Miracle-dz/UniFAR-FTUs huggingface dataset:
X-ray observations of luminous dusty quasars at z > 2

We present new X-ray observations of luminous heavily dust-reddened quasars (HRQs) selected from infrared (IR) sky surveys. HRQs appear to be a dominant population at high redshifts and the highest luminosities, and may be associated with a transitional ‘blowout’ phase of black hole and galaxy co-evolution models. Despite this, their high-energy properties have been poorly known. We use the overall sample of 10 objects with XMM–Newton coverage to study the high-energy properties of HRQs at $\langle$Lbol$\rangle$ =1047.5 erg s−1 and $\langle$z$\rangle$ =2.5. For seven sources with strong X-ray detections, we perform spectral analyses. These find a median X-ray luminosity of $\left\langle L_{\rm 2\! -\! 10\, keV} \right\rangle = 10^{45.1}$ erg s−1, comparable to the most powerful X-ray quasars known. The gas column densities are NH = (1–8) × 1022 cm−2, in agreement with the amount of dust extinction observed. The dust-to-gas ratios are sub-Galactic, but are higher than found in local AGN. The intrinsic X-ray luminosities of HRQs are weak compared to the mid-IR ($L_{\rm 6\, \mu m}$) and bolometric luminosities (Lbol), in agreement with findings for other luminous quasar samples. For instance, the X-ray to bolometric corrections range from κbol ≈ 50 to 3000. The moderate absorption levels and accretion rates close to the Eddington limit ($\langle$λEdd$\rangle$ =1.06) are in agreement with a quasar blowout phase. Indeed, we find that the HRQs lie in the forbidden region of the NH–λEdd plane, and therefore that radiation pressure feedback on the dusty interstellar medium may be driving a phase of blowout that has been ongoing for a few 105 yr. The wider properties, including [O iii] narrow-line region kinematics, broadly agree with this interpretation.
Geodesic coordinates for the pressure metric at the Fuchsian locus

We prove that the Hitchin parametrization provides geodesic coordinates at the Fuchsian locus for the pressure metric in the Hitchin component $\mathcal{H}_{3}(S)$ of surface group representations into $PSL(3,\mathbb{R})$. The proof consists of the following elements: we compute first derivatives of the pressure metric using the thermodynamic formalism. We invoke a gauge-theoretic formula to compute first and second variations of reparametrization functions by studying flat connections from Hitchin's equations and their parallel transports. We then extend these expressions of integrals over closed geodesics to integrals over the two-dimensional surface. Symmetries of the Liouville measure then provide cancellations, which show that the first derivatives of the pressure metric tensors vanish at the Fuchsian locus.
A Youla Operator State-Space Framework for Stably Realizable Distributed Control

This paper deals with the problem of distributed control synthesis. We seek to find structured controllers that are stably realizable over the underlying network. We address the problem using an operator form of discrete-time linear systems. This allows for uniform treatment of various classes of linear systems, e.g., Linear Time Invariant (LTI), Linear Time Varying (LTV), or linear switched systems. We combine this operator representation for linear systems with the classical Youla parameterization to characterize the set of stably realizable controllers for a given network structure. Using this Youla Operator State-Space (YOSS) framework, we show that if the structure satisfies certain subspace like assumptions, then both stability and performance problems can be formulated as convex optimization and more precisely as tractable model-matching problems to any a priori accuracy. Furthermore, we show that the structured controllers found from our approach can be stably realized over the network and provide a generalized separation principle.
A Three-phase Approach to Grain Surface Chemistry in Protoplanetary Disks: Gas, Ice Surfaces, and Ice Mantles of Dust Grains

We study the effects of grain surface reactions on the chemistry of protoplanetary disks where gas, ice surface layers, and icy mantles of dust grains are considered as three distinct phases. Gas-phase and grain surface chemistry is found to be mainly driven by photoreactions and dust temperature gradients. The icy disk interior has three distinct chemical regions: (i) the inner midplane with low far-UV (FUV) fluxes and warm dust (≳15 K) that lead to the formation of complex organic molecules, (ii) the outer midplane with higher FUV from the interstellar medium and cold dust where hydrogenation reactions dominate, and (iii) a molecular layer above the midplane but below the water condensation front where photodissociation of ices affects gas-phase compositions. Some common radicals, e.g., CN and C2H, exhibit a two-layered vertical structure and are abundant near the CO photodissociation front and near the water condensation front. The three-phase approximation in general leads to lower vertical column densities than two-phase models for many gas-phase molecules owing to reduced desorption, e.g., H2O, CO2, HCN, and HCOOH decrease by roughly two orders of magnitude. Finally, we find that many observed gas-phase species originate near the water condensation front; photoprocesses determine their column densities, which do not vary significantly with key disk properties such as mass and dust/gas ratio.
Stabilizing Generative Adversarial Network Training: A Survey

