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Deflection of light due to massive objects was predicted by Einstein in his General Theory of Relativity. This deflection of light has been calculated by many researchers in past, for spherically symmetric objects. But, in reality, most of these gravitating objects are not spherical instead they are ellipsoidal ( oblate) in shape. The objective of the present work is to study theoretically the effect of this ellipticity on the trajectory of a light ray. Here, we obtain a converging series expression for the deflection of a light ray due to an ellipsoidal gravitating object, characterised by an ellipticity parameter. As a boundary condition, by setting the ellipticity parameter to be equal to zero, we get back the same expression for deflection as due to Schwarzschild object. It is also found that the additional contribution in deflection angle due to this ellipticity though small, but could be typically higher than the similar contribution caused by the rotation of a celestial object. Therefore for a precise estimate of the deflection due to a celestial object, the calculations presented here would be useful.

2104.14168

Recently, learning-based approaches for 3D model reconstruction have attracted attention owing to its modern applications such as Extended Reality(XR), robotics and self-driving cars. Several approaches presented good performance on reconstructing 3D shapes by learning solely from images, i.e., without using 3D models in training. Challenges, however, remain in texture generation due to the gap between 2D and 3D modals. In previous work, the grid sampling mechanism from Spatial Transformer Networks was adopted to sample color from an input image to formulate texture. Despite its success, the existing framework has limitations on searching scope in sampling, resulting in flaws in generated texture and consequentially on rendered 3D models. In this paper, to solve that issue, we present a novel sampling algorithm by optimizing the gradient of predicted coordinates based on the variance on the sampling image. Taking into account the semantics of the image, we adopt Frechet Inception Distance (FID) to form a loss function in learning, which helps bridging the gap between rendered images and input images. As a result, we greatly improve generated texture. Furthermore, to optimize 3D shape reconstruction and to accelerate convergence at training, we adopt part segmentation and template learning in our model. Without any 3D supervision in learning, and with only a collection of single-view 2D images, the shape and texture learned by our model outperform those from previous work. We demonstrate the performance with experimental results on a publically available dataset.

2104.14169

Proactive tile-based virtual reality (VR) video streaming employs the current tracking data of a user to predict future requested tiles, then renders and delivers the predicted tiles to be requested before playback. Very recently, privacy protection in VR video streaming starts to raise concerns. However, existing privacy protection may fail even with federated learning at head mounted display (HMD). This is because when the HMD requests the predicted requested tiles and the prediction is accurate, the real requested tiles and corresponding user behavior-related data can still be recovered at multi-access edge computing server. In this paper, we consider how to protect privacy even with accurate predictors and investigate the impact of privacy requirement on the quality of experience (QoE). To this end, we first add extra camouflaged tile requests in addition to real tile requests and model the privacy requirement as the spatial degree of privacy (sDoP). By ensuring sDoP, the real tile requests can be hidden and privacy can be protected. Then, we jointly optimize the durations for prediction, computing, and transmitting, aimed at maximizing the privacy-aware QoE given arbitrary predictor and configured resources. From the obtained optimal closed-form solution, we find that the increase of sDoP improves the capability of communication and computing hence improves QoE, but degrades the prediction performance hence degrades the QoE. The overall impact depends on which factor dominates the QoE. Simulation with two predictors on a real dataset verifies the analysis and shows that the overall impact of sDoP is to improve the QoE.

2104.14170

We study systems of String Equations where block variables need to be assigned strings so that their concatenation gives a specified target string. We investigate this problem under a multivariate complexity framework, searching for tractable special cases such as systems of equations with few block variables or few equations. Our main results include a polynomial-time algorithm for size-2 equations, and hardness for size-3 equations, as well as hardness for systems of two equations, even with tight constraints on the block variables. We also study a variant where few deletions are allowed in the target string, and give XP algorithms in this setting when the number of block variables is constant.

2104.14171

We study the average number $\mathcal{A}(G)$ of colors in the non-equivalent colorings of a graph $G$. We show some general properties of this graph invariant and determine its value for some classes of graphs. We then conjecture several lower bounds on $\mathcal{A}(G)$ and prove that these conjectures are true for specific classes of graphs such as triangulated graphs and graphs with maximum degree at most 2.

2104.14172

Many fundamental machine learning tasks can be formulated as a problem of learning with vector-valued functions, where we learn multiple scalar-valued functions together. Although there is some generalization analysis on different specific algorithms under the empirical risk minimization principle, a unifying analysis of vector-valued learning under a regularization framework is still lacking. In this paper, we initiate the generalization analysis of regularized vector-valued learning algorithms by presenting bounds with a mild dependency on the output dimension and a fast rate on the sample size. Our discussions relax the existing assumptions on the restrictive constraint of hypothesis spaces, smoothness of loss functions and low-noise condition. To understand the interaction between optimization and learning, we further use our results to derive the first generalization bounds for stochastic gradient descent with vector-valued functions. We apply our general results to multi-class classification and multi-label classification, which yield the first bounds with a logarithmic dependency on the output dimension for extreme multi-label classification with the Frobenius regularization. As a byproduct, we derive a Rademacher complexity bound for loss function classes defined in terms of a general strongly convex function.

2104.14173

Molecular communication via diffusion (MCvD) is considered as one of the most feasible communication paradigms for nanonetworks, especially for bio-nanonetworks which are usually in water-rich biological environments. Two effects that deteriorates the signal in MCvD are noise and inter-symbol interference (ISI). The expected channel impulse response of MCvD has a long and slow attenuating tail due to molecular diffusion which causes ISI and further limits the slow data rate of MCvD. The extent that ISI and noise are suppressed in an MCvD system determines its effectiveness, especially at a high data rate. Although ISI-suppression approaches have been investigated, most of them are addressed as non-essential parts in other topics, such as signal detection or modulation. Furthermore, most of the state-of-the-art ISI-suppression approaches are performed by subtracting the estimated ISI from the total signal. In this work, we investigate ISI-suppression from a new perspective of filters to filter ISI out without any ISI estimation. The principles for a good design of ISI-suppression filters in MCvD are investigated. Based on the principles, an ISI-suppression filter with good anti-noise capability and an associated signal detection scheme is proposed for MCvD scenarios with both ISI and noise. We compare the proposed scheme with the state-of-the-art ISI-suppression approaches. The result manifests that the proposed ISI-suppression scheme could recover signals deteriorated severely by both ISI and noise, which could not be effectively detected by the state-of-the-art ISI-suppression approaches.

2104.14174

We present initial limit Datalog, a new extensible class of constrained Horn clauses for which the satisfiability problem is decidable. The class may be viewed as a generalisation to higher-order logic (with a simple restriction on types) of the first-order language limit Datalog$_Z$ (a fragment of Datalog modulo linear integer arithmetic), but can be instantiated with any suitable background theory. For example, the fragment is decidable over any countable well-quasi-order with a decidable first-order theory, such as natural number vectors under componentwise linear arithmetic, and words of a bounded, context-free language ordered by the subword relation. Formulas of initial limit Datalog have the property that, under some assumptions on the background theory, their satisfiability can be witnessed by a new kind of term model which we call entwined structures. Whilst the set of all models is typically uncountable, the set of all entwined structures is recursively enumerable, and model checking is decidable.

2104.14175

We introduce a dense and a dilute loop model on causal dynamical triangulations. Both models are characterised by a geometric coupling constant $g$ and a loop parameter $\alpha$ in such a way that the purely geometric causal triangulation model is recovered for $\alpha=1$. We show that the dense loop model can be mapped to a solvable planar tree model, whose partition function we compute explicitly and use to determine the critical behaviour of the loop model. The dilute loop model can likewise be mapped to a planar tree model; however, a closed-form expression for the corresponding partition function is not obtainable using the standard methods employed in the dense case. Instead, we derive bounds on the critical coupling $g_c$ and apply transfer matrix techniques to examine the critical behaviour for $\alpha$ small.

2104.14176

The evaluation of robot capabilities to navigate human crowds is essential to conceive new robots intended to operate in public spaces. This paper initiates the development of a benchmark tool to evaluate such capabilities; our long term vision is to provide the community with a simulation tool that generates virtual crowded environment to test robots, to establish standard scenarios and metrics to evaluate navigation techniques in terms of safety and efficiency, and thus, to install new methods to benchmarking robots' crowd navigation capabilities. This paper presents the architecture of the simulation tools, introduces first scenarios and evaluation metrics, as well as early results to demonstrate that our solution is relevant to be used as a benchmark tool.

2104.14177

We report the synthesis and crystal structure of an organic inorganic compound, ethylenediammonium lead iodide, NH3CH2CH2NH3PbI4. Synchrotron based single crystal X-ray diffraction experiments revealed that the pristine and thermally treated crystals differ in the organic cation behaviour, which is characterized by a partial disorder in the thermally treated crystal. Based on current voltage measurements, increased disorder of the organic cation is associated with enhanced photoconductivity. This compound could be a potential candidate for interface engineering in lead halide perovskite-based optoelectronic devices.

2104.14178

The time evolution of a two-component collisionless plasma is modeled by the Vlasov-Poisson system. In this work, the setting is two and one-half dimensional, that is, the distribution functions of the particles species are independent of the third space dimension. We consider the case that an external magnetic field is present in order to confine the plasma in a given infinitely long cylinder. After discussing global well-posedness of the corresponding Cauchy problem, we construct stationary solutions which indeed have support away from their confinement device. Then, in the main part of this work we investigate the stability of such steady states, both with respect to perturbations in the initial data, where we employ the energy-Casimir method, and also with respect to perturbations in the external magnetic field.

2104.14179

Kinetic models of biochemical systems used in the modern literature often contain hundreds or even thousands of variables. While these models are convenient for detailed simulations, their size is often an obstacle to deriving mechanistic insights. One way to address this issue is to perform an exact model reduction by finding a self-consistent lower-dimensional projection of the corresponding dynamical system. Recently, a new algorithm CLUE has been designed and implemented, which allows one to construct an exact linear reduction of the smallest possible dimension such that the fixed variables of interest are preserved. It turned out that allowing arbitrary linear combinations (as opposed to zero-one combinations used in the prior approaches) may yield a much smaller reduction. However, there was a drawback: some of the new variables did not have clear physical meaning, thus making the reduced model harder to interpret. We design and implement an algorithm that, given an exact linear reduction, re-parametrizes it by performing an invertible transformation of the new coordinates to improve the interpretability of the new variables. We apply our algorithm to three case studies and show that "uninterpretable" variables disappear entirely in all the case studies. The implementation of the algorithm and the files for the case studies are available at https://github.com/xjzhaang/LumpingPostiviser.

2104.14180

We consider an electronic bound state of the usual, non-relativistic, molecular Hamiltonian with Coulomb interactions and fixed nuclei. Away from appropriate collisions, we prove the real analyticity of all the reduced densities and density matrices, that are associated to this bound state. We provide a similar result for the associated reduced current density.

