Miracle-dz/UniFAR-FTUs huggingface dataset:
Masses of double neutron star mergers

Aims. The mass discrepancy between the observed population of double neutron star binaries by radio pulsar observations and gravitational-wave observation requires an explanation. Methods. Binary population synthesis calculations are performed, and their results are compared with the radio and the gravitational-wave observations simultaneously. Results. Simulations of binary evolution are used to link different observations of double neutron star binaries with each other. The progenitor of GW190425 is investigated in more detail. A distribution of masses and merger times of the possible progenitors is presented. Conclusions. A mass discrepancy between the radio pulsars in the Milky Way with another neutron star companion and the inferred masses from gravitational-wave observations of those kind of merging systems is naturally found in binary evolution.
Generating Automatic Curricula via Self-Supervised Active Domain Randomization

Goal-directed Reinforcement Learning (RL) traditionally considers an agent interacting with an environment, prescribing a real-valued reward to an agent proportional to the completion of some goal. Goal-directed RL has seen large gains in sample efficiency, due to the ease of reusing or generating new experience by proposing goals. One approach,self-play, allows an agent to "play" against itself by alternatively setting and accomplishing goals, creating a learned curriculum through which an agent can learn to accomplish progressively more difficult goals. However, self-play has been limited to goal curriculum learning or learning progressively harder goals within a single environment. Recent work on robotic agents has shown that varying the environment during training, for example with domain randomization, leads to more robust transfer. As a result, we extend the self-play framework to jointly learn a goal and environment curriculum, leading to an approach that learns the most fruitful domain randomization strategy with self-play. Our method, Self-Supervised Active Domain Randomization(SS-ADR), generates a coupled goal-task curriculum, where agents learn through progressively more difficult tasks and environment variations. By encouraging the agent to try tasks that are just outside of its current capabilities, SS-ADR builds a domain randomization curriculum that enables state-of-the-art results on varioussim2real transfer tasks. Our results show that a curriculum of co-evolving the environment difficulty together with the difficulty of goals set in each environment provides practical benefits in the goal-directed tasks tested.
A Mystery in Chamaeleon: Serendipitous Discovery of a Galactic Symbiotic Nova

We present the serendipitous discovery of a low optical-luminosity nova occurring in a D-type symbiotic binary star system in the Milky Way. We lay out the extensive archival data alongside new follow-up observations related to the stellar object CN Cha in the constellation of Chamaeleon. The object had long period (250 days), high amplitude (3 mag) optical variability in its recent past, preceding an increase in optical brightness by 8 magnitudes and a persistence at this brightness for about 3 yr, followed by a period of 1.4 mag yr−1 dimming. The object’s current optical luminosity seems to be dominated by Hα emission, which also exhibits blueshifted absorption (a P-Cygni-like profile). After consideration of a number of theories to explain these myriad observations, we determine that CN Cha is most likely a symbiotic (an evolved-star–white-dwarf binary) system that has undergone a long-duration, low optical brightness, nova, placing it squarely in the class of so-called “slow novae,” of which there are only a few known examples. The duration of the optical plateau in CN Cha would make it the shortest timescale plateau of any known slow symbiotic novae.
The Capacity and Optimal Signaling for Gaussian MIMO Channels Under Interference Constraints

Gaussian MIMO channel under the joint transmit (Tx) and interference power constraints (TPC and IPC) is studied. A closed-form solution for the optimal Tx covariance matrix in the general case is obtained using the KKT-based approach, up to dual variables. A number of more explicit closed-form solutions are given with optimal dual variables, including full-rank and rank-1 (beamforming) cases as well as the case of identical eigenvectors (typical for massive MIMO settings), which differer significantly from the standard water-filling solution. A “whitening” filter is shown to be an important part of optimal precoding under interference constraints. Sufficient and necessary conditions for each constraint to be redundant are given. Capacity scaling with the SNR is shown to be determined by a natural linear-algebraic structure of sub-spaces induced by channel matrices of multiple users. A number of unusual properties of optimal Tx covariance matrix under the joint TPC and IPC are pointed out and a bound on its rank is established. An interplay between the TPC and IPC is studied, including the transition from power-limited to interference-limited regimes as the Tx power increases. While closed-from solutions for optimal dual variables are given in some special cases, an iterative bisection algorithm (IBA) is proposed to find optimal dual variables in the general case and its convergence is proved for some special cases. Numerical experiments illustrate its efficient performance. Bounds for the optimal dual variables are given.
Piracy Resistant Watermarks for Deep Neural Networks.

