cssi/SciSciGPT-SciSciNet huggingface dataset:
Optimal Battery Participation in Frequency Regulation Markets

Battery participants in performance-based frequency regulation markets must consider the cost of battery aging in their operating strategies to maximize market profits. In this paper, we solve this problem by proposing an optimal control policy and an optimal bidding policy based on realistic market settings and an accurate battery aging model. The proposed control policy has a threshold structure and achieves near-optimal performance with respect to an offline controller that has complete future information. The proposed bidding policy considers the optimal control policy to maximize market profits while satisfying the market performance requirement through a chance-constraint. It factors the value of performance and supports a tradeoff between higher profits and a lower risk of violating performance requirements. We demonstrate the optimality of both policies using simulations. A case study based on the PJM Interconnection LLC (PJM) regulation market shows that the approach is effective at maximizing operating profits.
Classification of vertex-transitive cubic partial cubes

Partial cubes are graphs isometrically embeddable into hypercubes. In this paper it is proved that every cubic, vertex-transitive partial cube is isomorphic to one of the following graphs: $K_2 \, \square \, C_{2n}$, for some $n\geq 2$, the generalized Petersen graph $G(10,3)$, the cubic permutahedron, the truncated cuboctahedron, or the truncated icosidodecahedron. This classification is a generalization of results of Bresar et al.~from 2004 on cubic mirror graphs, it includes all cubic, distance-regular partial cubes (Weichsel, 1992), and presents a contribution to the classification of all cubic partial cubes.
Soficity and variations on Higman’s group

A group is sofic when every finite subset can be well approximated in a finite symmetric group. No example of a non-sofic group is known. Higman's group, which is a circular amalgamation of four copies of the Baumslag--Solitar group, is a candidate. Here we contribute to the discussion of the problem of its soficity in two ways. We construct variations on Higman's group replacing the Baumslag--Solitar group by other groups $G$. We give an elementary condition on $G$, enjoyed for example by $\mathbb{Z} \wr \mathbb{Z}$ and the integral Heisenberg group, under which the resulting group is sofic. We then use soficity to deduce that there exist permutations of $\mathbb{Z} / n\mathbb{Z}$ that are seemingly pathological in that they have order dividing four and yet locally they behave like exponential functions over most of their domains. Our approach is based on that of Helfgott and Juschenko, who recently showed the soficity of Higman's group would imply some the existence of some similarly pathological functions. Our results call into question their suggestion that this might be a step towards proving the existence of a non-sofic group.
Elementary subalgebras of Lie algebras

Abstract We initiate the investigation of the projective varieties E ( r , g ) of elementary subalgebras of dimension r of a (p-restricted) Lie algebra g for various r ≥ 1 . These varieties E ( r , g ) are the natural ambient varieties for generalized support varieties for restricted representations of g . We identify these varieties in special cases, revealing their interesting and varied geometric structures. We also introduce invariants for a finite dimensional u ( g ) -module M, the local ( r , j ) -radical rank and local ( r , j ) -socle rank, functions which are lower/upper semicontinuous on E ( r , g ) . Examples are given of u ( g ) -modules for which some of these rank functions are constant.
Deep Predictive Models for Collision Risk Assessment in Autonomous Driving

In this paper, we investigate a predictive approach for collision risk assessment in autonomous and assisted driving. A deep predictive model is trained to anticipate imminent accidents from traditional video streams. In particular, the model learns to identify cues in RGB images that are predictive of hazardous upcoming situations. In contrast to previous work, our approach incorporates (a) temporal information during decision making, (b) multi-modal information about the environment, as well as the proprioceptive state and steering actions of the controlled vehicle, and (c) information about the uncertainty inherent to the task. To this end, we discuss Deep Predictive Models and present an implementation using a Bayesian Convolutional LSTM. Experiments in a simple simulation environment show that the approach can learn to predict impending accidents with reasonable accuracy, especially when multiple cameras are used as input sources.
Revisiting differentially private linear regression: optimal and adaptive prediction & estimation in unbounded domain.

We revisit the problem of linear regression under a differential privacy constraint. By consolidating existing pieces in the literature, we clarify the correct dependence of the feature, label and coefficient domains in the optimization error and estimation error, hence revealing the delicate price of differential privacy in statistical estimation and statistical learning. Moreover, we propose simple modifications of two existing DP algorithms: (a) posterior sampling, (b) sufficient statistics perturbation, and show that they can be upgraded into **adaptive** algorithms that are able to exploit data-dependent quantities and behave nearly optimally **for every instance**. Extensive experiments are conducted on both simulated data and real data, which conclude that both AdaOPS and AdaSSP outperform the existing techniques on nearly all 36 data sets that we test on.
A correspondence principle between (hyper)graph theory and probability theory, and the (hyper)graph removal lemma

