Miracle-dz/UniFAR-FTUs huggingface dataset:
The nuclear environment of the NLS1 Mrk 335: Obscuration of the X-ray line emission by a variable outflow

We present XMM–Newton, NuSTAR, Swift, and Hubble Space Telescope observations of the Narrow-line Seyfert 1 galaxy Mrk 335 in a protracted low state in 2018 and 2019. The X-ray flux is at the lowest level so far observed, and the extremely low continuum flux reveals a host of soft X-ray emission lines from photoionized gas. The simultaneous UV flux drop suggests that the variability is intrinsic to the source, and we confirm this with broad-band X-ray spectroscopy. The dominance of the soft X-ray lines at low energies and distant reflection at high energies, is therefore due to the respective emission regions being located far enough from the X-ray source that they have not yet seen the flux drop. Between the two XMM–Newton spectra, taken 6 months apart, the emission line ratio in the O vii triplet changes drastically. We attribute this change to a drop in the ionization of intervening warm absorption, which means that the absorber must cover a large fraction of the line emitting region, and extend much further from the black hole than previously assumed. The HST spectrum, taken in 2018, shows that new absorption features have appeared on the blue wings of C iii*, Ly α, N v, Si iv, and C iv, likely due to absorbing gas cooling in response to the low flux state.
On the interstellar Von Neumann micro self-reproducing probes

Abstract In this paper we consider efficiency of self-reproducing extraterrestrial Von-Neumann micro scale robots and analyse the observational characteristics. By examining the natural scenario of moving in the HII clouds, it has been found that the timescale of replication might be several years and even less – making the process of observation quite promising. We have shown that by encountering the interstellar protons the probes might be visible at least in the infrared energy band and the corresponding luminosities might reach enormous values.
On Structural Invariants in the Energy-Based Control of Infinite-Dimensional Port-Hamiltonian Systems with In-Domain Actuation

This contribution deals with energy-based in-domain control of systems governed by partial differential equations with spatial domain up to dimension two. We exploit a port-Hamiltonian system description based on an underlying jet-bundle formalism, where we restrict ourselves to systems with 2nd-order Hamiltonian. A certain power-conserving interconnection enables the application of a dynamic control law based on structural invariants. Furthermore, we use various examples such as beams and plates with in-domain actuation to demonstrate the capability of our approach.
ORES

Algorithmic systems---from rule-based bots to machine learning classifiers---have a long history of supporting the essential work of content moderation and other curation work in peer production projects. From counter-vandalism to task routing, basic machine prediction has allowed open knowledge projects like Wikipedia to scale to the largest encyclopedia in the world, while maintaining quality and consistency. However, conversations about how quality control should work and what role algorithms should play have generally been led by the expert engineers who have the skills and resources to develop and modify these complex algorithmic systems. In this paper, we describe ORES: an algorithmic scoring service that supports real-time scoring of wiki edits using multiple independent classifiers trained on different datasets. ORES decouples several activities that have typically all been performed by engineers: choosing or curating training data, building models to serve predictions, auditing predictions, and developing interfaces or automated agents that act on those predictions. This meta-algorithmic system was designed to open up socio-technical conversations about algorithms in Wikipedia to a broader set of participants. In this paper, we discuss the theoretical mechanisms of social change ORES enables and detail case studies in participatory machine learning around ORES from the 5 years since its deployment.
End-to-End Boundary Aware Networks for Medical Image Segmentation

Fully convolutional neural networks (CNNs) have proven to be effective at representing and classifying textural information, thus transforming image intensity into output class masks that achieve semantic image segmentation. In medical image analysis, however, expert manual segmentation often relies on the boundaries of anatomical structures of interest. We propose boundary aware CNNs for medical image segmentation. Our networks are designed to account for organ boundary information, both by providing a special network edge branch and edge-aware loss terms, and they are trainable end-to-end. We validate their effectiveness on the task of brain tumor segmentation using the BraTS 2018 dataset. Our experiments reveal that our approach yields more accurate segmentation results, which makes it promising for more extensive application to medical image segmentation.
Flag versions of quiver Grassmannians for Dynkin quivers have no odd cohomology over $\mathbb{Z}$

