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In the most recent assessment by the Programme for the International Assessment of Adult Competencies (PIAAC), approximately 19% of adults in the United States scored at or below Level 1 in literacy (National Center for Education Statistics [NCES], 2019a). Adults who performed at Level 1 were only able to identify one key piece of information from short real-world texts, and adults who scored below Level 1 struggled with this basic task. Because the PIAAC is based on a nationally representative sample, this grim finding suggests that almost one in five adults have difficulty understanding everyday texts, which include key documents like workplace memos, medical forms, and notices from children's schools. Thus, these adults' low reading skills impede their functionality in today's information-driven society. In addition to gathering data on the U.S. household population, the PIAAC Survey of Incarcerated Adults was conducted during the 2014 National Supplement phase of data collection to provide information on this particularly vulnerable segment of adults (Rampey et al., 2016). The purpose of the present study was to examine potential differences between low-skilled incarcerated adults and their counterparts in the general U.S. household sample who completed the Reading Components Supplement based on individual characteristics reported by PIAAC test-takers on the background questionnaire. Thus, the primary research questions were: Are demographic characteristics (i.e., age, race/ethnicity, educational attainment, self-reported learning disability (LD) status, native language status, overall health, and parental education levels) related to the literacy performance of low-skilled adults (U.S. household and prison sample)? Do the relationships of demographics to literacy performance vary based on sample type (U.S. household vs. prison sample)? Given the exploratory and novel nature of directly comparing these two samples, the study did not make specific hypotheses about each demographic category in relation to literacy performance, but instead selected these demographics based on the heterogeneity of the samples included.

We study the problem of locally private mean estimation of high-dimensional vectors in the Euclidean ball. Existing algorithms for this problem either incur sub-optimal error or have high communication and/or run-time complexity. We propose a new algorithmic framework, ProjUnit, for private mean estimation that yields algorithms that are computationally efficient, have low communication complexity, and incur optimal error up to a $1+o(1)$-factor. Our framework is deceptively simple: each randomizer projects its input to a random low-dimensional subspace, normalizes the result, and then runs an optimal algorithm such as PrivUnitG in the lower-dimensional space. In addition, we show that, by appropriately correlating the random projection matrices across devices, we can achieve fast server run-time. We mathematically analyze the error of the algorithm in terms of properties of the random projections, and study two instantiations. Lastly, our experiments for private mean estimation and private federated learning demonstrate that our algorithms empirically obtain nearly the same utility as optimal ones while having significantly lower communication and computational cost., Comment: Added the correct github link

We consider online learning problems in the realizable setting, where there is a zero-loss solution, and propose new Differentially Private (DP) algorithms that obtain near-optimal regret bounds. For the problem of online prediction from experts, we design new algorithms that obtain near-optimal regret ${O} \big( \varepsilon^{-1} \log^{1.5}{d} \big)$ where $d$ is the number of experts. This significantly improves over the best existing regret bounds for the DP non-realizable setting which are ${O} \big( \varepsilon^{-1} \min\big\{d, T^{1/3}\log d\big\} \big)$. We also develop an adaptive algorithm for the small-loss setting with regret $O(L^\star\log d + \varepsilon^{-1} \log^{1.5}{d})$ where $L^\star$ is the total loss of the best expert. Additionally, we consider DP online convex optimization in the realizable setting and propose an algorithm with near-optimal regret $O \big(\varepsilon^{-1} d^{1.5} \big)$, as well as an algorithm for the smooth case with regret $O \big( \varepsilon^{-2/3} (dT)^{1/3} \big)$, both significantly improving over existing bounds in the non-realizable regime.

