cssi/SciSciGPT-SciSciNet huggingface dataset:
Optimal destabilizing centers and equivariant K-stability

We give an algebraic proof of the equivalence of equivariant K-semistability (resp. equivariant K-polystability) with geometric K-semistability (resp. geometric K-polystability). Along the way we also prove the existence and uniqueness of minimal optimal destabilizing centers on K-unstable log Fano pairs.
Ordering for Communication-Efficient Quickest Change Detection in a Decomposable Graphical Model

A quickest change detection problem is considered in a sensor network with observations whose statistical dependency structure across the sensors before and after the change is described by a decomposable graphical model (DGM). Distributed computation methods for this problem are proposed that are capable of producing the optimum centralized test statistic. The DGM leads to the proper way to collect nodes into local groups equivalent to cliques in the graph, such that a clique statistic which summarizes all the clique sensor data can be computed within each clique. The clique statistics are transmitted to a decision maker to produce the optimum centralized test statistic. In order to further improve communication efficiency, an ordered transmission approach is proposed where transmissions of the clique statistics to the fusion center are ordered and then adaptively halted when sufficient information is accumulated. This procedure is always guaranteed to provide the optimal change detection performance, despite not transmitting all the statistics from all the cliques. A lower bound on the average number of transmissions saved by ordered transmissions is provided and for the case where the change seldom occurs the lower bound approaches approximately half the number of cliques provided a well behaved distance measure between the distributions of the sensor observations before and after the change is sufficiently large. We also extend the approach to the case when the graph structure is different under each hypothesis. Numerical results show significant savings using the ordered transmission approach and validate the theoretical findings.
Ferromagnetism in a Semiconductor with Mobile Carriers via Low-Level Nonmagnetic Doping

We show that doped cubic iron pyrite, which is a diamagnetic semiconductor, becomes ferromagnetic when $p$-type doped. We furthermore find that this material can exhibit high spin polarization both for tunneling and transport devices. These results are based on first-principles electronic structure and transport calculations. This illustrates the use of $p$-type doping without magnetic impurities as a strategy for obtaining ferromagnetic semiconducting behavior, with implications for spintronic applications that require both magnetic ordering and good mobility. This is a combination that has been difficult to achieve by doping semiconductors with magnetic impurities. We show that phosphorus and arsenic may be effective dopants for achieving this behavior.
Hate in Cyberspace: Regulating Hate Speech On the Internet

The Internet is a global network providing connections for many forms of speech. All the processes of message transmission occur in real space through a system of identifiable algorithms. The information is posted on the Web by individuals or groups intending it to be read by and to affect a limited or expansive audience. The worldwide potentials for the Internet offer a mechanism for spreading democracy and commercial entrepreneurialship throughout the world. However, the Internet is also a breeding ground for hate groups who use it to expand their membership and to solidify their forces. The packages of information about how to instigate a racial war or to limit the opportunities for identifiable groups do not exist in a virtual world, absent from reality. False messages which are intended to stifle and exploit existing negative stereotypes impact individuals' lives and the societies where they reside. They strengthen the purveyors of racism, anti-Semitism, sexism, and gay-bashers. They also intimidate traditional scapegoats and limit their ability to exercise the full extent of their fundamental right to autonomy. In this article, I argue that criminal penalties should be imposed on persons who intend harm and violence against identifiable groups. I begin with a technological explanation of the Internet. I next turn to the proliferation of Internet sites that spread messages promoting racial and ethnic hatred and oppression. I then demonstrate the harm to hate posses to egalitarian democracy. The next part discusses how Canada and Germany have managed to honor freedom of speech on the Internet while contemporaneously prohibiting hate propaganda. Finally, Part V considers whether it is appropriate to enact laws prohibiting the distribution of hate speech on the Internet, and if so, to what extent such expression may constitutionally be limited.
A Wasserstein inequality and minimal Green energy on compact manifolds

Abstract Let M be a smooth, compact d−dimensional manifold, d ≥ 3 , without boundary and let G : M × M → R ∪ { ∞ } denote the Green's function of the Laplacian −Δ (normalized to have mean value 0). We prove a bound on the cost of transporting Dirac measures in { x 1 , … , x n } ⊂ M to the normalized volume measure dx in terms of the Green's function of the Laplacian W 2 ( 1 n ∑ k = 1 n δ x k , d x ) ≲ M 1 n 1 / d + 1 n | ∑ k , l = 1 k ≠ l n G ( x k , x l ) | 1 / 2 . We obtain the same result for the Coulomb kernel G ( x , y ) = 1 / ‖ x − y ‖ d − 2 on the sphere S d , for d ≥ 3 , where we show that W 2 ( 1 n ∑ k = 1 n δ x k , d x ) ≲ 1 n 1 / d + 1 n | ∑ k , l = 1 k ≠ l n ( 1 ‖ x k − x l ‖ d − 2 − c d ) | 1 2 , where c d is the constant that normalizes the Coulomb kernel to have mean value 0. We use this to show that minimizers of the discrete Green energy on compact manifolds have optimal rate of convergence W 2 ( 1 n ∑ k = 1 n δ x k , d x ) ≲ n − 1 / d . The second inequality implies the same result for minimizers of the Coulomb energy on S d which was recently proven by Marzo & Mas.
Linear Regression Games: Convergence Guarantees to Approximate Out-of-Distribution Solutions.

