cssi/SciSciGPT-SciSciNet huggingface dataset:
Hamburger moment sequences and their moment subsequences

Partial sequences and subsequences of Hamburger moment sequences arise where data have been lost or interpolation is sought. All the relevant questions dealing with full moment sequences, such as positivity and partial positivity, relationship between the corresponding measures and determinacy can be posed for the partial data. In this paper, we characterize the moment subsequences of Hamburger moment sequences, give a description of certain completable patterns of partial sequences and provide a simple relationship between the Cauchy transform of the moment sequence and its moment subsequences. A general result for perturbation of moment sequences by arbitrary sequences is also given.
Smooth, Efficient, and Interruptible Zooming and Panning

This paper introduces a novel technique for smooth and efficient zooming and panning based on dynamical systems in hyperbolic space. Unlike the technique of van Wijk and Nuij, the animations produced by our technique are smooth at the endpoints and when interrupted by a change of target. To analyze the results of our technique, we introduce world/screen diagrams, a novel technique for visualizing zooming and panning animations.
Complexity of a disjoint matching problem on bipartite graphs

We consider the following question: given an ( X , Y ) -bigraph G and a set S ź X , does G contain two disjoint matchings M 1 and M 2 such that M 1 saturates X and M 2 saturates S? When | S | ź | X | - 1 , this question is solvable by finding an appropriate factor of the graph. In contrast, we show that when S is allowed to be an arbitrary subset of X, the problem is NP-hard. We consider the problem of finding two disjoint matchings in a bipartite graph.One matching should be perfect, the other should saturate some given vertex set.We prove that this problem is NP-hard.This strengthens a hardness result due to Frieze.
A proof of Jones' conjecture

Abstract In this paper, we prove that Wright's equation y ′ ( t ) = − α y ( t − 1 ) { 1 + y ( t ) } has a unique slowly oscillating periodic solution for parameter values α ∈ ( π 2 , 1.9 ] , up to time translation. This result proves Jones' Conjecture formulated in 1962, that there is a unique slowly oscillating periodic orbit for all α > π 2 . Furthermore, there are no isolas of periodic solutions to Wright's equation; all periodic orbits arise from Hopf bifurcations.
Cellular automata using infinite computations

Abstract This paper presents an application of the Infinite Unit Axiom, introduced by Yaroslav Sergeyev, (see [10] , [11] , [12] , [13] ) to the development of one-dimensional cellular automata. This application allows the establishment of a new and more precise metric on the space of definition for one-dimensional cellular automata, whereby accuracy of computations is increased. Using this new metric, open disks are defined and the number of points in each disk is computed. The forward dynamics of a cellular automaton map are also studied via defined equivalence classes. Using the Infinite Unit Axiom, the number of configurations that stay close to a given configuration under the shift automaton map can now be computed.
Unique equilibrium states for Bonatti-Viana diffeomorphisms

We show that the robustly transitive diffeomorphisms constructed by Bonatti and Viana have unique equilibrium states for natural classes of potentials. In particular, we characterize the SRB measure as the unique equilibrium state for a suitable geometric potential. The techniques developed are applicable to a wide class of DA diffeomorphisms, and persist under $C^1$ perturbations of the map. These results are an application of general machinery developed by the first and last named authors.
VBPR: visual Bayesian Personalized Ranking from implicit feedback

Modern recommender systems model people and items by discovering or 'teasing apart' the underlying dimensions that encode the properties of items and users' preferences toward them. Critically, such dimensions are uncovered based on user feedback, often in implicit form (such as purchase histories, browsing logs, etc.); in addition, some recommender systems make use of side information, such as product attributes, temporal information, or review text. However one important feature that is typically ignored by existing personalized recommendation and ranking methods is the visual appearance of the items being considered. In this paper we propose a scalable factorization model to incorporate visual signals into predictors of people's opinions, which we apply to a selection of large, real-world datasets. We make use of visual features extracted from product images using (pre-trained) deep networks, on top of which we learn an additional layer that uncovers the visual dimensions that best explain the variation in people's feedback. This not only leads to significantly more accurate personalized ranking methods, but also helps to alleviate cold start issues, and qualitatively to analyze the visual dimensions that influence people's opinions.
{GAZELLE}: A Low Latency Framework for Secure Neural Network Inference

