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Number of zeros seen by a particle around small clusters of other particles is encoded in the root partition, and partly characterizes the correlations in fractional quantum Hall trial wavefunctions. We explore a generalization wherein we consider the counting of zeros seen by a cluster of particles on another cluster. Numbers of such zeros between clusters in the Laughlin wavefunctions are fully determined by the root partition. However, such a counting is unclear for general Jain states where a polynomial expansion is difficult. Here we consider the simplest state beyond the Laughlin wavefunction, namely a state containing a single quasiparticle of the Laughlin state. We show numerically and analytically that in the trial wavefunction for the quasiparticle of the Laughlin state, counting of zeros seen by a cluster on another cluster depends on the relative dimensions of the two clusters. We further ask if the patterns in the counting of zeros extend, in at least an approximate sense, to wavefunctions beyond the trial states. Using numerical computations in systems up to $N=9$, we present results for the statistical distribution of zeros around particle clusters at the center of an FQH droplet in the ground state of a Hamiltonian that is perturbed away from the $V_1$ interaction (short-range repulsion). Evolution of this distribution with the strength of the perturbation shows that the counting of zeros is altered by even a weak perturbation away from the parent Hamiltonian, though the perturbations do not change the phase of the system., Comment: 14 pages, 11 figures

In steady-state, the plasma loss rate to the chamber wall is balanced by the ionization rate in the hot-filament discharges. This balance in the loss rate and ionization rate maintains the quasi--neutrality of the bulk plasma. In this report, we have studied the properties of bulk plasma in the presence of an auxiliary (additional) biased metal disk, which is working as a sink, in low-pressure helium plasma. A single Langmuir probe and emissive probe are used to characterize the plasma for various biases (positive and negative) to the metal disk, which was placed along the discharge axis inside the plasma. It is observed that only a positively biased disk increases the plasma potential, electron temperature, and plasma density. Moreover, the plasma parameters remain unaltered when the disk is negatively biased. The observed results for two different-sized positively metal disks are compared with an available theoretical model and found an opposite behavior of plasma density variation with the disk bias voltages at given discharge condition. The role of the primary energetic electron population in determining the plasma parameters is discussed. These experimental results are qualitatively explained on the basis of electrostatic confinement arising due to the loss of electrons to a biased metal disk electrode., Comment: 7, 6 figures

We propose a risk measure and construct an infectious diseases hazard map for India. Given an outbreak location, a hazard index is assigned to each city using an effective distance that depends on inter-city mobilities instead of geographical distance. We demonstrate its utility using an SIR model augmented with air, rail, and road data between top 446 cities. Simulations show that the effective distance from outbreak location reliably predicts the time of arrival of infection in other cities. The hazard index predictions compare well with the observed spread of SARS-CoV-2. The hazard map can be useful in other outbreaks also., Comment: 25 pages, 8 figures, including supplementary material

We provide closed-form approximations to the performance gain achieved in a RIS-assisted communication. We then consider a network deployment of RIS and Transmitter-Receiver pairs and use these approximate expressions to provide equivalence models which state that the performance of a RIS-equipped network is similar to the performance of an appropriately spatially scaled system. We provide a way of assigning available RIS to assist communication between several transmitter-receiver pairs. Several such approximations are expected to spawn from this study, with more clarity on structural aspects of the gains achieved in RIS-assisted communications., Comment: 4 pages, 6 figures

We investigate the presence of a black hole black string phase transition in Einstein Gauss Bonnet (EGB) gravity in the large dimension limit. The merger point is the static spacetime connecting the black string phase with the black hole phase. We consider several ranges of the Gauss-Bonnet parameter. We find that there is a range when the Gauss-Bonnet corrections are subordinate to the Einstein gravity terms in the large dimension limit, and yet the merger point geometry does not approach a black hole away from the neck. We cannot rule out a topology changing phase transition as argued by Kol. However as the merger point geometry does not approach the black hole geometry asymptotically it is not obvious that the transition is directly to a black hole phase. We also demonstrate that for another range of the Gauss-Bonnet parameter, the merger point geometry approaches the black hole geometry asymptotically when a certain parameter depending on the Gauss-Bonnet parameter $\alpha$ and on the parameters in the Einstein-Gauss-Bonnet black hole metric is small enough., Comment: 23 pages, modifications, subsection added

