cssi/SciSciGPT-SciSciNet huggingface dataset:
Multiplicative perturbation bounds for multivariate multiple linear regression in Schatten p-norms

Abstract Multivariate multiple linear regression (MMLR), which occurs in a number of practical applications, generalizes traditional least squares (multivariate linear regression) to multiple right-hand sides. We extend recent MLR analyses to sketched MMLR in general Schatten p-norms by interpreting the sketched problem as a multiplicative perturbation. Our work represents an extension of Maher's results on Schatten p-norms. We derive expressions for the exact and perturbed solutions in terms of projectors for easy geometric interpretation. We also present a geometric interpretation of the action of the sketching matrix in terms of relevant subspaces. We show that a key term in assessing the accuracy of the sketched MMLR solution can be viewed as a tangent of a largest principal angle between subspaces under some assumptions. Our results enable additional interpretation of the difference between an orthogonal and oblique projector with the same range.
Strongyloides RNA-seq Browser: a web-based software platform for on-demand bioinformatics analyses of Strongyloides species

Soil-transmitted gastrointestinal parasitic nematodes infect approximately 1 billion people worldwide, predominantly in low-resource communities. Skin-penetrating gastrointestinal nematodes in the genus Strongyloides are emerging as model systems for mechanistic studies of soil-transmitted helminths due to the growing availability of functional genomics tools for these species. To facilitate future genomics studies of Strongyloides species, we have designed a web-based application, the Strongyloides RNA-seq Browser, that provides an open source, user-friendly portal for accessing and analyzing Strongyloides genomic expression data. Specifically, the Strongyloides RNA-seq Browser takes advantage of alignment-free read mapping tools and R-based transcriptomics tools to re-analyze publicly available RNA sequencing datasets from four Strongyloides species: Strongyloides stercoralis, Strongyloides ratti, Strongyloides papillosus, and Strongyloides venezuelensis. This application permits on-demand exploration and quantification of gene expression across life stages without requiring previous coding experience. Here, we describe this interactive application and demonstrate how it may be used by nematode researchers to conduct a standard set of bioinformatics queries.
The association between blast exposure and transdiagnostic health symptoms on systemic inflammation.

Chronic elevation of systemic inflammation is observed in a wide range of disorders including PTSD, depression, and traumatic brain injury. Although previous work has demonstrated a link between inflammation and various diagnoses separately, few studies have examined transdiagnostic symptoms and inflammation within the same model. The objective of this study was to examine relationships between psychiatric and health variables and systemic inflammation and to determine whether mild traumatic brain injury (mTBI) and/or exposure to blast munitions moderate these relationships. Confirmatory factor analysis in a large sample (N = 357) of post-9/11 Veterans demonstrated a good fit to a four-factor model reflecting traumatic stress, affective, somatic, and metabolic latent variables. Hierarchical regression models revealed that each of the latent variables were associated with higher levels of systemic inflammation. However, the strongest relationship with inflammation emerged among those who had both war-zone blast exposures and metabolic dysregulation, even after adjusting for mental health latent variables. Exploratory analyses showed that blast exposure was associated with metabolic dysregulation in a dose-response manner, with self-reported closer blast proximity associated with the greatest metabolic dysregulation. Together, these results provide a greater understanding of the types of symptoms most strongly associated with inflammation and underscore the importance of maintaining a healthy lifestyle to reduce the impact of obesity and other metabolic symptoms on future chronic disease in younger to middle-aged Veterans.
Adversarially Robust Estimate and Risk Analysis in Linear Regression

Adversarially robust learning aims to design algorithms that are robust to small adversarial perturbations on input variables. Beyond the existing studies on the predictive performance to adversarial samples, our goal is to understand statistical properties of adversarially robust estimates and analyze adversarial risk in the setup of linear regression models. By discovering the statistical minimax rate of convergence of adversarially robust estimators, we emphasize the importance of incorporating model information, e.g., sparsity, in adversarially robust learning. Further, we reveal an explicit connection of adversarial and standard estimates, and propose a straightforward two-stage adversarial learning framework, which facilitates to utilize model structure information to improve adversarial robustness. In theory, the consistency of the adversarially robust estimator is proven and its Bahadur representation is also developed for the statistical inference purpose. The proposed estimator converges in a sharp rate under either low-dimensional or sparse scenario. Moreover, our theory confirms two phenomena in adversarially robust learning: adversarial robustness hurts generalization, and unlabeled data help improve the generalization. In the end, we conduct numerical simulations to verify our theory.
CADA: Communication-Adaptive Distributed Adam.