2104.14181

Particle scattering is a powerful tool to unveil the nature of various subatomic phenomena. The key quantity is the scattering amplitude whose analytic structure carries the information of the quantum states. In this work, we demonstrate our first step attempt to extract the pole configuration of inelastic scatterings using the deep learning method. Among various problems, motivated by the recent new hadron phenomena, we develop a curriculum learning method of deep neural network to analyze coupled channel scattering problems. We show how effectively the method works to extract the pole configuration associated with resonances in the $\pi N$ scatterings.

2104.14182

We consider finite and infinite-dimensional first-order consensus systems with timeconstant interaction coefficients. For symmetric coefficients, convergence to consensus is classically established by proving, for instance, that the usual variance is an exponentially decreasing Lyapunov function. We investigate here the convergence to consensus in the non-symmetric case: we identify a positive weight which allows to define a weighted mean corresponding to the consensus, and obtain exponential convergence towards consensus. Moreover, we compute the sharp exponential decay rate.

2104.14183

This work investigates uncertainty-aware deep learning (DL) in tactile robotics based on a general framework introduced recently for robot vision. For a test scenario, we consider optical tactile sensing in combination with DL to estimate the edge pose as a feedback signal to servo around various 2D test objects. We demonstrate that uncertainty-aware DL can improve the pose estimation over deterministic DL methods. The system estimates the uncertainty associated with each prediction, which is used along with temporal coherency to improve the predictions via a Kalman filter, and hence improve the tactile servo control. The robot is able to robustly follow all of the presented contour shapes to reduce not only the error by a factor of two but also smooth the trajectory from the undesired noisy behaviour caused by previous deterministic networks. In our view, as the field of tactile robotics matures in its use of DL, the estimation of uncertainty will become a key component in the control of physically interactive tasks in complex environments.

2104.14184

We investigate inference of variable-length codes in other domains of computer science, such as noisy information transmission or information retrieval-storage: in such topics, traditionally mostly constant-length codewords act. The study is relied upon the two concepts of independent and closed sets. We focus to those word relations whose images are computed by applying some peculiar combinations of deletion, insertion, or substitution. In particular, characterizations of variable-length codes that are maximal in the families of $\tau$-independent or $\tau$-closed codes are provided.

2104.14185

Current dense symmetric eigenvalue (EIG) and singular value decomposition (SVD) implementations may suffer from the lack of concurrency during the tridiagonal and bidiagonal reductions, respectively. This performance bottleneck is typical for the two-sided transformations due to the Level-2 BLAS memory-bound calls. Therefore, the current state-of-the-art EIG and SVD implementations may achieve only a small fraction of the system's sustained peak performance. The QR-based Dynamically Weighted Halley (QDWH) algorithm may be used as a pre-processing step toward the EIG and SVD solvers, while mitigating the aforementioned bottleneck. QDWH-EIG and QDWH-SVD expose more parallelism, while relying on compute-bound matrix operations. Both run closer to the sustained peak performance of the system, but at the expense of performing more FLOPS than the standard EIG and SVD algorithms. In this paper, we introduce a new QDWH-based solver for computing the partial spectrum for EIG (QDWHpartial-EIG) and SVD (QDWHpartial-SVD) problems. By optimizing the rational function underlying the algorithms only in the desired part of the spectrum, QDWHpartial-EIG and QDWHpartial-SVD algorithms efficiently compute a fraction (say 1-20%) of the corresponding spectrum. We develop high-performance implementations of QDWHpartial-EIG and QDWHpartial-SVD on distributed-memory anymore systems and demonstrate their numerical robustness. Experimental results using up to 36K MPI processes show performance speedups for QDWHpartial-SVD up to 6X and 2X against PDGESVD from ScaLAPACK and KSVD, respectively. QDWHpartial-EIG outperforms PDSYEVD from ScaLAPACK up to 3.5X but remains slower compared to ELPA. QDWHpartial-EIG achieves, however, a better occupancy of the underlying hardware by extracting higher sustained peak performance than ELPA, which is critical moving forward with accelerator-based supercomputers.

2104.14186

Let g be a complex semi-simple Lie algebra and g be a semisimple subalgebra of g. Consider the branching problem of decomposing the simple g-representations V as a sum of simple grepresentations V. When g = g x g, it is the tensor product decomposition. The multiplicity space Mult(V, V) satisfies V = $\oplus$ V Mult(V, V) $\otimes$ V, where the sum runs over the isomorphism classes of simple g-representations. In the case when g is spherical of minimal rank, we describe Mult(V, V) as the intersection of kernels of powers of root operators in some weight space of the dual space V * of V. When g = g x g, we recover by geometric methods a well known result.

2104.14187

Since its inception, the E.U.'s Common Agricultural Policy (CAP) aimed at ensuring an adequate and stable farm income. While recognizing that the CAP pursues a larger set of objectives, this thesis focuses on the impact of the CAP on the level and the stability of farm income in Italian farms. It uses microdata from a high standardized dataset, the Farm Accountancy Data Network (FADN), that is available in all E.U. countries. This allows if perceived as useful, to replicate the analyses to other countries. The thesis first assesses the Income Transfer Efficiency (i.e., how much of the support translate to farm income) of several CAP measures. Secondly, it analyses the role of a specific and relatively new CAP measure (i.e., the Income Stabilisation Tool - IST) that is specifically aimed at stabilising farm income. The assessment of the potential use of Machine Learning procedures to develop an adequate ratemaking in IST. These are used to predict indemnity levels because this is an essential point for a similar insurance scheme. The assessment of ratemaking is challenging: indemnity distribution is zero-inflated, not-continuous, right-skewed, and several factors can potentially explain it. We address these problems by using Tweedie distributions and three Machine Learning procedures. The objective is to assess whether this improves the ratemaking by using the prospective application of the Income Stabilization Tool in Italy as a case study. We look at the econometric performance of the models and the impact of using their predictions in practice. Some of these procedures efficiently predict indemnities, using a limited number of regressors, and ensuring the scheme's financial stability.

2104.14188

We introduce and parameterize a chemomechanical model of microtubule dynamics on the dimer level, which is based on the allosteric tubulin model and includes attachment, detachment and hydrolysis of tubulin dimers as well as stretching of lateral bonds, bending at longitudinal junctions, and the possibility of lateral bond rupture and formation. The model is computationally efficient such that we reach sufficiently long simulation times to observe repeated catastrophe and rescue events at realistic tubulin concentrations and hydrolysis rates, which allows us to deduce catastrophe and rescue rates. The chemomechanical model also allows us to gain insight into microscopic features of the GTP-tubulin cap structure and microscopic structural features triggering microtubule catastrophes and rescues. Dilution simulations show qualitative agreement with experiments. We also explore the consequences of a possible feedback of mechanical forces onto the hydrolysis process and the GTP-tubulin cap structure.

2104.14189

This paper aims at solving FX market volatility modeling problem and finding the most becoming approach to this task. Validity of two competing approaches, classical econometric generalized conditional heteroscedasticity and mathematical (singular spectrum analysis and dynamical systems stability analysis) are tested on major currency pairs (EUR/USD, USD/JPY, GBP/USD) and unique high-frequency USD/RUB data. The study shows that both mathematical tools, understudied in econometric discourse, have a great potential in scope of discussed problematic, as for all experiments covered in this research, both of them show promising results.

2104.14190

We investigate the Hi envelope of the young, massive GMCs in the star-forming regions N48 and N49, which are located within the high column density Hi ridge between two kpc-scale supergiant shells, LMC 4 and LMC 5. New long-baseline Hi 21 cm line observations with the Australia Telescope Compact Array (ATCA) were combined with archival shorter baseline data and single dish data from the Parkes telescope, for a final synthesized beam size of 24.75" by 20.48", which corresponds to a spatial resolution of ~ 6 pc in the LMC. It is newly revealed that the Hi gas is highly filamentary, and that the molecular clumps are distributed along filamentary Hi features. In total 39 filamentary features are identified and their typical width is ~ 21 (8-49) [pc]. We propose a scenario in which the GMCs were formed via gravitational instabilities in atomic gas which was initially accumulated by the two shells and then further compressed by their collision. This suggests that GMC formation involves the filamentary nature of the atomic medium.

2104.14191

Ultralight scalars, which are states that are either exactly massless or much lighter than any other massive particle in the model, appear in many new physics scenarios. Axions and majorons constitute well-motivated examples of this type of particle. In this work, we explore the phenomenology of these states in low-energy leptonic observables adopting a model independent approach that includes both scalar and pseudoscalar interactions. Then, we consider processes in which the ultralight scalar $\phi$ is directly produced, such as $\mu \to e \, \phi$, or acts as a mediator, as in $\tau \to \mu \mu \mu$. Finally, contributions to the charged leptons magnetic and electric moments are studied as well. In particular, it is shown that the muon $g-2$ anomaly can be explained provided a mechanism for suppressing the experimental bounds on the coupling between the ultralight scalar and a pair of muons is introduced.

2104.14192

This paper is concerned with multiplicity results for parametric singular double phase problems in $\mathbb{R}^N$ via the Nehari manifold approach. It is shown that the problem under consideration has at least two nontrivial weak solutions provided the parameter is sufficiently small. The idea is to split the Nehari manifold into three disjoint parts minimizing the energy functional on two of them. The third set turns out to be the empty set for small values of the parameter.

2104.14193

Two-loop MHV amplitudes in planar ${\cal N} = 4$ supersymmetric Yang Mills theory are known to exhibit many intriguing forms of cluster-algebraic structure. We leverage this structure to upgrade the symbols of the eight- and nine-particle amplitudes to complete analytic functions. This is done by systematically projecting onto the components of these amplitudes that take different functional forms, and matching each component to an ansatz of multiple polylogarithms with negative cluster-coordinate arguments. The remaining additive constant can be determined analytically by comparing the collinear limit of each amplitude to known lower-multiplicity results. We also observe that the nonclassical part of each of these amplitudes admits a unique decomposition in terms of a specific $A_3$ cluster polylogarithm, and explore the numerical behavior of the remainder function along lines in the positive region.

2104.14194

Nickel-based complex oxides have served as a playground for decades in the quest for a copper-oxide analog of the high-temperature (high-Tc) superconductivity. They may provide key points towards understanding the mechanism of the high-Tc and an alternative route for a room-temperature superconductor. The recent discovery of superconductivity in the infinite-layer nickelate thin films has put this pursuit to an end. Having complete control in material preparation and a full understanding of the properties and electronic structures becomes the center of gravity of current research in nickelates. Thus far, material synthesis remains challenging. The demonstration of perfect diamagnetism is still missing, and understanding the role of the interface and bulk to the superconducting properties is still lacking. Here, we synthesized high-quality Nd0.8Sr0.2NiO2 thin films with different thicknesses and investigated the interface and strain effects on the electrical, magnetic and optical properties. The perfect diamagnetism is demonstrated, confirming the occurrence of superconductivity in the thin films. Unlike the thick films in which the normal state Hall coefficient (RH) changes signs from negative to positive as the temperature decreases, the RH of the films thinner than 6.1-nm remains negative at the whole temperature range below 300 K, suggesting a thickness-driven band structure modification. The X-ray spectroscopy reveals the Ni-O hybridization nature in doped finite-layer nickelates, and the hybridization is enhanced as the thickness decreases. Consistent with band structure calculations on nickelate/SrTiO3 interfaces, the interface and strain effect induce the dominating electron-like band in the ultrathin film, thus causing the sign-change of the RH.