Recently, invariant risk minimization (IRM) (Arjovsky et al.) was proposed as a promising solution to address out-of-distribution (OOD) generalization. In Ahuja et al., it was shown that solving for the Nash equilibria of a new class of "ensemble-games" is equivalent to solving IRM. In this work, we extend the framework in Ahuja et al. for linear regressions by projecting the ensemble-game on an $\ell_{\infty}$ ball. We show that such projections help achieve non-trivial OOD guarantees despite not achieving perfect invariance. For linear models with confounders, we prove that Nash equilibria of these games are closer to the ideal OOD solutions than the standard empirical risk minimization (ERM) and we also provide learning algorithms that provably converge to these Nash Equilibria. Empirical comparisons of the proposed approach with the state-of-the-art show consistent gains in achieving OOD solutions in several settings involving anti-causal variables and confounders.
Activity Recognition with Moving Cameras and Few Training Examples: Applications for Detection of Autism-Related Headbanging

Activity recognition computer vision algorithms can be used to detect the presence of autism-related behaviors, including what are termed “restricted and repetitive behaviors”, or stimming, by diagnostic instruments. Examples of stimming include hand flapping, spinning, and head banging. One of the most significant bottlenecks for implementing such classifiers is the lack of sufficiently large training sets of human behavior specific to pediatric developmental delays. The data that do exist are usually recorded with a handheld camera which is itself shaky or even moving, posing a challenge for traditional feature representation approaches for activity detection which capture the camera's motion as a feature. To address these issues, we first document the advantages and limitations of current feature representation techniques for activity recognition when applied to head banging detection. We then propose a feature representation consisting exclusively of head pose keypoints. We create a computer vision classifier for detecting head banging in home videos using a time-distributed convolutional neural network (CNN) in which a single CNN extracts features from each frame in the input sequence, and these extracted features are fed as input to a long short-term memory (LSTM) network. On the binary task of predicting head banging and no head banging within videos from the Self Stimulatory Behaviour Dataset (SSBD), we reach a mean F1-score of 90.77% using 3-fold cross validation (with individual fold F1-scores of 83.3%, 89.0%, and 100.0%) when ensuring that no child who appeared in the train set was in the test set for all folds. This work documents a successful process for training a computer vision classifier which can detect a particular human motion pattern with few training examples and even when the camera recording the source clip is unstable. The process of engineering useful feature representations by visually inspecting the representations, as described here, can be a useful practice by designers and developers of interactive systems detecting human motion patterns for use in mobile and ubiquitous interactive systems.
A reduced model for solute transport in compliant blood vessels with arbitrary axial velocity profile

Abstract The main result of this paper is the formulation and derivation of a solute transport model in the case of a general velocity profile and in a blood vessel of varying cross section. We couple the resulting one dimensional advection-diffusion equation to a reduced model for blood flow in a compliant vessel, and we obtain a system of equations describing the evolution of the vessel area, blood momentum, and radially averaged solute concentration. In the special case of a no-slip axial velocity profile, we specify a relation between the diffusion coefficient and the shape of the profile. This relation is investigated numerically with physiological boundary data for blood flow, and the results show that the Coriolis parameter has an impact in the transient regime of solute transport.
Two-type annihilating systems on the complete and star graph

Abstract Red and blue particles are placed in equal proportion throughout either the complete or star graph and iteratively sampled to take simple random walk steps. Mutual annihilation occurs when particles with different colors meet. We compare the time it takes to extinguish every particle to the analogous time in the (simple to analyze) one-type setting. Additionally, we study the effect of asymmetric particle speeds.
PPARγ regulates hypoxia‐induced Nox4 expression in human pulmonary artery smooth muscle cells through NF‐κB

NADPH oxidases are a major source of superoxide production in the vasculature. The constitutively active Nox4 subunit, which is selectively upregulated in the lungs of human subjects and experiment...
Mitigating information imperfections in proxy contests: The effect of dissidents' proxy solicitation

