cssi/SciSciGPT-SciSciNet huggingface dataset:
On incorporating forecasts into linear state space model Markov decision processes

Weather forecast information will very likely find increasing application in the control of future energy systems. In this paper, we introduce an augmented state space model formulation with linear dynamics, within which one can incorporate forecast information that is dynamically revealed alongside the evolution of the underlying state variable. We use the martingale model for forecast evolution (MMFE) to enforce the necessary consistency properties that must govern the joint evolution of forecasts with the underlying state. The formulation also generates jointly Markovian dynamics that give rise to Markov decision processes (MDPs) that remain computationally tractable. This paper is the first to enforce MMFE consistency requirements within an MDP formulation that preserves tractability. This article is part of the theme issue 'The mathematics of energy systems'.
Digital Pathology-based Study of Cell- and Tissue-level Morphologic Features in Serous Borderline Ovarian Tumor and High-grade Serous Ovarian Cancer

Background: Serous borderline ovarian tumor (SBOT) and high-grade serous ovarian cancer (HGSOC) are two distinct subtypes of epithelial ovarian tumors, with markedly different biologic background, behavior, prognosis, and treatment. However, the histologic diagnosis of serous ovarian tumors can be subjectively variable and labor-intensive as multiple tumor slides/blocks need to be thoroughly examined to search for these features. Materials and Methods: We developed a novel informatics system to facilitate objective and scalable diagnosis screening for SBOT and HGSOC. The system was built upon Groovy scripts and QuPath to enable interactive annotation and data exchange. Results: The system was used to successfully detect cellular boundaries and extract an expanded set of cellular features representing cell- and tissue-level characteristics. The performance of cell-level classification for both tumor and stroma cells achieved >90% accuracy. The performance of differentiating HGSOC versus SBOT achieved 91%–95% accuracy for 6485 imaging patches which have sufficient tumor and stroma cells (minimum of ten each) and 97% accuracy for classifying patients when aggregating the results to whole-slide image based on consensus. Conclusions: Cellular features digitally extracted from pathological images can be used for cell classification and SBOT v. HGSOC differentiation. Introducing digital pathology into ovarian cancer research could be beneficial to discover potential clinical implications. A larger cohort is required to further evaluate the system.
An improved approximation algorithm for maximin shares

Abstract Fair division is a fundamental problem in various multi-agent settings, where the goal is to divide a set of resources among agents in a fair manner. We study the case where m indivisible items need to be divided among n agents with additive valuations using the popular fairness notion of maximin share (MMS). An MMS allocation provides each agent a bundle worth at least her maximin share. While it is known that such an allocation need not exist [1] , [2] , a series of remarkable work [1] , [3] , [4] , [5] , [6] provided approximation algorithms for a 2 3 -MMS allocation in which each agent receives a bundle worth at least 2 3 times her maximin share. More recently, Ghodsi et al. [7] showed the existence of a 3 4 -MMS allocation and a PTAS to find a ( 3 4 − ϵ )-MMS allocation for an ϵ > 0 . Most of the previous works utilize intricate algorithms and require agents' approximate MMS values, which are computationally expensive to obtain. In this paper, we develop a new approach that gives a simple algorithm for showing the existence of a 3 4 -MMS allocation. Furthermore, our approach is powerful enough to be easily extended in two directions: First, we get a strongly polynomial time algorithm to find a 3 4 -MMS allocation, where we do not need to approximate the MMS values at all. Second, we show that there always exists a ( 3 4 + 1 12 n ) -MMS allocation, improving the best previous factor. This improves the approximation guarantee, most notably for small n. We note that 3 4 was the best factor known for n > 4 .
Reinforcement Learning for Beam Pattern Design in Millimeter Wave and Massive MIMO Systems

Deploying large scale antenna arrays is a key characteristic of current and future wireless communication systems. However, due to some non-ideal practical conditions, such as the unknown array geometry or possible hardware impairments, the accurate channel state information becomes hard to acquire. This impedes the design of beamforming/combining vectors that are crucial to fully exploit the potential of the large-scale MIMO systems or to combat the high path-loss in millimeter wave (mmWave) communications. In this paper, we propose a novel solution that leverages deep reinforcement learning (DRL) to learn the beam pattern that is optimized for a group of users without the explicit knowledge of the channels. Simulation results show that the developed solution is capable of finding the near optimal beam pattern with quantized phase shifters and with only requiring the beamforming gain feedback from the users.
POST-COITAL CONTRACEPTION: MEDICAL AND SOCIAL FACTORS OF THE MORNING AFTER PILL

Abstract 200 patients who were administered diethyl-stilbesterol after requesting post-coital contraception are presented. Demographic data, contraceptive habits, medical and psychological findings are analyzed. There were only three failures, all of whom had multiple sexual exposures during the treatment cycle. Recommendations for treatment of these patients are offered.
Beyond Scaling: Calculable Error Bounds of the Power-of-Two-Choices Mean-Field Model in Heavy-Traffic