The growing popularity of cloud-based machine learning raises a natural question about the privacy guarantees that can be provided in such a setting. Our work tackles this problem in the context where a client wishes to classify private images using a convolutional neural network (CNN) trained by a server. Our goal is to build efficient protocols whereby the client can acquire the classification result without revealing their input to the server, while guaranteeing the privacy of the server's neural network. To this end, we design Gazelle, a scalable and low-latency system for secure neural network inference, using an intricate combination of homomorphic encryption and traditional two-party computation techniques (such as garbled circuits). Gazelle makes three contributions. First, we design the Gazelle homomorphic encryption library which provides fast algorithms for basic homomorphic operations such as SIMD (single instruction multiple data) addition, SIMD multiplication and ciphertext permutation. Second, we implement the Gazelle homomorphic linear algebra kernels which map neural network layers to optimized homomorphic matrix-vector multiplication and convolution routines. Third, we design optimized encryption switching protocols which seamlessly convert between homomorphic and garbled circuit encodings to enable implementation of complete neural network inference. We evaluate our protocols on benchmark neural networks trained on the MNIST and CIFAR-10 datasets and show that Gazelle outperforms the best existing systems such as MiniONN (ACM CCS 2017) by 20 times and Chameleon (Crypto Eprint 2017/1164) by 30 times in online runtime. Similarly when compared with fully homomorphic approaches like CryptoNets (ICML 2016) we demonstrate three orders of magnitude faster online run-time.
SQuAD: 100,000+ Questions for Machine Comprehension of Text

We present the Stanford Question Answering Dataset (SQuAD), a new reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the corresponding reading passage. We analyze the dataset to understand the types of reasoning required to answer the questions, leaning heavily on dependency and constituency trees. We build a strong logistic regression model, which achieves an F1 score of 51.0%, a significant improvement over a simple baseline (20%). However, human performance (86.8%) is much higher, indicating that the dataset presents a good challenge problem for future research. The dataset is freely available at this https URL
On Unifying Deep Generative Models

Deep generative models have achieved impressive success in recent years. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), as emerging families for generative model learning, have largely been considered as two distinct paradigms and received extensive independent studies respectively. This paper aims to establish formal connections between GANs and VAEs through a new formulation of them. We interpret sample generation in GANs as performing posterior inference, and show that GANs and VAEs involve minimizing KL divergences of respective posterior and inference distributions with opposite directions, extending the two learning phases of classic wake-sleep algorithm, respectively. The unified view provides a powerful tool to analyze a diverse set of existing model variants, and enables to transfer techniques across research lines in a principled way. For example, we apply the importance weighting method in VAE literatures for improved GAN learning, and enhance VAEs with an adversarial mechanism that leverages generated samples. Experiments show generality and effectiveness of the transferred techniques.
Vertical perimeter versus horizontal perimeter

The discrete Heisenberg group $\mathbb{H}_{\mathbb{Z}}^{2k+1}$ is the group generated by $a_1,b_1,\ldots,a_k,b_k,c$, subject to the relations $[a_1,b_1]=\ldots=[a_k,b_k]=c$ and $[a_i,a_j]=[b_i,b_j]=[a_i,b_j]=[a_i,c]=[b_i,c]=1$ for every distinct $i,j\in \{1,\ldots,k\}$. Denote $S=\{a_1^{\pm 1},b_1^{\pm 1},\ldots,a_k^{\pm 1},b_k^{\pm 1}\}$. The horizontal boundary of $\Omega\subset \mathbb{H}_{\mathbb{Z}}^{2k+1}$, denoted $\partial_{h}\Omega$, is the set of all $(x,y)\in \Omega\times (\mathbb{H}_{\mathbb{Z}}^{2k+1}\setminus \Omega)$ such that $x^{-1}y\in S$. The horizontal perimeter of $\Omega$ is $|\partial_{h}\Omega|$. For $t\in \mathbb{N}$, define $\partial^t_{v} \Omega$ to be the set of all $(x,y)\in \Omega\times (\mathbb{H}_{\mathsf{Z}}^{2k+1}\setminus \Omega)$ such that $x^{-1}y\in \{c^t,c^{-t}\}$. The vertical perimeter of $\Omega$ is defined by $|\partial_{v}\Omega|= \sqrt{\sum_{t=1}^\infty |\partial^t_{v}\Omega|^2/t^2}$. It is shown here that if $k\ge 2$, then $|\partial_{v}\Omega|\lesssim \frac{1}{k} |\partial_{h}\Omega|$. The proof of this "vertical versus horizontal isoperimetric inequality" uses a new structural result that decomposes sets of finite perimeter in the Heisenberg group into pieces that admit an "intrinsic corona decomposition." This allows one to deduce an endpoint $W^{1,1}\to L_2(L_1)$ boundedness of a certain singular integral operator from a corresponding lower-dimensional $W^{1,2}\to L_2(L_2)$ boundedness. The above inequality has several applications, including that any embedding into $L_1$ of a ball of radius $n$ in the word metric on $\mathbb{H}_{\mathbb{Z}}^{5}$ incurs bi-Lipschitz distortion that is at least a constant multiple of $\sqrt{\log n}$. It follows that the integrality gap of the Goemans--Linial semidefinite program for the Sparsest Cut Problem on inputs of size $n$ is at least a constant multiple of $\sqrt{\log n}$.
Quivers, long exact sequences and Horn type inequalities

