cssi/SciSciGPT-SciSciNet huggingface dataset:
Realization of Ground-State Artificial Skyrmion Lattices at Room Temperature

Magnetic Skyrmions exhibit topologically protected quantum states, not only offering exciting new mechanisms for ultrahigh density and low dissipation information storage, but also providing an ideal platform for explorations of unique topological phenomena. Prerequisite are systems exhibiting skyrmion lattices at ambient conditions. Here, we demonstrate the realization of artificial Bloch skyrmion lattices over extended areas in their ground state at room temperature by patterning asymmetric magnetic nanodots with controlled circularity on an underlayer film with perpendicular magnetic anisotropy (PMA) [1], shown in Fig. 1.
Finite-Time Resilient Formation Control with Bounded Inputs

In this paper we consider the problem of a multiagent system achieving a formation in the presence of misbehaving or adversarial agents. We introduce a novel continuous time resilient controller to guarantee that normally behaving agents can converge to a formation with respect to a set of leaders. The controller employs a norm-based filtering mechanism, and unlike most prior algorithms, also incorporates input bounds. In addition, the controller is shown to guarantee convergence in finite time. A sufficient condition for the controller to guarantee convergence is shown to be a graph theoretical structure which we denote as Resilient Directed Acyclic Graph (RDAG). Further, we employ our filtering mechanism on a discrete time system which is shown to have exponential convergence. Our results are demonstrated through simulations.
On Convergence of Epanechnikov Mean Shift

Epanechnikov Mean Shift is a simple yet empirically very effective algorithm for clustering. It localizes the centroids of data clusters via estimating modes of the probability distribution that generates the data points, using the "optimal" Epanechnikov kernel density estimator. However, since the procedure involves non-smooth kernel density functions,the convergence behavior of Epanechnikov mean shift lacks theoretical support as of this writing---most of the existing analyses are based on smooth functions and thus cannot be applied to Epanechnikov Mean Shift. In this work, we first show that the original Epanechnikov Mean Shift may indeed terminate at a non-critical point, due to the non-smoothness nature. Based on our analysis, we propose a simple remedy to fix it. The modified Epanechnikov Mean Shift is guaranteed to terminate at a local maximum of the estimated density, which corresponds to a cluster centroid, within a inite number of iterations. We also propose a way to avoid running the Mean Shift iterates from every data point, while maintaining good clustering accuracies under non-overlapping spherical Gaussian mixture models. This further pushes Epanechnikov Mean Shift to handle very large and high-dimensional data sets. Experiments show surprisingly good performance compared to the Lloyd's K-means algorithm and the EM algorithm.
Maximum tolerated dose versus metronomic scheduling in the treatment of metastatic cancers

Although optimal control theory has been used for the theoretical study of anti-cancerous drugs scheduling optimization, with the aim of reducing the primary tumor volume, the effect on metastases is often ignored. Here, we use a previously published model for metastatic development to define an optimal control problem at the scale of the entire organism of the patient. In silico study of the impact of different scheduling strategies for anti-angiogenic and cytotoxic agents (either in monotherapy or in combination) is performed to compare a low-dose, continuous, metronomic administration scheme with a more classical maximum tolerated dose schedule. Simulation results reveal differences between primary tumor reduction and control of metastases but overall suggest use of the metronomic protocol.
More Robust Doubly Robust Off-policy Evaluation

We study the problem of off-policy evaluation (OPE) in reinforcement learning (RL), where the goal is to estimate the performance of a policy from the data generated by another policy(ies). In particular, we focus on the doubly robust (DR) estimators that consist of an importance sampling (IS) component and a performance model, and utilize the low (or zero) bias of IS and low variance of the model at the same time. Although the accuracy of the model has a huge impact on the overall performance of DR, most of the work on using the DR estimators in OPE has been focused on improving the IS part, and not much on how to learn the model. In this paper, we propose alternative DR estimators, called more robust doubly robust (MRDR), that learn the model parameter by minimizing the variance of the DR estimator. We first present a formulation for learning the DR model in RL. We then derive formulas for the variance of the DR estimator in both contextual bandits and RL, such that their gradients w.r.t.~the model parameters can be estimated from the samples, and propose methods to efficiently minimize the variance. We prove that the MRDR estimators are strongly consistent and asymptotically optimal. Finally, we evaluate MRDR in bandits and RL benchmark problems, and compare its performance with the existing methods.
Optimal Quantization for Piecewise Uniform Distributions

Quantization for probability distribution refers to the idea of estimating a given probability by a discrete probability supported by a finite number of points. In this paper, firstly a general approach to this process is outlined using independent random variables and ergodic maps; these give asymptotically optimal $n$-means and $n$th quantization errors. Then secondly two piecewise uniform distributions are considered on $\mathbb R$: one with infinite number of pieces and one with finite number of pieces. For these two probability measures, we describe the optimal sets of $n$-means and the $n$th quantization errors for all $n\in \mathbb N$. It is seen that for uniform distribution with infinite number of pieces to determine the optimal sets of $n$-means for $n\geq 2$ one needs to know an optimal set of $(n-1)$-means, but with finite number of pieces one can directly determine the optimal sets of $n$-means and the $n$th quantization errors for all $n\in \mathbb N$.
Distribution‐constrained optimal stopping

