cssi/SciSciGPT-SciSciNet huggingface dataset:
Global well-posedness for nonlinear wave equations with supercritical source and damping terms

Abstract We prove the global well-posedness of weak solutions for nonlinear wave equations with supercritical source and damping terms on a three-dimensional torus T 3 of the prototype u t t − Δ u + | u t | m − 1 u t = | u | p − 1 u , ( x , t ) ∈ T 3 × R + ; u ( 0 ) = u 0 ∈ H 1 ( T 3 ) ∩ L m + 1 ( T 3 ) , u t ( 0 ) = u 1 ∈ L 2 ( T 3 ) , where 1 ≤ p ≤ min ⁡ { 2 3 m + 5 3 , m } . Notably, p is allowed to be larger than 6.
Towards lightweight convolutional neural networks for object detection

We propose model with larger spatial size of feature maps and evaluate it on object detection task. With the goal to choose the best feature extraction network for our model we compare several popular lightweight networks. After that we conduct a set of experiments with channels reduction algorithms in order to accelerate execution. Our vehicle detection models are accurate, fast and therefore suit for embedded visual applications. With only 1.5 GFLOPs our best model gives 95.13 AP on validation subset of challenging DETRAC dataset. The smallest of our models is the first to achieve real-time inference speed on CPU with reasonable accuracy drop to 91.72 AP.
Multiband Electronic Structure of Magnetic Quantum Dots: Numerical Studies

Semiconductor quantum dots (QDs) doped with magnetic impurities have been a focus of continuous research for a couple of decades. A significant effort has been devoted to studies of magnetic polarons (MP) in these nanostructures. These collective states arise through exchange interaction between a carrier confined in a QD and localized spins of the magnetic impurities (typically: Mn). We discuss our theoretical description of various MP properties in self-assembled QDs. We present a self-consistent, temperature-dependent approach to MPs formed by a valence band hole. We use the Luttinger-Kohn k.p Hamiltonian to account for the important effects of spin-orbit interaction.
Accelerated first-order methods for hyperbolic programming

We develop a framework for applying accelerated methods to general hyperbolic programming, including linear, second-order cone, and semidefinite programming as special cases. The approach replaces a hyperbolic program with a convex optimization problem whose smooth objective function is explicit, and for which the only constraints are linear equations (one more linear equation than for the original problem). Virtually any first-order method can be applied. An iteration bound for a representative accelerated method is derived.
Scale invariance of immune system response rates and times: perspectives on immune system architecture and implications for artificial immune systems

Most biological rates and times decrease systematically with increasing organism body size. We use an ordinary differential equation (ODE) model of West Nile Virus in birds to show that pathogen replication rates decline with host body size, but natural immune system (NIS) response rates do not change systematically with body size. The scale-invariant detection and response of the NIS is surprising since the NIS has to search for small quantities of pathogens through larger physical spaces in larger organisms, and also respond by producing larger absolute quantities of antibody in larger organisms. We hypothesize that the NIS has evolved an architecture to efficiently neutralize pathogens. We investigate three different hypothesized NIS architectures using an Agent Based Model (ABM). We find that a sub-modular NIS architecture, in which lymph node number and size both increase sublinearly with body size, efficiently balances the tradeoff between local pathogen detection and global response. This leads to nearly scale-invariant detection and response consistent with experimental data. Similar to the NIS, physical space and resources are also important constraints on distributed systems, for example low-powered robots connected by short-range wireless communication. We show that the sub-modular design principles of the NIS can be applied to problems such as distributed robot control to efficiently balance the tradeoff between local search for a solution and global response or proliferation of the solution. We demonstrate that the lymphatic network of the NIS efficiently balances local and global communication, and we suggest a new approach for Artificial Immune Systems (AIS) that uses a sub-modular architecture to facilitate distributed search.
The minimal resultant locus

Let K be a complete, algebraically closed nonarchimedean valued field, and let f(z) in K(z) be a rational function of degree d at least 2. We give an algorithm to determine whether f(z) has potential good reduction over K, based on a geometric reformulation of the problem using the Berkovich Projective Line. We show the minimal resultant is is either achieved at a single point in the Berkovich line, or on a segment, and that minimal resultant locus is contained in the tree in spanned by the fixed points and the poles of f(z). When f(z) is defined over the rationals, the algorithm runs in probabilistic polynomial time. If f(z) has potential good reduction, and is defined over a subfield H of K, we show there is an extension L/H in K with degree at most (d + 1)^2 such that f(z) achieves good reduction over L.
SlugNERDS: A Named Entity Recognition Tool for Open Domain Dialogue Systems

