cssi/SciSciGPT-SciSciNet huggingface dataset:
Improved a priori bounds for thermal fluid equations

We consider two hydrodynamic model problems (one incompressible and one compressible) with three dimensional fluid flow on the torus and temperature-dependent viscosity and conductivity. The ambient heat for the fluid is transported by the flow and fed by the local energy dissipation, modeling the transfer of kinetic energy into thermal energy through fluid friction. Both the viscosity and conductivity grow with the local temperature. We prove a strong a priori bound on the enstrophy of the velocity weighed against the temperature for initial data of arbitrary size, requiring only that the conductivity be proportionately larger than the viscosity (and, in the incompressible case, a bound on the temperature as a Muckenhoupt weight).
Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers

We consider the problem of estimating a function defined over $n$ locations on a $d$-dimensional grid (having all side lengths equal to $n^{1/d}$). When the function is constrained to have discrete total variation bounded by $C_n$, we derive the minimax optimal (squared) $\ell_2$ estimation error rate, parametrized by $n, C_n$. Total variation denoising, also known as the fused lasso, is seen to be rate optimal. Several simpler estimators exist, such as Laplacian smoothing and Laplacian eigenmaps. A natural question is: can these simpler estimators perform just as well? We prove that these estimators, and more broadly all estimators given by linear transformations of the input data, are suboptimal over the class of functions with bounded variation. This extends fundamental findings of Donoho and Johnstone (1998) on 1-dimensional total variation spaces to higher dimensions. The implication is that the computationally simpler methods cannot be used for such sophisticated denoising tasks, without sacrificing statistical accuracy. We also derive minimax rates for discrete Sobolev spaces over $d$-dimensional grids, which are, in some sense, smaller than the total variation function spaces. Indeed, these are small enough spaces that linear estimators can be optimal---and a few well-known ones are, such as Laplacian smoothing and Laplacian eigenmaps, as we show. Lastly, we investigate the adaptivity of the total variation denoiser to these smaller Sobolev function spaces.
Tensor diagrams and cluster algebras

Abstract The rings of SL ( V ) invariants of configurations of vectors and linear forms in a finite-dimensional complex vector space V were explicitly described by Hermann Weyl in the 1930s. We show that when V is 3-dimensional, each of these rings carries a natural cluster algebra structure (typically, many of them) whose cluster variables include Weyl's generators. We describe and explore these cluster structures using the combinatorial machinery of tensor diagrams. A key role is played by the web bases introduced by G. Kuperberg.
Shading Annotations in the Wild

Understanding shading effects in images is critical for a variety of vision and graphics problems, including intrinsic image decomposition, shadow removal, image relighting, and inverse rendering. As is the case with other vision tasks, machine learning is a promising approach to understanding shading - but there is little ground truth shading data available for real-world images. We introduce Shading Annotations in the Wild (SAW), a new large-scale, public dataset of shading annotations in indoor scenes, comprised of multiple forms of shading judgments obtained via crowdsourcing, along with shading annotations automatically generated from RGB-D imagery. We use this data to train a convolutional neural network to predict per-pixel shading information in an image. We demonstrate the value of our data and network in an application to intrinsic images, where we can reduce decomposition artifacts produced by existing algorithms. Our database is available at http://opensurfaces.cs.cornell.edu/saw.
Assessment of optimal flexibility in ensemble of frequency responsive loads

Potential of electrical loads in providing grid ancillary services is often limited due to the uncertainties associated with the load behavior. A knowledge of the expected uncertainties with a load control program would invariably yield to better informed control policies, opening up the possibility of extracting the maximal load control potential without affecting grid operations. In the context of frequency responsive load control, a probabilistic uncertainty analysis framework is presented to quantify the expected error between the target and actual load response, under uncertainties in the load dynamics. A closed-form expression of an optimal demand flexibility, minimizing the expected error in actual and committed flexibility, is provided. Analytical results are validated through Monte Carlo simulations of ensembles of electric water heaters.
Local Perturb-and-MAP for Structured Prediction

Conditional random fields (CRFs) provide a powerful tool for structured prediction, but cast significant challenges in both the learning and inference steps. Approximation techniques are widely used in both steps, which should be considered jointly to guarantee good performance (a.k.a. "inferning"). Perturb-and-MAP models provide a promising alternative to CRFs, but require global combinatorial optimization and hence they are usable only on specific models. In this work, we present a new Local Perturb-and-MAP (locPMAP) framework that replaces the global optimization with a local optimization by exploiting our observed connection between locPMAP and the pseudolikelihood of the original CRF model. We test our approach on three different vision tasks and show that our method achieves consistently improved performance over other approximate inference techniques optimized to a pseudolikelihood objective. Additionally, we demonstrate that we can integrate our method in the fully convolutional network framework to increase our model's complexity. Finally, our observed connection between locPMAP and the pseudolikelihood leads to a novel perspective for understanding and using pseudolikelihood.
On the existence of affine Landau–Ginzburg phases in gauged linear sigma models