We solve the problem of optimal stopping of a Brownian motion subject to the constraint that the stopping time's distribution is a given measure consisting of finitely many atoms. In particular, we show that this problem can be converted to a finite sequence of state‐constrained optimal control problems with additional states corresponding to the conditional probability of stopping at each possible terminal time. The proof of this correspondence relies on a new variation of the dynamic programming principle for state‐constrained problems, which avoids measurable selections. We emphasize that distribution constraints lead to novel and interesting mathematical problems on their own, but also demonstrate an application in mathematical finance to model‐free superhedging with an outlook on volatility.
Rigged modules I: Modules over dual operator algebras and the Picard group

Abstract In a previous paper we generalized the theory of W ⁎ -modules to the setting of modules over nonselfadjoint dual operator algebras, obtaining the class of weak ⁎ -rigged modules. At that time we promised a forthcoming paper devoted to other aspects of the theory. We fulfill this promise in the present work and its sequel “Rigged modules II”, giving many new results about weak ⁎ -rigged modules and their tensor products. We also discuss the Picard group of weak* closed subalgebras of a commutative algebra. For example, we compute the weak Picard group of H ∞ ( D ) , and prove that for a weak* closed function algebra A , the weak Picard group is a semidirect product of the automorphism group of A , and the subgroup consisting of symmetric equivalence bimodules.
A continuous analogue of the tensor-train decomposition

Abstract We develop new approximation algorithms and data structures for representing and computing with multivariate functions using the functional tensor-train (FT), a continuous extension of the tensor-train (TT) decomposition. The FT represents functions using a tensor-train ansatz by replacing the three-dimensional TT cores with univariate matrix-valued functions. The main contribution of this paper is a framework to compute the FT that employs adaptive approximations of univariate fibers, and that is not tied to any tensorized discretization. The algorithm can be coupled with any univariate linear or nonlinear approximation procedure. We demonstrate that this approach can generate multivariate function approximations that are several orders of magnitude more accurate, for the same cost, than those based on the conventional approach of compressing the coefficient tensor of a tensor-product basis. Our approach is in the spirit of other continuous computation packages such as Chebfun, and yields an algorithm which requires the computation of “continuous” matrix factorizations such as the LU and QR decompositions of vector-valued functions. To support these developments, we describe continuous versions of an approximate maximum-volume cross approximation algorithm and of a rounding algorithm that re-approximates an FT by one of lower ranks. We demonstrate that our technique improves accuracy and robustness, compared to TT and quantics-TT approaches with fixed parameterizations, of high-dimensional integration, differentiation, and approximation of functions with local features such as discontinuities and other nonlinearities.
Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee

We introduce and analyze a new technique for model reduction for deep neural networks. While large networks are theoretically capable of learning arbitrarily complex models, overfitting and model redundancy negatively affects the prediction accuracy and model variance. Our Net-Trim algorithm prunes (sparsifies) a trained network layer-wise, removing connections at each layer by solving a convex optimization program. This program seeks a sparse set of weights at each layer that keeps the layer inputs and outputs consistent with the originally trained model. The algorithms and associated analysis are applicable to neural networks operating with the rectified linear unit (ReLU) as the nonlinear activation. We present both parallel and cascade versions of the algorithm. While the latter can achieve slightly simpler models with the same generalization performance, the former can be computed in a distributed manner. In both cases, Net-Trim significantly reduces the number of connections in the network, while also providing enough regularization to slightly reduce the generalization error. We also provide a mathematical analysis of the consistency between the initial network and the retrained model. To analyze the model sample complexity, we derive the general sufficient conditions for the recovery of a sparse transform matrix. For a single layer taking independent Gaussian random vectors of length $N$ as inputs, we show that if the network response can be described using a maximum number of $s$ non-zero weights per node, these weights can be learned from $\mathcal{O}(s\log N)$ samples.
Timeout control in distributed systems using perturbation analysis: Multiple communication links

Timeout control is a simple mechanism used when direct feedback is either impossible, unreliable, or too costly, as is often the case in distributed systems. Its effectiveness is determined by a timeout threshold parameter and our goal is to quantify the effect of this parameter on the system behavior. In this paper, we extend previous results to the case where there are N transmitting nodes making use of a common communication link bandwidth. After deriving the stochastic hybrid model for this problem, we apply Infinitesimal Perturbation Analysis to find the derivative estimates of aggregate average goodput of the system. We also derive the derivative estimate of the goodput of a transmitter with respect to its own timeout threshold which can be used for local and hence, distributed optimization.
Blowup of solutions of the hydrostatic Euler equations