Abstract We give necessary and sufficient inequalities for the existence of long exact sequences of m finite abelian p-groups with fixed isomorphism types. This problem is related to some generalized Littlewood–Richardson coefficients that we define in this paper. We also show how this problem is related to eigenvalues of Hermitian matrices satisfying certain (in)equalities. When m = 3 , we recover the Horn type inequalities that solve the saturation conjecture for Littlewood–Richardson coefficients and Horn's conjecture.
Electric field of a point charge in truncated hyperbolic motion

We find the electric field of a point charge in ‘truncated hyperbolic motion’, in which the charge moves at a constant velocity followed by motion with a constant acceleration in its instantaneous rest frame. The same Lienard–Wiechert formula holds for the acceleration phase and the constant velocity phase of the charge’s motion. The only modification is that the formula giving the retarded time is different for the constant velocity motion than it was for the accelerated motion. The electric field lines are continuous as the retarded time increases through the transition time between constant velocity and accelerated motion. As the transition time approaches negative infinity the electric field develops a delta function contribution that has been introduced by others as necessary to preserve Gauss’s law for the electric field.
Sliced-Inverse-Regression--Aided Rotated Compressive Sensing Method for Uncertainty Quantification

Compressive-sensing--based uncertainty quantification methods have become a powerful tool for problems with limited data. In this work, we use the sliced inverse regression (SIR) method to provide ...
On the singularities of effective loci of line bundles

Abstract We prove that every irreducible component of semi-regular loci of effective line bundles in the Picard scheme of a smooth projective variety has at worst rational singularities. This generalizes Kempf's result on rational singularities of W d 0 for smooth curves. We also work out an example of such loci for a ruled surface.
Domain Decomposition for Heterojunction Problems in Semiconductors

We present a domain decomposition approach for the simulation of charge transport in heterojunction semiconductors. The problem is characterized by a large variation of primary variables across an interface region of a size much smaller than the device scale, and requires a multiscale approach in which that region is modeled as an internal boundary. The model combines drift diffusion equations on subdomains coupled by thermionic emission heterojunction model on the interface which involves a nonhomogeneous jump computed at fine scale with Density Functional Theory. Our full domain decomposition approach extends our previous work for the potential equation only, and we present perspectives on its HPC implementation. The model can be used, e.g., for the design of higher efficiency solar cells for which experimental results are not available. More generally, our algorithm is naturally parallelizable and is a new domain decomposition paradigm for problems with multiscale phenomena associated with internal interfaces and/or boundary layers.
Multi-lingual neural title generation for e-Commerce browse pages

To provide better access of the inventory to buyers and better search engine optimization, e-Commerce websites are automatically generating millions of browse pages. A browse page consists of a set of slot name/value pairs within a given category, grouping multiple items which share some characteristics. These browse pages require a title describing the content of the page. Since the number of browse pages are huge, manual creation of these titles is infeasible. Previous statistical and neural approaches depend heavily on the availability of large amounts of data in a language. In this research, we apply sequence-to-sequence models to generate titles for high-resource as well as low-resource languages by leveraging transfer learning. We train these models on multi-lingual data, thereby creating one joint model which can generate titles in various different languages. Performance of the title generation system is evaluated on three different languages; English, German, and French, with a particular focus on low-resourced French language.
A game theoretical approach to broadcast information diffusion in social networks

One major function of social networks (e.g., massive online social networks) is the dissemination of information, such as scientific knowledge, news, and rumors. Information can be propagated by the users of the network via natural connections in written, oral or electronic form. The information passing from a sender to receivers and back (in the form of comments) involves all of the actors considering their knowledge, trust, and popularity, which shape their publishing and commenting strategies. To understand such human aspects of the information dissemination, we propose a game theoretical model of a one-way information forwarding and feedback mechanism in a star-shaped social network that takes into account the personalities of the communicating actors.
An ELU Network with Total Variation for Image Denoising

In this paper, we propose a novel convolutional neural network (CNN) for image denoising, which uses exponential linear unit (ELU) as the activation function. We investigate the suitability by analyzing ELU’s connection with trainable nonlinear reaction diffusion model (TNRD) and residual denoising. On the other hand, batch normalization (BN) is indispensable for residual denoising and convergence purpose. However, direct stacking of BN and ELU degrades the performance of CNN. To mitigate this issue, we design an innovative combination of activation layer and normalization layer to exploit and leverage the ELU network, and discuss the corresponding rationale. Moreover, inspired by the fact that minimizing total variation (TV) can be applied to image denoising, we propose a TV regularized L2 loss to evaluate the training effect during the iterations. Finally, we conduct extensive experiments, showing that our model outperforms some recent and popular approaches on Gaussian denoising with specific or randomized noise levels for both gray and color images.
Power control and mode selection for VBR video streaming in D2D networks

