cssi/SciSciGPT-SciSciNet huggingface dataset:
The Myths and Realities of Correctional Severity: Evidence from the National Corrections Reporting Program on Sentencing Practices

The forces driving U.S. prison growth are poorly understood. This article examines one factor that has received insufficient attention: changes in time served. It demonstrates that time served has not risen dramatically in recent years, even declining in some jurisdictions. It also shows that time served is fairly short: median release times are approximately one to two years. Thus, admissions practices, not longer sentences, appear to drive prison growth. This article also examines whether time served varies across different types of inmates. Young, Hispanic, and violent offenders appear to serve longer sentences; race and sex appear to be of minor importance. Copyright 2011, Oxford University Press.
In Defense of Client-Lawyer Confidentiality ... and Its Exceptions ...

The current scope of required client confidentiality in the Restatement (Third) of the Law Governing Lawyers and the Model rules of Professional Conduct reflect its long standing purposes. Exceptions to the rule should be governed by the same moral justifications.
The Crime of Aggression and Humanitarian Intervention on Behalf of Women

This article is part of a larger project to analyse the rarely-considered gender aspects of the crime of aggression and to explore whether or not the amendments adding the crime of aggression to the Statute of the International Criminal Court (ICC) represent an advancement for women. This piece focuses on the potential for the new provisions to chill bona fide exercises of humanitarian intervention given that (1) the crime is expansively drafted to potentially cover all uses of sovereign force, (2) delegates rejected efforts by the United States to include an express exception for military operations launched to prevent the commission of other crimes within the jurisdiction of the ICC, and (3) other proposals that would have prevented humanitarian interventions from being considered 'acts of aggression' were not fully explored or implemented. The article acknowledges that feminist theory may never fully come to terms with a notion of humanitarian intervention given the doctrine's valorisation of militarism, especially in light of the fact that women are so often excluded from decisions about uses of force. It nonetheless argues that if we want to hold out the possibility of humanitarian intervention being deployed in defence of women, elements of the new provisions (such as the terms 'manifest', 'character', 'gravity', and 'consequences') should be interpreted to exclude situations involving the nascent responsibility to protect doctrine.
Spin stiffness of chromium-based van der Waals ferromagnets

Low temperature magnetization of CrI3, CrSiTe3and CrGeTe3single crystals were systematically studied. Based on the temperature dependence of extrapolated spontaneous magnetization from magnetic isotherms measured at different temperatures, the spin stiffness constant (D) and spin excitation gap (Δ) were extracted according to Bloch's law. For spin stiffness,Dis estimated to be 27 ± 6 meV A2, 20 ± 3 meV A2and 38 ± 7 meV A2for CrI3, CrSiTe3and CrGeTe3respectively. Spin excitation gaps determined via Bloch's formulation have larger error bars yielding 0.59 ± 0.34 meV (CrI3), 0.37 ± 0.22 meV (CrSiTe3) and 0.28 ± 0.19 meV (CrGeTe3). Among all three studied compounds, larger spin stiffness value leads to higher ferromagnetic transition temperature.
Topological Analysis of Magnetic Reconnection in Kinetic Plasma Simulations

Magnetic reconnection is a ubiquitous plasma process in which oppositely directed magnetic field lines break and rejoin, resulting in a change of the magnetic field topology. Reconnection generates magnetic islands: regions enclosed by magnetic field lines and separated by reconnection points. Proper identification of these features is important to understand particle acceleration and overall behavior of plasma. We present a contour-tree based visualization for robust and objective identification of islands and reconnection points in two-dimensional (2D) magnetic reconnection simulations. The application of this visualization to a simple simulation has revealed a physical phenomenon previously not reported, resulting in a more comprehensive understanding of magnetic reconnection.
Executive functions in two-way dual-language education: A mechanism for academic performance

Children across the United States are increasingly learning academic content through two-way dual-language education (http://www.cal.org/twi/). This education model provides instruction through two...
Indirect versus direct effects of freshwater browning on larval fish foraging

Fish foraging and energy flow between trophic levels are predicted to decline with freshwater browning due to decreased light availability. Studies investigating these predictions have focused on j...
Using Spectral Graph Theory to Map Qubits onto Connectivity-limited Devices

We propose an efficient heuristic for mapping the logical qubits of quantum algorithms to the physical qubits of connectivity-limited devices, adding a minimal number of connectivity-compliant SWAP gates. In particular, given a quantum circuit, we construct an undirected graph with edge weights a function of the two-qubit gates of the quantum circuit. Taking inspiration from spectral graph drawing, we use an eigenvector of the graph Laplacian to place logical qubits at coordinate locations. These placements are then mapped to physical qubits for a given connectivity. We primarily focus on one-dimensional connectivities and sketch how the general principles of our heuristic can be extended for use in more general connectivities.
The Mackey bijection for complex reductive groups and continuous fields of reduced group C*-algebras