In this paper we prove that for a certain class of initial data, smooth solutions of the hydrostatic Euler equations blow up in finite time.
Resolving force indeterminacy in contact dynamics using compatibility conditions

Contact dynamics (CD) is a powerful method to solve the dynamics of large systems of colliding rigid bodies. CD can be computationally more efficient than classical penalty-based discrete element methods (DEM) for simulating contact between stiff materials such as rock, glass, or engineering metals. However, by idealizing bodies as perfectly rigid, contact forces computed by CD can be non-unique due to indeterminacy in the contact network, which is a common occurence in dense granular flows. We propose a frictionless CD method that is designed to identify only the unique set of contact forces that would be predicted by a soft particle method, such as DEM, in the limit of large stiffness. The method involves applying an elastic compatibility condition to the contact forces, which maintains no-penetration constraints but filters out force distributions that could not have arisen from stiff elastic contacts. The method can be used as a post-processing step that could be integrated into existing CD codes with minimal effort. We demonstrate its efficacy in a variety of indeterminate problems, including some involving multiple materials, non-spherical shapes, and nonlinear contact constitutive laws.
X‐ray reflectivity of chemically vapor‐deposited diamond single crystals in the Laue geometry

The absolute X-ray reflectivity of chemically vapor-deposited (CVD) single-crystal diamond plates was measured in the Laue geometry in the double-crystal non-dispersive setting with an asymmetric Si beam-conditioner crystal. The measurements were supplemented by rocking-curve topography. The measured reflectivity curves are examined in the framework of the Darwin-Hamilton approach using a set of two independent parameters: the characteristic thickness of mosaic blocks and their average angular misorientation. Owing to strong extinction effects, the width of the reflectivity curves does not directly represent the average misorientation of the blocks. Two different sets of parameters were found for the 111 asymmetric reflection in the two different scattering configurations (beam compression and beam expansion). Analysis of the rocking-curve topographs shows that this discrepancy can be attributed to inhomogeneity of the diamond crystal microstructure.
Enforcing template representability and temporal consistency for adaptive sparse tracking

Sparse representation has been widely studied in visual tracking, which has shown promising tracking performance. Despite a lot of progress, the visual tracking problem is still a challenging task due to appearance variations over time. In this paper, we propose a novel sparse tracking algorithm that well addresses temporal appearance changes, by enforcing template representability and temporal consistency (TRAC). By modeling temporal consistency, our algorithm addresses the issue of drifting away from a tracking target. By exploring the templates' long-term-short-term representability, the proposed method adaptively updates the dictionary using the most descriptive templates, which significantly improves the robustness to target appearance changes. We compare our TRAC algorithm against the state-of-the-art approaches on 12 challenging benchmark image sequences. Both qualitative and quantitative results demonstrate that our algorithm significantly outperforms previous state-of-the-art trackers.
GreenDataFlow: Minimizing the Energy Footprint of Global Data Movement

The global data movement over Internet has an estimated energy footprint of 100 terawatt hours per year, costing the world economy billions of dollars. The networking infrastructure together with source and destination nodes involved in the data transfer contribute to overall energy consumption. Although considerable amount of research has rendered power management techniques for the networking infrastructure, there has not been much prior work focusing on energy-aware data transfer solutions for minimizing the power consumed at the end-systems. In this paper, we introduce a novel application-layer solution based on historical analysis and real-time tuning called GreenDataFlow, which aims to achieve high data transfer throughput while keeping the energy consumption at the minimal levels. GreenDataFlow supports service level agreements (SLAs) which give the service providers and the consumers the ability to fine tune their goals and priorities in this optimization process. Our experimental results show that GreenDataFlow outperforms the closest competing state-of-the art solution in this area 50% for energy saving and 2.5× for the achieved end-to-end performance.
An empirical performance study of Intra-vehicular Wireless Sensor Networks under WiFi and Bluetooth interference

Intra-Vehicular Wireless Sensor Network (IVWSN) is a new automotive architecture that applies wireless technologies to the communications between Electrical Control Units (ECUs) and sensors. It can potentially help achieve better fuel economy, reduce wiring complexity, and support additional new applications. In the existing works, most of the popular wireless technologies applied on IVWSNs occupy the same 2.4 GHz ISM frequency bands as WiFi and Bluetooth do. It is therefore essential to evaluate the performance of IVWSNs under interference from WiFi and Bluetooth devices, especially when these devices are inside the vehicle. In this paper, we report the results of a comprehensive experimental study of IVWSNs based on ZigBee and Bluetooth Low Energy under WiFi and Bluetooth interference. The impact of the interference from Bluetooth and WiFi devices can be clearly observed from the experiments. The results of the experiments conducted suggest that Bluetooth Low Energy technology outperforms ZigBee technology in the context of IVWSNs when WiFi interference exists in the car.
Structure theory of singular spaces