Abstract This study examines dissidents' expenses related to proxy solicitation activities in a contested director election. I find that dissidents' solicitation expenses are associated with higher chances of winning board seats. This association is consistent with my conjecture that dissidents extend their efforts in proxy solicitation and thus persuade more shareholders in dissidents' favor, but this is not likely driven by retained solicitor's reputation, dissidents' opportunistic behavior for reimbursement, or other popular strategic activities in proxy contests. Furthermore, I show that proxy solicitation activities are considered to be an information channel that substitutes for investors' own search for proxy statements from the SEC's EDGAR. Overall, this study suggests that proxy solicitation plays an important role in proxy contests.
Neuronal variability reflects probabilistic inference tuned to natural image statistics

Neuronal activity in sensory cortex fluctuates over time and across repetitions of the same input. This variability is often considered detrimental to neural coding. The theory of neural sampling proposes instead that variability encodes the uncertainty of perceptual inferences. In primary visual cortex (V1), modulation of variability by sensory and non-sensory factors supports this view. However, it is unknown whether V1 variability reflects the statistical structure of visual inputs, as would be required for inferences correctly tuned to the statistics of the natural environment. Here we combine analysis of image statistics and recordings in macaque V1 to show that probabilistic inference tuned to natural image statistics explains the widely observed dependence between spike count variance and mean, and the modulation of V1 activity and variability by spatial context in images. Our results show that the properties of a basic aspect of cortical responses—their variability—can be explained by a probabilistic representation tuned to naturalistic inputs. The neural sampling theory suggests that neuronal variability encodes the uncertainty of probabilistic inferences. This paper shows that response variability in primary visual cortex reflects the statistical structure of visual inputs, as required for inferences correctly tuned to the statistics of the natural environment.
The cognitive load of inpatient consults: A convergent parallel mixed methods study using the consult cognitive load instrument.

11027Background: Consultation is crucial for inpatient care and a primary responsibility of fellows. Understanding the cognitive load associated with the complex skill of consultation would enhance...
Existence of Weak Solutions for Non-Simple Elastic Surface Models

We consider a class of models for nonlinearly elastic surfaces in this work. We have in mind thin, highly deformable structures modeled directly as two-dimensional nonlinearly elastic continua, accounting for finite membrane and bending strains and thickness change. We assume that the stored-energy density is polyconvex with respect to the second gradient of the deformation, and we require that it grow unboundedly as the local area ratio approaches zero. For sufficiently fast growth, we show that the latter is uniformly bounded away from zero at an energy minimizer. With this in hand, we rigorously derive the weak form of the Euler-Lagrange equilibrium equations.
Sensor Placement With Optimal Precision for Temperature Estimation of Battery Systems

The temperature distribution in the battery significantly impacts the short-term and long-term performance of battery systems. Therefore, efficient, safe, and reliable battery system operation requires an accurate estimation of the temperature field. The current industry standard for sensors to battery cell ratio is quite frugal. Thus, the problem of sensor placement for accurate temperature estimation becomes non-trivial, especially for large-scale systems. In this letter, we explore a greedy approach for sensor placement suitable for large-scale battery systems. An observer to estimate the thermal field is designed in an $\mathcal {H}_{\infty }$ framework while simultaneously minimizing the sensor precisions, thus lowering the overall thermal management system’s economic cost.
Unique flight characteristics of the AD-1 oblique-wing research airplane

Flight characteristics associated with an oblique-wing airplane have been studied with limited scope and complexity using the AD-1 research vehicle. The AD-1 is a low-speed, low-cost, manned airplane with an aeroelastically tailored wing that can be pivoted 0 to 60 deg asymmetrically. Results of the flight tests include aerodynamic parameter extraction, verification of the aeroelastic wing design criteria, trim requirements, stall characteristics, and an evaluation of the handling qualities and basic control system requirements. Some of the unique characteristics of these results that pertain to the oblique-wing design are presented.
Keep It Simple: Unsupervised Simplification of Multi-Paragraph Text

This work presents Keep it Simple (KiS), a new approach to unsupervised text simplification which learns to balance a reward across three properties: fluency, salience and simplicity. We train the model with a novel algorithm to optimize the reward (k-SCST), in which the model proposes several candidate simplifications, computes each candidate’s reward, and encourages candidates that outperform the mean reward. Finally, we propose a realistic text comprehension task as an evaluation method for text simplification. When tested on the English news domain, the KiS model outperforms strong supervised baselines by more than 4 SARI points, and can help people complete a comprehension task an average of 18% faster while retaining accuracy, when compared to the original text.
Defending against Backdoors in Federated Learning with Robust Learning Rate.