Using a mean-field theory of signal propagation, we analyze the evolution of correlations between two signals propagating forward through a deep ReLU network with correlated weights. Signals become highly correlated in deep ReLU networks with uncorrelated weights. We show that ReLU networks with anti-correlated weights can avoid this fate and have a chaotic phase where the signal correlations saturate below unity. Consistent with this analysis, we find that networks initialized with anti-correlated weights can train faster (in a teacher-student setting) by taking advantage of the increased expressivity in the chaotic phase. Combining this with a previously proposed strategy of using an asymmetric initialization to reduce dead node probability, we propose an initialization scheme that allows faster training and learning than the best-known initializations.

Statistical distances (SDs), which quantify the dissimilarity between probability distributions, are central to machine learning and statistics. A modern method for estimating such distances from data relies on parametrizing a variational form by a neural network (NN) and optimizing it. These estimators are abundantly used in practice, but corresponding performance guarantees are partial and call for further exploration. In particular, there seems to be a fundamental tradeoff between the two sources of error involved: approximation and estimation. While the former needs the NN class to be rich and expressive, the latter relies on controlling complexity. This paper explores this tradeoff by means of non-asymptotic error bounds, focusing on three popular choices of SDs -- Kullback-Leibler divergence, chi-squared divergence, and squared Hellinger distance. Our analysis relies on non-asymptotic function approximation theorems and tools from empirical process theory. Numerical results validating the theory are also provided.

Quantum control of large spin registers is crucial for many applications ranging from spectroscopy to quantum information. A key factor that determines the efficiency of a register for implementing a given information processing task is its network topology. One particular type, called star-topology, involves a central qubit uniformly interacting with a set of ancillary qubits. A particular advantage of the star-topology quantum registers is in the efficient preparation of large entangled states, called NOON states, and their generalized variants. Thanks to the robust generation of such correlated states, spectral simplicity, ease of polarization transfer from ancillary qubits to the central qubit, as well as the availability of large spin-clusters, the star-topology registers have been utilized for several interesting applications over the last few years. Here we review some recent progress with the star-topology registers, particularly via nuclear magnetic resonance methods., Comment: 15 pages, 10 figures

A thermodynamic framework has been developed for a class of amorphous polymers used in fused deposition modeling (FDM), in order to predict the residual stresses and the accompanying distortion of the geometry of the printed part (warping). When a polymeric melt is cooled, the inhomogeneous distribution of temperature causes spatially varying volumetric shrinkage resulting in the generation of residual stresses. Shrinkage is incorporated into the framework by introducing an isotropic volumetric expansion/contraction in the kinematics of the body. We show that the parameter for shrinkage also appears in the systematically derived rate-type constitutive relation for the stress. The solidification of the melt around the glass transition temperature is emulated by drastically increasing the viscosity of the melt. In order to illustrate the usefulness and efficacy of the derived constitutive relation, we consider four ribbons of polymeric melt stacked on each other such as those extruded using a flat nozzle: each layer laid instantaneously and allowed to cool for one second before another layer is laid on it. Each layer cools, shrinks and warps until a new layer is laid, at which time the heat from the newly laid layer flows and heats up the bottom layers. The residual stresses of the existing and newly laid layers readjust to satisfy equilibrium. Such mechanical and thermal interactions amongst layers result in a complex distribution of residual stresses. The plane strain approximation predicts nearly equibiaxial tensile stress conditions in the core region of the solidified part, implying that a pre-existing crack in that region is likely to propagate and cause failure of the part during service. The free-end of the interface between the first and the second layer is subjected to the largest magnitude of combined shear and tension in the plane with a propensity for delamination.

