cssi/SciSciGPT-SciSciNet huggingface dataset:
Square Kilometre Array key science: a progressive retrospective

I summarize the science drivers presented at the workshop for Phase I of the Square Kilometre Array: “Advancing Astrophysics with the Square Kilometre Array”. I build from the historical perspective of the original Key Science programs: “Science with a Square Kilometre Array”, and consider progress in astrophysics since 2004. I then present my “score card” of the primary science drivers proposed by the Science Working Groups, and further developed in the white papers and presentations at the meeting. The science case for the SKA phase I is compelling, with the right mix of killer applications (eg. pulsars and gravity, 21cm cosmology), foundational radio astronomy (eg. cosmic magnetism, baryon cycle, high energy phenomena, large molecule chemistry and protoplanetary disks), and high risk-high return “game-changing” programs (eg. fast radio bursts, BAO intensity mapping, SETI). The case for real-time data spigots that allow for commensal observing is also strong. Ultimately, the greatest discoveries that will come from the SKA are likely even richer still, and beyond prognostication.
A multidimensional analogue of the Rademacher-Gaussian tail comparison

We prove a dimension-free tail comparison between the Euclidean norms of sums of independent random vectors uniformly distributed in centred Euclidean spheres and properly rescaled standard Gaussian random vectors. 2010 Mathematics Subject Classification. Primary 60E15; Secondary 60G15, 60G50.
Unitary representations of the groups of measurable and continuous functions with values in the circle

Abstract We give a classification of unitary representations of certain Polish, not necessarily locally compact, groups: the groups of all measurable functions with values in the circle and the groups of all continuous functions on compact, second countable, zero-dimensional spaces with values in the circle. In the proofs of our classification results, certain structure theorems and factorization theorems for linear operators are used.
Embedding-Based Speaker Adaptive Training of Deep Neural Networks.

An embedding-based speaker adaptive training (SAT) approach is proposed and investigated in this paper for deep neural network acoustic modeling. In this approach, speaker embedding vectors, which are a constant given a particular speaker, are mapped through a control network to layer-dependent element-wise affine transformations to canonicalize the internal feature representations at the output of hidden layers of a main network. The control network for generating the speaker-dependent mappings is jointly estimated with the main network for the overall speaker adaptive acoustic modeling. Experiments on large vocabulary continuous speech recognition (LVCSR) tasks show that the proposed SAT scheme can yield superior performance over the widely-used speaker-aware training using i-vectors with speaker-adapted input features.
Ray: a distributed framework for emerging AI applications

The next generation of AI applications will continuously interact with the environment and learn from these interactions. These applications impose new and demanding systems requirements, both in terms of performance and flexibility. In this paper, we consider these requirements and present Ray--a distributed system to address them. Ray implements a unified interface that can express both task-parallel and actor-based computations, supported by a single dynamic execution engine. To meet the performance requirements, Ray employs a distributed scheduler and a distributed and fault-tolerant store to manage the system's control state. In our experiments, we demonstrate scaling beyond 1.8 million tasks per second and better performance than existing specialized systems for several challenging reinforcement learning applications.
A lower bound for the least prime in an Arithmetic progression

Fix $k$ a positive integer, and let $\ell$ be coprime to $k$. Let $p(k,\ell)$ denote the smallest prime equivalent to $\ell \pmod{k}$, and set $P(k)$ to be the maximum of all the $p(k,\ell)$. We seek lower bounds for $P(k)$. In particular, we show that for almost every $k$ one has $P(k) \gg \phi(k) \log k \log_2 k \log_4 k / \log_3 k,$ answering a question of Ford, Green, Konyangin, Maynard, and Tao. We rely on their recent work on large gaps between primes. Our main new idea is to use sieve weights to capture not only primes, but also small multiples of primes. We also give a heuristic which suggests that $\liminf_{k} \frac{P(k)}{ \phi(k) \log^2 k} = 1.$
Blowup with vorticity control for a 2D model of the Boussinesq equations

Abstract We propose a system of equations with nonlocal flux in two space dimensions which is closely modeled after the 2D Boussinesq equations in a hyperbolic flow scenario. Our equations involve a vorticity stretching term and a non-local Biot-Savart law and provide insight into the underlying intrinsic mechanisms of singularity formation. We prove stable, controlled finite time blowup involving upper and lower bounds on the vorticity up to the time of blowup for a wide class of initial data.
Using Sentence-Level LSTM Language Models for Script Inference

There is a small but growing body of research on statistical scripts, models of event sequences that allow probabilistic inference of implicit events from documents. These systems operate on structured verb-argument events produced by an NLP pipeline. We compare these systems with recent Recurrent Neural Net models that directly operate on raw tokens to predict sentences, finding the latter to be roughly comparable to the former in terms of predicting missing events in documents.
Factorization length distribution for affine semigroups I: Numerical semigroups with three generators