In dialogue systems, the tasks of named entity recognition (NER) and named entity linking (NEL) are vital preprocessing steps for understanding user intent, especially in open domain interaction where we cannot rely on domain-specific inference. UCSC's effort as one of the funded teams in the 2017 Amazon Alexa Prize Contest has yielded Slugbot, an open domain social bot, aimed at casual conversation. We discovered several challenges specifically associated with both NER and NEL when building Slugbot, such as that the NE labels are too coarse-grained or the entity types are not linked to a useful ontology. Moreover, we have discovered that traditional approaches do not perform well in our context: even systems designed to operate on tweets or other social media data do not work well in dialogue systems. In this paper, we introduce Slugbot's Named Entity Recognition for dialogue Systems (SlugNERDS), a NER and NEL tool which is optimized to address these issues. We describe two new resources that we are building as part of this work: SlugEntityDB and SchemaActuator. We believe these resources will be useful for the research community.
Online tensor methods for learning latent variable models

We introduce an online tensor decomposition based approach for two latent variable modeling problems namely, (1) community detection, in which we learn the latent communities that the social actors in social networks belong to, and (2) topic modeling, in which we infer hidden topics of text articles. We consider decomposition of moment tensors using stochastic gradient descent. We conduct optimization of multilinear operations in SGD and avoid directly forming the tensors, to save computational and storage costs. We present optimized algorithm in two platforms. Our GPU-based implementation exploits the parallelism of SIMD architectures to allow for maximum speed-up by a careful optimization of storage and data transfer, whereas our CPU-based implementation uses efficient sparse matrix computations and is suitable for large sparse data sets. For the community detection problem, we demonstrate accuracy and computational efficiency on Facebook, Yelp and DBLP data sets, and for the topic modeling problem, we also demonstrate good performance on the New York Times data set. We compare our results to the state-of-the-art algorithms such as the variational method, and report a gain of accuracy and a gain of several orders of magnitude in the execution time.
Depth-dependent hysteresis in adhesive elastic contacts at large surface roughness

Contact force–indentation depth measurements in contact experiments involving compliant materials, such as polymers and gels, show a hysteresis loop whose size depends on the maximum indentation depth. This depth-dependent hysteresis (DDH) is not explained by classical contact mechanics theories and was believed to be due to effects such as material viscoelasticity, plasticity, surface polymer interdigitation, and moisture. It has been observed that the DDH energy loss initially increases and then decreases with roughness. A mechanics model based on the occurrence of adhesion and roughness related small-scale instabilities was presented by one of the authors for explaining DDH. However, that model only applies in the regime of infinitesimally small surface roughness, and consequently it does not capture the decrease in energy loss with surface roughness at the large roughness regime. We present a new mechanics model that applies in the regime of large surface roughness based on the Maugis–Dugdale theory of adhesive elastic contacts and Nayak’s theory of rough surfaces. The model captures the trend of decreasing energy loss with increasing roughness. It also captures the experimentally observed dependencies of energy loss on the maximum indentation depth, and material and surface properties.
Distributionally Robust Graphical Models

In many structured prediction problems, complex relationships between variables are compactly defined using graphical structures. The most prevalent graphical prediction methods---probabilistic graphical models and large margin methods---have their own distinct strengths but also possess significant drawbacks. Conditional random fields (CRFs) are Fisher consistent, but they do not permit integration of customized loss metrics into their learning process. Large-margin models, such as structured support vector machines (SSVMs), have the flexibility to incorporate customized loss metrics, but lack Fisher consistency guarantees. We present adversarial graphical models (AGM), a distributionally robust approach for constructing a predictor that performs robustly for a class of data distributions defined using a graphical structure. Our approach enjoys both the flexibility of incorporating customized loss metrics into its design as well as the statistical guarantee of Fisher consistency. We present exact learning and prediction algorithms for AGM with time complexity similar to existing graphical models and show the practical benefits of our approach with experiments.
Asian option as a fixed-point

We characterize the price of an Asian option, a financial contract, as a fixed-point of a non-linear operator. In recent years, there has been interest in incorporating changes of regime into the parameters describing the evolution of the underlying asset price, namely the interest rate and the volatility, to model sudden exogenous events in the economy. Asian options are particularly interesting because the payoff depends on the integrated asset price. We study the case of both floating- and fixed-strike Asian call options with arithmetic averaging when the asset follows a regime-switching geometric Brownian motion with coefficients that depend on a Markov chain. The typical approach to finding the value of a financial option is to solve an associated system of coupled partial differential equations. Alternatively, we propose an iterative procedure that converges to the value of this contract with geometric rate using a classical fixed-point theorem.
Amperean Pairing and the Pseudogap Phase of Cuprate Superconductors

Cuprates exhibit high-temperature superconductivity, but many of their properties above the transition temperature remain mysterious. Scientists propose that a different kind of fluctuating superconductivity is responsible for these strange properties.
Bandits and Experts in Metric Spaces

