Search

Over 2000 Gamma ray sources identified by the Large Area Telescope (LAT) on NASA's Fermi Gamma-ray Space Telescope are considered unassociated, meaning that they have no known counterparts in any other frequency regime. We have carried out an image-based search for steep spectrum radio sources, with in-band spectral index less than -1.4, within the error regions of Fermi unassociated sources using 1 to 1.4 GHz radio data from the MeerKAT Absorption Line Survey (MALS) Data Release. MALS DR1 with a median rms noise of 22 to 25 microJy and 735,649 sources is a significant advance over past image-based searches with improvements in sensitivity, resolution and bandwidth. Steep spectrum candidates were identified using a combination of in-band spectral indices from MALS and existing radio surveys. We developed an optical and infrared source classification scheme in order to distinguish between galactic pulsars and radio galaxies. In total, we identify nine pulsar candidates towards six Fermi sources that are worthy of follow-up for pulsation searches. We also report 41 steep spectrum radio galaxy candidates that may be of interest in searches for high-redshift radio galaxies. We show that MALS due to its excellent continuum sensitivity can detect 80 percent of the known pulsar population. This exhibits the promise of identifying exotic pulsar candidates with future image-based surveys with the Square Kilometre and its precursors., Comment: 13 pages, 5 figures, Accepted for publication in ApJ

We consider the formulation of a symbolic execution (SE) procedure for functional programs that interact with effectful, opaque libraries. Our procedure allows specifications of libraries and abstract data type (ADT) methods that are expressed in Linear Temporal Logic over Finite Traces (LTLf), interpreting them as symbolic finite automata (SFAs) to enable intelligent specification-guided path exploration in this setting. We apply our technique to facilitate the falsification of complex data structure safety properties in terms of effectful operations made by ADT methods on underlying opaque representation type(s). Specifications naturally characterize admissible traces of temporally-ordered events that ADT methods (and the library methods they depend upon) are allowed to perform. We show how to use these specifications to construct feasible symbolic input states for the corresponding methods, as well as how to encode safety properties in terms of this formalism. More importantly, we incorporate the notion of symbolic derivatives, a mechanism that allows the SE procedure to intelligently underapproximate the set of precondition states it needs to explore, based on the automata structures implicit in the provided specifications and the safety property that is to be falsified. Intuitively, derivatives enable symbolic execution to exploit temporal constraints defined by trace-based specifications to quickly prune unproductive paths and discover feasible error states. Experimental results on a wide-range of challenging ADT implementations demonstrate the effectiveness of our approach., Comment: To appear at POPL'25

Despite significant advancements in large language models (LLMs) that enhance robot agents' understanding and execution of natural language (NL) commands, ensuring the agents adhere to user-specified constraints remains challenging, particularly for complex commands and long-horizon tasks. To address this challenge, we present three key insights, equivalence voting, constrained decoding, and domain-specific fine-tuning, which significantly enhance LLM planners' capability in handling complex tasks. Equivalence voting ensures consistency by generating and sampling multiple Linear Temporal Logic (LTL) formulas from NL commands, grouping equivalent LTL formulas, and selecting the majority group of formulas as the final LTL formula. Constrained decoding then uses the generated LTL formula to enforce the autoregressive inference of plans, ensuring the generated plans conform to the LTL. Domain-specific fine-tuning customizes LLMs to produce safe and efficient plans within specific task domains. Our approach, Safe Efficient LLM Planner (SELP), combines these insights to create LLM planners to generate plans adhering to user commands with high confidence. We demonstrate the effectiveness and generalizability of SELP across different robot agents and tasks, including drone navigation and robot manipulation. For drone navigation tasks, SELP outperforms state-of-the-art planners by 10.8% in safety rate (i.e., finishing tasks conforming to NL commands) and by 19.8% in plan efficiency. For robot manipulation tasks, SELP achieves 20.4% improvement in safety rate. Our datasets for evaluating NL-to-LTL and robot task planning will be released in github.com/lt-asset/selp.

We discuss the magnetic ground state and properties of a frustrated two-dimensional classical Heisenberg model of interacting hexagonal clusters of spins. The energy of the ground states is found exactly for arbitrary values of $J_1$ (intra-cluster couplings) and $J_2$ (inter-cluster couplings). Our main results concern a frustrated region of the phase diagram, where we show that the set of ground states has a degeneracy larger than that due to global rotation symmetry. Furthermore, the ground state manifold does not have a fixed total magnetization~: there is a range of allowed values. At finite temperature, our Monte-Carlo simulations show that the entropy selects the most probable value of the total magnetization, while the histogram of the Monte-Carlo time series is non-trivial. This model is a first step towards modelling properties of a class of frustrated magnetic structures composed of coupled spin clusters., Comment: 6 pages, 7 figures, submitted to Phys Rev B Letters

We present the second data release of the MeerKAT Absorption Line Survey (MALS), consisting of wideband continuum catalogues of 391 pointings observed at L~band. The full wideband catalogue covers 4344 deg$^2$ of sky, reaches a depth of 10 $\mu$Jy beam$^{-1}$, and contains 971,980 sources. With its balance between survey depth and sky coverage, MALS DR2 covers five orders of magnitude of flux density, presenting a robust view of the extragalactic radio source population down to 200 $\mu$Jy. Using this catalogue, we perform a measurement of the cosmic radio dipole, an anisotropy in the number counts of radio sources with respect to the cosmic background, analogous to the dipole found in the cosmic microwave background (CMB). For this measurement, we present the characterisation of completeness and noise properties of the catalogue, and show that a declination-dependent systematic affects the number density of faint sources. In the dipole measurement on the MALS catalogue, we recover reasonable dipole measurements once we model the declination systematic with a linear fit between the size of the major axis of the restoring beam and the amount of sources of each pointing. The final results are consistent with the CMB dipole in terms of direction and amplitude, unlike many recent measurements of the cosmic radio dipole made with other centimetre wavelength catalogues, which generally show a significantly larger amplitude. This result demonstrates the value of dipole measurements with deeper and more sparse radio surveys, as the population of faint sources probed may have had a significant impact on the measured dipole., Comment: 25 pages, 14 figures. Accepted for publication in Astronomy & Astrophysics. The MALS wideband catalogues and images are publicly available at https://mals.iucaa.in