This paper provides a recipe for deriving calculable approximation errors of mean-field models in heavy-traffic with the focus on the well-known load balancing algorithm --- power-of-two-choices (Po2). The recipe combines Stein's method for linearized mean-field models and State Space Concentration (SSC) based on geometric tail bounds. In particular, our approach divides the state space into two regions, a neighborhood near the mean-field equilibrium and the complement of that. We first use a tail bound to show that the steady-state probability being outside the neighborhood is small. Then, we use a linearized mean-field model and Stein's method to characterize the generator difference, which provides the dominant term of the approximation error. From the dominant term, we are able to obtain an asymptotically-tight bound and a nonasymptotic upper bound, both are calculable bounds, not order-wise scaling results like most results in the literature. Finally, we compare the theoretical bounds with numerical evaluations to show the effectiveness of our results. We note that the simulation results show that both bounds are valid even for small size systems such as a system with only ten servers.
Aeroelastic response of swept aircraft wings in a compressible flow field

The present study addresses the subcritical aeroelastic response of swept wings, in various flight speed regimes, to arbitrary time-dependent external excitations. The methodology based on the concept of indicial functions is carried out in time and frequency domains. As a result of this approach, the proper unsteady aerodynamic loads necessary to study the subcritical aeroelastic response of the open/closed loop aeroelastic systems, and of flutter instability, respectively are obtained. Validation of the aeroelastic model is provided, and applications to subcritical aeroelastic response to blast pressure signatures are illustrated. In this context, an original representation of the aeroelastic response in the phase-space is displayed, and pertinent conclusions on the implications of a number of selected parameters of the system are outlined.
Linearization for High-Speed Current-Steering DACs Using Neural Networks

This paper proposes a novel foreground linearization scheme for a high-speed current-steering (CS) digital-to-analog converter (DAC). The technique leverages neural networks (NNs) to derive a lookup-table (LUT) that maps the inverse of the DAC transfer characteristic onto the input codes. The algorithm is shown to improve conventional methods by at least 6dB in terms of intermodulation (IM) performance for frequencies up to 9GHz on a state-of-the-art 10-bit CS-DAC operating at 40.96GS/s (gigasamples-per-second) in 14nm CMOS.
Deception Detection in Group Video Conversations using Dynamic Interaction Networks

Predicting groups of people who are jointly deceptive is critical in settings such as sales pitches and negotiations. Past work on deception in videos focuses on detecting single deceivers and uses facial or visual features only. We propose the concept of Face-to Face Interaction Networks (FFINs) and Negative Interaction Networks (NINs) to model interactions within a group of people. The use of FFINs and NINs in this paper enables us to leverage network relations in predicting face-to-face deception for the first time. We will use a dataset of 185 videos from a deception-based game called Resistance for deception based prediction using FFINs. We first characterize the behavior of individuals, pairs, and groups of deceptive participants compared to non-deceptive participants. Our analysis reveals that less engaged deceivers are identified early. Moreover, pairs of deceivers tend to avoid mutual interaction and focus their attention on non-deceivers. In contrast, non-deceivers interact with everyone equally. We propose the notion of Negative Interaction Networks (NINs) and create a belief propagation neural net algorithm called BPNN based on dynamic FFINs and NINs to detect deceivers from videos that are just 1 minute long. We show that our method outperforms recent state-of-the-art computer vision, graph embedding, and ensemble methods by at least 20.9% AUROC in identifying deception from videos.
Past Experiences and Investment Decisions: Evidence from Real Estate Markets

This paper investigates how market participants form risk perspectives through a sequence of information shocks. Guided by a theoretical Bayesian learning model, we exploit a natural experiment afforded by the fracking boom in Pennsylvania in the late-2000s. We empirically examine whether familiarity with historical conventional gas explorations affects the willingness to pay for houses near fracking wells. We find the local real estate market is very efficient with participants rapidly collecting and processing market–relevant new information. We also find that participants discount historical events and rely on current information to estimate the risk of a change in market conditions.
Piezoacoustics for precision control of electrons floating on helium.

Piezoelectric surface acoustic waves (SAWs) are powerful for investigating and controlling elementary and collective excitations in condensed matter. In semiconductor two-dimensional electron systems SAWs have been used to reveal the spatial and temporal structure of electronic states, produce quantized charge pumping, and transfer quantum information. In contrast to semiconductors, electrons trapped above the surface of superfluid helium form an ultra-high mobility, two-dimensional electron system home to strongly-interacting Coulomb liquid and solid states, which exhibit non-trivial spatial structure and temporal dynamics prime for SAW-based experiments. Here we report on the coupling of electrons on helium to an evanescent piezoelectric SAW. We demonstrate precision acoustoelectric transport of as little as ~0.01% of the electrons, opening the door to future quantized charge pumping experiments. We also show SAWs are a route to investigating the high-frequency dynamical response, and relaxational processes, of collective excitations of the electronic liquid and solid phases of electrons on helium. Hybrid devices based on electrons on helium may find application in quantum devices. Here the authors demonstrate surface acoustic wave driven acoustoelectric transport of electrons on superfluid helium.
Do sin tax hikes spur cheating in interpersonal exchange