We introduce a correspondence principle (analogous to the Furstenberg Correspondence Principle) that allows one to extract an infinite random graph or hypergraph from a sequence of increasingly large deterministic graphs or hypergraphs. As an application we present a new (infinitary) proof of the hypergraph removal lemma of Nagle-Schacht-Rodl-Skokan and Gowers, which does not require the hypergraph regularity lemma and requires significantly less computation. This in turn gives new proofs of several corollaries of the hypergraph removal lemma, such as Szemeredi’s Theorem on arithmetic progressions.
Singly generated planar algebras of small dimension, Part III

The first two authors classified subfactor planar algebra generated by a non-trivial 2-box subject to the condition that the dimension of 3-boxes is at most 12 in Part I; 13 in Part II of this series. They are the group planar algebra for $\mathbb{Z}_3$, the Fuss-Catalan planar algebra ; and the group/subgroup planar algebra for $\mathbb{Z}_2\subset \mathbb{Z}_5\rtimes \mathbb{Z}_2$. In the present paper, we extend the classification to 14 dimensional 3-boxes. They are all BMW. Precisely it contains a depth 3 one from quantum $SO(3)$, and a one-parameter family from quantum $Sp(4)$.
Total communicability as a centrality measure

We examine node centrality measures based on the notion of total communicability, defined in terms of the row sums of matrix functions of the adjacency matrix of the network. Our main focus is on the matrix exponential and the resolvent, which have natural interpretations in terms of walks on the underlying graph. While such measures have been used before for ranking nodes in a network, we show that they can be computed very rapidly even in the case of large networks. Furthermore, we propose the (normalized) total sum of node communicabilities as a useful measure of network connectivity. Extensive numerical studies are conducted in order to compare this centrality measure with the closely related ones of subgraph centrality [E. Estrada and J. A. Rodriguez-Velazquez, Phys. Rev. E, 71 (2005), 056103] and Katz centrality [L. Katz, Psychometrica, 18 (1953), pp. 39–43]. Both synthetic and real-world networks are used in the computations.
Synthetic and Natural Noise Both Break Neural Machine Translation

Character-based neural machine translation (NMT) models alleviate out-of-vocabulary issues, learn morphology, and move us closer to completely end-to-end translation systems. Unfortunately, they are also very brittle and easily falter when presented with noisy data. In this paper, we confront NMT models with synthetic and natural sources of noise. We find that state-of-the-art models fail to translate even moderately noisy texts that humans have no trouble comprehending. We explore two approaches to increase model robustness: structure-invariant word representations and robust training on noisy texts. We find that a model based on a character convolutional neural network is able to simultaneously learn representations robust to multiple kinds of noise.
Coded Caching for Heterogeneous Systems: An Optimization Perspective

In cache-aided networks, the server populates the cache memories at the users during low-traffic periods in order to reduce the delivery load during peak-traffic hours. In turn, there exists a fundamental tradeoff between the delivery load on the server and the cache sizes at the users. In this paper, we study this tradeoff in a multicast network, where the server is connected to users with unequal cache sizes and the number of users is less than or equal to the number of library files. We propose centralized uncoded placement and linear delivery schemes which are optimized by solving a linear program. Additionally, we derive a lower bound on the delivery memory tradeoff with uncoded placement that accounts for the heterogeneity in cache sizes. We explicitly characterize this tradeoff for the case of three end-users, as well as an arbitrary number of end-users when the total memory size at the users is small, and when it is large. Next, we consider a system where the server is connected to the users via rate-limited links of different capacities and the server assigns the users’ cache sizes subject to a total cache budget. We characterize the optimal cache sizes that minimize the delivery completion time with uncoded placement and linear delivery. In particular, the optimal memory allocation balances between assigning larger cache sizes to users with low capacity links and uniform memory allocation.
A PATCHWORK QUILT SEWN FROM BROWNIAN FABRIC: REGULARITY OF POLYMER WEIGHT PROFILES IN BROWNIAN LAST PASSAGE PERCOLATION

In last passage percolation models lying in the Kardar–Parisi–Zhang (KPZ) universality class, the energy of long energy-maximizing paths may be studied as a function of the paths’ pair of endpoint locations. Scaled coordinates may be introduced, so that these maximizing paths, or polymers, now cross unit distances with unit-order fluctuations, and have scaled energy, or weight, of unit order. In this article, we consider Brownian last passage percolation in these scaled coordinates. In the narrow wedge case, when one endpoint of such polymers is fixed, say at . This result is proved by harnessing an understanding of the uniform coalescence structure in the field of polymers developed in Hammond [‘Exponents governing the rarity of disjoint polymers in Brownian last passage percolation’, Preprint (2017a), arXiv:1709.04110] using techniques from Hammond (2016) and [‘Modulus of continuity of polymer weight profiles in Brownian last passage percolation’, Preprint (2017b), arXiv:1709.04115].
Computing homology invariants of Legendrian knots

