cssi/SciSciGPT-SciSciNet huggingface dataset:
P2C2: Peer-to-Peer Car Charging

With rising concerns over fossil fuel depletion and the impact of Internal Combustion Engine (ICE) vehicles on our climate, the transportation industry is observing a rapid proliferation of Electric Vehicles (EVs). Yet, people continue to use ICE vehicles over EVs due to consumer worries over issues such as limited range, limited battery life, long charging times, and the lack of EV charging stations. Existing solutions to these problems, such as building more charging stations, increasing battery capacity, and road-charging have not been proven efficient so far. In this paper, we propose Peer-to-PeerCarC harging (P2C2), ahighly scalable novel technique for charging EVs on-the-go with minimal cost overhead. We allow EVs to share charge among each other based on the instructions from a cloud-based control system. The control system assigns and guides EVs for charge sharing. We also introduce Mobile Charging Stations (MoCS), which are high battery capacity vehicles that are used to replenish the overall charge in the vehicle networks. We have implemented P2C2 and integrated it with the traffic simulator, SUMO. We observe promising results with up to 65% reduction in the number of EV halts and with up to 24.4% reduction in required battery capacity without any extra halts.
Wearable Respiration Monitoring: Interpretable Inference With Context and Sensor Biomarkers

Continuous monitoring of breathing rate (BR), minute ventilation (VE), and other respiratory parameters could transform care for and empower patients with chronic cardio-pulmonary conditions, such as asthma. However, the clinical standard for measuring respiration, namely Spirometry, is hardly suitable for continuous use. Wearables can track many physiological signals, like ECG and motion, yet respiration tracking faces many challenges. In this work, we infer respiratory parameters from wearable ECG and wrist motion signals. We propose a modular and generalizable classification-regression pipeline to utilize available context information, such as physical activity, in learning context-conditioned inference models. Novel morphological and power domain features from the wearable ECG are extracted to use with these models. Exploratory feature selection methods are incorporated in this pipeline to discover application-driven interpretable biomarkers. Using data from 15 subjects, we evaluate two implementations of the proposed inference pipeline: for BR and VE. Each implementation compares generalized linear model, random forest, support vector machine, Gaussian process regression, and neighborhood component analysis as regression models. Permutation, regularization, and relevance determination methods are used to rank the ECG features to identify robust ECG biomarkers across models and activities. This work demonstrates the potential of wearable sensors not only in continuous monitoring, but also in designing biomarker-driven preventive measures.
Comparing the writing skills of autistic and nonautistic university students: A collaboration with autistic university students:

The writing skills of autistic university students have received very little empirical attention. Previous research has suggested that autistic people may struggle with writing, in part, due to cha...
Machine Boss: rapid prototyping of bioinformatic automata.

MOTIVATION Many software libraries for using Hidden Markov Models in bioinformatics focus on inference tasks, such as likelihood calculation, parameter-fitting and alignment. However, construction of the state machines can be a laborious task, automation of which would be time-saving and less error-prone. RESULTS We present Machine Boss, a software tool implementing not just inference and parameter-fitting algorithms, but also a set of operations for manipulating and combining automata. The aim is to make prototyping of bioinformatics HMMs as quick and easy as the construction of regular expressions, with one-line 'recipes' for many common applications. We report data from several illustrative examples involving protein-to-DNA alignment, DNA data storage and nanopore sequence analysis. AVAILABILITY AND IMPLEMENTATION Machine Boss is released under the BSD-3 open source license and is available from http://machineboss.org/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Neural Network Coding

In this paper we introduce Neural Network Coding (NNC), a data-driven approach to joint source and network coding. In NNC, the encoders at each source and intermediate node, as well as the decoder at each destination node, are neural networks which are all trained jointly for the task of communicating correlated sources through a network of noisy point-to-point links. The NNC scheme is application-specific and makes use of a training set of data, instead of making assumptions on the source statistics. In addition, it can adapt to any arbitrary network topology and power constraint. We show empirically that, for the task of transmitting MNIST images over a network, the NNC scheme shows improvement over baseline schemes, especially in the low-SNR regime.
Automated detection of corrosion in used nuclear fuel dry storage canisters using residual neural networks

Abstract Nondestructive evaluation methods play an important role in ensuring component integrity and safety in many industries. Operator fatigue can play a critical role in the reliability of such methods. This is important for inspecting high value assets or assets with a high consequence of failure, such as aerospace and nuclear components. Recent advances in convolution neural networks can support and automate these inspection efforts. This paper proposes using residual neural networks (ResNets) for real-time detection of corrosion, including iron oxide discoloration, pitting and stress corrosion cracking, in dry storage stainless steel canisters housing used nuclear fuel. The proposed approach crops nuclear canister images into smaller tiles, trains a ResNet on these tiles, and classifies images as corroded or intact using the per-image count of tiles predicted as corroded by the ResNet. The results demonstrate that such a deep learning approach allows to detect the locus of corrosion via smaller tiles, and at the same time to infer with high accuracy whether an image comes from a corroded canister. Thereby, the proposed approach holds promise to automate and speed up nuclear fuel canister inspections, to minimize inspection costs, and to partially replace human-conducted onsite inspections, thus reducing radiation doses to personnel.
Measuring similarity between curves on 2-manifolds via homotopy area