Abstract Most factorization invariants in the literature extract extremal factorization behavior, such as the maximum and minimum factorization lengths. Invariants of intermediate size, such as the mean, median, and mode factorization lengths are more subtle. We use techniques from analysis and probability to describe the asymptotic behavior of these invariants. Surprisingly, the asymptotic median factorization length is described by a number that is usually irrational.
Sparse matrix decompositions and graph characterizations

Abstract Zeros in positive definite correlation matrices arise frequently in probability and statistics, and are intimately related to the notion of stochastic independence. The question of when zeros (i.e., sparsity) in a positive definite matrix A are preserved in its Cholesky decomposition, and vice versa, was addressed by Paulsen et al. [V.I. Paulsen, S.C. Power, R.R. Smith, Schur products and matrix completions, J. Funct. Anal. 85 (1989) 151–178]. In particular, they prove that for the pattern of zeros in A to be retained in the Cholesky decomposition of A , the pattern of zeros in A has to necessarily correspond to a chordal (or decomposable) graph associated with a specific type of vertex ordering. This result therefore also yields a characterization of chordal graphs in terms of sparse positive definite matrices, and has proved to be extremely useful in probabilistic and statistical analysis of Markov random fields. Now, consider a positive definite matrix A and its Cholesky decomposition given by A = LDL T , where L is lower triangular with unit diagonal entries, and D a diagonal matrix with positive entries. In this paper, we prove that a necessary and sufficient condition for zeros (i.e., sparsity) in a positive definite matrix A to be preserved in its associated Cholesky matrix L , and in addition also preserved in the inverse of the Cholesky matrix L - 1 , is that the pattern of zeros corresponds to a co-chordal or homogeneous graph associated with a specific type of vertex ordering. We proceed to provide a second characterization of this class of graphs in terms of determinants of submatrices that correspond to cliques in the graph. These results add to the growing body of literature in the field of sparse matrix decompositions, and also prove to be critical ingredients in the probabilistic analysis of an important class of Markov random fields.
Numerically Erasure-robust Frames

Abstract Given a channel with additive noise and adversarial erasures, the task is to design a frame that allows for stable signal reconstruction from transmitted frame coefficients. To meet these specifications, we introduce numerically erasure-robust frames. We first consider a variety of constructions, including random frames, equiangular tight frames and group frames. Later, we show that arbitrarily large erasure rates necessarily induce numerical instability in signal reconstruction. We conclude with a few observations, including some implications for maximal equiangular tight frames and sparse frames.
Balancing interpretability and predictive accuracy for unsupervised tensor mining

The PARAFAC tensor decomposition has enjoyed an increasing success in exploratory multi-aspect data mining scenarios. A major challenge remains the estimation of the number of latent factors (i.e., the rank) of the decomposition, which is known to yield high-quality, interpretable results. Previously, AutoTen, an automated tensor mining method which leverages a well-known quality heuristic from the field of Chemometrics, the Core Consistency Diagnostic (CORCONDIA), in order to automatically determine the rank for the PARAFAC decomposition, was proposed. In this work, building upon AutoTen, we set out to explore the trade-off between 1) the interpretability of the results (as expressed by CORCONDIA), and 2) the predictive accuracy of the decomposition, towards improving rank estimation quality. Our preliminary results indicate that striking a good balance in that trade-off yields high-quality rank estimation, towards achieving unsupervised tensor mining.
Training Detection-Range-Frugal Cooperative Collision Avoidance Models for Quadcopters via Neuroevolution

Cooperative autonomous approaches to avoiding collisions among small Unmanned Aerial Vehicles (UAVs) is central to safe integration of UAVs within the civilian airspace. One potential online cooperative approach is the concept of reciprocal actions, where both UAVs take pre-trained mutually coherent actions that do not require active online coordination (thereby avoiding the computational burden and risk associated with it). This paper presents a learning based approach to train such reciprocal maneuvers. Neuroevolution, which uses evolutionary algorithms to simultaneously optimize the topology and weights of neural networks, is used as the learning method -- which operates over a set of sample approach scenarios. Unlike most existing work (that minimize travel distance, energy or risk), the training process here focuses on the objective of minimizing the required detection range; this has important practical implications w.r.t. alleviating the dependency on sophisticated sensing and their reliability under various environments. A specialized design of experiments and line search is used to identify the minimum detection range for each sample scenarios. In order to allow an efficient training process, a classifier is used to discard actions (without simulating them) where the controller would fail. The model obtained via neuroevolution is observed to generalize well to (i.e., successful collision avoidance over) unseen approach scenarios.
An MILP approach for real-time optimal controller synthesis with Metric Temporal Logic specifications

