cssi/SciSciGPT-SciSciNet huggingface dataset:
The turbulent bubble break-up cascade. Part 2. Numerical simulations of breaking waves

Breaking waves generate a distribution of bubble sizes that evolves over time. Knowledge of how this distribution evolves is of practical importance for maritime and climate studies. The analytical framework developed in Part 1 (Chan, Johnson & Moin, J. Fluid Mech., vol. 912, 2021, A42) examined how this evolution is governed by the bubble-mass flux from large- to small-bubble sizes which depends on the rate of break-up events and the distribution of child bubble sizes. These statistics are measured in Part 2 as ensemble-averaged functions of time by simulating ensembles of breaking waves, and identifying and tracking individual bubbles and their break-up events. The large-scale break-up dynamics is seen to be statistically unsteady, and two intervals with distinct characteristics were identified. In the first interval, the dissipation rate and bubble-mass flux are quasi-steady, and the theoretical analysis of Part 1 is supported by all observed statistics, including the expected as small bubbles are also depleted more quickly. This suggests the emergence of different physical mechanisms during different phases of the breaking-wave evolution, although size-local break-up remains a dominant theme. Parts 1 and 2 present an analytical toolkit for population balance analysis in two-phase flows.
Prestrain-induced bandgap tuning in 3D-printed tensegrity-inspired lattice structures

Abstract In this letter, we provide experimental evidence of bandgap tunability with global prestrain in additively-manufactured tensegrity-inspired lattice structures. These lattices are extremely lightweight and designed to exhibit a nonlinear compressive response that mimics that of a tensegrity structure. We fabricate them out of a stiff polymer but, owing to their peculiar design, they are compliant and remain elastic up to high levels of precompression. In turn, unlike tunable metamaterials made of soft polymers, the response of our lattices is not dominated by damping. We perform experiments on a one-dimensional lattice subject to longitudinal wave excitation and varying levels of static longitudinal precompression, and observe continuous tuning of both the wave speed and the location and width of the lowest-frequency bandgap.
Bi-Directional Domain Adaptation for Sim2Real Transfer of Embodied Navigation Agents

Deep reinforcement learning models are notoriously data hungry, yet real-world data is expensive and time consuming to obtain. The solution that many have turned to is to use simulation for training before deploying the robot in a real environment. Simulation offers the ability to train large numbers of robots in parallel, and offers an abundance of data. However, no simulation is perfect, and robots trained solely in simulation fail to generalize to the real-world, resulting in a “sim-vs-real gap”. How can we overcome the trade-off between the abundance of less accurate, artificial data from simulators and the scarcity of reliable, real-world data? In this letter, we propose Bi-directional Domain Adaptation (BDA), a novel approach to bridge the sim-vs-real gap in both directions– real2sim to bridge the visual domain gap, and sim2real to bridge the dynamics domain gap. We demonstrate the benefits of BDA on the task of PointGoal Navigation. BDA with only 5 k real-world (state, action, next-state) samples matches the performance of a policy fine-tuned with $\sim$ 600 k samples, resulting in a speed-up of $\sim 120\times$ .
Network Optimization via Smooth Exact Penalty Functions Enabled by Distributed Gradient Computation

This article proposes a distributed algorithm for a network of agents to solve an optimization problem with separable objective function and locally coupled constraints. Our strategy is based on reformulating the original constrained problem as the unconstrained optimization of a smooth (continuously differentiable) exact penalty function. Computing the gradient of this penalty function in a distributed way is challenging even under the separability assumptions on the original optimization problem. Our technical approach shows that the distributed computation problem for the gradient can be formulated as a system of linear algebraic equations defined by separable problem data. To solve it, we design an exponentially fast, input-to-state stable distributed algorithm that does not require the individual agent matrices to be invertible. We employ this strategy to compute the gradient of the penalty function at the current network state. Our distributed algorithmic solver for the original constrained optimization problem interconnects this estimation with the prescription of having the agents follow the resulting direction. Numerical simulations illustrate the convergence and robustness properties of the proposed algorithm.
Varieties with small dual varieties, I.

Let X be a complex projective nonlinear n-fold in IP". Let X*_IP m be the dual variety of X. Landman defines the defect of X to be def(X)=N-1- dimX*. For most examples, def(X)=0 (i.e. X* is a hypersurface). The main purpose of this paper is to investigate those varieties with positive defect. Assume that def(X)=k>0. Let H be a general tangent hyperplane of X. The contact locus of H with X is a k dimensional linear space L in X [15]. We show that
A Data-Driven Study to Discover, Characterize, and Classify Convergence Bidding Strategies in California ISO Energy Market