Abstract We study the New York City taxi market to examine whether an excise tax hike on cigarettes corresponds to smoker taxi drivers more frequently cheating their customers. Increased cheating could be motivated by both financial pressures and as a reaction to unfair treatment (as surveyed smokers view cigarette tax hikes as quite unfair). We examine this question using detailed ride-level data where we can identify a rare but fraudulent overcharging technique (cheating) and a subsample of taxi drivers who smoke (affected taxpayers, identified via tickets for smoking in a cab). In difference-in-differences regressions we find that following a cigarette tax hike, taxi drivers who smoke are approximately 1.5 times more likely to cheat customers than other drivers. Our findings are strongest in the subsample of smokers with consistently low earnings.
Prediction and Optimal Feedback Steering of Probability Density Functions for Safe Automated Driving

We propose a stochastic prediction-control framework to promote safety in automated driving by directly controlling the joint state probability density functions (PDFs) subject to the vehicle dynamics via trajectory-level state feedback. To illustrate the main ideas, we focus on a multi-lane highway driving scenario although the proposed framework can be adapted to other contexts. The computational pipeline consists of a PDF prediction layer, followed by a PDF control layer. The prediction layer performs moving horizon nonparametric forecasts for the ego and the non-ego vehicles' stochastic states, and thereby derives safe target PDF for the ego. The latter is based on the forecasted collision probabilities, and promotes the probabilistic safety for the ego. The PDF control layer designs a feedback that optimally steers the joint state PDF subject to the controlled ego dynamics while satisfying the endpoint PDF constraints. Our computation for the PDF prediction layer leverages the structure of the controlled Liouville PDE to evolve the joint PDF values, as opposed to empirically approximating the PDFs. Our computation for the PDF control layer leverages the differential flatness structure in vehicle dynamics. We harness recent theoretical and algorithmic advances in optimal mass transport, and the Schrodinger bridge. The numerical simulations illustrate the efficacy of the proposed framework.
SARSA(0) Reinforcement Learning over Fully Homomorphic Encryption

We consider a cloud-based control architecture in which the local plants outsource the control synthesis task to the cloud. In particular, we consider a cloud-based reinforcement learning (RL), where updating the value function is outsourced to the cloud. To achieve confidentiality, we implement computations over Fully Homomorphic Encryption (FHE). We use a CKKS encryption scheme and a modified SARSA(0) reinforcement learning to incorporate the encryption-induced delays. We then give a convergence result for the delayed updated rule of SARSA(0) with a blocking mechanism. We finally present a numerical demonstration via implementing on a classical pole-balancing problem.
Automation and the future of work: Assessing the role of labor flexibility

Abstract We study the economic incentives for automation when labor and machines are perfect substitutes. Labor may still be employed in production, even when it is a costlier input than robots on a productivity-adjusted basis. This occurs if firms face idiosyncratic risk, adjusting the stock of machines is costly, and workers can be hired and fired quickly enough. Even though labor survives, jobs become less stable, as workers are hired in short-lived bursts to cope with shocks. We calibrate a general equilibrium, multi-industry version of our model to match data on robot adoption in US manufacturing sectors, and use it to compute the employment and labor share consequences of progress in automation technology. A fall in the relative price of robots leads to relatively few jobs losses, while reductions in adjustment costs, or improvements in relative robot productivity, can be far more disruptive. The model-implied semi-elasticity of aggregate employment to robot penetration (number of robots per thousand employees) ranges between 0.01% and 0.12%, depending on the underlying source of increased robot adoption, consistent with findings in the empirical literature. In an extension, we show that reduced-form hiring and firing costs unambiguously depress long-run employment.
Determining County-Level Counterfactuals for Evaluation of Population Health Interventions : A Novel Application of K-Means Cluster Analysis.

ObjectivesEvaluating population health initiatives at the community level necessitates valid counterfactual communities, which includes having similar population composition, health care access, an...
Does revenue-expense matching play a differential role in analysts’ earnings and revenue forecasts?

Abstract This paper investigates whether matching has differential implications for the accuracy of analysts' earnings and revenue forecasts. We construct a novel measure of firm-level matching and document that matching improves analysts' earnings forecasts to a greater extent than their revenue forecasts. We also document matching's differential impact on analysts' earnings and sales forecasts by proposing a new count metric capturing a wedge in the accuracy of earnings and revenue forecasts. In additional tests, we report that the differential impact of matching is less (more) pronounced in a situation where the balance sheet (income statement) orientation likely dominates. We also report that matching's differential role is weaker (stronger) when firms have high intangible intensity (analysts have appropriate resources or expertise). In short window tests, matching's role in analysts' forecast revisions is more pronounced for earnings than sales forecasts. Overall, these results show how analysts benefit from better revenue-expense matching.
Batch Optimization for DNA Synthesis