Federated learning (FL) allows a set of agents to collaboratively train a model without sharing their potentially sensitive data. This makes FL suitable for privacy-preserving applications. At the same time, FL is susceptible to adversarial attacks due to decentralized and unvetted data. One important line of attacks against FL is the backdoor attacks. In a backdoor attack, an adversary tries to embed a backdoor functionality to the model during training that can later be activated to cause a desired misclassification. To prevent backdoor attacks, we propose a lightweight defense that requires minimal change to the FL protocol. At a high level, our defense is based on carefully adjusting the aggregation server's learning rate, per dimension and per round, based on the sign information of agents' updates. We first conjecture the necessary steps to carry a successful backdoor attack in FL setting, and then, explicitly formulate the defense based on our conjecture. Through experiments, we provide empirical evidence that supports our conjecture, and we test our defense against backdoor attacks under different settings. We observe that either backdoor is completely eliminated, or its accuracy is significantly reduced. Overall, our experiments suggest that our defense significantly outperforms some of the recently proposed defenses in the literature. We achieve this by having minimal influence over the accuracy of the trained models. In addition, we also provide convergence rate analysis for our proposed scheme.
On the Probability That a Rocky Planet’s Composition Reflects Its Host Star

The bulk density of a planet, as measured by mass and radius, is a result of planet structure and composition. Relative proportions of iron core, rocky mantle, and gaseous envelopes are degenerate for a given density. This degeneracy is reduced for rocky planets without significant gaseous envelopes when the structure is assumed to be a differentiated iron core and rocky mantle, in which the core mass fraction (CMF) is a first-order description of a planet's bulk composition. A rocky planet's CMF may be derived both from bulk density and by assuming the planet reflects the host star's major rock-building elemental abundances (Fe, Mg, and Si). Contrasting CMF measures, therefore, shed light on the outcome diversity of planet formation from processes including mantle stripping, out-gassing, and/or late-stage volatile delivery. We present a statistically rigorous analysis of the consistency of these two CMF measures accounting for observational uncertainties of planet mass and radius and host-star chemical abundances. We find that these two measures are unlikely to be resolvable as statistically different unless the bulk density CMF is at least 40% greater than or 50% less than the CMF as inferred from the host star. Applied to 11 probable rocky exoplanets, Kepler-107c has a CMF as inferred from bulk density that is significantly greater than the inferred CMF from its host star (2$\sigma$) and is therefore likely an iron-enriched super-Mercury. K2-229b, previously described as a super-Mercury, however, does not meet the threshold for a super-Mercury at a 1- or 2- $\sigma$ level.
Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation

Multi-task learning (MTL) aims to improve the generalization of several related tasks by learning them jointly. As a comparison, in addition to the joint training scheme, modern meta-learning allows unseen tasks with limited labels during the test phase, in the hope of fast adaptation over them. Despite the subtle difference between MTL and meta-learning in the problem formulation, both learning paradigms share the same insight that the shared structure between existing training tasks could lead to better generalization and adaptation. In this paper, we take one important step further to understand the close connection between these two learning paradigms, through both theoretical analysis and empirical investigation. Theoretically, we first demonstrate that MTL shares the same optimization formulation with a class of gradient-based meta-learning (GBML) algorithms. We then prove that for over-parameterized neural networks with sufficient depth, the learned predictive functions of MTL and GBML are close. In particular, this result implies that the predictions given by these two models are similar over the same unseen task. Empirically, we corroborate our theoretical findings by showing that, with proper implementation, MTL is competitive against state-of-the-art GBML algorithms on a set of few-shot image classification benchmarks. Since existing GBML algorithms often involve costly second-order bi-level optimization, our first-order MTL method is an order of magnitude faster on large-scale datasets such as mini-ImageNet. We believe this work could help bridge the gap between these two learning paradigms, and provide a computationally efficient alternative to GBML that also supports fast task adaptation.
Experimental Evaluation of Multiprecision Strategies for GMRES on GPUs

Support for lower precision computation is becoming more common in accelerator hardware due to lower power usage, reduced data movement and increased computational performance. However, computational science and engineering (CSE) problems require double precision accuracy in several domains. This conflict between hardware trends and application needs has resulted in a need for multiprecision strategies at the linear algebra algorithms level if we want to exploit the hardware to its full potential while meeting the accuracy requirements. In this paper, we focus on preconditioned sparse iterative linear solvers, a key kernel in several CSE applications. We present a study of multiprecision strategies for accelerating this kernel on GPUs. We seek the best methods for incorporating multiple precisions into the GMRES linear solver; these include iterative refinement and parallelizable preconditioners. Our work presents strategies to determine when multiprecision GMRES will be effective and to choose parameters for a multiprecision iterative refinement solver to achieve better performance. We use an implementation that is based on the Trilinos library and employs Kokkos Kernels for performance portability of linear algebra kernels. Performance results demonstrate the promise of multiprecision approaches and demonstrate even further improvements are possible by optimizing low-level kernels.
Reconstruction of plant-pollinator networks from observational data.