Convergence bidding, has been adopted in recent years by most Independent System Operators (ISOs) in the United States as a relatively new market mechanism to enhance market efficiency. Convergence bidding affects many aspects of the operation of the electricity markets and there is currently a gap in the literature on understanding how the market participants strategically select their convergence bids in practice. To address this open Problem, in this paper, we study three years of real-world market data from the California ISO energy market. First, we provide a data - driven overview of all submitted convergence bids (CBs) and analyze the performance of each individual convergence bidder based on the number of their submitted CBs, the number of locations that they placed the CBs, the percentage of submitted supply or demand and CBs, the amount of cleared CBs, and their gained profit or loss. Next, we scrutinize the bidding strategies of the 13 largest market players that account for 75 % of all CBs in. the California ISO market. We identify quantitative features to characterize and distinguish their different convergence bidding strategies. This analysis results in revealing three different classes of CB strategies that are used in practice. We identify the differences between. these strategic bidding classes and compare their advantages and disadvantages. We also explain how some of the most active market participants are using bidding strategies that do not any of the strategic bidding methods that currently exist in the literature.
Generation and utilization of a HEK-293T murine GM-CSF expressing cell line

Macrophages and dendritic cells (DCs) are innate immune cells that play a key role in defense against pathogens. In vitro cultures of bone marrow-derived macrophages (BMDMs) and dendritic cells (BMDCs) are well-established and valuable methods for immunological studies. Typically, commercially available recombinant GM-CSF is utilized to generate BMDCs and is also used to culture alveolar macrophages. We have generated a new HEK-293T cell line expressing murine GM-CSF that secretes high levels of GM-CSF (~180 ng/ml) into complete media as an alternative to commercial GM-CSF. Differentiation of dendritic cells and expression of various markers were kinetically assessed using the GM-CSF HEK293T cell line, termed supGM-CSF and compared directly to purified commercial GMCSF. After 7-9 days of cell culture the supGM-CSF yielded twice as many viable cells compared to the commercial purified GM-CSF. In addition to differentiating BMDCs, the supGM-CSF can be utilized to culture functionally active alveolar macrophages. Collectively, our results show that supernatant from our GM-CSF HEK293T cell line supports the differentiation of mouse BMDCs or alveolar macrophage culturing, providing an economical alternative to purified GM-CSF.
Prevalence and Burden on Childhood Maltreatment among Children Presenting to US Trauma Centers

Purpose: Non-accidental trauma (NAT) is one of the common causes of injury in children the United States (US). Abuse and maltreatment affects 2 per 100,000 children annually and may go unrecognized. The aim of this study to quantify the burden and epidemiology of NAT in the US pediatric population. Methods: The National Trauma Data Bank (NTDB 2010-2015) was queried for pediatric trauma patients. Children presenting for non-accidental trauma …
Optimal Control of Convection-Cooling and Numerical Implementation

Abstract This paper is concerned with the problem of enhancing convection-cooling via active control of the incompressible velocity field, described by a stationary diffusion–convection model. This essentially leads to a bilinear optimal control problem. A rigorous proof of the existence of an optimal control is presented and the first order optimality conditions are derived for solving the control using a variational inequality. Moreover, the second order sufficient conditions are established to characterize the local minimizer. Finally, numerical experiments are conducted utilizing finite elements methods together with nonlinear iterative schemes, to demonstrate and validate the effectiveness of our control design.
Nonconvex Consensus ADMM for Cooperative Lane Change Maneuvers of Connected Automated Vehicles

Abstract Connected and automated vehicles (CAVs) offer huge potential to improve the performance of automated vehicles (AVs) without communication capabilities, especially in situations when the vehicles (or agents) need to be cooperative to accomplish their maneuver. Lane change maneuvers in dense traffic, e.g., are very challenging for non-connected AVs. To alleviate this problem, we propose a holistic distributed lane change control scheme for CAVs which relies on vehicle-to-vehicle communication. The originally centralized optimal control problem is embedded into a consensus-based Alternating Direction Method of Multipliers framework to solve it in a distributed receding horizon fashion. Although agent dynamics render the underlying optimal control problem nonconvex, we propose a problem reformulation that allows to derive convergence guarantees. In the distributed setting, every agent needs to solve a nonlinear program (NLP) locally. To obtain a real-time solution of the local NLPs, we utilize the optimization engine OpEn which implements the proximal averaged Newton method for optimal control (PANOC). Simulation results prove the efficacy and real-time capability of our approach.
Competing Epidemics on Graphs - Global Convergence and Coexistence

The dynamics of the spread of contagions such as viruses, infectious diseases or even rumors/opinions over contact networks (graphs) have effectively been captured by the well known Susceptible-Infected-Susceptible (SIS) epidemic model in recent years. When it comes to competition between two such contagions spreading on overlaid graphs, their propagation is captured by so-called bi-virus epidemic models. Analysis of such dynamical systems involve the identification of equilibrium points and its convergence properties, which determine whether either of the viruses dies out, or both survive together. We demonstrate how the existing works are unsuccessful in characterizing a large subset of the model parameter space, including all parameters for which the competitiveness of the bi-virus system is significant enough to attain coexistence of the epidemics. In this paper, we fill in this void and obtain convergence results for the entirety of the model parameter space; giving precise conditions (necessary and sufficient) under which the system globally converges to a trichotomy of possible outcomes: a virus-free state, a single-virus state, and to a coexistence state – the first such result.
Microwave photon number resolving detector using the topological surface state of superconducting cadmium arsenide