The fundamental idea of this work is to synthesize reactive controllers such that closed-loop execution trajectories of the system satisfies desired specifications that ensure correct system behaviors, while optimizing a desired performance criteria. We define the correctness of a system's behavior according to the system's relation to the environment, for example, the output trajectories of the system terminate in a goal set without entering an unsafe set. Using Metric Temporal Logic (MTL) specifications we can capture complex system behaviors and timing requirements, such as the output trajectories must pass through a number of way-points within a certain time frame before terminating in the goal set. Given a Mixed Logical Dynamical (MLD) system and system specifications in terms of MTL formula or simpler reach-avoid specifications, our goal is to find a closed-loop trajectory that satisfies the specifications, in non-deterministic environments. Using an MILP framework we search over the space of input signals to obtain such valid trajectories of the system, by adding constraints to satisfy the MTL formula only when necessary, to avoid the exponential complexity of solving MILP problems. We also present experimental results for planning a path in real-time for a mobile robot through a dynamically changing environment with a desired task specification.
The Utility Cost of Robust Privacy Guarantees

Consider a data publishing setting for a data set with public and private features. The objective of the publisher is to maximize the amount of information about the public features in a revealed data set, while keeping the information leaked about the private features bounded. The goal of this paper is to analyze the performance of privacy mechanisms that are constructed to match the distribution learned from the data set. Two distinct scenarios are considered: (i) mechanisms are designed to provide a privacy guarantee for the learned distribution; and (ii) mechanisms are designed to provide a privacy guarantee for every distribution in a given neighborhood of the learned distribution. For the first scenario, given any privacy mechanism, upper bounds on the difference between the privacy-utility guarantees for the learned and true distributions are presented. In the second scenario, upper bounds on the reduction in utility incurred by providing a uniform privacy guarantee are developed.
Unsupervised Feature Learning from Temporal Data

Current state-of-the-art classification and detection algorithms rely on supervised training. In this work we study unsupervised feature learning in the context of temporally coherent video data. We focus on feature learning from unlabeled video data, using the assumption that adjacent video frames contain semantically similar information. This assumption is exploited to train a convolutional pooling auto-encoder regularized by slowness and sparsity. We establish a connection between slow feature learning to metric learning and show that the trained encoder can be used to define a more temporally and semantically coherent metric. Our main assumption is that data samples that are temporal neighbors are also likely to be neighbors in the latent space. For example, adjacent frames in a video sequence are more likely to be semantically similar than non-adjacent frames. This assumption naturally leads to the slowness prior on features which was introduced in SFA (Wiskott & Sejnowski (2002)). Temporal coherence can be exploited by assuming a prior on the features extracted from the temporal data sequence. One such prior is that the features should vary slowly with respect to time. In the discrete time setting this prior corresponds to minimizing an L p norm of the difference of feature vectors for temporally adjacent inputs. Consider a video sequence withT frames, ifzt represents the feature vector extracted from the frame at timet then the slowness prior corresponds to minimizing PT t=1 kzt zt 1kp. To avoid the degenerate solution zt = z0 for t = 1:::T , a second term is introduced which encourages data samples that are not temporal neighbors to be separated by at least a distance ofm-units in feature space, wherem is known as the margin. In the temporal setting this corresponds to minimizing max(0;mk zt zt0kp), wherejt t 0 j > 1. Together the two terms form the loss function introduced in Hadsell et al. (2006) as a dimension reduction and data visualization algorithm known as DrLIM. Assume that there is a differentiable mapping from input space to feature space which operates on individual temporal samples. Denote this mapping by G and assume it is parametrized by a set of trainable coefficients denoted byW . That is,zt = GW(xt). The per-sample loss function can be written as:
Nonuniform dependence on initial data for compressible gas dynamics: The periodic Cauchy problem

Abstract We start with the classic result that the Cauchy problem for ideal compressible gas dynamics is locally well posed in time in the sense of Hadamard; there is a unique solution that depends continuously on initial data in Sobolev space H s for s > d / 2 + 1 where d is the space dimension. We prove that the data to solution map for periodic data in two dimensions although continuous is not uniformly continuous.
Use Privacy in Data-Driven Systems: Theory and Experiments with Machine Learnt Programs

This paper presents an approach to formalizing and enforcing a class of use privacy properties in data-driven systems. In contrast to prior work, we focus on use restrictions on proxies (i.e. strong predictors) of protected information types. Our definition relates proxy use to intermediate computations that occur in a program, and identify two essential properties that characterize this behavior: 1) its result is strongly associated with the protected information type in question, and 2) it is likely to causally affect the final output of the program. For a specific instantiation of this definition, we present a program analysis technique that detects instances of proxy use in a model, and provides a witness that identifies which parts of the corresponding program exhibit the behavior. Recognizing that not all instances of proxy use of a protected information type are inappropriate, we make use of a normative judgment oracle that makes this inappropriateness determination for a given witness. Our repair algorithm uses the witness of an inappropriate proxy use to transform the model into one that provably does not exhibit proxy use, while avoiding changes that unduly affect classification accuracy. Using a corpus of social datasets, our evaluation shows that these algorithms are able to detect proxy use instances that would be difficult to find using existing techniques, and subsequently remove them while maintaining acceptable classification performance.
Interfacial fluid instabilities and Kapitsa pendula.