Empirical measurements of ecological networks such as food webs and mutualistic networks are often rich in structure but also noisy and error-prone, particularly for rare species for which observations are sparse. Focusing on the case of plant–pollinator networks, we here describe a Bayesian statistical technique that allows us to make accurate estimates of network structure and ecological metrics from such noisy observational data. Our method yields not only estimates of these quantities, but also estimates of their statistical errors, paving the way for principled statistical analyses of ecological variables and outcomes. We demonstrate the use of the method with an application to previously published data on plant–pollinator networks in the Seychelles archipelago and Kosciusko National Park, calculating estimates of network structure, network nestedness, and other characteristics. Networks describe the intricate patterns of interaction occurring within ecological systems, but they are unfortunately difficult to construct from data. Here, the authors show how Bayesian statistical techniques can separate structure from noise in networks gathered in observational studies of plant-pollinator systems.
Sensitivity of steady states in a degenerately damped stochastic Lorenz system

We study stability of solutions for a randomly driven and degenerately damped version of the Lorenz ’63 model. Specifically, we prove that when damping is absent in one of the temperature component...
Probabilistic Performance Bounds for Randomized Sensor Selection in Kalman Filtering

We consider the problem of randomly choosing the sensors of a linear time-invariant dynamical system subject to process and measurement noise. Each sensor is sampled independently and from the same distribution for the purpose of state estimation by Kalman filtering. Due to our randomized sampling procedure, the estimation error covariance cannot be bounded in a deterministic sense. Using tools from random matrix theory, we derive probabilistic bounds on the steady-state estimation error covariance in the semi-definite sense for an arbitrary sampling distribution. Our bounds are functions of several tunable parameters of interest, such as the number of sampled sensors and the likelihood that our bounds hold. We indirectly improve the performance of our Kalman filter for the maximum eigenvalue metric by finding the optimal sampling distribution. By minimizing the maximum eigenvalue of the upper bound, we are able to minimize the maximum eigenvalue of the steady-state estimation error covariance, the actual metric of interest. We identify the subset of sensors to sample with high frequency through the optimal sampling distribution. We illustrate our findings through several insightful simulations and comparisons with multiple sampling policies.
Investment Incentives in Near-Optimal Mechanisms

In many real-world resource allocation problems, optimization is computationally intractable, so any practical allocation mechanism must be based on an approximation algorithm. We study investment incentives in strategy-proof mechanisms that use such approximations. In sharp contrast with the Vickrey-Clark-Groves mechanism, for which individual returns on investments are aligned with social welfare, we find that some algorithms that approximate efficient allocation arbitrarily well can nevertheless create misaligned investment incentives that lead to arbitrarily bad overall outcomes. However, if a near-efficient algorithm "excludes bossy negative externalities," then its outcomes remain near-efficient even after accounting for investments. A weakening of this "XBONE" condition is necessary and sufficient for the result.
Improved Product-Based High-Dimensional Expanders

High-dimensional expanders generalize the notion of expander graphs to higher-dimensional simplicial complexes. In contrast to expander graphs, only a handful of high-dimensional expander constructions have been proposed, and no elementary combinatorial construction with near-optimal expansion is known. In this paper, we introduce an improved combinatorial high-dimensional expander construction, by modifying a previous construction of Liu, Mohanty, and Yang (ITCS 2020), which is based on a high-dimensional variant of a tensor product. Our construction achieves a spectral gap of Ω(1/(k²)) for random walks on the k-dimensional faces, which is only quadratically worse than the optimal bound of Θ(1/k). Previous combinatorial constructions, including that of Liu, Mohanty, and Yang, only achieved a spectral gap that is exponentially small in k. We also present reasoning that suggests our construction is optimal among similar product-based constructions.
Fast and flexible estimation of effective migration surfaces.

Spatial population genetic data often exhibits ‘isolation-by-distance,’ where genetic similarity tends to decrease as individuals become more geographically distant. The rate at which genetic similarity decays with distance is often spatially heterogeneous due to variable population processes like genetic drift, gene flow, and natural selection. Petkova et al., 2016 developed a statistical method called Estimating Effective Migration Surfaces (EEMS) for visualizing spatially heterogeneous isolation-by-distance on a geographic map. While EEMS is a powerful tool for depicting spatial population structure, it can suffer from slow runtimes. Here, we develop a related method called Fast Estimation of Effective Migration Surfaces (FEEMS). FEEMS uses a Gaussian Markov Random Field model in a penalized likelihood framework that allows for efficient optimization and output of effective migration surfaces. Further, the efficient optimization facilitates the inference of migration parameters per edge in the graph, rather than per node (as in EEMS). With simulations, we show conditions under which FEEMS can accurately recover effective migration surfaces with complex gene-flow histories, including those with anisotropy. We apply FEEMS to population genetic data from North American gray wolves and show it performs favorably in comparison to EEMS, with solutions obtained orders of magnitude faster. Overall, FEEMS expands the ability of users to quickly visualize and interpret spatial structure in their data.
Model-Based Event-Triggered Control over Lossy Networks