We prove a simple criterion for the existence of an affine Landau-Ginzburg point in the Kahler moduli space of a gauged linear sigma model.
N-point locality for vertex operators: Normal ordered products, operator product expansions, twisted vertex algebras

Abstract In this paper we study fields satisfying N -point locality and their properties. We obtain residue formulae for N -point local fields in terms of derivatives of delta functions and Bell polynomials. We introduce the notion of the space of descendants of N -point local fields which includes normal ordered products and coefficients of operator product expansions. We show that examples of N -point local fields include the vertex operators generating the boson–fermion correspondences of types B, C and D-A. We apply the normal ordered products of these vertex operators to the setting of the representation theory of the double-infinite rank Lie algebras b ∞ , c ∞ , d ∞ . Finally, we show that the field theory generated by N -point local fields and their descendants has a structure of a twisted vertex algebra.
Extension properties of asymptotic property C and finite decomposition complexity

Abstract We prove extension theorems for several geometric properties such as asymptotic property C (APC), finite decomposition complexity (FDC), strict finite decomposition complexity (sFDC) which are weakenings of Gromov's finite asymptotic dimension (FAD). The context of all theorems is a finitely generated group G with a word metric and a coarse quasi-action on a metric space X . We assume that the quasi-stabilizers have a property P 1 , and X has the same or sometimes a weaker property P 2 . Then G also has property P 2 . We show some sample applications, discuss constraints to further generalizations, and illustrate the flexibility that the weak quasi-action assumption allows.
Visiting Newton's Atelier before the Principia, 1679-1684

The worksheets that presumably contained Newton's early development of the fundamental concepts in his Principia have been lost. A plausible reconstruction of this development is presented based on...
Multilinear dyadic operators and their commutators

We obtain a generalized paraproduct decomposition of the pointwise product of two or more functions that naturally gives rise to multilinear dyadic paraproducts and Haar multipliers. We then study the boundedness properties of these multilinear operators and their commutators with dyadic BMO functions. We also characterize the dyadic BMO functions via the boundedness of (a) certain paraproducts, and (b) the commutators of multilinear Haar multipliers and paraproduct operators.
Optimally Combining Classifiers Using Unlabeled Data

We develop a worst-case analysis of aggregation of classifier ensembles for binary classification. The task of predicting to minimize error is formulated as a game played over a given set of unlabeled data (a transductive setting), where prior label information is encoded as constraints on the game. The minimax solution of this game identifies cases where a weighted combination of the classifiers can perform significantly better than any single classifier.
The AJ-Conjecture and cabled knots over torus knots

We show that most cabled knots over torus knots in S3 satisfy the AJ-conjecture, namely each (r, s)-cabled knot over each (p, q)-torus knot satisfies the AJ-conjecture if r is not a number between 0 and pqs.
Best of many worlds: Robust model selection for online supervised learning

We introduce algorithms for online, full-information prediction that are computationally efficient and competitive with contextual tree experts of unknown complexity, in both probabilistic and adversarial settings. We incorporate a novel probabilistic framework of structural risk minimization into existing adaptive algorithms and show that we can robustly learn not only the presence of stochastic structure when it exists, but also the correct model order. When the stochastic data is actually realized from a predictor in the model class considered, we obtain regret bounds that are competitive with the regret of an optimal algorithm that possesses strong side information about both the true model order and whether the process generating the data is stochastic or adversarial. In cases where the data does not arise from any of the models, our algorithm selects models of higher order as we play more rounds. We display empirically improved \textit{overall prediction error} over other adversarially robust approaches.
Regularity of almost periodic modulo scaling solutions for mass-critical NLS and applications

In this paper, we consider the Lx solution u to mass critical NLS iut + ∆u = ±|u| 4 d u. We prove that in dimensions d ≥ 4, if the solution is spherically symmetric and is almost periodic modulo scaling, then it must lie in H x for some e > 0. Moreover, the kinetic energy of the solution is localized uniformly in time. One important application of the theorem is a simplified proof of the scattering conjecture for mass critical NLS without reducing to three enemies [17], [18]. As another important application, we establish a Liouville type result for Lx initial data with ground state mass. We prove that if a radial Lx solution to focusing mass critical problem has the ground state mass and does not scatter in both time directions, then it must be global and coincide with the solitary wave up to symmetries. Here the ground state is the unique, positive, radial solution to elliptic equation ∆Q − Q + Q 4 d = 0. This is the first rigidity type result in scale invariant space Lx.
Non-parametric estimation of Jensen-Shannon Divergence in Generative Adversarial Network training