Abstract In this paper we develop a structure theory of Einstein manifolds or manifolds with lower Ricci curvature bounds for certain singular spaces that arise as geometric limits of sequences of Riemannian manifolds. This theory generalizes the results that were obtained by Cheeger, Colding and Naber in the smooth setting. In the course of the paper, we will carefully characterize the assumptions that we have to impose on this sequence of Riemannian manifolds in order to guarantee that the individual results hold. An important aspect of our approach is that we don't need impose any Ricci curvature bounds on the sequence of Riemannian manifolds leading to the singular limit. The Ricci curvature bounds will only be required to hold on the regular part of the limit and we will not impose any (synthetic) curvature condition on its singular part. The theory developed in this paper will have applications in the blowup analysis of certain geometric equations in which we study scales that are much larger than the local curvature scale. In particular, this theory will have applications in the study of Ricci flows of bounded scalar curvature, which we will describe in a subsequent paper.
Alexandria: Extensible Framework for Rapid Exploration of Social Media

The Alexandria system under development at IBM Research provides an extensible framework and platform for supporting a variety of big-data analytics and visualizations. The system is currently focused on enabling rapid exploration of text-based social media data. The system provides tools to help with constructing "domain models" (i.e., Families of keywords and extractors to enable focus on tweets and other social media documents relevant to a project), to rapidly extract and segment the relevant social media and its authors, to apply further analytics (such as finding trends and anomalous terms), and visualizing the results. The system architecture is centered around a variety of REST-based service APIs to enable flexible orchestration of the system capabilities, these are especially useful to support knowledge-worker driven iterative exploration of social phenomena. The architecture also enables rapid integration of Alexandria capabilities with other social media analytics system, as has been demonstrated through an integration with IBM Research's SystemG. This paper describes a prototypical usage scenario for Alexandria, along with the architecture and key underlying analytics.
C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching

Lightweight, source-to-source transformation approaches to implementing MCMC for probabilistic programming languages are popular for their simplicity, support of existing deterministic code, and ability to execute on existing fast runtimes [1]. However, they are also inecient, requiring a complete re-execution of the program on every Metropolis Hastings proposal. We present a new extension to the lightweight approach, C3, which enables ecient, incrementalized re-execution of MH proposals. C3 is based on two core ideas: transforming probabilistic programs into continuation passing style (CPS), and caching the results of function calls. It is particularly eective at speeding up recursive programs with many local latent variables. We show that on several common such models, C3 reduces proposal runtime by 20-100x, in some cases reducing runtime complexity from linear in model size to constant. We also demonstrate nearly an order of magnitude speedup on a complex inverse procedural modeling application.
Moduli of fibered surface pairs from twisted stable maps

In this paper, we use the theory of twisted stable maps to construct compactifications of the moduli space of pairs $$(X \rightarrow C, S + F)$$ where $$X \rightarrow C$$ is a fibered surface, S is a sum of sections, F is a sum of marked fibers, and $$(X,S+F)$$ is a stable pair in the sense of the minimal model program. This generalizes the work of Abramovich–Vistoli, who compactified the moduli space of fibered surfaces with no marked fibers. Furthermore, we compare our compactification to Alexeev’s space of stable maps and the KSBA compactification. As an application, we describe the boundary of a compactification of the moduli space of elliptic surfaces.
Multiresolution Analysis on Compact Riemannian Manifolds

In the chapter "Multiresolution Analysis on Compact Riemannian Manifolds" Isaac Pesenson describes multiscale analysis, sampling, interpolation and approximation of functions defined on manifolds. His main achievements are: construction on manifolds of bandlimited and space-localized frames which have Parseval property and construction of variational splines on manifolds. Such frames and splines enable multiscale analysis on arbitrary compact manifolds, and they already found a number of important applications (statistics, CMB, crystallography) related to such manifolds as two-dimensional sphere and group of its rotations.
Codon Context Optimization in Synthetic Gene Design

Advances in de novo synthesis of DNA and computational gene design methods make possible the customization of genes by direct manipulation of features such as codon bias and mRNA secondary structure. Codon context is another feature significantly affecting mRNA translational efficiency, but existing methods and tools for evaluating and designing novel optimized protein coding sequences utilize untested heuristics and do not provide quantifiable guarantees on design quality. In this study we examine statistical properties of codon context measures in an effort to better understand the phenomenon. We analyze the computational complexity of codon context optimization and design exact and efficient heuristic gene recoding algorithms under reasonable constraint models. We also present a web-based tool for evaluating codon context bias in the appropriate context.
Log-Concavity and Symplectic Flows