The purpose of this paper is to make a further contribution to the Mackey bijection for a complex reductive group G, between the tempered dual of G and the unitary dual of the associated Cartan motion group. We shall construct an embedding of the C*-algebra of the motion group into the reduced C*-algebra of G, and use it to characterize the continuous field of reduced group C*-algebras that is associated to the Mackey bijection. We shall also obtain a new characterization of the Mackey bijection using the same embedding.
Enhance CNN Robustness Against Noises for Classification of 12-Lead ECG with Variable Length

Electrocardiogram (ECG) is the most widely used diagnostic tool to monitor the condition of the cardiovascular system. Deep neural networks (DNNs), have been developed in many research labs for automatic interpretation of ECG signals to identify potential abnormalities in patient hearts. Studies have shown that given a sufficiently large amount of data, the classification accuracy of DNNs could reach human-expert cardiologist level. However, despite of the excellent performance in classification accuracy, it has been shown that DNNs are highly vulnerable to adversarial noises which are subtle changes in input of a DNN and lead to a wrong class-label prediction with a high confidence. Thus, it is challenging and essential to improve robustness of DNNs against adversarial noises for ECG signal classification -a life-critical application. In this work, we designed a CNN for classification of 12-lead ECG signals with variable length, and we applied three defense methods to improve robustness of this CNN for this classification task. The ECG data in this study is very challenging because the sample size is limited, and the length of each ECG recording varies in a large range. The evaluation results show that our customized CNN reached satisfying F1 score and average accuracy, comparable to the top-6 entries in the CPSC2018 ECG classification challenge, and the defense methods enhanced robustness of our CNN against adversarial noises and white noises, with a minimal reduction in accuracy on clean data.
Maximizing BCI Human Feedback using Active Learning

Recent advancements in Learning from Human Feedback present an effective way to train robot agents via inputs from non-expert humans, without a need for a specially designed reward function. However, this approach needs a human to be present and attentive during robot learning to provide evaluative feedback. In addition, the amount of feedback needed grows with the level of task difficulty and the quality of human feedback might decrease over time because of fatigue. To overcome these limitations and enable learning more robot tasks with higher complexities, there is a need to maximize the quality of expensive feedback received and reduce the amount of human cognitive involvement required. In this work, we present an approach that uses active learning to smartly choose queries for the human supervisor based on the uncertainty of the robot and effectively reduces the amount of feedback needed to learn a given task. We also use a novel multiple buffer system to improve robustness to feedback noise and guard against catastrophic forgetting as the robot learning evolves. This makes it possible to learn tasks with more complexity using lesser amounts of human feedback compared to previous methods. We demonstrate the utility of our proposed method on a robot arm reaching task where the robot learns to reach a location in 3D without colliding with obstacles. Our approach is able to learn this task faster, with less human feedback and cognitive involvement, compared to previous methods that do not use active learning.
The Role of Edges in Line Drawing Perception

It has often been conjectured that the effectiveness of line drawings can be explained by the similarity of edge images to line drawings. This article presents several problems with explaining line...
Saccharomyces cerevisiae Requires CFF1 To Produce 4-Hydroxy-5-Methylfuran-3(2H)-One, a Mimic of the Bacterial Quorum-Sensing Autoinducer AI-2.

ABSTRACT Quorum sensing is a process of cell-to-cell communication that bacteria use to orchestrate collective behaviors. Quorum sensing depends on the production, release, and detection of extracellular signal molecules called autoinducers (AIs) that accumulate with increasing cell density. While most AIs are species specific, the AI called AI-2 is produced and detected by diverse bacterial species, and it mediates interspecies communication. We recently reported that mammalian cells produce an AI-2 mimic that can be detected by bacteria through the AI-2 receptor LuxP, potentially expanding the role of the AI-2 system to interdomain communication. Here, we describe a second molecule capable of interdomain signaling through LuxP, 4-hydroxy-5-methylfuran-3(2H)-one (MHF), that is produced by the yeast Saccharomyces cerevisiae. Screening the S. cerevisiae deletion collection revealed Cff1p, a protein with no known role, to be required for MHF production. Cff1p is proposed to be an enzyme, with structural similarity to sugar isomerases and epimerases, and substitution at the putative catalytic residue eliminated MHF production in S. cerevisiae. Sequence analysis uncovered Cff1p homologs in many species, primarily bacterial and fungal, but also viral, archaeal, and higher eukaryotic. Cff1p homologs from organisms from all domains can complement a cff1ΔS. cerevisiae mutant and restore MHF production. In all cases tested, the identified catalytic residue is conserved and required for MHF to be produced. These findings increase the scope of possibilities for interdomain interactions via AI-2 and AI-2 mimics, highlighting the breadth of molecules and organisms that could participate in quorum sensing. IMPORTANCE Quorum sensing is a cell-to-cell communication process that bacteria use to monitor local population density. Quorum sensing relies on extracellular signal molecules called autoinducers (AIs). One AI called AI-2 is broadly made by bacteria and used for interspecies communication. Here, we describe a eukaryotic AI-2 mimic, 4-hydroxy-5-methylfuran-3(2H)-one, (MHF), that is made by the yeast Saccharomyces cerevisiae, and we identify the Cff1p protein as essential for MHF production. Hundreds of viral, archaeal, bacterial, and eukaryotic organisms possess Cff1p homologs. This finding, combined with our results showing that homologs from all domains can replace S. cerevisiae Cff1p, suggests that like AI-2, MHF is widely produced. Our results expand the breadth of organisms that may participate in quorum-sensing-mediated interactions.
As a Matter of Tension: Kinetic Energy Spectra in MHD Turbulence