We prove that flag versions of quiver Grassmannians (also knows as Lusztig's fibers) for Dynkin quivers (types $A$, $D$, $E$) have no odd cohomology over $\mathbb{Z}$. Moreover, for types $A$ and $D$ we prove that these varieties have $\alpha$-partitions into affine spaces. We also show that to prove the same statement for type $E$, it is enough to check this for indecomposable representations. We also give a flag version of the result of Irelli-Esposito-Franzen-Reineke on rigid representations: we prove that flag versions of quiver Grassmannians for rigid representations have a diagonal decomposition. In particular, they have no odd cohomology over $\mathbb{Z}$.
Factorized MultiClass Boosting

In this paper, we introduce a new approach to multiclass classification problem. We decompose the problem into a series of regression tasks, that are solved with CART trees. The proposed method works significantly faster than state-of-the-art solutions while giving the same level of model quality. The algorithm is also robust to imbalanced datasets, allowing to reach high-quality results in significantly less time without class re-balancing.
Sunny and Dark Outside?! Improving Answer Consistency in VQA through Entailed Question Generation

While models for Visual Question Answering (VQA) have steadily improved over the years, interacting with one quickly reveals that these models lack consistency. For instance, if a model answers “red” to “What color is the balloon? ”, it might answer “no” if asked, “Is the balloon red? ”. These responses violate simple notions of entailment and raise questions about how effectively VQA models ground language. In this work, we introduce a dataset, ConVQA, and metrics that enable quantitative evaluation of consistency in VQA. For a given observable fact in an image (e.g. the balloon’s color), we generate a set of logically consistent question-answer (QA) pairs (e.g. Is the balloon red? ) and also collect a human-annotated set of common-sense based consistent QA pairs (e.g. Is the balloon the same color as tomato sauce? ). Further, we propose a consistency-improving data augmentation module, a Consistency Teacher Module (CTM). CTM automatically generates entailed (or similar-intent) questions for a source QA pair and fine-tunes the VQA model if the VQA’s answer to the entailed question is consistent with the source QA pair. We demonstrate that our CTM-based training improves the consistency of VQA models on the Con-VQA datasets and is a strong baseline for further research.
Crocoddyl: An Efficient and Versatile Framework for Multi-Contact Optimal Control

We introduce Crocoddyl (Contact RObot COntrol by Differential DYnamic Library), an open-source framework tailored for efficient multi-contact optimal control. Crocoddyl efficiently computes the state trajectory and the control policy for a given predefined sequence of contacts. Its efficiency is due to the use of sparse analytical derivatives, exploitation of the problem structure, and data sharing. It employs differential geometry to properly describe the state of any geometrical system, e.g. floating-base systems. Additionally, we propose a novel optimal control algorithm called Feasibility-driven Differential Dynamic Programming (FDDP). Our method does not add extra decision variables which often increases the computation time per iteration due to factorization. FDDP shows a greater globalization strategy compared to classical Differential Dynamic Programming (DDP) algorithms. Concretely, we propose two modifications to the classical DDP algorithm. First, the backward pass accepts infeasible state-control trajectories. Second, the rollout keeps the gaps open during the early "exploratory" iterations (as expected in multipleshooting methods with only equality constraints). We showcase the performance of our framework using different tasks. With our method, we can compute highly-dynamic maneuvers (e.g. jumping, front-flip) within few milliseconds.
Emergent dual topology in the three-dimensional Kane-Mele 
Pt2HgSe3