Large pools of synthetic DNA molecules have been recently used to reliably store significant volumes of digital data. While DNA as a storage medium has enormous potential because of its high storage density, its practical use is currently severely limited because of the high cost and low throughput of available DNA synthesis technologies. We study the role of batch optimization in reducing the cost of large scale DNA synthesis, which translates to the following algorithmic task. Given a large pool $S$ of random quaternary strings of fixed length, partition $S$ into batches in a way that minimizes the sum of the lengths of the shortest common supersequences across batches. We introduce two ideas for batch optimization that both improve (in different ways) upon a naive baseline: (1) using both (ACGT)* and its reverse (TGCA)* as reference strands, and batching appropriately, and (2) batching via the quantiles of an appropriate ordering of the strands. We also prove asymptotically matching lower bounds on the cost of DNA synthesis, showing that one cannot improve upon these two ideas. Our results uncover a surprising separation between two cases that naturally arise in the context of DNA data storage: the asymptotic cost savings of batch optimization are significantly greater in the case where strings in $S$ do not contain repeats of the same character (homopolymers), as compared to the case where strings in $S$ are unconstrained. A full version of this paper is accessible at: https://arxiv.org/abs/2011.14532
Lucid: a language for control in the data plane

Programmable switch hardware makes it possible to move fine-grained control logic inside the network data plane, improving performance for a wide range of applications. However, applications with integrated control are inherently hard to write in existing data-plane programming languages such as P4. This paper presents Lucid, a language that raises the level of abstraction for putting control functionality in the data plane. Lucid introduces abstractions that make it easy to write sophisticated data-plane applications with interleaved packet-handling and control logic, specialized type and syntax systems that prevent programmer bugs related to data-plane state, and an open-sourced compiler that translates Lucid programs into P4 optimized for the Intel Tofino. These features make Lucid general and easy to use, as we demonstrate by writing a suite of ten different data-plane applications in Lucid. Working prototypes take well under an hour to write, even for a programmer without prior Tofino experience, have around 10x fewer lines of code compared to P4, and compile efficiently to real hardware. In a stateful firewall written in Lucid, we find that moving control from a switch's CPU to its data-plane processor using Lucid reduces the latency of performance-sensitive operations by over 300X.
A linear noise approximation for stochastic epidemic models fit to partially observed incidence counts

Stochastic epidemic models (SEMs) fit to incidence data are critical to elucidating outbreak dynamics, shaping response strategies, and preparing for future epidemics. SEMs typically represent counts of individuals in discrete infection states using Markov jump processes (MJPs), but are computationally challenging as imperfect surveillance, lack of subject-level information, and temporal coarseness of the data obscure the true epidemic. Analytic integration over the latent epidemic process is impossible, and integration via Markov chain Monte Carlo (MCMC) is cumbersome due to the dimensionality and discreteness of the latent state space. Simulation-based computational approaches can address the intractability of the MJP likelihood, but are numerically fragile and prohibitively expensive for complex models. A linear noise approximation (LNA) that approximates the MJP transition density with a Gaussian density has been explored for analyzing prevalence data in large-population settings, but requires modification for analyzing incidence counts without assuming that the data are normally distributed. We demonstrate how to reparameterize SEMs to appropriately analyze incidence data, and fold the LNA into a data augmentation MCMC framework that outperforms deterministic methods, statistically, and simulation-based methods, computationally. Our framework is computationally robust when the model dynamics are complex and applies to a broad class of SEMs. We evaluate our method in simulations that reflect Ebola, influenza, and SARS-CoV-2 dynamics, and apply our method to national surveillance counts from the 2013--2015 West Africa Ebola outbreak.
A new formulation of time domain boundary integral equation for acoustic wave scattering in the presence of a uniform mean flow

It has been well-known that under the assumption of a constant uniform mean flow, the acoustic wave propagation equation can be formulated as a boundary integral equation, in both the time domain and the frequency domain. Compared with solving partial differential equations, numerical methods based on the boundary integral equation have the advantage of a reduced spatial dimension and, hence, requiring only a surface mesh. However, the constant uniform mean flow assumption, while convenient for formulating the integral equation, does not satisfy the solid wall boundary condition wherever the body surface is not aligned with the uniform mean flow. In this paper, we argue that the proper boundary condition for the acoustic wave should not have its normal velocity be zero everywhere on the solid surfaces, as has been applied in the literature. A careful study of the acoustic energy conservation equation is presented that shows such a boundary condition in fact leads to erroneous source or sink points on solid surfaces not aligned with the mean flow. A new solid wall boundary condition is proposed that conserves the acoustic energy and a new time domain boundary integral equation is derived. In addition to conserving the acoustic energy, another significant advantage of the new equation is that it is considerably simpler than previous formulations. In particular, tangential derivatives of the solution on the solid surfaces are no longer needed in the new formulation, which greatly simplifies numerical implementation. Furthermore, stabilization of the new integral equation by Burton-Miller type reformulation is presented. The stability of the new formulation is studied theoretically as well as numerically by an eigenvalue analysis. Numerical solutions are also presented that demonstrate the stability of the new formulation.
Rorα Enforces Stability of the T-Helper-17 Cell Effector Program