Generative Adversarial Networks (GANs) are a type of Generative Models, which received wide attention for their ability to model complex real-world data. With a considerably large number of potential applications and a promise to alleviate some of the major issues of the field of Artificial Intelligence, recent years have seen rapid progress in the field of GANs. Despite the relative success, the process of training GANs remains challenging, suffering from instability issues such as mode collapse and vanishing gradients. This survey summarizes the approaches and methods employed for the purpose of stabilizing GAN training procedure. We provide a brief explanation behind the workings of GANs and categorize employed stabilization techniques outlining each of the approaches. Finally, we discuss the advantages and disadvantages of each of the methods, offering a comparative summary of the literature on stabilizing GAN training procedure.
The adjacency algebra of a graph, with an application to affine planes

We study the algebra generated by the adjacency operators on an edge-coloured graph. We stress the importance of graphs endowed with a certain type of distance function, defined on the set of vertices, and taking its values in the adjacency algebra. These include, in particular, the graphs on which a group acts strongly transitively, in a certain sense, as well as all association schemes, and all buildings in the sense of Tits. About buidings, we prove more specifically that the usual distance function with values in the associated Coxeter group can be taken to define a distance with values in the adjacency algebra, which is itself none other than the corresponding Iwahori-Hecke algebra. We also prove that strong transitivity in our sense reduces, in the case of buildings, to the standard notion of transitivity on pairs of incident chambers and appartments. We give a number of examples, and in particular we study the adjacency algebra of (the chamber system of) an affine plane (this is neither a building nor an association scheme). We describe this algebra completely, showing that it depends only on the order of the plane; we show that a distance function as above always exists; and we show that, when an affine plane admits a strongly transitive action in our sense, it must be Desarguesian. We study circle geometries in the same fashion. Both situations involve a deformation of the group algebra of the symmetric group of degree 3, which is similar to a Iwahori-Hecke algebra, yet different. The representation theory of these algebras is worked out. We conclude by associating an algebra to each Buekenhout diagram. The adjacency algebra of (the chamber system of) a geometry which belongs to a given diagram is a homomorphic image of the latter. Many open questions arise.
Deploying the NASA Valkyrie Humanoid for IED Response: An Initial Approach and Evaluation Summary

As part of a feasibility study, this paper shows the NASA Valkyrie humanoid robot performing an end-to-end improvised explosive device (IED) response task. To demonstrate and evaluate robot capabilities, sub-tasks highlight different locomotion, manipulation, and perception requirements: traversing uneven terrain, passing through a narrow passageway, opening a car door, retrieving a suspected IED, and securing the IED in a total containment vessel (TCV). For each sub-task, a description of the technical approach and the hidden challenges that were overcome during development are presented. The discussion of results, which explicitly includes existing limitations, is aimed at motivating continued research and development to enable practical deployment of humanoid robots for IED response. For instance, the data shows that operator pauses contribute to 50% of the total completion time, which implies that further work is needed on user interfaces for increasing task completion efficiency. **Disclaimer: Trade names and trademarks are used in this report for identification only. Their usage does not constitute an official endorsement, either expressed or implied, by the National Aeronautics and Space Administration
Optimizing the Transition Waste in Coded Elastic Computing

Motivated by recently available services in the cloud computing industry, e.g., EC2 Spot or Azure Batch, where spare/low-priority virtual machines are offered at a fraction of the price of the on-demand instances but can be preempted on short notice, we investigate coded computing solutions over elastic resources, where the set of available machines may change in the middle of the computation. Our contributions are two-fold: We first propose an efficient method to minimize the transition waste, a newly introduced concept quantifying the total number of tasks that existing machines have to abandon or take on anew when a machine joins or leaves, for the cyclic elastic task allocation scheme recently proposed in the literature (Yang et al. ISIT’19). We then proceed to generalize such a scheme and introduce new task allocation schemes based on finite geometry that achieve zero transition wastes as long as the number of active machines varies within a fixed range. The proposed solutions can be applied on top of existing coded computing schemes tolerating stragglers.
MIMO Assisted Networks Relying on Large Intelligent Surfaces: A Stochastic Geometry Model