Recently, the very first large-gap Kane-Mele quantum spin Hall insulator was predicted to be monolayer jacutingaite (Pt$_2$HgSe$_3$), a naturally-occurring exfoliable mineral discovered in Brazil in 2008. The stacking of quantum spin Hall monolayers into a van-der-Waals layered crystal typically leads to a (0;001) weak topological phase, which does not protect the existence of surface states on the (001) surface. Unexpectedly, recent angle-resolved photoemission spectroscopy experiments revealed the presence of surface states dispersing over large areas of the 001-surface Brillouin zone of jacutingaite single crystals. The 001-surface states have been shown to be topologically protected by a mirror Chern number $C_M=-2$, associated with a nodal line gapped by spin-orbit interactions. Here, we extend the two-dimensional Kane-Mele model to bulk jacutingaite and unveil the microscopic origin of the gapped nodal line and the emerging crystalline topological order. By using maximally-localized Wannier functions, we identify a large non-trivial second nearest-layer hopping term that breaks the standard paradigm of weak topological insulators. Complemented by this term, the predictions of the Kane-Mele model are in remarkable agreement with recent experiments and first-principles simulations, providing an appealing conceptual framework also relevant for other layered materials made of stacked honeycomb lattices.
Spectral Non-Convex Optimization for Dimension Reduction with Hilbert-Schmidt Independence Criterion

The Hilbert Schmidt Independence Criterion (HSIC) is a kernel dependence measure that has applications in various aspects of machine learning. Conveniently, the objectives of different dimensionality reduction applications using HSIC often reduce to the same optimization problem. However, the nonconvexity of the objective function arising from non-linear kernels poses a serious challenge to optimization efficiency and limits the potential of HSIC-based formulations. As a result, only linear kernels have been computationally tractable in practice. This paper proposes a spectral-based optimization algorithm that extends beyond the linear kernel. The algorithm identifies a family of suitable kernels and provides the first and second-order local guarantees when a fixed point is reached. Furthermore, we propose a principled initialization strategy, thereby removing the need to repeat the algorithm at random initialization points. Compared to state-of-the-art optimization algorithms, our empirical results on real data show a run-time improvement by as much as a factor of $10^5$ while consistently achieving lower cost and classification/clustering errors. The implementation source code is publicly available on this https URL.
Joint Learning of Unsupervised Object-Based Perception and Control

This paper is concerned with object-based perception control (OPC), which allows for joint optimization of hierarchical object-based perception and decision making. We define the OPC framework by extending the Bayesian brain hypothesis to support object-based latent representations and propose an unsupervised end-to-end solution method. We develop a practical algorithm and analyze the convergence of the perception model update. Experiments on a high-dimensional pixel environment justify the learning effectiveness of our object-based perception control approach.
Learning Vector-valued Functions with Local Rademacher Complexity

We consider a general family of problems of which the output space admits vector-valued structure, covering a broad family of important domains, e.g. multi-label learning and multi-class classification. By using local Rademacher complexity and unlabeled data, we derived novel data-dependent excess risk bounds for vector-valued functions in both linear space and kernel space. The proposed bounds are much sharper than existing bounds and can be applied into specific vector-valued tasks in terms of different hypotheses sets and loss functions. Theoretical analysis motivates us to devise a unified learning framework for vector-valued functions based which is solved by proximal gradient descent on the primal, achieving a much better tradeoff between accuracy and efficiency. Empirical results on several benchmark datasets show that the proposed algorithm outperforms compared methods significantly, which coincides with our theoretical analysis.
Blandford-Znajek process as Alfvénic superradiance

The superradiant scattering of Alfv\'en waves (Alfv\'enic superradiance) in a forcefree magnetosphere is discussed to reveal the relationship between the Blandford-Znajek (BZ) process and superradiance. For simplicity, we consider a four-dimensional rotating black string spacetime of which each $z=\text{const}$ slice is a Ba\~nados-Teitelboim-Zanelli solution as an analogy of the equatorial plane of the Kerr spacetime. Then, it is confirmed that the condition for Alfv\'enic superradiance coincides with that for the BZ process, and the wave amplification can be very large due to a resonant scattering for some parameter sets of the wave frequency and the angular velocity of the magnetic field line. Moreover, by analysis of the Poynting flux, we first show that the BZ process can be interpreted as the long wavelength limit of Alfv\'enic superradiance.
Global Locality in Event Extraction