Entanglement is a key resource for a wide range of tasks in quantum information and computing. Thus, verifying availability of this quantum resource is essential. Extensive research on entanglement detection has led to no-go theorems (Lu et al. [Phys. Rev. Lett., 116, 230501 (2016)]) that highlight the need for full state tomography (FST) in the absence of adaptive or joint measurements. Recent advancements, as proposed by Zhu, Teo, and Englert [Phys. Rev. A, 81, 052339, 2010], introduce a single-parameter family of entanglement witness measurements which are capable of conclusively detecting certain entangled states and only resort to FST when all witness measurements are inconclusive. We find a variety of realistic noisy two-qubit quantum states $\mathcal{F}$ that yield conclusive results under this witness family. We solve the problem of detecting entanglement among $K$ quantum states in $\mathcal{F}$, of which $m$ states are entangled, with $m$ potentially unknown. We recognize a structural connection of this problem to the Bad Arm Identification problem in stochastic Multi-Armed Bandits (MAB). In contrast to existing quantum bandit frameworks, we establish a new correspondence tailored for entanglement detection and term it the $(m,K)$-quantum Multi-Armed Bandit. We implement two well-known MAB policies for arbitrary states derived from $\mathcal{F}$, present theoretical guarantees on the measurement/sample complexity and demonstrate the practicality of the policies through numerical simulations. More broadly, this paper highlights the potential for employing classical machine learning techniques for quantum entanglement detection., Comment: 20 pages, 5 figures

Motivated by risk-sensitive reinforcement learning scenarios, we consider the problem of policy evaluation for variance in a discounted reward Markov decision process (MDP). For this problem, a temporal difference (TD) type learning algorithm with linear function approximation (LFA) exists in the literature, though only asymptotic guarantees are available for this algorithm. We derive finite sample bounds that hold (i) in the mean-squared sense; and (ii) with high probability, when tail iterate averaging is employed with/without regularization. Our bounds exhibit exponential decay for the initial error, while the overall bound is $O(1/t)$, where $t$ is the number of update iterations of the TD algorithm. Further, the bound for the regularized TD variant is for a universal step size. Our bounds open avenues for analysis of actor-critic algorithms for mean-variance optimization in a discounted MDP.

We consider the problem of estimating the Optimized Certainty Equivalent (OCE) risk from independent and identically distributed (i.i.d.) samples. For the classic sample average approximation (SAA) of OCE, we derive mean-squared error as well as concentration bounds (assuming sub-Gaussianity). Further, we analyze an efficient stochastic approximation-based OCE estimator, and derive finite sample bounds for the same. To show the applicability of our bounds, we consider a risk-aware bandit problem, with OCE as the risk. For this problem, we derive bound on the probability of mis-identification. Finally, we conduct numerical experiments to validate the theoretical findings.

Functional programs typically interact with stateful libraries that hide state behind typed abstractions. One particularly important class of applications are data structure implementations that rely on such libraries to provide a level of efficiency and scalability that may be otherwise difficult to achieve. However, because the specifications of the methods provided by these libraries are necessarily general and rarely specialized to the needs of any specific client, any required application-level invariants must often be expressed in terms of additional constraints on the (often) opaque state maintained by the library. In this paper, we consider the specification and verification of such representation invariants using symbolic finite automata (SFA). We show that SFAs can be used to succinctly and precisely capture fine-grained temporal and data-dependent histories of interactions between functional clients and stateful libraries. To facilitate modular and compositional reasoning, we integrate SFAs into a refinement type system to qualify stateful computations resulting from such interactions. The particular instantiation we consider, Hoare Automata Types (HATs), allows us to both specify and automatically type-check the representation invariants of a datatype, even when its implementation depends on stateful library methods that operate over hidden state. We also develop a new bidirectional type checking algorithm that implements an efficient subtyping inclusion check over HATs, enabling their translation into a form amenable for SMT-based automated verification. We present extensive experimental results on an implementation of this algorithm that demonstrates the feasibility of type-checking complex and sophisticated HAT-specified OCaml data structure implementations., Comment: PLDI'24

Data-driven neural path planners are attracting increasing interest in the robotics community. However, their neural network components typically come as black boxes, obscuring their underlying decision-making processes. Their black-box nature exposes them to the risk of being compromised via the insertion of hidden malicious behaviors. For example, an attacker may hide behaviors that, when triggered, hijack a delivery robot by guiding it to a specific (albeit wrong) destination, trapping it in a predefined region, or inducing unnecessary energy expenditure by causing the robot to repeatedly circle a region. In this paper, we propose a novel approach to specify and inject a range of hidden malicious behaviors, known as backdoors, into neural path planners. Our approach provides a concise but flexible way to define these behaviors, and we show that hidden behaviors can be triggered by slight perturbations (e.g., inserting a tiny unnoticeable object), that can nonetheless significantly compromise their integrity. We also discuss potential techniques to identify these backdoors aimed at alleviating such risks. We demonstrate our approach on both sampling-based and search-based neural path planners.

Superconductivity was recently reported in several quasicrystalline systems. These are materials which are structurally ordered, but since they are not translationally invariant, the usual BCS theory does not apply. At the present time, the underlying mechanism and the properties of the superconducting phase are insufficiently understood. To gain a better understanding of quasiperiodic superconductors, we consider the attractive Hubbard model on the Fibonacci chain, and examine its low-temperature superconducting phase in detail using the Bogoliubov-de Gennes mean-field approach. We obtain superconducting solutions as a function of the parameters controlling the physical properties of the system: the strength of the Hubbard attraction $U$, the chemical potential $\mu$, and the strength of the modulation of the Fibonacci Hamiltonian, $w$. We find that there is a bulk transition at a critical temperature that obeys a power law in $U$. The local superconducting order parameter is self-similar both in real and perpendicular space. The local densities of states vary from site to site, however, the width of the superconducting gap is the same on all sites. The interplay between the Hubbard attraction and the intrinsic gaps of the Fibonacci chain results in a complex zero-temperature $\mu$-$U$ phase diagram with insulating domes surrounded by superconducting regions. Finally, we show that tuning $w$ from weak to strong quasicrystalline modulation gives rise to qualitatively different thermodynamic behaviors as could be observed by measuring the specific heat.

