Miracle-dz/UniFAR-FTUs huggingface dataset:
Discovery of Double-ring Structure in the Supernova Remnant N103B: Evidence for Bipolar Winds from a Type Ia Supernova Progenitor

The geometric structure of supernova remnants (SNR) provides a clue to unveiling the pre-explosion evolution of their progenitors. Here we present an X-ray study of N103B (0509–68.7), a Type Ia SNR in the Large Magellanic Cloud, that is known to be interacting with dense circumstellar matter (CSM). Applying our novel method for feature extraction to deep Chandra observations, we have successfully resolved the CSM, Fe-rich ejecta, and intermediate-mass element (IME) ejecta components, and revealed each of their spatial distributions. Remarkably, the IME ejecta component exhibits a double-ring structure, implying that the SNR expands into an hourglass-shape cavity and thus forms bipolar bubbles of the ejecta. This interpretation is supported by more quantitative spectroscopy that reveals a clear bimodality in the distribution of the ionization state of the IME ejecta. These observational results can be naturally explained if the progenitor binary system had formed a dense CSM torus on the orbital plane prior to the explosion, providing further evidence that the SNR N103B originates from a single-degenerate progenitor.
HOPPY: An Open-source Kit for Education with Dynamic Legged Robots

This paper introduces HOPPY, an open-source, low-cost, robust, and modular kit for robotics education. The robot dynamically hops around a rotating gantry with a fixed base. The kit is intended to lower the entry barrier for studying dynamic robots and legged locomotion with real systems. It bridges the theoretical content of fundamental robotic courses with real dynamic robots by facilitating and guiding the software and hardware integration. This paper describes the topics which can be studied using the kit, lists its components, discusses preferred practices for implementation, presents results from experiments with the simulator and the real system, and suggests further improvements. A simple heuristic-based controller is described to achieve velocities up to 1.7m/s, navigate small objects, and mitigate external disturbances when the robot is aided by a counterweight. HOPPY was utilized as the subject of a semester-long project for the Robot Dynamics and Control course at the University of Illinois at Urbana-Champaign. The positive feedback from the students and instructors about the hands-on activities during the course motivates us to share this kit and continue improving it in the future.
Contents, Contexts, and Basics of Contextuality

This is a non-technical introduction into theory of contextuality. More precisely, it presents the basics of a theory of contextuality called Contextuality-by-Default (CbD). One of the main tenets of CbD is that the identity of a random variable is determined not only by its content (that which is measured or responded to) but also by contexts, systematically recorded conditions under which the variable is observed; and the variables in different contexts possess no joint distributions. I explain why this principle has no paradoxical consequences, and why it does not support the holistic"everything depends on everything else"view. Contextuality is defined as the difference between two differences: (1) the difference between content-sharing random variables when taken in isolation, and (2) the difference between the same random variables when taken within their contexts. Contextuality thus defined is a special form of context-dependence rather than a synonym for the latter. The theory applies to any empirical situation describable in terms of random variables. Deterministic situations are trivially noncontextual in CbD, but some of them can be described by systems of epistemic random variables, in which random variability is replaced with epistemic uncertainty. Mathematically, such systems are treated as if they were ordinary systems of random variables.
Adaptive posterior convergence in sparse high dimensional clipped generalized linear models

We develop a framework to study posterior contraction rates in sparse high dimensional generalized linear models (GLM). We introduce a new family of GLMs, denoted by clipped GLM, which subsumes many standard GLMs and makes minor modification of the rest. With a sparsity inducing prior on the regression coefficients, we delineate sufficient conditions on true data generating density that leads to minimax optimal rates of posterior contraction of the coefficients in `1 norm. Our key contribution is to develop sufficient conditions commensurate with the geometry of the clipped GLM family, propose prior distributions which do not require any knowledge of the true parameters and avoid any assumption on the growth rate of the true coefficient vector.
Cloth manipulation planning on basis of mesh representations with incomplete domain knowledge and voxel-to-mesh estimation