As companies continue to invest heavily in larger, more accurate and more robust deep learning models, they are exploring approaches to monetize their models while protecting their intellectual property. Model licensing is promising, but requires a robust tool for owners to claim ownership of models, i.e. a watermark. Unfortunately, current designs have not been able to address piracy attacks, where third parties falsely claim model ownership by embedding their own "pirate watermarks" into an already-watermarked model. We observe that resistance to piracy attacks is fundamentally at odds with the current use of incremental training to embed watermarks into models. In this work, we propose null embedding, a new way to build piracy-resistant watermarks into DNNs that can only take place at a model's initial training. A null embedding takes a bit string (watermark value) as input, and builds strong dependencies between the model's normal classification accuracy and the watermark. As a result, attackers cannot remove an embedded watermark via tuning or incremental training, and cannot add new pirate watermarks to already watermarked models. We empirically show that our proposed watermarks achieve piracy resistance and other watermark properties, over a wide range of tasks and models. Finally, we explore a number of adaptive counter-measures, and show our watermark remains robust against a variety of model modifications, including model fine-tuning, compression, and existing methods to detect/remove backdoors. Our watermarked models are also amenable to transfer learning without losing their watermark properties.
Common envelope evolution of massive stars

Abstract The discovery via gravitational waves of binary black hole systems with total masses greater than 60Mʘ has raised interesting questions for stellar evolution theory. Among the most promising formation channels for these systems is one involving a common envelope binary containing a low metallicity, core helium burning star with mass ⁓30 – 40Mʘ and a black hole with mass ⁓30 – 40Mʘ. For this channel to be viable, the common envelope binary must eject more than half the giant star’s mass and reduce its orbital separation by as much as a factor of 80. We discuss issues faced in numerically simulating the common envelope evolution of such systems and present a 3D AMR simulation of the dynamical inspiral of a low-metallicity red supergiant with a massive black hole companion.
The complexity of knapsack problems in wreath products

We prove new complexity results for computational problems in certain wreath products of groups and (as an application) for free solvable group. For a finitely generated group we study the so-called power word problem (does a given expression $u_1^{k_1} \ldots u_d^{k_d}$, where $u_1, \ldots, u_d$ are words over the group generators and $k_1, \ldots, k_d$ are binary encoded integers, evaluate to the group identity? ) and knapsack problem (does a given equation $u_1^{x_1} \ldots u_d^{x_d} = v$, where $u_1, \ldots, u_d,v$ are words over the group generators and $x_1,\ldots,x_d$ are variables, has a solution in the natural numbers). We prove that the power word problem for wreath products of the form $G \wr \mathbb{Z}$ with $G$ nilpotent and iterated wreath products of free abelian groups belongs to $\mathsf{TC}^0$. As an application of the latter, the power word problem for free solvable groups is in $\mathsf{TC}^0$. On the other hand we show that for wreath products $G \wr \mathbb{Z}$, where $G$ is a so called uniformly strongly efficiently non-solvable group (which form a large subclass of non-solvable groups), the power word problem is $\mathsf{coNP}$-hard. For the knapsack problem we show $\mathsf{NP}$-completeness for iterated wreath products of free abelian groups and hence free solvable groups. Moreover, the knapsack problem for every wreath product $G \wr \mathbb{Z}$, where $G$ is uniformly efficiently non-solvable, is $\Sigma^2_p$-hard.
Progressive Identification of True Labels for Partial-Label Learning