The onset and development of instabilities is one of the central problems in fluid mechanics. Here we develop a connection between instabilities of free fluid interfaces and inverted pendula. When acted upon solely by the gravitational force, the inverted pendulum is unstable. This position can be stabilized by the Kapitsa phenomenon, in which high-frequency low-amplitude vertical vibrations of the base creates a fictitious force which opposes the gravitational force. By transforming the dynamical equations governing a fluid interface into an appropriate pendulum-type equation, we demonstrate how stability can be induced in fluid systems by properly tuned vibrations. We construct a “dictionary”-type relationship between various pendula and the classical Rayleigh-Taylor, Kelvin-Helmholtz, Rayleigh-Plateau and the self-gravitational instabilities. This makes several results in control theory and dynamical systems directly applicable to the study of tunable fluid instabilities, where the critical wavelength depends on the external forces or the instability is suppressed entirely. We suggest some applications and instances of the effect ranging in scale from microns to the radius of a galaxy.
Linear piecewise-deterministic Markov processes with families of random discrete events

We consider a class of piecewise-deterministic Markov processes where the state evolves according to a linear dynamical system. This continuous time evolution is interspersed by discrete events that occur at random times and change (reset) the state based on a linear affine map. In particular, we consider two families of mutually independent discrete events, with the first family of resets occurring at exponentially-distributed times. The second family of resets is generally-distributed, in the sense that, the time intervals between successive events are independent and identically distributed random variables that follow an arbitrary continuous positively-valued probability density function. For this class of stochastic systems, we provide explicit conditions that lead to finite stationary moments, and the corresponding exact closed-form moment formulas. These results are illustrated on an example drawn from systems biology, where a protein is expressed in bursts at exponentially-distributed time intervals, decays within the cell cycle, and is randomly divided among daughter cells when generally-distributed cell-division events occur. Our analysis leads to novel results for the mean and noise levels in protein copy numbers, and we decompose the noise levels into components arising from stochastic expression, random cell-cycle times, and partitioning. Interestingly, these individual noise contributions behave differently as cell division times become more random. In summary, the paper expands the class of stochastic hybrid systems for which statistical moments can be derived exactly without any approximations, and these results have applications for studying random phenomena in diverse areas.
Supersequences, rearrangements of sequences, and the spectrum of bases in additive number theory

The set A={an}n=1∞ of nonnegative integers is an asymptotic basis of order h if every sufficiently large integer can be represented as the sum of h elements of A. If an∼αnh for some real number α>0, then α is called an additive eigenvalue of order h. The additive spectrum of order h is the set N(h) consisting of all additive eigenvalues of order h. It is proved that there is a positive number ηh⩽1/h! such that N(h)=(0,ηh) or N(h)=(0,ηh]. The proof uses results about the construction of supersequences of sequences with prescribed asymptotic growth, and also about the asymptotics of rearrangements of infinite sequences. For example, it is proved that there does not exist a strictly increasing sequence of integers B={bn}n=1∞ such that bn∼2n and B contains a subsequence {bnk}k=1∞ such that bnk∼3k.
Decomposition bounds for marginal MAP

Marginal MAP inference involves making MAP predictions in systems defined with latent variables or missing information. It is significantly more difficult than pure marginalization and MAP tasks, for which a large class of efficient and convergent variational algorithms, such as dual decomposition, exist. In this work, we generalize dual decomposition to a generic power sum inference task, which includes marginal MAP, along with pure marginalization and MAP, as special cases. Our method is based on a block coordinate descent algorithm on a new convex decomposition bound, that is guaranteed to converge monotonically, and can be parallelized efficiently. We demonstrate our approach on marginal MAP queries defined on real-world problems from the UAI approximate inference challenge, showing that our framework is faster and more reliable than previous methods.
LinNet: Probabilistic Lineup Evaluation Through Network Embedding