In this paper, we investigate the problem of power control for streaming variable-bit-rate (VBR) videos in a device-to-device (D2D) wireless network. A VBR video traffic model that considers video frame sizes and playout buffers at the mobile users is adopted. A setup with one pair of D2D users (DUs) and one cellular user (CU) is considered and three modes, namely cellular mode, dedicated mode and reuse mode, are employed. Mode selection for the data delivery is determined and the transmit powers of the base station (BS) and device transmitter are optimized with the goal of maximizing the overall transmission rate while VBR video data can be delivered to the CU and DU without causing playout buffer underflows or overflows. A low-complexity algorithm is proposed. Through simulations with VBR video traces over fading channels, we demonstrate that video delivery with mode selection and power control achieves a better performance than just using a single mode throughout the transmission.
Split functions, Fourier transforms and multipliers

We study the \(L^p\) norm of the Fourier transform of \(S_t\!f\), where \(S_t\) is a splitting operator. If \(m\) is a Fourier multiplier, we also study the operator norm of \(S_t m\). Most of our results assume \(p\) is an even integer. They are often stronger when \(f\) or \(m\) has compact support.
Optimal Angular Resolution for Face-Symmetric Drawings

Let G be a graph that may be drawn in the plane in such a way that all internal faces are centrally symmetric convex polygons. We show how to find a drawing of this type that maximizes the angular resolution of the drawing, the minimum angle between any two incident edges, in polynomial time, by reducing the problem to one of finding parametric shortest paths in an auxiliary graph. The running time is at most O(t), where t is a parameter of the input graph that is at most O(n). Submitted: March 2009 Reviewed: June 2009 Revised: July 2010 Accepted: August 2011 Final: September 2011 Published: September 2011 Article type: Regular paper Communicated by: P. Eades E-mail addresses: eppstein@ics.uci.edu (David Eppstein) kwortman@fullerton.edu (Kevin A.
Hilbert-Kunz Functions for Normal Rings

Let (R,m,k) be an excellent, local, normal ring of characteristic p with a perfect residue field and dim R=d. Let M be a finitely generated R-module. We show that there exists a real number beta(M) such that lambda(M/I^[q]M) = e_{HK}(M) q^d + beta(M) q^{d-1} + O(q^{d-2}).
Similarity-based Multi-label Learning

Multi-label classification is an important learning problem with many applications. In this work, we propose a similarity-based approach for multi-label learning called SML. We also introduce a similarity-based approach for predicting the label set size. SML is amenable to streaming data and online learning, naturally able to handle changes in the problem domain, robust to training data with skewed class label sets, accurate with low variance, and lends itself to an efficient parallel implementation. The experimental results demonstrate the effectiveness of SML for multi-label classification where it is shown to compare favorably with a wide variety of existing algorithms across a range of evaluation criterion.
Matrix Optimal Mass Transport: A Quantum Mechanical Approach

In this paper, we describe a possible generalization of the Wasserstein-2 metric, originally defined on the space of scalar probability densities, to the space of Hermitian matrices with trace one and to the space of matrix-valued probability densities. Our approach follows a control-theoretic optimization formulation of the Wasserstein-2 metric, having its roots in fluid dynamics, and utilizes certain results from the quantum mechanics of open systems, in particular the Lindblad equation. It allows determining the gradient flow for the quantum entropy relative to this matricial Wasserstein metric.
Amari–Chentsov Connections and their Geodesics on Homogeneous Spaces of Diffeomorphism Groups

We study the family of Amari–Chentsov α-connections on the homogeneous space $ {{{\mathcal{D}(M)}} \left/ {{{{\mathcal{D}}_{\mu }}(M)}} \right.} $ of diffeomorphisms modulo volume-preserving diffeomorphisms of a compact manifold M. We show that in some cases their geodesic equations yield completely integrable Hamiltonian systems. Bibliography: 10 titles.
Double posets and the antipode of QSym

A quasisymmetric function is assigned to every double poset (that is, every finite set endowed with two partial orders) and any weight function on its ground set. This generalizes well-known objects such as monomial and fundamental quasisymmetric functions, (skew) Schur functions, dual immaculate functions, and quasisymmetric $\left(P, \omega\right)$-partition enumerators. We prove a formula for the antipode of this function that holds under certain conditions (which are satisfied when the second order of the double poset is total, but also in some other cases); this restates (in a way that to us seems more natural) a result by Malvenuto and Reutenauer, but our proof is new and self-contained. We generalize it further to an even more comprehensive setting, where a group acts on the double poset by automorphisms.
Theoretical design of a shallow donor in diamond by lithium-nitrogen codoping.

