tuning software; including a production exascale data processing system.
The possibility of application of B-spline family wavelets in diagnostic signal processing

Nowadays, wavelets are widely used in many applications, e.g. signal processing. The application of wavelet transform allows to obtain considerably more information about technical state of machine (especially for non-stationary signals) than traditional methods, e.g. Fourier transform. The aim of the paper is to analyze the goodness of B-spline family wavelets in diagnostic signal processing. Three types of B-spline wavelets were investigated. Their applicability was verified using degree of scalogram density on synthetic and operational signals. Results of the analysis of B-spline family wavelets show that proposed approach gives more accurate results in comparison with other chosen wavelets and can be applied in industrial diagnostics.
ConnectedUNets++: Mass Segmentation from Whole Mammographic Images

Deep learning has made a breakthrough in medical image segmentation in recent years due to its ability to extract high-level features without the need for prior knowledge. In this context, U-Net is one of the most advanced medical image segmentation models, with promising results in mammography. Despite its excellent overall performance in segmenting multimodal medical images, the traditional U-Net structure appears to be inadequate in various ways. There are certain U-Net design modifications, such as MultiResUNet, Connected-UNets, and AU-Net, that have improved overall performance in areas where the conventional U-Net architecture appears to be deficient. Following the success of UNet and its variants, we have presented two enhanced versions of the Connected-UNets architecture: ConnectedUNets+ and ConnectedUNets++. In ConnectedUNets+, we have replaced the simple skip connections of Connected-UNets architecture with residual skip connections, while in ConnectedUNets++, we have modified the encoder-decoder structure along with employing residual skip connections. We have evaluated our proposed architectures on two publicly available datasets, the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and INbreast.
Useful Confidence Measures: Beyond the Max Score

An important component in deploying machine learning (ML) in safety-critic applications is having a reliable measure of confidence in the ML model's predictions. For a classifier $f$ producing a probability vector $f(x)$ over the candidate classes, the confidence is typically taken to be $\max_i f(x)_i$. This approach is potentially limited, as it disregards the rest of the probability vector. In this work, we derive several confidence measures that depend on information beyond the maximum score, such as margin-based and entropy-based measures, and empirically evaluate their usefulness, focusing on NLP tasks with distribution shifts and Transformer-based models. We show that when models are evaluated on the out-of-distribution data ``out of the box'', using only the maximum score to inform the confidence measure is highly suboptimal. In the post-processing regime (where the scores of $f$ can be improved using additional in-distribution held-out data), this remains true, albeit less significant. Overall, our results suggest that entropy-based confidence is a surprisingly useful measure.
Asymmetrical tidal tails of open star clusters: stars crossing their cluster's prah challenge Newtonian gravitation

After their birth a signiﬁcant fraction of all stars pass through the tidal threshold (práh) of their cluster of origin into the classical tidal tails. The asymmetry between the number of stars in the leading and trailing tails tests gravitational theory. All ﬁve open clusters with tail data (Hyades, Praesepe, Coma Berenices, COIN-Gaia 13, NGC 752) have visibly more stars within 𝑑 cl ≈ 50 pc of their centre in their leading than their trailing tail. Using the Jerabkova-compact-convergent-point (CCP) method, the extended tails have been mapped out for four nearby 600 − 2000 Myr old open clusters to 𝑑 cl > 50 pc. These are on near-circular Galactocentric orbits, a formula for estimating the orbital eccentricity of an open cluster being derived. Applying the Phantom of Ramses code to this problem, in Newtonian gravitation the tails are near-symmetrical. In Milgromian dynamics (MOND) the asymmetry reaches the observed values for 50 < 𝑑 cl / pc < 200, being maximal near peri-galacticon, and can slightly invert near apo-galacticon, and the Küpper epicyclic overdensities are asymmetrically spaced. Clusters on circular orbits develop orbital eccentricity due to the asymmetrical spill-out, therewith spinning up opposite to their orbital angular momentum. This positive dynamical feedback suggests Milgromian open clusters to demise rapidly as their orbital eccentricity keeps increasing. Future work is necessary to better delineate the tidal tails around open clusters of diﬀerent ages and to develop a Milgromian direct 𝑛 -body code.
Faster Projection-Free Augmented Lagrangian Methods via Weak Proximal Oracle