Measuring the similarity of curves is a fundamental problem arising in many application fields. There has been considerable interest in several such measures, both in Euclidean space and in more general setting such as curves on Riemannian surfaces or curves in the plane minus a set of obstacles. However, so far, efficiently computable similarity measures for curves on general surfaces remain elusive. This paper aims at developing a natural curve similarity measure that can be easily extended and computed for curves on general orientable 2-manifolds. Specifically, we measure similarity between homotopic curves based on how hard it is to deform one curve into the other one continuously, and define this "hardness'' as the minimum possible surface area swept by a homotopy between the curves. We consider cases where curves are embedded in the plane or on a triangulated orientable surface with genus $g$, and we present efficient algorithms (which are either quadratic or near linear time, depending on the setting) for both cases. The results are also extended to comparing simple cycles (simple closed curves) in the plane or on a sphere, although the algorithms become less efficient by a factor of n. We also discuss the case of cycles on surfaces, which remains open.
Unboxing MAC Protocol Design Optimization Using Deep Learning

Evolving amendments of 802.11 standards feature a large set of physical and MAC layer control parameters to support the increasing communication objectives spanning application requirements and network dynamics. The significant growth and penetration of various devices come along with a tremendous increase in the number of applications supporting various domains and services which will impose a never-before-seen burden on wireless networks. The challenge however, is that each scenario requires a different wireless protocol functionality and parameter setting to optimally determine how to tune these functionalities and parameters to adapt to varying network scenarios. The traditional trial-error approach of manual tuning of parameters is not just becoming difficult to repeat but also sub-optimal for different networking scenarios. In this paper, we describe how we can leverage a deep reinforcement learning framework to be trained to learn the relation between different parameters in the physical and MAC layer and show that how our learning-based approach could help us in getting insights about protocol design optimization task.
Connecting the q-multiplicative convolution and the finite difference convolution

Abstract In [1] , Branden, Krasikov, and Shapiro study root location preservation properties of finite difference operators. They then conjecture a characterization of linear finite difference operators on polynomials of fixed degree which preserve root mesh properties, via a natural polynomial convolution. We prove this conjecture using two methods. The first develops a novel connection between the additive (Walsh) and multiplicative (Grace-Szego) convolutions, which can be generically used to transfer results from multiplicative to additive. We then use this to transfer an analogous result, due to Lamprecht [4] , which demonstrates logarithmic root mesh preservation properties of a certain q-multiplicative convolution operator. The second method proves the result directly using a modification of Lamprecht's proof of the logarithmic root mesh result. We present his original argument in a streamlined fashion and then make the appropriate alterations to apply it to the additive case.
Small cap decouplings

We develop a toolbox for proving decouplings into boxes with diameter smaller than the canonical scale. As an application of this new technique, we solve three problems for which earlier methods have failed. We start by verifying the small cap decoupling for the parabola. Then we find sharp estimates for exponential sums with small frequency separation on the moment curve in $$\mathbb {R}^3$$ . This part of the work relies on recent improved Kakeya-type estimates for planar tubes, as well as on new multilinear incidence bounds for plates and planks. We also combine our method with the recent advance on the reverse square function estimate, in order to prove small cap decoupling into square-like caps for the two dimensional cone. The Appendix by Roger Heath-Brown contains an application of the new exponential sum estimates for the moment curve, to the Riemann zeta-function.
A Random Matrix Approach to Absorption in Free Products

This paper gives a free entropy theoretic perspective on amenable absorption results for free products of tracial von Neumann algebras. In particular, we give the first free entropy proof of Popa's famous result that the generator MASA in a free group factor is maximal amenable, and we partially recover Houdayer's results on amenable absorption and Gamma stability. Moreover, we give a unified approach to all these results using $1$-bounded entropy. We show that if $\mathcal{M} = \mathcal{P} * \mathcal{Q}$, then $\mathcal{P}$ absorbs any subalgebra of $\mathcal{M}$ that intersects it diffusely and that has $1$-bounded entropy zero (which includes amenable and property Gamma algebras as well as many others). In fact, for a subalgebra $\mathcal{P} \leq \mathcal{M}$ to have this absorption property, it suffices for $\mathcal{M}$ to admit random matrix models that have exponential concentration of measure and that "simulate" the conditional expectation onto $\mathcal{P}$.
EXPORT-GROWTH NEXUS WHILE USING THE BLOCK EXOGENEITY: EMPIRICAL EVIDENCE FROM PAKISTAN