The astronomical community is grappling with the increasing volume and complexity of data produced by modern telescopes, due to difficulties in reducing, accessing, analyzing, and combining archives of data. To address this challenge, we propose the establishment of a coordinating body, an "entity," with the specific mission of enhancing the interoperability, archiving, distribution, and production of both astronomical data and software. This report is the culmination of a workshop held in February 2023 on the Future of Astronomical Data Infrastructure. Attended by 70 scientists and software professionals from ground-based and space-based missions and archives spanning the entire spectrum of astronomical research, the group deliberated on the prevailing state of software and data infrastructure in astronomy, identified pressing issues, and explored potential solutions. In this report, we describe the ecosystem of astronomical data, its existing flaws, and the many gaps, duplication, inconsistencies, barriers to access, drags on productivity, missed opportunities, and risks to the long-term integrity of essential data sets. We also highlight the successes and failures in a set of deep dives into several different illustrative components of the ecosystem, included as an appendix., Comment: 59 pages; please send comments and/or questions to foadi@googlegroups.com

We theoretically study the real-space distribution of the supercurrent that flows under a uniform vector potential in a two-dimensional quasiperiodic structure. This is done by considering the attractive Hubbard model on the quasiperiodic Ammann-Beenker structure and studying the superconducting phase within the Bogoliubov-de Gennes mean-field theory. Decomposing the local supercurrent into the paramagnetic and diamagnetic components, we numerically investigate their dependencies on average electron density, temperature, and the angle of the applied vector potential. We find that the diamagnetic current locally violates the current conservation law, necessitating compensation from the paramagnetic current, even at zero temperature. The paramagnetic current shows exotic behaviors in the quasiperiodic structure, such as local currents which are oriented transversally or reversely to that of the applied vector potential., Comment: 14 pages, 11 figures

The MeerKAT Absorption Line Survey (MALS) has observed 391 telescope pointings at L-band (900 - 1670 MHz) at $\delta\lesssim$ $+20\deg$. We present radio continuum images and a catalog of 495,325 (240,321) radio sources detected at a signal-to-noise ratio (SNR) $>$5 over an area of 2289 deg$^2$ (1132 deg$^2$) at 1006 MHz (1381 MHz). Every MALS pointing contains a central bright radio source ($S_{1\,\mathrm{GHz}} \gtrsim 0.2$ Jy). The median spatial resolution is $12^{\prime\prime}$ ($8^{\prime\prime}$). The median rms noise away from the pointing center is 25 $\mu$Jy beam$^{-1}$ (22 $\mu$Jy beam$^{-1}$) and is within $\sim$ 15% of the achievable theoretical sensitivity. The flux density scale ratio and astrometric accuracy deduced from multiply observed sources in MALS are less than 1% (8% scatter) and $1^{\prime\prime}$, respectively. Through comparisons with NVSS and FIRST at 1.4 GHz, we establish the catalog's accuracy in the flux density scale and astrometry to be better than 6% (15% scatter) and $0.8^{\prime\prime}$, respectively. The median flux density offset is higher (9%) for an alternate beam model based on holographic measurements. The MALS radio source counts at 1.4 GHz are in agreement with literature. We estimate spectral indices ($\alpha$) of a subset of 125,621 sources (SNR$>$8), confirm the flattening of spectral indices with decreasing flux density and identify 140 ultra steep-spectrum ($\alpha<-1.3$) sources as prospective high-$z$ radio galaxies ($z>2$). We have identified 1308 variable and 122 transient radio sources comprising primarily of AGN that demonstrate long-term (26 years) variability in their observed flux densities. The MALS catalogs and images are publicly available at https://mals.iucaa.in., Comment: 64 pages, 25 figures, accepted for publication in the ApJS (full version of the paper with complete tables is available at DR1 release notes)

Our understanding of physical properties of quasicrystals owes a great deal to studies of tight-binding models constructed on quasiperiodic tilings. Among the large number of possible quasiperiodic structures, two dimensional tilings are of particular importance -- in their own right, but also for information regarding properties of three dimensional systems. We provide here a users manual for those wishing to construct and study physical properties of the 8-fold Ammann-Beenker quasicrystal, a good starting point for investigations of two dimensional quasiperiodic systems. This tiling has a relatively straightforward construction. Thus, geometrical properties such as the type and number of local environments can be readily found by simple analytical computations. Transformations of sites under discrete scale changes -- called inflations and deflations -- are easier to establish compared to the celebrated Penrose tiling, for example. We have aimed to describe the methodology with a minimum of technicalities but in sufficient detail so as to enable non-specialists to generate quasiperiodic tilings and periodic approximants, with or without disorder. The discussion of properties includes some relations not previously published, and examples with figures., Comment: Review article to appear in Israel Journal of Chemistry

Parser combinators are a well-known mechanism used for the compositional construction of parsers, and have shown to be particularly useful in writing parsers for rich grammars with data-dependencies and global state. Verifying applications written using them, however, has proven to be challenging in large part because of the inherently effectful nature of the parsers being composed and the difficulty in reasoning about the arbitrarily rich data-dependent semantic actions that can be associated with parsing actions. In this paper, we address these challenges by defining a parser combinator framework called Morpheus equipped with abstractions for defining composable effects tailored for parsing and semantic actions and a rich specification language used to define safety properties over the constituent parsers comprising a program. Even though its abstractions yield many of the same expressivity benefits as other parser combinator systems, Morpheus is carefully engineered to yield a substantially more tractable automated verification pathway. We demonstrate its utility in verifying a number of realistic, challenging parsing applications, including several cases that involve non-trivial data-dependent relations., Comment: 41 pages

We consider a queue-channel model that captures the waiting time-dependent degradation of information bits as they wait to be transmitted. Such a scenario arises naturally in quantum communications, where quantum bits tend to decohere rapidly. Trailing the capacity results obtained recently for certain queue-channels, this paper aims to construct practical channel codes for the erasure queue-channel (EQC) -- a channel characterized by highly correlated erasures, governed by the underlying queuing dynamics. Our main contributions in this paper are twofold: (i) We propose a generic `wrapper' based on interleaving across renewal blocks of the queue to convert any capacity-achieving block code for a memoryless erasure channel to a capacity-achieving code for the EQC. Next, due to the complexity involved in implementing interleaved systems, (ii) we study the performance of LDPC and Polar codes without any interleaving. We show that standard Ar{\i}kan's Polar transform polarizes the EQC for certain restricted class of erasure probability functions. We also highlight some possible approaches and the corresponding challenges involved in proving polarization of a general EQC., Comment: 10 pages, 5 figures, accepted to IEEE ISIT 2023

The single electron spectrum and wavefunctions in quasicrystals continue to be a fascinating problem, with few known solutions, especially in two and higher dimensions. This paper investigates the energy spectra and gap structures in tight-binding models on a quasiperiodic tiling in two dimensions. By varying a continuous parameter, we follow the evolution of the band structure from the discrete molecular or atomic states, to the multifractal states well-known from previous studies. We propose a scheme for labeling gaps in finite approximants. It is equivalent in the limit of infinite systems to the one presented by Kellendonk and Putnam based on the algebraic structure of this quasiperiodic system., Comment: Final version, with revised gap label definitions

Test input generators are an important part of property-based testing (PBT) frameworks. Because PBT is intended to test deep semantic and structural properties of a program, the outputs produced by these generators can be complex data structures, constrained to satisfy properties the developer believes is most relevant to testing the function of interest. An important feature expected of these generators is that they be capable of producing all acceptable elements that satisfy the function's input type and generator-provided constraints. However, it is not readily apparent how we might validate whether a particular generator's output satisfies this coverage requirement. Typically, developers must rely on manual inspection and post-mortem analysis of test runs to determine if the generator is providing sufficient coverage; these approaches are error-prone and difficult to scale as generators become more complex. To address this important concern, we present a new refinement type-based verification procedure for validating the coverage provided by input test generators, based on a novel interpretation of types that embeds ``must-style'' underapproximate reasoning principles as a fundamental part of the type system. The types associated with expressions now capture the set of values guaranteed to be produced by the expression, rather than the typical formulation that uses types to represent the set of values an expression may produce. Beyond formalizing the notion of coverage types in the context of a rich core language with higher-order procedures and inductive datatypes, we also present a detailed evaluation study to justify the utility of our ideas.

