cssi/SciSciGPT-SciSciNet huggingface dataset:
Asynchronous Coded Caching With Uncoded Prefetching

Coded caching is a technique that promises huge reductions in network traffic in content-delivery networks. However, the original formulation and several subsequent contributions in the area, assume that the file requests from the users are synchronized, i.e., they arrive at the server at the same time. In this work, we formulate and study the coded caching problem when the file requests from the users arrive at different times. We assume that each user also has a prescribed deadline by which they want their request to be completed. In the offline case, we assume that the server knows the arrival times before starting transmission and in the online case, the user requests are revealed to the server over time. We present a linear programming formulation for the offline case that minimizes the overall transmission rate from the server subject to the constraint that each user meets his/her deadline. While the online case is much harder, we introduce a novel heuristic for it and show that under certain conditions, with high probability the request of each user can be satisfied with her/his deadline. Our simulation results indicate that in the presence of mild asynchronism, much of the benefit of coded caching can still be leveraged.
Some remarks about the maximal perimeter of convex sets with respect to probability measures

In this note, we study the maximal perimeter of a convex set in ℝn with respect to various classes of measures. Firstly, we show that for a probability measure μ on ℝn, satisfying very mild assumpt...
Energy Demand-Side Management: New Perspectives for a New Era.

Over the past decade and a half, state governments have assumed greater responsibility over demand-side management (DSM) operations. Whereas DSM programs formerly were initiated primarily by utilities or state public utility commissions, they are now becoming increasingly state-initiated and incentivized through funding mechanisms or efficiency-level mandates. The supporting literature, however, has yet to respond to these changes and to verify that DSM funding or mandates are effective policy mechanisms. Furthermore, the supporting literature has yet to resolve some of the research design and methodological challenges that traditionally plague DSM evaluations. As states continue to expand their energy and climate policy efforts, and the federal government considers the possibility of national decarbonization policies, of which DSM is a key strategy, the need for empirical research on the effectiveness of DSM programs will grow. This essay describes the current status of DSM efforts in the U.S. and explores how these programs affect electricity operations. The relationship between DSM policy and program efforts and the amount of saved electricity is tested with a methodological approach aimed at minimizing the selection bias that is inherent in the nonexperimental research design. Results confirm that state-run DSM efforts contribute to electricity savings across the country. Public benefit funds coupled with performance incentives are found to encourage utility participation in DSM programs. Energy efficiency portfolio standards and performance incentives effectively promote electricity savings, but public benefit funds without the support of other DSM policies are not significant drivers of either DSM program participation or total DSM electricity savings. © 2011 by the Association for Public Policy Analysis and Management.
Design of linear parameter varying quadratic regulator in polynomial chaos framework

We present a new theoretical framework for designing linear parameter varying controllers in the polynomial chaos framework. We assume the scheduling variable to be random and apply polynomial chaos approach to synthesize the controller for the resulting linear stochastic dynamical system. Two algorithms are presented that minimize the performance objective with respect to the stochastic system. The first algorithm is based on Galerkin projection and the second algorithm is based on stochastic collocation. LPV controllers from both the algorithms are shown to outperform classical LPV designs with respect to regulator design for nonlinear missile system.
On the impact of different voltage unbalance metrics in distribution system optimization

Abstract With increasing penetrations of single-phase, rooftop solar PV installations, the relative variations in per-phase loading and associated voltage unbalance are expected to increase. High voltage unbalance may increase network losses and lead to failure of three-phase equipment such as motor loads. However, solar PV panels are connected to the grid through inverters, which can provide reactive power support and may mitigate some of these negative effects. In this paper, we utilize a three-phase AC optimal power flow (OPF) formulation to minimize voltage unbalance using reactive power from solar PV inverters. When considering actions to reduce voltage unbalance, it is important to recognize that various organizations such as IEC, NEMA and IEEE provide different and partially inconsistent definitions of voltage unbalance in their power quality standards. This paper analyzes the impact of the different voltage unbalance metrics using different combinations of voltage unbalance objectives and constraints. For our analysis, the optimization scheme is tested on two unbalanced low-voltage distribution networks. We observe that minimizing voltage unbalance defined by one standard might actually increase voltage unbalance as defined by another standard, potentially resulting in equipment damage. We also observe that minimizing voltage unbalance does not always lead to lower network losses. However, considerable reduction in voltage unbalance with low network losses can be achieved by minimizing the losses while simultaneously enforcing limits on multiple definitions of voltage unbalance.
Statistical mechanical approximations to more efficiently determine polymorph free energy differences for small organic molecules

Methods to efficiently determine the relative stability of polymorphs of organic crystals are highly desired in crystal structure predictions (CSPs). Current methodologies include calculating the f...
Uniqueness of two-convex closed ancient solutions to the mean curvature flow