Export-growth nexus while using the block exogeneity: empirical evidence from pakistan - The core objective of the study is
Connectivity for quantum graphs

Abstract In Quantum Information Theory there is a construction for quantum channels, appropriately called a quantum graph, that generalizes the confusability graph construction for classical channels in classical information theory. In this paper a definition of connectedness for quantum graphs is provided, which generalizes the classical definition. It is shown that several examples of well-known quantum graphs (quantum Hamming cubes and quantum expanders) are connected. A quantum version of a particular case of the classical tree-packing theorem from Graph Theory is also proved. Generalizations for the related notions of k-connectedness and of orthogonal representation are also proposed for quantum graphs, and it is shown that orthogonal representations have the same implications for connectedness as they do in the classical case.
Low-rank matrix completion and denoising under Poisson noise

This paper considers the problem of estimating a low-rank matrix from the observation of all or a subset of its entries in the presence of Poisson noise. When we observe all entries, this is a problem of matrix denoising; when we observe only a subset of the entries, this is a problem of matrix completion. In both cases, we exploit an assumption that the underlying matrix is low-rank. Specifically, we analyze several estimators, including a constrained nuclear-norm minimization program, nuclear-norm regularized least squares, and a nonconvex constrained low-rank optimization problem. We show that for all three estimators, with high probability, we have an upper error bound (in the Frobenius norm error metric) that depends on the matrix rank, the fraction of the elements observed, and maximal row and column sums of the true matrix. We furthermore show that the above results are minimax optimal (within a universal constant) in classes of matrices with low rank and bounded row and column sums. We also extend these results to handle the case of matrix multinomial denoising and completion.
Robot Packing With Known Items and Nondeterministic Arrival Order

This article formulates two variants of packing problems in which the set of items is known, but the arrival order is unknown. The goal is to certify that the items can be packed in a given container and/or to optimize the size or cost of a container so that that the items are guaranteed to be packable, regardless of arrival order. The nondeterministically ordered packing (NDOP) variant asks to generate a certificate that a packing plan exists for every ordering of items. Quasi-online packing (QOP) asks to generate a partially observable packing policy that chooses the location of each item as their arrival order is revealed one-by-one, with all of the remaining items certified to be packable regardless of their future arrival order. Theoretical analysis demonstrates that even the simple subproblem of verifying the feasibility of a packing policy is NP-complete. Despite this worst case complexity, practical solvers for both NDOP and QOP are developed. Multiple extensions to the basic nondeterministic problem are presented, including packing with a fixed-capacity buffer and packing with equivalent objects. Experiments demonstrate that these algorithms can be applied to packing irregular 3-D shapes with stability and manipulator loading constraints. Note to Practitioners —Automatic packing of objects into containers, such as a shipping box, has applications in warehouse automation for e-commerce applications and less-than-truckload shipping. In many scenarios, a container needs to be preselected for a set of objects before they arrive, and the order of the objects cannot be controlled. This article studies the theoretical foundations of packing problems, in which the set of items is known, but the arrival order is unknown. The algorithms presented in this article can certify whether a container can hold a set of objects in the case where the arrival order is revealed at once, one-by-one, and where a limited set of objects can be put aside in a buffer before packing. These algorithms can be used to choose optimal containers and packing strategies, both for robot and human packers.
Improved Virus Isoelectric Point Estimation by Exclusion of Known and Predicted Genome-Binding Regions

ABSTRACT Knowledge of the isoelectric points (pIs) of viruses is beneficial for predicting virus behavior in environmental transport and physical/chemical treatment applications. However, the empirically measured pIs of many viruses have thus far defied simple explanation, let alone prediction, based on the ionizable amino acid composition of the virus capsid. Here, we suggest an approach for predicting the pI of nonenveloped viruses by excluding capsid regions that stabilize the virus polynucleotide via electrostatic interactions. This method was applied first to viruses with known polynucleotide-binding regions (PBRs) and/or three-dimensional (3D) structures. Then, PBRs were predicted in a group of 32 unique viral capsid proteome sequences via conserved structures and sequence motifs. Removing predicted PBRs resulted in a significantly better fit to empirical pI values. After modification, mean differences between theoretical and empirical pI values were reduced from 2.1 ± 2.4 to 0.1 ± 1.7 pH units. IMPORTANCE This model fits predicted pIs to empirical values for a diverse set of viruses. The results suggest that many previously reported discrepancies between theoretical and empirical virus pIs can be explained by coulombic neutralization of PBRs of the inner capsid. Given the diversity of virus capsid structures, this nonarbitrary, heuristic approach to predicting virus pI offers an effective alternative to a simplistic, one-size-fits-all charge model of the virion. The accurate, structure-based prediction of PBRs of the virus capsid employed here may also be of general interest to structural virologists.
Discovery of a Novel Simian Pegivirus in Common Marmosets (Callithrix jacchus) with Lymphocytic Enterocolitis