T helper 17 (Th17) cells regulate mucosal barrier defenses, but also promote multiple autoinflammatory diseases. Although many molecular determinants of Th17 cell differentiation have been described, the transcriptional programs that sustain Th17 cells in vivo remain obscure. The transcription factor RORγt is critical for Th17 cell differentiation, but a distinct role of the closely-related RORα, which is co-expressed in Th17 cells, is not known. Here we demonstrate that, although dispensable for Th17 cell differentiation, RORα governs optimal Th17 responses in peripheral tissues. Thus, the absence of RORα in T cells led to significant reductions in both RORγt expression and effector function amongst Th17 cells, due to need for cooperative RORα and RORγt binding to a newly-identified Rorc enhancer element that is essential for Th17 lineage maintenance in vivo. Altogether, these data point to a nonredundant role of RORα in Th17 lineage maintenance via reinforcement of the RORγt transcriptional program.
A non-ergodic effective amplitude ground-motion model for California

A new non-ergodic ground-motion model (GMM) for effective amplitude spectral (EAS) values for California is presented in this study. EAS, which is defined in Goulet et al. (Effective amplitude spectrum (eas) as a metric for ground motion modeling using fourier amplitudes, 2018), is a smoothed rotation-independent Fourier amplitude spectrum of the two horizontal components of an acceleration time history. The main motivation for developing a non-ergodic EAS GMM, rather than a spectral acceleration GMM, is that the scaling of EAS does not depend on spectral shape, and therefore, the more frequent small magnitude events can be used in the estimation of the non-ergodic terms. The model is developed using the California subset of the NGAWest2 dataset (Ancheta in PEER NGA-West2 database. Tech. rep., PEER, Berkeley, CA, 2013). The Bayless and Abrahamson (Bull Seismol Soc Am 109(5): 2088-2105, https://doi.org/10.1785/0120190077 , 2019b) (BA18) ergodic EAS GMM was used as backbone to constrain the average source, path, and site scaling. The non-ergodic GMM is formulated as a Bayesian hierarchical model: the non-ergodic source and site terms are modeled as spatially varying coefficients following the approach of Landwehr et al. (Bull Seismol Soc Am 106(6):2574-2583. https://doi.org/10.1785/0120160118 , 2016), and the non-ergodic path effects are captured by the cell-specific anelastic attenuation attenuation following the approach of Dawood and Rodriguez-Marek (Bull Seismol Soc Am 103(2B):1360-1372, https://doi.org/10.1785/0120120125 , 2013). Close to stations and past events, the mean values of the non-ergodic terms deviate from zero to capture the systematic effects and their epistemic uncertainty is small. In areas with sparse data, the epistemic uncertainty of the non-ergodic terms is large, as the systematic effects cannot be determined. The non-ergodic total aleatory standard deviation is approximately 30 to $$40\%$$ smaller than the total aleatory standard deviation of BA18. This reduction in the aleatory variability has a significant impact on hazard calculations at large return periods. The epistemic uncertainty of the ground motion predictions is small in areas close to stations and past events.
Learning forecasts of rare stratospheric transitions from short simulations

Nonlinear atmospheric dynamics produce rare events that are hard to predict and attribute due to many interacting degrees of freedom. Sudden stratospheric warming event is a model example. Approximately once every other year, the winter polar vortex in the boreal stratosphere rapidly breaks down, inducing a shift in midlatitude surface weather patterns persisting for up to 2-3 months. In principle, lengthy numerical simulations can be used to predict and understand these rare transitions. For complex models, however, the cost of the direct numerical simulation approach is often prohibitive. We describe an alternative approach which only requires relatively short-duration computer simulations of the system. The methodology is illustrated by applying it to a prototype model of an SSW event developed by Holton and Mass (1976) and driven with stochastic forcing. While highly idealized, the model captures the essential nonlinear dynamics of SSWs and exhibits the key forecasting challenge: the dramatic separation in timescales between the dynamics of a single event and the return time between successive events. We compute optimal forecasts of sudden warming events and quantify the limits of predictability. Statistical analysis relates these optimal forecasts to a small number of interpretable physical variables. Remarkably, we are able to estimate these quantities using a data set of simulations much shorter than the timescale of the warming event. This methodology is designed to take full advantage of the high-dimensional data from models and observations, and can be employed to find detailed predictors of many complex rare events arising in climate dynamics.
Computer-aided diagnosis of low grade endometrial stromal sarcoma (LGESS)