In this paper we consider closed non-collapsed ancient solutions to the mean curvature flow ($n \ge 2$) which are uniformly two-convex. We prove that any two such ancient solutions are the same up to translations and scaling. In particular, they must coincide up to translations and scaling with the rotationally symmetric closed ancient non-collapsed solution constructed by Brian White in (2000), and by Robert Haslhofer and Or Hershkovits in (2016).
Treating Irrigation Water Using High-Performance Membranes for Monovalent Selective Electrodialysis

The most common desalination technology for treating brackish irrigation water is reverse osmosis (RO). RO yields product waters low in monovalent ions that are harmful to crops (Na+ and Cl–) and i...
Leveraging the Template and Anchor Framework for Safe, Online Robotic Gait Design

Online control design using a high-fidelity, full-order model for a bipedal robot can be challenging due to the size of the state space of the model. A commonly adopted solution to overcome this challenge is to approximate the fullorder model (anchor) with a simplified, reduced-order model (template), while performing control synthesis. Unfortunately it is challenging to make formal guarantees about the safety of an anchor model using a controller designed in an online fashion using a template model. To address this problem, this paper proposes a method to generate safety-preserving controllers for anchor models by performing reachability analysis on template models by relying on functions that bound the difference between the two models. This paper describes how this reachable set can be incorporated into a Model Predictive Control framework to select controllers that result in safe walking on the anchor model in an online fashion. The method is illustrated on a 5-link RABBIT model, and is shown to allow the robot to walk safely while utilizing controllers designed in an online fashion.
Distributed Multi-Target Tracking for Autonomous Vehicle Fleets

We present a scalable distributed target tracking algorithm based on the alternating direction method of multipliers that is well-suited for a fleet of autonomous cars communicating over a vehicle-to-vehicle network. Each sensing vehicle communicates with its neighbors to execute iterations of a Kalman filter-like update such that each agent’s estimate approximates the centralized maximum a posteriori estimate without requiring the communication of measurements. We show that our method outperforms the Consensus Kalman Filter in recovering the centralized estimate given a fixed communication bandwidth. We also demonstrate the algorithm in a high fidelity urban driving simulator (CARLA), in which 50 autonomous cars connected on a time-varying communication network track the positions and velocities of 50 target vehicles using on-board cameras.
Methodology for optimizing composite design via biological pattern generation mechanisms

Abstract Mechanistic capabilities found in nature have influenced a variety of successful functional designs in engineering. However, the unique combinations of mechanical properties found in natural materials have not been readily adapted into synthetic materials, due to a disconnect between biological principles and engineering applications. Current biomimetic material approaches tend to involve mimicking nature's microstructure geometries or mimicking nature's adaptive design process through brute force element-by-element composite optimization techniques. While the adaptive approach promotes the generation of application-specific microstructure geometries, the element-by-element optimization techniques encompass a large design space that is directly related to the number of elements in the system. In contrast, a novel methodology is proposed in this paper that merges biological pattern generation mechanisms observed in the Gray-Scott model, with an evolutionary-inspired genetic algorithm to create adaptive bio-inspired composite geometries optimized for stiffness and toughness. The results reveal that this methodology significantly reduces the optimization parameter space from tens of thousands of parameters to only four or five. In addition, the resultant composite geometries improved upon the overall combination of stiffness and toughness by a factor of 13, when compared to published brute force element-by-element techniques.
Tools for Analyzing R Code the Tidy Way

With the current emphasis on reproducibility and replicability, there is an increasing need to examine how data analyses are conducted. In order to analyze the between researcher variability in data analysis choices as well as the aspects within the data analysis pipeline that contribute to the variability in results, we have created two R packages: matahari and tidycode. These packages build on methods created for natural language processing; rather than allowing for the processing of natural language, we focus on R code as the substrate of interest. The matahari package facilitates the logging of everything that is typed in the R console or in an R script in a tidy data frame. The tidycode package contains tools to allow for analyzing R calls in a tidy manner. We demonstrate the utility of these packages as well as walk through two examples.
Petz reconstruction in random tensor networks

We illustrate the ideas of bulk reconstruction in the context of random tensor network toy models of holography. Specifically, we demonstrate how the Petz reconstruction map works to obtain bulk operators from the boundary data by exploiting the replica trick. We also take the opportunity to comment on the differences between coarse-graining and random projections.
An Upper Bound on the State-Space Complexity of Brandubh

Before chess came to Northern Europe there was Tafl, a family of asymmetric strategy board games associated strongly with the Vikings. The purpose of this paper is to study the combinatorial state-space complexity of an Irish variation of Tafl called Brandubh. Brandubh was chosen because of its asymmetric goals for the two players, but also its overall complexity well below that of chess, which should make it tractable for strong solving. Brandubh’s rules and characteristics are used to gain an understanding of the overall state-space complexity of the game. State-spaces will consider valid piece positions, a generalized rule set, and accepted final state conditions. From these states the upper bound for the complexity of strongly solving Brandubh is derived. Great effort has been placed on thoroughly accounting for all potential states and excluding invalid ones for the game. Overall, the upper bound complexity for solving the game is around 1014 states, between that of connect four and draughts (checkers).
Baba is Y’all: Collaborative Mixed-Initiative Level Design