We numerically study quenches from a fully ordered state to the ferromagnetic regime of the chiral $\mathbb{Z}_3$ clock model, where the physics can be understood in terms of sparse domain walls of six flavors. As in the previously studied models, the spread of entangled domain wall pairs generated by the quench lead to a linear growth of entropy with time, upto a time $\ell/2v_g$ in size-$\ell$ subsystems in the bulk where $v_g$ is the maximal group velocity of domain walls. In small subsystems located in the bulk, the entropy continues to further grow towards $\ln 3$, as domain walls traverse the subsystem and increment the population of the two oppositely ordered states, restoring the $\mathbb{Z}_3$ symmetry. The latter growth in entropy is seen also in small subsystems near an open boundary in a non-chiral clock model. In contrast to this, in the case of the chiral model, the entropy of small subsystems near an open boundary saturates. We rationalize the difference in behavior in terms of qualitatively different scattering properties of domain walls at the open boundary in the chiral model. We also present empirical results for entropy growth, correlation spread, and energies of longitudinal-field-induced bound states of domain wall pairs in the chiral model.

We present results from applying the SNAD anomaly detection pipeline to the third public data release of the Zwicky Transient Facility (ZTF DR3). The pipeline is composed of 3 stages: feature extraction, search of outliers with machine learning algorithms and anomaly identification with followup by human experts. Our analysis concentrates in three ZTF fields, comprising more than 2.25 million objects. A set of 4 automatic learning algorithms was used to identify 277 outliers, which were subsequently scrutinised by an expert. From these, 188 (68%) were found to be bogus light curves -- including effects from the image subtraction pipeline as well as overlapping between a star and a known asteroid, 66 (24%) were previously reported sources whereas 23 (8%) correspond to non-catalogued objects, with the two latter cases of potential scientific interest (e. g. 1 spectroscopically confirmed RS Canum Venaticorum star, 4 supernovae candidates, 1 red dwarf flare). Moreover, using results from the expert analysis, we were able to identify a simple bi-dimensional relation which can be used to aid filtering potentially bogus light curves in future studies. We provide a complete list of objects with potential scientific application so they can be further scrutinised by the community. These results confirm the importance of combining automatic machine learning algorithms with domain knowledge in the construction of recommendation systems for astronomy. Our code is publicly available at https://github.com/snad-space/zwad, Comment: 31 pages, 28 figures, 4 tables, accepted for publication in MNRAS

The problem of inverse reinforcement learning (IRL) is relevant to a variety of tasks including value alignment and robot learning from demonstration. Despite significant algorithmic contributions in recent years, IRL remains an ill-posed problem at its core; multiple reward functions coincide with the observed behavior and the actual reward function is not identifiable without prior knowledge or supplementary information. This paper presents an IRL framework called Bayesian optimization-IRL (BO-IRL) which identifies multiple solutions that are consistent with the expert demonstrations by efficiently exploring the reward function space. BO-IRL achieves this by utilizing Bayesian Optimization along with our newly proposed kernel that (a) projects the parameters of policy invariant reward functions to a single point in a latent space and (b) ensures nearby points in the latent space correspond to reward functions yielding similar likelihoods. This projection allows the use of standard stationary kernels in the latent space to capture the correlations present across the reward function space. Empirical results on synthetic and real-world environments (model-free and model-based) show that BO-IRL discovers multiple reward functions while minimizing the number of expensive exact policy optimizations., Comment: Accepted to 34th Conference on Neural Information Processing Systems (NeurIPS 2020). Includes Appendix. 21 pages

Atmospheric escape is an important factor shaping the exoplanet population and hence drives our understanding of planet formation. Atmospheric escape from giant planets is driven primarily by the stellar X-ray and extreme-ultraviolet (EUV) radiation. Furthermore, EUV and longer wavelength UV radiation power disequilibrium chemistry in the middle and upper atmosphere. Our understanding of atmospheric escape and chemistry, therefore, depends on our knowledge of the stellar UV fluxes. While the far-ultraviolet fluxes can be observed for some stars, most of the EUV range is unobservable due to the lack of a space telescope with EUV capabilities and, for the more distant stars, to interstellar medium absorption. Thus, it becomes essential to have indirect means for inferring EUV fluxes from features observable at other wavelengths. We present here analytic functions for predicting the EUV emission of F-, G-, K-, and M-type stars from the log $R'_{HK}$ activity parameter that is commonly obtained from ground-based optical observations of the Ca II H&K lines. The scaling relations are based on a collection of about 100 nearby stars with published log $R'_{HK}$ and EUV flux values, where the latter are either direct measurements or inferences from high-quality far-ultraviolet (FUV) spectra. The scaling relations presented here return EUV flux values with an accuracy of about three, which is slightly lower than that of other similar methods based on FUV or X-ray measurements., Comment: Accepted for publication by A&A