From 2010 to 2015, 73 common marmosets (Callithrix jacchus) housed at the Wisconsin National Primate Research Center (WNPRC) were diagnosed postmortem with lymphocytic enterocolitis. We used unbiased deep-sequencing to screen the blood of deceased enterocolitis-positive marmosets for viruses. In five out of eight common marmosets with lymphocytic enterocolitis, we discovered a novel pegivirus not present in ten matched, clinically normal controls. The novel virus, which we named Southwest bike trail virus (SOBV), is most closely related (68% nucleotide identity) to a strain of simian pegivirus A isolated from a three-striped night monkey (Aotus trivirgatus). We screened 146 living WNPRC common marmosets for SOBV, finding an overall prevalence of 34% (50/146). Over four years, 85 of these 146 animals died or were euthanized. Histological examination revealed 27 SOBV-positive marmosets from this cohort had lymphocytic enterocolitis, compared to 42 SOBV-negative marmosets, indicating no association between SOBV and disease in this cohort (p = 0.0798). We also detected SOBV in two of 33 (6%) clinically normal marmosets screened during transfer from the New England Primate Research Center, suggesting SOBV could be exerting confounding influences on comparisons of common marmoset studies from multiple colonies.
A Kinetic Rate Model of Novel Upconversion Nanostructures for High-Efficiency Photovoltaics

We present a computational model for predicting the net solar energy conversion efficiency of a single-junction solar cell backed by a solid-state photon upconverter. We describe and model the upconversion process using a series of kinetic rate equations to calculate equilibrium populations in a single upconversion nanostructure. We determine the internal upconversion quantum efficiency of the proposed nanostructure as a function of intentionally introduced photon energy sacrifice. We integrate the results of this model with the detailed balance method for estimating solar cell efficiency to determine the optimal nanostructure design to achieve maximum net solar energy conversion efficiency for a host solar cell backed by this upconverter. We show that net efficiencies in excess of 40% could be possible for a wide-bandgap host cell using this upconversion nanostructure.
Unsupervised Wasserstein Distance Guided Domain Adaptation for 3D Multi-Domain Liver Segmentation

Deep neural networks have shown exceptional learning capability and generalizability in the source domain when massive labeled data is provided. However, the well-trained models often fail in the target domain due to the domain shift. Unsupervised domain adaptation aims to improve network performance when applying robust models trained on medical images from source domains to a new target domain. In this work, we present an approach based on the Wasserstein distance guided disentangled representation to achieve 3D multi-domain liver segmentation. Concretely, we embed images onto a shared content space capturing shared feature-level information across domains and domain-specific appearance spaces. The existing mutual information-based representation learning approaches often fail to capture complete representations in multi-domain medical imaging tasks. To mitigate these issues, we utilize Wasserstein distance to learn more complete representation, and introduces a content discriminator to further facilitate the representation disentanglement. Experiments demonstrate that our method outperforms the state-of-the-art on the multi-modality liver segmentation task.
Dynamic State Analysis of a Driven Magnetic Pendulum Using Ordinal Partition Networks and Topological Data Analysis

The use of complex networks for time series analysis has recently shown to be useful as a tool for detecting dynamic state changes for a wide variety of applications. In this work, we implement the commonly used ordinal partition network to transform a time series into a network for detecting these state changes for the simple magnetic pendulum. The time series that we used are obtained experimentally from a base-excited magnetic pendulum apparatus, and numerically from the corresponding governing equations.The magnetic pendulum provides a relatively simple, non-linear example demonstrating transitions from periodic to chaotic motion with the variation of system parameters. For our method, we implement persistent homology, a shape measuring tool from Topological Data Analysis (TDA), to summarize the shape of the resulting ordinal partition networks as a tool for detecting state changes. We show that this network analysis tool provides a clear distinction between periodic and chaotic time series. Another contribution of this work is the successful application of the networks-TDA pipeline, for the first time, to signals from non-autonomous nonlinear systems. This opens the door for our approach to be used as an automatic design tool for studying the effect of design parameters on the resulting system response. Other uses of this approach include fault detection from sensor signals in a wide variety of engineering operations.
Counting Polygon Triangulations is Hard

We prove that it is $$\#{\mathsf {P}}$$ -complete to count the triangulations of a (non-simple) polygon.
Order-disorder behavior at thin film oxide interfaces

Abstract Order-disorder processes fundamentally determine the structure and properties of many important oxide systems for energy and computing applications. While these processes have been intensively studied in bulk materials, they are less investigated and understood for nanostructured oxides in highly non-equilibrium conditions. These systems can now be realized through a range of deposition techniques and probed at exceptional spatial and chemical resolution, leading to a greater focus on interface dynamics. Here we survey a selection of recent studies of order-disorder behavior at thin film oxide interfaces, with a particular emphasis on the emergence of order during synthesis and disorder in extreme irradiation environments. We summarize key trends and identify directions for future study in this growing research area.
Phytoplankton Temporal Strategies Increase Entropy Production in a Marine Food Web Model.