Let (M,ω) be a compact, connected symplectic 2n-dimensional manifold on which an (n− 2)-dimensional torus T acts effectively and Hamiltonianly. Under the assumption that there is an effective complementary 2-torus acting on M with symplectic orbits, we show that the Duistermaat-Heckman measure of the T -action is log-concave. This verifies the logarithmic concavity conjecture for a class of inequivalent T -actions. Then we use this conjecture to prove the following: if there is an effective symplectic action of an (n − 2)-dimensional torus T on a compact, connected symplectic 2n-dimensional manifold that admits an effective complementary symplectic action of a 2-torus with symplectic orbits, then the existence of T -fixed points implies that the T -action is Hamiltonian. As a consequence of this, we give new proofs of a classical theorem by McDuff about S1-actions, and some of its recent extensions.
Three-dimensional noncompact $\kappa$-solutions that are Type I forward and backward

As indicated by the third author in [19], there is a gap in the previous version of this paper by the first two authors [5]. We provide in this version an argument to fix the aforementioned gap. The main proposition, whose proof uses Perelman's techniques, is implied by Ding [9] and is covered by [19]. Our approach, however, is different from theirs. In addition, we prove a necessary and sufficient condition for a three-dimensional $\kappa$-solution to form a forward singularity. We hope that this condition is helpful in the classification of all three-dimensional $\kappa$-solutions. Up to now, the only main progress on such a classification, as conjectured by Perelman, is by Brendle [2].
On minimum identifying codes in some Cartesian product graphs

An identifying code in a graph is a dominating set that also has the property that the closed neighborhood of each vertex in the graph has a distinct intersection with the set. The minimum cardinality of an identifying code, or ID code, in a graph G is called the ID code number of G and is denoted $$\gamma ^\mathrm{ID}(G)$$ . In this paper, we give upper and lower bounds for the ID code number of the prism of a graph, or $$G\Box K_2$$ . In particular, we show that $$\gamma ^\mathrm{ID}(G \Box K_2) \ge \gamma ^\mathrm{ID}(G)$$ and we show that this bound is sharp. We also give upper and lower bounds for the ID code number of grid graphs and a general upper bound for $$\gamma ^\mathrm{ID}(G\Box K_2)$$ .
The Complexity of Bit Retrieval

Bit retrieval is the problem of reconstructing a periodic binary sequence from its periodic autocorrelation, with applications in cryptography and x-ray crystallography. After defining the problem, with and without noise, we describe and compare various algorithms for solving it. A geometrical constraint satisfaction algorithm, relaxed-reflect-reflect, is currently the best algorithm for noisy bit retrieval.
Allele-Specific Silencing Ameliorates Restrictive Cardiomyopathy Attributable to a Human Myosin Regulatory Light Chain Mutation

Background: Restrictive cardiomyopathy is a rare heart disease associated with mutations in sarcomeric genes and with phenotypic overlap with hypertrophic cardiomyopathy. There is no approved thera...
Hierarchical Metric Learning and Matching for 2D and 3D Geometric Correspondences

Interest point descriptors have fueled progress on almost every problem in computer vision. Recent advances in deep neural networks have enabled task-specific learned descriptors that outperform hand-crafted descriptors on many problems. We demonstrate that commonly used metric learning approaches do not optimally leverage the feature hierarchies learned in a Convolutional Neural Network (CNN), especially when applied to the task of geometric feature matching. While a metric loss applied to the deepest layer of a CNN, is often expected to yield ideal features irrespective of the task, in fact the growing receptive field as well as striding effects cause shallower features to be better at high precision matching tasks. We leverage this insight together with explicit supervision at multiple levels of the feature hierarchy for better regularization, to learn more effective descriptors in the context of geometric matching tasks. Further, we propose to use activation maps at different layers of a CNN, as an effective and principled replacement for the multi-resolution image pyramids often used for matching tasks. We propose concrete CNN architectures employing these ideas, and evaluate them on multiple datasets for 2D and 3D geometric matching as well as optical flow, demonstrating state-of-the-art results and generalization across datasets.
Predictive Second Order Sliding Control of Constrained Linear Systems with Application to Automotive Control Systems

This paper presents a new predictive second order sliding controller (PSSC) formulation for setpoint tracking of constrained linear systems. The PSSC scheme is developed by combining the concepts of model predictive control (MPC) and second order discrete sliding mode control. In order to guarantee the feasibility of the PSSC during setpoint changes, a virtual reference variable is added to the PSSC cost function to calculate the closest admissible set point. The states of the system are then driven asymptotically to this admissible setpoint by the control action of the PSSC. The performance of the proposed PSSC is evaluated for an advanced automotive engine case study, where a high fidelity physics-based model of a reactivity controlled compression ignition (RCCI) engine is utilized to serve as the virtual test-bed for the simulations. Considering the hard physical constraints on the RCCI engine states and control inputs, simultaneous tracking of engine load and optimal combustion phasing is a challenging objective to achieve. The simulation results of testing the proposed PSSC on the high fidelity RCCI model show that the developed predictive controller is able to track desired engine load and combustion phasing setpoints, with minimum steady state error, and no overshoot. Moreover, the simulation results confirm the robust tracking performance of the PSSC during transient operations, in the presence of engine cyclic variability.
The Galois action and cohomology of a relative homology group of Fermat curves