Synthesizing planning and control policies in robotics is a fundamental task, further complicated by factors such as complex logic specifications and high-dimensional robot dynamics. This paper presents a novel reinforcement learning approach to solving high-dimensional robot navigation tasks with complex logic specifications by co-learning planning and control policies. Notably, this approach significantly reduces the sample complexity in training, allowing us to train high-quality policies with much fewer samples compared to existing reinforcement learning algorithms. In addition, our methodology streamlines complex specification extraction from map images and enables the efficient generation of long-horizon robot motion paths across different map layouts. Moreover, our approach also demonstrates capabilities for high-dimensional control and avoiding suboptimal policies via policy alignment. The efficacy of our approach is demonstrated through experiments involving simulated high-dimensional quadruped robot dynamics and a real-world differential drive robot (TurtleBot3) under different types of task specifications.

The number counts of homogeneous samples of radio sources are a tried and true method of probing the large scale structure of the Universe, as most radio sources outside the galactic plane are at cosmological distances. As such they are expected to trace the cosmic radio dipole, an anisotropy analogous to the dipole seen in the cosmic microwave background (CMB). Results have shown that although the cosmic radio dipole matches the direction of the CMB dipole, it has a significantly larger amplitude. This result challenges our assumption of the Universe being isotropic, which can have large repercussions for the current cosmological paradigm. Though significant measurements have been made, sensitivity to the radio dipole is generally hampered by systematic effects that can cause large biases in the measurement. Here we assess these systematics with data from the MeerKAT Absorption Line Survey (MALS). We present the analysis of ten MALS pointings, focusing on systematic effects that could lead to an inhomogeneous catalogue. We describe the calibration and creation of full band continuum images and catalogues, producing a combined catalogue containing 16,313 sources and covering 37.5 square degrees of sky down to a sensitivity of 10 $\mu$Jy/beam. We measure the completeness, purity, and flux recovery statistics for these catalogues using simulated data. We investigate different source populations in the catalogues by looking at flux densities and spectral indices, and how they might influence source counts. Using the noise characteristics of the pointings, we find global measures that can be used to correct for the incompleteness of the catalogue, producing corrected number counts down to 100 - 200 $\mu$Jy. We show that we can homogenise the catalogues and properly account for systematic effects. We determine that we can measure the dipole to $3\sigma$ significance with 100 MALS pointings., Comment: 30 pages, 25 figures. Accepted for publication in Astronomy & Astrophysics

We report the detection of stimulated hydrogen radio recombination line (RRL) emission from ionized gas in a $z=0.89$ galaxy using 580--1670 MHz observations from the MeerKAT Absorption Line Survey (MALS). The RRL emission originates in a galaxy that intercepts and strongly lenses the radio blazar PKS 1830-211 ($z=2.5$). This is the second detection of RRLs outside of the local universe and the first clearly associated with hydrogen. We detect effective H144$\alpha$ (and H163$\alpha$) transitions at observed frequencies of 1156 (798) MHz by stacking 17 (27) RRLs with 21$\sigma$ (14$\sigma$) significance. The RRL emission contains two main velocity components and is coincident in velocity with HI 21 cm and OH 18 cm absorption. We use the RRL spectral line energy distribution and a Bayesian analysis to constrain the density ($n_e$) and the volume-averaged pathlength ($\ell$) of the ionized gas. We determine $\log( n_e ) = 2.0_{-0.7}^{+1.0}$ cm$^{-3}$ and $\log( \ell ) = -0.7_{-1.1}^{+1.1}$ pc towards the north east (NE) lensed image, likely tracing the diffuse thermal phase of the ionized ISM in a thin disk. Towards the south west (SW) lensed image, we determine $\log( n_e ) = 3.2_{-1.0}^{+0.4}$ cm$^{-3}$ and $\log( \ell ) = -2.7_{-0.2}^{+1.8}$ pc, tracing gas that is more reminiscent of H II regions. We estimate a star formation (surface density) rate of $\Sigma_{\mathrm{SFR}} \sim 0.6$ M$_{\odot}$ yr$^{-1}$ kpc$^{-2}$ or SFR $\sim 50$ M$_{\odot}$ yr$^{-1}$, consistent with a star-forming main sequence galaxy of $M_{\star} \sim 10^{11}$ M$_{\odot}$. The discovery presented here opens up the possibility of studying ionized gas at high redshifts using RRL observations from current and future (e.g., SKA and ngVLA) radio facilities., Comment: 17 pages, 10 figures, 2 tables, accepted in ApJ

In this era of fast and large-scale opinion formation, a mathematical understanding of opinion evolution, a.k.a. opinion dynamics, acquires importance. Linear graph-based dynamics and bounded confidence dynamics are the two popular models for opinion dynamics in social networks. Stochastic bounded confidence (SBC) opinion dynamics was proposed as a general framework that incorporates both these dynamics as special cases and also captures the inherent stochasticity and noise (errors) in real-life social exchanges. Although SBC dynamics is quite general and realistic, its analysis is more challenging. This is because SBC dynamics is nonlinear and stochastic, and belongs to the class of Markov processes that have asymptotically zero drift and unbounded jumps. The asymptotic behavior of SBC dynamics was characterized in prior works. However, they do not shed light on its finite-time behavior, which is often of interest in practice. We take a stride in this direction by analyzing the finite-time behavior of a two-agent system and a bistar graph, which are crucial to the understanding of general multi-agent dynamics. In particular, we show that the opinion difference between the two agents is well-concentrated around zero under the conditions that lead to asymptotic stability of the SBC dynamics., Comment: A preliminary version of this paper appeared in the proceedings of COMmunication Systems & NETworkS (COMSNETS) 2022. arXiv admin note: substantial text overlap with arXiv:2112.04373