2104.14195

We consider a one-dimensional stochastic differential equation driven by a Wiener process, where the diffusion coefficient depends on an ergodic fast process. The averaging principle is satisfied: it is well-known that the slow component converges in distribution to the solution of an averaged equation, with generator determined by averaging the square of the diffusion coefficient. We propose a version of the averaging principle, where the solution is interpreted as the sum of two terms: one depending on the average of the diffusion coefficient, the other giving fluctuations around that average. Both the average and fluctuation terms contribute to the limit, which illustrates why it is required to average the square of the diffusion coefficient to find the limit behavior.

2104.14196

We present the hybrid hadron string dynamic (HydHSD) model connecting the parton-hadron-string dynamic model (PHSD) and a hydrodynamic model taking into account shear viscosity within the Israel-Stewart approach. The performance of the code is tested on the pion and proton rapidity and transverse mass distributions calculated for Au+Au and Pb+Pb collision at AGS--SPS energies. The influence of the switch time from transport to hydro models, the viscous parameter, and freeze-out time are discussed. Since the applicability of the Israel-Stewart hydrodynamics assumes the perturbative character of the viscous stress tensor, $\pi^{\mu\nu}$, which should not exceed the ideal energy-momentum tensor, $T_{\rm id}^{\mu\nu}$, hydrodynamical codes usually rescale the shear stress tensor if the inequality $\|\pi^{\mu\nu}\|\ll \|T_{\rm id}^{\mu\nu}\|$ is not fulfilled in some sense. We show that the form of the corresponding condition plays an important role in the sensitivity of hydrodynamic calculations to the viscous parameter -- a ratio of the shear viscosity to the entropy density, $\eta/s$. It is shown that the constraints used in the vHLLE and MUSIC models give the same results for the observables. With these constraints, the rapidity distributions and transverse momentum spectra are most sensitive to a change of the $\eta/s$ ratio. As an alternative, a strict condition is used. We performed global fits the rapidity and transverse mass distribution of pion and protons. It was also found that $\eta/s$ as a function of the collision energy monotonically increases from $E_{\rm lab}=6A$GeV up to $E_{\rm lab}=40A$GeV and saturates for higher SPS energies. We observe that it is difficult to reproduce simultaneously pion and proton rapidity distribution within our model with the present choice of the equation of state without a phase transition.

2104.14197

We design numerical schemes for a class of slow-fast systems of stochastic differential equations, where the fast component is an Ornstein-Uhlenbeck process and the slow component is driven by a fractional Brownian motion with Hurst index $H>1/2$. We establish the asymptotic preserving property of the proposed scheme: when the time-scale parameter goes to $0$, a limiting scheme which is consistent with the averaged equation is obtained. With this numerical analysis point of view, we thus illustrate the recently proved averaging result for the considered SDE systems and the main differences with the standard Wiener case.

2104.14198

We estimate the short- to medium term impact of six major past pandemic crises on the CO2 emissions and energy transition to renewable electricity. The results show that the previous pandemics led on average to a 3.4-3.7% fall in the CO2 emissions in the short-run (1-2 years since the start of the pandemic). The effect is present only in the rich countries, as well as in countries with the highest pandemic death toll (where it disappears only after 8 years) and in countries that were hit by the pandemic during economic recessions. We found that the past pandemics increased the share of electricity generated from renewable sources within the fiveyear horizon by 1.9-2.3 percentage points in the OECD countries and by 3.2-3.9 percentage points in countries experiencing economic recessions. We discuss the implications of our findings in the context of CO2 emissions and the transition to renewable energy in the post-COVID-19 era.

2104.14199

Recommender systems have achieved great success in modeling user's preferences on items and predicting the next item the user would consume. Recently, there have been many efforts to utilize time information of users' interactions with items to capture inherent temporal patterns of user behaviors and offer timely recommendations at a given time. Existing studies regard the time information as a single type of feature and focus on how to associate it with user preferences on items. However, we argue they are insufficient for fully learning the time information because the temporal patterns of user preference are usually heterogeneous. A user's preference for a particular item may 1) increase periodically or 2) evolve over time under the influence of significant recent events, and each of these two kinds of temporal pattern appears with some unique characteristics. In this paper, we first define the unique characteristics of the two kinds of temporal pattern of user preference that should be considered in time-aware recommender systems. Then we propose a novel recommender system for timely recommendations, called TimelyRec, which jointly learns the heterogeneous temporal patterns of user preference considering all of the defined characteristics. In TimelyRec, a cascade of two encoders captures the temporal patterns of user preference using a proposed attention module for each encoder. Moreover, we introduce an evaluation scenario that evaluates the performance on predicting an interesting item and when to recommend the item simultaneously in top-K recommendation (i.e., item-timing recommendation). Our extensive experiments on a scenario for item recommendation and the proposed scenario for item-timing recommendation on real-world datasets demonstrate the superiority of TimelyRec and the proposed attention modules.

2104.14200

Very recently, To et al.~have experimentally explored granular flow in a cylindrical silo, with a bottom wall that rotates horizontally with respect to the lateral wall \cite{Kiwing2019}. Here, we numerically reproduce their experimental findings, in particular, the peculiar behavior of the mass flow rate $Q$ as a function of the frequency of rotation $f$. Namely, we find that for small outlet diameters $D$ the flow rate increased with $f$, while for larger $D$ a non-monotonic behavior is confirmed. Furthermore, using a coarse-graining technique, we compute the macroscopic density, momentum, and the stress tensor fields. These results show conclusively that changes in the discharge process are directly related to changes in the flow pattern from funnel flow to mass flow. Moreover, by decomposing the mass flux (linear momentum field) at the orifice into two main factors: macroscopic velocity and density fields, we obtain that the non-monotonic behavior of the linear momentum is caused by density changes rather than by changes in the macroscopic velocity. In addition, by analyzing the spatial distribution of the kinetic stress, we find that for small orifices increasing rotational shear enhances the mean kinetic pressure $\langle p^k \rangle$ and the system dilatancy. This reduces the stability of the arches, and, consequently, the volumetric flow rate increases monotonically. For large orifices, however, we detected that $\langle p^k \rangle$ changes non-monotonically, which might explain the non-monotonic behavior of $Q$ when varying the rotational shear.

2104.14201

Uncertainty quantification is a key aspect in robotic perception, as overconfident or point estimators can lead to collisions and damages to the environment and the robot. In this paper, we evaluate scalable approaches to uncertainty quantification in single-view supervised depth learning, specifically MC dropout and deep ensembles. For MC dropout, in particular, we explore the effect of the dropout at different levels in the architecture. We demonstrate that adding dropout in the encoder leads to better results than adding it in the decoder, the latest being the usual approach in the literature for similar problems. We also propose the use of depth uncertainty in the application of pseudo-RGBD ICP and demonstrate its potential for improving the accuracy in such a task.

2104.14202

Recent researches on unsupervised domain adaptation (UDA) have demonstrated that end-to-end ensemble learning frameworks serve as a compelling option for UDA tasks. Nevertheless, these end-to-end ensemble learning methods often lack flexibility as any modification to the ensemble requires retraining of their frameworks. To address this problem, we propose a flexible ensemble-distillation framework for performing semantic segmentation based UDA, allowing any arbitrary composition of the members in the ensemble while still maintaining its superior performance. To achieve such flexibility, our framework is designed to be robust against the output inconsistency and the performance variation of the members within the ensemble. To examine the effectiveness and the robustness of our method, we perform an extensive set of experiments on both GTA5 to Cityscapes and SYNTHIA to Cityscapes benchmarks to quantitatively inspect the improvements achievable by our method. We further provide detailed analyses to validate that our design choices are practical and beneficial. The experimental evidence validates that the proposed method indeed offer superior performance, robustness and flexibility in semantic segmentation based UDA tasks against contemporary baseline methods.

2104.14203

Electricity exchanges offer several trading possibilities for market participants: starting with futures products through the spot market consisting of the auction and continuous part, and ending with the balancing market. This variety of choice creates a new question for traders - when to trade to maximize the gain. This problem is not trivial especially for trading larger volumes as the market participants should also consider their own price impact. The following paper raises this issue considering two markets: the hourly EPEX Day-Ahead Auction and the quarter-hourly EPEX Intraday Auction. We consider a realistic setting which includes a forecasting study and a suitable evaluation. For a meaningful optimization many price scenarios are considered that we obtain using bootstrap with models that are well-known and researched in the electricity price forecasting literature. The own market impact is predicted by mimicking the demand or supply shift in the respectful auction curves. A number of trading strategies is considered, e.g. minimization of the trading costs, risk neutral or risk averse agents. Additionally, we provide theoretical results for risk neutral agents. Especially we show when the optimal trading path coincides with the solution that minimizes transaction costs. The application study is conducted using the German market data, but the presented methods can be easily utilized with other two auction-based markets. They could be also generalized to other market types, what is discussed in the paper as well. The empirical results show that market participants could increase their gains significantly compared to simple benchmark strategies.

2104.14204

We present the novel Efficient Line Segment Detector and Descriptor (ELSD) to simultaneously detect line segments and extract their descriptors in an image. Unlike the traditional pipelines that conduct detection and description separately, ELSD utilizes a shared feature extractor for both detection and description, to provide the essential line features to the higher-level tasks like SLAM and image matching in real time. First, we design the one-stage compact model, and propose to use the mid-point, angle and length as the minimal representation of line segment, which also guarantees the center-symmetry. The non-centerness suppression is proposed to filter out the fragmented line segments caused by lines' intersections. The fine offset prediction is designed to refine the mid-point localization. Second, the line descriptor branch is integrated with the detector branch, and the two branches are jointly trained in an end-to-end manner. In the experiments, the proposed ELSD achieves the state-of-the-art performance on the Wireframe dataset and YorkUrban dataset, in both accuracy and efficiency. The line description ability of ELSD also outperforms the previous works on the line matching task.

2104.14205

Quasi-equilibrium approximation is a widely used closure approximation approach for model reduction with applications in complex fluids, materials science, etc. It is based on the maximum entropy principle and leads to thermodynamically consistent coarse-grain models. However, its high computational cost is a known barrier for fast and accurate applications. Despite its good mathematical properties, there are very few works on the fast and efficient implementations of quasi-equilibrium approximations. In this paper, we give efficient implementations of quasi-equilibrium approximations for antipodally symmetric problems on unit circle and unit sphere using polynomial and piecewise polynomial approximations. Comparing to the existing methods using linear or cubic interpolations, our approach achieves high accuracy (double precision) with much less storage cost. The methods proposed in this paper can be directly extended to handle other moment closure approximation problems.