Abstract For an odd prime p satisfying Vandiver's conjecture, we give explicit formulae for the action of the absolute Galois group G Q ( ζ p ) on the homology of the degree p Fermat curve, building on work of Anderson. Further, we study the invariants and the first Galois cohomology group which are associated with obstructions to rational points on the Fermat curve.
Efficient parallelization for AMR MHD multiphysics calculations; implementation in AstroBEAR

Current adaptive mesh refinement (AMR) simulations require algorithms that are highly parallelized and manage memory efficiently. As compute engines grow larger, AMR simulations will require algorithms that achieve new levels of efficient parallelization and memory management. We have attempted to employ new techniques to achieve both of these goals. Patch or grid based AMR often employs ghost cells to decouple the hyperbolic advances of each grid on a given refinement level. This decoupling allows each grid to be advanced independently. In AstroBEAR we utilize this independence by threading the grid advances on each level with preference going to the finer level grids. This allows for global load balancing instead of level by level load balancing and allows for greater parallelization across both physical space and AMR level. Threading of level advances can also improve performance by interleaving communication with computation, especially in deep simulations with many levels of refinement. While we see improvements of up to 30% on deep simulations run on a few cores, the speedup is typically more modest (5-20%) for larger scale simulations. To improve memory management we have employed a distributed tree algorithm that requires processors to only store and communicate local sections of the AMR tree structure with neighboring processors. Using this distributed approach we are able to get reasonable scaling efficiency (>80%) out to 12288 cores and up to 8 levels of AMR - independent of the use of threading.
Computational content analysis of negative tweets for obesity, diet, diabetes, and exercise

Social media based digital epidemiology has the potential to support faster response and deeper understanding of public health related threats. This study proposes a new framework to analyze unstructured health related textual data via Twitter users' post (tweets) to characterize the negative health sentiments and non-health related concerns in relations to the corpus of negative sentiments, regarding Diet Diabetes Exercise, and Obesity (DDEO). Through the collection of 6 million Tweets for one month, this study identified the prominent topics of users as it relates to the negative sentiments. Our proposed framework uses two text mining methods, sentiment analysis and topic modeling, to discover negative topics. The negative sentiments of Twitter users support the literature narratives and the many morbidity issues that are associated with DDEO and the linkage between obesity and diabetes. The framework offers a potential method to understand the publics' opinions and sentiments regarding DDEO. More importantly, this research provides new opportunities for computational social scientists, medical experts, and public health professionals to collectively address DDEO-related issues.
Computing Exact Minimum Cuts Without Knowing the Graph

We give query-efficient algorithms for the global min-cut and the s-t cut problem in unweighted, undirected graphs. Our oracle model is inspired by the submodular function minimization problem: on query S \subset V, the oracle returns the size of the cut between S and V \ S. We provide algorithms computing an exact minimum $s$-$t$ cut in $G$ with ~{O}(n^{5/3}) queries, and computing an exact global minimum cut of G with only ~{O}(n) queries (while learning the graph requires ~{\Theta}(n^2) queries).
Acoustic Waves Generated by the Spin Precession

We study the generation of acoustic waves by a precessing spin of a nanoparticle deposited on the surface of a solid. Our approach elucidates macroscopic dynamics of the Einstein–de Haas effect. It is based upon the solution of parameter-free equations of motion mandated by the conservation of the total angular momentum: spin + mechanical. We show that the amplitude of the acoustic waves generated by the spin precession may be sufficient for detection by a tunneling electron microscope.
Eigenvalue estimates and differential form Laplacians on Alexandrov spaces

We give upper bounds on the eigenvalues of the differential form Laplacian on a compact Riemannian manifold. The proof uses Alexandrov spaces with curvature bounded below. We also construct differential form Laplacians on Alexandrov spaces. Under a local biLipschitz assumption on the Alexandrov space, which is conjecturally always satisfied, we show that the differential form Laplacian has a compact resolvent. We identify its kernel with an intersection homology group.
Network utility maximization with heterogeneous traffic flows

We consider the Network Utility Maximization (NUM) problem for wireless networks in the presence of arbitrary types of flows, including unicast, broadcast, multicast, and anycast traffic. Building upon the recent framework of a universal control policy (UMW), we design a utility optimal cross-layer admission control, routing and scheduling policy, called UMW+. The UMW+ policy takes packet level actions based on a precedence-relaxed virtual network. Using Lyapunov optimization techniques, we show that UMW+ maximizes network utility, while simultaneously keeping the physical queues in the network stable. Extensive simulation results validate the performance of UMW+ demonstrating both optimal utility performance and bounded average queue occupancy. Moreover, we establish a precise one-to-one correspondence between the dynamics of the virtual queues under the UMW+ policy, and the dynamics of the dual variables of an associated offline NUM program, under a subgradient algorithm. This correspondence sheds further insight into our understanding of UMW+.
ON TOPOLOGICAL COMPLEXITY OF TWISTED PRODUCTS