Scoring the factuality of a generated summary involves measuring the degree to which a target text contains factual information using the input document as support. Given the similarities in the problem formulation, previous work has shown that Natural Language Inference models can be effectively repurposed to perform this task. As these models are trained to score entailment at a sentence level, several recent studies have shown that decomposing either the input document or the summary into sentences helps with factuality scoring. But is fine-grained decomposition always a winning strategy? In this paper we systematically compare different granularities of decomposition -- from document to sub-sentence level, and we show that the answer is no. Our results show that incorporating additional context can yield improvement, but that this does not necessarily apply to all datasets. We also show that small changes to previously proposed entailment-based scoring methods can result in better performance, highlighting the need for caution in model and methodology selection for downstream tasks., Comment: Generation, Evaluation & Metrics (GEM) Workshop 2022

We consider maximizing the long-term average reward in a single server queue, where the reward obtained for a job is a non-increasing function of its sojourn time. The motivation behind this work comes from multiple applications, including quantum information processing and multimedia streaming. We introduce a new service discipline, shortest predicted sojourn time (SPST), which, in simulations, performs better than well-known disciplines. We also present some limited analytical guarantees for this highly intricate problem., Comment: 8 pages, 6 figures

We present deep L-Band observations of the equatorial field centered on the z=6.3 SDSS QSO, reaching a 1 sigma sensitivity of ~2.5 uJy at the center of the field. We extracted a catalog of 1489 radio sources down to a flux density of ~12.5 uJy (5 sigma) over a field of view of ~ 30' diameter. We derived the source counts accounting for catalog reliability and completeness, and compared them with others available in the literature. Our source counts are among the deepest available so far, and, overall, are consistent with recent counts' determinations and models. We detected for the first time in the radio band the SDSS J1030+0524 QSO (26 +/- 5 uJy). We derived its optical radio loudness R_O = 0.62 +/- 0.12, which makes it the most radio quiet AGN at z >~ 6 discovered so far and detected at radio wavelengths. We unveiled extended diffuse radio emission associated with the lobes of a bright FRII radio galaxy located close to the center of the J1030 field, which is likely to become the future BCG of a protocluster at z=1.7. The lobes' complex morphology, coupled with the presence of X-ray diffuse emission detected around the FRII galaxy lobes, may point toward an interaction between the radio jets and the external medium. We also investigated the relation between radio and X-ray luminosity for a sample of 243 X-ray-selected objects obtained from 500 ks Chandra observations of the same field, and spanning a wide redshift range (0 ~< z ~< 3). Focused on sources with a spectroscopic redshift and classification, we found that sources hosted by ETG and AGN follow Log(L_R)/Log(L_X) linear correlations with slopes of ~0.6 and ~0.8, respectively. This is interpreted as a likely signature of different efficiency in the accretion process. Finally, we found that most of these sources (>~87%) show a radio-to-X-ray radio loudness R_X < -3.5, classifying these objects as radio quiet., Comment: 18 pages, 15 figures, 5 table. Accepted for publication in A&A

CASA, the Common Astronomy Software Applications, is the primary data processing software for the Atacama Large Millimeter/submillimeter Array (ALMA) and the Karl G. Jansky Very Large Array (VLA), and is frequently used also for other radio telescopes. The CASA software can handle data from single-dish, aperture-synthesis, and Very Long Baseline Interferometery (VLBI) telescopes. One of its core functionalities is to support the calibration and imaging pipelines for ALMA, VLA, VLA Sky Survey (VLASS), and the Nobeyama 45m telescope. This paper presents a high-level overview of the basic structure of the CASA software, as well as procedures for calibrating and imaging astronomical radio data in CASA. CASA is being developed by an international consortium of scientists and software engineers based at the National Radio Astronomical Observatory (NRAO), the European Southern Observatory (ESO), the National Astronomical Observatory of Japan (NAOJ), and the Joint Institute for VLBI European Research Infrastructure Consortium (JIV-ERIC), under the guidance of NRAO., Comment: Accepted for publication in PASP (20 pages, 4 figures). Joint publication with CASA-VLBI paper

We consider critical eigenstates in a two dimensional quasicrystal and their evolution as a function of disorder. By exact diagonalization of finite size systems we show that the evolution of properties of a typical wave-function is non-monotonic. That is, disorder leads to states delocalizing, until a certain crossover disorder strength is attained, after which they start to localize. Although this non-monotonic behavior is only present in finite-size systems and vanishes in the thermodynamic limit, the crossover disorder strength decreases logarithmically slowly with system size, and is quite large even for very large approximants. The non-monotonic evolution of spatial properties of eigenstates can be observed in the anomalous dimensions of the wave-function amplitudes, in their multifractal spectra, and in their dynamical properties. We compute the two-point correlation functions of wave-function amplitudes and show that these follow power laws in distance and energy, consistent with the idea that wave-functions retain their multifractal structure on a scale which depends on disorder strength. Dynamical properties are studied as a function of disorder. We find that the diffusion exponents do not reflect the non-monotonic wave-function evolution. Instead, they are essentially independent of disorder until disorder increases beyond the crossover value, after which they decrease rapidly, until the strong localization regime is reached. The differences between our results and earlier studies on geometrically disordered ``phason-flip'' models lead us to propose that the two models are in different universality classes. We conclude by discussing some implications of our results for transport and a proposal for a Mott hopping mechanism between power law localized wave-functions, in moderately disordered quasicrystals., Comment: New figures and references added (23 pages, 13 figures)

We consider a two-dimensional generalization of the Su-Schrieffer-Heeger model which is known to possess a non-trivial topological band structure. For this model, which is characterized by a single parameter, the hopping ratio $0 \leq r\leq 1$, the inhomogeneous superconducting phases induced by an attractive $U$ Hubbard interaction are studied using mean field theory. We show, analytically and by numerical diagonalization, that in lattices with open boundaries, phases with enhanced superconducting order on the corners or the edges can appear, depending on the filling. For finite samples at half filling, the corner site superconducting transition temperature can be much larger than that of the bulk. A novel proximity effect thus arises for $T_{c,bulk} < T

Component-based synthesis seeks to build programs using the APIs provided by a set of libraries. Oftentimes, these APIs have effects, which make it challenging to reason about the correctness of potential synthesis candidates. This is because changes to global state made by effectful library procedures affect how they may be composed together, yielding an intractably large search space that can confound typical enumerative synthesis techniques. If the nature of these effects are exposed as part of their specification, however, deductive synthesis approaches can be used to help guide the search for components. In this paper, we present a new specification-guided synthesis procedure that uses Hoare-style pre- and post-conditions to express fine-grained effects of potential library component candidates to drive a bi-directional synthesis search strategy. The procedure alternates between a forward search process that seeks to build larger terms given an existing context but which is otherwise unaware of the actual goal, alongside a backward search mechanism that seeks terms consistent with the desired goal but which is otherwise unaware of the context from which these terms must be synthesized. To further improve efficiency and scalability, we integrate a conflict-driven learning procedure into the synthesis algorithm that provides a semantic characterization of previously encountered unsuccessful search paths that is used to prune the space of possible candidates as synthesis proceeds. We have implemented our ideas in a tool called Cobalt and demonstrate its effectiveness on a number of challenging synthesis problems defined over OCaml libraries equipped with effectful specifications.