Magnetized turbulence is ubiquitous in many astrophysical and terrestrial systems but no complete, uncontested theory even in the simplest form, magnetohydrodynamics (MHD), exists. Many theories and phenomenologies focus on the joint (kinetic and magnetic) energy fluxes and spectra. We highlight the importance of treating kinetic and magnetic energies separately to shed light on MHD turbulence dynamics. We conduct an implicit large eddy simulation of subsonic, super-Alfvenic MHD turbulence and analyze the scale-wise energy transfer over time. Our key finding is that the kinetic energy spectrum develops a scaling of approximately $k^{-4/3}$ in the stationary regime as the kinetic energy cascade is suppressed by magnetic tension. This motivates a reevaluation of existing MHD turbulence theories with respect to a more differentiated modeling of the energy fluxes.
A canonical purification for the entanglement wedge cross-section

In AdS/CFT we consider a class of bulk geometric quantities inside the entanglement wedge called reflected minimal surfaces. The areas of these surfaces are dual to the entanglement entropy associated to a canonical purification (the GNS state) that we dub the reflected entropy. From the bulk point of view, we show that half the area of the reflected minimal surface gives a reinterpretation of the notion of the entanglement wedge cross-section. We prove some general properties of the reflected entropy and introduce a novel replica trick in CFTs for studying it. The duality is established using a recently introduced approach to holographic modular flow. We also consider an explicit holographic construction of the canonical purification, introduced by Engelhardt and Wall; the reflected minimal surfaces are simply RT surfaces in this new spacetime. We contrast our results with the entanglement of purification conjecture, and finally comment on the continuum limit where we find a relation to the split property: the reflected entropy computes the von Neumann entropy of a canonical splitting type-I factor introduced by Doplicher and Longo.
Attention recruits frontal cortex in human infants

Young infants learn about the world by overtly shifting their attention to perceptually salient events. In adults, attention recruits several brain regions spanning the frontal and parietal lobes. However, it is unclear whether these regions are sufficiently mature in infancy to support attention and, more generally, how infant attention is supported by the brain. We used event-related functional magnetic resonance imaging (fMRI) in 24 sessions from 20 awake behaving infants 3 mo to 12 mo old while they performed a child-friendly attentional cuing task. A target was presented to either the left or right of the infant's fixation, and offline gaze coding was used to measure the latency with which they saccaded to the target. To manipulate attention, a brief cue was presented before the target in three conditions: on the same side as the upcoming target (valid), on the other side (invalid), or on both sides (neutral). All infants were faster to look at the target on valid versus invalid trials, with valid faster than neutral and invalid slower than neutral, indicating that the cues effectively captured attention. We then compared the fMRI activity evoked by these trial types. Regions of adult attention networks activated more strongly for invalid than valid trials, particularly frontal regions. Neither behavioral nor neural effects varied by infant age within the first year, suggesting that these regions may function early in development to support the orienting of attention. Together, this furthers our mechanistic understanding of how the infant brain controls the allocation of attention.
Anti-reflection coated vacuum window for the Primordial Inflation Polarization ExploreR (PIPER) balloon-borne instrument

Measuring the faint polarization signal of the cosmic microwave background (CMB) not only requires high optical throughput and instrument sensitivity but also control over systematic effects. Polarimetric cameras or receivers used in this setting often employ dielectric vacuum windows, filters, or lenses to appropriately prepare light for detection by cooled sensor arrays. These elements in the optical chain are typically designed to minimize reflective losses and hence improve sensitivity while minimizing potential imaging artifacts such as glint and ghosting. The Primordial Inflation Polarization ExploreR (PIPER) is a balloon-borne instrument designed to measure the polarization of the CMB radiation at the largest angular scales and characterize astrophysical dust foregrounds. PIPER’s twin telescopes and detector systems are submerged in an open-aperture liquid helium bucket dewar. A fused-silica window anti-reflection (AR) coated with polytetrafluoroethylene is installed on the vacuum cryostat that houses the cryogenic detector arrays. Light passes from the skyward portions of the telescope to the detector arrays through this window, which utilizes an indium seal to prevent superfluid helium leaks into the vacuum cryostat volume. The AR coating implemented reduces reflections from each interface to <1% compared to ∼10% from an uncoated window surface. The AR coating procedure and room temperature optical measurements of the window are presented. The indium vacuum sealing process is also described in detail, and test results characterizing its integrity to superfluid helium leaks are provided.
The Law and Economics of “Forced” Technology Transfer and Its Implications for Trade and Investment Policy (and the U.S.–China Trade War)