$^{15}$N($\alpha$,$\gamma$)$^{19}$F is believed to be the primary means of stellar nucleosynthesis of fluorine. Here we present the use of a single-fluid bubble chamber to measure the cross section of the time-inverse photo-dissociation reaction. The method benefits from a luminosity increase of several orders of magnitude due to the use of a thicker liquid target -- when compared to thin films or gas targets -- and from the reciprocity theorem. We discuss the results of an experiment at the Thomas Jefferson National Accelerator Facility, where the cross section of the photodisintegration process $^{19}$F($\gamma$, $\alpha$)$^{15}$N was measured by bombarding a superheated fluid of C$_3$F$_8$ with bremsstrahlung $\gamma$-rays produced by impinging a 4 - 5.5 MeV electron beam on a Cu radiator. From the photodissociation yield the cross section was extracted by performing a convolution with a Monte Carlo-generated $\gamma$-ray beam spectrum. The measurement produced a cross section that was then time inverted using the reciprocity theorem. The cross section for the $^{15}$N($\alpha$,$\gamma$)$^{19}$F reaction was determined down to a value in the range of hundreds of picobarns. With further improvements of the experimental setup the technique could potentially push cross section measurements down to the single picobarn range., Comment: Corrected Typos. Accepted for Publication in Physical Review C

We construct a holographic map that takes the semi-classical state of an evaporating black hole and its Hawking radiation to a microscopic model that reflects the scrambling dynamics of the black hole. The microscopic model is given by a nested sequence of random unitaries, each one implementing a scrambling time step of the black hole evolution. Differently from other models, energy conservation and the thermal nature of the Hawking radiation are taken into account. We show that the QES formula follows for the entropy of multiple subsets of the radiation and black hole. We further show that a version of entanglement wedge reconstruction can be proved by computing suitable trace norms and quantum fidelities involving the action of a unitary on a subset of Hawking partners. If the Hawking partner is in an island, its unitary can be reconstructed by a unitary on the radiation. We also adopt a similar setup and analyse reconstruction of unitaries acting on an infalling system., Comment: published version

A canonical algorithm for log-concave sampling is the Langevin Algorithm, aka the Langevin Diffusion run with some discretization stepsize $\eta > 0$. This discretization leads the Langevin Algorithm to have a stationary distribution $\pi_{\eta}$ which differs from the stationary distribution $\pi$ of the Langevin Diffusion, and it is an important challenge to understand whether the well-known properties of $\pi$ extend to $\pi_{\eta}$. In particular, while concentration properties such as isoperimetry and rapidly decaying tails are classically known for $\pi$, the analogous properties for $\pi_{\eta}$ are open questions with algorithmic implications. This note provides a first step in this direction by establishing concentration results for $\pi_{\eta}$ that mirror classical results for $\pi$. Specifically, we show that for any nontrivial stepsize $\eta > 0$, $\pi_{\eta}$ is sub-exponential (respectively, sub-Gaussian) when the potential is convex (respectively, strongly convex). Moreover, the concentration bounds we show are essentially tight. We also show that these concentration bounds extend to all iterates along the trajectory of the Langevin Algorithm, and to inexact implementations which use sub-Gaussian estimates of the gradient. Key to our analysis is the use of a rotation-invariant moment generating function (aka Bessel function) to study the stationary dynamics of the Langevin Algorithm. This technique may be of independent interest because it enables directly analyzing the discrete-time stationary distribution $\pi_{\eta}$ without going through the continuous-time stationary distribution $\pi$ as an intermediary., Comment: Added Section 6 (extensions to concentration of the trajectory and inexact gradients)

We identify and characterize unital completely positive (UCP) maps on finite dimensional $C^*$-algebras for which the Choi-Effros product extended to the space generated by peripheral eigenvectors matches with the original product. We analyze a decomposition of general UCP maps in finite dimensions into persistent and transient parts. It is shown that UCP maps on finite dimensional $C^*$-algebras with spectrum contained in the unit circle are $\ast$-automorphisms., Comment: 16 pages

Privately learning statistics of events on devices can enable improved user experience. Differentially private algorithms for such problems can benefit significantly from interactivity. We argue that an aggregation protocol can enable an interactive private federated statistics system where user's devices maintain control of the privacy assurance. We describe the architecture of such a system, and analyze its security properties.