Spin-orbital liquids are quantum disordered states in systems with entangled spin and orbital degrees of freedom. We study exactly solvable spin-orbital models in two dimensions with selected Heisenberg-, Kitaev-, and $\Gamma$-type interactions, as well as external magnetic fields. These models realize a variety of spin-orbital-liquid phases featuring dispersing Majorana fermions with Fermi surfaces, nodal Dirac or quadratic band touching points, or full gaps. In particular, we show that Zeeman magnetic fields can stabilize nontrivial flux patterns and induce metamagnetic transitions between states with different topological character. Solvable nearest-neighbor biquadratic spin-orbital perturbations can be tuned to stabilize zero-energy flat bands. We discuss in detail the examples of $\mathrm{SO}(2)$- and $\mathrm{SO}(3)$-symmetric spin-orbital models on the square and honeycomb lattices, and use group-theoretical arguments to generalize to $\mathrm{SO}(\nu)$-symmetric models with arbitrary integer $\nu > 1$. These results extend the list of exactly solvable models with spin-orbital-liquid ground states and highlight the intriguing general features of such exotic phases. Our models are thus excellent starting points for more realistic modellings of candidate materials., Comment: 18 pages, 11 figures; v3 (expanded conclusion and appendix, additional minor changes)

States of strongly interacting particles are of fundamental interest in physics, and can produce exotic emergent phenomena and topological structures. We consider here two-dimensional electrons in a magnetic field, and, departing from the standard practice of restricting to the lowest LL, introduce a model short-range interaction that is infinitely strong compared to the cyclotron energy. We demonstrate that this model lends itself to an exact solution for the ground as well as excited states at arbitrary filling factors $\nu<1/2p$ and produces a fractional quantum Hall effect at fractions of the form $\nu=n/(2pn+ 1)$, where n and p are integers. The fractional quantum Hall states of our model share many topological properties with the corresponding Coulomb ground states in the lowest Landau level, such as the edge physics and the fractional charge of the excitations.

The recent increase in volume and complexity of available astronomical data has led to a wide use of supervised machine learning techniques. Active learning strategies have been proposed as an alternative to optimize the distribution of scarce labeling resources. However, due to the specific conditions in which labels can be acquired, fundamental assumptions, such as sample representativeness and labeling cost stability cannot be fulfilled. The Recommendation System for Spectroscopic follow-up (RESSPECT) project aims to enable the construction of optimized training samples for the Rubin Observatory Legacy Survey of Space and Time (LSST), taking into account a realistic description of the astronomical data environment. In this work, we test the robustness of active learning techniques in a realistic simulated astronomical data scenario. Our experiment takes into account the evolution of training and pool samples, different costs per object, and two different sources of budget. Results show that traditional active learning strategies significantly outperform random sampling. Nevertheless, more complex batch strategies are not able to significantly overcome simple uncertainty sampling techniques. Our findings illustrate three important points: 1) active learning strategies are a powerful tool to optimize the label-acquisition task in astronomy, 2) for upcoming large surveys like LSST, such techniques allow us to tailor the construction of the training sample for the first day of the survey, and 3) the peculiar data environment related to the detection of astronomical transients is a fertile ground that calls for the development of tailored machine learning algorithms., Comment: Accepted to the 2020 IEEE Symposium Series on Computational Intelligence