Stochastic gradient descent (SGD) has taken the stage as the primary workhorse for large-scale machine learning. It is often used with its adaptive variants such as AdaGrad, Adam, and AMSGrad. This paper proposes an adaptive stochastic gradient descent method for distributed machine learning, which can be viewed as the communication-adaptive counterpart of the celebrated Adam method - justifying its name CADA. The key components of CADA are a set of new rules tailored for adaptive stochastic gradients that can be implemented to save communication upload. The new algorithms adaptively reuse the stale Adam gradients, thus saving communication, and still have convergence rates comparable to original Adam. In numerical experiments, CADA achieves impressive empirical performance in terms of total communication round reduction.
A meta-learning based distribution system load forecasting model selection framework

Abstract This paper presents a meta-learning based, automatic distribution system load forecasting model selection framework. The framework includes the following processes: feature extraction, candidate model preparation and labeling, offline training, and online model recommendation. Using load forecasting needs and data characteristics as input features, multiple metalearners are used to rank the candidate load forecast models based on their forecasting accuracy. Then, a scoring-voting mechanism is proposed to weights recommendations from each meta-leaner and make the final recommendations. Heterogeneous load forecasting tasks with different temporal and technical requirements at different load aggregation levels are set up to train, validate, and test the performance of the proposed framework. Simulation results demonstrate that the performance of the meta-learning based approach is satisfactory in both seen and unseen forecasting tasks.
Stability analysis of complementarity systems with neural network controllers

Complementarity problems, a class of mathematical optimization problems with orthogonality constraints, are widely used in many robotics tasks, such as locomotion and manipulation, due to their ability to model non-smooth phenomena (e.g., contact dynamics). In this paper, we propose a method to analyze the stability of complementarity systems with neural network controllers. First, we introduce a method to represent neural networks with rectified linear unit (ReLU) activations as the solution to a linear complementarity problem. Then, we show that systems with ReLU network controllers have an equivalent linear complementarity system (LCS) description. Using the LCS representation, we turn the stability verification problem into a linear matrix inequality (LMI) feasibility problem. We demonstrate the approach on several examples, including multi-contact problems and friction models with non-unique solutions.
Global stability of trajectory tracking of robot under PD control

It is shown for the first time that, even if there exist nonlinear unknown dynamics, aPD feedback control without higher-order nonlinear compensation can guarantee global stability for the trajectory following problem of a robot manipulator. ThePD control under investigation is a position and velocity feedback control with a time-varying gain, and does not contain any higher-order nonlinearity. The proposed control is in general continuous and does not require any knowledge of robotic systems except size bounding function on nonlinear dynamics. Asymptotic stability of velocity tracking error and arbitrarily small position tracking error are guaranteed. Another novel and interesting result shown in this paper is that a measure on protection against saturation of actuators has been incorporated into consideration of control design and robustness analysis.
Development of an objective index, neural activity score (NAS), reveals neural network ontogeny and treatment effects on microelectrode arrays.

Microelectrode arrays (MEAs) are valuable tools for electrophysiological analysis, providing assessment of neural network health and development. Analysis can be complex, however, requiring intensive processing of large data sets consisting of many activity parameters, leading to information loss as studies subjectively report relatively few metrics in the interest of simplicity. In screening assays, many groups report simple overall activity (i.e. firing rate) but omit network connectivity changes (e.g. burst characteristics and synchrony) that may not be evident from basic parameters. Our goal was to develop an objective process to capture most of the valuable information gained from MEAs in neural development and toxicity studies. We implemented principal component analysis (PCA) to reduce the high dimensionality of MEA data. Upon analysis, we found the first principal component was strongly correlated to time, representing neural culture development; therefore, factor loadings were used to create a single index score—named neural activity score (NAS)—reflecting neural maturation. For validation, we applied NAS to studies analyzing various treatments. In all cases, NAS accurately recapitulated expected results, suggesting viability of NAS to measure network health and development. This approach may be adopted by other researchers using MEAs to analyze complicated treatment effects and multicellular interactions.
Predicting In-Hospital Mortality in Patients Undergoing Percutaneous Coronary Intervention