The Chekanov–Eliashberg differential graded algebra of a Legendrian knot L is a rich source of Legendrian knot invariants, as is the theory of generating families. The set P(L) of homology groups of augmentations of the Chekanov–Eliashberg algebra is an invariant, as is a count of objects from the theory of generating families called graded normal rulings. This paper gives two results demonstrating the usefulness of computing the homology group of an augmentation using a combinatorial interpretation of a generating family called a Morse complex sequence (MCS). First, we show that if the projection of L to the xz-plane has exactly 4 cusps, then |P(L)| ≤ 1. Second, we show that two augmentations associated to the same graded normal ruling by the many-to-one map between augmentations and graded normal rulings defined by Ng and Sabloff [The correspondence between augmentations and rulings for Legendrian knots, Pacific J. Math.224(1) (2006) 141–150] need not have isomorphic homology groups.
MODERN MONETARY CIRCUIT THEORY, STABILITY OF INTERCONNECTED BANKING NETWORK, AND BALANCE SHEET OPTIMIZATION FOR INDIVIDUAL BANKS

A modern version of monetary circuit theory with a particular emphasis on stochastic underpinning mechanisms is developed. It is explained how money is created by the banking system as a whole and by individual banks. The role of central banks as system stabilizers and liquidity providers is elucidated. It is shown how in the process of money creation banks become naturally interconnected. A novel extended structural default model describing the stability of the Interconnected banking network is proposed. The purpose of bank capital and liquidity is explained. Multi-period constrained optimization problem for bank balance sheet is formulated and solved in a simple case. Both theoretical and practical aspects are covered.
Scientific progress despite irreproducibility: A seeming paradox

It appears paradoxical that science is producing outstanding new results and theories at a rapid rate at the same time that researchers are identifying serious problems in the practice of science that cause many reports to be irreproducible and invalid. Certainly, the practice of science needs to be improved, and scientists are now pursuing this goal. However, in this perspective, we argue that this seeming paradox is not new, has always been part of the way science works, and likely will remain so. We first introduce the paradox. We then review a wide range of challenges that appear to make scientific success difficult. Next, we describe the factors that make science work-in the past, present, and presumably also in the future. We then suggest that remedies for the present practice of science need to be applied selectively so as not to slow progress and illustrate with a few examples. We conclude with arguments that communication of science needs to emphasize not just problems but the enormous successes and benefits that science has brought and is now bringing to all elements of modern society.
Waddling Random Walk: Fast and Accurate Mining of Motif Statistics in Large Graphs

Algorithms for mining very large graphs, such as those representing online social networks, to discover the relative frequency of small subgraphs within them are of high interest to sociologists, computer scientists and marketeers alike. However, the computation of these network motif statistics via naive enumeration is infeasible for either its prohibitive computational costs or access restrictions on the full graph data. Methods to estimate the motif statistics based on random walks by sampling only a small fraction of the subgraphs in the large graph address both of these challenges. In this paper, we present a new algorithm, called the Waddling Random Walk (WRW), which estimates the concentration of motifs of any size. It derives its name from the fact that it sways a little to the left and to the right, thus also sampling nodes not directly on the path of the random walk. The WRW algorithm achieves its computational efficiency by not trying to enumerate subgraphs around the random walk but instead using a randomized protocol to sample subgraphs in the neighborhood of the nodes visited by the walk. In addition, WRW achieves significantly higher accuracy (measured by the closeness of its estimate to the correct value) and higher precision (measured by the low variance in its estimations) than the current state-of-the-art algorithms for mining subgraph statistics. We illustrate these advantages in speed, accuracy and precision using simulations on well-known and widely used graph datasets representing real networks.
Free resolutions of orbit closures of Dynkin quivers

We use the Kempf-Lascoux-Weyman geometric technique in order to determine the minimal free resolutions of some orbit closures of quivers. As a consequence, we obtain that for Dynkin quivers orbit closures of 1-step representations are normal with rational singularities. For Dynkin quivers of type $A$, we describe explicit minimal generators of the defining ideals of orbit closures of 1-step representations. Using this, we provide an algorithm for type $A$ quivers for describing an efficient set of generators of the defining ideal of the orbit closure of any representation.
Sharp L 1-Poincaré inequalities correspond to optimal hypersurface cuts

Let \({\Omega \subset \mathbb{R}^n}\) be a convex set. If \({u: \Omega \rightarrow \mathbb{R}}\) has mean 0, then we have the classical Poincare inequality $$\|u \|_{L^p} \leq c_p {\rm diam}(\Omega) \| \nabla u \|_{L^p}$$ with sharp constants \({c_2 = 1/\pi}\) (Payne and Weinberger, 1960) and c1 = 1/2 (Acosta and Duran, 2005) independent of the dimension. The sharp constants cp for 1 < p < 2 have recently been found by Valtorta (2012) and Ferone, Nitsch and Trombetti (2012). The purpose of this short paper is to prove a much stronger inequality in the endpoint L1: we combine results of Cianchi and Kannan, Lovasz and Simonovits to show that $$\left\|u\right\|_{L^{1}(\Omega)}\leq \frac{2}{\log{2}} M_{}(\Omega)\left\|\nabla u\right\|_{L^{1}(\Omega)},$$ where \({M(\Omega)}\) is the average distance between a point in \({\Omega}\) and the center of gravity of \({\Omega}\) . If \({\Omega}\) is a regular simplex in \({\mathbb{R}^n}\) , this yields an improvement by a factor of \({\sim \sqrt{n}}\) .
Geometric plurisubharmonicity and convexity - an introduction