Online prediction from experts is a fundamental problem in machine learning and several works have studied this problem under privacy constraints. We propose and analyze new algorithms for this problem that improve over the regret bounds of the best existing algorithms for non-adaptive adversaries. For approximate differential privacy, our algorithms achieve regret bounds of $\tilde{O}(\sqrt{T \log d} + \log d/\varepsilon)$ for the stochastic setting and $\tilde{O}(\sqrt{T \log d} + T^{1/3} \log d/\varepsilon)$ for oblivious adversaries (where $d$ is the number of experts). For pure DP, our algorithms are the first to obtain sub-linear regret for oblivious adversaries in the high-dimensional regime $d \ge T$. Moreover, we prove new lower bounds for adaptive adversaries. Our results imply that unlike the non-private setting, there is a strong separation between the optimal regret for adaptive and non-adaptive adversaries for this problem. Our lower bounds also show a separation between pure and approximate differential privacy for adaptive adversaries where the latter is necessary to achieve the non-private $O(\sqrt{T})$ regret., Comment: Removed the results for the realizable setting which we uploaded with additional results for that setting in a separate paper. Added a proof sketch for the lower bound

Recovering linear subspaces from data is a fundamental and important task in statistics and machine learning. Motivated by heterogeneity in Federated Learning settings, we study a basic formulation of this problem: the principal component analysis (PCA), with a focus on dealing with irregular noise. Our data come from $n$ users with user $i$ contributing data samples from a $d$-dimensional distribution with mean $\mu_i$. Our goal is to recover the linear subspace shared by $\mu_1,\ldots,\mu_n$ using the data points from all users, where every data point from user $i$ is formed by adding an independent mean-zero noise vector to $\mu_i$. If we only have one data point from every user, subspace recovery is information-theoretically impossible when the covariance matrices of the noise vectors can be non-spherical, necessitating additional restrictive assumptions in previous work. We avoid these assumptions by leveraging at least two data points from each user, which allows us to design an efficiently-computable estimator under non-spherical and user-dependent noise. We prove an upper bound for the estimation error of our estimator in general scenarios where the number of data points and amount of noise can vary across users, and prove an information-theoretic error lower bound that not only matches the upper bound up to a constant factor, but also holds even for spherical Gaussian noise. This implies that our estimator does not introduce additional estimation error (up to a constant factor) due to irregularity in the noise. We show additional results for a linear regression problem in a similar setup., Comment: In NeurIPS 2022

We consider a semilinear heat equation involving a forcing term which depends only on the space variable. To start with, the existence of a local mild solution is proved through an application of the Banach fixed-point theorem. With the help of carefully defined test functions, we then prove the nonexistence of global weak solutions. The most crucial step is to find the function $d(x)$ used in our proofs, which seems to depends only upon the considered vector fields. This leads to lower bounds for a possible critical Fujita-type exponent. The same function $d(x)$ could lead to a potential norm function which would be most suitable while working with these vector fields. Section 4 is the attraction of this paper in which we apply our approach to all of the vector fields discussed by Biagi, Bonfiglioli and Bramanti, giving rise to Grushin-type and Engel-type PDOs, and more. An upper bound for the blow-up time of local solutions is also provided in each of these cases., Comment: Sections 4 and 5 are now combined into a single section. The revised Section 4 includes a broader range of general results, complemented by blow-up time estimates provided after each non-existence result

Sampling from a high-dimensional distribution is a fundamental task in statistics, engineering, and the sciences. A canonical approach is the Langevin Algorithm, i.e., the Markov chain for the discretized Langevin Diffusion. This is the sampling analog of Gradient Descent. Despite being studied for several decades in multiple communities, tight mixing bounds for this algorithm remain unresolved even in the seemingly simple setting of log-concave distributions over a bounded domain. This paper completely characterizes the mixing time of the Langevin Algorithm to its stationary distribution in this setting (and others). This mixing result can be combined with any bound on the discretization bias in order to sample from the stationary distribution of the continuous Langevin Diffusion. In this way, we disentangle the study of the mixing and bias of the Langevin Algorithm. Our key insight is to introduce a technique from the differential privacy literature to the sampling literature. This technique, called Privacy Amplification by Iteration, uses as a potential a variant of R\'enyi divergence that is made geometrically aware via Optimal Transport smoothing. This gives a short, simple proof of optimal mixing bounds and has several additional appealing properties. First, our approach removes all unnecessary assumptions required by other sampling analyses. Second, our approach unifies many settings: it extends unchanged if the Langevin Algorithm uses projections, stochastic mini-batch gradients, or strongly convex potentials (whereby our mixing time improves exponentially). Third, our approach exploits convexity only through the contractivity of a gradient step -- reminiscent of how convexity is used in textbook proofs of Gradient Descent. In this way, we offer a new approach towards further unifying the analyses of optimization and sampling algorithms.