Generative Adversarial Networks (GANs) have become a widely popular framework for generative modelling of high-dimensional datasets. However their training is well-known to be difficult. This work presents a rigorous statistical analysis of GANs providing straight-forward explanations for common training pathologies such as vanishing gradients. Furthermore, it proposes a new training objective, Kernel GANs, and demonstrates its practical effectiveness on large-scale real-world data sets. A key element in the analysis is the distinction between training with respect to the (unknown) data distribution, and its empirical counterpart. To overcome issues in GAN training, we pursue the idea of smoothing the Jensen-Shannon Divergence (JSD) by incorporating noise in the input distributions of the discriminator. As we show, this effectively leads to an empirical version of the JSD in which the true and the generator densities are replaced by kernel density estimates, which leads to Kernel GANs.
Stylistic clusters and the Syrian/South Syrian tradition of first-millennium BCE Levantine ivory carving: a machine learning approach

Abstract Thousands of first-millennium BCE ivory carvings have been excavated from Neo-Assyrian sites in Mesopotamia (primarily Nimrud, Khorsabad, and Arslan Tash), hundreds of miles from their Levantine production contexts. At present, their specific manufacture dates and workshop localities are unknown. Relying on subjective, visual methods, scholars have grappled with their classification and regional attribution for over a century. This study combines visual approaches with machine learning techniques to offer data-driven perspectives on the classification and attribution of this Iron Age corpus. The study sample consists of 162 sculptures of female figures that have been conventionally attributed to three main regional carving traditions: “Phoenician,” “North Syrian,” and “Syrian/South Syrian”. We have developed an algorithm that clusters the ivories based on a combination of descriptive and anthropometric data. The resulting categories, which are based on purely statistical criteria, show good agreement with conventional art historical classifications, while revealing new insights, especially with regard to the “Syrian/South Syrian” tradition. Specifically, we have determined that objects of the Syrian/South Syrian tradition might be more closely related to Phoenician objects than to North Syrian objects. We also reconsider the classification of a subset of “Phoenician” objects, and we confirm Syrian/South Syrian stylistic subgroups, the geographic distribution of which might illuminate Neo-Assyrian acquisition networks. Additionally, we have identified the features in our cluster assignments that might be diagnostic of regional traditions. In short, our study both corroborates traditional visual classifications and demonstrates how machine learning techniques may be employed to retrieve complementary information not accessible through an exclusively visual analysis.
Long time asymptotic behavior of the focusing nonlinear Schrödinger equation

Abstract We study the Cauchy problem for the focusing nonlinear Schrodinger (fNLS) equation. Using the ∂ ‾ generalization of the nonlinear steepest descent method we compute the long-time asymptotic expansion of the solution ψ ( x , t ) in any fixed space-time cone C ( x 1 , x 2 , v 1 , v 2 ) = { ( x , t ) ∈ R 2 : x = x 0 + v t with x 0 ∈ [ x 1 , x 2 ] , v ∈ [ v 1 , v 2 ] } up to an (optimal) residual error of order O ( t − 3 / 4 ) . In each cone C the leading order term in this expansion is a multi-soliton whose parameters are modulated by soliton–soliton and soliton–radiation interactions as one moves through the cone. Our results require that the initial data possess one L 2 ( R ) moment and (weak) derivative and that it not generate any spectral singularities.
Minimax Theory for High-dimensional Gaussian Mixtures with Sparse Mean Separation

While several papers have investigated computationally and statistically efficient methods for learning Gaussian mixtures, precise minimax bounds for their statistical performance as well as fundamental limits in high-dimensional settings are not well-understood. In this paper, we provide precise information theoretic bounds on the clustering accuracy and sample complexity of learning a mixture of two isotropic Gaussians in high dimensions under small mean separation. If there is a sparse subset of relevant dimensions that determine the mean separation, then the sample complexity only depends on the number of relevant dimensions and mean separation, and can be achieved by a simple computationally efficient procedure. Our results provide the first step of a theoretical basis for recent methods that combine feature selection and clustering.
FORMAL FROBENIUS MANIFOLD STRUCTURE ON EQUIVARIANT COHOMOLOGY

For a closed Kahler manifold with a Hamiltonian action of a connected compact Lie group by holomorphic isometries, we construct a formal Frobenius manifold structure on the equivariant cohomology by exploiting a natural DGBV algebra structure on the Cartan model.
Global in time existence of self-interacting scalar field in de Sitter spacetimes

Abstract We prove the existence of a global in time solution of the semilinear Klein–Gordon equation in the de Sitter space–time. The coefficients of the equation depend on spatial variables as well, that make results applicable to the space–time with the time slices being Riemannian manifolds.
Maximal operators associated to multiplicative characters

We show that the natural analogue of the Carleson-Hunt inequality fails for multiplicative characters.
Endomorphism algebras of Jacobians of certain superelliptic curves

Abstract Text Let p be a prime, and q a power of p. Using Galois theory, we show that over a field K of characteristic zero, the endomorphism algebras of the Jacobians of certain superelliptic curves y q = f ( x ) are products of cyclotomic fields. Video For a video summary of this paper, please click here or visit http://www.youtube.com/watch?v=z5ZzOy1K_Ec .
Coloring trees in reverse mathematics