We propose a new substitutional impurity complex in diamond composed of a lithium atom that is tetrahedrally coordinated by four nitrogen atoms (${\mathrm{LiN}}_{4}$). Density functional calculations are consistent with the hydrogenic impurity model, both supporting the prediction that this complex is a shallow donor with an activation energy of $0.27\ifmmode\pm\else\textpm\fi{}0.06\text{ }\text{ }\mathrm{eV}$. Three paths to the experimental realization of the ${\mathrm{LiN}}_{4}$ complex in diamond are proposed and theoretically analyzed.
Computing Approximate Statistical Discrepancy.

Consider a geometric range space (X,A) where X is comprised of the union of a red set R and blue set B. Let Phi(A) define the absolute difference between the fraction of red and fraction of blue points which fall in the range A. The maximum discrepancy range A^* = arg max_{A in (X,A)} Phi(A). Our goal is to find some A^ in (X,A) such that Phi(A^*) - Phi(A^) <= epsilon. We develop general algorithms for this approximation problem for range spaces with bounded VC-dimension, as well as significant improvements for specific geometric range spaces defined by balls, halfspaces, and axis-aligned rectangles. This problem has direct applications in discrepancy evaluation and classification, and we also show an improved reduction to a class of problems in spatial scan statistics.
The Weighted Connection and Sectional Curvature for Manifolds With Density

In this paper we study sectional curvature bounds for Riemannian manifolds with density from the perspective of a weighted torsion-free connection introduced recently by the last two authors. We develop two new tools for studying weighted sectional curvature bounds: a new weighted Rauch comparison theorem and a modified notion of convexity for distance functions. As applications we prove generalizations of theorems of Preissman and Byers for negative curvature, the (homeomorphic) quarter-pinched sphere theorem, and Cheeger’s finiteness theorem. We also improve results of the first two authors for spaces of positive weighted sectional curvature and symmetry.
The squared-error of generalized LASSO: A precise analysis

We consider the problem of estimating an unknown but structured signal x0 from its noisy linear observations y = Ax0 + z ∈ ℝm. To the structure of x0 is associated a structure inducing convex function f(·). We assume that the entries of A are i.i.d. standard normal N(0, 1) and z ~ N(0, σ2Im). As a measure of performance of an estimate x* of x0 we consider the “Normalized Square Error” (NSE) ∥x* - x0∥22/σ2. For sufficiently small σ, we characterize the exact performance of two different versions of the well known LASSO algorithm. The first estimator is obtained by solving the problem argminx ∥y - Ax∥2 + λf(x). As a function of λ, we identify three distinct regions of operation. Out of them, we argue that “RON” is the most interesting one. When λ ∈ RON, we show that the NSE is Df(x0, λ)/m-Df(x0, λ) for small σ, where Df(x0, λ) is the expected squared-distance of an i.i.d. standard normal vector to the dilated subdifferential λ · ∂f(x0). Secondly, we consider the more popular estimator argminx 1/2∥y - Ax∥22. + στ f(x). We propose a formula for the NSE of this estimator by establishing a suitable mapping between this and the previous estimator over the region RON. As a useful side result, we find explicit formulae for the optimal estimation performance and the optimal penalty parameters λ* and τ*.
A note on kernels, images, and cokernels in the perverse category

We discuss the relationship between kernels, images and cokernels of morphisms between perverse sheaves and induced maps on stalk cohomology.
Better Algorithms for Stochastic Bandits with Adversarial Corruptions

We study the stochastic multi-armed bandits problem in the presence of adversarial corruption. We present a new algorithm for this problem whose regret is nearly optimal, substantially improving upon previous work. Our algorithm is agnostic to the level of adversarial contamination and can tolerate a significant amount of corruption with virtually no degradation in performance.
Using Bi-Directional Information Exchange to Improve Decentralized Schedule-Driven Traffic Control

Recent work in decentralized, schedule-driven traffic control has demonstrated the ability to improve the efficiency of traffic flow in complex urban road networks. In this approach, a scheduling agent is associated with each intersection. Each agent senses the traffic approaching its intersection and in real-time constructs a schedule that minimizes the cumulative wait time of vehicles approaching the intersection over the current look-ahead horizon. In order to achieve network level coordination in a scalable manner, scheduling agents communicate only with their direct neighbors. Each time an agent generates a new intersection schedule it communicates its expected outflows to its downstream neighbors as a prediction of future demand and these outflows are appended to the downstream agent’s locally perceived demand. In this paper, we extend this basic coordination algorithm to additionally incorporate the complementary flow of information reflective of an intersection’s current congestion level to its upstream neighbors. We present an asynchronous decentralized algorithm for updating intersection schedules and congestion level estimates based on these bi-directional information flows. By relating this algorithm to the self-optimized decision making of the basic operation, we are able to approach network-wide optimality and reduce inefficiency due to strictly self-interested intersection control decisions.
Detecting DGA domains with recurrent neural networks and side information