Deep learning networks have shown promising results in fast magnetic resonance imaging (MRI) reconstruction. In our work, we develop deep networks to further improve the quantitative and the perceptual quality of reconstruction. To begin with, we propose reconsynergynet (RSN), a network that combines the complementary benefits of independently operating on both the image and the Fourier domain. For a single-coil acquisition, we introduce deep cascade RSN (DC-RSN), a cascade of RSN blocks interleaved with data fidelity (DF) units. Secondly, we improve the structure recovery of DC-RSN for T2 weighted Imaging (T2WI) through assistance of T1 weighted imaging (T1WI), a sequence with short acquisition time. T1 assistance is provided to DC-RSN through a gradient of log feature (GOLF) fusion. Furthermore, we propose perceptual refinement network (PRN) to refine the reconstructions for better visual information fidelity (VIF), a metric highly correlated to radiologists opinion on the image quality. Lastly, for multi-coil acquisition, we propose variable splitting RSN (VS-RSN), a deep cascade of blocks, each block containing RSN, multi-coil DF unit, and a weighted average module. We extensively validate our models DC-RSN and VS-RSN for single-coil and multi-coil acquisitions and report the state-of-the-art performance. We obtain a SSIM of 0.768, 0.923, 0.878 for knee single-coil-4x, multi-coil-4x, and multi-coil-8x in fastMRI. We also conduct experiments to demonstrate the efficacy of GOLF based T1 assistance and PRN., Comment: Accepted in CMIG 2021

While notable progress has been made in specifying and learning objectives for general cyber-physical systems, applying these methods to distributed multi-agent systems still pose significant challenges. Among these are the need to (a) craft specification primitives that allow expression and interplay of both local and global objectives, (b) tame explosion in the state and action spaces to enable effective learning, and (c) minimize coordination frequency and the set of engaged participants for global objectives. To address these challenges, we propose a novel specification framework that allows natural composition of local and global objectives used to guide training of a multi-agent system. Our technique enables learning expressive policies that allow agents to operate in a coordination-free manner for local objectives, while using a decentralized communication protocol for enforcing global ones. Experimental results support our claim that sophisticated multi-agent distributed planning problems can be effectively realized using specification-guided learning., Comment: ECML-PKDD 2022

Neural network policies trained using Deep Reinforcement Learning (DRL) are well-known to be susceptible to adversarial attacks. In this paper, we consider attacks manifesting as perturbations in the observation space managed by the external environment. These attacks have been shown to downgrade policy performance significantly. We focus our attention on well-trained deterministic and stochastic neural network policies in the context of continuous control benchmarks subject to four well-studied observation space adversarial attacks. To defend against these attacks, we propose a novel defense strategy using a detect-and-denoise schema. Unlike previous adversarial training approaches that sample data in adversarial scenarios, our solution does not require sampling data in an environment under attack, thereby greatly reducing risk during training. Detailed experimental results show that our technique is comparable with state-of-the-art adversarial training approaches.

We study the fundamental limits of classical communication using quantum states that decohere as they traverse through a network of queues. We consider a network of Markovian queues, known as a Jackson network, with a single source or multiple sources and a single destination. Qubits are communicated through this network with inevitable buffering at intermediate nodes. We model each node as a `queue-channel,' wherein as the qubits wait in buffer, they continue to interact with the environment and suffer a waiting time-dependent noise. Focusing on erasures, we first obtain explicit classical capacity expressions for simple topologies such as tandem queue-channel and parallel queue-channel. Using these as building blocks, we characterize the classical capacity of a general quantum Jackson network with waiting time-dependent erasures. Throughout, we study two types of quantum networks, namely, (i) Repeater-assisted and (ii) Repeater-less. We also obtain optimal pumping rates and routing probabilities to maximize capacity in simple topologies. More broadly, our work quantifies the impact of delay-induced decoherence on the fundamental limits of classical communication over quantum networks., Comment: 8 pages

We report on the discovery and localization of fast radio bursts (FRBs) from the MeerTRAP project, a commensal fast radio transient-detection programme at MeerKAT in South Africa. Our hybrid approach combines a coherent search with an average field-of-view of 0.4 $\rm deg^{2}$ with an incoherent search utilizing a field-of-view of $\sim$1.27 $\rm deg^{2}$ (both at 1284~MHz). Here, we present results on the first three FRBs: FRB 20200413A (DM=1990.05 pc cm$^{-3}$), FRB 20200915A (DM=740.65 pc cm$^{-3}$), and FRB 20201123A (DM=433.55 pc cm$^{-3}$). FRB 20200413A was discovered only in the incoherent beam. FRB 20200915A (also discovered only in the incoherent beam) shows speckled emission in the dynamic spectrum which cannot be explained by interstellar scintillation in our Galaxy or plasma lensing, and might be intrinsic to the source. FRB 20201123A shows a faint post-cursor burst about 200 ms after the main burst and warrants further follow-up to confirm whether it is a repeating FRB. FRB 20201123A also exhibits significant temporal broadening consistent with scattering by a turbulent medium. The broadening exceeds that predicted for medium along the sightline through our Galaxy. We associate this scattering with the turbulent medium in the environment of the FRB in the host galaxy. Within the approximately $1'$ localization region of FRB 20201123A, we identify one luminous galaxy ($r \approx 15.67$; J173438.35$-$504550.4) that dominates the posterior probability for a host association. The galaxy's measured properties are consistent with other FRB hosts with secure associations., Comment: 14 pages, 9 figures, 3 tables, accepted for publication in MNRAS

We derive a new eight dimensional matrix representation of the Maxwell equations for a linear homogeneous medium and extend it to the case of a linear inhomogneous medium. This derivation starts ab initio with the Maxwell equations and uses arguments based on the algebra of the Pauli matrices. This process leads automatically to the matrix representation based on the Riemann-Silberstein-Weber (RSW) vector. The new representation for the homogeneous medium is a direct sum of four Pauli matrix blocks. This aspect of the new representation should make it suitable for studying the propagation of electromagnetic waves in a linear inhomogeneous medium adopting the techniques of quantum mechanics treating the inhomogeneity as a perturbation. The new representation is used to rederive the Mukunda-Simon-Sudarshan matrix substitution rule for transition from the Helmholtz scalar wave optics to the Maxwell vector wave optics., Comment: 50 pages. The previous version has been substantially revised. Journal Reference: Results in Optics (in press)