Which of your team’s possible lineups has the best chances against each of your opponent’s possible lineups? To answer this question, we develop LinNet (which stands for LINeup NETwork). LinNet exploits the dynamics of a directed network that captures the performance of lineups during their matchups. The nodes of this network represent the different lineups, while an edge from node B to node A exists if lineup \({\lambda }_A\) has outperformed lineup \({\lambda }_B\). We further annotate each edge with the corresponding performance margin (point margin per minute). We then utilize this structure to learn a set of latent features for each node (i.e., lineup) using the node2vec framework. Consequently, using the latent, learned features, LinNet builds a logistic regression model for the probability of lineup \({\lambda }_A\) outperforming lineup \({\lambda }_B\). We evaluate the proposed method by using NBA lineup data from the five seasons between 2007–08 and 2011–12. Our results indicate that our method has an out-of-sample accuracy of 68%. In comparison, utilizing simple network centrality metrics (i.e., PageRank) achieves an accuracy of just 53%, while using the adjusted plus-minus of the players in the lineup for the same prediction problem provides an accuracy of only 55%. We have also explored the adjusted lineups’ plus-minus as our predictors and obtained an accuracy of 59%. Furthermore, the probability output of LinNet is well-calibrated as indicated by the Brier score and the reliability curve. One of the main benefits of LinNet is its generic nature that allows it to be applied in different sports since the only input required is the lineups’ matchup network, i.e., not any sport-specific features are needed.
What We Learn about Girls' Education from Interventions that Do Not Focus on Girls

Despite dramatic global gains in access to education, 130 million girls of school age remain out of school. Among those who do enter, too many do not gain the essential skills to succeed after they complete their schooling. Previous efforts to synthesize evidence on how to improve educational outcomes for girls have tended to focus on interventions that are principally targeted to girls, such as girls' latrines or girls' scholarships. But if general, non-targeted interventions -- those that benefit both girls and boys -- significantly improve girls' education, then focusing only on girl-targeted interventions may miss some of the best investments for improving educational opportunities for girls in absolute terms. This review brings together evidence from 270 educational interventions from 177 studies in 54 low- and middle-income countries and identifies their impacts on girls, regardless of whether the interventions specifically target girls. The review finds that to improve access and learning, general interventions deliver gains for girls that are comparable to girl-targeted interventions. At the same time, many more general interventions have been tested, providing a broader menu of options for policy makers. General interventions have similar impacts for girls as for boys. Many of the most effective interventions to improve access for girls are household-based (such as cash transfer programs), and many of the most effective interventions to improve learning for girls involve improving the pedagogy of teachers. Girl-targeted interventions may make the most sense when addressing constraints that are unique to girls.
Avoiding Latent Variable Collapse with Generative Skip Models

Variational autoencoders (VAEs) learn distributions of high-dimensional data. They model data with a deep latent-variable model and then fit the model by maximizing a lower bound of the log marginal likelihood. VAEs can capture complex distributions, but they can also suffer from an issue known as "latent variable collapse," especially if the likelihood model is powerful. Specifically, the lower bound involves an approximate posterior of the latent variables; this posterior "collapses" when it is set equal to the prior, i.e., when the approximate posterior is independent of the data. While VAEs learn good generative models, latent variable collapse prevents them from learning useful representations. In this paper, we propose a simple new way to avoid latent variable collapse by including skip connections in our generative model; these connections enforce strong links between the latent variables and the likelihood function. We study generative skip models both theoretically and empirically. Theoretically, we prove that skip models increase the mutual information between the observations and the inferred latent variables. Empirically, we study images (MNIST and Omniglot) and text (Yahoo). Compared to existing VAE architectures, we show that generative skip models maintain similar predictive performance but lead to less collapse and provide more meaningful representations of the data.
Exploring the phase structure of 12-flavor $SU(3)$

We are studying the SU(3) gauge theory with 12 staggered fermions, searching for the endpoint of the line of first-order phase transitions in the mass–beta plane. This endpoint plays an important role in our understanding of the phase diagram of this model. Having found this endpoint with high statistics on a small lattice using unimproved staggered fermions, we are working to find it on larger lattices and with improved actions. For an action improved with nHYP-smeared staggered fermions, we discuss the effect of slowly turning off the improvement on the broken shift symmetry phase.
ZnSnS3: Structure Prediction, Ferroelectricity, and Solar Cell Applications

The rapid growth of the solar energy industry is driving a strong demand for high performance, efficient photoelectric materials. In particular, ferroelectrics composed of earth-abundant elements may be useful in solar cell applications due to their large internal polarization. Unfortunately, wide band gaps prevent many such materials from absorbing light in the visible to mid-infrared range. Here, we address the band gap issue by investigating the effects of substituting sulfur for oxygen in the perovskite structure ZnSnO3. Using evolutionary methods, we identify the stable and metastable structures of ZnSnS3 and compare them to those previously characterized for ZnSnO3. Our results suggest that the most stable structure of ZnSnS3 is the monoclinic structure, followed by the metastable ilmenite and lithium niobate structures. The latter structure is highly polarized, possessing a significantly reduced band gap of 1.28 eV. These desirable characteristics make it a prime candidate for solar cell applications.
On the Failure of BD-N and BD, and an Application to the Anti-Specker Property