2104.14206

Scene graph generation has emerged as an important problem in computer vision. While scene graphs provide a grounded representation of objects, their locations and relations in an image, they do so only at the granularity of proposal bounding boxes. In this work, we propose the first, to our knowledge, framework for pixel-level segmentation-grounded scene graph generation. Our framework is agnostic to the underlying scene graph generation method and address the lack of segmentation annotations in target scene graph datasets (e.g., Visual Genome) through transfer and multi-task learning from, and with, an auxiliary dataset (e.g., MS COCO). Specifically, each target object being detected is endowed with a segmentation mask, which is expressed as a lingual-similarity weighted linear combination over categories that have annotations present in an auxiliary dataset. These inferred masks, along with a novel Gaussian attention mechanism which grounds the relations at a pixel-level within the image, allow for improved relation prediction. The entire framework is end-to-end trainable and is learned in a multi-task manner with both target and auxiliary datasets.

2104.14207

Deep learning (DL) frameworks have been extensively designed, implemented, and used in software projects across many domains. However, due to the lack of knowledge or information, time pressure, complex context, etc., various uncertainties emerge during the development, leading to assumptions made in DL frameworks. Though not all the assumptions are negative to the frameworks, being unaware of certain assumptions can result in critical problems (e.g., system vulnerability and failures, inconsistencies, and increased cost). As the first step of addressing the critical problems, there is a need to explore and understand the assumptions made in DL frameworks. To this end, we conducted an exploratory study to understand self-claimed assumptions (SCAs) about their distribution, classification, and impacts using code comments from nine popular DL framework projects on GitHub. The results are that: (1) 3,084 SCAs are scattered across 1,775 files in the nine DL frameworks, ranging from 1,460 (TensorFlow) to 8 (Keras) SCAs. (2) There are four types of validity of SCAs: Valid SCA, Invalid SCA, Conditional SCA, and Unknown SCA, and four types of SCAs based on their content: Configuration and Context SCA, Design SCA, Tensor and Variable SCA, and Miscellaneous SCA. (3) Both valid and invalid SCAs may have an impact within a specific scope (e.g., in a function) on the DL frameworks. Certain technical debt is induced when making SCAs. There are source code written and decisions made based on SCAs. This is the first study on investigating SCAs in DL frameworks, which helps researchers and practitioners to get a comprehensive understanding on the assumptions made. We also provide the first dataset of SCAs for further research and practice in this area.

2104.14208

To determine whether some often-used lexical association measures assign high scores to n-grams that chance could have produced as frequently as observed, we used an extension of Fisher's exact test to sequences longer than two words to analyse a corpus of four million words. The results, based on the precision-recall curve and a new index called chance-corrected average precision, show that, as expected, simple-ll is extremely effective. They also show, however, that MI3 is more efficient than the other hypothesis tests-based measures and even reaches a performance level almost equal to simple-ll for 3-grams. It is additionally observed that some measures are more efficient for 3-grams than for 2-grams, while others stagnate.

2104.14209

Graph representation learning has become a ubiquitous component in many scenarios, ranging from social network analysis to energy forecasting in smart grids. In several applications, ensuring the fairness of the node (or graph) representations with respect to some protected attributes is crucial for their correct deployment. Yet, fairness in graph deep learning remains under-explored, with few solutions available. In particular, the tendency of similar nodes to cluster on several real-world graphs (i.e., homophily) can dramatically worsen the fairness of these procedures. In this paper, we propose a biased edge dropout algorithm (FairDrop) to counter-act homophily and improve fairness in graph representation learning. FairDrop can be plugged in easily on many existing algorithms, is efficient, adaptable, and can be combined with other fairness-inducing solutions. After describing the general algorithm, we demonstrate its application on two benchmark tasks, specifically, as a random walk model for producing node embeddings, and to a graph convolutional network for link prediction. We prove that the proposed algorithm can successfully improve the fairness of all models up to a small or negligible drop in accuracy, and compares favourably with existing state-of-the-art solutions. In an ablation study, we demonstrate that our algorithm can flexibly interpolate between biasing towards fairness and an unbiased edge dropout. Furthermore, to better evaluate the gains, we propose a new dyadic group definition to measure the bias of a link prediction task when paired with group-based fairness metrics. In particular, we extend the metric used to measure the bias in the node embeddings to take into account the graph structure.

2104.14210

We theoretically investigate the phase and voltage correlation dynamics under a current noise including thermal and quantum fluctuations in a resistively and capacitively shunted Josephson (RCSJ) junction. Within the linear regime, an external current is found to shift and intensify the deterministic contributions in phase and voltage. In addition to the deterministic contribution, we observe the relaxation of autocorrelation functions of phase and voltage to finite values due to the current noise. We also find an earlier decay of coherence at a higher temperature in which thermal fluctuations dominate over quantum ones.

2104.14211

We seek to find the precursors of the Herbig Ae/Be stars in the solar vicinity within 500 pc from the Sun. We do this by creating an optically selected sample of intermediate mass T-Tauri stars (IMTT stars) here defined as stars of masses $1.5 M_{\odot}\leq M_* \leq 5 M_{\odot}$ and spectral type between F and K3, from literature. We use literature optical photometry (0.4-1.25$\mu$m) and distances determined from \textit{Gaia} DR2 parallax measurements together with Kurucz stellar model spectra to place the stars in a HR-diagram. With Siess evolutionary tracks we identify intermediate mass T-Tauri stars from literature and derive masses and ages. We use Spitzer spectra to classify the disks around the stars into Meeus Group I and Group II disks based on their [F$_{30}$/F$_{13.5}$] spectral index. We also examine the 10$\mu$m silicate dust grain emission and identify emission from Polycyclic Aromatic Hydrocarbons (PAH). From this we build a qualitative picture of the disks around the intermediate mass T-Tauri stars and compare this with available spatially resolved images at infrared and at sub-millimeter wavelengths to confirm our classification. We find 49 intermediate mass T-Tauri stars with infrared excess. The identified disks are similar to the older Herbig Ae/Be stars in disk geometries and silicate dust grain population. Spatially resolved images at infra-red and sub-mm wavelengths suggest gaps and spirals are also present around the younger precursors to the Herbig Ae/Be stars. Comparing the timescale of stellar evolution towards the main sequence and current models of protoplanetary disk evolution the similarity between Herbig Ae/Be stars and the intermediate mass T-Tauri stars points towards an evolution of Group I and Group II disks that are disconnected, and that they represent two different evolutionary paths.

2104.14212

We introduce the tree distance, a new distance measure on graphs. The tree distance can be computed in polynomial time with standard methods from convex optimization. It is based on the notion of fractional isomorphism, a characterization based on a natural system of linear equations whose integer solutions correspond to graph isomorphism. By results of Tinhofer (1986, 1991) and Dvo\v{r}\'ak (2010), two graphs G and H are fractionally isomorphic if and only if, for every tree T, the number of homomorphisms from T to G equals the corresponding number from T to H, which means that the tree distance of G and H is zero. Our main result is that this correspondence between the equivalence relations "fractional isomorphism" and "equal tree homomorphism densities" can be extended to a correspondence between the associated distance measures. Our result is inspired by a similar result due to Lov\'asz and Szegedy (2006) and Borgs, Chayes, Lov\'asz, S\'os, and Vesztergombi (2008) that connects the cut distance of graphs to their homomorphism densities (over all graphs), which is a fundamental theorem in the theory of graph limits. We also introduce the path distance of graphs and take the corresponding result of Dell, Grohe, and Rattan (2018) for exact path homomorphism counts to an approximate level. Our results answer an open question of Grohe (2020). We establish our main results by generalizing our definitions to graphons as this allows us to apply techniques from functional analysis. We prove the fairly general statement that, for every "reasonably" defined graphon pseudometric, an exact correspondence to homomorphism densities can be turned into an approximate one. We also provide an example of a distance measure that violates this reasonableness condition. This incidentally answers an open question of Greb\'ik and Rocha (2021).

2104.14213

Quantitative trading is an integral part of financial markets with high calculation speed requirements, while no quantum algorithms have been introduced into this field yet. We propose quantum algorithms for high-frequency statistical arbitrage trading in this work by utilizing variable time condition number estimation and quantum linear regression.The algorithm complexity has been reduced from the classical benchmark O(N^2d) to O(sqrt(d)(kappa)^2(log(1/epsilon))^2 )). It shows quantum advantage, where N is the length of trading data, and d is the number of stocks, kappa is the condition number and epsilon is the desired precision. Moreover, two tool algorithms for condition number estimation and cointegration test are developed.

2104.14214

The stochastic network calculus (SNC) holds promise as a framework to calculate probabilistic performance bounds in networks of queues. A great challenge to accurate bounds and efficient calculations are stochastic dependencies between flows due to resource sharing inside the network. However, by carefully utilizing the basic SNC concepts in the network analysis the necessity of taking these dependencies into account can be minimized. To that end, we fully unleash the power of the pay multiplexing only once principle (PMOO, known from the deterministic network calculus) in the SNC analysis. We choose an analytic combinatorics presentation of the results in order to ease complex calculations. In tree-reducible networks, a subclass of a general feedforward networks, we obtain a perfect analysis in terms of avoiding the need to take internal flow dependencies into account. In a comprehensive numerical evaluation, we demonstrate how this unleashed PMOO analysis can reduce the known gap between simulations and SNC calculations significantly, and how it favourably compares to state-of-the art SNC calculations in terms of accuracy and computational effort. Driven by these promising results, we also consider general feedforward networks, when some flow dependencies have to be taken into account. To that end, the unleashed PMOO analysis is extended to the partially dependent case and a case study of a canonical example topology, known as the diamond network, is provided, again displaying favourable results over the state of the art.

2104.14215

(abridged) Context. The origin of hot exozodiacal dust and its connection with outer dust reservoirs remains unclear. Aims. We aim to explore the possible connection between hot exozodiacal dust and warm dust reservoirs (> 100 K) in asteroid belts. Methods. We use precision near-infrared interferometry with VLTI/PIONIER to search for resolved emission at H band around a selected sample of nearby stars. Results. Our observations reveal the presence of resolved near-infrared emission around 17 out of 52 stars, four of which are shown to be due to a previously unknown stellar companion. The 13 other H-band excesses are thought to originate from the thermal emission of hot dust grains. Taking into account earlier PIONIER observations, and after reevaluating the warm dust content of all our PIONIER targets through spectral energy distribution modeling, we find a detection rate of 17.1(+8.1)(-4.6)% for H-band excess around main sequence stars hosting warm dust belts, which is statistically compatible with the occurrence rate of 14.6(+4.3)(-2.8)% found around stars showing no signs of warm dust. After correcting for the sensitivity loss due to partly unresolved hot disks, under the assumption that they are arranged in a thin ring around their sublimation radius, we however find tentative evidence at the 3{\sigma} level that H-band excesses around stars with outer dust reservoirs (warm or cold) could be statistically larger than H-band excesses around stars with no detectable outer dust. Conclusions. Our observations do not suggest a direct connection between warm and hot dust populations, at the sensitivity level of the considered instruments, although they bring to light a possible correlation between the level of H-band excesses and the presence of outer dust reservoirs in general.