In a multi-armed bandit problem, an online algorithm chooses from a set of strategies in a sequence of trials to maximize the total payoff of the chosen strategies. While the performance of bandit algorithms with a small finite strategy set is well understood, bandit problems with large strategy sets are still a topic of active investigation, motivated by practical applications, such as online auctions and web advertisement. The goal of such research is to identify broad and natural classes of strategy sets and payoff functions that enable the design of efficient solutions. In this work, we study a general setting for the multi-armed bandit problem, in which the strategies form a metric space, and the payoff function satisfies a Lipschitz condition with respect to the metric. We refer to this problem as the Lipschitz MAB problem. We present a solution for the multi-armed bandit problem in this setting. That is, for every metric space, we define an isometry invariant that bounds from below the performance of Lipschitz MAB algorithms for this metric space, and we present an algorithm that comes arbitrarily close to meeting this bound. Furthermore, our technique gives even better results for benign payoff functions. We also address the full-feedback (“best expert”) version of the problem, where after every round the payoffs from all arms are revealed.
Laboratory-Based X-ray Absorption Spectroscopy on a Working Pouch Cell Battery at Industrially-Relevant Charging Rates

Li-ion battery (LIB) research has continuing importance for the entire range of applications from consumer products to vehicle electrification and grid stabilization. In many cases, standard electrochemical methods only provide an overall voltage or specific capacity, giving an inadequate description of parallel redox processes or chemical gradients at the particle and pack level. X-ray absorption fine structure (XAFS) is frequently used to augment bulk electrochemical data, as it provides element-specific changes in oxidation state and local atomic structure. Such microscopic descriptors are crucial for elucidating charge transfer and structural changes associated with bonding or site mixing, two key factors in evaluating state of charge and modes of cell failure. However, the impact of XAFS on LIB research has been significantly constrained by a logistical barrier: contemporary XAFS work is performed almost exclusively at synchrotron x-ray light sources, where beamtime is infrequent and experiment time-frames are limited. Here we show that modern laboratory based XAFS can not only be applied to, e.g., characterization of ex situ LIB electrode materials, but can also be used for operando studies at industrially-relevant charging rates in a standard pouch cell preparation. Such capability enables accelerated discovery of new materials and improved operation modes for LIBs.
Convergence of the $J$-flow on toric manifolds

We show that on a Kahler manifold whether the $J$-flow converges or not is independent of the chosen background metric in its Kahler class. On toric manifolds we give a numerical characterization of when the $J$-flow converges, verifying a conjecture in [19] (M. Lejmi and G. Szekelyhidi, “The $J$-flow and stability”) in this case. We also strengthen existing results on more general inverse $\sigma_k$ equations on Kahler manifolds.
Optimal vertex fault tolerant spanners (for fixed stretch)

A k-spanner of a graph G is a sparse subgraph H whose shortest path distances match those of G up to a multiplicative error k. In this paper we study spanners that are resistant to faults. A subgraph H ⊆ G is an f vertex fault tolerant (VFT) k-spanner if H \ F is a k-spanner of G \ F for any small set F of f vertices that might "fail." One of the main questions in the area is: what is the minimum size of an f fault tolerant k-spanner that holds for all n node graphs (as a function of f, k and n)? This question was first studied in the context of geometric graphs [Levcopoulos et al. STOC '98, Czumaj and Zhao SoCG '03] and has more recently been considered in general undirected graphs [Chechik et al. STOC '09, Dinitz and Krauthgamer PODC '11]. In this paper, we settle the question of the optimal size of a VFT spanner, in the setting where the stretch factor k is fixed. Specifically, we prove that every (undirected, possibly weighted) n-node graph G has a (2k − 1)-spanner resilient to f vertex faults with Of Ok(f1−1/k) edges, and this is fully optimal (unless the famous Erdos Girth Conjecture is false). Our lower bound even generalizes to imply that no data structure capable of approximating distG\F(s, t) similarly can beat the space usage of our spanner in the worst case. To the best of our knowledge, this is the first instance in fault tolerant network design in which introducing fault tolerance to the structure increases the size of the (non-FT) structure by a sublinear factor in f. Another advantage of this result is that our spanners are constructed by a very natural and simple greedy algorithm, which is the obvious extension of the standard greedy algorithm used to build spanners in the non-faulty setting. We also consider the edge fault tolerant (EFT) model, defined analogously with edge failures rather than vertex failures. We show that the same spanner upper bound applies in this setting. Our data structure lower bound extends to the case k = 2 (and hence we close the EFT problem for 3-approximations), but it falls to Ω(f1/2−1/(2k) · n1+1/k) for k ≥ 3. We leave it as an open problem to close this gap.
Thermalization Time Bounds for Pauli Stabilizer Hamiltonians

We prove a general lower bound to the spectral gap of the Davies generator for Hamiltonians that can be written as the sum of commuting Pauli operators. These Hamiltonians, defined on the Hilbert space of N-qubits, serve as one of the most frequently considered candidates for a self-correcting quantum memory. A spectral gap bound on the Davies generator establishes an upper limit on the life time of such a quantum memory and can be used to estimate the time until the system relaxes to thermal equilibrium when brought into contact with a thermal heat bath. The bound can be shown to behave as λ≥O(N^(−1) exp(−2βϵ)), where ϵ is a generalization of the well known energy barrier for logical operators. Particularly in the low temperature regime we expect this bound to provide the correct asymptotic scaling of the gap with the system size up to a factor of N^(−1). Furthermore, we discuss conditions and provide scenarios where this factor can be removed and a constant lower bound can be proven.
Polarization of electric-field noise near metallic surfaces