We present a collaborative mixed-initiative system for building levels for the puzzle game "Baba is You". Unlike previous mixed-initiative systems, Baba is Y’all is designed for collaborative asynchronous creation by multiple users over the internet. The system includes several AI-assisted features to help designers, including a level evolver and an automated player for playtesting. The level archives catalogues levels according to which mechanics are implemented and not implemented, allowing the system to ask users to design levels with specific combinations of mechanics. We describe the operation of the system and the results of small-scale informal user test, and discuss future development paths for this system as well as for collaborative mixed-initiative systems in general.
Learning compact physics‐aware delayed photocurrent models using dynamic mode decomposition

Radiation-induced photocurrent in semiconductor devices can be simulated using complex physics-based models, which are accurate, but computationally expensive. This presents a challenge for implementing device characteristics in high-level circuit simulations where it is computationally infeasible to evaluate detailed models for multiple individual circuit elements. In this work we demonstrate a procedure for learning compact delayed photocurrent models that are efficient enough to implement in large-scale circuit simulations, but remain faithful to the underlying physics. Our approach utilizes Dynamic Mode Decomposition (DMD), a system identification technique for learning reduced order discrete-time dynamical systems from time series data based on singular value decomposition. To obtain physics-aware device models, we simulate the excess carrier density induced by radiation pulses by solving numerically the Ambipolar Diffusion Equation, then use the simulated internal state as training data for the DMD algorithm. Our results show that the significantly reduced order delayed photocurrent models obtained via this method accurately approximate the dynamics of the internal excess carrier density -- which can be used to calculate the induced current at the device boundaries -- while remaining compact enough to incorporate into larger circuit simulations.
Partitioned exponential methods for coupled multiphysics systems

Abstract Multiphysics problems involving two or more coupled physical phenomena are ubiquitous in science and engineering. This work develops a new partitioned exponential approach for the time integration of multiphysics problems. After a possible semi-discretization in space, the class of problems under consideration is modeled by a system of ordinary differential equations where the right-hand side is a summation of two component functions, each corresponding to a given set of physical processes. The partitioned-exponential methods proposed herein evolve each component of the system via an exponential integrator, and information between partitions is exchanged via coupling terms. The traditional approach to constructing exponential methods, based on the variation-of-constants formula, is not directly applicable to partitioned systems. Rather, our approach to developing new partitioned-exponential families is based on a general-structure additive formulation of the schemes. Two method formulations are considered, one based on a linear-nonlinear splitting of the right hand component functions, and another based on approximate Jacobians. The paper develops classical (non-stiff) order conditions theory for partitioned exponential schemes based on particular families of T-trees and B-series theory. Several practical methods of third order are constructed that extend the Rosenbrock-type and EPIRK families of exponential integrators. Several implementation optimizations specific to the application of these methods to reaction-diffusion systems are also discussed. Numerical experiments reveal that the new partitioned-exponential methods can perform better than traditional unpartitioned exponential methods on some problems.
Simple sheaves for knot conormals

We classify the simple sheaves microsupported along the conormal bundle of a knot. We also establish a correspondence between simple sheaves up to local systems and augmentations, explaining the underlying reason why knot contact homology representations detect augmentations.
G-LBM:Generative Low-dimensional Background Model Estimation from Video Sequences

In this paper, we propose a computationally tractable and theoretically supported non-linear low-dimensional generative model to represent real-world data in the presence of noise and sparse outliers. The non-linear low-dimensional manifold discovery of data is done through describing a joint distribution over observations, and their low-dimensional representations (i.e. manifold coordinates). Our model, called generative low-dimensional background model (G-LBM) admits variational operations on the distribution of the manifold coordinates and simultaneously generates a low-rank structure of the latent manifold given the data. Therefore, our probabilistic model contains the intuition of the non-probabilistic low-dimensional manifold learning. G-LBM selects the intrinsic dimensionality of the underling manifold of the observations, and its probabilistic nature models the noise in the observation data. G-LBM has direct application in the background scenes model estimation from video sequences and we have evaluated its performance on SBMnet-2016 and BMC2012 datasets, where it achieved a performance higher or comparable to other state-of-the-art methods while being agnostic to different scenes. Besides, in challenges such as camera jitter and background motion, G-LBM is able to robustly estimate the background by effectively modeling the uncertainties in video observations in these scenarios. (The code and models are available at: https://github.com/brezaei/G-LBM.)
Rare-event extinction on stochastic networks