Abstract Background Standardization of risk is critical in benchmarking and quality improvement efforts for percutaneous coronary interventions (PCIs). In 2018, the CathPCI Registry was updated to include additional variables to better classify higher-risk patients. Objectives This study sought to develop a model for predicting in-hospital mortality risk following PCI incorporating these additional variables. Methods Data from 706,263 PCIs performed between July 2018 and June 2019 at 1,608 sites were used to develop and validate a new full and pre-catheterization model to predict in-hospital mortality, and a simplified bedside risk score. The sample was randomly split into a development cohort (70%, n = 495,005) and a validation cohort (30%, n = 211,258). The authors created 1,000 bootstrapped samples of the development cohort and used stepwise selection logistic regression on each sample. The final model included variables that were selected in at least 70% of the bootstrapped samples and those identified a priori due to clinical relevance. Results In-hospital mortality following PCI varied based on clinical presentation. Procedural urgency, cardiovascular instability, and level of consciousness after cardiac arrest were most predictive of in-hospital mortality. The full model performed well, with excellent discrimination (C-index: 0.943) in the validation cohort and good calibration across different clinical and procedural risk cohorts. The median hospital risk-standardized mortality rate was 1.9% and ranged from 1.1% to 3.3% (interquartile range: 1.7% to 2.1%). Conclusions The risk of mortality following PCI can be predicted in contemporary practice by incorporating variables that reflect clinical acuity. This model, which includes data previously not captured, is a valid instrument for risk stratification and for quality improvement efforts.
Secondary Energization in Compressing Plasmoids during Magnetic Reconnection

Plasmoids -- magnetized quasi-circular structures formed self-consistently in reconnecting current sheets -- were previously considered to be the graveyards of energetic particles. In this paper, we demonstrate the important role of plasmoids in shaping the particle energy spectrum in relativistic reconnection (i.e., with upstream magnetization $\sigma_{\rm up} \gg 1$). Using two dimensional particle-in-cell simulations in pair plasmas with $\sigma_{\rm up}=10$ and $100$, we study a secondary particle energization process that takes place inside compressing plasmoids. We demonstrate that plasmoids grow in time, while their interiors compress, amplifying the internal magnetic field. The magnetic field felt by particles injected in an isolated plasmoid increases linearly with time, which leads to particle energization as a result of magnetic moment conservation. For particles injected with a power-law distribution function, this energization process acts in such a way that the shape of the injected power law is conserved, while producing an additional non-thermal tail $f(E)\propto E^{-3}$ at higher energies followed by an exponential cutoff. The cutoff energy, which increases with time as $E_{\rm cut}\propto\sqrt{t}$, can greatly exceed $\sigma_{\rm up} m_e c^2$. We analytically predict the secondary acceleration timescale and the shape of the emerging particle energy spectrum, which can be of major importance in certain astrophysical systems, such as blazar jets.
Identifications of some highly-ionized iron and nickel lines in the 200–400 Å region of the solar spectrum

A number of previously unclassified multiplets of Fexiv, xiii, xii, and xi produced by transitions of the type 3s 23p n -3s3p n+1 are identified in the XUV spectrum of the Sun. The iron lines account for most of the previously unidentified strong lines between 330 and 370 A. Solar observations of especial value for the investigation of the 300–400 A region were the slitless spectroheliograms of September 22, 1968 (Purcell and Tousey, 1969) and November 4, 1969 (Tousey, 1971) — on which the image of a flare was recorded. Other solar identifications in the same spectral region include the resonance lines of Nixvii and Nixviii, and one 3p-3d multiplet of Fexiii. The solar blend at 417 A involving the Fexv inter-combination line and Sxiv is resolved.
Theory of Faradaically Modulated Redox Active Electrodes for Electrochemically Mediated Selective Adsorption Processes