The authors propose a microwave photon number detector with single-photon resolution using cadmium arsenide at low temperature, with the graphene-like surface state acting as the absorber and the superconducting bulk as the bolometer.
An Algebraic Approach to Electron Interactions in Quantum Hall Systems

Let m denote the number of quasielectrons (QEs) in a quantum Hall system containing N particles altogether. We show in several general cases that for systems containing m QEs in a single angular momentum shell above N − m Fermions in an incompressible quantum liquid (IQL) state having filling factor ν =1/3 that there always exists a configuration whose symmetric correlation function G is nonzero. This extends recent comparable results concerning the IQL state. As a consequence, one can obtain (explicitly) a configuration having a nonzero G for all N ≤ 8 particle systems containing any number of QEs. To establish our result, we construct a family of multi-graphs on N vertices satisfying certain restraints on the degrees of the vertices and possessing the property that whenever one computes the linear symmetrization of the graph monomial of any member of the family, the result is always nonzero. The nonzero linear symmetrization that is obtained in each case is in fact an example of what is called a relative semi-invariant of a (generic) binary form of degree N. Thus, in addition to providing new correlation functions for systems of interacting Fermions containing QEs, our construction is of potential interest from both the invariant and graph theoretic standpoints.
Database of Wannier tight-binding Hamiltonians using high-throughput density functional theory.

Wannier tight-binding Hamiltonians (WTBH) provide a computationally efficient way to predict electronic properties of materials. In this work, we develop a computational workflow for high-throughput Wannierization of density functional theory (DFT) based electronic band structure calculations. We apply this workflow to 1771 materials (1406 3D and 365 2D), and we create a database with the resulting WTBHs. We evaluate the accuracy of the WTBHs by comparing the Wannier band structures to directly calculated spin-orbit coupling DFT band structures. Our testing includes k-points outside the grid used in the Wannierization, providing an out-of-sample test of accuracy. We illustrate the use of WTBHs with a few example applications. We also develop a web-app that can be used to predict electronic properties on-the-fly using WTBH from our database. The tools to generate the Hamiltonian and the database of the WTB parameters are made publicly available through the websites https://github.com/usnistgov/jarvis and https://jarvis.nist.gov/jarviswtb . Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14161406
Dynamics of global gene expression and chromatin accessibility of the peripheral nervous system in animal models of persistent pain

Efforts to understand genetic variability involved in an individual's susceptibility to chronic pain support a role for upstream regulation by epigenetic mechanisms. To examine the transcriptomic and epigenetic basis of chronic pain that resides in the peripheral nervous system, we used RNA-seq and ATAC-seq of the rat dorsal root ganglion (DRG) to identify novel molecular pathways associated with pain hypersensitivity in two well-studied persistent pain models induced by Chronic Constriction Injury (CCI) of the sciatic nerve and intra-plantar injection of Complete Freund's Adjuvant (CFA) in rats. Our RNA-seq studies identify a variety of biological process related to synapse organization, membrane potential, transmembrane transport, and ion binding. Interestingly, genes that encode transcriptional regulators were disproportionately downregulated in both models. Our ATAC-seq data provide a comprehensive map of changes in chromatin accessibility in the DRG. A total of 1123 regions showed changes in chromatin accessibility in one or both models when compared to the naive and 31 shared differentially accessible regions (DAR)s. Functional annotation of the DARs identified disparate molecular functions enriched for each pain model which suggests that chromatin structure may be altered differently following sciatic nerve injury and hind paw inflammation. Motif analysis identified 17 DNA sequences known to bind transcription factors in the CCI DARs and 33 in the CFA DARs. Two motifs were significantly enriched in both models. Our improved understanding of the changes in chromatin accessibility that occur in chronic pain states may identify regulatory that play a central role in changing gene expression in the DRG. F32NR015728 (KES), KL2 TR003108 (KES), NS110598 (YG), NS117761 (YG), Johns Hopkins Blaustein Pain Research Fund (SDT).
Automated Identification of Patients With Immune-Related Adverse Events From Clinical Notes Using Word Embedding and Machine Learning.