Context. Observationally constraining the atmospheric temperature-pressure (TP) profile of exoplanets is an important step forward for improving planetary atmosphere models, further enabling one to place the detection of spectral features and the measurement of atomic and molecular abundances through transmission and emission spectroscopy on solid ground. Aims. The aim is to constrain the TP profile of the ultra-hot Jupiter KELT-9b by fitting synthetic spectra to the observed H$\alpha$ and H$\beta$ lines and identify why self-consistent planetary TP models are unable to fit the observations. Methods. We construct 126 one-dimensional TP profiles varying the lower and upper atmospheric temperatures, as well as the location and gradient of the temperature rise. For each TP profile, we compute transmission spectra of the H$\alpha$ and H$\beta$ lines employing the Cloudy radiative transfer code, which self-consistently accounts for non-local thermodynamic equilibrium (NLTE) effects. Results. The TP profiles leading to best fit the observations are characterised by an upper atmospheric temperature of 10000-11000 K and by an inverted temperature profile at pressures higher than 10$^{-4}$ bar. We find that the assumption of local thermodynamic equilibrium (LTE) leads to overestimate the level population of excited hydrogen by several orders of magnitude, and hence to significantly overestimate the strength of the Balmer lines. The chemical composition of the best fitting models indicate that the high upper atmospheric temperature is most likely driven by metal photoionisation and that FeII and FeIII have comparable abundances at pressures lower than 10$^{-6}$ bar, possibly making the latter detectable. Conclusions. Modelling the atmospheres of ultra-hot Jupiters requires one to account for metal photoionisation. [abridged], Comment: Accepted for publication by A&A

Bringing the success of modern machine learning (ML) techniques to mobile devices can enable many new services and businesses, but also poses significant technical and research challenges. Two factors that are critical for the success of ML algorithms are massive amounts of data and processing power, both of which are plentiful, yet highly distributed at the network edge. Moreover, edge devices are connected through bandwidth- and power-limited wireless links that suffer from noise, time-variations, and interference. Information and coding theory have laid the foundations of reliable and efficient communications in the presence of channel imperfections, whose application in modern wireless networks have been a tremendous success. However, there is a clear disconnect between the current coding and communication schemes, and the ML algorithms deployed at the network edge. In this paper, we challenge the current approach that treats these problems separately, and argue for a joint communication and learning paradigm for both the training and inference stages of edge learning., Comment: 13 pages, 5 figures

We study transitions between topological phases featuring emergent fractionalized excitations in two-dimensional models for Mott insulators with spin and orbital degrees of freedom. The models realize fermionic quantum critical points in fractionalized Gross-Neveu$^\ast$ universality classes in (2+1) dimensions. They are characterized by the same set of critical exponents as their ordinary Gross-Neveu counterparts, but feature a different energy spectrum, reflecting the nontrivial topology of the adjacent phases. We exemplify this in a square-lattice model, for which an exact mapping to a $t$-$V$ model of spinless fermions allows us to make use of large-scale numerical results, as well as in a honeycomb-lattice model, for which we employ $\epsilon$-expansion and large-$N$ methods to estimate the critical behavior. Our results are potentially relevant for Mott insulators with $d^1$ electronic configurations and strong spin-orbit coupling, or for twisted bilayer structures of Kitaev materials., Comment: 6+6 pages, 2+3 figures; v3 (minor changes, discussion on strong-coupling limit)

In this article, we prove the proximinality of closed unit ball of $M$-ideals of compact operators. We also prove the ball proximinality of $M$-embedded spaces in their biduals. Moreover, we show that $\mathcal{K}(\ell_1)$, the space of compact operators on $\ell_1$, is ball proximinal in $\mathcal{B}(\ell_1)$, the space of bounded operators on $\ell_1$, even though $\mathcal{K}(\ell_1)$ is not an $M$-ideal in $\mathcal{B}(\ell_1)$.

B$_0$-VPG graphs are intersection graphs of vertical and horizontal line segments on a plane. Cohen, Golumbic, Trotter, and Wang [Order, 2016] pose the question of characterizing B$_0$-VPG permutation graphs. We respond here by characterizing B$_0$-VPG cocomparability graphs. This characterization also leads to a polynomial time recognition and B$_0$-VPG drawing algorithm for the class. Our B$_0$-VPG drawing algorithm starts by fixing any one of the many posets $P$ whose cocomparability graph is the input graph $G$. The drawing we obtain not only visualizes $G$ in that one can distinguish comparable pairs from incomparable ones, but one can also identify which among a comparable pair is larger in $P$ from this visualization., Comment: v1: Appears in the Proceedings of the 28th International Symposium on Graph Drawing and Network Visualization (GD 2020), v2: Changed order of authors and added ORCID, results unchanged