Electrochemically mediated selective adsorption is an emerging electrosorption technique that utilizes Faradaically enhanced redox active electrodes, which can adsorb ions not only electrostatically, but also electrochemically. The superb selectivity (>100) of this technique enables selective removal of toxic or high-value target ions under low energy consumption. Here, we develop a general theoretical framework to describe the competitive electrosorption phenomena involving multiple ions and surface-bound redox species. The model couples diffusion, convection and electromigration with competitive surface adsorption reaction kinetics, consistently derived from non-equilibrium thermodynamics. To optimize the selective removal of the target ions, design criteria were derived analytically from physically relevant dimensionless groups and time scales, where the propagation of the target anions concentration front is the limiting step. Detailed computational studies are reported for three case studies that cover a wide range of inlet concentration ratios between the competing ions. And in all three cases, target anions in the electrosorption cell forms a self-sharpening reaction-diffusion wave front. Based on the model, a three-step stop-flow operation scheme with a pure stripping solution of target anions is proposed that optimizes the ion adsorption performance and increases the purity of the regeneration stream to almost 100%, which is beneficial for downstream processing.
A Unified Conditional Disentanglement Framework For Multimodal Brain Mr Image Translation

Multimodal MRI provides complementary and clinically relevant information to probe tissue condition and to characterize various diseases. However, it is often difficult to acquire sufficiently many modalities from the same subject due to limitations in study plans, while quantitative analysis is still demanded. In this work, we propose a unified conditional disentanglement framework to synthesize any arbitrary modality from an input modality. Our framework hinges on a cycleconstrained conditional adversarial training approach, where it can extract a modality-invariant anatomical feature with a modality-agnostic encoder and generate a target modality with a conditioned decoder. We validate our framework on four MRI modalities, including T1-weighted, T1 contrast enhanced, T2-weighted, and FLAIR MRI, from the BraTS’18 database, showing superior performance on synthesis quality over the comparison methods. In addition, we report results from experiments on a tumor segmentation task carried out with synthesized data.
Fractal Dimension and Lower Bounds for Geometric Problems

We study the complexity of geometric problems on spaces of low fractal dimension. It was recently shown in Sidiropoulos and Sridhar (33rd International Symposium on Computational Geometry (Brisbane 2017). Leibniz Int. Proc. Inform., vol. 77, # 58. Leibniz-Zent. Inform., Wadern, 2017) that several problems admit improved solutions when the input is a pointset in Euclidean space with fractal dimension smaller than the ambient dimension. In this paper we prove nearly-matching lower bounds, thus establishing nearly-optimal bounds for various problems as a function of the fractal dimension. More specifically, we show that for any integer $$d > 1$$ , any $$\delta \in (1,d)$$ , and any $$n \in {\mathbb {N}}$$ , there exists a set X of n points in $${\mathbb {R}}^{d}$$ , with fractal dimension $$\delta $$ such that for any $$\varepsilon > 0$$ and $$c \ge 1$$ , any c-spanner of X has treewidth $$\Omega ( n^{1-1/(\delta - \epsilon )}/c^{d-1} )$$ . This lower bound matches the previous upper bound. The construction used to prove this lower bound on the treewidth of spanners, can also be used to derive lower bounds on the running time of algorithms for various problems, assuming the Exponential Time Hypothesis. We provide two prototypical results of this type: The above results nearly match previously known upper bounds from [op. cit.], and generalize analogous lower bounds for the case of ambient dimension due to Marx and Sidiropoulos (30th Annual Symposium on Computational Geometry (Kyoto 2014), pp. 67–76. ACM, New York, 2014).
Streaming and Distributed Algorithms for Robust Column Subset Selection