PURPOSEAlthough immune checkpoint inhibitors (ICIs) have substantially improved survival in patients with advanced malignancies, they are associated with a unique spectrum of side effects termed im...
Two-beam coupling in the production of quantum correlated images by four-wave mixing

We investigate the effect of 2-beam coupling in different imaging geometries in generating intensity-difference squeezing from four-wave mixing (4WM) in Rb atomic vapors. A recently-introduced dual-seeding technique can cancel out the classical noise in a seeded four-wave mixing process. This dual-seeding technique, however, can introduce new complications that involve 2-beam coupling between different seeded spatial modes in the atomic vapor and can ruin squeezing at frequencies on the order of the atomic linewidth and below. This complicates some forms of quantum imaging using these systems. Here we show that seeding the 4WM process with skew rays can eliminate the excess noise caused by 2-beam coupling. To avoid 2-beam coupling in bright, seeded images, it is important to re-image the object in the gain medium, instead of focussing through it.
Commons Management in Migrant Communities

This article examines whether (and why) migrant communities are less likely to support institutions for managing common pool resources. Focusing on Buvuma Island, which is situated in Uganda’s portion of Lake Victoria, I study the efforts at locally supporting forestry regulations among randomly selected communities. These communities have varying proportions of both immigrants and prospective out-migrants, and they are confronting the degradation of adjacent forest reserves. The evidence from survey data on 293 randomly selected heads of households suggests that migrant communities are less likely to support common pool resource institutions. The same evidence suggests that the lower likelihood of support among migrant communities has more to do with their weaker relationships (of reputation, trust, and reciprocity) than their expectations about the institutional net-benefits.
Detecting and phasing minor single-nucleotide variants from long-read sequencing data.

Cellular genetic heterogeneity is common in many biological conditions including cancer, microbiome, and co-infection of multiple pathogens. Detecting and phasing minor variants play an instrumental role in deciphering cellular genetic heterogeneity, but they are still difficult tasks because of technological limitations. Recently, long-read sequencing technologies, including those by Pacific Biosciences and Oxford Nanopore, provide an opportunity to tackle these challenges. However, high error rates make it difficult to take full advantage of these technologies. To fill this gap, we introduce iGDA, an open-source tool that can accurately detect and phase minor single-nucleotide variants (SNVs), whose frequencies are as low as 0.2%, from raw long-read sequencing data. We also demonstrate that iGDA can accurately reconstruct haplotypes in closely related strains of the same species (divergence ≥0.011%) from long-read metagenomic data. Cellular genetic heterogeneity is common across biological conditions, yet application of long-read sequencing to this subject is limited by error rates. Here, the authors present iGDA, a tool for detection and phasing of minor variants from long-read sequencing data, allowing accurate reconstruction of haplotypes.
Sex- and Subtype-Specific Adaptations in Excitatory Signaling Onto Deep-Layer Prelimbic Cortical Pyramidal Neurons After Chronic Alcohol Exposure

Long-term alcohol use results in behavioral deficits including impaired working memory, elevated anxiety, and blunted inhibitory control that is associated with prefrontal cortical (PFC) dysfunction. Preclinical observations demonstrate multiple impairments in GABAergic neurotransmission onto deep-layer principal cells (PCs) in the prelimbic cortex that suggest dependence-related cortical dysfunction is the product of elevated excitability in these cells. Despite accumulating evidence showing alcohol-induced changes in interneuron signaling onto PCs differ between sexes, there is limited data explicitly evaluating sex-specific ethanol effects on excitatory signaling onto deep-layer PCs that may further contribute to deficits in PFC-dependent behaviors. To address this, we conducted electrophysiological and behavioral tests in both male and female Sprague-Dawley rats to evaluate the effects of chronic ethanol exposure. Among our observations, we report a marked enhancement in glutamatergic signaling onto deep-layer PCs in male, but not female, rats after alcohol exposure. This phenomenon was furthermore specific to a sub-class of PC, sub-cortically projecting Type-A cells, and coincided with enhanced anxiety-like behavior, but no observable deficit in working memory. In contrast, female rats displayed alcohol-induced facilitation in working memory performance with no change in expression of anxiety-like behavior. Together, these results suggest fundamental differences in alcohol effects on cell activity, cortical sub-circuits, and PFC-dependent behaviors across male and female rats.
Falx: Synthesis-Powered Visualization Authoring

Modern visualization tools aim to allow data analysts to easily create exploratory visualizations. When the input data layout conforms to the visualization design, users can easily specify visualizations by mapping data columns to visual channels of the design. However, when there is a mismatch between data layout and the design, users need to spend significant effort on data transformation. We propose Falx, a synthesis-powered visualization tool that allows users to specify visualizations in a similarly simple way but without needing to worry about data layout. In Falx, users specify visualizations using examples of how concrete values in the input are mapped to visual channels, and Falx automatically infers the visualization specification and transforms the data to match the design. In a study with 33 data analysts on four visualization tasks involving data transformation, we found that users can effectively adopt Falx to create visualizations they otherwise cannot implement.
Comparison of two-dimensional and three-dimensional droplet trajectory calculations in the vicinity of finite wings

Computational predictions of ice accretion on flying aircraft most commonly rely on modeling in two dimensions (2D). These 2D methods treat an aircraft geometry either as wing-like with infinite span, or as an axisymmetric body. Recently, fully three dimensional (3D) methods have been introduced that model an aircrafts true 3D shape. Because 3D methods are more computationally expensive than 2D methods, 2D methods continue to be widely used. However, a 3D method allows us to investigate whether it is valid to continue applying 2D methods to a finite wing. The extent of disagreement between LEWICE, a 2D method, and LEWICE3D, a 3D method, in calculating local collection efficiencies at the leading edge of finite wings is investigated in this paper.
An exact solver for the Weston-Watkins SVM subproblem