Due to the exponential growth of biomedical literature, event and relation extraction are important tasks in biomedical text mining. Most work in relation extraction detect a single entity pair mention on a short span of text, which is not ideal due to long sentences that appear in biomedical contexts. We propose an approach to both event and relation extraction, for simultaneously predicting relationships between all mention pairs in a text. Our model includes a set of multi-head attentions and convolutions, an adaptation of the transformer architecture, which offers self-attention the ability to strengthen dependencies among related elements, and models the interaction between features extracted by multiple attention heads. Experiment results demonstrate that our approach outperforms the state-of-the-art on a set of benchmark biomedical corpora including BioNLP 2009, 2011, 2013 and BioCreative 2017 shared tasks.
MPC-Net: A First Principles Guided Policy Search

We present an Imitation Learning approach for the control of dynamical systems with a known model. Our policy search method is guided by solutions from Model Predictive Control (MPC). Typical policy search methods of this kind minimize a distance metric between the guiding demonstrations and the learned policy. Our loss function, however, corresponds to the minimization of the control Hamiltonian, which derives from the principle of optimality. Therefore, our algorithm directly attempts to solve the optimality conditions with a parameterized class of control laws. Additionally, the proposed loss function explicitly encodes the constraints of the optimal control problem and we provide numerical evidence that its minimization achieves improved constraint satisfaction. We train a mixture-of-expert neural network architecture for controlling a quadrupedal robot and show that this policy structure is well suited for such multimodal systems. The learned policy can successfully stabilize different gaits on the real walking robot from less than 10 min of demonstration data.
Online Trajectory Generation With Distributed Model Predictive Control for Multi-Robot Motion Planning

We present a distributed model predictive control (DMPC) algorithm to generate trajectories in real-time for multiple robots. We adopted the on-demand collision avoidance method presented in previous work to efficiently compute non-colliding trajectories in transition tasks. An event-triggered replanning strategy is proposed to account for disturbances. Our simulation results show that the proposed collision avoidance method can reduce, on average, around 50% of the travel time required to complete a multi-agent point-to-point transition when compared to the well-studied Buffered Voronoi Cells (BVC) approach. Additionally, it shows a higher success rate in transition tasks with a high density of agents, with more than 90% success rate with 30 palm-sized quadrotor agents in a 18 $\text{m}^3$ arena. The approach was experimentally validated with a swarm of up to 20 drones flying in close proximity.
Insights and algorithms for the multivariate square-root lasso.

We study the multivariate square-root lasso, a method for fitting the multivariate response linear regression model with dependent errors. This estimator minimizes the nuclear norm of the residual matrix plus a convex penalty. Unlike some existing methods for multivariate response linear regression, which require explicit estimates of the error covariance matrix or its inverse, the multivariate square-root lasso criterion implicitly adapts to dependent errors and is convex. To justify the use of this estimator, we establish an error bound which illustrates that like the univariate square-root lasso (Belloni et al., 2011), the multivariate square-root lasso is pivotal with respect to the unknown error covariance matrix. Based on our theory, we propose a simple tuning approach which requires fitting the model for only a single value of the tuning parameter, i.e., does not require cross-validation. We propose two algorithms to compute the estimator: a prox-linear alternating direction method of multipliers algorithm, and a fast first order algorithm which can be applied in special cases. In both simulation studies and a real data application, we show that the multivariate square-root lasso can outperform more computationally intensive methods which estimate both the regression coefficient matrix and error precision matrix.
A Momentum-conserving Accretion Disk Wind in the Narrow-line Seyfert 1 I Zwicky 1