We develop a trait-based model founded on the hypothesis that biological systems evolve and organize to maximize entropy production by dissipating chemical and electromagnetic free energy over longer time scales than abiotic processes by implementing temporal strategies. A marine food web consisting of phytoplankton, bacteria, and consumer functional groups is used to explore how temporal strategies, or the lack thereof, change entropy production in a shallow pond that receives a continuous flow of reduced organic carbon plus inorganic nitrogen and illumination from solar radiation with diel and seasonal dynamics. Results show that a temporal strategy that employs an explicit circadian clock produces more entropy than a passive strategy that uses internal carbon storage or a balanced growth strategy that requires phytoplankton to grow with fixed stoichiometry. When the community is forced to operate at high specific growth rates near 2 d−1, the optimization-guided model selects for phytoplankton ecotypes that exhibit complementary for winter versus summer environmental conditions to increase entropy production. We also present a new type of trait-based modeling where trait values are determined by maximizing entropy production rather than by random selection.
Fermilab 4.3 MeV electron cooler

The Recycler Electron Cooler (REC) was the first cooler working at a relativistic energy ( γ = 9.5). It was successfully developed in 1995-2004 and was in operation at Fermilab in 2005–2011, providing cooling of antiprotons in the Recycler ring. After introducing the physics of electron cooling and the REC system, this paper describes measurements carried out to tune the electron beam and optimize its cooling properties. In particular, we discuss the cooling strategy adopted for maximizing the collider integrated luminosity.
Sidebands due to Quasi-periodic Oscillations in 4U 1626–67

The low-mass X-ray binary pulsar 4U 1626-67 shows 0.048 Hz quasi-periodic oscillations (QPOs) and red noise variability, as well as coherent pulsations at the 0.130 Hz neutron star spin frequency. Power density spectra of observations made with the Rossi X-Ray Timing Explorer show significant sidebands separated from the pulsar spin frequency (and its harmonics) by the QPO frequency. These show that the instantaneous amplitude of the coherent pulsations is modulated by the amplitude of the QPOs. This phenomenon is expected in models such as the magnetospheric beat frequency model, where the QPOs originate near the polar caps of the neutron star. In the 4-8 keV energy range, however, the lower frequency sidebands are significantly stronger than their higher frequency complements; this is inconsistent with the magnetospheric beat frequency model. We suggest that the 0.048 Hz QPOs are produced instead by a structure orbiting the neutron star at the QPO frequency. This structure crosses the line of sight once per orbit and attenuates the pulsar beam, producing the symmetric (amplitude modulation) sidebands. It also reprocesses the pulsar beam at the beat frequencies between the neutron star spin frequency and the QPOs, producing the excess variability observed in the lower frequency sidebands. Quite independently, we find no evidence that the red noise variability modulates the amplitude of the coherent pulsations. This is also in contrast to the expectations of the magnetospheric beat frequency model and differs from the behavior in some high-mass X-ray binary pulsars.
Bayesian Optimization for Materials Design

WeintroduceBayesian optimization, a technique developed for optimizing time-consuming engineering simulations and for fitting machine learning models on large datasets. Bayesian optimization guides the choice of experiments during materials design and discovery to find good material designs in as few experiments as possible. We focus on the case when materials designs are parameterized by a low-dimensional vector. Bayesian optimization is built on a statistical technique called Gaussian process regression, which allows predicting the performance of a new design based on previously tested designs. After providing a detailed introduction to Gaussian process regression, we describe two Bayesian optimization methods: expected improvement, for design problems with noise-free evaluations; and the knowledge-gradient method, which generalizes expected improvement and may be used in design problems with noisy evaluations. Both methods are derived using a value-of-information analysis, and enjoy one-step Bayes-optimality.
Theory of electron transport and emission from a semiconductor nanotip

An effective mass based model accounting for the conduction band quantization in a high aspect ratio semiconductor nanotip is developed to describe injected electron transport and subsequent electron emission from the nanotip. A transfer matrix formalism is used to treat electron scattering induced by the variation in the tip diameter and in the electron emission. Numerical analysis of the scattering and emission probabilities is performed for the diamond parametrized nanotip model. Our scattering and emission models are further combined with a Monte Carlo (MC) approach to simulate electron transport through the nanotip. The MC simulations, also accounting for the electron-phonon scattering and externally applied electric field, are performed for a minimal nanotip model and an equivalent width diamond slab. An effect of the level quantization, electron scattering due to the nanotip diameter variation, and electron-phonon scattering on the nanotip emission properties are identified and compared with the case of a bulk slab.
Heat-to-Work Conversion by Exploiting Full or Partial Correlations of Quantum Particles