Recent empirical evidence suggests that the Weston-Watkins support vector machine is among the best performing multiclass extensions of the binary SVM. Current state-of-the-art solvers repeatedly solve a particular subproblem approximately using an iterative strategy. In this work, we propose an algorithm that solves the subproblem exactly using a novel reparametrization of the Weston-Watkins dual problem. For linear WW-SVMs, our solver shows significant speed-up over the state-of-the-art solver when the number of classes is large. Our exact subproblem solver also allows us to prove linear convergence of the overall solver.
Multi-channel transformer transducer for speech recognition

Multi-channel inputs offer several advantages over single-channel, to improve the robustness of on-device speech recognition systems. Recent work on multi-channel transformer, has proposed a way to incorporate such inputs into end-to-end ASR for improved accuracy. However, this approach is characterized by a high computational complexity, which prevents it from being deployed in on-device systems. In this paper, we present a novel speech recognition model, Multi-Channel Transformer Transducer (MCTT), which features end-to-end multi-channel training, low computation cost, and low latency so that it is suitable for streaming decoding in on-device speech recognition. In a far-field in-house dataset, our MCTT outperforms stagewise multi-channel models with transformer-transducer up to 6.01% relative WER improvement (WERR). In addition, MCTT outperforms the multi-channel transformer up to 11.62% WERR, and is 15.8 times faster in terms of inference speed. We further show that we can improve the computational cost of MCTT by constraining the future and previous context in attention computations.
Prospects for quantum enhancement with diabatic quantum annealing

Optimization, sampling and machine learning are topics of broad interest that have inspired significant developments and new approaches in quantum computing. One such approach is quantum annealing (QA). In this Review, we assess the prospects for algorithms within the general framework of QA to achieve a quantum speedup relative to classical state-of-the-art methods. We argue for continued exploration in the QA framework on the basis that improved coherence times and control capabilities will enable the near-term exploration of several heuristic quantum optimization algorithms. These continuous-time Hamiltonian computation algorithms rely on control protocols that are more advanced than those in traditional ground-state QA, while still being considerably simpler than those used in gate-model implementations. The inclusion of coherent diabatic transitions to excited states results in a generalization we refer to collectively as diabatic quantum annealing, which we believe is the most promising route to quantum enhancement within this framework. Other promising variants of traditional QA include reverse annealing, continuous-time quantum walks and analogues of parameterized quantum circuit ansatzes for machine learning. Most of these algorithms have no known efficient classical simulations, making them worthy of further investigation with quantum hardware in the intermediate-scale regime. Quantum annealing is a widely used heuristic algorithm for optimization and sampling, implemented in commercial processors. This Review provides a critical assessment of the field and points to new opportunities for a quantum advantage via recently developed alternative quantum annealing protocols.
Influence of Sideslip on the Kinematics of the Helicopter in Steady Coordinated Turns

A steep coordinated helical turn at extreme angles of attack with inherent sideslip is of primary interest in this study. Unlike fixed-wing aircraft, the helicopter in a steady coordinated turn will inherently sideslip. A set of exact kinematic equations describing this motion in steady helical turns has been developed, and a rational definition for the load factor that best characterizes a coordinated turn for a helicopter has been proposed. An analysis has also been completed on the effects of sideslip on the kinematic relationships in a coordinated turn which is based on new closed-form solutions which relate the aircraft angular rates and pitch and roll attitudes to the turn parameters, angle of attack, and sideslip. The results show that the bank angle of the aircraft can differ markedly from the tilt angle of the normal load factor and that the normal load factor can also differ substantially from the accelerometer reading along the vertical body axis of the aircraft. Generally, sideslip has a strong influence on the pitch attitude and roll rate of the helicopter. The study also indicates that pitch rate is independent of angle of attack in a coordinated turn and that in the absence of sideslip, angular rates about the stability axes are independent of the aerodynamic characteristics of the aircraft.
Sequential Attacks on Kalman Filter-based Forward Collision Warning Systems

Kalman Filter (KF) is widely used in various domains to perform sequential learning or variable estimation. In the context of autonomous vehicles, KF constitutes the core component of many Advanced Driver Assistance Systems (ADAS), such as Forward Collision Warning (FCW). It tracks the states (distance, velocity etc.) of relevant traffic objects based on sensor measurements. The tracking output of KF is often fed into downstream logic to produce alerts, which will then be used by human drivers to make driving decisions in near-collision scenarios. In this paper, we study adversarial attacks on KF as part of the more complex machine-human hybrid system of Forward Collision Warning. Our attack goal is to negatively affect human braking decisions by causing KF to output incorrect state estimations that lead to false or delayed alerts. We accomplish this by sequentially manipulating measure ments fed into the KF, and propose a novel Model Predictive Control (MPC) approach to compute the optimal manipulation. Via experiments conducted in a simulated driving environment, we show that the attacker is able to successfully change FCW alert signals through planned manipulation over measurements prior to the desired target time. These results demonstrate that our attack can stealthily mislead a distracted human driver and cause vehicle collisions.
Mirror Descent and the Information Ratio