We consider the issue of information recovery for an object carrying energy and entropy into a black hole using the generalized entropy formalism, in the context of JT gravity where the backreaction problem can be solved exactly. We verify the main aspects of the Hayden-Preskill scenario but with some refinements. We show that the information is encoded in the Hawking radiation in a redundant way, as expected for a quantum error correcting code. We show how quantum extremal surfaces associated to information recovery have the form of a python's lunch and thereby show that the complexity of decoding is exponential in a combination of the entropy shift of the black hole and the entropy of the object. We also show that an infalling observer must have a smooth experience at the horizon and we calculate their endurance proper time inside the black hole before they are radiated out., Comment: minor corrections

It is shown that the operator space generated by peripheral eigenvectors of a unital completely positive map on a von Neumann algebra has a $C^*$-algebra structure. This extends the notion of non-commutative Poisson boundary by including the point spectrum of the map contained in the unit circle. The main ingredient is dilation theory. This theory provides a simple formula for the new product. The notion has implications to our understanding of quantum dynamics. For instance, it is shown that the peripheral Poisson boundary remains invariant in discrete quantum dynamics., Comment: Appendix is added. Accepted for publication in the Israel Journal of Mathematics

The shuffle model of differential privacy has gained significant interest as an intermediate trust model between the standard local and central models [EFMRTT19; CSUZZ19]. A key result in this model is that randomly shuffling locally randomized data amplifies differential privacy guarantees. Such amplification implies substantially stronger privacy guarantees for systems in which data is contributed anonymously [BEMMRLRKTS17]. In this work, we improve the state of the art privacy amplification by shuffling results both theoretically and numerically. Our first contribution is the first asymptotically optimal analysis of the R\'enyi differential privacy parameters for the shuffled outputs of LDP randomizers. Our second contribution is a new analysis of privacy amplification by shuffling. This analysis improves on the techniques of [FMT20] and leads to tighter numerical bounds in all parameter settings., Comment: Errata added. 14 pages, 4 figures

Cross-device federated learning is an emerging machine learning (ML) paradigm where a large population of devices collectively train an ML model while the data remains on the devices. This research field has a unique set of practical challenges, and to systematically make advances, new datasets curated to be compatible with this paradigm are needed. Existing federated learning benchmarks in the image domain do not accurately capture the scale and heterogeneity of many real-world use cases. We introduce FLAIR, a challenging large-scale annotated image dataset for multi-label classification suitable for federated learning. FLAIR has 429,078 images from 51,414 Flickr users and captures many of the intricacies typically encountered in federated learning, such as heterogeneous user data and a long-tailed label distribution. We implement multiple baselines in different learning setups for different tasks on this dataset. We believe FLAIR can serve as a challenging benchmark for advancing the state-of-the art in federated learning. Dataset access and the code for the benchmark are available at \url{https://github.com/apple/ml-flair}.