Abstract The tree theorem for pairs ( TT 2 2 ), first introduced by Chubb, Hirst, and McNicholl, asserts that given a finite coloring of pairs of comparable nodes in the full binary tree 2 ω , there is a set of nodes isomorphic to 2 ω which is homogeneous for the coloring. This is a generalization of the more familiar Ramsey's theorem for pairs ( RT 2 2 ), which has been studied extensively in computability theory and reverse mathematics. We answer a longstanding open question about the strength of TT 2 2 , by showing that this principle does not imply the arithmetic comprehension axiom ( ACA 0 ) over the base system, recursive comprehension axiom ( RCA 0 ), of second-order arithmetic. Combined with a recent result of Patey's that TT 2 2 is strictly stronger than RT 2 2 , this establishes TT 2 2 as the first known example of a natural combinatorial principle to occupy the interval strictly between ACA 0 and RT 2 2 . The proof of this fact uses an extension of the bushy tree forcing method, and develops new techniques for dealing with combinatorial statements formulated on trees, rather than on ω.
Chromatic Numbers of Stable Kneser Hypergraphs via Topological Tverberg-Type Theorems

Kneser's 1955 conjecture -- proven by Lov\'asz in 1978 -- asserts that in any partition of the $k$-subsets of $\{1, 2, \dots, n\}$ into $n-2k-3$ parts, one part contains two disjoint sets. Schrijver showed that one can restrict to significantly fewer $k$-sets and still observe the same intersection pattern. Alon, Frankl, and Lov\'asz proved a different generalization of Kneser's conjecture for $r$ pairwise disjoint sets. Dolnikov generalized Lov\'asz' result to arbitrary set systems, while K\v{r}\'{i}\v{z} did the same for the $r$-fold extension of Kneser's conjecture. Here we prove a common generalization of all of these results. Moreover, we prove additional strengthenings by determining the chromatic number of certain sparse stable Kneser hypergraphs, and further develop a general approach to establishing lower bounds for chromatic numbers of hypergraphs using a combination of methods from equivariant topology and intersection results for convex hulls of points in Euclidean space.
Functorial compactification of linear spaces

We define compactifications of vector spaces which are functorial with respect to certain linear maps. These "many-body" compactifications are manifolds with corners, and the linear maps lift to b-maps in the sense of Melrose. We derive a simple criterion under which the lifted maps are in fact b-fibrations, and identify how these restrict to boundary hypersurfaces. This theory is an application of a general result on the iterated blow-up of cleanly intersecting submanifolds which extends related results in the literature.
Spreading Speeds and Linear Determinacy for Two Species Competition Systems with Nonlocal Dispersal in Periodic Habitats

The current paper is concerned with the existence of spreading speeds and linear determinacy for two species competition systems with nonlocal dispersal in time and space periodic habitats. The notion of spreading speed intervals for such a system is first introduced via the natural features of spreading speeds. The existence and lower bounds of spreading speed intervals are then established. When the periodic dependence of the habitat is only on the time variable, the existence of a single spreading speed is proved. It also shows that, under certain conditions, the spreading speed interval in any direction is a singleton, and, moreover, the linear determinacy holds.
A CLASSIFICATION OF H-PRIMES OF QUANTUM PARTIAL FLAG VARIETIES

We classify the invariant prime ideals of a quantum partial flag variety under the action of the related maximal torus. As a result we construct a bijection between them and the torus orbits of symplectic leaves of the standard Poisson structure on the corresponding flag variety. It was previously shown by K. Goodearl and the author that the latter are precisely the Lusztig strata of the partial flag variety.
Multiple operator integrals in perturbation theory

The purpose of this survey article is to give an introduction to double operator integrals and multiple operator integrals and to discuss various applications of such operator integrals in perturbation theory. We start with the Birman–Solomyak approach to define double operator integrals and consider applications in estimating operator differences $$f(A)-f(B)$$ for self-adjoint operators A and B. Next, we present the Birman–Solomyak approach to the Lifshits–Krein trace formula that is based on double operator integrals. We study the class of operator Lipschitz functions, operator differentiable functions, operator Holder functions, obtain Schatten–von Neumann estimates for operator differences. Finally, we consider in Chapter 1 estimates of functions of normal operators and functions of d-tuples of commuting self-adjoint operators under perturbations. In Chapter 2 we define multiple operator integrals in the case when the integrands belong to the integral projective tensor product of $$L^\infty $$ spaces. We consider applications of such multiple operator integrals to the problem of the existence of higher operator derivatives and to the problem of estimating higher operator differences. We also consider connections with trace formulae for functions of operators under perturbations of class $${\varvec{S}}_m$$ , $$m\ge 2$$ . In the last chapter we define Haagerup-like tensor products of the first kind and of the second kind and we use them to study functions of noncommuting self-adjoint operators under perturbation. We show that for functions f in the Besov class $$B_{\infty ,1}^1({\mathbb R}^2)$$ and for $$p\in [1,2]$$ we have a Lipschitz type estimate in the Schatten–von Neumann norm $${\varvec{S}}_p$$ for functions of pairs of noncommuting self-adjoint operators, but there is no such a Lipschitz type estimate in the norm of $${\varvec{S}}_p$$ with $$p>2$$ as well as in the operator norm. We also use triple operator integrals to estimate the trace norms of commutators of functions of almost commuting self-adjoint operators and extend the Helton–Howe trace formula for arbitrary functions in the Besov space $$B_{\infty ,1}^1({\mathbb R}^2)$$ .
Learning to Describe Differences Between Pairs of Similar Images