goes from 1 to k. Thus, it deﬁnes the nonreduced topological complexity whichis by one larger.The topological complexity is homotopy invariant. Therefore one can deﬁne thetopological complexity TC(π) of a discrete group π as TC(K(π,1)) where K(G,1)is an Eilenberg-Maclane complex. It is known thatcd(π) ≤ TC(π) ≤ 2cd(π)where cd(π) is the cohomological dimension of a group π [Br]. M. Farber proposeda natural question:1.1. Problem (Farber). Whatkindofadiscretegroupinvariant isTC(π)?Note that for groups with inﬁnite cohomological dimension TC(π) = ∞. Thus,Farber’squestionmakessenseonlyforgroupsadmitting aﬁnitedimensionalEilenberg-MacLane complex, in particular, for torsion free groups. It is known that the re-duced Lusternik-Schnirelman category of a group π agrees with the cohomologicaldimension,cat(K(π,1)) = cd(π).In view of the equality TC(G) = catG [F3] for all topological groups G and the factthat K(π,1) is homotopy equivalent to a topological group for abelian π, we obtainthat TC(π) = cd(π) for abelian groups. For free nonabelian groups the otherbound is taken, TC(π) = 2cd(π). Using the wedge formula for the topologicalcomplexity [Dr3] Yu. Rudyak noticed that for all values k and n with n ≤ k ≤ 2nthere is a group π with cd(π) = n and TC(π) = k [Ru].
Near-optimal target learning with stochastic binary signals

We study learning in a noisy bisection model: specifically, Bayesian algorithms to learn a target value V given access only to noisy realizations of whether V is less than or greater than a threshold θ. At step t = 0,1, 2,..., the learner sets threshold θt and observes a noisy realization of sign(V - θt). After T steps, the goal is to output an estimate V which is within an e-tolerance of V. This problem has been studied, predominantly in environments with a fixed error probability q < ½ for the noisy realization of sign (V - θt). In practice, it is often the case that q can approach ½, especially as θ → V, and there is little known when this happens. We give a pseudo-Bayesian algorithm which provably converges to V. When the true prior matches our algorithm's Gaussian prior, we show near-optimal expected performance. Our methods extend to the general multiple-threshold setting where the observation noisily indicates which of k ≥ 2 regions V belongs to.
GEOMETRIC ERGODICITY OF TWO{DIMENSIONAL HAMILTONIAN SYSTEMS WITH A LENNARD{JONES{LIKE REPULSIVE POTENTIAL

Molecular dynamics simulation is among the most important and widely used tools in the study of molecular systems, providing fundamental insights into molecular mechanisms at a level of detail unattainable by experimental methods [AT87, Lea96, FS96, Sch02]. Usage of molecular dynamics spans a diverse array of fields, from physics and chemistry, to molecular and cellular biology, to engineering and materials science. Due to their size and complexity, simulations of large systems such as biological macromolecules (DNA, RNA, proteins, carbohydrates, and lipids) are typically performed under a classical mechanics representation. A critical requirement of such simulations is ergodicity, or convergence in the limit to the equilibrium (typically canonical) Boltzmann measure μ(dq, dp) = Z(β)−1e−βH(q,p)dqdp. Although ergodicity is commonly assumed, recently [CS08] showed that all commonly used deterministic dynamics methods for simulating the canonical (constanttemperature) ensemble fail to be ergodic. They also showed that introduction of a stochastic hybrid Monte Carlo (HMC) correction guarantees ergodicity; however, HMC scales poorly with system dimension and is rarely used for macromolecules. [CS08] also show empirically that more commonly used stochastic Langevin dynamics [Pas94] appear to exhibit ergodic behavior, but were unable to provide rigorous proof. The key difficulty in applying existing arguments [MSH02] is the appearance of singularities in the potential U(q). Most modern molecular mechanics forcefields [PCC95, BBO83, JTR88] take the form
A bound on the spectral radius of hypergraphs with e edges

Abstract For r ≥ 3 , let f r : [ 0 , ∞ ) → [ 1 , ∞ ) be the unique analytic function such that f r ( ( k r ) ) = ( k − 1 r − 1 ) for any k ≥ r − 1 . We prove that the spectral radius of an r-uniform hypergraph H with e edges is at most f r ( e ) . The equality holds if and only if e = ( k r ) for some positive integer k and H is the union of a complete r-uniform hypergraph K k r and some possible isolated vertices. This result generalizes the classical Stanley's theorem on graphs.
Maximizing Activity in Ising Networks via the TAP Approximation