It is shown how information contained in the pairwise correlations (in general, partial) between atoms of a gas can be used to completely convert heat taken from a thermostat into mechanical work in a process of relaxation of the system to its thermal equilibrium state. Both classical correlations and quantum correlations (entanglement) are considered. The amount of heat converted into work is proportional to the entropy defect of the initial state of the system. For fully correlated particles, in the case of entanglement the amount of work obtained per particle is twice as large as in the case of classical correlations. However, in the case of entanglement, the amount of work does not depend on the degree of correlation, in contrast to the case of classical correlations. The results explicitly demonstrate the equivalence relation between information and work for the case of two-particle correlations.
Lattice Methods and the Nuclear Few- and Many-Body Problem

This chapter builds upon the review of lattice methods and effective field theory of the previous chapter. We begin with a brief overview of lattice calculations using chiral effective field theory and some recent applications. We then describe several methods for computing scattering on the lattice. After that we focus on the main goal, explaining the theory and algorithms relevant to lattice simulations of nuclear few- and many-body systems. We discuss the exact equivalence of four different lattice formalisms, the Grassmann path integral, transfer matrix operator, Grassmann path integral with auxiliary fields, and transfer matrix operator with auxiliary fields. Along with our analysis we include several coding examples and a number of exercises for the calculations of few- and many-body systems at leading order in chiral effective field theory.
Exotic smooth structures on nonpositively curved symmetric spaces

We construct series of examples of exotic smooth structures on compact locally symmetric spaces of noncompact type. In particular, we obtain higher rank examples, which do not support Riemannian metric of nonpositive curvature. The examples are obtained by taking the connected sum with an exotic sphere. To detect the change of the smooth structure we use a tangential map from the locally symmetric space its dual compact type twin. AMS Classification 53C35; 57T15, 55R37, 57R99
On the Incidence and Kinematics of Strong Mg II Absorbers

We present the results of two complementary investigations into the nature of strong (rest equivalent width, Wr > 1.0 A) Mg II absorption systems at high redshift. The first line of questioning examines the complete Sloan Digital Sky Survey Data Release 3 set of quasar spectra to determine the evolution of the incidence of strong Mg II absorption. This search resulted in 7421 confirmed Mg II systems of Wr > 1.0 A, yielding a >95% complete statistical sample of 4835 absorbers (systems detected in S/N > 7 spectral regions) spanning a redshift range 0.35 0.8, indicating that the product of the number density and gas cross section of halos hosting strong Mg II is unevolving at these redshifts. In contrast, one observes a decline in lMg(X) at z 1.0 A were discovered. These systems are characterized by the presence of numerous components spread over an average velocity width of Δv ≈ 200 km s-1. Also, absorption due to more highly ionized species (e.g., Al III, C IV, Si IV) tends to display kinematic profiles similar to the corresponding Mg II and Fe II absorption. We consider all of these results in light of two competing theories previously introduced to explain strong Mg II absorption: post-starburst, supernova-driven galactic winds and accreting gas in the halos of massive galaxies. The latter model is especially disfavored by the absence of evolution in lMg(X) at z > 1. We argue that the strong Mg II phenomenon primarily arises from feedback processes in relatively low mass galactic halos related to star formation.
20 years of developments in optical frequency comb technology and applications

Optical frequency combs were developed nearly two decades ago to support the world’s most precise atomic clocks. Acting as precision optical synthesizers, frequency combs enable the precise transfer of phase and frequency information from a high-stability reference to hundreds of thousands of tones in the optical domain. This versatility, coupled with near-continuous spectroscopic coverage from microwave frequencies to the extreme ultra-violet, has enabled precision measurement capabilities in both fundamental and applied contexts. This review takes a tutorial approach to illustrate how 20 years of source development and technology has facilitated the journey of optical frequency combs from the lab into the field. Optical frequency combs were realized nearly two decades ago to support the development of the world’s most precise atomic clocks, but their versatility has since made them useful instruments well beyond their original goal, and spans across a wide variety of fundamental and applied physics in a wide range of wavelengths. Fortier and Baumann present a comprehensive review of developments in optical frequency comb technology and a view to the future with these technologies.
THE LOS ALAMOS SUPERNOVA LIGHT-CURVE PROJECT: COMPUTATIONAL METHODS

We have entered the era of explosive transient astronomy, in which current and upcoming real-time surveys such as the Large Synoptic Survey Telescope, the Palomar Transient Factory, and the Panoramic Survey Telescope and Rapid Response System will detect supernovae in unprecedented numbers. Future telescopes such as the James Webb Space Telescope may discover supernovae from the earliest stars in the universe and reveal their masses. The observational signatures of these astrophysical transients are the key to unveiling their central engines, the environments in which they occur, and to what precision they will pinpoint cosmic acceleration and the nature of dark energy. We present a new method for modeling supernova light curves and spectra with the radiation hydrodynamics code RAGE coupled with detailed monochromatic opacities in the SPECTRUM code. We include a suite of tests that demonstrate how the improved physics and opacities are indispensable to modeling shock breakout and light curves when radiation and matter are tightly coupled.
Quantum Hall effect in higher dimensions