The event-triggered control problem over lossy communication networks is addressed in this paper. Although packet dropouts have been considered in the implementation of event-triggered controllers, the assumption of protocols that employ acknowledgement messages persists. This paper provides an approach that relaxes such assumption. An event-based controller is implemented at the sensor node of the networked and uncertain system and it transmits feedback measurements to the controller node at asynchronous time instants. The transmitted packets of information are subject to dropouts by the lossy network. We show that the uncertain system can be asymptotically stabilized, that a positive minimum inter-event time exists, and that the proposed approach does not require acknowledgement messages.
AutoInt: Automatic Integration for Fast Neural Volume Rendering

Numerical integration is a foundational technique in scientific computing and is at the core of many computer vision applications. Among these applications, neural volume rendering has recently been proposed as a new paradigm for view synthesis, achieving photorealistic image quality. However, a fundamental obstacle to making these methods practical is the extreme computational and memory requirements caused by the required volume integrations along the rendered rays during training and inference. Millions of rays, each requiring hundreds of forward passes through a neural network are needed to approximate those integrations with Monte Carlo sampling. Here, we propose automatic integration, a new framework for learning efficient, closed-form solutions to integrals using coordinate-based neural networks. For training, we instantiate the computational graph corresponding to the derivative of the coordinate-based network. The graph is fitted to the signal to integrate. After optimization, we reassemble the graph to obtain a network that represents the antiderivative. By the fundamental theorem of calculus, this enables the calculation of any definite integral in two evaluations of the network. Applying this approach to neural rendering, we improve a tradeoff between rendering speed and image quality: improving render times by greater than 10× with a tradeoff of reduced image quality.
Maximizing Performance of Quantum Cascade Laser-Pumped Molecular Lasers

Quantum-cascade-laser- (QCL) pumped molecular lasers (QPMLs) have recently been introduced as a source of powerful ($g\phantom{\rule{-2pt}{0ex}}1$ mW) tunable ($g\phantom{\rule{-2pt}{0ex}}1$ THz) narrow-band ($l\phantom{\rule{-2pt}{0ex}}10$ kHz) continuous-wave terahertz radiation. The performance of these lasers depends critically on molecular collision physics, pump saturation, and on the design of the laser cavity. Using a validated three-level model that captures the essential collision and saturation behaviors of the QPML gas nitrous oxide (${\mathrm{N}}_{2}\mathrm{O}$), we explore how the threshold pump power and output terahertz power depend on the pump power and gas pressure, as well as on the diameter, length, and output-coupler transmissivity of a cylindrical cavity. The analysis indicates that maximum power occurs as pump saturation is minimized in a manner that depends much more sensitively on pressure than on cell diameter, length, or transmissivity. A near-optimal compact laser cavity can produce tens of milliwatts of power tunable over frequencies above 1 THz when pumped by a multiwatt QCL.
Growth of topological insulator Bi2Se3 particles on GaAs via droplet epitaxy

The discovery of topological insulators (TIs) and their unique electronic properties has motivated research into a variety of applications, including quantum computing. It has been proposed that TI surface states will be energetically discretized in a quantum dot nanoparticle. These discretized states could then be used as basis states for a qubit that is more resistant to decoherence. In this work, prototypical TI Bi2Se3 nanoparticles are grown on GaAs (001) using the droplet epitaxy technique, and we demonstrate the control of nanoparticle height, area, and density by changing the duration of bismuth deposition and substrate temperature. Within the growth window studied, nanoparticles ranged from 5 to 15 nm in height with an 8–18 nm equivalent circular radius, and the density could be relatively well controlled by changing the substrate temperature and bismuth deposition time.
Combined Eco-Routing and Power-Train Control of Plug-In Hybrid Electric Vehicles in Transportation Networks

We study the problem of eco-routing for Plug-In Hybrid Electric Vehicles (PHEVs) to minimize the overall energy consumption cost. We propose an algorithm which can simultaneously calculate an energy-optimal route (eco-route) for a PHEV and an optimal power-train control strategy over this route. In order to show the effectiveness of our method in practice, we use a HERE Maps API to apply our algorithms based on traffic data in the city of Boston with more than 110,000 links. Moreover, we validate the performance of our eco-routing algorithm using speed profiles collected from a traffic simulator (SUMO) as input to a high-fidelity energy model to calculate energy consumption costs. Our results show significant energy savings (around 12%) for PHEVs with a near real-time execution time for the algorithm.
Variable-length Feedback Codes with Several Decoding Times for the Gaussian Channel