We give the first single-pass streaming algorithm for Column Subset Selection with respect to the entrywise $\ell_p$-norm with $1 \leq p < 2$. We study the $\ell_p$ norm loss since it is often considered more robust to noise than the standard Frobenius norm. Given an input matrix $A \in \mathbb{R}^{d \times n}$ ($n \gg d$), our algorithm achieves a multiplicative $k^{\frac{1}{p} - \frac{1}{2}}\text{poly}(\log nd)$-approximation to the error with respect to the best possible column subset of size $k$. Furthermore, the space complexity of the streaming algorithm is optimal up to a logarithmic factor. Our streaming algorithm also extends naturally to a 1-round distributed protocol with nearly optimal communication cost. A key ingredient in our algorithms is a reduction to column subset selection in the $\ell_{p,2}$-norm, which corresponds to the $p$-norm of the vector of Euclidean norms of each of the columns of $A$. This enables us to leverage strong coreset constructions for the Euclidean norm, which previously had not been applied in this context. We also give the first provable guarantees for greedy column subset selection in the $\ell_{1, 2}$ norm, which can be used as an alternative, practical subroutine in our algorithms. Finally, we show that our algorithms give significant practical advantages on real-world data analysis tasks.
Iterative Model Predictive Control for Piecewise Systems

In this letter, we present an iterative Model Predictive Control (MPC) design for piecewise nonlinear systems. We consider finite time control tasks where the goal of the controller is to steer the system from a starting configuration to a goal state while minimizing a cost function. First, we present an algorithm that leverages a feasible trajectory that completes the task to construct a control policy which guarantees that state and input constraints are recursively satisfied and that the closed-loop system reaches the goal state in finite time. Utilizing this construction, we present a policy iteration scheme that iteratively generates safe trajectories which have non-decreasing performance. Finally, we test the proposed strategy on a discretized Spring Loaded Inverted Pendulum (SLIP) model with massless legs. We show that our methodology is robust to changes in initial conditions and disturbances acting on the system. Furthermore, we demonstrate the effectiveness of our policy iteration algorithm in a minimum time control task.
Normalization by distributional resampling of high throughput single-cell RNA-sequencing data.

Motivation Normalization to remove technical or experimental artifacts is critical in the analysis of single-cell RNA-sequencing experiments, even those for which unique molecular identifiers (UMIs) are available. The majority of methods for normalizing single-cell RNA-sequencing data adjust average expression in library size (LS), allowing the variance and other properties of the gene-specific expression distribution to be non-constant in LS. This often results in reduced power and increased false discoveries in downstream analyses, a problem which is exacerbated by the high proportion of zeros present in most datasets. Results To address this, we present Dino, a normalization method based on a flexible negative-binomial mixture model of gene expression. As demonstrated in both simulated and case study datasets, by normalizing the entire gene expression distribution, Dino is robust to shallow sequencing, sample heterogeneity, and varying zero proportions, leading to improved performance in downstream analyses in a number of settings. Availability The R package, Dino, is available on GitHub at https://github.com/JBrownBiostat/Dino. The Dino package is further archived and freely available on Zenodo at https://doi.org/10.5281/zenodo.4897558. Supplementary information Supplementary data are available at Bioinformatics online.
Settling SETH vs. approximate sparse directed unweighted diameter (up to (NU)NSETH)

We prove several tight results on the fine-grained complexity of approximating the diameter of a graph. First, we prove that, for any e>0, assuming the Strong Exponential Time Hypothesis (SETH), there are no near-linear time 2−e-approximation algorithms for the Diameter of a sparse directed graph, even in unweighted graphs. This result shows that a simple near-linear time 2-approximation algorithm for Diameter is optimal under SETH, answering a question from a survey of Rubinstein and Vassilevska-Williams (SIGACT ’19) for the case of directed graphs. In the same survey, Rubinstein and Vassilevska-Williams also asked if it is possible to show that there are no 2−e approximation algorithms for Diameter in a directed graph in O(n1.499) time. We show that, assuming a hypothesis called NSETH, one cannot use a deterministic SETH-based reduction to rule out the existence of such algorithms. Extending the techniques in these two results, we characterize whether a 2−e approximation algorithm running in time O(n1+δ) for the Diameter of a sparse directed unweighted graph can be ruled out by a deterministic SETH-based reduction for every δ∈(0,1) and essentially every e∈(0,1), assuming NSETH. This settles the SETH-hardness of approximating the diameter of sparse directed unweighted graphs for deterministic reductions, up to NSETH. We make the same characterization for randomized SETH-based reductions, assuming another hypothesis called NUNSETH. We prove additional hardness and non-reducibility results for undirected graphs.
Uniqueness of compact ancient solutions to three-dimensional Ricci flow