We establish a connection between the stability of mirror descent and the information ratio by Russo and Van Roy (2014). Our analysis shows that mirror descent with suitable loss estimators and exploratory distributions enjoys the same bound on the adversarial regret as the bounds on the Bayesian regret for information-directed sampling. Along the way, we develop the theory for information-directed sampling and provide an efficient algorithm for adversarial bandits for which the regret upper bound matches exactly the best known information-theoretic upper bound. Keywords: Bandits, partial monitoring, mirror descent, information theory.
Predict and Write: Using K-Means Clustering to Extend the Lifetime of NVM Storage

Non-volatile memory (NVM) technologies suffer from limited write endurance. To address this challenge, we propose Predict and Write (PNW), a K/V-store that uses a clustering-based machine learning approach to extend the lifetime of NVMs. PNW decreases the number of bit flips for PUT/UPDATE operations by determining the best memory location an updated value should be written to. PNW leverages the indirection level of K/V-stores to freely choose the target memory location for any given write based on its value. PNW organizes NVM addresses in a dynamic address pool clustered by the similarity of the data values they refer to. We show that, by choosing the right target memory location for a given PUT/UPDATE operation, the number of total bit flips and cache lines can be reduced by up to 85% and 56% over the state of the art.
Optimal Design of Limited Partnership Agreements

General partners (GP) are sometimes paid on a deal-by-deal basis and other times on a whole-portfolio basis. When is one method of payment better than the other? I show that when assets (projects or firms) are highly correlated or when GPs have low reputation, whole-portfolio contracting is superior to deal-by-deal contracting. In this case, by bundling payouts together, whole- portfolio contracting enhances incentives for GPs to exert e ort. Therefore, it is better suited to alleviate the moral hazard problem which is stronger than the adverse selection problem in the case of high correlation of assets or low reputation of GPs. In contrast, for low correlation of assets or high reputation of GPs, information asymmetry concerns dominate and deal-by- deal contracts become optimal, as they can efficiently weed out bad projects one by one. These results shed light on recent empirical findings on the relationship between investors and venture capitalists.
FDeblur-GAN: Fingerprint Deblurring using Generative Adversarial Network

While working with fingerprint images acquired from crime scenes, mobile cameras, or low-quality sensors, it becomes difficult for automated identification systems to verify the identity due to image blur and distortion. We propose a fingerprint deblurring model FDeblur-GAN, based on the conditional Generative Adversarial Networks (cGANs) and multi-stage framework of the stack GAN. Additionally, we integrate two auxiliary sub-networks into the model for the deblurring task. The first sub-network is a ridge extractor model. It is added to generate ridge maps to ensure that fingerprint information and minutiae are preserved in the deblurring process and prevent the model from generating erroneous minutiae. The second sub-network is a verifier that helps the generator to preserve the ID information during the generation process. Using a database of blurred fingerprints and corresponding ridge maps, the deep network learns to deblur from the input blurry samples. We evaluate the proposed method in combination with two different fingerprint matching algorithms. We achieved an accuracy of 95.18% on our fingerprint database for the task of matching deblurred and ground truth fingerprints.
Data-Driven Scenario Optimization for Automated Controller Tuning with Probabilistic Performance Guarantees

Systematic design and verification of advanced control strategies for complex systems under uncertainty largely remains an open problem. Despite the promise of black-box optimization methods for automated controller tuning, they generally lack formal guarantees on the solution quality, which is especially important in the control of safety-critical systems. This paper focuses on obtaining closed-loop performance guarantees for automated controller tuning, which can be formulated as a black-box optimization problem under uncertainty. We use recent advances in non-convex scenario theory to provide a distribution-free bound on the probability of the closed-loop performance measures. To mitigate the computational complexity of the data-driven scenario optimization method, we restrict ourselves to a discrete set of candidate tuning parameters. We propose to generate these candidates using constrained Bayesian optimization run multiple times from different random seed points. We apply the proposed method for tuning an economic nonlinear model predictive controller for a semibatch reactor modeled by seven highly nonlinear differential equations.
Extreme values of the resurgence for homogeneous ideals in polynomial rings

Abstract We show that two ostensibly different versions of the asymptotic resurgence introduced by E. Guardo, B. Harbourne and A. Van Tuyl in 2013 are the same. We also show that the resurgence and asymptotic resurgence attain their maximal values simultaneously, if at all, which we apply to a conjecture of E. Grifo. For radical ideals of points, we show that the resurgence and asymptotic resurgence attain their minimal values simultaneously. In addition, we introduce an integral closure version of the resurgence and relate it to the other versions of the resurgence. In closing we provide various examples and raise some related questions, and we finish with some remarks about computing the resurgence.
Direct observation of topological charge impacting skyrmion bubble stability in Pt/Ni/Co asymmetric superlattices