We give the natural topological model for ¬BD-N, and use it to show that the closure of spaces with the anti-Specker property under product does not imply BD-N. Also, the natural topological model for ¬BD is presented. Finally, for some of the realizability models known indirectly to falsify BD-N ,i t is brought out in detail how BD-N fails.
Finding Streams in Knowledge Graphs to Support Fact Checking

The volume of information generated online makes it impossible to manually fact-check all claims. Computational approaches for fact checking may be the key to help mitigate the risks of massive misinformation spread. Such approaches can be designed to not only be scalable and effective at assessing veracity of dubious claims, but also to boost a human fact checker's productivity by surfacing relevant facts and patterns to aid their analysis. We present a novel, unsupervised network-flow based approach to determine the truthfulness of a statement of fact expressed in the form of a triple. We view a knowledge graph of background information about real-world entities as a flow network, and show that computational fact checking then amounts to finding a "knowledge stream" connecting the subject and object of the triple. Evaluation on a range of real-world and hand-crafted datasets of facts reveals that this network-flow model can be very effective in discerning true statements from false ones, outperforming existing algorithms on many test cases. Moreover, the model is expressive in its ability to automatically discover several useful patterns and surface relevant facts that may help a human fact checker.
The Throughput-Outage Tradeoff of Wireless One-Hop Caching Networks

We consider a wireless device-to-device (D2D) network where the nodes have precached information from a library of available files. Nodes request files at random. If the requested file is not in the on-board cache, then it is downloaded from some neighboring node via one-hop local communication. An outage event occurs when a requested file is not found in the neighborhood of the requesting node, or if the network admission control policy decides not to serve the request. We characterize the optimal throughput-outage tradeoff in terms of tight scaling laws for various regimes of the system parameters, when both the number of nodes and the number of files in the library grow to infinity. Our analysis is based on Gupta and Kumar protocol model for the underlying D2D wireless network, widely used in the literature on capacity scaling laws of wireless networks without caching. Our results show that the combination of D2D spectrum reuse and caching at the user nodes yields a per-user throughput independent of the number of users, for any fixed outage probability in (0, 1). This implies that the D2D caching network is scalable: even though the number of users increases, each user achieves constant throughput. This behavior is very different from the classical Gupta and Kumar result on ad hoc wireless networks, for which the per-user throughput vanishes as the number of users increases. Furthermore, we show that the user throughput is directly proportional to the fraction of cached information over the whole file library size. Therefore, we can conclude that D2D caching networks can turn memory into bandwidth (i.e., doubling the on-board cache memory on the user devices yields a 100% increase of the user throughout).
Computing permanents of complex diagonally dominant matrices and tensors

We prove that for any λ > 1, fixed in advance, the permanent of an n × n complex matrix, where the absolute value of each diagonal entry is at least λ times bigger than the sum of the absolute values of all other entries in the same row, can be approximated within any relative error 0 < ϵ < 1 in quasi-polynomial nO(lnn-lnϵ) time. We extend this result to multidimensional permanents of tensors and apply it to weighted counting of perfect matchings in hypergraphs.
Exact volume of hyperbolic 2-bridge links

W. Thurston suggested a method for computing hyperbolic volume of hyperbolic 3-manifolds, based on a triangulation of the manifold. The method was implemented by J. Weeks in the program SnapPea, which produces a decimal approximation as a result. For hyperbolic 2-bridge links, we give formulae that allow one to find the exact volume, i.e. to construct a polynomial and to find volume as an analytic function of one of its roots. The computation is performed directly from a reduced, alternating link diagram.
Effects of confinement for single-well potentials

We study bound states generated by a unique potential minimum in the situation where the system is strongly confined to a bounded region containing the minimum (by imposing Dirichlet boundary conditions). In this case, the eigenvalues of the confined system differ from those of the unconfined system by an exponentially small quantity in the semiclassical limit. An asymptotic expansion for this shift is established. The formulas are evaluated explicitly for the harmonic oscillator and an application to the Coulomb potential at a fixed angular momentum is given.
Big Ramsey degrees and topological dynamics

We consider Fraisse structures whose objects have finite big Ramsey degree and ask what consequences this has for the dynamics of the automorphism group. Motivated by a theorem of D. Devlin about the partition properties of the rationals, we define the notion of a big Ramsey structure, a single structure which codes the big Ramsey degrees of a given Fraisse structure. This in turn leads to the definition of a completion flow; we show that if a Fraisse structure admits a big Ramsey structure, then the automorphism group admits a unique universal completion flow. We also discuss the problem of when big Ramsey structures exist and explore connections to the notion of oscillation stability defined by Kechris, Pestov, and Todorcevic
Classically integral quadratic forms excepting at most two values