“Forced technology transfer” is a central issue in the ongoing U.S.–China trade row. The phrase encompasses a number of different practices, but the most significant according to various commentators involve measures that require foreign investors in China to partner with domestic entities as a condition of making an investment, either by forming a joint venture or affording Chinese investors a controlling equity stake. These “corporate structure requirements” empower prospective Chinese partners to bargain for technology transfer as a condition of forming a new venture or otherwise enable them to learn the details of foreign technology through participation in the business enterprise. Foreign investors are free to reject such requirements and forego the associated investment opportunities, and in this sense any technology transfer pursuant to China’s requirements is “consensual.” For ease of reference, this essay refers to these corporate structure requirements as CSR. The analysis to follow examines the economics of CSR from both the national and global welfare perspectives. It indicates how CSR may undercut the national welfare of the USA even if it is profitable for U.S. investors. The global welfare implications of CSR, however, are much less clear, which offers an explanation for the absence of any constraints on CSR in typical trade agreements. A clear role for restrictions on CSR does emerge, however, in investment agreements that seek to eliminate investment protectionism by requiring “pre-establishment national treatment” for foreign investors. This analysis has immediate policy implications for the ongoing trade dispute with China.
Services Ecosystem

Summary form only given. Services businesses have become very exciting growth opportunities for the industry. How to design, model, and implement business services using IT technology is becoming a challenging issue. With the introduction of service-oriented architecture (SOA) and Web services, componentizing enterprises and services based on patterns has paved a way to running a successful services business. In this talk, the author describes the services ecosystem, methodology and supporting techniques used to build, operate, and manage business services.
The ethical obligations of lawyers, law students and law professors telling stories on web logs

This article examines how blogging has developed and considers the ethics of blogging and its impact on the legal profession. It examines blog entries from lawyers, law professors and law students and suggests that the rules of the Bar may be colliding with the manner of online storytelling occurring by legal professionals. The article takes an in-depth look at how blogging has impacted legal education and the relationship between faculty and students. It proposes ways in which incorporating blogging assignments into law school courses can assist students in developing ethical story-telling on web logs.
Model Predictive Control with Forward-Looking Persistent Excitation

Abstract This work deals with the dual-control problem of simultaneous regulation and model parameter estimation in model predictive control. We propose an adaptive model predictive control which guarantees a persistently exciting closed loop sequence by only looking forward in time into the prediction horizon. Earlier works needed to look backwards and preserve prior regressor data. With the new approach, under the assumption of a known periodic persistently exciting reference trajectory around the equilibrium, we demonstrate exponential convergence of nonlinear systems under the influence of the adaptive model predictive control combined with a recursive least squares identifier with forgetting factor despite bounded noise. The results are, at this stage, local in state and parameter estimate space.
On reaction network implementations of neural networks.

This paper is concerned with the utilization of deterministically modelled chemical reaction networks for the implementation of (feed-forward) neural networks. We develop a general mathematical fra...
Ultrathin Broadband Reflective Optical Limiter

Optical limiters are nonlinear devices that feature decreasing transmittance with increasing incident optical intensity, and thus can protect sensitive components from high-intensity illumination. The ideal optical limiter reflects rather than absorbs light in its active ("limiting") state, minimizing risk of damage to the limiter itself. Previous efforts to realize reflective limiters were based on embedding nonlinear layers into relatively thick multilayer photonic structures, resulting in substantial fabrication complexity, reduced speed and, in some instances, limited working bandwidth. We overcome these tradeoffs by using the insulator-to-metal transition in vanadium dioxide (VO2) to achieve intensity-dependent modulation of resonant transmission through aperture antennas. Due to the dramatic change of optical properties across the insulator-to-metal transition, low-quality-factor resonators were sufficient to achieve high on-off ratios in device transmittance. As a result, our ultra-thin reflective limiter (thickness ~1/100 of the free-space wavelength) is broadband in terms of operating wavelength (> 2 um at 10 um) and angle of incidence (up to ~50$°$ away from the normal).
Simulating magnetic monopole-defect dynamics

We present simulations of one magnetic monopole interacting with multiple magnetic singularities. Three-dimensional plots of the energy density are constructed from explicit solutions to the Bogomolny equation obtained by Blair, Cherkis, and Durcan. Animations follow trajectories derived from collective coordinate mechanics on the multi-centered Taub-NUT monopole moduli space. We supplement our numerical results with a complete analytic treatment of the single-defect case.
Learning-Based Real-Time Event Identification Using Rich Real PMU Data