We consider the problem of extinction processes on random networks with a given structure. For sufficiently large well-mixed populations, the process of extinction of one or more state variable components occurs in the tail of the quasi-stationary probability distribution, thereby making it a rare event. Here we show how to extend the theory of large deviations to random networks to predict extinction times. In particular, we use the theory to find the most probable path leading to extinction. We apply the methodology to epidemic models and discover how mean extinction times scale with epidemiological and network parameters in Erdős-Renyi networks. The results are shown to compare quite well with Monte Carlo simulations of the network in predicting both the most probable paths to extinction and mean extinction times.
Identification and Analysis of Young Star Cluster Candidates in M31

We present a method for finding clusters of young stars in M31 using broadband WFPC2 data from the HST data archive. Applying our identification method to 13 WFPC2 fields, covering an area of ~60 arcmin2, has revealed 79 new candidate young star clusters in these portions of the M31 disk. Most of these clusters are small (5 pc), young (~10-200 Myr) star groups located within large OB associations. We have estimated the reddening values and the ages of each candidate individually by fitting isochrones to the stellar photometry. We provide a catalog of the candidates including rough approximations of their reddenings and ages. We also look for patterns of cluster formation with galactocentric distance, but our rough estimates are not precise enough to reveal any clear patterns.
Finite volume effects and quenched chiral logarithms

We have measured the valence pion mass and the valence chiral condensate on lattice configurations generated with and without dynamical fermions. We find that our data and that of others is well represented by a linear relationship between mπ2 and the valence quark mass, with a non-zero intercept. For our data, we relate the intercept to finite volume effects visible in the valence chiral condensate. We see no evidence for the singular behavior expected from quenched chiral logarithms.
Suboptimal stabilizing controllers for linearly solvable system

This paper presents a novel method to synthesize stochastic control Lyapunov functions for a class of nonlinear, stochastic control systems. In this work, the classical nonlinear Hamilton-Jacobi-Bellman partial differential equation is transformed into a linear partial differential equation for a class of systems with a particular constraint on the stochastic disturbance. It is shown that this linear partial differential equation can be relaxed to a linear differential inclusion, allowing for approximating polynomial solutions to be generated using sum of squares programming. It is shown that the resulting solutions are stochastic control Lyapunov functions with a number of compelling properties. In particular, a-priori bounds on trajectory suboptimality are shown for these approximate value functions. The result is a technique whereby approximate solutions may be computed with non-increasing error via a hierarchy of semidefinite optimization problems.
Logarithmic trace and orbifold products

The purpose of this article is to give a purely equivariant definition of orbifold Chow rings of quotient Deligne-Mumford stacks. This completes a program begun in [JKK] for quotients by finite groups. The key to our construction is the definition (Section 6.1) of a twisted pullback in equivariant K-theory, KG(X)→KG(IG2(X)) taking nonnegative elements to nonnegative elements. (Here IG2(X)={(g1,g2,x)|g1x=g2x=x}⊂G×G×X.) The twisted pullback is defined using data about fixed loci of elements of finite order in G but depends only on the underlying quotient stack (Theorem 6.3). In our theory, the twisted pullback of the class T∈KG(X), corresponding to the tangent bundle to [X/G], replaces the obstruction bundle of the corresponding moduli space of twisted stable maps. When G is finite, the twisted pullback of the tangent bundle agrees with the class R(m) given in [JKK, Definition 1.5]. However, unlike in [JKK] we need not compare our class to the class of the obstruction bundle of Fantechi and Gottsche [FG] in order to prove that it is a nonnegative integral element of KG(IG2(X)). We also give an equivariant description of the product on the orbifold K-theory of [X/G]. Our orbifold Riemann-Roch theorem (Theorem 7.3) states that there is an orbifold Chern character homomorphism which induces an isomorphism of a canonical summand in the orbifold Grothendieck ring with the orbifold Chow ring. As an application we show (see Theorem 8.7) that if X=[X/G], then there is an associative orbifold product structure on K(X)⊗C distinct from the usual tensor product
Bagger–Witten line bundles on moduli spaces of elliptic curves

In this paper, we discuss Bagger–Witten line bundles over moduli spaces of SCFTs. We review how in general they are “fractional” line bundles, not honest line bundles, twisted on triple overlaps. W...
Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News

Although many fact-checking systems have been developed in academia and industry, fake news is still proliferating on social media. These systems mostly focus on fact-checking but usually neglect online users who are the main drivers of the spread of misinformation. How can we use fact-checked information to improve users’ consciousness of fake news to which they are exposed? How can we stop users from spreading fake news? To tackle these questions, we propose a novel framework to search for fact-checking articles, which address the content of an original tweet (that may contain misinformation) posted by online users. The search can directly warn fake news posters and online users (e.g. the posters' followers) about misinformation, discourage them from spreading fake news, and scale up verified content on social media. Our framework uses both text and images to search for fact-checking articles, and achieves promising results on real-world datasets. Our code and datasets are released at https://github.com/nguyenvo09/EMNLP2020.
Self-intersection numbers and random surfaces on the lattice