Partial-label learning (PLL) is a typical weakly supervised learning problem, where each training instance is equipped with a set of candidate labels among which only one is the true label. Most existing methods elaborately designed learning objectives as constrained optimizations that must be solved in specific manners, making their computational complexity a bottleneck for scaling up to big data. The goal of this paper is to propose a novel framework of PLL with flexibility on the model and optimization algorithm. More specifically, we propose a novel estimator of the classification risk, theoretically analyze the classifier-consistency, and establish an estimation error bound. Then we propose a progressive identification algorithm for approximately minimizing the proposed risk estimator, where the update of the model and identification of true labels are conducted in a seamless manner. The resulting algorithm is model-independent and loss-independent, and compatible with stochastic optimization. Thorough experiments demonstrate it sets the new state of the art.
Indirect Adversarial Attacks via Poisoning Neighbors for Graph Convolutional Networks

Graph convolutional neural networks, which learn aggregations over neighbor nodes, have achieved great performance in node classification tasks. However, recent studies reported that such graph convolutional node classifier can be deceived by adversarial perturbations on graphs. Abusing graph convolutions, a node’s classification result can be influenced by poisoning its neighbors. Given an attributed graph and a node classifier, how can we evaluate robustness against such indirect adversarial attacks? Can we generate strong adversarial perturbations which are effective on not only one-hop neighbors, but more far from the target? In this paper, we demonstrate that the node classifier can be deceived with high-confidence by poisoning just a single node even two-hops or more far from the target. Towards achieving the attack, we propose a new approach which searches smaller perturbations on just a single node far from the target. In our experiments, our proposed method shows 99% attack success rate within two-hops from the target in two datasets. We also demonstrate that $m -$layer graph convolutional neural networks have chance to be deceived by our indirect attack within m-hop neighbors. The proposed attack can be used as a benchmark in future defense attempts to develop graph convolutional neural networks with having adversary robustness.
Discovery of technetium- and niobium-rich S stars: The case for bitrinsic stars

Context. S stars are late-type giants with overabundances of s-process elements. They come in two flavors depending on the presence or lack of presence of technetium (Tc), an element without stable isotopes. Intrinsic S stars are Tc-rich and genuine asymptotic giant branch (AGB) stars, while extrinsic S stars owe their s-process over abundances to the pollution from a former AGB companion, which is now a white dwarf (WD). In addition to Tc, another distinctive feature between intrinsic and extrinsic S stars is the overabundance of niobium (Nb) in the latter class. Indeed, since the mass transfer occurred long ago, 93Zr had time to decay into the only stable isotope of Nb, 93Nb, causing its overabundance. Aims. We discuss the case of the S stars BD+79°156 and o1 Ori, whose specificity lies in sharing the distinctive features of both intrinsic and extrinsic S stars, namely the presence of Tc along with a Nb overabundance. Methods. We used high-resolution HERMES optical spectra, MARCS model atmospheres of S stars, Gaia DR2 parallaxes, and STAREVOL evolutionary tracks to determine the stellar parameters and chemical abundances of the two S stars, and to locate them in the Hertzsprung-Russell (HR) diagram. Results. BD+79°156 is the first clear case of a bitrinsic star, that is, a doubly s-process-enriched object, first through mass transfer in a binary system and then through internal nucleosynthesis that is responsible for the Tc-enrichment in BD+79°156, which must, therefore, have reached the AGB phase of its evolution. This hybrid nature of the s-process pattern in BD+79°156 is supported by its binary nature and its location in the HR diagram that is just beyond the onset of the third dredge-up on the AGB. The Tc-rich, binary S-star o1 Ori with a WD companion was another long-standing candidate for a similar hybrid s-process enrichment. However, the marginal overabundance of Nb derived in o1 Ori does not allow one to trace evidence of large amounts of pollution coming from the AGB progenitor of its current WD companion unambiguously. As a side product, the current study offers a new way of detecting binary AGB stars with WD companions by identifying their Tc-rich nature along with a Nb overabundance.
Optimizing Federated Queries Based on the Physical Design of a Data Lake