A large-scale deployment of phasor measurement units (PMUs) that reveal the inherent physical laws of power systems from a data perspective enables an enhanced awareness of power system operation. However, the high-granularity and non-stationary nature of PMU data and imperfect data quality could bring great technical challenges for real-time system event identification. To address these challenges, this paper proposes a two-stage learning-based framework. In the first stage, a Markov transition field (MTF) algorithm is exploited to extract the latent data features by encoding temporal dependency and transition statistics of PMU data in graphs. Then, a spatial pyramid pooling (SPP)-aided convolutional neural network (CNN) is established to efficiently and accurately identify power events. The proposed method fully builds on and is also tested on a large real-world dataset from several tens of PMU sources (and the corresponding event logs), located across the U.S., with a time span of two consecutive years. The numerical results validate that our method has high identification accuracy while showing good robustness against poor data quality.
The effects of the BP Deepwater Horizon oil spill on housing markets

Abstract When the Deepwater Horizon oil rig exploded in 2010, it resulted in the largest off-shore oil spill in United States history. Economic theory dictates that the oil damage and restitution payments that resulted from the spill should be capitalized into property values. To measure the extent of this capitalization, we create a novel dataset by linking surveys of the location and severity of oil observed along over 4,300 miles of the Gulf Coast to measures of local housing market outcomes. We then perform hedonic-style analysis to determine the net effects of the spill on affected real estate markets. In doing so, we provide the first plausibly causal estimates of the effect of the spill on affected housing markets throughout the Gulf region. Identification comes from a triple-difference framework that exploits the random nature of both the spill and the spatial distribution of oil that affected coastal communities, as well as controls for the confounding effects of the housing market crash. Results suggest that on net, the BP oil spill caused a significant decline in home prices of between 4% and 8% that persisted until at least 2015. This implies housing markets capitalized $3.8 billion to $5.0 billion in spill damage inclusive of clean-up and restitution effects. These results are robust to numerous alternative definitions of treatment and control groups.
Static Stages for Heterogeneous Programming

Heterogeneous hardware is central to modern advances in performance and efficiency. Mainstream programming models for heterogeneous architectures, however, sacrifice safety and expressiveness in favor of low-level control over performance details. The interfaces between hardware units consist of verbose, unsafe APIs; hardware-specific languages make it difficult to move code between units; and brittle preprocessor macros complicate the task of specializing general code for efficient accelerated execution. We propose a unified low-level programming model for heterogeneous systems that offers control over performance, safe communication constructs, cross-device code portability, and hygienic metaprogramming for specialization. The language extends constructs from multi-stage programming to separate code for different hardware units, to communicate between them, and to express compile-time code optimization. We introduce static staging, a different take on multi-stage programming that lets the compiler generate all code and communication constructs ahead of time. To demonstrate our approach, we use static staging to implement BraidGL, a real-time graphics programming language for CPU-GPU systems. Current real-time graphics software in OpenGL uses stringly-typed APIs for communication and unsafe preprocessing to generate specialized GPU code variants. In BraidGL, programmers instead write hybrid CPU-GPU software in a unified language. The compiler statically generates target-specific code and guarantees safe communication between the CPU and the graphics pipeline stages. Example scenes demonstrate the language's productivity advantages: BraidGL eliminates the safety and expressiveness pitfalls of OpenGL and makes common specialization techniques easy to apply. The case study demonstrates how static staging can express core placement and specialization in general heterogeneous programming.
On the three-dimensional spatial correlations of curved dislocation systems

Coarse-grained descriptions of dislocation motion in crystalline metals inherently represent a loss of information regarding dislocation-dislocation interactions. In the present work, we consider a coarse-graining framework capable of re-capturing these interactions by means of the dislocation-dislocation correlation functions. The framework depends on a convolution length to define slip-system-specific dislocation densities. Following a statistical definition of this coarse-graining process, we define a spatial correlation function which will allow the arrangement of the discrete line system at two points—and thus the strength of their interactions at short range—to be recaptured into a mean field description of dislocation dynamics. Through a statistical homogeneity argument, we present a method of evaluating this correlation function from discrete dislocation dynamics simulations. Finally, results of this evaluation are shown in the form of the correlation of dislocation densities on the same slip-system. These correlation functions are seen to depend weakly on plastic strain, and in turn, the dislocation density, but are seen to depend strongly on the convolution length. Implications of these correlation functions in regard to continuum dislocation dynamics as well as future directions of investigation are also discussed.
Benefits of deep learning classification of continuous noninvasive brain-computer interface control