Large intelligent surfaces (LISs) constitute a promising performance enhancement for next-generation (NG) wireless networks in terms of enhancing both their spectrum efficiency (SE) and energy efficiency (EE). Hence we conceive a LIS-aided multiple-input multiple-output framework for providing wireless services to randomly roaming users and analyze its performance by utilizing stochastic geometry tools. As such, each user receives the superposed signals reflected by multiple LISs. We aim for serving multiple users by jointly designing the passive beamforming weight at the LISs and detection weight vectors at the users. As a benefit, the intra-cell interference imposed by the LISs can be suppressed. In an effort to evaluate the performance of the proposed framework, we first derive new channel statistics for characterizing the effective channel gains. Then, we derive closed-form expressions both for the outage probability and for the ergodic rate of users. For gleaning further insights, we investigate both the diversity orders of outage probability and the high signal-to-noise (SNR) slopes of ergodic rate. We also derive the SE and EE of the proposed framework. Our analytical results demonstrate that the specific fading environments encountered between the LISs and users have almost no impact on the diversity orders attainted. Numerical results are provided to confirm that: i) the high-SNR slope of the proposed framework is one; and ii) the SE and EE can be significantly enhanced by increasing the number of LISs.
Omnipush: accurate, diverse, real-world dataset of pushing dynamics with RGB-D video

Pushing is a fundamental robotic skill. Existing work has shown how to exploit models of pushing to achieve a variety of tasks, including grasping under uncertainty, in-hand manipulation and clearing clutter. Such models, however, are approximate, which limits their applicability.Learning-based methods can reason directly from raw sensory data with accuracy, and have the potential to generalize to a wider diversity of scenarios. However, developing and testing such methods requires rich-enough datasets. In this paper we introduce Omnipush, a dataset with high variety of planar pushing behavior.In particular, we provide 250 pushes for each of 250 objects, all recorded with RGB-D and a high precision tracking system. The objects are constructed so as to systematically explore key factors that affect pushing–the shape of the object and its mass distribution–which have not been broadly explored in previous datasets, and allow to study generalization in model learning.Omnipush includes a benchmark for meta-learning dynamic models, which requires algorithms that make good predictions and estimate their own uncertainty. We also provide an RGB video prediction benchmark and propose other relevant tasks that can be suited with this dataset. Data and code are available at https://web.mit. edu/mcube/omnipush-dataset/.
Practical Period Finding on IBM Q - Quantum Speedups in the Presence of Errors

We implemented Simon's quantum period finding circuit for functions $\mathbb{F}_2^n \rightarrow \mathbb{F}_2^n$ with period $\vec s \in \mathbb{F}_2^n$ up to $n=7$ on the 14-qubit quantum device IBM Q 16 Melbourne. Our experiments show that with a certain probability $\tau(n)$ we measure erroneous vectors that are not orthogonal to $\vec s$. While Simon's algorithm for extracting $\vec s$ runs in polynomial time in the error-free case $\tau(n)=0$, we show that the problem of extracting $\vec s \in \mathbb{F}_2^n$ in the general setting $0 \leq \tau(n) \leq \frac 1 2$ is as hard as solving LPN (Learning Parity with Noise) with parameters $n$ and $\tau(n)$. Hence, in the error-prone case we may not hope to find periods in time polynomial in $n$. However, we also demonstrate theoretically and experimentally that erroneous quantum measurements are still useful to find periods faster than with purely classical algorithms, even for large errors $\tau(n)$ close to $\frac 1 2$.
Unsupervised Doodling and Painting with Improved SPIRAL

We investigate using reinforcement learning agents as generative models of images (extending arXiv:1804.01118). A generative agent controls a simulated painting environment, and is trained with rewards provided by a discriminator network simultaneously trained to assess the realism of the agent's samples, either unconditional or reconstructions. Compared to prior work, we make a number of improvements to the architectures of the agents and discriminators that lead to intriguing and at times surprising results. We find that when sufficiently constrained, generative agents can learn to produce images with a degree of visual abstraction, despite having only ever seen real photographs (no human brush strokes). And given enough time with the painting environment, they can produce images with considerable realism. These results show that, under the right circumstances, some aspects of human drawing can emerge from simulated embodiment, without the need for external supervision, imitation or social cues. Finally, we note the framework's potential for use in creative applications.
Spectral shock detection for dynamically developing discontinuities

Pseudospectral schemes are a class of numerical methods capable of solving smooth problems with high accuracy thanks to their exponential convergence to the true solution. When applied to discontinuous problems, such as fluid shocks and material interfaces, due to the Gibbs phenomenon, pseudospectral solutions lose their superb convergence and suffer from spurious oscillations across the entire computational domain. Luckily, there exist theoretical remedies for these issues which have been successfully tested in practice for cases of well defined discontinuities. We focus on one piece of this procedure---detecting a discontinuity in spectral data. We show that realistic applications require treatment of discontinuities dynamically developing in time and that it poses challenges associated with shock detection. More precisely, smoothly steepening gradients in the solution spawn spurious oscillations due to insufficient resolution, causing premature shock identification and information loss. We improve existing spectral shock detection techniques to allow us to automatically detect true discontinuities and identify cases for which post-processing is required to suppress spurious oscillations resulting from the loss of resolution. We then apply these techniques to solve an inviscid Burgers' equation in 1D, demonstrating that our method correctly treats genuine shocks caused by wave breaking and removes oscillations caused by numerical constraints.
Formal Language Constraints for Markov Decision Processes