A wide array of complex biological, social, and physical systems have recently been shown to be quantitatively described by Ising models, which lie at the intersection of statistical physics and machine learning. Here, we study the fundamental question of how to optimize the state of a networked Ising system given a budget of external influence. In the continuous setting where one can tune the influence applied to each node, we propose a series of approximate gradient ascent algorithms based on the Plefka expansion, which generalizes the naive mean field and TAP approximations. In the discrete setting where one chooses a small set of influential nodes, the problem is equivalent to the famous influence maximization problem in social networks with an additional stochastic noise term. In this case, we provide sufficient conditions for when the objective is submodular, allowing a greedy algorithm to achieve an approximation ratio of $1-1/e$. Additionally, we compare the Ising-based algorithms with traditional influence maximization algorithms, demonstrating the practical importance of accurately modeling stochastic fluctuations in the system.
Shifted Distinct-part Partition Identities in Arithmetic Progressions

The partition function p(n), which counts the number of partitions of a positive integer n, is widely studied. Here, we study partition functions pS(n) that count partitions of n into distinct parts satisfying certain congruence conditions. A shifted partition identity is an identity of the form $${{p_{S_1}} (n - H) = {p_{S_2}} (n)}$$ for all n in some arithmetic progression. Several identities of this type have been discovered, including two infinite families found by Alladi. In this paper, we use the theory of modular functions to determine the necessary and sufficient conditions for such an identity to exist. In addition, for two specific cases, we extend Alladi’s theorem to other arithmetic progressions.
A column generation algorithm for vehicle scheduling and routing problems

Abstract In this paper, we consider a variant of a truckload open vehicle routing problem with time windows, which is suitable for modeling vehicle routing operations during a humanitarian crisis. We present two integer linear programming models to formulate the problem. The first one is an arc-based mixed integer linear programming model that can be solved using a general purpose solver. For the second formulation, we first devise a task adjacency graph, that we use to develop a path-based integer linear program. We then propose a column generation framework to solve large-scale instances. Finally, we perform numerical experiments to compare the performance of the two models. Our computational results show that the path-based formulation solved using our proposed column generation framework outperforms the arc-based mixed integer linear program in solution time, without sacrificing solution quality.
Survival of retinal ganglion cells after damage to the occipital lobe in humans is activity-dependent

Damage to the optic radiations or primary visual cortex leads to blindness in all or part of the contralesional visual field. Such damage disconnects the retina from its downstream targets and, ove...
Reversible Markov structures on divisible set partitions

We study k-divisible partition structures, which are families of random set partitions whose block sizes are divisible by an integer k = 1; 2;:::. In this setting, exchangeability corresponds to the usual invariance under relabeling by arbitrary permutations; however, for k > 1, the ordinary deletion maps on partitions no longer preserve divisibility, and so a random deletion procedure is needed to obtain a partition structure. We describe explicit Chinese restauranttype seating rules for generating families of exchangeable k-divisible partitions that are consistent under random deletion. We further introduce the notion of Markovian partition structures, which are ensembles of exchangeable Markov chains on k-divisible partitions that are consistent under a random process of Markovian deletion. The Markov chains we study are reversible and rene the
Critical Pebbling Numbers of Graphs

We dene three new pebbling parameters of a connected graph G, the r-, g-, and ucritical pebbling numbers. Together with the pebbling number, the optimal pebbling number, the number of vertices n and the diameter d of the graph, this yields 7 graph parameters. We determine the relationships between these parameters. We investigate properties of the r-critical pebbling number, and distinguish between greedy graphs, thrifty graphs, and graphs for which the r-critical pebbling number is 2 d .
Large-scale randomized-coordinate descent methods with non-separable linear constraints

We develop randomized block coordinate descent (CD) methods for linearly constrained convex optimization. Unlike other large-scale CD methods, we do not assume the constraints to be separable, but allow them be coupled linearly. To our knowledge, ours is the first CD method that allows linear coupling constraints, without making the global iteration complexity have an exponential dependence on the number of constraints. We present algorithms and theoretical analysis for four key (convex) scenarios: (i) smooth; (ii) smooth + separable nonsmooth; (iii) asynchronous parallel; and (iv) stochastic. We discuss some architectural details of our methods and present preliminary results to illustrate the behavior of our algorithms.
On intrinsic ergodicity and weakenings of the specification property

Abstract Since seminal work of Bowen [2] , it has been known that the specification property implies various useful properties about an expansive topological dynamical system, among them uniqueness of the measure of maximal entropy (often referred to as intrinsic ergodicity). Weakenings of the specification property have been defined and profitably applied in various works such as [6] , [9] , [11] , [16] , [17] . It has been an open question (see p. 798 of [4] ) whether two of these properties, which we here call almost specification and non-uniform specification, imply intrinsic ergodicity for expansive topological systems. We answer this question negatively by exhibiting examples of subshifts with multiple measures of maximal entropy with disjoint support which have non-uniform specification with any gap function f ( n ) = O ( ln ⁡ n ) or almost specification with any mistake function g ( n ) ≥ 4 . We also show some results in the opposite direction, showing that subshifts with non-uniform specification with gap function f ( n ) = o ( ln ⁡ n ) or almost specification with mistake function g ( n ) = 1 cannot have multiple measures of maximal entropy with disjoint support.