We characterize the magnetic properties and domain structure of Pt/Ni/Co asymmetric superlattices in comparison to the more established Pt/Co/Ni structure. This reversal in stacking sequence leads to a marked drop in interfacial magnetic anisotropy and the magnitude of the interfacial Dzyaloshinskii–Moriya interaction as inferred from the domain wall (DW) structure, which we speculate could be related to a degradation of the Pt/Co interface when Pt is deposited on top of the Co layer. Lorentz transmission electron microscopy exclusively reveals Neel-type DWs and, with a perpendicular field, Neel skyrmions in the Pt/Co/Ni films. Conversely, the Pt/Ni/Co samples show only achiral Bloch DWs, which leads to the formation of achiral Bloch and type II bubbles at an increased perpendicular field. Combined with the reduced anisotropy leading to greater bubble densities, the latter case makes for an excellent test bed to examine the benefits of topological charge on stability. Simultaneous observation of Bloch and type II bubbles shows a roughly 50 mT larger annihilation field for the former. An in-plane component to the magnetic field is shown to both impact the structure of the formed bubbles and separately suppress the topological benefit.
Efficiencies of Magnetic Field Amplification and Electron Acceleration in Young Supernova Remnants: Global Averages and Kepler’s Supernova Remnant

Particle acceleration to suprathermal energies in strong astrophysical shock waves is a widespread phenomenon, generally explained by diffusive shock acceleration. Such shocks can also amplify upstream magnetic field considerably beyond simple compression. The complex plasma physics processes involved are often parameterized by assuming that shocks put some fraction $\epsilon_e$ of their energy into fast particles, and another fraction $\epsilon_B$ into magnetic field. Modelers of shocks in supernovae, supernova remnants, and gamma-ray bursters, among other locations, often assume typical values for these fractions, presumed to remain constant in time. However, it is rare that enough properties of a source are independently constrained that values of the epsilons can be inferred directly. Supernova remnants (SNRs) can provide such circumstances. Here we summarize results from global fits to spatially integrated emission in six young SNRs, finding $10^{-4} \le \epsilon_e \le 0.05$ and $0.001 \le \epsilon_B \le 0.1$. These large variations might be put down to the differing ages and environments of these SNRs, so we conduct a detailed analysis of a single remnant, that of Kepler's supernova. Both epsilons can be determined at seven different locations around the shock, and we find even larger ranges for both epsilons, as well as for their ratio (thus independent of the shock energy itself). We conclude that unknown factors have a large influence on the efficiency of both processes. Shock obliquity, upstream neutral fraction, or other possibilities need to be explored, while calculations assuming fixed values of the epsilons should be regarded as provisional.
Social Bubbles and Superspreaders: Source Identification for Contagion Processes on Hypertrees

Previous work shows that for contagions on extended star networks, there is a simple, closed-form expression for a highly accurate approximation to the maximum likelihood infection source. Here, we generalize that result to a class of hypertrees which, although somewhat structurally analogous, provides a much richer representation space. This approach can be used to estimate patient zero sources, even when the infection has been propagated via large group gatherings rather than person-to-person spread, and when it is spreading through interrelated social bubbles with varying degrees of overlap. In contact tracing contexts, this estimator may be used to identify the source of a local outbreak, which can then be used for forward tracing or further backward tracing.
Nontrivial maturation metastate-average state in a one-dimensional long-range Ising spin glass: Above and below the upper critical range

Understanding the low-temperature pure state structure of spin glasses remains an open problem in the field of statistical mechanics of disordered systems. Here we study Monte Carlo dynamics, performing simulations of the growth of correlations following a quench from infinite temperature to a temperature well below the spin-glass transition temperature ${T}_{c}$ for a one-dimensional Ising spin-glass model with diluted long-range interactions. In this model, the probability ${P}_{ij}$ that an edge ${i,j}$ has nonvanishing interaction falls as a power law with chord distance, ${P}_{ij}\ensuremath{\propto}1/{R}_{ij}^{2\ensuremath{\sigma}}$, and we study a range of values of $\ensuremath{\sigma}$ with $1/2l\ensuremath{\sigma}l1$. We consider a correlation function ${C}_{4}(r,t)$. A dynamic correlation length that shows power-law growth with time $\ensuremath{\xi}(t)\ensuremath{\propto}{t}^{1/z}$ can be identified in the data and, for large time $t, {C}_{4}(r,t)$ decays as a power law ${r}^{\ensuremath{-}{\ensuremath{\alpha}}_{d}}$ with distance $r$ when $r\ensuremath{\ll}\ensuremath{\xi}(t)$. The calculation can be interpreted in terms of the maturation metastate averaged Gibbs state, or MMAS, and the decay exponent ${\ensuremath{\alpha}}_{d}$ differentiates between a trivial MMAS (${\ensuremath{\alpha}}_{d}=0$), as expected in the droplet picture of spin glasses, and a nontrivial MMAS (${\ensuremath{\alpha}}_{d}\ensuremath{\ne}0$), as in the replica-symmetry-breaking (RSB) or chaotic pairs pictures. We find nonzero ${\ensuremath{\alpha}}_{d}$ even in the regime $\ensuremath{\sigma}g2/3$ which corresponds to short-range systems below six dimensions. For $\ensuremath{\sigma}l2/3$, the decay exponent ${\ensuremath{\alpha}}_{d}$ follows the RSB prediction for the decay exponent ${\ensuremath{\alpha}}_{s}=3\ensuremath{-}4\ensuremath{\sigma}$ of the static metastate, consistent with a conjectured statics-dynamics relation, while it approaches ${\ensuremath{\alpha}}_{d}=1\ensuremath{-}\ensuremath{\sigma}$ in the regime $2/3l\ensuremath{\sigma}l1$; however, it deviates from both lines in the vicinity of $\ensuremath{\sigma}=2/3$.
COMPARE: accelerating groupwise comparison in relational databases for data analytics