The optimization of query execution plans is known to be crucial for reducing the query execution time. In particular, query optimization has been studied thoroughly for relational databases over the past decades. Recently, the Resource Description Framework (RDF) became popular for publishing data on the Web. As a consequence, federations composed of different data models like RDF and relational databases evolved. One type of these federations are Semantic Data Lakes where every data source is kept in its original data model and semantically annotated with ontologies or controlled vocabularies. However, state-of-the-art query engines for federated query processing over Semantic Data Lakes often rely on optimization techniques tailored for RDF. In this paper, we present query optimization techniques guided by heuristics that take the physical design of a Data Lake into account. The heuristics are implemented on top of Ontario, a SPARQL query engine for Semantic Data Lakes. Using source-specific heuristics, the query engine is able to generate more efficient query execution plans by exploiting the knowledge about indexes and normalization in relational databases. We show that heuristics which take the physical design of the Data Lake into account are able to speed up query processing.
Analysis and Design of Vehicle Platooning Operations on Mixed-Traffic Highways

Platooning of connected and autonomous vehicles (CAVs) has a significant potential for throughput improvement. However, the interaction between CAVs and non-CAVs may limit the practically attainable improvement due to platooning. To better understand and address this limitation, we introduce a new fluid model of mixed-autonomy traffic flow and use this model to analyze and design platoon coordination strategies. We propose a tandem-link fluid model that considers randomly arriving platoons sharing highway capacity with non-CAVs. We derive verifiable conditions for stability of the fluid model by analyzing an underlying M/D/1 queuing process and establishing a Foster–Lyapunov drift condition for the fluid model. These stability conditions enable a quantitative analysis of highway throughput under various scenarios. The model is useful for designing platoon coordination strategies that maximize throughput and minimize delay. Such coordination strategies are provably optimal in the fluid model and are practically relevant. We also validate our results using standard macroscopic (cell transmission model) and microscopic (simulation for urban mobility) simulation models.
Physics-informed neural networks for solving nonlinear diffusivity and Biot’s equations

This paper presents the potential of applying physics-informed neural networks for solving nonlinear multiphysics problems, which are essential to many fields such as biomedical engineering, earthquake prediction, and underground energy harvesting. Specifically, we investigate how to extend the methodology of physics-informed neural networks to solve both the forward and inverse problems in relation to the nonlinear diffusivity and Biot’s equations. We explore the accuracy of the physics-informed neural networks with different training example sizes and choices of hyperparameters. The impacts of the stochastic variations between various training realizations are also investigated. In the inverse case, we also study the effects of noisy measurements. Furthermore, we address the challenge of selecting the hyperparameters of the inverse model and illustrate how this challenge is linked to the hyperparameters selection performed for the forward one.
Neural Clustering Processes

Probabilistic clustering models (or equivalently, mixture models) are basic building blocks in countless statistical models and involve latent random variables over discrete spaces. For these models, posterior inference methods can be inaccurate and/or very slow. In this work we introduce deep network architectures trained with labeled samples from any generative model of clustered datasets. At test time, the networks generate approximate posterior samples of cluster labels for any new dataset of arbitrary size. We develop two complementary approaches to this task, requiring either O(N) or O(K) network forward passes per dataset, where N is the dataset size and K the number of clusters. Unlike previous approaches, our methods sample the labels of all the data points from a well-defined posterior, and can learn nonparametric Bayesian posteriors since they do not limit the number of mixture components. Moreover, the algorithms are easily parallelized with a GPU. As a scientific application, we present a novel approach to neural spike sorting for high-density multielectrode arrays.
Afghanistan: the decimation of a people.