Cloth manipulation is common in both housework and manufacturing. However, robotic cloth manipulation remains challenging, especially for less controlled and open-goal settings. We consider the problem of open-goal planning for robotic cloth manipulation, with focus on the roles of cloth representation and epistemic uncertainty. Core of our system is a neural network trained as a forward model of cloth behaviour under manipulation, with planning performed through backpropagation. We introduce a neural network-based routine for estimating mesh representations from voxel input, and perform planning in mesh format internally. We address the problem of planning with incomplete domain knowledge by introducing an explicit epistemic uncertainty penalty, using prediction divergence between two instances of the forward model network as a proxy of epistemic uncertainty. This allows us to avoid plans with high epistemic uncertainty during planning. Finally, we introduce logic for handling restriction of grasp points to a discrete set of candidates, in order to accommodate graspability constraints imposed by robotic hardware. We evaluate the system’s mesh estimation, prediction, and planning ability on simulated cloth for sequences of one to three manipulations. Comparative experiments confirm that planning on basis of estimated meshes improves accuracy compared to voxel-based planning, and that epistemic uncertainty avoidance improves performance under conditions of incomplete domain knowledge. Planning time cost is a few seconds. We additionally present qualitative results on robot hardware. Our results indicate that representation format and epistemic uncertainty are important factors to consider for open-goal cloth manipulation planning.
Learning in Markets: Greed Leads to Chaos but Following the Price is Right

We study learning dynamics in distributed production economies such as blockchain mining, peer-to-peer file sharing and crowdsourcing. These economies can be modelled as multi-product Cournot competitions or all-pay auctions (Tullock contests) when individual firms have market power, or as Fisher markets with quasi-linear utilities when every firm has negligible influence on market outcomes. In the former case, we provide a formal proof that Gradient Ascent (GA) can be Li-Yorke chaotic for a step size as small as Θ(1/n), where n is the number of firms. In stark contrast, for the Fisher market case, we derive a Proportional Response (PR) protocol that converges to market equilibrium. The positive results on the convergence of the PR dynamics are obtained in full generality, in the sense that they hold for Fisher markets with any quasi-linear utility functions. Conversely, the chaos results for the GA dynamics are established even in the simplest possible setting of two firms and one good, and they hold for a wide range of price functions with different demand elasticities. Our findings suggest that by considering multi-agent interactions from a market rather than a game-theoretic perspective, we can formally derive natural learning protocols which are stable and converge to effective outcomes rather than being chaotic.
Velocity, Velocity Gradient and Vorticity Statistics of Grid Turbulence Obtained Using Digital Cross-correlation PIV

Grid turbulence is investigated using cross-correlation digital Particle Image Velocimetry (PIV) over a range of Taylor Reynolds Number (Re{\lambda}) from 5 to 44. Instantaneous velocity is measured directly and vorticity and velocity gradients are obtained indirectly. Measurements are taken at various downstream locations from the generating grid. Probability distribution functions (PDFs) are calculated for the fluctuating component of the velocity, the spatial velocity gradients and vorticity. The PDF of the velocity fluctuations has a Gaussian distribution while velocity gradients and vorticity are found to have non-Gaussian PDF distributions. The structure of the flow is investigated by calculating spatial autocorrelations for all measured and derived data. The spatial velocity autocorrelations differ from previous experimental measurements of grid turbulence, most of which have been determined from single-point measurements. This difference is believed to be due to differences in the way in which the measurements are made, and particularly to differences in the spatial size of the experimental domain.
Valid sequential inference on probability forecast performance

Probability forecasts for binary events play a central role in many applications. Their quality is commonly assessed with proper scoring rules, which assign forecasts a numerical score such that a correct forecast achieves a minimal expected score. In this paper, we construct e-values for testing the statistical significance of score differences of competing forecasts in sequential settings. E-values have been proposed as an alternative to p-values for hypothesis testing, and they can easily be transformed into conservative p-values by taking the multiplicative inverse. The e-values proposed in this article are valid in finite samples without any assumptions on the data generating processes. They also allow optional stopping, so a forecast user may decide to interrupt evaluation taking into account the available data at any time and still draw statistically valid inference, which is generally not true for classical p-value based tests. In a case study on postprocessing of precipitation forecasts, state-of-the-art forecasts dominance tests and e-values lead to the same conclusions.
Robust Maml: Prioritization Task Buffer with Adaptive Learning Process for Model-Agnostic Meta-Learning

Model agnostic meta-learning (MAML) is a popular state-of-the-art meta-learning algorithm that provides good weight initialization of a model given a variety of learning tasks. The model initialized by provided weight can be fine-tuned to an unseen task despite only using a small amount of samples and within a few adaptation steps. MAML is simple and versatile but requires costly learning rate tuning and careful design of the task distribution which affects its scalability and generalization. This paper proposes a more robust MAML based on an adaptive learning scheme and a prioritization task buffer (PTB) referred to as Robust MAML (RMAML) for improving scalability of training process and alleviating the problem of distribution mismatch. RMAML uses gradient-based hyper-parameter optimization to automatically find the optimal learning rate and uses the PTB to gradually adjust training task distribution toward testing task distribution over the course of training. Experimental results on meta reinforcement learning environments demonstrate a substantial performance gain as well as being less sensitive to hyper-parameter choice and robust to distribution mismatch.
Multiwavelength Observations of Fast Radio Bursts