Modern malware typically makes use of a domain generation algorithm (DGA) to avoid command and control domains or IPs being seized or sinkholed. This means that an infected system may attempt to access many domains in an attempt to contact the command and control server. Therefore, the automatic detection of DGA domains is an important task, both for the sake of blocking malicious domains and identifying compromised hosts. However, many DGAs use English wordlists to generate plausibly clean-looking domain names; this makes automatic detection difficult. In this work, we devise a notion of difficulty for DGA families called the smashword score; this measures how much a DGA family looks like English words. We find that this measure accurately reflects how much a DGA family's domains look like they are made from natural English words. We then describe our new modeling approach, which is a combination of a novel recurrent neural network architecture with domain registration side information. Our experiments show the model is capable of effectively identifying domains generated by difficult DGA families. Our experiments also show that our model outperforms existing approaches, and is able to reliably detect difficult DGA families such as matsnu, suppobox, rovnix, and others. The model's performance compared to the state of the art is best for DGA families that resemble English words. We believe that this model could either be used in a standalone DGA domain detector---such as an endpoint security application---or alternately the model could be used as a part of a larger malware detection system.
Work after Retirement: Worklife Transitions of Career Public Employess

Engaging in paid employment after claiming retirement benefits may be an important avenue for individuals to work longer as life expectancies rise. After separating from one's career employer, individuals may engage in paid work to stay active or to supplement their current level of retirement savings or both. Individuals who choose not to work after claiming may be expressing their preference to stay retired, perhaps because their retirement income is sufficient. However, the decision to work after claiming may be driven by the lack of retirement planning and insufficient savings, while the lack of post-claiming work may reflect the inability to find adequate employment opportunities. We use administrative records merged with panel data from several surveys of public employees in North Carolina to study the decision to engage in paid work after claiming retirement benefits. More than 60% of active workers plan to work after claiming benefits, while only around 42% of the same sample of individuals have engaged in post-claiming paid work in the first few years after leaving public sector employment. Despite this gap, stated work plans are strongly predictive of actual post-claiming work behavior. Our final analysis uses self-reported measures to gauge the financial well-being of our sample in the early years after leaving career employment.
Antifungal Potency and Modes of Action of a Novel Olive Tree Defensin Against Closely Related Ascomycete Fungal Pathogens

Antimicrobial peptides play a pivotal role in the innate immunity of plants. Defensins are cysteine-rich antifungal peptides with multiple modes of action. A novel Oleaceae-specific defensin gene family has been discovered in the genome sequences of wild and cultivated species of a perennial olive tree, Olea europaea. OefDef1.1, a member of this defensin family, potently inhibits the in-vitro growth of ascomycete fungal pathogens Botrytis cinerea and three Fusarium spp. OefDef1.1 rapidly permeabilizes the plasma membrane of the conidial and germling cells of B. cinerea. Interestingly, it induces reactive oxygen species and translocates to the cytoplasm only in the germlings but not in the conidia. In medium containing a high concentration of Na1+, antifungal activity of OefDef1.1 is significantly reduced. Surprisingly, a chimeric OefDef1.1 peptide containing the γ-core motif of a Medicago truncatula defensin, MtDef4, displays Na1+-tolerant antifungal activity. In a phospholipid-protein overlay assay, the chimeric peptide exhibits stronger binding to its phosphoinositide partners than OefDef1.1 and is also more potent in inhibiting gray mold disease on the surface of Nicotiana benthamiana and lettuce leaves than OefDef1.1. Significant differences are observed among the four ascomycete pathogens in their responses to OefDef1.1 in growth medium with or without the elevated concentration of Na1+. The varied responses of closely related ascomycete pathogens to this defensin have implications for engineering disease resistance in plants.
Cormorant: Covariant Molecular Neural Networks

We propose Cormorant, a rotationally covariant neural network architecture for learning the behavior and properties of complex many-body physical systems. We apply these networks to molecular systems with two goals: learning atomic potential energy surfaces for use in Molecular Dynamics simulations, and learning ground state properties of molecules calculated by Density Functional Theory. Some of the key features of our network are that (a) each neuron explicitly corresponds to a subset of atoms; (b) the activation of each neuron is covariant to rotations, ensuring that overall the network is fully rotationally invariant. Furthermore, the non-linearity in our network is based upon tensor products and the Clebsch-Gordan decomposition, allowing the network to operate entirely in Fourier space. Cormorant significantly outperforms competing algorithms in learning molecular Potential Energy Surfaces from conformational geometries in the MD-17 dataset, and is competitive with other methods at learning geometric, energetic, electronic, and thermodynamic properties of molecules on the GDB-9 dataset.
Complete set of stochastic Verlet-type thermostats for correct Langevin simulations

We present the complete set of stochastic Verlet-type algorithms that can provide correct statistical measures for both configurational and kinetic sampling in discrete-time Langevin systems. The a...
Mapping coercive institutions: The State Security Forces dataset, 1960–2010:

How rulers organize and use their security forces is thought to have important implications for regime survival, repression, and military effectiveness. While a number of studies provide insight in...
A Possible Approach to Fiscal Rules in Small Islands - Incorporating Natural Disasters and Climate Change

A big challenge for the economic development of small island countries is dealing with external shocks. The Pacific Islands are vulnerable to natural disasters, climate change, commodity price changes, and uncertain donor grants. The question that arises is how should small developing countries formulate a fiscal policy to achieve economic stability and fiscal sustainability when prone to various shocks? We study how natural disasters affect long-term debt dynamics and propose fiscal policy rules that could help insulate the economy from such unexpected shocks. We propose fiscal rules to address these shocks and uncertainties using the example of Papua New Guinea. Our study finds the advantages of expenditure rules, especially a recurrent expenditure rule based on non-resource and non-grant revenue, interdependently determined by government debt and budget balance targets with expected disaster shocks. This paper contributes to the literature and policy dialogue by theoretically analyzing the impact of natural disasters on debt sustainability and proposing fiscal rules against natural disasters and climate changes. Our fiscal policy framework is practically applicable for many developing countries facing increasing frequency and impact of natural disasters and climate change. Our rules-based fiscal framework is crucial for sustainable and countercyclical macroeconomic policies to build resilience against devastating natural hazards.
IRC Cross-Layer Design Exploration of Intermittent Robust Computation Units for IoTs

Energy-harvesting-powered computing offers intriguing and vast opportunities to dramatically transform the landscape of the Internet of Things (IoT) devices by utilizing ambient sources of energy to achieve battery-free computing. In order to operate within the restricted energy capacity and intermittency profile, it is proposed to innovate Intermittent Robust Computation (IRC) Unit as a new duty-cycle-variable computing approach leveraging the non-volatility inherent in spin-based switching devices. The foundations of IRC will be advanced from the device-level upwards, by extending a Spin Hall Effect Magnetic Tunnel Junction (SHE-MTJ) device. The device will then be used to realize SHE-MTJ Majority/Polymorphic Gate (MG/PG) logic approaches and libraries. Then a Logic-Embedded Flip-Flop (LE-FF) is developed to realize rudimentary Boolean logic functions along with an inherent state-holding capability within a compact footprint. Finally, the NV-Clustering synthesis procedure and corresponding tool module are proposed to instantiate the LE-FF library cells within conventional Register Transfer Language (RTL) specifications. This selectively clusters together logic and NV state-holding functionality, based on energy and area minimization criteria. It also realizes middleware-coherent, intermittent computation without checkpointing, micro-tasking, or software bloat and energy overheads vital to IoT. Simulation results for various benchmark circuits including ISCAS-89 validate functionality and power dissipation, area, and delay benefits.
On the Mechanism of Bilayer Separation by Extrusion, or Why Your LUVs Are Not Really Unilamellar.

Abstract Extrusion through porous filters is a widely used method for preparing biomimetic model membranes. Of primary importance in this approach is the efficient production of single bilayer (unilamellar) vesicles that eliminate the influence of interlamellar interactions and strictly define the bilayer surface area available to external reagents such as proteins. Submicroscopic vesicles produced using extrusion are widely assumed to be unilamellar, and large deviations from this assumption would impact interpretations from many model membrane experiments. Using three probe-free methods—small angle X-ray and neutron scattering and cryogenic electron microscopy—we report unambiguous evidence of extensive multilamellarity in extruded vesicles composed of neutral phosphatidylcholine lipids, including for the common case of neutral lipids dispersed in physiological buffer and extruded through 100-nm diameter pores. In such preparations, only ∼35% of lipids are externally accessible and this fraction is highly dependent on preparation conditions. Charged lipids promote unilamellarity as does decreasing solvent ionic strength, indicating the importance of electrostatic interactions in determining the lamellarity of extruded vesicles. Smaller extrusion pore sizes also robustly increase the fraction of unilamellar vesicles, suggesting a role for membrane bending. Taken together, these observations suggest a mechanistic model for extrusion, wherein the formation of unilamellar vesicles involves competition between bilayer bending and adhesion energies. The findings presented here have wide-ranging implications for the design and interpretation of model membrane studies, especially ensemble-averaged observations relying on the assumption of unilamellarity.
A Combinatorial Approach for Single-cell Variant Detection via Phylogenetic Inference.