Let $S \subseteq \mathbb{N}$ be finite. Is there a positive definite quadratic form that fails to represent only those elements in $S$? For $S = \emptyset$, this was solved (for classically integral forms) by the $15$-Theorem of Conway-Schneeberger in the early 1990s and (for all integral forms) by the $290$-Theorem of Bhargava-Hanke in the mid-2000s. In 1938 Halmos attempted to list all weighted sums of four squares that failed to represent $S=\{m\}$; of his $88$ candidates, he could provide complete justifications for all but one. In the same spirit, we ask, "for which $S = \{m, n\}$ does there exist a quadratic form excepting only the elements of $S$?" Extending the techniques of Bhargava and Hanke, we answer this question for quaternary forms. In the process, we prove what Halmos could not; namely, that $x^2+2y^2+7z^2+13w^2$ represents all positive integers except $5$. We develop new strategies to handle forms of higher dimensions, yielding an enumeration of and proofs for the $73$ possible pairs that a classically integral positive definite quadratic form may except.
Learning to Segment Breast Biopsy Whole Slide Images

We trained and applied an encoder-decoder model to semantically segment breast biopsy images into biologically meaningful tissue labels. Since conventional encoderdecoder networks cannot be applied directly on large biopsy images and the different sized structures in biopsies present novel challenges, we propose four modifications: (1) an input-aware encoding block to compensate for information loss, (2) a new dense connection pattern between encoder and decoder, (3) dense and sparse decoders to combine multi-level features, (4) a multi-resolution network that fuses the results of encoder-decoders run on different resolutions. Our model outperforms a feature-based approach and conventional encoder-decoders from the literature. We use semantic segmentations produced with our model in an automated diagnosis task and obtain higher accuracies than a baseline approach that employs an SVM for featurebased segmentation, both using the same segmentationbased diagnostic features.
The complex Frobenius theorem for rough involutive structures

We establish a version of the complex Frobenius theorem in the context of a complex subbundle S of the complexified tangent bundle of a manifold having minimal regularity. If the subbundle S defines the structure of a Levi-flat CR-manifold, it suffices that S be Lipschitz for our results to apply. A principal tool in the analysis is a precise version of the Newlander-Nirenberg theorem with parameters, for integrable almost complex structures with minimal regularity, which builds on recent work of the authors.
Spatial measures of genetic heterogeneity during carcinogenesis

In this work we explore the temporal dynamics of spatial heterogeneity during the process of tumorigenesis from healthy tissue. We utilize a spatial stochastic model of mutation accumulation and clonal expansion in a structured tissue to describe this process. Under a two-step tumorigenesis model, we first derive estimates of a non-spatial measure of diversity: Simpson’s Index, which is the probability that two individuals sampled at random from the population are identical, in the premalignant population. We next analyze two new measures of spatial population heterogeneity. In particular we study the typical length scale of genetic heterogeneity during the carcinogenesis process and estimate the extent of a surrounding premalignant clone given a clinical observation of a premalignant point biopsy. This evolutionary framework contributes to a growing literature focused on developing a better understanding of the spatial population dynamics of cancer initiation and progression.
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration

Computing optimal transport distances such as the earth mover's distance is a fundamental problem in machine learning, statistics, and computer vision. Despite the recent introduction of several algorithms with good empirical performance, it is unknown whether general optimal transport distances can be approximated in near-linear time. This paper demonstrates that this ambitious goal is in fact achieved by Cuturi's Sinkhorn Distances. This result relies on a new analysis of Sinkhorn iterations, which also directly suggests a new greedy coordinate descent algorithm Greenkhorn with the same theoretical guarantees. Numerical simulations illustrate that Greenkhorn significantly outperforms the classical Sinkhorn algorithm in practice.
Method and advantages of genetic algorithms in parameterization of interatomic potentials: Metal oxides

Abstract The method and the advantages of an evolutionary computing based approach using a steady state genetic algorithm (GA) for the parameterization of interatomic potentials for metal oxides within the shell model framework are developed and described. We show that the GA based methodology for the parameterization of interatomic force field functions is capable of (a) simultaneous optimization of the multiple phases or properties of a material in a single run, (b) facilitates the incremental re-optimization of the whole system as more data is made available for either additional phases or material properties not included in previous runs, and (c) successful global optimization in the presence of multiple local minima in the parameter space. As an example, we apply the method towards simultaneous optimization of four distinct crystalline phases of Barium Titanate (BaTiO3 or BTO) using an ab initio density functional theory (DFT) based reference dataset. We find that the optimized force field function is capable of the prediction of the two phases not used in the optimization procedure, and that many derived physical properties such as the equilibrium lattice constants, unit cell volume, elastic properties, coefficient of thermal expansion, and average electronic polarization are in good agreement with the experimental results available from the literature.
Capability considerations for enhancing safety on long duration crewed missions: Insights from a technical interchange meeting on autonomous crew operations