Electric-field noise in proximity to metallic surfaces is a poorly understood phenomenon that appears in different areas of physics. Trapped ion quantum information processors are particularly susceptible to this noise, leading to motional decoherence, which ultimately limits the fidelity of quantum operations. On the other hand, trapped ions present an ideal tool to study this effect, opening new possibilities in surface science. In this work we analyze and measure the polarization of the noise field in a microfabricated ion trap for various noise sources. We find that technical noise sources and noise emanating directly from the surface give rise to different degrees of polarization, which allows us to differentiate between the two noise sources. Based on this we demonstrate a method to infer the magnitude of surface noise in the presence of technical noise.
Triangle-free Uniquely 3-Edge Colorable Cubic Graphs

This paper presents infinitely many new examples of triangle-free uniquely 3-edge colorable cubic graphs. The only such graph previously known was given by Tutte in 1976.
Robustness Analysis of Synchrosqueezed Transforms

Synchrosqueezed transforms are non-linear processes for a sharpened time-frequency representation of wave-like components. They are ecient tools for identifying and analyzing wave-like components from their superposition. This paper is concerned with the statistical properties of compactly supported synchrosqueezed transforms for wave-like components embedded in a generalized Gaussian random process in multidimensional spaces. Guided by the theoretical analysis of these properties, new numerical implementations are proposed to reduce the noise uctuations of these transforms on noisy data. A MATLAB package SynLab together with several heavily noisy examples is provided to support these theoretical claims.
Limits of modified higher q, t-Catalan numbers

The $q,t$-Catalan numbers can be defined using rational functions, geometry related to Hilbert schemes, symmetric functions, representation theory, Dyck paths, partition statistics, or Dyck words. After decades of intensive study, it was eventually proved that all these definitions are equivalent. In this paper, we study the similar situation for higher $q,t$-Catalan numbers, where the equivalence of the algebraic and combinatorial definitions is still conjectural. We compute the limits of several versions of the modified higher $q,t$-Catalan numbers and show that these limits equal the generating function for integer partitions. We also identify certain coefficients of the higher $q,t$-Catalan numbers as enumerating suitable integer partitions, and we make some conjectures on the homological significance of the Bergeron-Garsia nabla operator.
Spatially adaptive image compression using a tiled deep network

Deep neural networks represent a powerful class of function approximators that can learn to compress and reconstruct images. Existing image compression algorithms based on neural networks learn quantized representations with a constant spatial bit rate across each image. While entropy coding introduces some spatial variation, traditional codecs have benefited significantly by explicitly adapting the bit rate based on local image complexity and visual saliency. This paper introduces an algorithm that combines deep neural networks with quality-sensitive bit rate adaptation using a tiled network. We demonstrate the importance of spatial context prediction and show improved quantitative (PSNR) and qualitative (subjective rater assessment) results compared to a non-adaptive baseline and a recently published image compression model based on fully-convolutional neural networks.
Computational and Statistical Tradeoffs in Learning to Rank

For massive and heterogeneous modern data sets, it is of fundamental interest to provide guarantees on the accuracy of estimation when computational resources are limited. In the application of learning to rank, we provide a hierarchy of rank-breaking mechanisms ordered by the complexity in thus generated sketch of the data. This allows the number of data points collected to be gracefully traded off against computational resources available, while guaranteeing the desired level of accuracy. Theoretical guarantees on the proposed generalized rank-breaking implicitly provide such trade-offs, which can be explicitly characterized under certain canonical scenarios on the structure of the data.
Shannon-Like Wavelet Frames on a Class of Nilpotent Lie Groups

We construct Shannon-like Parseval frame wavelets on a class of non commutative two-step nilpotent Lie groups. Our work was inspired by a construction given by Azita Mayeli on the Heisenberg group. The tools used here are representation theoretic. However, a great deal of Gabor theory is used for the construction of the wavelets. The construction obtained here is very explicit, and we are even able to compute an upper bound for the L2 norm for these Parseval frame wavelets. AMS Subject Classification: 22E25
The Two-Unicast Problem

We consider the communication capacity of wireline networks for a two-unicast traffic pattern. The network has two sources and two destinations with each source communicating an independent message to its own destination, subject to the capacity constraints on the directed edges of the network. We propose a simple outer bound for the problem that we call the generalized network sharing (GNS) bound. We show that this bound is the tightest edge-cut bound for two-unicast networks and is tight in several cases, though it is not tight in general. We also show that the problem of computing the GNS bound is NP complete. Finally, we show that despite its seeming simplicity, the two-unicast problem is a very difficult problem: the general network coding problem can be reduced to two-unicast. As a consequence, linear coding is insufficient to achieve capacity for general two-unicast networks, and non-Shannon inequalities are necessary for characterizing the capacity of general two-unicast networks.
Geometric Bijections Between Spanning Trees and Break Divisors