A central issue in machine learning is how to train models on sensitive user data. Industry has widely adopted a simple algorithm: Stochastic Gradient Descent with noise (a.k.a. Stochastic Gradient Langevin Dynamics). However, foundational theoretical questions about this algorithm's privacy loss remain open -- even in the seemingly simple setting of smooth convex losses over a bounded domain. Our main result resolves these questions: for a large range of parameters, we characterize the differential privacy up to a constant factor. This result reveals that all previous analyses for this setting have the wrong qualitative behavior. Specifically, while previous privacy analyses increase ad infinitum in the number of iterations, we show that after a small burn-in period, running SGD longer leaks no further privacy. Our analysis departs from previous approaches based on fast mixing, instead using techniques based on optimal transport (namely, Privacy Amplification by Iteration) and the Sampled Gaussian Mechanism (namely, Privacy Amplification by Sampling). Our techniques readily extend to other settings, e.g., strongly convex losses, non-uniform stepsizes, arbitrary batch sizes, and random or cyclic choice of batches., Comment: v2: improved exposition, slightly simplified proofs, all results unchanged

The recently proposed NR-ready integrated access and backhaul (IAB) architecture promises to bring a cost-efficient deployment solution for both coverage extension and capacity boosting in future 5G/5G+ systems. While its impact on the coverage extension was thoughtfully addressed in the literature, the effect of advanced functionalities such as multi-hop, multi-connectivity, and multi-beam operations on the throughput remains unclear. We review and characterize the system-level impact of these capabilities on the performance of self-backhauled IAB systems operating in half-duplex mode and utilizing millimeter-wave (mmWave) technology across both access and backhaul. Our results indicate that the throughput gain of multi-hopping and multi-beaming is significant even without multi-connectivity operation. Another important learning is that in all-mmWave systems with link blockage, multi-connectivity with link switching allows achieving self-load balancing. Finally, we outline future research directions., Comment: Accepted for publishing in the IEEE Communications Magazine

We study the problem of mean estimation of $\ell_2$-bounded vectors under the constraint of local differential privacy. While the literature has a variety of algorithms that achieve the asymptotically optimal rates for this problem, the performance of these algorithms in practice can vary significantly due to varying (and often large) hidden constants. In this work, we investigate the question of designing the protocol with the smallest variance. We show that PrivUnit (Bhowmick et al. 2018) with optimized parameters achieves the optimal variance among a large family of locally private randomizers. To prove this result, we establish some properties of local randomizers, and use symmetrization arguments that allow us to write the optimal randomizer as the optimizer of a certain linear program. These structural results, which should extend to other problems, then allow us to show that the optimal randomizer belongs to the PrivUnit family. We also develop a new variant of PrivUnit based on the Gaussian distribution which is more amenable to mathematical analysis and enjoys the same optimality guarantees. This allows us to establish several useful properties on the exact constants of the optimal error as well as to numerically estimate these constants.

Early detection of cardiovascular diseases is crucial for effective treatment and an electrocardiogram (ECG) is pivotal for diagnosis. The accuracy of Deep Learning based methods for ECG signal classification has progressed in recent years to reach cardiologist-level performance. In clinical settings, a cardiologist makes a diagnosis based on the standard 12-channel ECG recording. Automatic analysis of ECG recordings from a multiple-channel perspective has not been given enough attention, so it is essential to analyze an ECG recording from a multiple-channel perspective. We propose a model that leverages the multiple-channel information available in the standard 12-channel ECG recordings and learns patterns at the beat, rhythm, and channel level. The experimental results show that our model achieved a macro-averaged ROC-AUC score of 0.9216, mean accuracy of 88.85\%, and a maximum F1 score of 0.8057 on the PTB-XL dataset. The attention visualization results from the interpretable model are compared against the cardiologist's guidelines to validate the correctness and usability., Comment: IEEE:SMC 2021

Research with school-age readers suggests that the contributions of reading and language skills vary across reading comprehension assessments and proficiency levels. With a sample of 168 struggling adult readers, we estimated the explanatory effects of decoding, oral vocabulary, listening comprehension, fluency, background knowledge, and inferencing across three reading comprehension tests and across low, average, and high levels of performance. OLS regression models accounted for 66% of the variance in WJ Passage Comprehension scores with all competencies except listening comprehension as significant predictors; 43% of the variance in RAPID Reading Comprehension scores with decoding and listening comprehension as significant predictors; and 31% of the variance in RISE Reading Comprehension scores with decoding as a significant predictor. Quantile regression models and between-quantile slope comparisons showed that the effects of some predictors on reading comprehension varied across performance levels on one or more tests. Implications for instruction, assessment, and future research are discussed. [This paper will be published in "Reading and Writing: An Interdisciplinary Journal."]