In order to satisfy safety conditions, a reinforcement learned (RL) agent maybe constrained from acting freely, e.g., to prevent trajectories that might cause unwanted behavior or physical damage in a robot. We propose a general framework for augmenting a Markov decision process (MDP) with constraints that are described in formal languages over sequences of MDP states and agent actions. Constraint enforcement is implemented by filtering the allowed action set or by applying potential-based reward shaping to implement hard and soft constraint enforcement, respectively. We instantiate this framework using deterministic finite automata to encode constraints and propose methods of augmenting MDP observations with the state of the constraint automaton for learning. We empirically evaluate these methods with a variety of constraints by training Deep Q-Networks in Atari games as well as Proximal Policy Optimization in MuJoCo environments. We experimentally find that our approaches are effective in significantly reducing or eliminating constraint violations with either minimal negative or, depending on the constraint, a clear positive impact on final performance.
A Survey of Big Data Machine Learning Applications Optimization in Cloud Data Centers and Networks

This survey article reviews the challenges associated with deploying and optimizing big data applications and machine learning algorithms in cloud data centers and networks. The MapReduce programming model and its widely-used open-source platform; Hadoop, are enabling the development of a large number of cloud-based services and big data applications. MapReduce and Hadoop thus introduce innovative, efficient, and accelerated intensive computations and analytics. These services usually utilize commodity clusters within geographically-distributed data centers and provide cost-effective and elastic solutions. However, the increasing traffic between and within the data centers that migrate, store, and process big data, is becoming a bottleneck that calls for enhanced infrastructures capable of reducing the congestion and power consumption. Moreover, enterprises with multiple tenants requesting various big data services are challenged by the need to optimize leasing their resources at reduced running costs and power consumption while avoiding under or over utilization. In this survey, we present a summary of the characteristics of various big data programming models and applications and provide a review of cloud computing infrastructures, and related technologies such as virtualization, and software-defined networking that increasingly support big data systems. Moreover, we provide a brief review of data centers topologies, routing protocols, and traffic characteristics, and emphasize the implications of big data on such cloud data centers and their supporting networks. Wide ranging efforts were devoted to optimize systems that handle big data in terms of various applications performance metrics and/or infrastructure energy efficiency. Finally, some insights and future research directions are provided.
Benchmarking machine learning models on eICU critical care dataset

Progress of machine learning in critical care has been difficult to track, in part due to absence of public benchmarks. Other fields of research (such as vision and NLP) have already established various competitions and benchmarks, whereas only recent availability of large clinical datasets has enabled the possibility of public benchmarks. Taking advantage of this opportunity, we propose a public benchmark suite to address four areas of critical care, namely mortality prediction, estimation of length of stay, patient phenotyping and risk of decompensation. We define each task and compare the performance of both clinical models as well as baseline and deep models using eICU critical care dataset of around 73,000 patients. Furthermore, we investigate the impact of numerical variables as well as handling of categorical variables for each of the defined tasks.
General perfect fluid perturbations of homogeneous and orthogonal locally rotationally symmetric class II cosmologies

First order perturbations of homogeneous and hypersurface orthogonal LRS (Locally Rotationally Symmetric) class II cosmologies with a cosmological constant are considered in the framework of the 1+1+2 covariant decomposition of spacetime. The perturbations, which are of perfect fluid type, include general scalar, vector and tensor modes and extend some previous works where vorticity perturbations were excluded. A harmonic decomposition is performed and the field equations are then reduced to a set of eight evolution equations for eight harmonic coefficients, representing perturbations in density, shear, vorticity and the Weyl tensor, in terms of which all other variables can be expressed algebraically. This system decouples into two sub-systems, one for five and one for three coefficients. As previously known, vorticity perturbations cannot be generated to any order in a barytopic perfect fluid. Hence the time development of existing first order vorticity perturbations are seen to be completely determined by the background. However, an already existing vorticity will act as source terms in the evolution equations for the other quantities. In the high frequency approximation the four independent Weyl tensor harmonics evolve as gravitational waves on the anisotropic background in the same manner as in the case without vorticity, whereas vorticity gives a first order disturbance of sonic waves.
An Efficient Method of Image Fusion Using SWT & DTCWT

Image fusion is a technique of combining more than one image to form a high configured image with good results. Image Fusion technique is used in many distinct fields such as remote sensing, robotics, medical applications and under water image processing. The main aim of this project (An efficient method of image fusion using SWT and DTCWT) is to reduce uncertainty, decrease redundancy in the output, and maximize relevant information pertaining to an function or a task.