Abstract String theory in four dimensions has the unique feature that a topological term, the oriented self-intersection number, can be added to the usual action. It has been suggested that the corresponding theory of random surfaces would be free from the problem encountered in the scaling of the string tension. Unfortunately, in the usual dynamical triangulation it is not clear how to write such a term. We show that for random surfaces on a hypercubic lattice however, the analogue of the oriented self-intersection number I [ σ ] can be defined and computed in a straightforward way. Furthermore, I [ σ ] has a genuine topological meaning in the sense that it is invariant under the discrete analogue of continuous deformations. The resulting random surface model is no longer free and may lead to a non-trivial continuum limit.
TEMPERATURE OF ELECTRON FLUCTUATIONS IN AN ACCELERATED VACUUM

The electron vacuum fluctuations measured by $\langle \bar\psi \psi \rangle$ do not vanish in an externally applied electric field ℰ. For an exactly constant field, that is for vacuum fluctuations in presence of a constant accelerating force, we show that $\langle \bar\psi \psi \rangle$ has a Boson-like structure with spectral state density tanh-1(E/m) and temperature TM = eℰ/mπ = av/π. Considering the vacuum fluctuations of 'classical' gyromagnetic ratio g = 1 particles we find Fermi-like structure with the same spectral state density at a smaller temperature T1 = av/2π which corresponds to the Unruh temperature of an accelerated observer.
Characterization of photomultiplier tubes with a realistic model through GPU-boosted simulation

The accurate characterization of a photomultiplier tube (PMT) is crucial in a wide-variety of applications. However, current methods do not give fully accurate representations of the response of a PMT, especially at very low light levels. In this work, we present a new and more realistic model of the response of a PMT, called the cascade model, and use it to characterize two different PMTs at various voltages and light levels. The cascade model is shown to outperform the more common Gaussian model in almost all circumstances and to agree well with a newly introduced model independent approach. The technical and computational challenges of this model are also presented along with the employed solution of developing a robust GPU-based analysis framework for this and other non-analytical models.
Compression- and shear-driven jamming of U-shaped particles in two dimensions

We carry out numerical simulations of soft, U-shaped, frictionless particles in $$d=2$$ dimensions in order to explore the effects of complex particle shape on the jamming transition. We consider both cases of uniform compression-driven and shear-driven jamming as packing fraction $$\phi $$ and compression or shear rate is varied. Upon slow compression, jamming is found to occur when the isostatic condition is satisfied. Under driven steady state shearing, jamming occurs at a higher packing fraction $$\phi _J$$ than observed in compression. A growing relaxation time and translational correlation length is found as $$\phi $$ increases towards $$\phi _J$$ . We consider the orientational ordering and rotation of particles induced by the shear flow. Both nematic and tetratic ordering are found, but these decrease as $$\phi $$ increases to $$\phi _J$$ . At the jamming transition, the nematic ordering further decreases, while the tetratic ordering increases, but the orientational correlation lengths remain small throughout. The average angular velocity of the particles is found to increase as $$\phi $$ increases, saturating to a plateau just below $$\phi _J$$ , but then increasing again as $$\phi $$ increases above $$\phi _J$$ .
Fermi Non-detections of Four X-Ray Jet Sources and Implications for the IC/CMB Mechanism

E.T.M. acknowledges HST Grant GO-13676. E.T.M. and M.G. acknowledge NASA Grant NNX15AE55G. This research has made use of data from the MOJAVE database that is maintained by the MOJAVE team (Lister et al. 2009). This paper makes use of the following ALMA data: ADS/JAO.ALMA#2015.1.00932.S, ADS/JAO.ALMA#2016.1.01481.S, and ADS/JAO.ALMA#2016.1.01250.S. ALMA is a partnership of ESO (representing its member states), NSF (USA) and NINS (Japan), together with NRC (Canada), NSC and ASIAA (Taiwan), and KASI (Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO and NAOJ. The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc.
The Elemental Abundance Distributions of Milky Way Satellite Galaxies

The chemical compositions of the stars in Milky Way (MW) satellite galaxies reveals the history of gas flows and star formation (SF) intensity. This talk presented a Keck/DEIMOS spectroscopic survey of the Fe, Mg, Si, Ca, and Ti abundances of nearly 3000 red giants in eight MW dwarf satellites. The metallicity and alpha-to-iron ratio distributions obey the following trends: (1) The more luminous galaxies are more metal-rich, indicating that they retained gas more efficiently than the less luminous galaxies. (2) The shapes of the metallicity distributions of the more luminous galaxies require gas infall during their SF lifetimes. (3) At [Fe/H]< −1.5, [α/Fe] falls monotonically with increasing [Fe/H] in all MW satellites. One interpretation of these trends is that the SF timescale in any MW satellite is long enough that Type Ia supernovae exploded for nearly the entire SF lifetime.
Authentic science experiences with STEM datasets: post-secondary results and potential gender influences