Low grade endometrial stromal sarcoma (LGESS) accounts for about 0.2% of all uterine cancer cases. Approximately 75% of LGESS patients are initially misdiagnosed with leiomyoma, which is a type of benign tumor, also known as fibroids. In this research, uterine tissue biopsy images of potential LGESS patients are preprocessed using segmentation and stain normalization algorithms. We then apply a variety of classic machine learning and advanced deep learning models to classify tissue images as either benign or cancerous. For the classic techniques considered, the highest classification accuracy we attain is about 0.85, while our best deep learning model achieves an accuracy of approximately 0.87. These results clearly indicate that properly trained learning algorithms can aid in the diagnosis of LGESS.
The formula for the quasicentral modulus in the case of spectral measures on fractals

We prove a general ampliation homogeneity result for the quasicentral modulus of an n-tuple of operators with respect to the (p,1) Lorentz normed ideal. We use this to prove a formula involving Hausdorff measure for the quasicentral modulus of n-tuples of commuting Hermitian operators the spectrum of which is contained in certain Cantor-like self-similar fractals.
Householder Dice: A Matrix-Free Algorithm for Simulating Dynamics on Gaussian and Random Orthogonal Ensembles

This paper proposes a new algorithm, named Householder Dice (HD), for simulating dynamics on dense random matrix ensembles with rotational invariance. Examples include the Gaussian ensemble, the Haar-distributed random orthogonal ensemble, and their complex-valued counterparts. A “direct” approach to the simulation, where one first generates a dense $n \times n$ matrix from the ensemble, requires at least $\mathcal {O}(n^{2})$ resource in space and time. The HD algorithm overcomes this $\mathcal {O}(n^{2})$ bottleneck by using the principle of deferred decisions: rather than fixing the entire random matrix in advance, it lets the randomness unfold with the dynamics. At the heart of this matrix-free algorithm is an adaptive and recursive construction of (random) Householder reflectors. These orthogonal transformations exploit the group symmetry of the matrix ensembles, while simultaneously maintaining the statistical correlations induced by the dynamics. The memory and computation costs of the HD algorithm are $\mathcal {O}(nT)$ and $\mathcal {O}(nT^{2})$ , respectively, with $T$ being the number of iterations. When $T \ll n$ , which is nearly always the case in practice, the new algorithm leads to significant reductions in runtime and memory footprint. Numerical results demonstrate the promise of the HD algorithm as a new computational tool in the study of high-dimensional random systems.
Leibniz cohomology and connections on differentiable manifolds

Abstract We show how an affine connection on a Riemannian manifold occurs naturally as a cochain in the complex for Leibniz cohomology of vector fields with coefficients in the adjoint representation. The Leibniz coboundary of the Levi-Civita connection can be expressed as a sum of two terms, one the Laplace-Beltrami operator and the other a Ricci curvature term. The vanishing of this coboundary has an interpretation in terms of eigenfunctions of the Laplacian. Separately, we compute the Leibniz cohomology with adjoint coefficients for a certain family of vector fields on Euclidean R n corresponding to the affine orthogonal Lie algebra, n ≥ 3 .
LIFE: A Generalizable Autodidactic Pipeline for 3D OCT-A Vessel Segmentation.

Optical coherence tomography (OCT) is a non-invasive imaging technique widely used for ophthalmology. It can be extended to OCT angiography (OCT-A), which reveals the retinal vasculature with improved contrast. Recent deep learning algorithms produced promising vascular segmentation results; however, 3D retinal vessel segmentation remains difficult due to the lack of manually annotated training data. We propose a learning-based method that is only supervised by a self-synthesized modality named local intensity fusion (LIF). LIF is a capillary-enhanced volume computed directly from the input OCT-A. We then construct the local intensity fusion encoder (LIFE) to map a given OCT-A volume and its LIF counterpart to a shared latent space. The latent space of LIFE has the same dimensions as the input data and it contains features common to both modalities. By binarizing this latent space, we obtain a volumetric vessel segmentation. Our method is evaluated in a human fovea OCT-A and three zebrafish OCT-A volumes with manual labels. It yields a Dice score of 0.7736 on human data and \(0.8594\,\pm \,0.0275\) on zebrafish data, a dramatic improvement over existing unsupervised algorithms.
A Visual Tour of Carbon Export by Sinking Particles

To better quantify the oceans biological carbon pump, we resolved the diversity of sinking particles that transport carbon into the oceans interior, their contribution to carbon export, and their attenuation with depth. Sinking particles collected in sediment trap gel layers from 4 distinct ocean ecosystems were imaged, measured, and classified. The size and identity of particles was used to model their contribution to particulate organic carbon (POC) flux. Measured POC fluxes were reasonably predicted by particle images. Nine particle types were identified, and most of the compositional variability was driven by the relative contribution of aggregates, long cylindrical fecal pellets, and salp fecal pellets. While particle composition varied across locations and seasons, the entire range of compositions was measured at a single well-observed location in the subarctic North Pacific over 1 month, across 500 m of depth. The magnitude of POC flux was not consistently associated with a dominant particle class, but particle classes did influence flux attenuation. Long fecal pellets attenuated most rapidly with depth whereas certain other classes attenuated little or not at all with depth. Small particles (<100 m) consistently contributed [~]5% to total POC flux in samples with higher magnitude fluxes. The relative importance of these small particle classes (spherical mini pellets, short oval fecal pellets, and dense detritus) increased in low flux environments (up to 46% of total POC flux). Imaging approaches that resolve large variations in particle composition across ocean basins, depth, and time will help to better parameterize biological carbon pump models.
A Quintet of Quandaries: Five No-Go Theorems for Relational Quantum Mechanics