This is an essay on potential theory for geometric plurisubharmonic functions. It begins with a given closed subset G of the Grassmann bundle G(p,TX) of tangent p-planes to a riemannian manifold X. This determines a nonlinear partial differential equation which is convex but never uniformly elliptic (p<dimX). A surprising number of results in complex analysis carry over to this more general setting. The notions of: a G-submanifold, an upper semi-continuous G-plurisubharmonic function, a G-convex domain, a G-harmonic function, and a G-free submanifold, are defined. Results include a restriction theorem as well as the existence and uniqueness of solutions to the Dirichlet Problem for G-harmonic functions on G-convex domains.
Atomic-scale investigation of creep behavior in nanocrystalline Mg and Mg–Y alloys

Abstract Magnesium (Mg) and its alloys offer great potential for reducing vehicular mass and energy consumption due to their inherently low densities. Historically, widespread applicability has been limited by low strength properties compared to other structural Al-, Ti- and Fe-based alloys. However, recent studies have demonstrated high-specific-strength in a number of nanocrystalline Mg-alloys. Even so, applications of these alloys would be restricted to low-temperature automotive components due to microstructural instability under high temperature creep loading. Hence, this work aims to gain a better understanding of creep and associated deformation behavior of columnar nanocrystalline Mg and Mg–yttrium (Y) (up to 3 at.% Y (10 wt.% Y)) with a grain size of 5 nm and 10 nm. Using molecular dynamics (MD) simulations, nanocrystalline magnesium with and without local concentrations of yttrium is subjected to constant-stress loading ranging from 0 to 500 MPa at different initial temperatures ranging from 473 to 723 K. In pure Mg, the analyses of the diffusion coefficient and energy barrier reveal that at lower temperatures (i.e., T ∼573 K) lattice diffusion dominates the overall creep behavior. Further, we observe a negligible change (within the fitting error) in the overall secondary creep rate with creep activation energy changing from 1.128 to 1.154 eV for 0 to 3 at.% Y, respectively, indicating that stage two creep activity is insensitive to Y for a given grain size. We also present novel results showing that the ( 1 0 1 ¯ 1 ) , ( 1 0 1 ¯ 2 ) , ( 1 0 1 ¯ 3 ) and ( 1 0 1 ¯ 6 ) boundary sliding energies are reduced with the addition of yttrium. This softening effect in the presence of yttrium suggests that the experimentally observed high temperature beneficial effects of yttrium addition is likely to be attributed to some combination of other reported creep strengthening mechanisms or phenomena such as formation of stable yttrium oxides at grain boundaries or increased forest dislocation-based hardening.
Investigating Cybersecurity Issues in Active Traffic Management Systems

Active traffic management (ATM) systems have been introduced by transportation agencies to manage recurrent and non-recurrent congestion. ATM systems rely on the interconnectivity of components mad...
Modeling events with cascades of poisson processes

We present a probabilistic model of events in continuous time in which each event triggers a Poisson process of successor events. The ensemble of observed events is thereby modeled as a superposition of Poisson processes. Efficient inference is feasible under this model with an EM algorithm. Moreover, the EM algorithm can be implemented as a distributed algorithm, permitting the model to be applied to very large datasets. We apply these techniques to the modeling of Twitter messages and the revision history of Wikipedia.
Optimizing Lossy Compression Rate-Distortion from Automatic Online Selection between SZ and ZFP

With ever-increasing volumes of scientific data produced by high-performance computing applications, significantly reducing data size is critical because of limited capacity of storage space and potential bottlenecks on I/O or networks in writing/reading or transferring data. SZ and ZFP are two leading BSD licensed open source C/C++ libraries for compressed floating-point arrays that support high throughput read and write random access. However, their performance is not consistent across different data sets and across different fields of some data sets, which raises the need for an automatic online (during compression) selection between SZ and ZFP, with minimal overhead. In this paper, the automatic selection optimizes the rate-distortion, an important statistical quality metric based on the signal-to-noise ratio. To optimize for rate-distortion, we investigate the principles of SZ and ZFP. We then propose an efficient online, low-overhead selection algorithm that predicts the compression quality accurately for two compressors in early processing stages and selects the best-fit compressor for each data field. We implement the selection algorithm into an open-source library, and we evaluate the effectiveness of our proposed solution against plain SZ and ZFP in a parallel environment with 1,024 cores. Evaluation results on three data sets representing about 100 fields show that our selection algorithm improves the compression ratio up to 70 percent with the same level of data distortion because of very accurate selection (around 99 percent) of the bestfit compressor, with little overhead (less than 7 percent in the experiments).
Injective Presentations of Induced Modules over Cluster-Tilted Algebras