I Zwicky 1 (hereafter I Zw 1) is the prototype optical narrow-line Seyfert 1 galaxy. It is also a nearby (z = 0.0611), luminous quasi-stellar object (QSO), accreting close to the Eddington limit. XMM-Newton observations of I Zw 1 in 2015 reveal the presence of a broad and blueshifted P Cygni iron K profile, as observed through a blueshifted absorption trough at 9 keV and a broad excess of emission at 7 keV in the X-ray spectra. The profile can be well fitted with a wide-angle accretion disk wind, with an outflow velocity of at least −0.25c. In this respect, I Zw 1 may be analogous to the prototype fast wind detected in the QSO PDS 456, while its overall mass outflow rate is scaled down by a factor of 50, due to its lower black hole mass. The mechanical power of the fast wind in I Zw 1 is constrained to within 5%–15% of Eddington, while its momentum rate is of the order unity. Upper limits placed on the energetics of any molecular outflow, from its CO profile measured by IRAM, appear to rule out the presence of a powerful, large-scale, energy-conserving wind in this active galactic nucleus (AGN). We consider whether I Zw 1 may be similar to a number of other AGNs, such as PDS 456, where the large-scale galactic outflow is much weaker than what is anticipated from models of energy-conserving feedback.
Kiselev/CFT correspondence and black hole thermodynamics.

In this paper, we study the thermodynamic properties of Kiselev black hole and its holographic dual. We obtain the thermodynamic product formula for the Kiselev black hole. We consider Kiselev Black hole surrounded by both dust and radiation. We find that the area (or entropy) product formula for both cases is mass-independent as well as the case in the Einstein gravity, which interpreted as universal quantity. Moreover, we calculate the black hole entropy bound for both the inner and outer horizons. Furthermore, we show that the central charges of the left and right moving sectors are not the same via universal thermodynamic relations. Such universal relations lead us to calculate the central charge of conformal field theory (CFT). Finally, we see that the left and right of central charge of CFT are same.
Determining Key Spin-Orbitronic Parameters via Propagating Spin Waves

We characterize spin wave propagation and its modification by an electrical current in Permalloy(Py)/Pt bilayers with Py thickness between 4 and 20 nm. First, we analyze the frequency non-reciprocity of surface spin waves and extract from it the interfacial Dzyaloshinskii-Moriya interaction constant $D_s$ accounting for an additional contribution due to asymmetric surface anisotropies. Second, we measure the spin-wave relaxation rate and deduce from it the Py/Pt spin mixing conductance $g^{\uparrow\downarrow}_{eff}$. Last, applying a \textit{dc} electrical current, we extract the spin Hall conductivity $\sigma_{SH}$ from the change of spin wave relaxation rate due to the spin-Hall spin transfer torque. We obtain a consistent picture of the spin wave propagation data for different film thicknesses using a single set of parameters $D_s=0.25$ pJ/m, $g^{\uparrow\downarrow}_{eff} = 3.2\times 10^{19}$ m$^{-2}$ and $\sigma_{SH}=4\times10^{5}$ S/m.
The Nubo Virtual Services Marketplace

In this paper, we describe a virtual services marketplace, called Nubo, designed to connect buyers of virtual services (or tenants) with providers of those services on a cloud computing platform. The marketplace is implemented as a collection of distributed microservices along with a marketplace portal that runs as a Web application. The heart of Nubo is the Saranyu tenant and service management microservice. Saranyu is a decentralized application (dApp) built on top of the J.P. Morgan Quorum blockchain. Tenant and service accounts are represented as static (nonnegotiable) smart contracts written in the Solidity language. Quorum provides a tamper evident and tamper resistant distributed ledger, whereby multiple cloud and service providers can co-operate to provide service resources to tenants in a trustworthy fashion. Services offer resources consisting of a collection of attributes describing what the tenant can consume, and tenants subscribe to service resources through the Nubo Marketplace portal. The Service Manager microservice provides multitenant support for containerized services built for deployment and orchestration using Docker that were originally not designed to be managed through Saranyu. We discuss our design goals for Nubo, describe the overall architecture, discuss some details on how Saranyu uses the blockchain and smart contracts, and provide comprehensive performance and scalability data measured on the Saranyu REST API. The results indicate Saranyu is competitive with published results for comparable operations on the Havana release of OpenStack Keystone, but Saranyu provides a much richer collection of tenant and service management functionality than Keystone.