Objective. Noninvasive brain-computer interfaces (BCIs) assist paralyzed patients by providing access to the world without requiring surgical intervention. Prior work has suggested that EEG motor imagery based BCI can benefit from increased decoding accuracy through the application of deep learning methods, such as convolutional neural networks (CNNs).Approach. Here, we examine whether these improvements can generalize to practical scenarios such as continuous control tasks (as opposed to prior work reporting one classification per trial), whether valuable information remains latent outside of the motor cortex (as no prior work has compared full scalp coverage to motor only electrode montages), and the existing challenges to the practical implementation of deep-learning based continuous BCI control.Main results. We report that: (1) deep learning methods significantly increase offline performance compared to standard methods on an independent, large, and longitudinal online motor imagery BCI dataset with up to 4-classes and continuous 2D feedback; (2) our results suggest that a variety of neural biomarkers for BCI, including those outside the motor cortex, can be detected and used to improve performance through deep learning methods, and (3) tuning neural network output will be an important step in optimizing online BCI control, as we found the CNN models trained with full scalp EEG also significantly reduce the average trial length in a simulated online cursor control environment.Significance. This work demonstrates the benefits of CNNs classification during BCI control while providing evidence that electrode montage selection and the mapping of CNN output to device control will be important design choices in CNN based BCIs.
Energy scheduling for residential distributed energy resources with uncertainties using model-based predictive control

Abstract This paper proposes a reliable energy scheduling framework for distributed energy resources (DER) of a residential area to achieve an appropriate daily electricity consumption with the maximum affordable demand response. Renewable and non-renewable energy resources are available to respond to customers’ demands using different methods to manage energy. The optimal operation problem is a mixed-integer-linear-programming (MILP) investigated using model-based predictive control (MPC) to determine which dispatchable unit should be operated at what time and at what power level while satisfying practical constraints. Renewable energy sources (RES), particularly solar and wind energies, have recently expanded their electric power systems role. Although they are environment friendly and accessible, there are challenging issues regarding their performance, such as dealing with the variability and uncertainties concerned with them. This research investigates the energy management of these systems in three complementary scenarios. The first and second scenarios are respectively suitable for a market with a constant and inconstant price. Additionally, the third scenario is proposed to consider the role of uncertainties in RES, and it is designed to recompense the power shortage using non-renewable resources. The validity of methods is explored in a residential area for 24 h, and the results thoroughly demonstrate the competence of the proposed approach for decreasing the operation cost.
On the Effect of Rigid Swept Surface Waves on Turbulent Drag

Passive turbulent drag reduction techniques are of interest as a cost effective means to improve air vehicle fuel consumption. In the past, rigid surface waves slanted at an angle from the streamwise direction were deemed ineffective to reduce skin friction drag due to the pressure drag that they generate. A recent analysis seeking similarities to the spanwise shear stress generated by spatial Stokes layers suggested that there may be a range of wavelength, amplitude, and orientation in which the wavy surface would reduce turbulent drag. The present work explores, by experiments and Direct Numerical Simulations (DNS), the effect of swept wavy surfaces on skin friction and pressure drag. Plates with shallow and deep wave patterns were rapid-prototyped and tested using a drag balance in the 7x11 inch Low-Speed Wind Tunnel at the NASA LaRC Research Center. The measured drag o set between the wavy plates and the reference at plate is found to be within the experimental repeatability limit. Oil vapor flow measurements indicate a mean spanwise flow over the deep waves. The turbulent flow in channels with at walls, swept wavy walls and spatial Stokes spanwise velocity forcing was simulated at a friction Reynolds number of two hundred. The time-averaged and dynamic turbulent flow characteristics of the three channel types are compared. The drag obtained for the channel with shallow waves is slightly larger than for the at channel, within the range of the experiments. In the case of the large waves, the simulation over predicts the drag. The shortcomings of the Stokes layer analogy model for the estimation of the spanwise shear stress and drag are discussed.
Radiative properties of quantum emitters in boron nitride from excited state calculations and Bayesian analysis

Point defects in hexagonal boron nitride (hBN) have attracted growing attention as bright single-photon emitters. However, understanding of their atomic structure and radiative properties remains incomplete. Here we study the excited states and radiative lifetimes of over 20 native defects and carbon or oxygen impurities in hBN using ab initio density functional theory and GW plus Bethe-Salpeter equation calculations, generating a large data set of their emission energy, polarization and lifetime. We find a wide variability across quantum emitters, with exciton energies ranging from 0.3 to 4 eV and radiative lifetimes from ns to ms for different defect structures. Through a Bayesian statistical analysis, we identify various high-likelihood charge-neutral defect emitters, among which the native VNNB defect is predicted to possess emission energy and radiative lifetime in agreement with experiments. Our work advances the microscopic understanding of hBN single-photon emitters and introduces a computational framework to characterize and identify quantum emitters in 2D materials.
Long-term air pollution and other risk factors associated with COVID-19 at the census tract level in Colorado.