We investigate variable-length feedback (VLF) codes for the Gaussian point-to-point channel under maximal power, average error probability, and average decoding time constraints. Our proposed strategy chooses $K decoding times $n_{1}, n_{2}, \ldots, n_{K}$ rather than allowing decoding at any time $n=0,1,2, \ldots$ . We consider stop-feedback, which is one-bit feedback transmitted from the receiver to the transmitter at times $n_{1}, n_{2},\ldots$ only to inform her whether to stop. We prove an achievability bound for VLF codes with the asymptotic approximation $\ln M \approx \frac{NC(P)}{1-\epsilon}-\sqrt{N\ \ln_{(K-1)}(N)\frac{V(P)}{1-\epsilon}}$ , where $\ln(K)(\cdot)$ denotes the $K$ -fold nested logarithm function, $N$ is the average decoding time, and $C(P)$ and $V(P)$ are the capacity and dispersion of the Gaussian channel, respectively. Our achievability bound evaluates a non-asymptotic bound and optimizes the decoding times $n_{1}, \ldots, n_{K}$ within our code architecture.
Vertex distortion of lattice knots

The vertex distortion of a lattice knot is the supremum of the ratio of the distance between a pair of vertices along the knot and their distance in the l1-norm. Inspired by Gromov, Pardon and Blai...
Numerically stable coded matrix computations via circulant and rotation matrix embeddings

Polynomial based methods have recently been used in several works for mitigating the effect of stragglers in distributed matrix computations. However, they suffer from serious numerical issues owing to the condition number of the corresponding real Vandermonde-structured recovery matrices. For a system with $n$ worker nodes where $s$ can be stragglers the condition number grows exponentially in n. We present a novel coded computation approach that leverages the properties of circulant permutation and rotation matrices. Our scheme has an optimal recovery threshold and an upper bound on the worst case condition number of our recovery matrices which grows as ≈ $O$ (ns+6); in the practical scenario where $s$ is a constant, this grows polynomially in n. Our schemes leverage the well-behaved conditioning of complex Vandermonde matrices with parameters on the complex unit circle, while still working with computation over the reals. Exhaustive experimental results demonstrate that our proposed method has condition numbers that are orders of magnitude lower than prior work.
Measurement and analysis of GPU-accelerated applications with HPCToolkit

Abstract To address the challenge of performance analysis on the US DOE’s forthcoming exascale supercomputers, Rice University has been extending its HPCToolkit performance tools to support measurement and analysis of GPU-accelerated applications. To help developers understand the performance of accelerated applications as a whole, HPCToolkit’s measurement and analysis tools attribute metrics to calling contexts that span both CPUs and GPUs. To measure GPU-accelerated applications efficiently, HPCToolkit employs a novel wait-free data structure to coordinate monitoring and attribution of GPU performance. To help developers understand the performance of complex GPU code generated from high-level programming models, HPCToolkit constructs sophisticated approximations of call path profiles for GPU computations. To support fine-grained analysis and tuning, HPCToolkit uses PC sampling and instrumentation to measure and attribute GPU performance metrics to source lines, loops, and inlined code. To supplement fine-grained measurements, HPCToolkit can measure GPU kernel executions using hardware performance counters. To provide a view of how an execution evolves over time, HPCToolkit can collect, analyze, and visualize call path traces within and across nodes. Finally, on NVIDIA GPUs, HPCToolkit can derive and attribute a collection of useful performance metrics based on measurements using GPU PC samples. We illustrate HPCToolkit’s new capabilities for analyzing GPU-accelerated applications with several codes developed as part of the Exascale Computing Project.
Dislocation non-Hermitian skin effect

We demonstrate that crystal defects can act as a probe of intrinsic non-Hermitian topology. In particular, in point-gapped systems with periodic boundary conditions, a pair of dislocations may induce a non-Hermitian skin effect, where an extensive number of Hamiltonian eigenstates localize at only one of the two dislocations. An example of such a phase are two-dimensional systems exhibiting weak non-Hermitian topology, which are adiabatically related to a decoupled stack of Hatano-Nelson chains. Moreover, we show that strong two-dimensional point-gap topology may also result in a dislocation response, even when there is no skin effect present with open boundary conditions. For both cases, we directly relate their bulk topology to a stable dislocation non-Hermitian skin effect. Finally, and in stark contrast to the Hermitian case, we find that gapless non-Hermitian systems hosting bulk exceptional points also give rise to a well-localized dislocation response.
Singular Value Decomposition and Similarity Renormalization Group Evolution of Nuclear Interactions