Abstract The Jacobian group Jac ( G ) of a finite graph G is a group whose cardinality is the number of spanning trees of G. G also has a tropical Jacobian which has the structure of a real torus; using the notion of break divisors, An et al. obtained a polyhedral decomposition of the tropical Jacobian where vertices and cells correspond to elements of Jac ( G ) and spanning trees of G, respectively. We give a combinatorial description of bijections coming from this geometric setting. This provides a new geometric method for constructing bijections in combinatorics. We introduce a special class of geometric bijections that we call edge ordering maps, which have good algorithmic properties. Finally, we study the connection between our geometric bijections and the class of bijections introduced by Bernardi; in particular we prove a conjecture of Baker that planar Bernardi bijections are “geometric”. We also give sharpened versions of results by Baker and Wang on Bernardi torsors.
Learning Video Representations From Correspondence Proposals

Correspondences between frames encode rich information about dynamic content in videos. However, it is challenging to effectively capture and learn those due to their irregular structure and complex dynamics. In this paper, we propose a novel neural network that learns video representations by aggregating information from potential correspondences. This network, named CPNet, can learn evolving 2D fields with temporal consistency. In particular, it can effectively learn representations for videos by mixing appearance and long-range motion with an RGB-only input. We provide extensive ablation experiments to validate our model. CPNet shows stronger performance than existing methods on Kinetics and achieves the state-of-the-art performance on Something-Something and Jester. We provide analysis towards the behavior of our model and show its robustness to errors in proposals.
Classification of Interacting Topological Floquet Phases in One Dimension

Repeatedly driving a system with electromagnetic pulses can produce dramatically new quantum properties. A theoretical understanding of new types of quantum phases of interacting matter that exist only in the face of periodic driving is presented.
A locking clamp that enables high thermal and vibrational stability for kinematic optical mounts

One of the main pursuits of the MagAO-X project is imaging planets around nearby stars with the direct detection method utilizing an extreme AO system and a coronagraph and a large telescope. The MagAO-X astronomical coronagraph will be implemented on the 6.5 meter Clay Magellan Telescope in Chile. The 22 mirrors in the system require a high level of mirror stability. Our goal is less than 1 microradian drift in tilt per mirror per one degree Celsius change in temperature. There are no commercial 2inch kinematic optical mounts that are truly “zero-drift” from 0-20C. Our solution to this problem was to develop a locking clamp to keep our optics stable and fulfill our specifications. After performing temperature variation and thermal shock testing, we conclude that this novel locking clamp significantly increases the thermal stability of stainless steel mounts by ~10x but still allows accurate microradian positioning of a mirror. A provisional patent (#62/632,544) has been obtained for this mount.
Approximate Hypergraph Coloring under Low-discrepancy and Related Promises

A hypergraph is said to be X-colorable if its vertices can be colored with X colors so that no hyperedge is monochromatic. 2-colorability is a fundamental property (called Property B) of hypergraphs and is extensively studied in combinatorics. Algorithmically, however, given a 2-colorable k-uniform hypergraph, it is NP-hard to find a 2-coloring miscoloring fewer than a fraction 2^(-k+1) of hyperedges (which is trivially achieved by a random 2-coloring), and the best algorithms to color the hypergraph properly require about n^(1-1/k) colors, approaching the trivial bound of n as k increases. In this work, we study the complexity of approximate hypergraph coloring, for both the maximization (finding a 2-coloring with fewest miscolored edges) and minimization (finding a proper coloring using fewest number of colors) versions, when the input hypergraph is promised to have the following stronger properties than 2-colorability: (A) Low-discrepancy: If the hypergraph has a 2-coloring of discrepancy l << sqrt(k), we give an algorithm to color the hypergraph with about n^(O(l^2/k)) colors. However, for the maximization version, we prove NP-hardness of finding a 2-coloring miscoloring a smaller than 2^(-O(k)) (resp. k^(-O(k))) fraction of the hyperedges when l = O(log k) (resp. l=2). Assuming the Unique Games conjecture, we improve the latter hardness factor to 2^(-O(k)) for almost discrepancy-1 hypergraphs. (B) Rainbow colorability: If the hypergraph has a (k-l)-coloring such that each hyperedge is polychromatic with all these colors (this is stronger than a (l+1)-discrepancy 2-coloring), we give a 2-coloring algorithm that miscolors at most k^(-Omega(k)) of the hyperedges when l << sqrt(k), and complement this with a matching Unique Games hardness result showing that when l = sqrt(k), it is hard to even beat the 2^(-k+1) bound achieved by a random coloring. (C) Strong Colorability: We obtain similar (stronger) Min- and Max-2-Coloring algorithmic results in the case of (k+l)-strong colorability.
Inverse Sum Indeg Energy of Graphs

Suppose G is an n-vertex simple graph with vertex set {v 1 , ... , v n } and d i , i = 1, ... , n, is the degree of vertex v i in G. The ISI matrix S(G) = [s ij ] n×n of G is defined by s ij = [d i d j ]/[d i +d j ] if the vertices v i and v j are adjacent and s ij = 0 otherwise. The S-eigenvalues of G are the eigenvalues of its ISI matrix S(G). Recently, the notion of inverse sum indeg (henceforth, ISI) energy of graphs is introduced and is defined byn Σ i=1 n |τ i |, where τ i are the S-eigenvalues. We give ISI energy formula of some graph classes. We also obtain some bounds for ISI energy of graphs. In the end, we give some noncospectral equienergetic graphs with respect to inverse sum indeg energy.
When do luxury cars hit the road? Findings by a big data approach