Every cluster-tilted algebra B is the relation extension $C\ltimes \textup {Ext}^{2}_{C}(DC,C)$ of a tilted algebra C. A B-module is called induced if it is of the form M⊗ C B for some C-module M. We study the relation between the injective presentations of a C-module and the injective presentations of the induced B-module. Our main result is an explicit construction of the modules and morphisms in an injective presentation of any induced B-module. In the case where the C-module, and hence the B-module, is projective, our construction yields an injective resolution. In particular, it gives a module theoretic proof of the well-known 1-Gorenstein property of cluster-tilted algebras.
RUNAWAY M DWARF CANDIDATES FROM THE SLOAN DIGITAL SKY SURVEY

We present a sample of 20 runaway M dwarf candidates (RdMs) within 1 kpc of the Sun whose Galactocentric velocities exceed 400 km s$^{-1}$. The candidates were selected from the SDSS DR7 M Dwarf Catalog of West et al. (2011). Our RdMs have SDSS+USNO-B proper motions that are consistent with those recorded in the PPMXL, LSPM, and combined WISE+SDSS+2MASS catalogs. Sixteen RdMs are classified as dwarfs, while the remaining four RdMs are subdwarfs. We model the Galactic potential using a bulge-disk-halo profile (Kenyon et al. 2008; Brown et al. 2014). Our fastest RdM, with Galactocentric velocity 658.5 $\pm$ 236.9 km s$^{-1}$, is a possible hypervelocity candidate, as it is unbound in 77% of our simulations. About half of our RdMs have kinematics that are consistent with ejection from the Galactic center. Seven of our RdMs have kinematics consistent with an ejection scenario from M31 or M32 to within 2{\sigma}, although our distance-limited survey makes such a realization unlikely. No more than four of our RdMs may have originated from the Leo stream. We propose that to within measurement errors, most of our bound RdMs are likely disk runaways or halo objects, and may have been accelerated through a series of multi-body interactions within the Galactic disk or possibly supernovae explosions.
Upper Bounds for Bergman Kernels Associated to Positive Line Bundles with Smooth Hermitian Metrics

Off-diagonal upper bounds are established for Bergman kernels associated to powers \(L^\lambda \) of holomorphic line bundles L over compact complex manifolds, asymptotically as the power \(\lambda \) tends to infinity. The line bundle is assumed to be equipped with a Hermitian metric with positive curvature form, which is \(C^\infty \) but not necessarily real analytic. The bounds are of the form \(\exp (-h(\lambda )\sqrt{\lambda \log \lambda })\) where h tends to infinity at a non-universal rate. This form is best possible.
Beacon-referenced Mutual Pursuit in Three Dimensions

Motivated by real-world applications of unmanned aerial vehicles, this paper introduces a steering control law for a pair of agents maneuvering in the vicinity of a fixed beacon in a 3-dimensional setting. This control law is a modification of the previously studied 3-dimensional constant bearing (CB) pursuit law, in the sense that it incorporates an additional term to allocate attention to the beacon. We investigate the behavior of the closed-loop dynamics for a two-agent mutual pursuit system wherein each agent employs the beacon-referenced CB pursuit law with regards to the other agent and a stationary beacon. Under certain assumptions on the associated control parameters, we demonstrate that this problem admits circling equilibria with agents moving on circular orbits with a common radius, in planes perpendicular to a common axis passing through the beacon. As the common radius and distances from the beacon are determined by the choice of parameters in the pursuit law, this approach provides a means to engineer desired formations in a 3-dimensional setting.
Iterative Sequential Action Control for Stable, Model-Based Control of Nonlinear Systems

This paper presents iterative sequential action control (iSAC), a receding horizon approach for control of nonlinear systems. The iSAC method has a closed-form open-loop solution, which is iteratively updated between time steps by introducing constant control values applied for short duration. Application of a contractive constraint on the cost is shown to lead to closed-loop asymptotic stability under mild assumptions. The effect of asymptotically decaying disturbances on system trajectories is also examined. To demonstrate the applicability of iSAC, we employ a variety of systems and conditions, including a 13-dimensional quaternion-based quadrotor and NASA's Transition Region and Coronal Explorer (TRACE) spacecraft. Each system is tested in different scenarios, ranging from feasible and infeasible trajectory tracking to setpoint stabilization, with or without the presence of external disturbances. Finally, limitations of this paper are discussed.
Learning Soft Linear Constraints with Application to Citation Field Extraction