In this paper, we introduce the task of automatically generating text to describe the differences between two similar images. We collect a new dataset by crowd-sourcing difference descriptions for pairs of image frames extracted from video-surveillance footage. Annotators were asked to succinctly describe all the differences in a short paragraph. As a result, our novel dataset provides an opportunity to explore models that align language and vision, and capture visual salience. The dataset may also be a useful benchmark for coherent multi-sentence generation. We perform a first-pass visual analysis that exposes clusters of differing pixels as a proxy for object-level differences. We propose a model that captures visual salience by using a latent variable to align clusters of differing pixels with output sentences. We find that, for both single-sentence generation and as well as multi-sentence generation, the proposed model outperforms the models that use attention alone.
Projection-Free Bandit Convex Optimization

In this paper, we propose the first computationally efficient projection-free algorithm for bandit convex optimization (BCO) with a general convex constraint. We show that our algorithm achieves a sublinear regret of $O(nT^{4/5})$ (where $T$ is the horizon and $n$ is the dimension) for any bounded convex functions with uniformly bounded gradients. We also evaluate the performance of our algorithm against baselines on both synthetic and real data sets for quadratic programming, portfolio selection and matrix completion problems.
On the detection of concept changes in time-varying data stream by testing exchangeability

A martingale framework for concept change detection based on testing data exchangeability was recently proposed (Ho, 2005). In this paper, we describe the proposed change-detection test based on the Doob's Maximal Inequality and show that it is an approximation of the sequential probability ratio test (SPRT). The relationship between the threshold value used in the proposed test and its size and power is deduced from the approximation. The mean delay time before a change is detected is estimated using the average sample number of a SPRT. The performance of the test using various threshold values is examined on five different data stream scenarios simulated using two synthetic data sets. Finally, experimental results show that the test is effective in detecting changes in time-varying data streams simulated using three benchmark data sets.
Nonstandard hulls of locally uniform groups

We present a nonstandard hull construction for locally uniform groups in a spirit similar to Luxembourg’s construction of the nonstandard hull of a uniform space. Our nonstandard hull is a local group rather than a global group. We investigate how this construction varies as one changes the family of pseudometrics used to construct the hull. We use the nonstandard hull construction to give a nonstandard characterization of Enflo’s notion of groups that are uniformly free from small subgroups. We prove that our nonstandard hull is locally isomorphic to Pestov’s nonstandard hull for Banach-Lie groups. We also give some examples of infinite-dimensional Lie groups that are locally uniform.
Joint Placement and Allocation of Virtual Network Functions with Budget and Capacity Constraints

With the advent of Network Function Virtualization (NFV), network services that traditionally run on proprietary dedicated hardware can now be realized using Virtual Network Functions (VNFs) that are hosted on general-purpose commodity hardware. This new network paradigm offers a great flexibility to Internet service providers (ISPs) for efficiently operating their networks (collecting network statistics, enforcing management policies, etc However, introducing NFV requires an investment to deploy VNFs at certain network nodes (called VNF-nodes), which has to account for practical constraints such as the deployment budget and the VNF-node capacity. To that end, it is important to design a joint VNF-nodes placement and capacity allocation algorithm that can maximize the total amount of network flows that are fully processed by the VNF-nodes while respecting such practical constraints. In contrast to most prior work that often neglects either the budget constraint or the capacity constraint, we explicitly consider both of them. We prove that accounting for these constraints introduces several new challenges. Specifically, we prove that the studied problem is not only NP-hard but also non-submodular. To address these challenges, we introduce a novel relaxation method such that the objective function of the relaxed placement subproblem becomes submodular. Leveraging this useful submodular property, we propose two algorithms that achieve an approximation ratio of $\displaystyle \frac{1}{2} (1\ -\ 1/e)$ and $\displaystyle \frac{1}{3}(1\ -\ 1/e)$ for the original non-relaxed problem, respectively. Finally, we corroborate the effectiveness of the proposed algorithms through extensive evaluations using both trace-driven simulations and simulations based on synthesized network settings.
Online Matroid Intersection: Beating Half for Random Arrival