In this paper, we focus on a study of the timing of different kinds of cars on the road. This information will enable us to infer the life style of the car owners. The results can further be used to guide marketing towards car owners and setting auto insurance policies. Conventionally, this kind of study is carried out by sending out questionnaires, which is limited in scale and diversity. To solve this problem, we propose a fully automatic method to conduct this study at scale. Our study is based on publicly available surveillance camera data. Images from the public traffic cameras are downloaded every minute. After obtaining the images, we apply faster R-CNN (region-based convolutional neural network) to detect the cars in the downloaded images and a fine-tuned VGG16 model is used to recognize the car makes. Based on the recognition results, we present a data-driven analysis on the relationship between car makes and their appearing times, with implications on lifestyles.
Highlights from BNL and RHIC 2016

Highlights of news from Brookhaven National Laboratory (BNL) and results from the Relativistic Heavy Ion Collider (RHIC) in the period July 2015-2016 are presented. Transverse single spin asymmetries from polarized p+p collisions are presented for $\pi^0$ and jets as a function of Feynman $x_F$. An energy scan to study the $\sqrt{s}$ dependence of collectivity and flow for small systems was performed as well as a high luminosity Au$+$Au run. The failure of a quench protection diode resulted in a pause in the run to replace it, but otherwise performance of RHIC was the best ever. Experimental results discussed are an elegant measurement from STAR of the force between anti-protons using HBT correlations, flow in U+U collisions, an improved method of generating constituent quarks by PHENIX and new Number of Quark Participants (NQP) scaling of $E_T$ distributions in p$+$p,d$+$Au and Au$+$Au which worked well. New hard-scattering results as a function of $\sqrt{s}$ in Au$+$Au central collisions are presented. Also, measurements of the di-hadron acoplanarity for $\pi^0 +h$ and $\gamma+h$ in p+p collisions at $\sqrt{s}=500$ GeV are presented in terms of the out-of-plane transverse momentum $p_{\rm out}$ which differ from the prediction of the TMD framework of parton transverse momentum dynamics.
Every Hilbert space frame has a Naimark complement

Abstract Naimark complements for Hilbert space Parseval frames are one of the most fundamental and useful results in the field of frame theory. We will show actually all Hilbert space frames have Naimark complements which possess all the usual properties for Naimark complements of Parseval frames with one notable exception. Thus these complements can be used with regard to equiangular frames, the restricted isometry property, fusion frames, etc. Along the way, we will correct a mistake in a recent fusion frame paper where chordal distances for Naimark complements are computed incorrectly.
Power Control for Wireless VBR Video Streaming: From Optimization to Reinforcement Learning

In this paper, we investigate the problem of power control for streaming variable bit rate (VBR) videos over wireless links. A system model involving a transmitter (e.g., a base station) that sends VBR video data to a receiver (e.g., a mobile user) equipped with a playout buffer is adopted, as used in dynamic adaptive streaming video applications. In this setting, we analyze power control policies considering the following two objectives: 1) the minimization of the transmit power consumption and 2) the minimization of the transmission completion time of the communication session. In order to play the video without interruptions, the power control policy should also satisfy the requirement in which the VBR video data is delivered to the mobile user without causing playout buffer underflow or overflows. A directional water-filling algorithm, which provides a simple and concise interpretation of the necessary optimality conditions, is identified as the optimal offline policy. Following this, two online policies are proposed for power control based on channel side information (CSI) prediction within a short time window. Dynamic programming is employed to implement the optimal offline and the initial online power control policies that minimize the transmit power consumption in the communication session. Subsequently, reinforcement learning (RL)-based approach is employed for the second online power control policy. Through the simulation results, we show that the optimal offline power control policy that minimizes the overall power consumption leads to substantial energy savings compared with the strategy of minimizing the time duration of video streaming. We also demonstrate that the RL algorithm performs better than the dynamic programming-based online grouped water-filling (GWF) strategy unless the channel is highly correlated.
On cluster algebras arising from unpunctured surfaces II

Abstract We study cluster algebras with principal and arbitrary coefficient systems that are associated to unpunctured surfaces. We give a direct formula for the Laurent polynomial expansion of cluster variables in these cluster algebras in terms of certain paths on a triangulation of the surface. As an immediate consequence, we prove the positivity conjecture of Fomin and Zelevinsky for these cluster algebras. Furthermore, we obtain direct formulas for F-polynomials and g-vectors and show that F-polynomials have constant term equal to 1. As an application, we compute the Euler–Poincare characteristic of quiver Grassmannians in Dynkin type A and affine Dynkin type A ˜ .
Bounding the degree of Belyi polynomials