Data analysis often involves comparing subsets of data across many dimensions for finding unusual trends and patterns. While the comparison between subsets of data can be expressed using SQL, they tend to be complex to write, and suffer from poor performance over large and high-dimensional datasets. In this paper, we propose a new logical operator COMPARE for relational databases that concisely captures the enumeration and comparison between subsets of data and greatly simplifies the expressing of a large class of comparative queries. We extend the database engine with optimization techniques that exploit the semantics of COMPARE to significantly improve the performance of such queries. We have implemented these extensions inside Microsoft SQL Server, a commercial DBMS engine. Our extensive evaluation on synthetic and real-world datasets shows that COMPARE results in a significant speedup over existing approaches, including physical plans generated by today's database systems, user-defined function (UDF), as well as middleware solutions that compare subsets outside the databases.
How to Train Your Quadrotor: A Framework for Consistently Smooth and Responsive Flight Control via Reinforcement Learning

We focus on the problem of reliably training Reinforcement Learning (RL) models (agents) for stable low-level control in embedded systems and test our methods on a high-performance, custom-built quadrotor platform. A common but often under-studied problem in developing RL agents for continuous control is that the control policies developed are not always smooth. This lack of smoothness can be a major problem when learning controllers as it can result in control instability and hardware failure.Issues of noisy control are further accentuated when training RL agents in simulation due to simulators ultimately being imperfect representations of reality—what is known as the reality gap. To combat issues of instability in RL agents, we propose a systematic framework, REinforcement-based transferable Agents through Learning (RE+AL), for designing simulated training environments that preserve the quality of trained agents when transferred to real platforms. RE+AL is an evolution of the Neuroflight infrastructure detailed in technical reports prepared by members of our research group. Neuroflight is a state-of-the-art framework for training RL agents for low-level attitude control. RE+AL improves and completes Neuroflight by solving a number of important limitations that hindered the deployment of Neuroflight to real hardware. We benchmark RE+AL on the NF1 racing quadrotor developed as part of Neuroflight. We demonstrate that RE+AL significantly mitigates the previously observed issues of smoothness in RL agents. Additionally, RE+AL is shown to consistently train agents that are flight capable and with minimal degradation in controller quality upon transfer. RE+AL agents also learn to perform better than a tuned PID controller, with better tracking errors, smoother control, and reduced power consumption. To the best of our knowledge, RE+AL agents are the first RL-based controllers trained in simulation to outperform a well-tuned PID controller on a real-world controls problem that is solvable with classical control.
Predictors of zoonotic potential in helminths

Helminths are parasites that cause disease at considerable cost to public health and present a risk for emergence as novel human infections. Although recent research has elucidated characteristics ...
Anti-SARS-CoV-2 receptor-binding domain antibody evolution after mRNA vaccination.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection produces B cell responses that continue to evolve for at least one year. During that time, memory B cells express increasingly broad and potent antibodies that are resistant to mutations found in variants of concern1. As a result, vaccination of coronavirus disease 2019 (COVID-19) convalescent individuals with currently available mRNA vaccines produces high levels of plasma neutralizing activity against all variants tested1,2. Here we examine memory B cell evolution 5 months after vaccination with either Moderna (mRNA-1273) or Pfizer-BioNTech (BNT162b2) mRNA vaccines in a cohort of SARS-CoV-2 naive individuals. Between prime and boost, memory B cells produce antibodies that evolve increased neutralizing activity, but there is no further increase in potency or breadth thereafter. Instead, memory B cells that emerge 5 months after vaccination of naive individuals express antibodies that are similar to those that dominate the initial response. While individual memory antibodies selected over time by natural infection have greater potency and breadth than antibodies elicited by vaccination, the overall neutralizing potency of plasma is greater following vaccination. These results suggest that boosting vaccinated individuals with currently available mRNA vaccines will increase plasma neutralizing activity but may not produce antibodies with equivalent breadth to those obtained by vaccinating convalescent individuals.