For two matroids \(\mathcal {M}_1\) and \(\mathcal {M}_2\) defined on the same ground set E, the online matroid intersection problem is to design an algorithm that constructs a large common independent set in an online fashion. The algorithm is presented with the ground set elements one-by-one in a uniformly random order. At each step, the algorithm must irrevocably decide whether to pick the element, while always maintaining a common independent set. While the natural greedy algorithm—pick an element whenever possible—is half competitive, nothing better was previously known; even for the special case of online bipartite matching in the edge arrival model. We present the first randomized online algorithm that has a \(\frac{1}{2} + \delta \) competitive ratio in expectation, where \(\delta >0\) is a constant. The expectation is over the random order and the coin tosses of the algorithm. As a corollary, we also obtain the first linear time algorithm that beats half competitiveness for offline matroid intersection.
JuxtaPiton: Enabling Heterogeneous-ISA Research with RISC-V and SPARC FPGA Soft-cores

Energy efficiency has become an increasingly important concern in computer architecture due to the end of Dennard scaling. Heterogeneity has been explored as a way to achieve better energy efficiency and heterogeneous microarchitecture chips have become common in the mobile setting. Recent research has explored using heterogeneous-ISA, heterogeneous microarchitecture, general-purpose cores to achieve further energy efficiency gains. However, there is no open-source hardware implementation of a heterogeneous-ISA processor available for research, and effective research on heterogeneous-ISA processors necessitates the emulation speed provided by FPGA prototyping. This work describes our experiences creating JuxtaPiton by integrating a small RISC-V core into the OpenPiton framework, which uses a modified OpenSPARC T1 core. This is the first time a new core has been integrated with the OpenPiton framework, and JuxtaPiton is the first open-source, general-purpose, heterogeneous-ISA processor. JuxtaPiton inherits all the capabilities of OpenPiton, including vital FPGA emulation infrastructure which can boot full-stack Debian Linux. Using this infrastructure, we investigate area and timing effects of using the new RISC-V core on FPGA and the performance of the new core running microbenchmarks.
On Optimal Generalizability in Parametric Learning

We consider the parametric learning problem, where the objective of the learner is determined by a parametric loss function. Employing empirical risk minimization with possibly regularization, the inferred parameter vector will be biased toward the training samples. Such bias is measured by the cross validation procedure in practice where the data set is partitioned into a training set used for training and a validation set, which is not used in training and is left to measure the out-of-sample performance. A classical cross validation strategy is the leave-one-out cross validation (LOOCV) where one sample is left out for validation and training is done on the rest of the samples that are presented to the learner, and this process is repeated on all of the samples. LOOCV is rarely used in practice due to the high computational complexity. In this paper, we first develop a computationally efficient approximate LOOCV (ALOOCV) and provide theoretical guarantees for its performance. Then we use ALOOCV to provide an optimization algorithm for finding the regularizer in the empirical risk minimization framework. In our numerical experiments, we illustrate the accuracy and efficiency of ALOOCV as well as our proposed framework for the optimization of the regularizer.
4d SCFTs from negative-degree line bundles

We construct 4d $$ \mathcal{N}=1 $$ quantum field theories by compactifying the (2,0) theories on a Riemann surface with genus g and n punctures, where the normal bundle decomposes into a sum of two line bundles with possibly negative degrees p and q. Until recently, the only available field-theoretic constructions required the line bundle degrees to be nonnegative, although supergravity solutions were constructed in the literature for the zero-puncture case for all p and q. Here, we provide field-theoretic constructions and computations of the central charges of 4d $$ \mathcal{N}=1 $$ SCFTs that are the IR limit of M5-branes wrapping a surface with general p or q negative, for general genus g and number of maximal punctures n.
Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks

Classification of histologic patterns in lung adenocarcinoma is critical for determining tumor grade and treatment for patients. However, this task is often challenging due to the heterogeneous nature of lung adenocarcinoma and the subjective criteria for evaluation. In this study, we propose a deep learning model that automatically classifies the histologic patterns of lung adenocarcinoma on surgical resection slides. Our model uses a convolutional neural network to identify regions of neoplastic cells, then aggregates those classifications to infer predominant and minor histologic patterns for any given whole-slide image. We evaluated our model on an independent set of 143 whole-slide images. It achieved a kappa score of 0.525 and an agreement of 66.6% with three pathologists for classifying the predominant patterns, slightly higher than the inter-pathologist kappa score of 0.485 and agreement of 62.7% on this test set. All evaluation metrics for our model and the three pathologists were within 95% confidence intervals of agreement. If confirmed in clinical practice, our model can assist pathologists in improving classification of lung adenocarcinoma patterns by automatically pre-screening and highlighting cancerous regions prior to review. Our approach can be generalized to any whole-slide image classification task, and code is made publicly available at https://github.com/BMIRDS/deepslide .
Shared data science infrastructure for genomics data