The origin and phenomenology of the Fast Radio Burst (FRB) remains unknown despite more than a decade of efforts. Though several models have been proposed to explain the observed data, none is able to explain alone the variety of events so far recorded. The leading models consider magnetars as potential FRB sources. The recent detection of FRBs from the galactic magnetar SGR J1935+2154 seems to support them. Still, emission duration and energetic budget challenge all these models. Like for other classes of objects initially detected in a single band, it appeared clear that any solution to the FRB enigma could only come from a coordinated observational and theoretical effort in an as wide as possible energy band. In particular, the detection and localisation of optical/NIR or/and high-energy counterparts seemed an unavoidable starting point that could shed light on the FRB physics. Multiwavelength (MWL) search campaigns were conducted for several FRBs, in particular for repeaters. Here we summarize the observational and theoretical results and the perspectives in view of the several new sources accurately localised that will likely be identified by various radio facilities worldwide. We conclude that more dedicated MWL campaigns sensitive to the millisecond–minute timescale transients are needed to address the various aspects involved in the identification of FRB counterparts. Dedicated instrumentation could be one of the key points in this respect. In the optical/NIR band, fast photometry looks to be the only viable strategy. Additionally, small/medium size radiotelescopes co-pointing higher energies telescopes look a very interesting and cheap complementary observational strategy.
Instantaneous SED coding over a DMC

In this paper, we propose a novel code for transmitting a sequence of $n$ message bits in real time over a discrete-memoryless channel (DMC) with noiseless feedback, where the message bits stream into the encoder one by one at random time instants. Similar to existing posterior matching schemes with block encoding, the encoder in our work takes advantage of the channel feedback to form channel inputs that contain the information the decoder does not yet have, and that are distributed close to the capacity-achieving input distribution, but dissimilar to the existing posterior matching schemes, the encoder performs instantaneous encoding - it immediately weaves the new message bits into a continuing transmission. A posterior matching scheme by Naghshvar et al. partitions the source messages into groups so that the group posteriors have a small-enough difference (SED) to the capacity-achieving distribution, and transmits the group index that contains the actual message. Our code adopts the SED rule to apply to the evolving message alphabet that contains all the possible variable-length strings that the source could have emitted up to that time. Our instantaneous SED code achieves better delay-reliability tradeoffs than existing feedback codes over 2-input DMCs: we establish this dominance both by simulations and via an analysis comparing the performance of the instantaneous SED code to Burnashev's reliability function. Due to the message alphabet that grows exponentially with time $t$, the complexity of the instantaneous SED code is double-exponential in $t$. To overcome this complexity barrier to practical implementation, we design a low-complexity code for binary symmetric channels that we name the instantaneous type set SED code. It groups the message strings into sets we call type sets and tracks their prior and posterior probabilities jointly, resulting in the reduction of complexity from double-exponential to $O(t^{4})$. Simulation results show that the gap in performance between the instantaneous SED code and the instantaneous type-set SED code is negligible.
Decision Making of Connected Automated Vehicles at an Unsignalized Roundabout Considering Personalized Driving Behaviours

To improve the safety and efficiency of the intelligent transportation system, particularly in complex urban scenarios, in this paper a game theoretic decision-making framework is designed for connected automated vehicles (CAVs) at unsignalized roundabouts considering their personalized driving behaviours. Within the decision-making framework, a motion prediction module is designed and optimized using model predictive control (MPC) to enhance the effectiveness and accuracy of the decision-making algorithm. Besides, the payoff function of decision making is defined with the consideration of vehicle safety, ride comfort and travel efficiency. Additionally, the constraints of the decision-making problem are constructed. Based on the established decision-making model, Stackelberg game and grand coalition game approaches are adopted to address the decision making of CAVs at an unsignalized roundabout. Three testing cases considering personalized driving behaviours are carried out to verify the performance of the developed decision-making algorithms. The testing results show that the proposed game theoretic decision-making framework is able to make safe and reasonable decisions for CAVs in the complex urban scenarios, validating its feasibility and effectiveness. Stackelberg game approach shows its advantage in guaranteeing personalized driving objectives of individuals, while the grand coalition game approach is advantageous regarding the efficiency improvement of the transportation system.
S2AND: A Benchmark and Evaluation System for Author Name Disambiguation