Abstract Text Belyiʼs theorem states that a Riemann surface X, as an algebraic curve, is defined over Q ¯ if and only if there exists a holomorphic function B taking X to P 1 C with at most three critical values { 0 , 1 , ∞ } . By restricting to the case where X = P 1 C and our holomorphic functions are Belyi polynomials, for an algebraic number λ, we define a Belyi height H ( λ ) to be the minimal degree of the set of Belyi polynomials with B ( λ ) ∈ { 0 , 1 } . We prove for non-zero λ with non-zero p-adic valuation, the Belyi height of λ is greater than or equal to p using the combinatorics of Newton polygons. We also give examples of algebraic numbers with relatively low height and show that our bounds are sharp. Video For a video summary of this paper, please click here or visit http://www.youtube.com/watch?v=MJAodACJ4kM .
Finite stopping times for freely oscillating drop of a yield stress fluid

Abstract The paper addresses the question if there exists a finite stopping time for an unforced motion of a yield stress fluid with free surface. A variational inequality formulation is deduced for the problem of yield stress fluid dynamics with a free surface. The free surface is assumed to evolve with a normal velocity of the flow. We also consider capillary forces acting along the free surface. Based on the variational inequality formulation an energy equality is obtained, where kinetic and free energy rate of change is in a balance with the internal energy viscoplastic dissipation and the work of external forces. Further, the paper considers free small-amplitude oscillations of a droplet of Herschel-Bulkley fluid under the action of surface tension forces. Under certain assumptions it is shown that the finite stopping time T f of oscillations exists once the yield stress parameter is positive and the flow index α satisfies α ≥ 1. Results of several numerical experiments illustrate the analysis, reveal the dependence of T f on problem parameters and suggest an instantaneous transition of the whole drop from yielding state to the rigid one.
Ranks of matrices with few distinct entries

An L-matrix is a matrix whose off-diagonal entries belong to a set L, and whose diagonal is zero. Let N(r, L) be the maximum size of a square L-matrix of rank at most r. Many applications of linear algebra in extremal combinatorics involve a bound on N(r, L). We review some of these applications, and prove several new results on N(r, L). In particular, we classify the sets L for which N(r, L) is linear, and show that if N(r, L) is superlinear and L ⊂ Z, then N(r, L) is at least quadratic. As a by-product of the work, we asymptotically determine the maximum multiplicity of an eigenvalue λ in an adjacency matrix of a digraph of a given size.
Contextualizing the global relevance of local land change observations

To understand global changes in the Earth system, scientists must generalize globally from observations made locally and regionally. In land change science (LCS), local field-based observations are costly and time consuming, and generally obtained by researchers working at disparate local and regional case-study sites chosen for different reasons. As a result, global synthesis efforts in LCS tend to be based on non-statistical inferences subject to geographic biases stemming from data limitations and fragmentation. Thus, a fundamental challenge is the production of generalized knowledge that links evidence of the causes and consequences of local land change to global patterns and vice versa. The GLOBE system was designed to meet this challenge. GLOBE aims to transform global change science by enabling new scientific workflows based on statistically robust, globally relevant integration of local and regional observations using an online social-computational and geovisualization system. Consistent with the goals of Digital Earth, GLOBE has the capability to assess the global relevance of local case-study findings within the context of over 50 global biophysical, land-use, climate, and socio-economic datasets. We demonstrate the implementation of one such assessment – a representativeness analysis – with a recently published meta-study of changes in swidden agriculture in tropical forests. The analysis provides a standardized indicator to judge the global representativeness of the trends reported in the meta-study, and a geovisualization is presented that highlights areas for which sampling efforts can be reduced and those in need of further study. GLOBE will enable researchers and institutions to rapidly share, compare, and synthesize local and regional studies within the global context, as well as contributing to the larger goal of creating a Digital Earth.
Capacity-approaching PhaseCode for low-complexity compressive phase retrieval

In this paper, we tackle the general compressive phase retrieval problem. The problem is to recover (to within a global phase uncertainty) a K-sparse complex vector of length n, x ∈ ℂn, from the magnitudes of m linear measurements, y = |Ax|, where A ∈ ℂm×n can be designed, and the magnitudes are taken component-wise for vector Ax ∈ ℂm. We propose a variant of the PhaseCode algorithm, first introduced in [1], and show that under some mild assumptions, using an irregular left-degree sparse-graph code construction, the algorithm can recover almost all the K non-zero signal components using only slightly more than 4K measurements, with orderoptimal time and memory complexity of O(K). It is known that the fundamental limit for the number of measurements in compressive phase retrieval problem is 4K − o(K) [2, 3]. To the best of our knowledge, this is the first constructive capacityapproaching compressive phase retrieval algorithm: in fact, our algorithm is also order-optimal in complexity and memory.
A concentration theorem for projections

Suppose the random variable X ∈ ℝD has mean zero and finite second moments. We show that there is a precise sense in which almost all linear projections of X into ℝD (for d < D) look like a scale-mixture of spherical Gaussians—specifically, a mixture of distributions N(0, σ2Id) where the the σ values follow the same distribution as ‖X‖/√D. The extent of this effect depends upon the ratio of d to D, and upon a particular coefficient of eccentricity of X's distribution. We explore this result in a variety of experiments.
Injective modules under faithfully flat ring extensions