2104.14216

With the aim of better understanding the numerical properties of the lattice Boltzmann method (LBM), a general methodology is proposed to derive its hydrodynamic limits in the discrete setting. It relies on a Taylor expansion in the limit of low Knudsen numbers. With a single asymptotic analysis, two kinds of deviations with the Navier-Stokes (NS) equations are explicitly evidenced: consistency errors, inherited from the kinetic description of the LBM, and numerical errors attributed to its space and time discretization. The methodology is applied to the Bhatnagar-Gross-Krook (BGK), the regularized and the multiple relaxation time (MRT) collision models in the isothermal framework. Deviation terms are systematically confronted to linear analyses in order to validate their expressions, interpret them and provide explanations for their numerical properties. The low dissipation of the BGK model is then related to a particular pattern of its error terms in the Taylor expansion. Similarly, dissipation properties of the regularized and MRT models are explained by a phenomenon referred to as hyperviscous degeneracy. The latter consists in an unexpected resurgence of high-order Knudsen effects induced by a large numerical pre-factor. It is at the origin of over-dissipation and severe instabilities in the low-viscosity regime.

2104.14217

In this paper an approximation of the image of the closed ball of the space $L_p$ $(p>1)$ centered at the origin with radius $r$ under Hilbert-Schmidt integral operator $F(\cdot):L_p\rightarrow L_q$ $\displaystyle \left(\frac{1}{p}+\frac{1}{q}=1\right)$ is presented. An error estimation for given approximation is obtained.

2104.14218

We investigate which plane curves admit rational families of quasi-toric relations. This extends previous results of Takahashi and Tokunaga in the positive case and of the author in the negative case.

2104.14219

X-ray flux from the inner hot region around central compact object in a binary system illuminates the upper surface of an accretion disc and it behaves like a corona. This region can be photoionised by the illuminating radiation, thus can emit different emission lines. We study those line spectra in black hole X-ray binaries for different accretion flow parameters including its geometry. The varying range of model parameters captures maximum possible observational features. We also put light on the routinely observed Fe line emission properties based on different model parameters, ionization rate, and Fe abundances. We find that the Fe line equivalent width $W_{\rm E}$ decreases with increasing disc accretion rate and increases with the column density of the illuminated gas. Our estimated line properties are in agreement with observational signatures.

2104.14220

In this paper, we investigate influence of Earth's orbit on the shadow of Sgr A*. Motivated by inclination of the Earth's orbit that is not located at galactic plane, we consider the black hole shadow for arbitrary inclinations and different velocities of observers. It is found that rotation axis of a black hole might not be extracted from its shadow, since the ways of the shadow getting distorted depend not only on the spin of the black hole, but also velocities of observers. Namely, appearance of the shadow could be rotated by an angle in observers' celestial sphere for an observer in motion. In order to consider the Earth's orbit for the shadow of Sgr A*, we present a formalism for calculating the shadow in terms of the local velocity expansion. It shows that influence of the orbital velocity of the Earth on the shadows of Sgr A* is much larger than that of the displacement in Earth's orbit. The deviation of size of the shadow is around $10^{-4}$. And the deviation of the distortion parameter of the shadow is around $10^{-14}$.

2104.14221

Recently, there has been an increasing concern about the privacy issue raised by using personally identifiable information in machine learning. However, previous portrait matting methods were all based on identifiable portrait images. To fill the gap, we present P3M-10k in this paper, which is the first large-scale anonymized benchmark for Privacy-Preserving Portrait Matting. P3M-10k consists of 10,000 high-resolution face-blurred portrait images along with high-quality alpha mattes. We systematically evaluate both trimap-free and trimap-based matting methods on P3M-10k and find that existing matting methods show different generalization capabilities when following the Privacy-Preserving Training (PPT) setting, i.e., "training on face-blurred images and testing on arbitrary images". To devise a better trimap-free portrait matting model, we propose P3M-Net, which leverages the power of a unified framework for both semantic perception and detail matting, and specifically emphasizes the interaction between them and the encoder to facilitate the matting process. Extensive experiments on P3M-10k demonstrate that P3M-Net outperforms the state-of-the-art methods in terms of both objective metrics and subjective visual quality. Besides, it shows good generalization capacity under the PPT setting, confirming the value of P3M-10k for facilitating future research and enabling potential real-world applications. The source code and dataset will be made publicly available.

2104.14222

Complicated assembly processes can be described as a sequence of two main activities: grasping and insertion. While general grasping solutions are common in industry, insertion is still only applicable to small subsets of problems, mainly ones involving simple shapes in fixed locations and in which the variations are not taken into consideration. Recently, RL approaches with prior knowledge (e.g., LfD or residual policy) have been adopted. However, these approaches might be problematic in contact-rich tasks since interaction might endanger the robot and its equipment. In this paper, we tackled this challenge by formulating the problem as a regression problem. By combining visual and force inputs, we demonstrate that our method can scale to 16 different insertion tasks in less than 10 minutes. The resulting policies are robust to changes in the socket position, orientation or peg color, as well as to small differences in peg shape. Finally, we demonstrate an end-to-end solution for 2 complex assembly tasks with multi-insertion objectives when the assembly board is randomly placed on a table.

2104.14223

Due to the noticeable structural similarity and being neighborhood in periodic table of group-IV and -V elemental monolayers, whether the combination of group-IV and -V elements could have stable nanosheet structures with optimistic properties has attracted great research interest. In this work, we performed first-principles simulations to investigate the elastic, vibrational and electronic properties of the carbon nitride (CN) nanosheet in the puckered honeycomb structure with covalent interlayer bonding. It has been demonstrated that the structural stability of CN nanosheet is essentially maintained by the strong interlayer \so\ bonding between adjacent carbon atoms in the opposite atomic layers. A negative Poisson's ratio in the out-of-plane direction under biaxial deformation, and the extreme in-plane stiffness of CN nanosheet, only slightly inferior to the monolayer graphene, are revealed. Moreover, the highly anisotropic mechanical and electronic response of CN nanosheet to tensile strain have been explored.

2104.14224

"Changing-look quasars" (CLQs) are active galactic nuclei (AGN) showing extreme variability that results in a transition from Type 1 to Type 2. The short timescales of these transitions present a challenge to the unified model of AGN and the physical processes causing these transitions remain poorly understood. CLQs also provide interesting samples for the study of AGN host galaxies since the central emission disappears almost entirely. Previous searches for CLQs have utilised photometric variability or SDSS classification changes to systematically identify CLQs, this approach may miss lower luminosity CLQs. In this paper, we aim to use spectroscopic data to asses if analysis difference spectra can be used to detect further changing look quasars missed by photometric searches. We search SDSS-II DR 7 repeat spectra for sources that exhibit either a disappearance or appearance of both broad line emission and accretion disk continuum emission by directly analysing the difference spectrum between two epochs of observation. From a sample of 24,782 objects with difference spectra, our search yielded six CLQs within the redshift range $0.1 \leq z \leq 0.3$, including four newly identified sources. Spectral analysis indicates that changes in accretion rate can explain the changing-look behaviour. While a change in dust extinction fits the changes in spectral shape, the time-scales of the changes observed are too short for obscuration from torus clouds. Using difference spectra was shown to be an effective and sensitive way to detect CLQs. We recover CLQs an order of magnitude lower in luminosities than those found by photometric searches and achieve higher completeness than spectroscopic searches relying on pipeline classification.

2104.14225

We investigate how different fairness assumptions affect results concerning lock-freedom, a typical liveness property targeted by session type systems. We fix a minimal session calculus and systematically take into account all known fairness assumptions, thereby identifying precisely three interesting and semantically distinct notions of lock-freedom, all of which having a sound session type system. We then show that, by using a general merge operator in an otherwise standard approach to global session types, we obtain a session type system complete for the strongest amongst those notions of lock-freedom, which assumes only justness of execution paths, a minimal fairness assumption for concurrent systems.

2104.14226

Results. We illustrate our profile-fitting technique and present the K\,{\sc i} velocity structure of the dense ISM along the paths to all targets. As a validation test of the dust map, we show comparisons between distances to several reconstructed clouds with recent distance assignments based on different techniques. Target star extinctions estimated by integration in the 3D map are compared with their K\,{\sc i} 7699 A absorptions and the degree of correlation is found comparable to the one between the same K\,{\sc i} line and the total hydrogen column for stars distributed over the sky that are part of a published high resolution survey. We show images of the updated dust distribution in a series of vertical planes in the Galactic longitude interval 150-182.5 deg and our estimated assignments of radial velocities to the opaque regions. Most clearly defined K\,{\sc i} absorptions may be assigned to a dense dust cloud between the Sun and the target star. It appeared relatively straightforward to find a velocity pattern consistent will all absorptions and ensuring coherence between adjacent lines of sight, at the exception of a few weak lines. We compare our results with recent determinations of velocities of several clouds and find good agreement. These results demonstrate that the extinction-K\,{\sc i} relationship is tight enough to allow linking the radial velocity of the K\,{\sc i} lines to the dust clouds seen in 3D, and that their combination may be a valuable tool in building a 3D kinetic structure of the dense ISM. We discuss limitations and perspectives for this technique.

2104.14227

Gamma-Ray Integrated Detectors (GRID) is a student project designed to use multiple gamma-ray detectors carried by nanosatellites (CubeSat), forming a full-time and all-sky gamma-ray detection network to monitor the transient gamma-ray sky in the multi-messenger astronomy era. A compact CubeSat gamma-ray detector has been designed and implemented for GRID, including its hardware and firmware. The detector employs four Gd2Al2Ga3O12 : Ce (GAGG:Ce) scintillators coupled with four silicon photomultiplier (SiPM) arrays to achieve a high detection efficiency of gamma rays between 10 keV and 2 MeV with low power and small dimensions. The first detector designed by the undergraduate student team onboard a commercial CubeSat was launched into a Sun-synchronous orbit on 29 October 2018. The detector has been in a normal observation state and accumulated data for approximately 1 month after on-orbit functional and performance tests in 2019.

2104.14228

Due to the widespread use of tools and the development of text processing techniques, the size and range of clinical data are not limited to structured data. The rapid growth of recorded information has led to big data platforms in healthcare that could be used to improve patients' primary care and serve various secondary purposes. Patient similarity assessment is one of the secondary tasks in identifying patients who are similar to a given patient, and it helps derive insights from similar patients' records to provide better treatment. This type of assessment is based on calculating the distance between patients. Since representing and calculating the similarity of patients plays an essential role in many secondary uses of electronic records, this article examines a new data representation method for Electronic Medical Records (EMRs) while taking into account the information in clinical narratives for similarity computing. Some previous works are based on structured data types, while other works only use unstructured data. However, a comprehensive representation of the information contained in the EMR requires the effective aggregation of both structured and unstructured data. To address the limitations of previous methods, we propose a method that captures the co-occurrence of different medical events, including signs, symptoms, and diseases extracted via unstructured data and structured data. It integrates data as discriminative features to construct a temporal tree, considering the difference between events that have short-term and long-term impacts. Our results show that considering signs, symptoms, and diseases in every time interval leads to less MSE and more precision compared to baseline representations that do not consider this information or consider them separately from structured data.