Fractional conductance is measured by partitioning $\nu = 1$ edge state using gate-tunable fractional quantum Hall (FQH) liquids of filling 1/3 or 2/3 for current injection and detection. We observe two sets of FQH plateaus 1/9, 2/9, 4/9 and 1/6, 1/3, 2/3 at low and high magnetic field ends of the $\nu = 1$ plateau respectively. The findings are explained by magnetic field dependent equilibration of three FQH edge modes with conductance $e^2/3h$ arising from edge reconstruction. The results reveal remarkable enhancement of the equilibration lengths of the FQH edge modes with increasing field., Comment: Supplemental information included

In two dimensions, the topological order described by $\mathbb{Z}_2$ gauge theory coupled to free or weakly interacting fermions with a nonzero spectral Chern number $\nu$ is classified by $\nu \; \mathrm{mod}\; 16$ as predicted by Kitaev [Ann. Phys. 321, 2 (2006)]. Here we provide a systematic and complete construction of microscopic models realizing this so-called sixteenfold way of anyon theories. These models are defined by $\Gamma$ matrices satisfying the Clifford algebra, enjoy a global $\mathrm{SO}(\nu)$ symmetry, and live on either square or honeycomb lattices depending on the parity of $\nu$. We show that all these models are exactly solvable by using a Majorana representation and characterize the topological order by calculating the topological spin of an anyonic quasiparticle and the ground-state degeneracy. The possible relevance of the $\nu=2$ and $\nu=3$ models to materials with Kugel-Khomskii-type spin-orbital interactions is discussed., Comment: 6+9 pages, 2+1 figures, published version

In many information-theoretic channel coding problems, adding an input cost constraint to the operational setup amounts to restricting the optimization domain in the capacity formula. This paper shows that, in contrast to common belief, such a simple modification does not hold for the cost-constrained (CC) wiretap channel (WTC). The secrecy-capacity of the discrete memoryless (DM) WTC without cost constraints is described by a single auxiliary random variable. For the CC DM-WTC, however, we show that two auxiliaries are necessary to achieve capacity. Specifically, we first derive the secrecy-capacity formula, proving the direct part via superposition coding. Then, we provide an example of a CC DM-WTC whose secrecy-capacity cannot be achieved using a single auxiliary. This establishes the fundamental role of superposition coding over CC WTCs.

With Full Duplex (FD), wireless terminal is capable of transmitting and receiving data simultaneously in the same frequency resources, however, it introduces self interference and co-channel interference. Even though various signal processing techniques are emerged to cancel the self interference, the bottleneck for FD performance in cellular systems is the co-channel interference from the other uplink and downlink signals. In this work we have studied both the uplink and downlink performances of a FD cellular network, where users employ fractional power control in uplink. We use Matern Cluster Process to model the network, which provides a tractable and realistic model to characterize the user-base station distances which are needed for uplink power control. Based on the obtained coverage probabilities, rates and their robust approximations, we show that while FD improves downlink performance, it severely hurts the uplink performance. Also, we provide a trade-off between uplink and downlink performances. Our study suggests dense deployment of low power base stations can improve the performance of FD system., Comment: no longer valid

A distributed binary hypothesis testing (HT) problem over a noisy (discrete and memoryless) channel studied previously by the authors is investigated from the perspective of the strong converse property. It was shown by Ahlswede and Csisz\'{a}r that a strong converse holds in the above setting when the channel is rate-limited and noiseless. Motivated by this observation, we show that the strong converse continues to hold in the noisy channel setting for a special case of HT known as testing against independence (TAI). The proof utilizes the blowing up lemma and the recent change of measure technique of Tyagi and Watanabe as the key tools.

The inflated transiting hot Jupiter HD 209458b is one of the best studied objects since the beginning of exoplanet characterization. Transmission observations of this system between the mid infrared and the far ultraviolet have revealed the signature of atomic, molecular, and possibly aerosol species in the lower atmosphere of the planet, as well as escaping hydrogen and metals in the upper atmosphere. From a re-analysis of near-ultraviolet (NUV) transmission observations of HD 209458b, we detect ionized iron (Fe II) absorption in a 100 A-wide range around 2370 A, lying beyond the planetary Roche lobe. However, we do not detect absorption of equally strong Fe II lines expected to be around 2600 A. Further, we find no evidence for absorption by neutral magnesium (Mg I), ionized magnesium (Mg II), nor neutral iron (Fe I). These results avoid the conflict with theoretical models previously found by Vidal-Madjar et al. (2013), which detected Mg I but did not detect Mg II from this same data set. Our results indicate that hydrodynamic escape is strong enough to carry atoms as heavy as iron beyond the planetary Roche lobe, even for planets less irradiated than the extreme ultra-hot-Jupiters such as WASP-12b and KELT-9b. The detection of iron and non-detection of magnesium in the upper atmosphere of HD 209458b can be explained by a model in which the lower atmosphere forms (hence, sequesters) primarily magnesium-bearing condensates, rather than iron condensates. This is suggested by current microphysical models. The inextricable synergy between upper- and lower-atmosphere properties highlights the value of combining observations that probe both regions., Comment: Accepted for publication at AAS Journals