The demographic impact of the war in Afghanistan is analyzed using data from surveys conducted among the refugees in Pakistan in 1987. The data indicate that by the end of 1987 approximately nine percent of the population had been killed during the war totalling between 1 and 1.5 million persons. Consideration is given to trends in mortality over time the demographic characteristics of those killed emigration and geographical differences in war-related mortality. (ANNOTATION)
EFEITOS DA REPUTAÇÃO CORPORATIVA NO CUSTO DE CAPITAL PRÓPRIO EM EMPRESAS BRASILEIRAS LISTADAS

A reputacao corporativa e uma representacao da avaliacao dos usuarios a respeito das acoes da empresa, as quais podem aumentar o capital reputacional e reduzir os riscos inerentes a atividade. Assim sendo, essa pesquisa tem por objetivo verificar os efeitos contributivos da reputacao corporativa na reducao do custo de capital proprio das empresas brasileiras listadas. Como a reputacao corporativa e um conjunto de percepcoes de diferentes stakeholders , utilizou-se: i) o Indice de Sustentabilidade Empresarial (ISE) para captar percepcoes de investidores, agentes do meio ambiente, auditores independentes e organizacoes de finalidade publica, ii) o ranking da revista “As Melhores da Dinheiro” para captar percepcoes de analistas de mercado e iii) o Indice de Social Disclosure (ISD), elaborado por Goncalves et al. (2014), para captar percepcoes dos investidores e usuarios externos. Quanto ao custo de capital proprio, foi utilizado o modelo de crescimento de Gordon. Para concretizar o objetivo, foram analisadas 56 empresas presentes na carteira teorica do Ibovespa nos anos de 2008 a 2012. Os resultados evidenciam que as metricas de reputacao corporativa, tais como o ISE e o ISD contribuiram negativamente com o custo de capital, ao passo que o ranking da revista “As Melhores da Dinheiro” demonstrou ser positiva a contribuicao para com o custo de capital. Conclui-se, portanto, que a sinalizacao por parte das empresas por meio de metricas de reputacao corporativa, tais como evidenciacao social e listagem no ISE, contribui positivamente no capital reputacional da organizacao e, por consequencia, na reducao do risco e no custo de capital proprio.
Questioning the ethics of John Bohannon’s hoaxes and stings in the context of science publishing

Address for Correspondence: Jaime A. Teixeira da Silva, email: jaimetex[at]yahoo.com, P. O. Box 7, Miki-cho post office, Ikenobe 3011-2, Kagawa-ken, 761-0799 Article received on the 1st December, 2015. Article accepted on the 21st February, 2016. Conflict of Interest: The authors declare no conflicts of interest. Jaime A. Teixeira da Silva and Aceil Al-Khatib 2 1 Retired researcher, Japan 2 Jordan University of Science and Technology, Jordan
EMERGING MARkET SPREADS AT ThE TURN Of ThE CENTURy: A ROLLER COASTER SPREAdS dE MERCAdOS EMERGENTES EN El CAMBIO dE SIGlO:

This paper examines the empirical behavior of monthly secondary spreads from eighteen emerging market economies located in Asia, East Europe and Latin America from October 1997 to September 2002, a particularly turbulent period. A succession of events affected these economies such as the Asian crisis, the Russian default, the Brazilian devaluation, the Ecuadorian default, the Turkish crisis and the Argentine default. Our empirical estimations allow us to construct taxonomy of these crises. First, the Russian default and the Turkish crisis correspond to episodes of global reduction of portfolio flows to emerging sovereign debt markets. Second, the Brazilian devaluation was fundamentally an abatement of portfolio flows to Latin America and East Europe (except for Russia). Third, the Asian crisis and the Ecuadorian default were consistent with a rebalancing of portfolios in emerging markets. Fourth, although the Argentine crisis shares some similarities with the former crises, it is unique in the sense that it was fully anticipated long before it happened. Finally, in light of these results, policy-makers in emerging markets should be keenly aware of the possibility that their country might be hit by a crisis so structural reforms should also include policies that help to protect the country from these unruly episodes.
An Analysis of Regional Economic Growth in the

In this paper we examine more closely some of the forces that underlie economic growth at the county level. In an effort to describe a much more comprehensive regional economic growth model, we address a variety of different growth hypotheses by introducing a large number of growth related variables. When formulating our hypotheses and specifying our growth model we make liberal use of GIS mapping software to "paint" a picture of where growth spots exist and why. Our empirical estimation indicates amenities, state and local tax burdens, population, amount of agricultural activity, and demographics have important economic growth impacts.