One of the main challenges for ab initio nuclear many-body theory in the coming decade is the growth of computational and storage costs as calculations are extended to increasingly heavy, exotic, and structurally complex nuclei. Here, we investigate the factorization of nuclear interactions as a means to address this issue. We perform singular value decompositions (SVD) of current nucleon-nucleon interactions in partial wave representation and study the dependence of the singular value spectrum on interaction characteristics like regularization schemes and resolution scales. Next, we develop and implement the similarity renormalization group (SRG) evolution of the interaction in terms of the relevant singular vectors, and demonstrate that this SVD-SRG approach accurately preserves two-nucleon observables. We find that low-resolution interactions naturally allow the truncation of the SVD at low rank, and that a small number of relevant components is sufficient to capture the nuclear interaction and perform an accurate SRG evolution, while the Coulomb interaction requires special consideration. The rank is uniform across all partial waves and almost independent of the basis choice in the tested cases. This suggests an interpretation of the relevant singular components as mere representations of a small set of abstract operators that can describe the interaction and its SRG flow. Following the traditional workflow for nuclear interactions, we discuss how the transformation between the center-of-mass and laboratory frames creates redundant copies of the partial wave components when implemented in matrix representation, and we discuss strategies for mitigation. Finally, we test the low-rank approximation to the SRG-evolved interactions in many-body calculations using the in-medium SRG. By including nuclear radii in our analysis, we verify that the implementation of the SRG using the singular vectors of the interaction does not spoil the evolution of other observables.
Hidden Markov models for pipeline damage detection using piezoelectric transducers

Oil and gas pipeline leakages lead to not only enormous economic loss but also environmental disasters. How to detect the pipeline damages including leakages and cracks has attracted much research attention. One of the promising leakage detection method is to use lead zirconate titanate (PZT) transducers to detect the negative pressure wave when leakage occurs. PZT transducers can generate and detect guided waves for crack detection also. However, the negative pressure waves or guided stress waves may not be easily detected with environmental interference, e.g., the oil and gas pipelines in an offshore environment. In this paper, a Gaussian mixture model-hidden Markov model (GMM-HMM) method is proposed to process PZT transducers’ outputs for detecting the pipeline leakage and crack depth in changing environment and time-varying operational conditions. Leakages in different sections or crack depths are considered as different states in hidden Markov models (HMMs). One time-domain damage index and one frequency domain damage index are extracted from signals collected from PZT transducers, then extracted indices are formed as observation emissions in the HMM. The observation probability distribution matrix in HMM is initialized by a Gaussian mixture model (GMM) to address signal uncertainties. After the HMM parameter initialization, an iterative training process through the Baum–Welch algorithm is applied to get the optimized parameters of the GMM-HMM. Leakage location or crack depth is decided by the maximum posterior probability from the trained model. Two different experimental settings and results show that the GMM-HMM method can recognize the crack depth and leakage of pipeline such as whether there is a leakage, where the leakage is.
Shape recovery of deformed biomolecular droplets: Dependence on condensate viscoelasticity.

A theoretical study on the shape dynamics of phase-separated biomolecular droplets is presented, highlighting the importance of condensate viscoelasticity. Previous studies on shape dynamics have modeled biomolecular condensates as purely viscous, but recent data have shown them to be viscoelastic. Here, we present an exact analytical solution for the shape recovery dynamics of deformed biomolecular droplets. The shape recovery of viscous droplets has an exponential time dependence, with the time constant given by the “viscocapillary” ratio, i.e., viscosity over interfacial tension. In contrast, the shape recovery dynamics of viscoelastic droplets is multi-exponential, with shear relaxation yielding additional time constants. During shape recovery, viscoelastic droplets exhibit shear thickening (increase in apparent viscosity) at fast shear relaxation rates but shear thinning (decrease in apparent viscosity) at slow shear relaxation rates. These results highlight the importance of viscoelasticity and expand our understanding of how material properties affect condensate dynamics in general, including aging.
Adaptive Variational Quantum Imaginary Time Evolution Approach for Ground State Preparation

An adaptive variational quantum imaginary time evolution (AVQITE) approach is introduced that yields efficient representations of ground states for interacting Hamiltonians on near-term quantum computers. It is based on McLachlan's variational principle applied to imaginary time evolution of variational wave functions. The variational parameters evolve deterministically according to equations of motions that minimize the difference to the exact imaginary time evolution, which is quantified by the McLachlan distance. Rather than working with a fixed variational ansatz, where the McLachlan distance is constrained by the quality of the ansatz, the AVQITE method iteratively expands the ansatz along the dynamical path to keep the McLachlan distance below a chosen threshold. This ensures the state is able to follow the quantum imaginary time evolution path in the system Hilbert space rather than in a restricted variational manifold set by a predefined fixed ansatz. AVQITE is used to prepare ground states of H$_4$, H$_2$O and BeH$_2$ molecules, where it yields compact variational ansatze and ground state energies within chemical accuracy. Polynomial scaling of circuit depth with system size is demonstrated through a set of AVQITE calculations of quantum spin models. Finally, it is shown that quantum Lanczos calculations can also be naturally performed alongside AVQITE without additional quantum resource costs.