Background: Dataset skills are used in STEM fields from astronomy to zoology. Few fields explicitly teach students the skills to analyze datasets, and yet the increasing push for authentic science ...
Design principles and fundamental trade-offs in biomimetic light harvesting

Recent developments in synthetic and supramolecular chemistry have created opportunities to design organic systems with tailored nanoscale structure for various technological applications. A key application area is the capture of light energy and its conversion into electrochemical or chemical forms for photovoltaic or sensing applications. In this work we consider cylindrical assemblies of chromophores that model structures produced by several supramolecular techniques. Our study is especially guided by the versatile structures produced by virus-templated assembly. We use a multi-objective optimization framework to determine design principles and limitations in light harvesting performance for such assemblies, both in the presence and absence of disorder. We identify a fundamental trade-off in cylindrical assemblies that is encountered when attempting to maximize both efficiency of energy transfer and absorption bandwidth. We also rationalize the optimal design strategies and provide explanations for why various structures provide optimal performance. Most importantly, we find that the optimal design strategies depend on the amount of energetic and structural disorder in the system. The aim of these studies is to develop a program of quantum-informed rational design for construction of organic assemblies that have the same degree of tailored nanoscale structure as biological photosynthetic light harvesting complexes, and consequently have the potential to reproduce their remarkable light harvesting performance.
Ionization Equilibrium Timescales in Collisional Plasmas

Astrophysical shocks or bursts from a photoionizing source can disturb the typical collisional plasma found in galactic interstellar media or the intergalactic medium. The spectrum emitted by this plasma contains diagnostics that have been used to determine the time since the disturbing event, although this determination becomes uncertain as the elements in the plasma return to ionization equilibrium. A general solution for the equilibrium timescale for each element arises from the elegant eigenvector method of solution to the problem of a non-equilibrium plasma described by Masai and Hughes & Helfand. In general, the ionization evolution of an element Z in a constant electron temperature plasma is given by a coupled set of Z + 1 first-order differential equations. However, they can be recast as Z uncoupled first-order differential equations using an eigenvector basis for the system. The solution is then Z separate exponential functions, with the time constants given by the eigenvalues of the rate matrix. The smallest of these eigenvalues gives the scale of the slowest return to equilibrium independent of the initial conditions, while conversely the largest eigenvalue is the scale of the fastest change in the ion population. These results hold for an ionizing plasma, a recombining plasma, or even a plasma with random initial conditions, and will allow users of these diagnostics to determine directly if their best-fit result significantly limits the timescale since a disturbance or is so close to equilibrium as to include an arbitrarily long time.
Real-time automatic fetal brain extraction in fetal MRI by deep learning

Brain segmentation is a fundamental first step in neuroimage analysis. In the case of fetal MRI, it is particularly challenging and important due to the arbitrary orientation of the fetus, organs that surround the fetal head, and intermittent fetal motion. Several promising methods have been proposed but are limited in their performance in challenging cases and in realtime segmentation. We aimed to develop a fully automatic segmentation method that independently segments sections of the fetal brain in 2D fetal MRI slices in real-time. To this end, we developed and evaluated a deep fully convolutional neural network based on 2D U-net and autocontext, and compared it to two alternative fast methods based on 1) a voxelwise fully convolutional network and 2) a method based on SIFT features, random forest and conditional random field. We trained the networks with manual brain masks on 250 stacks of training images, and tested on 17 stacks of normal fetal brain images as well as 18 stacks of extremely challenging cases based on extreme motion, noise, and severely abnormal brain shape. Experimental results show that our U-net approach outperformed the other methods and achieved average Dice metrics of 96.52% and 78.83% in the normal and challenging test sets, respectively. With an unprecedented performance and a test run time of about 1 second, our network can be used to segment the fetal brain in real-time while fetal MRI slices are being acquired. This can enable real-time motion tracking, motion detection, and 3D reconstruction of fetal brain MRI.
On the nonlinearity of quantum dynamical entropy

Linearity of a dynamical entropy means that the dynamical entropy of the n-fold composition of a dynamical map with itself is equal to n times the dynamical entropy of the map for every positive integer n. We show that the quantum dynamical entropy introduced by Slomczynski and Życzkowski is nonlinear in the time interval between successive measurements of a quantum dynamical system. This is in contrast to Kolmogorov-Sinai dynamical entropy for classical dynamical systems, which is linear in time. We also compute the exact values of quantum dynamical entropy for the Hadamard walk with varying Luders-von Neumann instruments and partitions.Linearity of a dynamical entropy means that the dynamical entropy of the n-fold composition of a dynamical map with itself is equal to n times the dynamical entropy of the map for every positive integer n. We show that the quantum dynamical entropy introduced by Slomczynski and Życzkowski is nonlinear in the time interval between successive measurements of a quantum dynamical system. This is in contrast to Kolmogorov-Sinai dynamical entropy for classical dynamical systems, which is linear in time. We also compute the exact values of quantum dynamical entropy for the Hadamard walk with varying Luders-von Neumann instruments and partitions.
Comment: The 2005 Neyman Lecture: Dynamic Indeterminism in Science