Relational quantum mechanics (RQM) proposes an ontology of relations between physical systems, where any system can serve as an ‘observer’ and any physical interaction between systems counts as a ‘measurement’. Quantities take unique values spontaneously in these interactions, and the occurrence of such ‘quantum events’ is strictly relative to the observing system, making them ‘relative facts’. The quantum state represents the objective information that one system has about another by virtue of correlations between their physical variables. The ontology of RQM thereby strives to uphold the universality and completeness of quantum theory, while at the same time maintaining that the actualization of each unique quantum event is a fundamental physical event. Can RQM sustain this precarious balancing act? Here we present five no-go theorems that imply it cannot; something has to give way.
Evolving the olfactory system with machine learning

The convergent evolution of the fly and mouse olfactory system led us to ask whether the anatomic connectivity and functional logic of olfactory circuits would evolve in artificial neural networks trained to perform olfactory tasks. Artificial networks trained to classify odor identity recapitulate the connectivity inherent in the olfactory system. Input units are driven by a single receptor type, and units driven by the same receptor converge to form a glomerulus. Glomeruli exhibit sparse, unstructured connectivity onto a larger expansion layer of Kenyon cells. When trained to both classify odor identity and to impart innate valence onto odors, the network develops independent pathways for identity and valence classification. Thus, the defining features of fly and mouse olfactory systems also evolved in artificial neural networks trained to perform olfactory tasks. This implies that convergent evolution reflects an underlying logic rather than shared developmental principles.
Genetic liability for substance use associated with medical comorbidities in electronic health records of African- and European-ancestry individuals.

Polygenic risk scores (PRS) represent an individual's summed genetic risk for a trait and can serve as biomarkers for disease. Less is known about the utility of PRS as a means to quantify genetic risk for substance use disorders (SUDs) than for many other traits. Nonetheless, the growth of large, electronic health record-based biobanks makes it possible to evaluate the association of SUD PRS with other traits. We calculated PRS for smoking initiation, alcohol use disorder (AUD), and opioid use disorder (OUD) using summary statistics from the Million Veteran Program sample. We then tested the association of each PRS with its primary phenotype in the Penn Medicine BioBank (PMBB) using all available genotyped participants of African or European ancestry (AFR and EUR, respectively) (N = 18,612). Finally, we conducted phenome-wide association analyses (PheWAS) separately by ancestry and sex to test for associations across disease categories. Tobacco use disorder was the most common SUD in the PMBB, followed by AUD and OUD, consistent with the population prevalence of these disorders. All PRS were associated with their primary phenotype in both ancestry groups. PheWAS results yielded cross-trait associations across multiple domains, including psychiatric disorders and medical conditions. SUD PRS were associated with their primary phenotypes; however, they are not yet predictive enough to be useful diagnostically. The cross-trait associations of the SUD PRS are indicative of a broader genetic liability. Future work should extend findings to additional population groups and for other substances of abuse.
Online Threshold Tracking in Cyber-Physical-Human Systems Based on Binary Observations

This paper addresses the online threshold tracking problem in Cyber-Physical-Human Systems (CPHS) based on binary-valued satisfactory signals from users. We model the CPHS system as a linear discrete-time time-varying dynamic system and consider both the estimation error and the user experience in our cost function. For finite horizon cases, the optimal tracking strategy is provided and sufficient conditions are given to guarantee bounded cost and bounded uncertainty range. For infinite horizon cases, we propose a receding horizon strategy to track the threshold and prove that the performance difference between the receding horizon strategy and the optimal strategy is bounded. Case studies based on Braille display readers are given to demonstrate that our methods have fewer reading errors while maintaining good performance compared with the binary search method.
Noetherian operators and primary decomposition

Abstract Noetherian operators are differential operators that encode primary components of a polynomial ideal. We develop a framework, as well as algorithms, for computing Noetherian operators with local dual spaces, both symbolically and numerically. For a primary ideal, such operators provide an alternative representation to one given by a set of generators. This description fits well with numerical algebraic geometry, taking a step toward the goal of numerical primary decomposition.
Investigating External Interaction Modality and Design Between Automated Vehicles and Pedestrians at Crossings