Accurately segmenting a citation string into fields for authors, titles, etc. is a challenging task because the output typically obeys various global constraints. Previous work has shown that modeling soft constraints, where the model is encouraged, but not require to obey the constraints, can substantially improve segmentation performance. On the other hand, for imposing hard constraints, dual decomposition is a popular technique for efficient prediction given existing algorithms for unconstrained inference. We extend dual decomposition to perform prediction subject to soft constraints. Moreover, with a technique for performing inference given soft constraints, it is easy to automatically generate large families of constraints and learn their costs with a simple convex optimization problem during training. This allows us to obtain substantial gains in accuracy on a new, challenging citation extraction dataset.
Mach’s Principle and the Origin of Inertia

The current status of Mach’s principle is discussed within the context of general relativity. The inertial properties of a particle are determined by its mass and spin, since these characterize the irreducible unitary representations of the inhomogeneous Lorentz group. The origin of the inertia of mass and intrinsic spin are discussed and the inertia of intrinsic spin is studied via the coupling of intrinsic spin with rotation. The implications of spin-rotation coupling and the possibility of history dependence and nonlocality in relativistic physics are briefly mentioned.
Scott ranks of models of a theory

Abstract The Scott rank of a countable structure is a measure, coming from the proof of Scott's isomorphism theorem, of the complexity of that structure. The Scott spectrum of a theory (by which we mean a sentence of L ω 1 ω ) is the set of Scott ranks of countable models of that theory. In Z F C + P D we give a descriptive-set-theoretic classification of the sets of ordinals which are the Scott spectrum of a theory: they are particular Σ 1 1 classes of ordinals. Our investigation of Scott spectra leads to the resolution (in ZFC) of a number of open problems about Scott ranks. We answer a question of Montalban by showing, for each α ω 1 , that there is a Π 2 in theory with no models of Scott rank less than α. We also answer a question of Knight and Calvert by showing that there are computable models of high Scott rank which are not computably approximable by models of low Scott rank. Finally, we answer a question of Sacks and Marker by showing that δ 2 1 is the least ordinal α such that if the models of a computable theory T have Scott rank bounded below ω 1 , then their Scott ranks are bounded below α.
Spoken vowel classification using synchronization of phase transition nano-oscillators

The paradigm of biologically-inspired computing endows the components of a neural network with dynamical functionality, such as self-oscillations, and harnesses emergent physical phenomena like synchronization, to learn and classify complex temporal patterns. In this work, we exploit the synchronization dynamics of a network of ultra-compact, low power Vanadium dioxide (VO 2 ) based insulator-to-metal phase-transition nano-oscillators (IMT-NO) to classify complex temporal pattern for speech discrimination. We successfully train a network of four capacitively coupled IMT-NOs to recognize spoken vowels by tuning their oscillation frequencies electrically according to a real-time learning rule and achieve high recognition rates of 90.5% for spoken vowels. Such an energy-efficient compact hardware with a small number of functional elements are a promising technology option for edge artificial intelligence.
Learning Robotic Manipulation through Visual Planning and Acting.

Planning for robotic manipulation requires reasoning about the changes a robot can affect on objects. When such interactions can be modelled analytically, as in domains with rigid objects, efficient planning algorithms exist. However, in both domestic and industrial domains, the objects of interest can be soft, or deformable, and hard to model analytically. For such cases, we posit that a data-driven modelling approach is more suitable. In recent years, progress in deep generative models has produced methods that learn to `imagine' plausible images from data. Building on the recent Causal InfoGAN generative model, in this work we learn to imagine goal-directed object manipulation directly from raw image data of self-supervised interaction of the robot with the object. After learning, given a goal observation of the system, our model can generate an imagined plan -- a sequence of images that transition the object into the desired goal. To execute the plan, we use it as a reference trajectory to track with a visual servoing controller, which we also learn from the data as an inverse dynamics model. In a simulated manipulation task, we show that separating the problem into visual planning and visual tracking control is more sample efficient and more interpretable than alternative data-driven approaches. We further demonstrate our approach on learning to imagine and execute in 3 environments, the final of which is deformable rope manipulation on a PR2 robot.
Late-Holocene floodplain development, land-use, and hydroclimate-flood relationships on the lower Ohio River, US

Floodplain development, land-use, and flooding on the lower Ohio River are investigated with a 3100-year-long sediment archive from Avery Lake, a swale lake on the Black Bottom floodplain in southe...
A comparison of automated lesion segmentation approaches for chronic stroke T1-weighted MRI data.