Author Name Disambiguation (AND) is the task of resolving which author mentions in a bibliographic database refer to the same real-world person, and is a critical ingredient of digital library applications such as search and citation analysis. While many AND algorithms have been proposed, comparing them is difficult because they often employ distinct features and are evaluated on different datasets. In response to this challenge, we present S2AND, a unified benchmark dataset for AND on scholarly papers, as well as an open-source reference model implementation. Our dataset harmonizes eight disparate AND datasets into a uniform format, with a single rich feature set drawn from the Semantic Scholar (S2) database. Our evaluation suite for S2AND reports performance split by facets like publication year and number of papers, allowing researchers to track both global performance and measures of fairness across facet values. Our experiments show that because previous datasets tend to cover idiosyncratic and biased slices of the literature, algorithms trained to perform well on one on them may generalize poorly to others. By contrast, we show how training on a union of datasets in S2AND results in more robust models that perform well even on datasets unseen in training. The resulting AND model also substantially improves over the production algorithm in S2, reducing error by over 50% in terms of B3 F1. We release our unified dataset, model code, trained models, and evaluation suite to the research community.11https://github.com/allenai/S2AND/
Representative volume elements for matrix-inclusion composites - a computational study on periodizing the ensemble

We investigate volume-element sampling strategies for the stochastic homogenization of particle-reinforced composites and show, via computational experiments, that an improper treatment of particles intersecting the boundary of the computational cell may affect the accuracy of the computed effective properties. Motivated by recent results on a superior convergence rate of the systematic error for periodized ensembles compared to taking snapshots of ensembles, we conduct computational experiments for microstructures with circular, spherical and cylindrical inclusions and monitor the systematic errors in the effective thermal conductivity for snapshots of ensembles compared to working with microstructures sampled from periodized ensembles. We observe that the standard deviation of the apparent properties computed on microstructures sampled from the periodized ensembles decays at the scaling expected from the central limit theorem. In contrast, the standard deviation for the snapshot ensembles shows an inferior decay rate at high filler content. The latter effect is caused by additional long-range correlations that necessarily appear in particle-reinforced composites at high, industrially relevant, volume fractions. Periodized ensembles, however, appear to be less affected by these correlations. Our findings provide guidelines for working with digital volume images of material microstructures and the design of representative volume elements for computational homogenization.
On an irreversible investment problem with two-factor uncertainty

We consider a real options model for the optimal irreversible investment problem of a profit-maximizing company. The company has the opportunity to invest in a production plant capable of producing two products, of which the prices follow two independent geometric Brownian motions. After paying a constant sunk investment cost, the company sells the products on the market and thus receives a continuous stochastic revenue flow. This investment problem is set as a two-dimensional optimal stopping problem. We find that the optimal investment decision is triggered by a convex curve, which we characterize as the unique continuous solution to a nonlinear integral equation. Furthermore, we provide analytical and numerical comparative statics results of the dependency of the project's value and investment decision with respect to the model's parameters.
Photochemical Formation of 1,5-Diketones from Dibenzoylmethane and Some Quinones.

Irradiation (300 nm UV light) of dibenzoylmethane and 1,4-naphthoquinone in dichloromethane gave 1,5-dike-tone as the major product, along with β-hydroxyketone as the minor product. Anthraquinone and anthrone also added photochemically to dibenzoylmethane to give 1,5-diketones as the major products. In contrast, tetrahalo-1,4-benzoquinones added to dibenzoylmethane to give two types of 1,5-diketones via oxetane and cyclobutane intermediates. Comparison of the potential energy values of the photoproducts reveals that the 1,5-diketones are more stable than the corresponding oxetanes or cyclobutanes due to the ring-strain of the bicyclic compounds.
iWarded: A System for Benchmarking Datalog+/- Reasoning (technical report)

Recent years have seen increasing popularity of logic-based reasoning systems, with research and industrial interest as well as many flourishing applications in the area of Knowledge Graphs. Despite that, one can observe a substantial lack of specific tools able to generate nontrivial reasoning settings and benchmark scenarios. As a consequence, evaluating, analysing and comparing reasoning systems is a complex task, especially when they embody sophisticated optimizations and execution techniques that leverage the theoretical underpinnings of the adopted logic fragment. In this paper, we aim at filling this gap by introducing iWarded, a system that can generate very large, complex, realistic reasoning settings to be used for the benchmarking of logic-based reasoning systems adopting Datalog+/-, a family of extensions of Datalog that has seen a resurgence in the last few years. In particular, iWarded generates reasoning settings for Warded Datalog+/-, a language with a very good tradeoff between computational complexity and expressive power. In the paper, we present the iWarded system and a set of novel theoretical results adopted to generate effective scenarios. As Datalog-based languages are of general interest and see increasing adoption, we believe that iWarded is a step forward in the empirical evaluation of current and future systems.