Abstract Following recent work on the quantum Hall effect on S4, we solve the Landau problem on the complex projective spaces C P k and discuss quantum Hall states for such spaces. Unlike the case of S4, a finite spatial density can be obtained with a finite number of internal states for each particle. We treat the case of C P 2 in some detail considering both Abelian and nonAbelian background fields. The wavefunctions are obtained and incompressibility of the Hall states is shown. The case of C P 3 is related to the case of S4.
A micromirror array with annular partitioning for high-speed random-access axial focusing.

Dynamic axial focusing functionality has recently experienced widespread incorporation in microscopy, augmented/virtual reality (AR/VR), adaptive optics and material processing. However, the limitations of existing varifocal tools continue to beset the performance capabilities and operating overhead of the optical systems that mobilize such functionality. The varifocal tools that are the least burdensome to operate (e.g. liquid crystal, elastomeric or optofluidic lenses) suffer from low (≈100 Hz) refresh rates. Conversely, the fastest devices sacrifice either critical capabilities such as their dwelling capacity (e.g. acoustic gradient lenses or monolithic micromechanical mirrors) or low operating overhead (e.g. deformable mirrors). Here, we present a general-purpose random-access axial focusing device that bridges these previously conflicting features of high speed, dwelling capacity and lightweight drive by employing low-rigidity micromirrors that exploit the robustness of defocusing phase profiles. Geometrically, the device consists of an 8.2 mm diameter array of piston-motion and 48-μm-pitch micromirror pixels that provide 2π phase shifting for wavelengths shorter than 1100 nm with 10-90% settling in 64.8 μs (i.e., 15.44 kHz refresh rate). The pixels are electrically partitioned into 32 rings for a driving scheme that enables phase-wrapped operation with circular symmetry and requires <30 V per channel. Optical experiments demonstrated the array's wide focusing range with a measured ability to target 29 distinct resolvable depth planes. Overall, the features of the proposed array offer the potential for compact, straightforward methods of tackling bottlenecked applications, including high-throughput single-cell targeting in neurobiology and the delivery of dense 3D visual information in AR/VR.
Smart Contracts for Machine-to-Machine Communication: Possibilities and Limitations

Blockchain technologies, such as smart contracts, present a unique interface for machine-to-machine communication that provides a secure, append-only record that can be shared without trust and without a central administrator. We study the possibilities and limitations of using smart contracts for machine-to-machine communication by designing, implementing, and evaluating AGasP, an application for automated gasoline purchases. We find that using smart contracts allows us to directly address the challenges of transparency, longevity, and trust in IoT applications. However, real-world applications using smart contracts must address their important trade-offs, such as performance, privacy, and the challenge of ensuring they are written correctly.
Introduction to Supersymmetric Theory of Stochastics

Many natural and engineered dynamical systems, including all living objects, exhibit signatures of what can be called spontaneous dynamical long-range order (DLRO). This order’s omnipresence has long been recognized by the scientific community, as evidenced by a myriad of related concepts, theoretical and phenomenological frameworks, and experimental phenomena such as turbulence, 1/f noise, dynamical complexity, chaos and the butterfly effect, the Richter scale for earthquakes and the scale-free statistics of other sudden processes, self-organization and pattern formation, self-organized criticality, etc. Although several successful approaches to various realizations of DLRO have been established, the universal theoretical understanding of this phenomenon remained elusive. The possibility of constructing a unified theory of DLRO has emerged recently within the approximation-free supersymmetric theory of stochastics (STS). There, DLRO is the spontaneous breakdown of the topological or de Rahm supersymmetry that all stochastic differential equations (SDEs) possess. This theory may be interesting to researchers with very different backgrounds because the ubiquitous DLRO is a truly interdisciplinary entity. The STS is also an interdisciplinary construction. This theory is based on dynamical systems theory, cohomological field theories, the theory of pseudo-Hermitian operators, and the conventional theory of SDEs. Reviewing the literature on all these mathematical disciplines can be time consuming. As such, a concise and self-contained introduction to the STS, the goal of this paper, may be useful.
Triangulum II: Possibly a Very Dense Ultra-faint Dwarf Galaxy