In this study, we investigated the effectiveness and user acceptance of three external interaction modalities (i.e., visual, auditory, and visual+auditory) in promoting communications between automated vehicle systems (AVS) and pedestrians at a crosswalk through a large number of combined designs. For this purpose, an online survey was designed and distributed to 68 participants. All participants reported their overall preferences for safety, comfort, trust, ease of understanding, usability, and acceptance towards the systems. Results showed that the visual+auditory interaction modality was the mostly preferred, followed by the visual interaction modality and then the auditory one. We also tested different visual and auditory interaction methods, and found that “Pedestrian silhouette on the front of the vehicle” was the best preferred option while middle-aged participants liked “Chime” much better than young participants though it was overall better preferred than others. Finally, communication between the AVS and pedestrians' phones was not well received due to privacy concerns. These results provided important interface design recommendations in identifying better combination of visual and auditory designs and therefore improving AVS communicating their intention with pedestrians.
Adaptive batching for Gaussian process surrogates with application in noisy level set estimation

We develop adaptive replicated designs for Gaussian process metamodels of stochastic experiments. Adaptive batching is a natural extension of sequential design heuristics with the benefit of replication growing as response features are learned, inputs concentrate, and the metamodeling overhead rises. Motivated by the problem of learning the level set of the mean simulator response we develop four novel schemes: Multi-Level Batching (MLB), Ratchet Batching (RB), Adaptive Batched Stepwise Uncertainty Reduction (ABSUR), Adaptive Design with Stepwise Allocation (ADSA) and Deterministic Design with Stepwise Allocation (DDSA). Our algorithms simultaneously (MLB, RB and ABSUR) or sequentially (ADSA and DDSA) determine the sequential design inputs and the respective number of replicates. Illustrations using synthetic examples and an application in quantitative finance (Bermudan option pricing via Regression Monte Carlo) show that adaptive batching brings significant computational speed-ups with minimal loss of modeling fidelity.
A Game-Theoretic Framework for Controlled Islanding in the Presence of Adversaries

Controlled islanding effectively mitigates cascading failures by partitioning the power system into a set of disjoint islands. In this paper, we study the controlled islanding problem of a power system under disturbances introduced by a malicious adversary. We formulate the interaction between the grid operator and adversary using a game-theoretic framework. The grid operator first computes a controlled islanding strategy, along with the power generation for the post-islanding system to guarantee stability. The adversary observes the strategies of the grid operator. The adversary then identifies critical substations of the power system to compromise and trips the transmission lines that are connected with compromised substations. For our game formulation, we propose a double oracle algorithm based approach that solves the best response for each player. We show that the best responses for the grid operator and adversary can be formulated as mixed integer linear programs. In addition, the best response of the adversary is equivalent to a submodular maximization problem under a cardinality constraint, which can be approximated up to a \((1-\frac{1}{e})\) optimality bound in polynomial time. We compare the proposed approach with a baseline where the grid operator computes an islanding strategy by minimizing the power flow disruption without considering the possible response from the adversary. We evaluate both approaches using IEEE 9-bus, 14-bus, 30-bus, 39-bus, 57-bus, and 118-bus power system case study data. Our proposed approach achieves better performance than the baseline in about \(44\%\) of test cases, and on average it incurs about 12.27 MW less power flow disruption.
On eigenvectors of nilpotent Lie algebras of linear operators

We give a condition ensuring that the operators in a nilpotent Lie algebra of linear operators on a finite dimensional vector space have a common eigenvector.
A near-field radiative heat transfer device.

Recently, several reports have experimentally shown near-field radiative heat transfer (NFRHT) exceeding the far-field blackbody limit between planar surfaces1–5. However, owing to the difficulties associated with maintaining the nanosized gap required for measuring a near-field enhancement, these demonstrations have been limited to experiments that cannot be implemented in large-scale devices. This poses a bottleneck to the deployment of NFRHT concepts in practical applications. Here, we describe a device bridging laboratory-scale measurements and potential NFRHT engineering applications in energy conversion6,7 and thermal management8–10. We report a maximum NFRHT enhancement of approximately 28.5 over the blackbody limit with devices made of millimetre-sized doped Si surfaces separated by vacuum gap spacings down to approximately 110 nm. The devices use micropillars, separating the high-temperature emitter and low-temperature receiver, manufactured within micrometre-deep pits. These micropillars, which are about 4.5 to 45 times longer than the nanosize vacuum spacing at which radiation transfer takes place, minimize parasitic heat conduction without sacrificing the structural integrity of the device. The robustness of our devices enables gap spacing visualization by scanning electron microscopy (SEM) before performing NFRHT measurements. An enhancement of 28.5 times beyond the blackbody radiation limit is demonstrated between two plates 110 nm apart in a chip-scale device.
Certain invariant spaces of bounded measurable functions on a sphere

In their 1976 paper, Nagel and Rudin characterize the closed unitarily and Mobius invariant spaces of continuous and $$L^p$$ -functions on a sphere, for $$1\le p<\infty $$ . In this paper we provide an analogous characterization for the weak*-closed unitarily and Mobius invariant spaces of $$L^\infty $$ -functions on a sphere. We also investigate the weak*-closed unitarily and Mobius invariant algebras of $$L^\infty $$ -functions on a sphere.