A large percentage of adults throughout the world have low reading skills. Computer technologies can potentially help these adults improve their literacy in addition to instructors at literacy centers. AutoTutor was designed to teach comprehension strategies by implementing conversational "trialogues" in which two computer agents (tutor and peer) hold spoken interactions with the adult about words, sentences, and text in digital lessons. The agents model comprehension strategies, ask questions, and give feedback on adult answers. AutoTutor records in log files the adults' performance, namely the time and accuracy of answering questions in the conversation. We assessed the value of AutoTutor in a study with 52 adult literacy students in the United States and Canada who interacted with AutoTutor as part of a 4-month intervention with human instructors. Performance in AutoTutor was tracked at four theoretical discourse levels (words, explicit textbase, conceptual situation model, rhetorical structure) and also engagement, with an objective psychometric measure of comprehension skill both before and after the intervention. The results showed that AutoTutor provides nuanced performance and engagement measures that predicted comprehension improvements and can be used to guide formative assessment for instructors. [This paper was published in "Revista Signos" v54 n107 p1089-1113 2021.]

Background: Of the myriad of reading comprehension (RC) assessments used in schools, multiple-choice (MC) questions continue to be one of the most prevalent formats used by educators and researchers. Outcomes from RC assessments dictate many critical factors encountered during a student's academic career, and it is crucial that we gain a deeper understanding of the nuances of these assessments and the types of skills needed for their successful completion. The purpose of this exploratory study was to examine how different component skills (i.e., decoding, word recognition, reading fluency, RC and working memory) were related to students' response accuracy as they read a text and responded to MC questions. Methods: We monitored the eye movements of 73 third graders as they read an expository text and answered MC questions. We investigated whether the component skills differentially predicted accuracy across different question types and difficulty levels. Results: Results indicated that readers who answered MC questions correctly were able to identify when they needed to reread the text to find the answer and were better able to find the relevant area in the text compared with incorrect responders. Incorrect responders were less likely to reread the text to find the answer and generally had poorer precision when attempting to locate the answer in the text. Finally, the component skills relied upon by readers to answer RC questions were related to the type and difficulty of the questions. [This paper was published in "Journal of Research in Reading" v44 p737-756 2021.]

In this work, we propose a new algorithm ProjectiveGeometryResponse (PGR) for locally differentially private (LDP) frequency estimation. For a universe size of $k$ and with $n$ users, our $\varepsilon$-LDP algorithm has communication cost $\lceil\log_2k\rceil$ bits in the private coin setting and $\varepsilon\log_2 e + O(1)$ in the public coin setting, and has computation cost $O(n + k\exp(\varepsilon) \log k)$ for the server to approximately reconstruct the frequency histogram, while achieving the state-of-the-art privacy-utility tradeoff. In many parameter settings used in practice this is a significant improvement over the $ O(n+k^2)$ computation cost that is achieved by the recent PI-RAPPOR algorithm (Feldman and Talwar; 2021). Our empirical evaluation shows a speedup of over 50x over PI-RAPPOR while using approximately 75x less memory for practically relevant parameter settings. In addition, the running time of our algorithm is within an order of magnitude of HadamardResponse (Acharya, Sun, and Zhang; 2019) and RecursiveHadamardResponse (Chen, Kairouz, and Ozgur; 2020) which have significantly worse reconstruction error. The error of our algorithm essentially matches that of the communication- and time-inefficient but utility-optimal SubsetSelection (SS) algorithm (Ye and Barg; 2017). Our new algorithm is based on using Projective Planes over a finite field to define a small collection of sets that are close to being pairwise independent and a dynamic programming algorithm for approximate histogram reconstruction on the server side. We also give an extension of PGR, which we call HybridProjectiveGeometryResponse, that allows trading off computation time with utility smoothly.