This paper considers a convex composite optimization problem with affine constraints, which includes problems that take the form of minimizing a smooth convex objective function over the intersection of (simple) convex sets, or regularized with multiple (simple) functions. Motivated by high-dimensional applications in which exact projection/proximal computations are not tractable, we propose a \textit{projection-free} augmented Lagrangian-based method, in which primal updates are carried out using a \textit{weak proximal oracle} (WPO). In an earlier work, WPO was shown to be more powerful than the standard \textit{linear minimization oracle} (LMO) that underlies conditional gradient-based methods (aka Frank-Wolfe methods). Moreover, WPO is computationally tractable for many high-dimensional problems of interest, including those motivated by recovery of low-rank matrices and tensors, and optimization over polytopes which admit efficient LMOs. The main result of this paper shows that under a certain curvature assumption (which is weaker than strong convexity), our WPO-based algorithm achieves an ergodic rate of convergence of $O(1/T)$ for both the objective residual and feasibility gap. This result, to the best of our knowledge, improves upon the $O(1/\sqrt{T})$ rate for existing LMO-based projection-free methods for this class of problems. Empirical experiments on a low-rank and sparse covariance matrix estimation task and the Max Cut semidefinite relaxation demonstrate that of our method can outperform state-of-the-art LMO-based Lagrangian-based methods.
Full-disc 13CO(1–0) mapping across nearby galaxies of the EMPIRE survey and the CO-to-H2 conversion factor

Carbon monoxide (CO) provides crucial information about the molecular gas properties of galaxies. While $^{12}$CO has been targeted extensively, isotopologues such as $^{13}$CO have the advantage of being less optically thick and observations have recently become accessible across full galaxy discs. We present a comprehensive new dataset of $^{13}$CO(1-0) observations with the IRAM 30-m telescope of the full discs of 9 nearby spiral galaxies from the EMPIRE survey at a spatial resolution of $\sim$1.5kpc. $^{13}$CO(1-0) is mapped out to $0.7-1r_{25}$ and detected at high signal-to-noise throughout our maps. We analyse the $^{12}$CO(1-0)-to-$^{13}$CO(1-0) ratio ($\Re$) as a function of galactocentric radius and other parameters such as the $^{12}$CO(2-1)-to-$^{12}$CO(1-0) intensity ratio, the 70-to-160$\mu$m flux density ratio, the star-formation rate surface density, the star-formation efficiency, and the CO-to-H$_2$ conversion factor. We find that $\Re$ varies by a factor of 2 at most within and amongst galaxies, with a median value of 11 and larger variations in the galaxy centres than in the discs. We argue that optical depth effects, most likely due to changes in the mixture of diffuse/dense gas, are favored explanations for the observed $\Re$ variations, while abundance changes may also be at play. We calculate a spatially-resolved $^{13}$CO(1-0)-to-H$_2$ conversion factor and find an average value of $1.0\times10^{21}$ cm$^{-2}$ (this http URL^{-1}$ over our sample with a standard deviation of a factor of 2. We find that $^{13}$CO(1-0) does not appear to be a good predictor of the bulk molecular gas mass in normal galaxy discs due to the presence of a large diffuse phase, but it may be a better tracer of the mass than $^{12}$CO(1-0) in the galaxy centres where the fraction of dense gas is larger.
MEEV: Body Mesh Estimation On Egocentric Video