Professor Brillinger wrote a very stimulating paper on Neyman's life history and some of his contributions to applied statistics. The paper's central theme is to re view how Neyman used stochastic processes in data analysis. The paper contains a number of illuminating examples of Neyman and of Brillinger with other col laborators. I am honored to have been invited to be a discussant. Professor Brillinger quoted Neyman (1960), 'The time has arrived for the theory of stochastic processes to become an item of usual equipment of every applied statistician." In the post-Neyman era, data come in our way fast and in all forms, such as streams, functions, manifolds, random shapes, trees and images. The im portance of the theory of stochastic processes in ap plied statistics cannot be overemphasized. Brillinger's observation of Neyman's thought pro cesses in conducting applied research resonates with me. My discussion will be primarily to amplify it from a somewhat different perspective, namely from Ney man's teaching and his research projects on sampling and cancer. Included in the discussion will be recalls of
Mathematical formalism for isothermal linear irreversibility

We prove the equivalence among symmetricity, time reversibility and zero entropy production of the stationary solutions of linear stochastic differential equations. A sufficient and necessary rever...
Computing zeta functions of cyclic covers in large characteristic

We describe an algorithm to compute the zeta function of a cyclic cover of the projective line over a finite field of characteristic $p$ that runs in time $p^{1/2 + o(1)}$. We confirm its practicality and effectiveness by reporting on the performance of our SageMath implementation on a range of examples. The algorithm relies on Goncalves's generalization of Kedlaya's algorithm for cyclic covers, and Harvey's work on Kedlaya's algorithm for large characteristic.
Poincaré series of character varieties for nilpotent groups

For any compact and connected Lie group $G$ and any free abelian or free nilpotent group $\Gamma$ , we determine the cohomology of the path component of the trivial representation of the representation space (character variety) $Rep(\Gamma,G)_1$, with coefficients in a field $F$ with ${char} (F)$ either 0 or relatively prime to the order of the Weyl group $W$. We give explicit formulas for the Poincare series. In addition we study $G$-equivariant stable decompositions of subspaces $X(q,G)$ of the free monoid $J(G)$ generated by the Lie group $G$, obtained from finitely generated free nilpotent group representations.
Freiman-Ruzsa-type theory for small doubling constant

In this paper, we study the linear structure of sets A ⊂ with doubling constant σ( A ) A ):=| A + A |/| A |. In particular, we show that A is contained in a small affine subspace. We also show that A can be covered by at most four shifts of some subspace V with | V | ≤ | A |. Finally, we classify all binary sets with small doubling constant.
EVOLUTIONARY ANALYSIS OF GASEOUS SUB-NEPTUNE-MASS PLANETS WITH MESA

Sub-Neptune-sized exoplanets represent the most common types of planets in the Milky Way, yet many of their properties are unknown. Here, we present a prescription to adapt the capabilities of the stellar evolution toolkit Modules for Experiments in Stellar Astrophysics to model sub-Neptune-mass planets with H/He envelopes. With the addition of routines treating the planet core luminosity, heavy-element enrichment, atmospheric boundary condition, and mass-loss due to hydrodynamic winds, the evolutionary pathways of planets with diverse starting conditions are more accurately constrained. Using these dynamical models, we construct mass-composition relationships of planets from 1-400 M⊕ and investigate how mass-loss impacts their composition and evolution history. We demonstrate that planet radii are typically insensitive to the evolution pathway that brought the planet to its instantaneous mass, composition and age, with variations from hysteresis ≾2%. We find that planet envelope mass-loss timescales, τ_(env), vary non-monotonically with H/He envelope mass fractions (at fixed planet mass). In our simulations of young (100 Myr) low-mass (M_p ≾ 10M⊕) planets with rocky cores, τ_(env) is maximized at M_(env)/M_p = 1% to 3%. The resulting convergent mass-loss evolution could potentially imprint itself on the close-in planet population as a preferred H/He mass fraction of ~1%. Looking ahead, we anticipate that this numerical code will see widespread applications complementing both 3D models and observational exoplanet surveys.
Information-based centralization of locomotion in animals and robots.