Single-cell sequencing provides a powerful approach for elucidating intratumor heterogeneity by resolving cell-to-cell variability. However, it also poses additional challenges including elevated error rates, allelic dropout and non-uniform coverage. A recently introduced single-cell-specific mutation detection algorithm leverages the evolutionary relationship between cells for denoising the data. However, due to its probabilistic nature, this method does not scale well with the number of cells. Here, we develop a novel combinatorial approach for utilizing the genealogical relationship of cells in detecting mutations from noisy single-cell sequencing data. Our method, called scVILP, jointly detects mutations in individual cells and reconstructs a perfect phylogeny among these cells. We employ a novel Integer Linear Program algorithm for deterministically and efficiently solving the joint inference problem. We show that scVILP achieves similar or better accuracy but significantly better runtime over existing methods on simulated data. We also applied scVILP to an empirical human cancer dataset from a high grade serous ovarian cancer patient.
CLOTHO: directed test generation for weakly consistent database systems

Relational database applications are notoriously difficult to test and debug. Concurrent execution of database transactions may violate complex structural invariants that constraint how changes to the contents of one (shared) table affect the contents of another. Simplifying the underlying concurrency model is one way to ameliorate the difficulty of understanding how concurrent accesses and updates can affect database state with respect to these sophisticated properties. Enforcing serializable execution of all transactions achieves this simplification, but it comes at a significant price in performance, especially at scale, where database state is often replicated to improve latency and availability. To address these challenges, this paper presents a novel testing framework for detecting serializability violations in (SQL) database-backed Java applications executing on weakly-consistent storage systems. We manifest our approach in a tool, CLOTHO, that combines a static analyzer and model checker to generate abstract executions, discover serializability violations in these executions, and translate them back into concrete test inputs suitable for deployment in a test environment. To the best of our knowledge, CLOTHO, is the first automated test generation facility for identifying serializability anomalies of Java applications intended to operate in geo-replicated distributed environments. An experimental evaluation on a set of industry-standard benchmarks demonstrates the utility of our approach.
Hyperbolic structures on wreath products

The study of the poset of hyperbolic structures on a group $G$ was initiated in a paper by C.Abbott, S.Balasubramanya and D.Osin. However, this poset is still very far from being understood and several questions remain unanswered. In this paper, we give a complete description of the poset of hyperbolic structures on the lamplighter groups and obtain some partial results about more general wreath products. As a consequence of this result, we answer two open questions regarding quasi-parabolic structures: we give an example of a group with an uncountable chain of quasi-parabolic structures and prove that the Lamplighter groups all have finitely many quasi-parabolic structures.
The sharp constant in the weak (1,1) inequality for the square function: a new proof

In this note we give a new proof of the sharp constant C=e−1/2+∫10e−x2/2dx in the weak (1, 1) inequality for the dyadic square function. The proof makes use of two Bellman functions L and M related to the problem, and relies on certain relationships between L and M, as well as the boundary values of these functions, which we find explicitly. Moreover, these Bellman functions exhibit an interesting behavior: the boundary solution for M yields the optimal obstacle condition for L, and vice versa.
How do scientific disciplines evolve in applied sciences? The properties of scientific fission and ambidextrous scientific drivers

One of the fundamental questions in science is how scientific disciplines evolve and sustain progress in society. No studies to date allows us to explain the endogenous processes that support the evolution of scientific disciplines and emergence of new scientific fields in applied sciences of physics. This study confronts this problem here by investigating the evolution of experimental physics to explain and generalize some characteristics of the dynamics of applied sciences. Empirical analysis suggests properties about the evolution of experimental physics and in general of applied sciences, such as: a) scientific fission, the evolution of scientific disciplines generates a process of division into two or more research fields that evolve as autonomous entities over time; b) ambidextrous drivers of science, the evolution of science via scientific fission is due to scientific discoveries or new technologies; c) new driving research fields, the drivers of scientific disciplines are new research fields rather than old ones; d) science driven by development of general purpose technologies, the evolution of experimental physics and applied sciences is due to the convergence of experimental and theoretical branches of physics associated with the development of computer, information systems and applied computational science. Results also reveal that average duration of the upwave of scientific production in scientific fields supporting experimental physics is about 80 years. Overall, then, this study begins the process of clarifying and generalizing, as far as possible, some characteristics of the evolutionary dynamics of scientific disciplines that can lay a foundation for the development of comprehensive properties explaining the evolution of science as a whole for supporting fruitful research policy implications directed to advancement of science and technological progress in society.
A bound on frequency hopped GSM performance in co-channel interference

The GSM and DCS-1900 mobile telephone systems have a large number of signal processing and configuration parameters that must be set by the BS designer or system operator. A computationally efficient method of determining system performance is needed in order to conduct parameter tradeoffs, and select the best system configuration. In addition to fading and shadowing loss experienced by the signal, the required method must reflect large variations of interference power that occur within a speech frame, due to frequency hopping and discontinuous transmission. Such a method is described in this paper. An upper bound on the frame erasure rate is developed using a simplified channel model. The bound provides a simple test for whether or not a link can be closed. Cell coverage calculations based on this test are demonstrated for a single source in Rayleigh fading, and for a co-channel interference limited environment. Results in fading are within 0.4 dB of simulation results for a similar system.