Computing the noisy sum of real-valued vectors is an important primitive in differentially private learning and statistics. In private federated learning applications, these vectors are held by client devices, leading to a distributed summation problem. Standard Secure Multiparty Computation (SMC) protocols for this problem are susceptible to poisoning attacks, where a client may have a large influence on the sum, without being detected. In this work, we propose a poisoning-robust private summation protocol in the multiple-server setting, recently studied in PRIO. We present a protocol for vector summation that verifies that the Euclidean norm of each contribution is approximately bounded. We show that by relaxing the security constraint in SMC to a differential privacy like guarantee, one can improve over PRIO in terms of communication requirements as well as the client-side computation. Unlike SMC algorithms that inevitably cast integers to elements of a large finite field, our algorithms work over integers/reals, which may allow for additional efficiencies., Comment: 17 pages

Understanding the explosion mechanism of a core-collapse supernova (CCSN) is important to accurately model CCSN scenarios for different progenitor stars using model-observation comparisons. The uncertainties of various nuclear reaction rates relevant for CCSN scenarios strongly affect the accuracy of these stellar models. Out of these reactions, the $^{13}$N($\alpha$,$p$)$^{16}$O reaction has been found to affect various stages of a CCSN at varying temperatures. This work presents the first direct measurement of the $^{13}$N($\alpha$,$p$)$^{16}$O reaction performed using a 34.6 MeV beam of radioactive $^{13}$N ions and the active-target detector MUSIC (MUlti-Sampling Ionization Chamber) at Argonne National Laboratory. The resulting total $^{13}$N($\alpha$,$p$)$^{16}$O reaction cross sections from this measurement in the center-of-mass energy range of 3.26 - 6.02 MeV are presented and compared with calculations using the Hauser-Feshbach formalism. Uncertainties in the reaction rate have been dramatically reduced at CCSN temperatures., Comment: References 27 and 31 has been changed

We consider the effect of grey-body factors on the entanglement island prescription for computing the entropy of an arbitrary subset of the Hawking radiation of an evaporating black hole. When there is a non-trivial grey-body factor, the modes reflected back into the black hole affect the position of the quantum extremal surfaces at a subleading level with respect to the scrambling time. The grey-body factor allows us to analyse the role of irreversibility in the evaporation. In particular, we show that irreversibility allows multiple saddles to dominate the entropy, rather than just two as expected on the basis of Page's theorem. We show that these multiple saddles can be derived from a generalization of Page's theorem that involves a nested temporal sequence of unitary averages. We then consider how irreversibility affects the monogamy entanglement problem., Comment: 51 pages

Connection management is an important problem for any wireless network to ensure smooth and well-balanced operation throughout. Traditional methods for connection management (specifically user-cell association) consider sub-optimal and greedy solutions such as connection of each user to a cell with maximum receive power. However, network performance can be improved by leveraging machine learning (ML) and artificial intelligence (AI) based solutions. The next generation software defined 5G networks defined by the Open Radio Access Network (O-RAN) alliance facilitates the inclusion of ML/AI based solutions for various network problems. In this paper, we consider intelligent connection management based on the O-RAN network architecture to optimize user association and load balancing in the network. We formulate connection management as a combinatorial graph optimization problem. We propose a deep reinforcement learning (DRL) solution that uses the underlying graph to learn the weights of the graph neural networks (GNN) for optimal user-cell association. We consider three candidate objective functions: sum user throughput, cell coverage, and load balancing. Our results show up to 10% gain in throughput, 45-140% gain cell coverage, 20-45% gain in load balancing depending on network deployment configurations compared to baseline greedy techniques., Comment: paper accepted to the IEEE International Conference on Machine Learning and Applications (ICMLA 2021)

The confluence of 5G and AI is transforming wireless networks to deliver diverse services at the Edge, driving towards a vision of pervasive distributed intelligence. Future 6G networks will need to deliver quality of experience through seamless integration of communication, computation and AI. Therefore, networks must become intelligent, distributed, scalable, and programmable platforms across the continuum of data delivery to address the ever-increasing service requirements and deployment complexity. We present novel results across three research directions that are expected to be integral to 6G systems and also discuss newer 6G metrics.