Let R be a commutative ring and S be an R-algebra. It is well-known that if N is an injective R-module, then Hom(S,N) is an injective S-module. The converse is not true, not even if R is a commutative noetherian local ring and S is its completion, but it is close: It is a special case of our main theorem that in this setting, an R-module N with Ext^i(S,N)=0 for all i>0 is injective if Hom(S,N) is an injective S-module.
High-Quality Information Technology and Capital Investment Decisions

We examine the effect of high-quality information technology (IT) on management's capital investment decisions. Evaluating capital investment decisions with contemporary investment efficiency and l...
Structured Sparsity Promoting Functions

Motivated by the minimax concave penalty-based variable selection in high-dimensional linear regression, we introduce a simple scheme to construct structured sparsity promoting functions from convex sparsity promoting functions and their Moreau envelopes. Properties of these functions are developed by leveraging their structure. In particular, we provide sparsity guarantees for the general family of functions. We further study the behavior of the proximity operators of several special functions, including indicator functions of closed and convex sets, piecewise quadratic functions, and linear combinations of the two. To demonstrate these properties, several concrete examples are presented and existing instances are featured as special cases.
Transforming Opacity Verification to Nonblocking Verification in Modular Systems

We consider the verification of current-state and $K$ -step opacity for systems modeled as interacting nondeterministic finite-state automata. We describe a new methodology for compositional opacity verification that employs abstraction, in the form of a notion called opaque observation equivalence, and that leverages existing compositional nonblocking verification algorithms. The compositional approach is based on a transformation of the system, where the transformed system is nonblocking if and only if the original one is current-state opaque. Furthermore, we prove that $K$ -step opacity can also be inferred if the transformed system is nonblocking. We provide experimental results where current-state opacity is verified efficiently for a large scaled-up system.
Efficient Algorithms for Smooth Minimax Optimization

This paper studies first order methods for solving smooth minimax optimization problems $\min_x \max_y g(x,y)$ where $g(\cdot,\cdot)$ is smooth and $g(x,\cdot)$ is concave for each $x$. In terms of $g(\cdot,y)$, we consider two settings -- strongly convex and nonconvex -- and improve upon the best known rates in both. For strongly-convex $g(\cdot, y),\ \forall y$, we propose a new direct optimal algorithm combining Mirror-Prox and Nesterov's AGD, and show that it can find global optimum in $\widetilde{O}\left(1/k^2 \right)$ iterations, improving over current state-of-the-art rate of $O(1/k)$. We use this result along with an inexact proximal point method to provide $\widetilde{O}\left(1/k^{1/3} \right)$ rate for finding stationary points in the nonconvex setting where $g(\cdot, y)$ can be nonconvex. This improves over current best-known rate of $O(1/k^{1/5})$. Finally, we instantiate our result for finite nonconvex minimax problems, i.e., $\min_x \max_{1\leq i\leq m} f_i(x)$, with nonconvex $f_i(\cdot)$, to obtain convergence rate of $O(m^{1/3}\sqrt{\log m}/k^{1/3})$.
Two-spectra theorem with uncertainty

The goal of this paper is to combine ideas from the theory of mixed spectral problems for differential operators with new results in the area of the Uncertainty Principle in Harmonic Analysis (UP). Using recent solutions of Gap and Type Problems of UP we prove a version of Borg's two-spectra theorem for Schrodinger operators, allowing uncertainty in the placement of the eigenvalues. We give a formula for the exact 'size of uncertainty', calculated from the lengths of the intervals where the eigenvalues may occur. Among other applications, we describe pairs of indeterminate operators in the three-interval case of the mixed spectral problem. At the end of the paper we discuss further questions and open problems.
Compounding the Disturbance: Family Forest Owner Reactions to Invasive Forest Insects

Abstract Invasive forest insect and pathogens (FIP) are having significant, direct, adverse impacts. Interactions between FIPs and forest owners have the potential to create ecosystem impacts that compound direct impacts. We assessed family forest owners' responses to numerous contingent behavior, FIP-outbreak scenarios in the northeastern USA based on FIP outbreak attributes. The survey was divided into four versions and each respondent was given four hypothetical scenarios and asked to gauge their certainty of each response. Sixty-eight percent of the hypothetical scenario responses (n = 2752) indicated an intent to harvest as a result of FIPs, and 49% indicated this intent with certainty. Eighty-four percent of respondents (n = 688) would consider harvesting for at least one of the four hypothetical scenarios presented, and 67% of respondents were certain of their intent to harvest for at least one of the four hypothetical scenarios. Harvest intention increased with greater FIP-related tree mortality and decreased with delayed total tree mortality. Owners with larger holdings, who had previously harvested forest products, and live on their forestland had greater intentions to harvest in response to FIPs. Results suggest that FIPs could transform the regional harvest regime with socio-ecological impacts that are distinct from those caused by FIPs or harvesting alone.