In several applications such as clinical trials and financial portfolio optimization, the expected value (or the average reward) does not satisfactorily capture the merits of a drug or a portfolio. In such applications, risk plays a crucial role, and a risk-aware performance measure is preferable, so as to capture losses in the case of adverse events. This survey aims to consolidate and summarise the existing research on risk measures, specifically in the context of multi-armed bandits. We review various risk measures of interest, and comment on their properties. Next, we review existing concentration inequalities for various risk measures. Then, we proceed to defining risk-aware bandit problems, We consider algorithms for the regret minimization setting, where the exploration-exploitation trade-off manifests, as well as the best-arm identification setting, which is a pure exploration problem -- both in the context of risk-sensitive measures. We conclude by commenting on persisting challenges and fertile areas for future research., Comment: 11 pages; Unabridged version of a a survey paper of the same title accepted to IJCAI-ECAI, 2022

Model-free Deep Reinforcement Learning (DRL) controllers have demonstrated promising results on various challenging non-linear control tasks. While a model-free DRL algorithm can solve unknown dynamics and high-dimensional problems, it lacks safety assurance. Although safety constraints can be encoded as part of a reward function, there still exists a large gap between an RL controller trained with this modified reward and a safe controller. In contrast, instead of implicitly encoding safety constraints with rewards, we explicitly co-learn a Twin Neural Lyapunov Function (TNLF) with the control policy in the DRL training loop and use the learned TNLF to build a runtime monitor. Combined with the path generated from a planner, the monitor chooses appropriate waypoints that guide the learned controller to provide collision-free control trajectories. Our approach inherits the scalability advantages from DRL while enhancing safety guarantees. Our experimental evaluation demonstrates the effectiveness of our approach compared to DRL with augmented rewards and constrained DRL methods over a range of high-dimensional safety-sensitive navigation tasks., Comment: 8 pages, 6 figures

We report the discovery of PSR J1555-2908, a 1.79 ms radio and gamma-ray pulsar in a 5.6 hr binary system with a minimum companion mass of 0.052 $M_\odot$. This fast and energetic ($\dot E = 3 \times 10^{35}$ erg/s) millisecond pulsar was first detected as a gamma-ray point source in Fermi LAT sky survey observations. Guided by a steep spectrum radio point source in the Fermi error region, we performed a search at 820 MHz with the Green Bank Telescope that first discovered the pulsations. The initial radio pulse timing observations provided enough information to seed a search for gamma-ray pulsations in the LAT data, from which we derive a timing solution valid for the full Fermi mission. In addition to the radio and gamma-ray pulsation discovery and timing, we searched for X-ray pulsations using NICER but no significant pulsations were detected. We also obtained time-series r-band photometry that indicates strong heating of the companion star by the pulsar wind. Material blown off the heated companion eclipses the 820 MHz radio pulse during inferior conjunction of the companion for ~10% of the orbit, which is twice the angle subtended by its Roche lobe in an edge-on system., Comment: 15 pages, 6 figures, accepted by ApJ

In this era of fast and large-scale opinion formation, a mathematical understanding of opinion evolution, a.k.a. opinion dynamics, is especially important. Linear graph-based dynamics and bounded confidence dynamics are the two most popular models for opinion dynamics in social networks. Recently, stochastic bounded confidence opinion dynamics were proposed as a general framework that incorporates both these dynamics as special cases and also captures the inherent stochasticity and noise (errors) in real-life social exchanges. Although these dynamics are quite general and realistic, their analysis is particularly challenging compared to other opinion dynamics models. This is because these dynamics are nonlinear and stochastic, and belong to the class of Markov processes that have asymptotically zero drift and unbounded jumps. The asymptotic behavior of these dynamics was characterized in prior works. However, they do not shed light on their finite-time behavior, which is often of interest in practice. We take a stride in this direction by analyzing the finite time behavior of a two-agent system, which is fundamental to the understanding of multi-agent dynamics. In particular, we show that the opinion difference between the two agents is well concentrated around zero under the conditions that lead to asymptotic stability of the dynamics., Comment: Accepted in COMSNETS 2022

In this work we performed a spectral energy distribution (SED) analysis in the optical/infrared band of the host galaxy of a proto-brightest cluster galaxy (BCG, NVSS J103023+052426) in a proto-cluster at z = 1.7. We found that it features a vigorous star formation rate (SFR) of ${\sim}$570 $\mathrm{M_{\odot}}$/yr and a stellar mass of $M_{\ast} \sim 3.7 \times 10^{11}$ $\mathrm{M_{\odot}}$; the high corresponding specific SFR = $1.5 \pm 0.5$ $\mathrm{Gyr^{-1}}$ classifies this object as a starburst galaxy that will deplete its molecular gas reservoir in $\sim$ $3.5 \times 10^8$ yr. Thus, this system represents a rare example of a proto-BCG caught during the short phase of its major stellar mass assembly. Moreover, we investigated the nature of the host galaxy emission at 3.3 mm. We found that it originates from the cold dust in the interstellar medium, even though a minor non-thermal AGN contribution cannot be completely ruled out. Finally, we studied the polarized emission of the lobes at 1.4 GHz. We unveiled a patchy structure where the polarization fraction increases in the regions in which the total intensity shows a bending morphology; in addition, the magnetic field orientation follows the direction of the bendings. We interpret these features as possible indications of an interaction with the intracluster medium. This strengthens the hypothesis of positive AGN feedback, as inferred in previous studies of this object on the basis of X-ray/mm/radio analysis. In this scenario, the proto-BCG heats the surrounding medium and possibly enhances the SFR in nearby galaxies., Comment: 13 pages, 3 figures, 1 table. Accepted for publication in Galaxies

We demonstrate an optical receiver that achieves the quantum Chernoff bound for discriminating coherent states from thermal states in the multi-copy scenario. In contrast, we find that repeated use of the receiver approaching the Helstrom bound for single-copy measurement is sub-optimal in this multi-copy case. Furthermore, for a large class of multi-copy discrimination tasks between a pure and a mixed state, we prove that any Helstrom-bound achieving single-copy receiver is suboptimal by a factor of at least two in error-probability exponent compared to the multi-copy quantum Chernoff bound. This behavior has a classical analog in the performance gap between soft-decision and hard-decision receivers for detecting a multi-copy signal embedded in white Gaussian noise., Comment: 11 pages, 4 figures

Utility-Based Shortfall Risk (UBSR) is a risk metric that is increasingly popular in financial applications, owing to certain desirable properties that it enjoys. We consider the problem of estimating UBSR in a recursive setting, where samples from the underlying loss distribution are available one-at-a-time. We cast the UBSR estimation problem as a root finding problem, and propose stochastic approximation-based estimations schemes. We derive non-asymptotic bounds on the estimation error in the number of samples. We also consider the problem of UBSR optimization within a parameterized class of random variables. We propose a stochastic gradient descent based algorithm for UBSR optimization, and derive non-asymptotic bounds on its convergence.