Creating a scalable computational infrastructure to analyze the wealth of information contained in data repositories is difficult due to significant barriers in organizing, extracting and analyzing relevant data. Shared data science infrastructures like Boag is needed to efficiently process and parse data contained in large data repositories. The main features of Boag are inspired from existing languages for data intensive computing and can easily integrate data from biological data repositories. As a proof of concept, Boa for genomics, Boag, has been implemented to analyze RefSeq’s 153,848 annotation (GFF) and assembly (FASTA) file metadata. Boag provides a massive improvement from existing solutions like Python and MongoDB, by utilizing a domain-specific language that uses Hadoop infrastructure for a smaller storage footprint that scales well and requires fewer lines of code. We execute scripts through Boag to answer questions about the genomes in RefSeq. We identify the largest and smallest genomes deposited, explore exon frequencies for assemblies after 2016, identify the most commonly used bacterial genome assembly program, and address how animal genome assemblies have improved since 2016. Boag databases provide a significant reduction in required storage of the raw data and a significant speed up in its ability to query large datasets due to automated parallelization and distribution of Hadoop infrastructure during computations. In order to keep pace with our ability to produce biological data, innovative methods are required. The Shared Data Science Infrastructure, Boag, provides researchers a greater access to researchers to efficiently explore data in new ways. We demonstrate the potential of a the domain specific language Boag using the RefSeq database to explore how deposited genome assemblies and annotations are changing over time. This is a small example of how Boag could be used with large biological datasets.
Physicians' Forecasts of Medical Practice: Why is the Glass Half Empty?

Excerpt Why would nearly half of the physicians in the practice of medicine in this country not recommend their profession to a young person today as highly as they might have 10 years ago? In a po...
Dimension estimates for the set of points with non-dense orbit in homogeneous spaces

Let $$X = G/\Gamma $$ , where G is a Lie group and $$\Gamma $$ is a lattice in G, and let U be a subset of X whose complement is compact. We use the exponential mixing results for diagonalizable flows on X to give upper estimates for the Hausdorff dimension of the set of points whose trajectories miss U. This extends a recent result of Kadyrov et al. (Dyn Syst 30(2):149–157, 2015) and produces new applications to Diophantine approximation, such as an upper bound for the Hausdorff dimension of the set of weighted uniformly badly approximable systems of linear forms, generalizing an estimate due to Broderick and Kleinbock (Int J Number Theory 11(7):2037–2054, 2015).
Maximizing Diversity of Opinion in Social Networks

We study the problem of maximizing opinion diversity in a social network that includes opinion leaders with binary opposing opinions. The members of the network who are not leaders form their opinions using the French-DeGroot model of opinion dynamics. To quantify the diversity of such a system, we adapt two diversity measures from ecology to our setting, the Simpson Diversity Index and the Shannon Index. Using these two measures, we formalize the problem of how to place a single leader with opinion 1, given a network with a leader with opinion 0, so as to maximize the opinion diversity. We give analytical solutions to these problems for paths, cycles, and trees, and we highlight our results through a numerical example.
Sub-band Convolutional Neural Networks for Small-footprint Spoken Term Classification

This paper proposes a Sub-band Convolutional Neural Network for spoken term classification. Convolutional neural networks (CNNs) have proven to be very effective in acoustic applications such as spoken term classification, keyword spotting, speaker identification, acoustic event detection, etc. Unlike applications in computer vision, the spatial invariance property of 2D convolutional kernels does not fit acoustic applications well since the meaning of a specific 2D kernel varies a lot along the feature axis in an input feature map. We propose a sub-band CNN architecture to apply different convolutional kernels on each feature sub-band, which makes the overall computation more efficient. Experimental results show that the computational efficiency brought by sub-band CNN is more beneficial for small-footprint models. Compared to a baseline full band CNN for spoken term classification on a publicly available Speech Commands dataset, the proposed sub-band CNN architecture reduces the computation by 39.7% on commands classification, and 49.3% on digits classification with accuracy maintained.
Active Hypothesis Testing: Beyond Chernoff-Stein

An active hypothesis testing problem is formulated. In this problem, the agent can perform a fixed number of experiments and then decide on one of the hypotheses. The agent is also allowed to declare its experiments inconclusive if needed. The objective is to minimize the probability of making an incorrect inference (misclassification probability) while ensuring that the true hypothesis is declared conclusively with moderately high probability. For this problem, lower and upper bounds on the optimal misclassification probability are derived and these bounds are shown to be asymptotically tight. In the analysis, a sub-problem, which can be viewed as a generalization of the Chernoff-Stein lemma, is formulated and analyzed. A heuristic approach to strategy design is proposed and its relationship with existing heuristic strategies is discussed.
Routing Symmetric Demands in Directed Minor-Free Graphs with Constant Congestion.

The problem of routing in graphs using node-disjoint paths has received a lot of attention and a polylogarithmic approximation algorithm with constant congestion is known for undirected graphs [Chuzhoy and Li 2016] and [Chekuri and Ene 2013]. However, the problem is hard to approximate within polynomial factors on directed graphs, for any constant congestion [Chuzhoy, Kim and Li 2016]. Recently, [Chekuri, Ene and Pilipczuk 2016] have obtained a polylogarithmic approximation with constant congestion on directed planar graphs, for the special case of symmetric demands. We extend their result by obtaining a polylogarithmic approximation with constant congestion on arbitrary directed minor-free graphs, for the case of symmetric demands.
Priorities in surgical simulation research: What do the experts say?