Abstract As future flight crews on long duration deep space missions are expected to operate more autonomously, considerations must be given to onboard capabilities and human-computer teaming that will fortify the safety net traditionally provided by the Mission Control Center. In August 2018, the Human Factors and Behavioral Performance Element of NASA's Human Research Program convened a Technical Interchange Meeting (TIM) on Autonomous Crew Operations at NASA Ames Research Center to address how intelligent technologies can be utlilzed to augment crew capabilities to support real-time anomaly response. In this paper, we highlight three topic areas discussed at the TIM that have direct implications for future crew anomaly response capabilities: smart structures, cognitive assistants, and manpower.
Teleoperator Imitation with Continuous-Time Safety

Learning to effectively imitate human teleoperators, with generalization to unseen and dynamic environments, is a promising path to greater autonomy enabling robots to steadily acquire complex skills from supervision. We propose a new motion learning technique rooted in contraction theory and sum-of-squares programming for estimating a control law in the form of a polynomial vector field from a given set of demonstrations. Notably, this vector field is provably optimal for the problem of minimizing imitation loss while providing continuous-time guarantees on the induced imitation behavior. Our method generalizes to new initial and goal poses of the robot and can adapt in real-time to dynamic obstacles during execution, with convergence to teleoperator behavior within a well-defined safety tube. We present an application of our framework for pick-and-place tasks in the presence of moving obstacles on a 7-DOF KUKA IIWA arm. The method compares favorably to other learning-from-demonstration approaches on benchmark handwriting imitation tasks.
The failure of Kodaira vanishing for Fano varieties, and terminal singularities that are not Cohen-Macaulay

We show that the Kodaira vanishing theorem can fail on smooth Fano varieties of any characteristic p > 0. Taking cones over some of these varieties, we give the first examples of terminal singularities which are not Cohen-Macaulay. By a different method, we construct a terminal singularity of dimension 3 (the lowest possible) in characteristic 2 which is not Cohen-Macaulay.
Estimating Time-Varying Graphical Models

In this article, we study time-varying graphical models based on data measured over a temporal grid. Such models are motivated by the needs to describe and understand evolving interacting relations...
The effects of spaceflight and fracture healing on distant skeletal sites.

Spaceflight results in reduced mechanical loading of the skeleton, which leads to dramatic bone loss. Low bone mass is associated with increased fracture risk, and this combination may compromise future, long-term, spaceflight missions. Here, we examined the systemic effects of spaceflight and fracture surgery/healing on several non-injured bones within the axial and appendicular skeleton. Forty C57BL/6, male mice were randomized into the following groups: (1) Sham surgery mice housed on the earth (Ground + Sham); (2) Femoral segmental bone defect surgery mice housed on the earth (Ground + Surgery); (3) Sham surgery mice housed in spaceflight (Flight + Sham); and (4) Femoral segmental bone defect surgery mice housed in spaceflight (Flight + Surgery). Mice were 9 weeks old at the time of launch and were euthanized approximately 4 weeks after launch. Micro-computed tomography (μCT) was used to evaluate standard bone parameters in the tibia, humerus, sternebra, vertebrae, ribs, calvarium, mandible, and incisor. One intriguing finding was that both spaceflight and surgery resulted in virtually identical losses in tibial trabecular bone volume fraction, BV/TV (24–28% reduction). Another important finding was that surgery markedly changed tibial cortical bone geometry. Understanding how spaceflight, surgery, and their combination impact non-injured bones will improve treatment strategies for astronauts and terrestrial humans alike.
Test challenges of multi-gigabit serial buses

As the complexity and bandwidth requirements of modern electronics and avionics increase, digital signal interfaces are evolving. Parallel buses have given way to serial buses, electrical interfaces are being replaced by optical interfaces, and data rates are continually rising. While this proliferation of high-speed serial interfaces enables impressive new technologies, it can also create unique test challenges for a test engineer [1].
Reifenberg's Isoperimetric Inequality Revisited

We prove a generalization of Reifenberg’s isoperimetric inequality. The main result of this paper is used in Harrison and Pugh (General methods of elliptic minimization, Available on arxiv, 2016) to establish existence of a minimizer for an anisotropically-weighted area functional among a collection of surfaces which satisfies a set of axioms, namely being closed under certain deformations and Hausdorff limits. This problem is known as the axiomatic Plateau problem.
DFS: a dataset file system for data discovering users

Many research questions can be answered quickly and efficiently using data already collected for previous research. This practice is called secondary data analysis, and has gained popularity due to lower costs and improved research efficiency. In this paper we propose DFS, a file system to standardize the metadata representation of datasets, and DDU, a scalable architecture based on DFS for semi-automated metadata generation and data recommendation on the cloud, and explores their implications on datasets stored in digital libraries.