2104.14229

Geometrical chirality is a universal phenomenon that is encountered on many different length scales ranging from geometrical shapes of various living organisms to protein and DNA molecules. Interaction of chiral matter with chiral light - that is, electromagnetic field possessing a certain handedness - underlies our ability to discriminate enantiomers of chiral molecules. In this context, it is often desired to have an optical cavity that would efficiently couple to only a specific (right or left) molecular enantiomer, and not couple to the opposite one. Here, we demonstrate a single-handedness chiral optical cavity supporting only an eigenmode of a given handedness without the presence of modes of other helicity. Resonant excitation of the cavity with light of appropriate handedness enables formation of a helical standing wave with a uniform chirality density, while the opposite handedness does not cause any resonant effects. Furthermore, only chiral emitters of the matching handedness efficiently interact with such a chiral eigenmode, enabling the handedness-selective coupling light-matter strength. The proposed system expands the set of tools available for investigations of chiral matter and opens the door to studies of chiral electromagnetic vacuum.

2104.14230

Although the expansion of the Universe explicitly breaks the time-translation symmetry, cosmological predictions for the stochastic gravitational wave background (SGWB) are usually derived under the so-called stationary hypothesis. By dropping this assumption and keeping track of the time dependence of gravitational waves at all length scales, we derive the expected unequal-time (and equal-time) waveform of the SGWB generated by scaling sources, such as cosmic defects. For extinct and smooth enough sources, we show that all observable quantities are uniquely and analytically determined by the holomorphic Fourier transform of the anisotropic stress correlator. Both the strain power spectrum and the energy density parameter are shown to have an oscillatory fine structure, they significantly differ on large scales while running in phase opposition at large wavenumbers $k$. We then discuss scaling sources that are never extinct nor smooth and which generate a singular Fourier transform of the anisotropic stress correlator. For these, we find the appearance of interferences on top of the above-mentioned fine-structure as well as atypical behaviour at small scales. For instance, we expect the rescaled strain power spectrum $k^2 \mathcal{P}_h$ generated by long cosmic strings in the matter era to oscillate around a scale invariant plateau. These singular sources are also shown to produce orders of magnitude difference between the rescaled strain spectra and the energy density parameter suggesting that only the former should be used for making reliable observable predictions. Finally, we discuss how measuring such a fine structure in the SGWB could disambiguate the possible cosmological sources.

2104.14231

Reconfigurable optical systems are the object of continuing, intensive research activities, as they hold great promise for realizing a new generation of compact, miniaturized, and flexible optical devices. However, current reconfigurable systems often tune only a single state variable triggered by an external stimulus, thus, leaving out many potential applications. Here we demonstrate a reconfigurable multistate optical system enabled by phase transitions in vanadium dioxide (VO2). By controlling the phase-transition characteristics of VO2 with simultaneous stimuli, the responses of the optical system can be reconfigured among multiple states. In particular, we show a quadruple-state dynamic plasmonic display that responds to both temperature tuning and hydrogen-doping. Furthermore, we introduce an electron-doping scheme to locally control the phase-transition behavior of VO2, enabling an optical encryption device encoded by multiple keys. Our work points the way toward advanced multistate reconfigurable optical systems, which substantially outperform current optical devices in both breadth of capabilities and functionalities.

2104.14232

Transit photometry is perhaps the most successful method for detecting exoplanets to date. However, a substantial amount of signal processing is needed since the dip in the signal detected, an indication that there is a planet in transit, is minuscule compared to the overall background signal due mainly to its host star. In this paper, we put forth a doable and straightforward method to enhance the signal and reduce noise. We discuss how to achieve higher planetary signals by subtracting equal halves of the host star - a folded detection. This results in a light curve with a double peak-to-peak signal, 2R_p^2/R_s^2, compared to the usual transit. We derive an expression of the light curve and investigate the effect of two common noises: the white Gaussian background noise and the noise due to the occurrences of sunspots. We show that in both simulation and analytical expression, the folded transit reduces the effective noise by a factor of 1/sqrt(2). This reduction and the doubling of the signal enables: (1) less number of transit measurements to get a definitive transiting planet signal and (2) detection of smaller planetary radii with the usual transit with the same number of transit data. Furthermore, we show that in the presence of multiple sunspots, the estimation of planetary parameters is more accurate. While our calculations may be very simple, it covers the basic concept of planetary transits.

2104.14233

Attracted by its scalability towards practical codeword lengths, we revisit the idea of Turbo-autoencoders for end-to-end learning of PHY-Layer communications. For this, we study the existing concepts of Turbo-autoencoders from the literature and compare the concept with state-of-the-art classical coding schemes. We propose a new component-wise training algorithm based on the idea of Gaussian a priori distributions that reduces the overall training time by almost a magnitude. Further, we propose a new serial architecture inspired by classical serially concatenated Turbo code structures and show that a carefully optimized interface between the two component autoencoders is required. To the best of our knowledge, these serial Turbo autoencoder structures are the best known neural network based learned sequences that can be trained from scratch without any required expert knowledge in the domain of channel codes.

2104.14234

The use of deep neural networks (DNNs) in safety-critical applications like mobile health and autonomous driving is challenging due to numerous model-inherent shortcomings. These shortcomings are diverse and range from a lack of generalization over insufficient interpretability to problems with malicious inputs. Cyber-physical systems employing DNNs are therefore likely to suffer from safety concerns. In recent years, a zoo of state-of-the-art techniques aiming to address these safety concerns has emerged. This work provides a structured and broad overview of them. We first identify categories of insufficiencies to then describe research activities aiming at their detection, quantification, or mitigation. Our paper addresses both machine learning experts and safety engineers: The former ones might profit from the broad range of machine learning topics covered and discussions on limitations of recent methods. The latter ones might gain insights into the specifics of modern ML methods. We moreover hope that our contribution fuels discussions on desiderata for ML systems and strategies on how to propel existing approaches accordingly.

2104.14235

Learning to re-identify or retrieve a group of people across non-overlapped camera systems has important applications in video surveillance. However, most existing methods focus on (single) person re-identification (re-id), ignoring the fact that people often walk in groups in real scenarios. In this work, we take a step further and consider employing context information for identifying groups of people, i.e., group re-id. We propose a novel unified framework based on graph neural networks to simultaneously address the group-based re-id tasks, i.e., group re-id and group-aware person re-id. Specifically, we construct a context graph with group members as its nodes to exploit dependencies among different people. A multi-level attention mechanism is developed to formulate both intra-group and inter-group context, with an additional self-attention module for robust graph-level representations by attentively aggregating node-level features. The proposed model can be directly generalized to tackle group-aware person re-id using node-level representations. Meanwhile, to facilitate the deployment of deep learning models on these tasks, we build a new group re-id dataset that contains more than 3.8K images with 1.5K annotated groups, an order of magnitude larger than existing group re-id datasets. Extensive experiments on the novel dataset as well as three existing datasets clearly demonstrate the effectiveness of the proposed framework for both group-based re-id tasks. The code is available at https://github.com/daodaofr/group_reid.

2104.14236

Table Structure Recognition is an essential part of end-to-end tabular data extraction in document images. The recent success of deep learning model architectures in computer vision remains to be non-reflective in table structure recognition, largely because extensive datasets for this domain are still unavailable while labeling new data is expensive and time-consuming. Traditionally, in computer vision, these challenges are addressed by standard augmentation techniques that are based on image transformations like color jittering and random cropping. As demonstrated by our experiments, these techniques are not effective for the task of table structure recognition. In this paper, we propose TabAug, a re-imagined Data Augmentation technique that produces structural changes in table images through replication and deletion of rows and columns. It also consists of a data-driven probabilistic model that allows control over the augmentation process. To demonstrate the efficacy of our approach, we perform experimentation on ICDAR 2013 dataset where our approach shows consistent improvements in all aspects of the evaluation metrics, with cell-level correct detections improving from 92.16% to 96.11% over the baseline.

2104.14237

We prove that the absolute extendability constant of a finite metric space may be determined by computing relative projection constants of certain Lipschitz-free spaces. As an application, we show that $\mbox{ae}(3)=4/3$ and $\mbox{ae}(4)\geq (5+4\sqrt{2})/7$. Moreover, we discuss how to compute relative projection constants by solving linear programming problems.

2104.14238

Dense high-energy monoenergetic proton beams are vital for wide applications, thus modern laser-plasma-based ion acceleration methods are aiming to obtain high-energy proton beams with energy spread as low as possible. In this work, we put forward a quantum radiative compression method to post-compress a highly accelerated proton beam and convert it to a dense quasi-monoenergetic one. We find that when the relativistic plasma produced by radiation pressure acceleration collides head-on with an ultraintense laser beam, large-amplitude plasma oscillations are excited due to quantum radiation-reaction and the ponderomotive force, which induce compression of the phase space of protons located in its acceleration phase with negative gradient. Our three-dimensional spin-resolved QED particle-in-cell simulations show that hollow-structure proton beams with a peak energy $\sim$ GeV, relative energy spread of few percents and number $N_p\sim10^{10}$ (or $N_p\sim 10^9$ with a $1\%$ energy spread) can be produced in near future laser facilities, which may fulfill the requirements of important applications, such as, for radiography of ultra-thick dense materials, or as injectors of hadron colliders.

2104.14239

Although there is a clear indication that stages of residential decision making are characterized by their own stakeholders, activities, and outcomes, many studies on residential low-carbon technology adoption only implicitly address stage-specific dynamics. This paper explores stakeholder influences on residential photovoltaic adoption from a procedural perspective, so-called stakeholder dynamics. The major objective is the understanding of underlying mechanisms to better exploit the potential for residential photovoltaic uptake. Four focus groups have been conducted in close collaboration with the independent institute for social science research SINUS Markt- und Sozialforschung in East Germany. By applying a qualitative content analysis, major influence dynamics within three decision stages are synthesized with the help of egocentric network maps from the perspective of residential decision-makers. Results indicate that actors closest in terms of emotional and spatial proximity such as members of the social network represent the major influence on residential PV decision-making throughout the stages. Furthermore, decision-makers with a higher level of knowledge are more likely to move on to the subsequent stage. A shift from passive exposure to proactive search takes place through the process, but this shift is less pronounced among risk-averse decision-makers who continuously request proactive influences. The discussions revealed largely unexploited potential regarding the stakeholders local utilities and local governments who are perceived as independent, trustworthy and credible stakeholders. Public stakeholders must fulfill their responsibility in achieving climate goals by advising, assisting, and financing services for low-carbon technology adoption at the local level. Supporting community initiatives through political frameworks appears to be another promising step.

2104.14240

This paper investigates the problem of straight-line path following for magnetic helical microswimmers. The control objective is to make the helical microswimmer to converge to a straight line without violating the step-out frequency constraint. The proposed feedback control solution is based on an optimal decision strategy (ODS) that is cast as a trust-region subproblem (TRS), i.e., a quadratic program over a sphere. The ODS-based control strategy minimizes the difference between the microrobot velocity and an integral line-of-sight (ILOS)-based reference vector field while respecting the magnetic saturation constraints and ensuring the absolute continuity of the control input. Due to the embedded integral action in the reference vector field, the microswimmer will follow the desired straight line by compensating for the drift effect of the environmental disturbances as well as the microswimmer weight.