In this paper, we address two key challenges in deep reinforcement learning setting, sample inefficiency and slow learning, with a dual NN-driven learning approach. In the proposed approach, we use two deep NNs with independent initialization to robustly approximate the action-value function in the presence of image inputs. In particular, we develop a temporal difference (TD) error-driven learning approach, where we introduce a set of linear transformations of the TD error to directly update the parameters of each layer in the deep NN. We demonstrate theoretically that the cost minimized by the error-driven learning (EDL) regime is an approximation of the empirical cost and the approximation error reduces as learning progresses, irrespective of the size of the network. Using simulation analysis, we show that the proposed methods enables faster learning and convergence and requires reduced buffer size (thereby increasing the sample efficiency).

Learning to control complex systems using non-traditional feedback, e.g., in the form of snapshot images, is an important task encountered in diverse domains such as robotics, neuroscience, and biology (cellular systems). In this paper, we present a two neural-network (NN)-based feedback control framework to design control policies for systems that generate feedback in the form of images. In particular, we develop a deep $Q$-network (DQN)-driven learning control strategy to synthesize a sequence of control inputs from snapshot images that encode the information pertaining to the current state and control action of the system. Further, to train the networks we employ a direct error-driven learning (EDL) approach that utilizes a set of linear transformations of the NN training error to update the NN weights in each layer. We verify the efficacy of the proposed control strategy using numerical examples.

Semantic mapping is the task of providing a robot with a map of its environment beyond the open, navigable space of traditional Simultaneous Localization and Mapping (SLAM) algorithms by attaching semantics to locations. The system presented in this work reads door placards to annotate the locations of offices. Whereas prior work on this system developed hand-crafted detectors, this system leverages YOLOv2 for detection and a segmentation network for segmentation. Placards are localized by computing their pose from a homography computed from a segmented quadrilateral outline. This work also introduces an Interruptable Frontier Exploration algorithm, enabling the robot to explore its environment to construct its SLAM map while pausing to inspect placards observed during this process. This allows the robot to autonomously discover room placards without human intervention while speeding up significantly over previous autonomous exploration methods.

Quantum queue-channels arise naturally in the context of buffering in quantum networks, wherein the noise suffered by the quantum states depends on the time spent waiting in the buffer. It has been shown that the upper-bound on the classical capacity of an additive queue-channel has a simple expression, and is achievable for the erasure and depolarizing channels [IEEE JSAIT, 1(2):432-444]. In this paper, we characterise the classical capacity for the class of unital qubit queue-channels, and show that a simple product (non-entangled) decoding strategy is capacity-achieving. As an intermediate result, we derive an explicit capacity achieving product decoding strategy for any i.i.d. unital qubit channel, which could be of independent interest. As an important special case, we also derive the capacity and optimal decoding strategies for a symmetric generalized amplitude damping (GAD) queue-channel. Our results provide useful insights towards designing practical quantum communication networks, and highlight the need to explicitly model the impact of buffering., Comment: 25 pages with 3 Figures. Merges and supersedes our prior submissions arXiv:2107.13486 and arXiv:2110.02800

Fine-tuning pretrained models for automatically summarizing doctor-patient conversation transcripts presents many challenges: limited training data, significant domain shift, long and noisy transcripts, and high target summary variability. In this paper, we explore the feasibility of using pretrained transformer models for automatically summarizing doctor-patient conversations directly from transcripts. We show that fluent and adequate summaries can be generated with limited training data by fine-tuning BART on a specially constructed dataset. The resulting models greatly surpass the performance of an average human annotator and the quality of previous published work for the task. We evaluate multiple methods for handling long conversations, comparing them to the obvious baseline of truncating the conversation to fit the pretrained model length limit. We introduce a multistage approach that tackles the task by learning two fine-tuned models: one for summarizing conversation chunks into partial summaries, followed by one for rewriting the collection of partial summaries into a complete summary. Using a carefully chosen fine-tuning dataset, this method is shown to be effective at handling longer conversations, improving the quality of generated summaries. We conduct both an automatic evaluation (through ROUGE and two concept-based metrics focusing on medical findings) and a human evaluation (through qualitative examples from literature, assessing hallucination, generalization, fluency, and general quality of the generated summaries)., Comment: Accepted in Findings of the EMNLP 2021. Code is available at https://github.com/negrinho/medical_conversation_summarization

Programmers often leverage data structure libraries that provide useful and reusable abstractions. Modular verification of programs that make use of these libraries naturally rely on specifications that capture important properties about how the library expects these data structures to be accessed and manipulated. However, these specifications are often missing or incomplete, making it hard for clients to be confident they are using the library safely. When library source code is also unavailable, as is often the case, the challenge to infer meaningful specifications is further exacerbated. In this paper, we present a novel data-driven abductive inference mechanism that infers specifications for library methods sufficient to enable verification of the library's clients. Our technique combines a data-driven learning-based framework to postulate candidate specifications, along with SMT-provided counterexamples to refine these candidates, taking special care to prevent generating specifications that overfit to sampled tests. The resulting specifications form a minimal set of requirements on the behavior of library implementations that ensures safety of a particular client program. Our solution thus provides a new multi-abduction procedure for precise specification inference of data structure libraries guided by client-side verification tasks. Experimental results on a wide range of realistic OCaml data structure programs demonstrate the effectiveness of the approach.

The generalized amplitude damping channel (GADC) is considered an important model for quantum communications, especially over optical networks. We make two salient contributions in this paper apropos of this channel. First, we consider a symmetric GAD channel characterized by the parameter $n=1/2,$ and derive its exact classical capacity, by constructing a specific induced classical channel. We show that the Holevo quantity for the GAD channel equals the Shannon capacity of the induced binary symmetric channel, establishing at once the capacity result and that the GAD channel capacity can be achieved without the use of entanglement at the encoder or joint measurements at the decoder. Second, motivated by the inevitable buffering of qubits in quantum networks, we consider a generalized amplitude damping \emph{queue-channel} -- that is, a setting where qubits suffer a waiting time dependent GAD noise as they wait in a buffer to be transmitted. This GAD queue channel is characterized by non-i.i.d. noise due to correlated waiting times of consecutive qubits. We exploit a conditional independence property in conjunction with additivity of the channel model, to obtain a capacity expression for the GAD queue channel in terms of the stationary waiting time in the queue. Our results provide useful insights towards designing practical quantum communication networks, and highlight the need to explicitly model the impact of buffering., Comment: Comments welcome!