Low frequency perturbations at the boundary of critical quantum chains can be understood in terms of the sequence of boundary conditions imposed by them, as has been previously demonstrated in the Ising and related fermion models. Using extensive numerical simulations, we explore the scaling behavior of the Loschmidt echo under longitudinal field perturbations at the boundary of a critical $\mathbb{Z}_3$ Potts model. We show that at times much larger than the relaxation time after a boundary quench, the Loschmidt-echo has a power-law scaling as expected from interpreting the quench as insertion of boundary condition changing operators. Similar scaling is observed as a function of time-period under a low frequency square-wave pulse. We present numerical evidence which indicate that under a sinusoidal or triangular pulse, scaling with time period is modified by Kibble-Zurek effect, again similar to the case of the Ising model. Results confirm the validity, beyond the Ising model, of the treatment of the boundary perturbations in terms of the effect on boundary conditions.

Simultaneous orthogonal matching pursuit (SOMP) and block OMP (BOMP) are two widely used techniques for sparse support recovery in multiple measurement vector (MMV) and block sparse (BS) models respectively. For optimal performance, both SOMP and BOMP require \textit{a priori} knowledge of signal sparsity or noise variance. However, sparsity and noise variance are unavailable in most practical applications. This letter presents a novel technique called generalized residual ratio thresholding (GRRT) for operating SOMP and BOMP without the \textit{a priori} knowledge of signal sparsity and noise variance and derive finite sample and finite signal to noise ratio (SNR) guarantees for exact support recovery. Numerical simulations indicate that GRRT performs similar to BOMP and SOMP with \textit{a priori} knowledge of signal and noise statistics., Comment: 13 pages, 8 figures

We use linear stability analysis to show that an isotropic phase of elongated particles with dipolar flow fields can develop nematic order as a result of their activity. We argue that ordering is favoured if the particles are flow-aligning and is strongest if the wavevector of the order perturbation is neither parallel nor perpendicular to the nematic director. Numerical solutions of the hydrodynamic equations of motion of an active nematic confirm the results. The instability is contrasted to the well-known hydrodynamic instability of an ordered active nematic.

We present the photometric properties of 2210 newly identified dwarf galaxy candidates in the MATLAS fields. The Mass Assembly of early Type gaLAxies with their fine Structures (MATLAS) deep imaging survey mapped $\sim$142 deg$^2$ of the sky around nearby isolated early type galaxies using MegaCam on the Canada-France-Hawaii Telescope, reaching surface brightnesses of $\sim$ 28.5 - 29 in the g-band. The dwarf candidates were identified through a direct visual inspection of the images and by visually cleaning a sample selected using a partially automated approach, and were morphologically classified at the time of identification. Approximately 75% of our candidates are dEs, indicating that a large number of early type dwarfs also populate low density environments, and 23.2% are nucleated. Distances were determined for 13.5% of our sample using pre-existing $z_{spec}$ measurements and HI detections. We confirm the dwarf nature for 99% of this sub-sample based on a magnitude cut $M_g$ = -18. Additionally, most of these ($\sim$90%) have relative velocities suggesting that they form a satellite population around nearby massive galaxies rather than an isolated field sample. Assuming that the candidates over the whole survey are satellites of the nearby galaxies, we demonstrate that the MATLAS dwarfs follow the same scaling relations as dwarfs in the Local Group as well as the Virgo and Fornax clusters. We also find that the nucleated fraction increases with $M_g$, and find evidence of a morphology-density relation for dwarfs around isolated massive galaxies., Comment: 21 pages, 16 figures, 1 table. Accepted for publication in MNRAS; minor corrections to the text, and updated Figures 8 and 9 to match published version