Laevens et al. recently discovered Triangulum II (Tri II), a satellite of the Milky Way. Its Galactocentric distance is 36 kpc, and its luminosity is only 450 L_⊙. Using Keck/DEIMOS, we measured the radial velocities of six member stars within 1'.2 of the center of Tri II, and we found a velocity dispersion of σ_v = 5.1_(-1.4)^(+4.0) km s^(-1). We also measured the metallicities of three stars and found a range of 0.8 dex in [Fe/H]. The velocity and metallicity dispersions identify Tri II as a dark matter-dominated galaxy. The galaxy is moving very quickly toward the Galactic center v_(GSR) = -262 km s^(-1). Although it might be in the process of being tidally disrupted as it approaches pericenter, there is no strong evidence for disruption in our data set. The ellipticity is low, and the mean velocity, 〈|v_(helio)〉 =-382.1 ± 2.9 km s^(-1), rules out an association with the Triangulum–Andromeda substructure or the Pan-Andromeda Archaeological Survey stellar stream. If Tri II is in dynamical equilibrium, then it would have a mass-to-light ratio of 3600_(-2100)^(+3500) M}_⊙ L_⊙^(-1), the highest of any non-disrupting galaxy (those for which dynamical mass estimates are reliable). The density within the 3D half-light radius would be 4.8_(-3.5)^(+8.1) M⊙ pc^(-3), even higher than Segue 1. Hence, Tri II is an excellent candidate for the indirect detection of dark matter annihilation.
Transdimensional epsilon-near-zero modes in planar plasmonic nanostructures

The authors use quantum electrodynamics and a confinement-induced nonlocal response model to study the epsilon-near-zero modes of metallic films in the transdimensional regime of plasmonic materials. The paper uncovers new effects such as the plasmon mode degeneracy lifting and the dipole emitter coupling to the split epsilon-near-zero modes, leading to thickness-controlled spontaneous decay with up to three-orders-of-magnitude increased rates.
Energy partitions and evolution in a purely thermal solar flare

This paper presents a solely thermal flare, which we detected in the microwave range from the thermal gyro- and free–free emission it produced. An advantage of analyzing thermal gyro emission is its unique ability to precisely yield the magnetic field in the radiating volume. When combined with observationally deduced plasma density and temperature, these magnetic field measurements offer a straightforward way of tracking evolution of the magnetic and thermal energies in the flare. For the event described here, the magnetic energy density in the radio-emitting volume declines over the flare rise phase, then stays roughly constant during the extended peak phase, but recovers to the original level over the decay phase. At the stage where the magnetic energy density decreases, the thermal energy density increases; however, this increase is insufficient, by roughly an order of magnitude, to compensate for the magnetic energy decrease. When the magnetic energy release is over, the source parameters come back to nearly their original values. We discuss possible scenarios to explain this behavior.
Reverse Isolation and Backaction of the SLUG Microwave Amplifier

Fast, accurate measurement of superconducting qubits demands approaching the quantum limit in amplifying weak microwave probe tones. An ideal amplifier would provide $n\phantom{\rule{0}{0ex}}o\phantom{\rule{0}{0ex}}n\phantom{\rule{0}{0ex}}r\phantom{\rule{0}{0ex}}e\phantom{\rule{0}{0ex}}c\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}p\phantom{\rule{0}{0ex}}r\phantom{\rule{0}{0ex}}o\phantom{\rule{0}{0ex}}c\phantom{\rule{0}{0ex}}a\phantom{\rule{0}{0ex}}l$ (high forward, negligible reverse) gain, protecting the qubit from noisy downstream measurements. The superconducting low-inductance undulatory galvanometer (SLUG) microwave amplifier provides highly directional gain and minimal backaction on the qubit, as the authors demonstrate by studying its performance in a circuit without microwave circulators or isolators. This elimination of bulky, magnetic nonreciprocal elements shows a way to scalable measurement of large multiqubit arrays.
Scl, sails, and surgery

We establish a close connection between the stable commutator length in free groups and the geometry of sails (roughly, the boundary of the convex hull of the set of integer lattice points) in integral polyhedral cones. This connection allows us to show that the scl norm is piecewise rational linear in free products of Abelian groups, and that it can be computed via integer programing. Furthermore, we show that the scl spectrum of non-Abelian free groups contains elements congruent to every rational number modulo ℤ, and contains well-ordered sequences of values with ordinal type ω ω . Finally, we study families of elements w(p) in free groups obtained by surgery on a fixed element w in a free product of Abelian groups of higher rank, and show that scl(w(p)) → scl(w) as p → ∞.
On the Exponential Stability of Primal-Dual Gradient Dynamics

Continuous time primal-dual gradient dynamics (PDGD) that find a saddle point of a Lagrangian of an optimization problem have been widely used in systems and control. While the global asymptotic stability of such dynamics has been well-studied, it is less studied whether they are globally exponentially stable. In this letter, we study the PDGD for convex optimization with strongly convex and smooth objectives and affine equality or inequality constraints, and prove global exponential stability for such dynamics. Bounds on decaying rates are provided.
TOP QUARK TOTAL AND DIFFERENTIAL CROSS SECTIONS AT NNLO AND NNLL

I present recent NNLO-NNLL results for top quark hadroproduction at the Tevatron. The total cross section as well as transverse momentum and rapidity distributions are shown.