2104.14241

We report experimental and theoretical evidence of strong electron-plasmon interaction in n-doped single-layer MoS2. Angle-resolved photoemission spectroscopy (ARPES) measurements reveal the emergence of distinctive signatures of polaronic coupling in the electron spectral function. Calculations based on many-body perturbation theory illustrate that electronic coupling to two-dimensional (2D) carrier plasmons provides an exhaustive explanation of the experimental spectral features and their energies. These results constitute compelling evidence of the formation of plasmon-induced polaronic quasiparticles, suggesting that highly-doped transition-metal dichalcogenides may provide a new platform to explore strong-coupling phenomena between electrons and plasmons in 2D.

2104.14242

In this article, we analyze perinatal data with birth weight (BW) as primarily interesting response variable. Gestational age (GA) is usually an important covariate and included in polynomial form. However, in opposition to this univariate regression, bivariate modeling of BW and GA is recommended to distinguish effects on each, on both, and between them. Rather than a parametric bivariate distribution, we apply conditional copula regression, where marginal distributions of BW and GA (not necessarily of the same form) can be estimated independently, and where the dependence structure is modeled conditional on the covariates separately from these marginals. In the resulting distributional regression models, all parameters of the two marginals and the copula parameter are observation-specific. Besides biometric and obstetric information, data on drinking water contamination and maternal smoking are included as environmental covariates. While the Gaussian distribution is suitable for BW, the skewed GA data are better modeled by the three-parametric Dagum distribution. The Clayton copula performs better than the Gumbel and the symmetric Gaussian copula, indicating lower tail dependence (stronger dependence when both variables are low), although this non-linear dependence between BW and GA is surprisingly weak and only influenced by Cesarean section. A non-linear trend of BW on GA is detected by a classical univariate model that is polynomial with respect to the effect of GA. Linear effects on BW mean are similar in both models, while our distributional copula regression also reveals covariates' effects on all other parameters.

2104.14243

The goal of this work is to find the simplest UV completion of Accidental Composite Dark Matter Models (ACDM) that can dynamically generate an asymmetry for the DM candidate, the lightest \textit{dark baryon} (DCb), and simultaneously annihilate the symmetric component. In this framework the DCb is a bound state of a confining $\text{SU}(N)_{\text{DC}}$ gauge group, and can interact weakly with the visible sector. The constituents of the DCb can possess non-trivial charges under the Standard Model gauge group. The generation of asymmetry for such candidate is a two-flavor variation of the \emph{out-of-equilibrium} decay of a heavy scalar, with mass $M_\phi\gtrsim 10^{15}$ GeV. Below the scale of the scalars, the models recover accidental stability, or long-livedness, of the DM candidate. The symmetric component is annihilated by residual confined interactions provided that the mass of the DCb $m_{\text{DCb}} \lesssim 75$ TeV. We implement the mechanism of asymmetry generation, or a variation of it, in all the original ACDM models, managing to generate the correct asymmetry for DCb of masses in this range. For some of the models found, the stability of the DM candidate is not spoiled even considering generic GUT completions or asymmetry generation mechanisms in the visible sector.

2104.14244

Wasserstein distance induces a natural Riemannian structure for the probabilities on the Euclidean space. This insight of classical transport theory is fundamental for tremendous applications in various fields of pure and applied mathematics. We believe that an appropriate probabilistic variant, the adapted Wasserstein distance AW, can play a similar role for the class FP of filtered processes, i.e. stochastic processes together with a filtration. In contrast to other topologies for stochastic processes, probabilistic operations such as the Doob-decomposition, optimal stopping and stochastic control are continuous w.r.t. AW. We also show that (FP,AW) is a geodesic space, isometric to a classical Wasserstein space, and that martingales form a closed geodesically convex subspace.

2104.14245

Due to the increasing size of HPC machines, the fault presence is becoming an eventuality that applications must face. Natively, MPI provides no support for the execution past the detection of a fault, and this is becoming more and more constraining. With the introduction of ULFM (User Level Fault Mitigation library), it has been provided with a possible way to overtake a fault during the application execution at the cost of code modifications. ULFM is intrusive in the application and requires also a deep understanding of its recovery procedures. In this paper we propose Legio, a framework that lowers the complexity of introducing resiliency in an embarrassingly parallel MPI application. By hiding ULFM behind the MPI calls, the library is capable to expose resiliency features to the application in a transparent manner thus removing any integration effort. Upon fault, the failed nodes are discarded and the execution continues only with the non-failed ones. A hierarchical implementation of the solution has been also proposed to reduce the overhead of the repair process when scaling towards a large number of nodes. We evaluated our solutions on the Marconi100 cluster at CINECA, showing that the overhead introduced by the library is negligible and it does not limit the scalability properties of MPI. Moreover, we also integrated the solution in real-world applications to further prove its robustness by injecting faults.

2104.14246

In 2017 Skabelund constructed two new examples of maximal curves $\tilde{\mathcal{S}}_q$ and $\tilde{\mathcal{R}}_q$ as covers of the Suzuki and Ree curves, respectively. The resulting Skabelund curves are analogous to the Giulietti-Korchm\'aros cover of the Hermitian curve. In this paper a complete characterization of all Galois subcovers of the Skabelund curves $\tilde{\mathcal{S}}_q$ and $\tilde{\mathcal{R}}_q$ is given. Calculating the genera of the corresponding curves, we find new additions to the list of known genera of maximal curves over finite fields.

2104.14247

These lectures present some basic ideas and techniques in the spectral analysis of lattice Schrodinger operators with disordered potentials. In contrast to the classical Anderson tight binding model, the randomness is also allowed to possess only finitely many degrees of freedom. This refers to dynamically defined potentials, i.e., those given by evaluating a function along an orbit of some ergodic transformation (or of several commuting such transformations on higher-dimensional lattices). Classical localization theorems by Frohlich--Spencer for large disorders are presented, both for random potentials in all dimensions, as well as even quasi-periodic ones on the line. After providing the needed background on subharmonic functions, we then discuss the Bourgain-Goldstein theorem on localization for quasiperiodic Schrodinger cocycles assuming positive Lyapunov exponents.

2104.14248

In this paper we study a family of limsup sets that are defined using iterated function systems. Our main result is an analogue of Khintchine's theorem for these sets. We then apply this result to the topic of intrinsic Diophantine Approximation on self-similar sets. In particular, we define a new height function for an element of $\mathbb{Q}^d$ contained in a self-similar set in terms of its eventually periodic representations. For limsup sets defined with respect to this height function, we obtain a detailed description of their metric properties. The results of this paper hold in arbitrary dimensions and without any separation conditions on the underlying iterated function system.

2104.14249

Tractable safety-ensuring algorithms for cyber-physical systems are important in critical applications. Approaches based on Control Barrier Functions assume continuous enforcement, which is not possible in an online fashion. This paper presents two tractable algorithms to ensure forward invariance of discrete-time controlled cyber-physical systems. Both approaches are based on Control Barrier Functions to provide strict mathematical safety guarantees. The first algorithm exploits Lipschitz continuity and formulates the safety condition as a robust program which is subsequently relaxed to a set of affine conditions. The second algorithm is inspired by tube-NMPC and uses an affine Control Barrier Function formulation in conjunction with an auxiliary controller to guarantee safety of the system. We combine an approximate NMPC controller with the second algorithm to guarantee strict safety despite approximated constraints and show its effectiveness experimentally on a mini-Segway.

2104.14250

In this letter, we propose a computationally efficient method for joint selection of cancellation carriers (CCs) and calculation of their values minimizing the out-of-band (OOB) power in non-contiguous (NC-) OFDM transmission. The proposed new CCs selection method achieves higher OOB power attenuation than algorithms known from literature as well as noticable reception performance improvement.

2104.14251

The role of gravity in human motor control is at the same time obvious and difficult to isolate. It can be assessed by performing experiments in variable gravity. We propose that adiabatic invariant theory may be used to reveal nearly-conserved quantities in human voluntary rhythmic motion, an individual being seen as a complex time-dependent dynamical system with bounded motion in phase-space. We study an explicit realization of our proposal: An experiment in which we asked participants to perform $\infty-$ shaped motion of their right arm during a parabolic flight, either at self-selected pace or at a metronome's given pace. Gravity varied between $0$ and $1.8$ $g$ during a parabola. We compute the adiabatic invariants in participant's frontal plane assuming a separable dynamics. It appears that the adiabatic invariant in vertical direction increases linearly with $g$, in agreement with our model. Differences between the free and metronome-driven conditions show that participants' adaptation to variable gravity is maximal without constraint. Furthermore, motion in the participant's transverse plane induces trajectories that may be linked to higher-derivative dynamics. Our results show that adiabatic invariants are relevant quantities to show the changes in motor strategy in time-dependent environments.

2104.14252

Thanks to Atkinson (1938), we know the first two terms of the asymptotic formula for the square mean integral value of the Riemann zeta function $\zeta$ on the critical line. Following both his work and the approach of Titchmarsh (1986), we present an explicit version of the Atkinson formula, improving on a recent bound by Simoni\v{c} (2019). We use mostly classical tools, such as the approximate functional equation and the explicit convexity bounds of the zeta function given by Backlund (1918).

2104.14253

In the high energy limit of hadron collisions, the evolution of the gluon density in the longitudinal momentum fraction can be deduced from the Balitsky hierarchy of equations or, equivalently, from the nonlinear Jalilian-Marian-Iancu-McLerran-Weigert-Leonidov-Kovner (JIMWLK) equation. The solutions of the latter can be studied numerically by using its reformulation in terms of a Langevin equation. In this paper, we present a comprehensive study of systematic effects associated with the numerical framework, in particular the ones related to the inclusion of the running coupling. We consider three proposed ways in which the running of the coupling constant can be included: "square root" and "noise" prescriptions and the recent proposal by Hatta and Iancu. We implement them both in position and momentum spaces and we investigate and quantify the differences in the resulting evolved gluon distributions. We find that the systematic differences associated with the implementation technicalities can be of a similar magnitude as differences in running coupling prescriptions in some cases, or much smaller in other cases.

2104.14254

Low-rank tensors are an established framework for high-dimensional least-squares problems. We propose to extend this framework by including the concept of block-sparsity. In the context of polynomial regression each sparsity pattern corresponds to some subspace of homogeneous multivariate polynomials. This allows us to adapt the ansatz space to align better with known sample complexity results. The resulting method is tested in numerical experiments and demonstrates improved computational resource utilization and sample efficiency.