Abstract Previous nationwide studies have reported links between long-term concentrations of fine particulate matter (PM2.5) and COVID-19 infection and mortality rates. In order to translate these results to the state level, we use Bayesian hierarchical models to explore potential links between long-term PM2.5 concentrations and census tract-level rates of COVID-19 outcomes (infections, hospitalizations, and deaths) in Colorado. We explicitly consider how the uncertainty in PM2.5 estimates affects our results by comparing four different PM2.5 surfaces from academic and governmental organizations. After controlling for 20 census tract-level covariates, we find that our results depend heavily on the choice of PM2.5 surface. Using PM2.5 estimates from the United States EPA, we find that a 1 μg/m3 increase in long-term PM2.5 concentrations is associated with a statistically significant 26% increase in the relative risk of hospitalizations and a 34% increase in mortality. Results for all other surfaces and outcomes were not statistically significant. At the same time, we find a clear association between communities of color and COVID-19 outcomes at the Colorado census tract level that is minimally affected by the choice of PM2.5 surface. A per-interquartile range (IQR) increase in the percent of non-African American people of color was associated with a 31%, 43%, and 56% increase in the relative risk of infection, hospitalization, and mortality respectively, while a per-IQR increase in the proportion of non-Hispanic African Americans was associated with a 4% and 7% increase in the relative risk of infections and hospitalizations. The current disagreement among the different PM2.5 estimates is a key factor limiting our ability to link environmental exposures and health outcomes at the census tract level. These results have strong implications for the implementation of an equitable public health response during the crisis and suggest targeted areas for additional air monitoring in Colorado.
Fiscal stimulus as an optimal control problem

Abstract During the Great Recession, Democrats in the United States argued that government spending could be utilized to “grease the wheels” of the economy in order to create wealth and to increase employment; Republicans, on the other hand, contended that government spending is wasteful and discourages investment, thereby increasing unemployment. This past year we have found ourselves in the midst of another crisis where government spending and fiscal stimulus is again being considered as a solution. In the present paper, we address this question by formulating an optimal control problem generalizing the model of Radner and Shepp (1996). The model allows for the company to borrow continuously from the government. We prove that there exists an optimal strategy; rigorous verification proofs for its optimality are provided. We proceed to prove that government loans increase the expected value of a company. We also examine the consequences of different profit-taking behaviors among firms who receive fiscal stimulus.
HILUCSI: Simple, Robust, and Fast Multilevel ILU for Large-Scale Saddle-Point Problems from PDEs

Incomplete factorization is a widely used preconditioning technique for Krylov subspace methods for solving large-scale sparse linear systems. Its multilevel variants, such as ILUPACK, are more robust for many symmetric or unsymmetric linear systems than the traditional, single-level incomplete LU (or ILU) techniques. However, the previous multilevel ILU techniques still lacked robustness and efficiency for some large-scale saddle-point problems, which often arise from systems of partial differential equations (PDEs). We introduce HILUCSI, or Hierarchical Incomplete LU-Crout with Scalability-oriented and Inverse-based dropping. As a multilevel preconditioner, HILUCSI statically and dynamically permutes individual rows and columns to the next level for deferred factorization. Unlike ILUPACK, HILUCSI applies symmetric preprocessing techniques at the top levels but always uses unsymmetric preprocessing and unsymmetric factorization at the coarser levels. The deferring combined with mixed preprocessing enabled a unified treatment for nearly or partially symmetric systems, and simplified the implementation by avoiding mixed $1\times 1$ and $2\times 2$ pivots for symmetric indefinite systems. We show that this combination improves robustness for indefinite systems without compromising efficiency. Furthermore, to enable superior efficiency for large-scale systems with millions or more unknowns, HILUCSI introduces a scalability-oriented dropping in conjunction with a variant of inverse-based dropping. We demonstrate the effectiveness of HILUCSI for dozens of benchmark problems, including those from the mixed formulation of the Poisson equation, Stokes equations, and Navier-Stokes equations. We also compare its performance with ILUPACK, the supernodal ILUTP in SuperLU, and multithreaded direct solvers in PARDISO and MUMPS.
Low-cost, scalable, and automated fluid sampling for fluidics applications

Abstract We present colosseum, a low-cost, modular, and automated fluid sampling device for scalable fluidic applications. The colosseum fraction collector uses a single motor, can be built for less than $100 using off-the-shelf and 3D-printed components, and can be assembled in less than an hour. Build Instructions and source files are available at https://doi.org/10.5281/zenodo.4677604 .
NOx Emissions Performance and Correlation Equations for a Multipoint LDI Injector