Abstract Introduction Progressing the field of surgical simulation research cohesively requires organization. The purpose of this study was to establish contemporary research priorities utilizing Delphi methodology. Methods Surgical researchers with expertise in simulation-based research were invited to submit important questions for the field according to an organized framework. Thematic analysis was used to collapse questions into unique questions. In a second round, experts rated the importance of questions. In a third round, experts re-rated the importance of questions. A prioritized agenda was then created. Results Eighteen experts submitted 80 questions in round one, which were collapsed into 43. In the final round, experts rated the following question as the most important priority: “Does demonstrated competency in the simulation lab translate to clinical competency (OR etc.)?” Conclusions Our systematic approach identified multiple important questions to advance the field that may guide researchers and funding agencies alike.
Annual and cropping season environmental production conditions effects on smallholder technical efficiency in sub‐Saharan Africa: Evidence from Ethiopia

This article investigates the impact of environmental production conditions on smallholder farmers’ technical efficiency in Ethiopia for wheat, maize, and sorghum farming. We use a household panel data set with annual and cropping season environmental production conditions to estimate the technical efficiency scores. The results show that including environmental production conditions in the stochastic frontier has a significant impact on farmers’ technical efficiency scores. Technical efficiency scores improve when environmental production conditions are incorporated in the stochastic frontier. Thus, accounting for environmental production conditions reduces the inefficiencies that otherwise may be attributed to the characteristics of smallholder farmers.
Selling Failed Banks

The average FDIC loss to selling a failed bank is 28% of assets. We document that failed banks are predominantly sold to bidders within the same county, with similar assets business lines, when these bidders are well capitalized. Otherwise, they are acquired by less similar banks located further away. We interpret these facts within a model of auctions with budget constraints, in which poor capitalization of some potential acquirers drives a wedge between their willingness and ability to pay for failed banks. We document that this wedge drives misallocation, and partially explains the FDIC losses from failed bank sales. This article is protected by copyright. All rights reserved
Pancreas transplantation following total pancreatectomy for chronic pancreatitis

Background Total pancreatectomy for chronic pancreatitis leads to brittle diabetes and challenging glycemic control with half of all patients experiencing severe hypoglycemia, many requiring medical intervention or hospitalization. Pancreas transplantation has the potential to manage both the endocrine and the exocrine insufficiency in this patient population. Methods Between June 1, 2005, and July 1, 2016, 8 patients with brittle diabetes following total pancreatectomy underwent pancreas transplantation. All grafts had systemic venous and enteric exocrine drainage. Data included demographics, graft and patient survival, pre- and post-transplant supplementation with pancreatic enzymes, and narcotic usage. Results Patient survival rate at 1 and 3 years was 88%. Pancreas graft survival rate of those alive at 1 year was 100% and 86%, respectively. About 75% of these patients remained insulin-free until their time of death, loss of follow-up, or present day. Of the patients with maintained graft function at 3 years, none required further hospitalization for glycemic control. About 75% of these patients have also maintained exocrine function without pancreatic enzyme supplementation. Conclusions Pancreas transplant can treat both exocrine and endocrine insufficiency and give long-term insulin-free survival and should be considered as a viable treatment option for patients who have undergone total pancreatectomy for chronic pancreatitis.
CHURP: Dynamic-Committee Proactive Secret Sharing

We introduce CHURP (CHUrn-Robust Proactive secret sharing). CHURP enables secure secret-sharing in dynamic settings, where the committee of nodes storing a secret changes over time. Designed for blockchains, CHURP has lower communication complexity than previous schemes: $O(n)$ on-chain and $O(n^2)$ off-chain in the optimistic case of no node failures. CHURP includes several technical innovations: An efficient new proactivization scheme of independent interest, a technique (using asymmetric bivariate polynomials) for efficiently changing secret-sharing thresholds, and a hedge against setup failures in an efficient polynomial commitment scheme. We also introduce a general new technique for inexpensive off-chain communication across the peer-to-peer networks of permissionless blockchains. We formally prove the security of CHURP, report on an implementation, and present performance measurements.
Spectral distributions of periodic random matrix ensembles

Kologlu, Kopp and Miller compute the limiting spectral distribution of a certain class of real random matrix ensembles, known as k-block circulant ensembles, and discover that it is exactly equal t...
A new decoupling technique for the hermite cubic collocation equations arising from boundary value problems

Abstract We present a new decoupling technique for solving the linear systems arising from Hermite cubic collocation solutions to boundary value problems with both Dirichlet and Neumann boundary conditions. While the traditional approach yields a linear system of order 2 N ×2 N with bandwidth 2, our technique decouples this system into two systems, one with a tridiagonal system of order N −1× N −1 and the other with the identity matrix of order N × N . Besides cutting the work in half, our new approach results in a new tridiagonal system that exhibits the same desirable properties (e.g. symmetric, positive definite) as in the case of finite difference approximations. We validate our theoretical work with a number of experimental results, demonstrating both accuracy and stability.