2104.14255

We study how to design edge server placement and server scheduling policies under workload uncertainty for 5G networks. We introduce a new metric called resource pooling factor to handle unexpected workload bursts. Maximizing this metric offers a strong enhancement on top of robust optimization against workload uncertainty. Using both real traces and synthetic traces, we show that the proposed server placement and server scheduling policies not only demonstrate better robustness against workload uncertainty than existing approaches, but also significantly reduce the cost of service providers. Specifically, in order to achieve close-to-zero workload rejection rate, the proposed server placement policy reduces the number of required edge servers by about 25% compared with the state-of-the-art approach; the proposed server scheduling policy reduces the energy consumption of edge servers by about 13% without causing much impact on the service quality.

2104.14256

The quantum geometry of Bloch bands fundamentally affects a wide range of physical phenomena. For example, the quantum Hall effect is governed by the Chern number, and superconductivity by the distance between the Bloch states -- the quantum metric. Here, we show that key properties of a weakly interacting Bose-Einstein condensate (BEC) depend on the underlying quantum geometry, and in the flat band limit they radically depart from those of a dispersive system. The speed of sound becomes proportional to the quantum metric of the condensed state, and depends linearly on the interaction energy. The fraction of particles depleted out of the condensate and the quantum fluctuations of the density-density correlation obtain a finite value for infinitesimally small interactions directly determined by the quantum distance, in striking contrast to dispersive bands where they vanish with the interaction strength. Our results reveal that non-trivial quantum geometry allows stability of a flat band BEC and anomalously strong quantum correlation effects.

2104.14257

We construct the global phase portraits of inflationary dynamics in teleparallel gravity models with a scalar field nonminimally coupled to torsion scalar. The adopted set of variables can clearly distinguish between different asymptotic states as fixed points, including the kinetic and inflationary regimes. The key role in the description of inflation is played by the heteroclinic orbits which run from the asymptotic saddle points to the late time attractor point and are approximated by nonminimal slow roll conditions. To seek the asymptotic fixed points we outline a heuristic method in terms of the "effective potential" and "effective mass", which can be applied for any nonminimally coupled theories. As particular examples we study positive quadratic nonminimal couplings with quadratic and quartic potentials, and note how the portraits differ qualitatively from the known scalar-curvature counterparts. For quadratic models inflation can only occur at small nonminimal coupling to torsion, as for larger coupling the asymptotic de Sitter saddle point disappears from the physical phase space. Teleparallel models with quartic potentials are not viable for inflation at all, since for small nonminimal coupling the asymptotic saddle point exhibits weaker than exponential expansion, and for larger coupling disappears too.

2104.14258

The Dual-Frequency synthetic aperture radar (DFSAR) system manifested on the Chandrayaan-2 spacecraft represents a significant step forward in radar exploration of solid solar system objects. It combines SAR at two wavelengths (L- and S-bands) and multiple resolutions with several polarimetric modes in one lightweight ($\sim$ 20 kg) package. The resulting data from DFSAR support calculation of the 2$\times$2 complex scattering matrix for each resolution cell, which enables lunar near surface characterization in terms of radar polarization properties at different wavelengths and incidence angles. In this paper, we report on the calibration and preliminary performance characterization of DFSAR data based on the analysis of a sample set of crater regions on the Moon. Our calibration analysis provided a means to compare on-orbit performance with pre-launch measurements and the results matched with the pre-launch expected values. Our initial results show that craters in both permanently shadowed regions (PSRs) and non-PSRs that are classified as Circular Polarization Ratio (CPR)-anomalous in previous S-band radar analyses appear anomalous at L-band also. We also observe that material evolution and physical properties at their interior and proximal ejecta are decoupled. For Byrgius C crater region, we compare our analysis of dual-frequency radar data with the predicted behaviours of theoretical scattering models. If crater age estimates are available, comparison of their radar polarization properties at multiple wavelengths similar to that of the three unnamed south polar crater regions shown in this study may provide new insights into how the rockiness of craters evolves with time.

2104.14259

A formalisation of G\"odel's incompleteness theorems using the Isabelle proof assistant is described. This is apparently the first mechanical verification of the second incompleteness theorem. The work closely follows {\'S}wierczkowski (2003), who gave a detailed proof using hereditarily finite set theory. The adoption of this theory is generally beneficial, but it poses certain technical issues that do not arise for Peano arithmetic. The formalisation itself should be useful to logicians, particularly concerning the second incompleteness theorem, where existing proofs are lacking in detail.

2104.14260

Practical quantum computing is rapidly becoming a reality. To harness quantum computers' real potential in software applications, one needs to have an in-depth understanding of all such characteristics of quantum computing platforms (QCPs), relevant from the Software Engineering (SE) perspective. Restrictions on copying, deletion, the transmission of qubit states, a hard dependency on quantum algorithms are few, out of many, examples of QCP characteristics that have significant implications for building quantum software. Thus, developing quantum software requires a paradigm shift in thinking by software engineers. This paper presents the key findings from the SE perspective, resulting from an in-depth examination of state-of-the-art QCPs available today. The main contributions that we present include i) Proposing a general architecture of the QCPs, ii) Proposing a programming model for developing quantum software, iii) Determining architecturally significant characteristics of QCPs, and \textbf{iv)} Determining the impact of these characteristics on various Quality Attributes (QAs) and Software Development Life Cycle (SDLC) activities. We show that the nature of QCPs makes them useful mainly in specialized application areas such as scientific computing. Except for performance and scalability, most of the other QAs (e.g., maintainability, testability, and reliability) are adversely affected by different characteristics of a QCP.

2104.14261

We have developed spin-resolved resonant electron energy-loss spectroscopy (SR-rEELS) in the primary energy of 0.3--1.5 keV, which corresponds to the core excitations of $2p\to3d$ absorption of transition metals and $3d\to4f$ absorption of rare earths. Element-specific carrier and valence plasmons can be observed by using the resonance enhancement of core absorptions. Spin-resolved plasmons were also observed using a spin-polarized electron source from a GaAs/GaAsP strained superlattice photocathode. Furthermore, this primary energy corresponds to an electron penetration depth of 1 to 10 nm and thus provides bulk-sensitive EELS spectra. The methodology is expected to complement the element-selective observation of elementary excitations by resonant inelastic x-ray scattering and resonant photoelectron spectroscopy.

2104.14262

Ziegler introduced the idea of a good partition $\{X_{p}:p\in P\}$ of a $T_{3}$-topological space, where $P$ is a finite partially ordered set, satisfying $\overline{X_{p}}=\bigcup_{q\leqslant p}X_{q}$ for all $p\in P$. Good partitions of Stone spaces arise naturally in the study of $\omega$-categorical structures, and a key concept for studying them is that of a $p$-trim open set which meets precisely those $X_{q}$ for which $q\geqslant p$. This paper develops the theory of infinite partitions of Stone spaces indexed by a poset where the trim sets form a neighbourhood base for the topology. We study the interplay between order properties of the poset and topological properties of the partition, examine extensions and completions of such partitions, and derive necessary and sufficient conditions on the poset for the existence of the various types of partition studied. We also identify circumstances in which a second countable Stone space with a trim partition indexed by a given poset is unique up to homeomorphism, subject to choices on the isolated point structure and boundedness of the partition elements. One corollary of our results is that there is a partition $\{X_{r}:r\in[0,1]\}$ of the Cantor set such that $\overline{X_{r}}=\bigcup_{s\leqslant r}X_{s}\text{ for all }r\in[0,1]$.

2104.14263

Liquid State Machines are brain inspired spiking neural networks (SNNs) with random reservoir connectivity and bio-mimetic neuronal and synaptic models. Reservoir computing networks are proposed as an alternative to deep neural networks to solve temporal classification problems. Previous studies suggest 2nd order (double exponential) synaptic waveform to be crucial for achieving high accuracy for TI-46 spoken digits recognition. The proposal of long-time range (ms) bio-mimetic synaptic waveforms is a challenge to compact and power efficient neuromorphic hardware. In this work, we analyze the role of synaptic orders namely: {\delta} (high output for single time step), 0th (rectangular with a finite pulse width), 1st (exponential fall) and 2nd order (exponential rise and fall) and synaptic timescales on the reservoir output response and on the TI-46 spoken digits classification accuracy under a more comprehensive parameter sweep. We find the optimal operating point to be correlated to an optimal range of spiking activity in the reservoir. Further, the proposed 0th order synapses perform at par with the biologically plausible 2nd order synapses. This is substantial relaxation for circuit designers as synapses are the most abundant components in an in-memory implementation for SNNs. The circuit benefits for both analog and mixed-signal realizations of 0th order synapse are highlighted demonstrating 2-3 orders of savings in area and power consumptions by eliminating Op-Amps and Digital to Analog Converter circuits. This has major implications on a complete neural network implementation with focus on peripheral limitations and algorithmic simplifications to overcome them.

2104.14264

Code reviews are one of the effective methods to estimate defectiveness in source code. However, the existing methods are dependent on experts or inefficient. In this paper, we improve the performance (in terms of speed and memory usage) of our existing code review assisting tool--CRUSO. The central idea of the approach is to estimate the defectiveness for an input source code by using the defectiveness score of similar code fragments present in various StackOverflow (SO) posts. The significant contributions of our paper are i) SOpostsDB: a dataset containing the PVA vectors and the SO posts information, ii) CRUSO-P: a code review assisting system based on PVA models trained on \emph{SOpostsDB}. For a given input source code, CRUSO-P labels it as {Likely to be defective, Unlikely to be defective, Unpredictable}. To develop CRUSO-P, we processed >3 million SO posts and 188200+ GitHub source files. CRUSO-P is designed to work with source code written in the popular programming languages {C, C#, Java, JavaScript, and Python}. CRUSO-P outperforms CRUSO with an improvement of 97.82% in response time and a storage reduction of 99.15%. CRUSO-P achieves the highest mean accuracy score of 99.6% when tested with the C programming language, thus achieving an improvement of 5.6% over the existing method.

2104.14265

Weighted monadic second-order logic is a weighted extension of monadic second-order logic that captures exactly the behaviour of weighted automata. Its semantics is parameterized with respect to a semiring on which the values that weighted formulas output are evaluated. Gastin and Monmege (2018) gave abstract semantics for a version of weighted monadic second-order logic to give a more general and modular proof of the equivalence of the logic with weighted automata. We focus on the abstract semantics of the logic and we give a complete axiomatization both for the full logic and for a fragment without general sum, thus giving a more fine-grained understanding of the logic. We discuss how common decision problems for logical languages can be adapted to the weighted setting, and show that many of these are decidable, though they inherit bad complexity from the underlying first- and second-order logics. However, we show that a weighted adaptation of satisfiability is undecidable for the logic when one uses the abstract interpretation.

2104.14266

We present the design and experimental validation of source seeking control algorithms for a unicycle mobile robot that is equipped with novel 3D-printed flexible graphene-based piezoresistive airflow sensors. Based solely on a local gradient measurement from the airflow sensors, we propose and analyze a projected gradient ascent algorithm to solve the source seeking problem. In the case of partial sensor failure, we propose a combination of Extremum-Seeking Control with our projected gradient ascent algorithm. For both control laws, we prove the asymptotic convergence of the robot to the source. Numerical simulations were performed to validate the algorithms and experimental validations are presented to demonstrate the efficacy of the proposed methods.

2104.14267