Lean Direct Injection (LDI) is a combustor concept that reduces nitrogen oxides (NOx) emissions.This paper looks at a 3-zone multipoint LDI concept developed by Parker Hannifin Corporation. The concept was tested in a flame-tube test facility at NASA Glenn Research Center. Due to test facility limitations, such as inlet air temperature and pressure, the flame-tube test was not able to cover the full set of engine operation conditions. Three NOx correlation equations were developed based on assessing NOx emissions dependencies on inlet air pressure (P3), inlet air temperature (T3), and fuel air equivalence ratio(theta) to estimate the NOx emissions at the unreachable high engine power conditions. As the results, the NOx emissions are found to be a strong function of combustion inlet air temperature and fuel air equivalence ratio but a weaker function of inlet air pressure. With these three equations, the NOx emissions performance of this injector concept is calculated as a 66 reduction relative to the ICAO CAEP-6 standard using a 55:1 pressure-ratio engine cycle. Uncertainty in the NOx emissions estimation increases as the extrapolation range departs from the experimental conditions. Since maximum inlet air pressure tested was less than 50 of the full power engine inlet air pressure, a future experiment at higher inlet air pressure conditions is needed to confirm the NOx emissions dependency on inlet air pressure.
Self-supervised Geometric Perception

We present self-supervised geometric perception (SGP), the first general framework to learn a feature descriptor for correspondence matching without any ground-truth geometric model labels (e.g., camera poses, rigid transformations). Our first contribution is to formulate geometric perception as an optimization problem that jointly optimizes the feature descriptor and the geometric models given a large corpus of visual measurements (e.g., images, point clouds). Under this optimization formulation, we show that two important streams of research in vision, namely robust model fitting and deep feature learning, correspond to optimizing one block of the unknown variables while fixing the other block. This analysis naturally leads to our second contribution – the SGP algorithm that performs alternating minimization to solve the joint optimization. SGP iteratively executes two meta-algorithms: a teacher that performs robust model fitting given learned features to generate geometric pseudo-labels, and a student that performs deep feature learning under noisy supervision of the pseudo-labels. As a third contribution, we apply SGP to two perception problems on large-scale real datasets, namely relative camera pose estimation on MegaDepth and point cloud registration on 3DMatch. We demonstrate that SGP achieves state-of-the-art performance that is on-par or superior to the supervised oracles trained using ground-truth labels.1
Universal Covertness for Discrete Memoryless Sources

Consider a sequence $X^{n}$ of length $n$ emitted by a Discrete Memoryless Source (DMS) with unknown distribution $p_{X}$ . The objective is to construct a lossless source code that maps $X^{n}$ to a sequence $\widehat {Y}^{m}$ of length $m$ that is indistinguishable, in terms of Kullback-Leibler divergence, from a sequence emitted by another DMS with known distribution $p_{Y}$ . The main result is the existence of a coding scheme that performs this task with an optimal ratio $m/n$ equal to $H(X)/H(Y)$ , the ratio of the Shannon entropies of the two distributions, as $n$ goes to infinity. The coding scheme overcomes the challenges created by the lack of knowledge about $p_{X}$ by a type-based universal lossless source coding scheme that produces as output an almost uniformly distributed sequence, followed by another type-based coding scheme that jointly performs source resolvability and universal lossless source coding. The result recovers and extends previous results that either assume $p_{X}$ or $p_{Y}$ uniform, or $p_{X}$ known. The price paid for these generalizations is the use of common randomness with vanishing rate, whose length scales as the logarithm of $n$ . By allowing common randomness larger than the logarithm of $n$ but still negligible compared to $n$ , a constructive low-complexity encoding and decoding counterpart to the main result is also provided for binary sources by means of polar codes.
Exoskeleton-Based Multimodal Action and Movement Recognition: Identifying and Developing the Optimal Boosted Learning Approach

This paper makes two scientific contributions to the field of exoskeleton-based action and movement recognition. First, it presents a novel machine learning and pattern recognition-based framework that can detect a wide range of actions and movements - walking, walking upstairs, walking downstairs, sitting, standing, lying, stand to sit, sit to stand, sit to lie, lie to sit, stand to lie, and lie to stand, with an overall accuracy of 82.63%. Second, it presents a comprehensive comparative study of different learning approaches - Random Forest, Artificial Neural Network, Decision Tree, Multiway Decision Tree, Support Vector Machine, k-NN, Gradient Boosted Trees, Decision Stump, Auto MLP, Linear Regression, Vector Linear Regression, Random Tree, Naive Bayes, Naive Bayes (Kernel), Linear Discriminant Analysis, Quadratic Discriminant Analysis, and Deep Learning applied to this framework. The performance of each of these learning approaches was boosted by using the AdaBoost algorithm, and the Cross Validation approach was used for training and testing. The results show that in boosted form, the kNN classifier outperforms all the other boosted learning approaches and is, therefore, the optimal learning method for this purpose. The results presented and discussed uphold the importance of this work to contribute towards augmenting the abilities of exoskeleton-based assisted and independent living of the elderly in the future of Internet of Things-based living environments, such as Smart Homes. As a specific use case, we also discuss how the findings of our work are relevant for augmenting the capabilities of the Hybrid Assistive Limb exoskeleton, a highly functional lower limb exoskeleton.