Accurate stroke lesion segmentation is a critical step in the neuroimaging processing pipeline for assessing the relationship between poststroke brain structure, function, and behavior. Many multimodal segmentation algorithms have been developed for acute stroke neuroimaging, yet few algorithms are effective with only a single T1-weighted (T1w) anatomical MRI. This is a critical gap because multimodal MRI is not commonly available due to time and cost constraints in the stroke rehabilitation setting. Although several attempts to automate the segmentation of chronic lesions on single-channel T1w MRI have been made, these approaches have not been systematically evaluated on a large dataset. We performed an exhaustive review of the literature and identified one semiautomated and three fully automated approaches for segmentation of chronic stroke lesions using T1w MRI within the last 10 years: Clusterize, automated lesion identification (ALI), Gaussian naive Bayes lesion detection (lesionGnb), and lesion identification with neighborhood data analysis (LINDA). We evaluated each method on a large T1w stroke dataset (N = 181). LINDA was the most computationally expensive approach, but performed best across the three main evaluation metrics (median values: dice coefficient = 0.50, Hausdorff's distance = 36.34 mm, and average symmetric surface distance = 4.97 mm). lesionGnb had the highest recall/least false negatives (median = 0.80). However, across the automated methods, many lesions were either misclassified (ALI: 28, lesionGnb: 39, LINDA: 45) or not identified (ALI: 24, LINDA: 23, lesionGnb: 0). Segmentation accuracy in all automated methods were influenced by size (small: worst) and stroke territory (brainstem, cerebellum: worst) of the lesion. To facilitate reproducible science, our analysis files have been made publicly available online.
Tracy-Widom Asymptotics for a River Delta Model

We study an oriented first passage percolation model for the evolution of a river delta. This model is exactly solvable and occurs as the low temperature limit of the beta random walk in random environment. We analyze the asymptotics of an exact formula from [13] to show that, at any fixed positive time, the width of a river delta of length L approaches a constant times \(L^{2/3}\) with Tracy-Widom GUE fluctuations of order \(L^{4/9}\). This result can be rephrased in terms of particle systems. We introduce an exactly solvable particle system on the integer half line and show that after running the system for only finite time the particle positions have Tracy-Widom fluctuations.
Cellular properties of intrinsically photosensitive retinal ganglion cells during postnatal development

Melanopsin-expressing, intrinsically photosensitive retinal ganglion cells (ipRGCs) respond directly to light and have been shown to mediate a broad variety of visual behaviors in adult animals. ipRGCs are also the first light sensitive cells in the developing retina, and have been implicated in a number of retinal developmental processes such as pruning of retinal vasculature and refinement of retinofugal projections. However, little is currently known about the properties of the six ipRGC subtypes during development, and how these cells act to influence retinal development. We therefore sought to characterize the structure, physiology, and birthdate of the most abundant ipRGC subtypes, M1, M2, and M4, at discrete postnatal developmental timepoints. We utilized whole cell patch clamp to measure the electrophysiological and morphological properties of ipRGC subtypes through postnatal development. We also used EdU labeling to determine the embryonic timepoints at which ipRGC subtypes terminally differentiate. Our data show that ipRGC subtypes are distinguishable from each other early in postnatal development. Additionally, we find that while ipRGC subtypes terminally differentiate at similar embryonic stages, the subtypes reach adult-like morphology and physiology at different developmental timepoints. This work provides a broad assessment of ipRGC morphological and physiological properties during the postnatal stages at which they are most influential in modulating retinal development, and lays the groundwork for further understanding of the specific role of each ipRGC subtype in influencing retinal and visual system development.
Learning Fashion Compatibility Across Apparel Categories for Outfit Recommendation

This paper addresses the problem of generating recommendations for completing the outfit given that a user is interested in a particular apparel item. The proposed method is based on a siamese network used for feature extraction followed by a fully-connected network used for learning a fashion compatibility metric. The embeddings generated by the siamese network are augmented with color histogram features motivated by the important role that color plays in determining fashion compatibility. The training of the network is formulated as a maximum a posteriori (MAP) problem where Laplacian distributions are assumed for the filters of the siamese network to promote sparsity and matrix-variate normal distributions are assumed for the weights of the metric network to efficiently exploit correlations between the input units of each fully-connected layer.
PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text

We consider open-domain question answering (QA) where answers are drawn from either a corpus, a knowledge base (KB), or a combination of both of these. We focus on a setting in which a corpus is supplemented with a large but incomplete KB, and on questions that require non-trivial (e.g., “multi-hop”) reasoning. We describe PullNet, an integrated framework for (1) learning what to retrieve and (2) reasoning with this heterogeneous information to find the best answer. PullNet uses an iterative process to construct a question-specific subgraph that contains information relevant to the question. In each iteration, a graph convolutional network (graph CNN) is used to identify subgraph nodes that should be expanded using retrieval (or “pull”) operations on the corpus and/or KB. After the subgraph is complete, another graph CNN is used to extract the answer from the subgraph. This retrieve-and-reason process allows us to answer multi-hop questions using large KBs and corpora. PullNet is weakly supervised, requiring question-answer pairs but not gold inference paths. Experimentally PullNet improves over the prior state-of-the art, and in the setting where a corpus is used with incomplete KB these improvements are often dramatic. PullNet is also often superior to prior systems in a KB-only setting or a text-only setting.
Robustness of the Sobol' indices to distributional uncertainty