The centralization of locomotor control from weak and local coupling to strong and global is hard to assess outside of particular modeling frameworks. We developed an empirical, model-free measure of centralization that compares information between control signals and both global and local states. A second measure, co-information, quantifies the net redundancy in global and local control. We first validate that our measures predict centralization in simulations of phase-coupled oscillators. We then test how centralization changes with speed in freely running cockroaches. Surprisingly, across all speeds centralization is constant and muscle activity is more informative of the global kinematic state (the averages of all legs) than the local state of that muscle’s leg. Finally we use a legged robot to show that mechanical coupling alone can change the centralization of legged locomotion. The results of these systems span a design space of centralization and co-information for biological and robotic systems. Model-based centralization schemes, though able to quantify locomotion control in animals and bio-inspired robots, are limited to specific systems. Here, the authors report a generalized information-based centralization scheme that unifies existing models and can be applied to different systems.
Allocation of Divisible Goods Under Lexicographic Preferences

We present a simple and natural non-pricing mechanism for allocating divisible goods among strategic agents having lexicographic preferences. Our mechanism has favorable properties of strategy-proofness (incentive compatibility). In addition (and even when extended to the case of Leontief bundles) it enjoys Pareto efficiency, envy-freeness, and time efficiency.
Discrete Solitary Waves in Systems with Nonlocal Interactions and the Peierls–Nabarro Barrier

We study a class of discrete focusing nonlinear Schrodinger equations (DNLS) with general nonlocal interactions. We prove the existence of onsite and offsite discrete solitary waves, which bifurcate from the trivial solution at the endpoint frequency of the continuous spectrum of linear dispersive waves. We also prove exponential smallness, in the frequency-distance to the bifurcation point, of the Peierls–Nabarro energy barrier (PNB), as measured by the difference in Hamiltonian or mass functionals evaluated on the onsite and offsite states. These results extend those of the authors for the case of nearest neighbor interactions to a large class of nonlocal short-range and long-range interactions. The appearance of distinct onsite and offsite states is a consequence of the breaking of continuous spatial translation invariance. The PNB plays a role in the dynamics of energy transport in such nonlinear Hamiltonian lattice systems.
A SYSTEMATIC ANALYSIS OF CAUSTIC METHODS FOR GALAXY CLUSTER MASSES

We quantify the expected observed statistical and systematic uncertainties of the escape velocity as a measure of the gravitational potential and total mass of galaxy clusters. We focus our attention on low redshift (z ≤0.15) clusters, where large and deep spectroscopic datasets currently exist. Utilizing a suite of Millennium Simulation semi-analytic galaxy catalogs, we find that the dynamical mass, as traced by either the virial relation or the escape velocity, is robust to variations in how dynamical friction is applied to orphan galaxies in the mock catalogs (i.e., those galaxies whose dark matter halos have fallen below the resolution limit). We find that the caustic technique recovers the known halo masses (M 200) with a third less scatter compared to the virial masses. The bias we measure increases quickly as the number of galaxies used decreases. For N gal > 25, the scatter in the escape velocity mass is dominated by projections along the line-of-sight. Algorithmic uncertainties from the determination of the projected escape velocity profile are negligible. We quantify how target selection based on magnitude, color, and projected radial separation can induce small additional biases into the escape velocity masses. Using N gal = 150 (25), the caustic technique has a per cluster scatter in ln (M|M 200) of 0.3 (0.5) and bias 1% ± 3} (16% ± 5}) for clusters with masses >1014 M ☉ at z < 0.15.
The magnetic moment enigma in Fe-based high temperature superconductors

The determination of the most appropriate starting point for the theoretical description of Fe-based materials hosting high-temperature superconductivity remains among the most important unsolved problem in this relatively new field. Most of the work to date has focused on the pnictides, with LaFeAsO, BaFe(2)As(2) and LiFeAs being representative parent compounds of three families known as 1111, 122 and 111, respectively. This topical review examines recent progress in this area, with particular emphasis on the implication of experimental data which have provided evidence for the presence of electron itinerancy and the detection of local spin moments. In light of the results presented, the necessity of a theoretical framework contemplating the presence and the interplay between itinerant electrons and large spin moments is discussed. It is argued that the physics at the heart of the macroscopic properties of pnictides Fe-based high-temperature superconductors appears to be far more complex and interesting than initially predicted.
On sumfree subsets of hypercubes

We consider the possible sizes of large sumfree sets contained in the discrete hypercube $\{1,...,n\}^k$, and we determine upper and lower bounds for the maximal size as $n$ becomes large. We also discuss a continuous analogue in which our lower bound remains valid and our upper bound can be strengthened, and we consider the generalization of both problems to $l$-fold-sumfree sets.
Consequences of Low Energy Dynamical Supersymmetry Breaking

Relatively simple models can be constructed in which supersymmetry is dynamically broken at energies of $10^5-10^7$ GeV. Models of this kind do not suffer from the naturalness and cosmological difficulties of conventional supergravity models, and make definite predictions for the spectrum of supersymmetric particle masses. Thus ``Renormalizable Visible Sector Models'' are a viable alternative to more conventional approaches. This talk mostly summarizes the results of a recent paper written with Michael Dine and Yuri Shirman.