This technical report introduces our solution, MEEV, proposed to the EgoBody Challenge at ECCV 2022. Captured from head-mounted devices, the dataset consists of human body shape and motion of interacting people. The EgoBody dataset has challenges such as occluded body or blurry image. In order to overcome the challenges, MEEV is designed to exploit multiscale features for rich spatial information. Besides, to overcome the limited size of dataset, the model is pre-trained with the dataset aggregated 2D and 3D pose estimation datasets. Achieving 82.30 for MPJPE and 92.93 for MPVPE, MEEV has won the EgoBody Challenge at ECCV 2022, which shows the ef-fectiveness of the proposed method. The code is available at https: //github.com/clovaai/meev
Mappings of generalized finite distortion and continuity

We study continuity properties of Sobolev mappings , , that satisfy the following generalized finite distortion inequality for almost every . Here and are measurable functions. Note that when , we recover the class of mappings of finite distortion, which are always continuous. The continuity of arbitrary solutions, however, turns out to be an intricate question. We fully solve the continuity problem in the case of bounded distortion , where a sharp condition for continuity is that is in the Zygmund space for some . We also show that one can slightly relax the boundedness assumption on to an exponential class with , and still obtain continuous solutions when with . On the other hand, for all with , we construct a discontinuous solution with and , including an example with and .
No-Box Attacks on 3D Point Cloud Classification

Adversarial attacks pose serious challenges for deep neural network (DNN)-based analysis of various input signals. In the case of 3D point clouds, methods have been developed to identify points that play a key role in network decision, and these become crucial in generating existing adversarial attacks. For example, a saliency map approach is a popular method for identifying adversarial drop points, whose removal would significantly impact the network decision. Generally, methods for identifying adversarial points rely on the access to the DNN model itself to determine which points are critically important for the model's decision. This paper aims to provide a novel viewpoint on this problem, where adversarial points can be predicted without access to the target DNN model, which is referred to as a ``no-box'' attack. To this end, we define 14 point cloud features and use multiple linear regression to examine whether these features can be used for adversarial point prediction, and which combination of features is best suited for this purpose. Experiments show that a suitable combination of features is able to predict adversarial points of four different networks -- PointNet, PointNet++, DGCNN, and PointConv -- significantly better than a random guess and comparable to white-box attacks. Additionally, we show that no-box attack is transferable to unseen models. The results also provide further insight into DNNs for point cloud classification, by showing which features play key roles in their decision-making process.
Pointly-Supervised Panoptic Segmentation

In this paper, we propose a new approach to applying point-level annotations for weakly-supervised panoptic segmentation. Instead of the dense pixel-level labels used by fully supervised methods, point-level labels only provide a single point for each target as supervision, significantly reducing the annotation burden. We formulate the problem in an end-to-end framework by simultaneously generating panoptic pseudo-masks from point-level labels and learning from them. To tackle the core challenge, i.e., panoptic pseudo-mask generation, we propose a principled approach to parsing pixels by minimizing pixel-to-point traversing costs, which model semantic similarity, low-level texture cues, and high-level manifold knowledge to discriminate panoptic targets. We conduct experiments on the Pascal VOC and the MS COCO datasets to demonstrate the approach's effectiveness and show state-of-the-art performance in the weakly-supervised panoptic segmentation problem. Codes are available at https://github.com/BraveGroup/PSPS.git.
Estimating Boltzmann Averages for Protein Structural Quantities Using Sequential Monte Carlo

Sequential Monte Carlo (SMC) methods are widely used to draw samples from intractable target distributions. Particle degeneracy can hinder the use of SMC when the target distribution is highly constrained or multimodal. As a motivating application, we consider the problem of sampling protein structures from the Boltzmann distribution. This paper proposes a general SMC method that propagates multiple descendants for each particle, followed by resampling to maintain the desired number of particles. Simulation studies demonstrate the efficacy of the method for tackling the protein sampling problem. As a real data example, we use our method to estimate the number of atomic contacts for a key segment of the SARS-CoV-2 viral spike protein.