We present the first evidence that adaptive learning techniques can boost the discovery of unusual objects within astronomical light curve data sets. Our method follows an active learning strategy where the learning algorithm chooses objects which can potentially improve the learner if additional information about them is provided. This new information is subsequently used to update the machine learning model, allowing its accuracy to evolve with each new information. For the case of anomaly detection, the algorithm aims to maximize the number of scientifically interesting anomalies presented to the expert by slightly modifying the weights of a traditional Isolation Forest (IF) at each iteration. In order to demonstrate the potential of such techniques, we apply the Active Anomaly Discovery (AAD) algorithm to 2 data sets: simulated light curves from the PLAsTiCC challenge and real light curves from the Open Supernova Catalog. We compare the AAD results to those of a static IF. For both methods, we performed a detailed analysis for all objects with the ~2% highest anomaly scores. We show that, in the real data scenario, AAD was able to identify ~80\% more true anomalies than the IF. This result is the first evidence that AAD algorithms can play a central role in the search for new physics in the era of large scale sky surveys., Comment: 10 pages, 5 figures, updated to include PLAsTiCC results

A two-terminal distributed binary hypothesis testing problem over a noisy channel is studied. The two terminals, called the observer and the decision maker, each has access to $n$ independent and identically distributed samples, denoted by $\mathbf{U}$ and $\mathbf{V}$, respectively. The observer communicates to the decision maker over a discrete memoryless channel, and the decision maker performs a binary hypothesis test on the joint probability distribution of $(\mathbf{U},\mathbf{V})$ based on $\mathbf{V}$ and the noisy information received from the observer. The trade-off between the exponents of the type I and type II error probabilities is investigated. Two inner bounds are obtained, one using a separation-based scheme that involves type-based compression and unequal error-protection channel coding, and the other using a joint scheme that incorporates type-based hybrid coding. The separation-based scheme is shown to recover the inner bound obtained by Han and Kobayashi for the special case of a rate-limited noiseless channel, and also the one obtained by the authors previously for a corner point of the trade-off. Finally, we show via an example that the joint scheme achieves a strictly tighter bound than the separation-based scheme for some points of the error-exponents trade-off.

The hardware-software co-optimization of neural network architectures is becoming a major stream of research especially due to the emergence of commercial neuromorphic chips such as the IBM Truenorth and Intel Loihi. Development of specific neural network architectures in tandem with the design of the neuromorphic hardware considering the hardware constraints will make a huge impact in the complete system level application. In this paper, we study various neural network architectures and propose one that is hardware-friendly for a neuromorphic hardware with crossbar array of synapses. Considering the hardware constraints, we demonstrate how one may design the neuromorphic hardware so as to maximize classification accuracy in the trained network architecture, while concurrently, we choose a neural network architecture so as to maximize utilization in the neuromorphic cores. We also proposed a framework for mapping a neural network onto a neuromorphic chip named as the Mapping and Debugging (MaD) framework. The MaD framework is designed to be generic in the sense that it is a Python wrapper which in principle can be integrated with any simulator tool for neuromorphic chips., Comment: 18 pages, 14 figures, 4 tables, submitted to Frontiers in Neuroscience

The Lunar Ultraviolet Cosmic Imager (LUCI) is a near-ultraviolet (NUV) telescope with all-spherical mirrors, designed and built to fly as a scientific payload on a lunar mission with Team Indus - the original Indian entry to the Google Lunar X-Prize. Observations from the Moon provide a unique opportunity of a stable platform with an unobstructed view of the space at all wavelengths due to the absence of atmosphere and ionosphere. LUCI is an 80 mm aperture telescope, with a field of view of 27.6'x 20.4' and a spatial resolution of 5'', will scan the sky in the NUV (200-320 nm) domain to look for transient sources. We describe here the assembly, alignment, and calibration of the complete instrument. LUCI is now in storage in a class 1000 clean room and will be delivered to our flight partner in readiness for flight., Comment: Accepted for publication in Astrophysics and Space Science. Added new references and corrected typo, in the updated version