Global sensitivity analysis (GSA) is used to quantify the influence of uncertain variables in a mathematical model. Prior to performing GSA, the user must specify (or implicitly assume), a probability distribution to model the uncertainty, and possibly statistical dependencies, of the variables. Determining this distribution is challenging in practice as the user has limited and imprecise knowledge of the uncertain variables. This article analyzes the robustness of the Sobol' indices, a commonly used tool in GSA, to changes in the distribution of the uncertain variables. A method for assessing such robustness is developed which requires minimal user specification and no additional evaluations of the model. Theoretical and computational aspects of the method are considered and illustrated through examples.
Herding an Adversarial Attacker to a Safe Area for Defending Safety-Critical Infrastructure

This paper investigates a problem of defending safety-critical infrastructure from an adversarial aerial attacker in an urban environment. A circular arc formation of defenders is formed around the attacker, and vector-field based guidance laws herd the attacker to a predefined safe area in the presence of rectangular obstacles. The defenders' formation is defined based on a novel vector field that imposes super-elliptic contours around the obstacles, to closely resemble their rectangular shape. A novel finite-time stabilizing controller is proposed to guide the defenders to their desired formation, while avoiding obstacles and inter-agent collisions. The efficacy of the approach is demonstrated via simulation results.
Deep Geometric Prior for Surface Reconstruction

The reconstruction of a discrete surface from a point cloud is a fundamental geometry processing problem that has been studied for decades, with many methods developed. We propose the use of a deep neural network as a geometric prior for surface reconstruction. Specifically, we overfit a neural network representing a local chart parameterization to part of an input point cloud using the Wasserstein distance as a measure of approximation. By jointly fitting many such networks to overlapping parts of the point cloud, while enforcing a consistency condition, we compute a manifold atlas. By sampling this atlas, we can produce a dense reconstruction of the surface approximating the input cloud. The entire procedure does not require any training data or explicit regularization, yet, we show that it is able to perform remarkably well: not introducing typical overfitting artifacts, and approximating sharp features closely at the same time. We experimentally show that this geometric prior produces good results for both man-made objects containing sharp features and smoother organic objects, as well as noisy inputs. We compare our method with a number of well-known reconstruction methods on a standard surface reconstruction benchmark.
Uniqueness from gauge invariance and the Adler zero

In this paper we provide detailed proofs for some of the uniqueness results presented in ref. [1]. We show that: (1) Yang-Mills and General Relativity tree-level amplitudes are completely determined by gauge invariance in n − 1 particles, with minimal assumptions on the singularity structure; (2) scalar non-linear sigma model and Dirac-Born-Infeld tree-level amplitudes are fixed by imposing full locality and the Adler zero condition (vanishing in the single soft limit) on n − 1 particles. We complete the proofs by showing uniqueness order by order in the single soft expansion for Yang-Mills and General Relativity, and the double soft expansion for NLSM and DBI. We further present evidence for a greater conjecture regarding Yang-Mills amplitudes, that a maximally constrained gauge invariance alone leads to both locality and unitarity, without any assumptions on the existence of singularities. In this case the solution is not unique, but a linear combination of amplitude numerators.
Behavioral Operations: Past, Present, and Future

Behavioral operations is now an established and fundamental research field dedicated to understanding how the behavior of managers, workers, and customers influences operational decisions and outco...
Moderately Complex Paxos Made Simple: High-Level Executable Specification of Distributed Algorithms

This paper describes the application of a high-level language and method in developing simpler specifications of more complex variants of the Paxos algorithm for distributed consensus. The specifications are for Multi-Paxos with preemption, replicated state machine, and reconfiguration and optimized with state reduction and failure detection. The language is DistAlgo. The key is to express complex control flows and synchronization conditions precisely at a high level, using nondeterministic waits and message-history queries. We obtain complete executable specifications that are almost completely declarative--updating only a number for the protocol round besides the sets of messages sent and received.We show the following results: (1) English and pseudocode descriptions of distributed algorithms can be captured completely and precisely at a high level, without adding, removing, or reformulating algorithm details to fit lower-level, more abstract, or less direct languages. (2) We created higher-level control flows and synchronization conditions than all previous specifications, and obtained specifications that are much simpler and smaller, even matching or smaller than abstract specifications that omit many algorithm details. (3) The simpler specifications led us to easily discover useless replies, unnecessary delays, and liveness violations (if messages can be lost) in previous published specifications, by just following the simplified algorithm flows. (4) The resulting specifications can be executed directly, and we can express optimizations cleanly, yielding drastic performance improvement over naive execution and facilitating a general method for merging processes. (5) We systematically translated the resulting specifications into TLA+ and developed machine-checked safety proofs, which also allowed us to detect and fix a subtle safety violation in an earlier unpublished specification. Additionally, we show the basic concepts in Paxos that are fundamental in many distributed algorithms and show that they are captured concisely in our specifications.