In this paper, we study the classification of $$\kappa $$ -noncollapsed ancient solutions to three-dimensional Ricci flow on $$S^3$$ . We prove that such a solution is either isometric to a family of shrinking round spheres, or the Type II ancient solution constructed by Perelman.
Evaluation of Multiple Flow Constrained Area Capacity Setting Methods for Collaborative Trajectory Options Program

The purpose of this study was to compare flow constrained area (FCA) capacity setting methods for Collaborative Trajectory Options Program (CTOP) as they pertain to the Integrated Demand Management (IDM) concept. IDM uses flow balancing to manage air traffic across multiple FCAs with a common downstream constraint, as well as constraints at the respective FCA locations. FCA capacity rates can be set manually, but generating capacities for multiple, interdependent FCAs could potentially over-burden a user. A new enhancement to CTOP called the FCA Balance Algorithm (FBA) was developed at NASA Ames Research Center to improve the process of allocating capacity across multiple flow constrained segments in the airspace. The FBA evaluates the predicted demand and capacity across multiple FCAs and dynamically generates capacity settings for the FCAs that best meet capacity limits for all identified constraints. In a human-in-the-loop simulation study, both manual and automated capacity setting methods were evaluated in terms of their overall feasibility using measures of system performance, human performance, and qualitative feedback. Subject matter experts were asked to use three different methods to allocate capacity to three FCAs, either (1) by manually setting capacity for every 60-minute time window, (2) by manually setting capacity for every 15-minute time window, or (3) by using the FBA capability to automatically generate capacity settings. Results showed no significant differences in terms of overall system performance, indicated by similar ground delay and airport throughput numbers between methods. However, differences in individual strategies afforded by the manual methods allowed some participants to achieve system-wide delay that was much lower than the average. The FBA was the fastest method of capacity setting, and it received the lowest subjective rating scores on physical task load, mental task load, task difficulty and task complexity out of the three methods. Finally, participants explained through qualitative feedback that there were many benefits to using the FBA, such as ease of use, accuracy, and low risk of human input error. Participants did not experience the same limitations with the FBA that they did with the manual methods, such as reduced accuracy in the 60-minute manual condition, or high complexity in the 15-minute/manual condition. These results suggest that the FBA automation enhancement to CTOP maintains system performance while improving human performance. Therefore, the FBA could be introduced as a way to mitigate operator workload while planning a CTOP.
Bogoliubov Quasiparticles in Superconducting Qubits

Extending the qubit coherence times is a crucial task in building quantum information processing devices. In the three-dimensional cavity implementations of circuit QED, the coherence of superconducting qubits was improved dramatically due to cutting the losses associated with the photon emission. Next frontier in improving the coherence includes the mitigation of the adverse effects of superconducting quasiparticles. In these lectures, we review the basics of the quasiparticles dynamics, their interaction with the qubit degree of freedom, their contribution to the qubit relaxation rates, and approaches to control their effect.
Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification

Existing bias mitigation methods to reduce disparities in model outcomes across cohorts have focused on data augmentation, debiasing model embeddings, or adding fairness-based optimization objectives during training. Separately, certified word substitution robustness methods have been developed to decrease the impact of spurious features and synonym substitutions on model predictions. While their end goals are different, they both aim to encourage models to make the same prediction for certain changes in the input. In this paper, we investigate the utility of certified word substitution robustness methods to improve equality of odds and equality of opportunity on multiple text classification tasks. We observe that certified robustness methods improve fairness, and using both robustness and bias mitigation methods in training results in an improvement in both fronts
Development of the ARIES Parachute System

The design and testing of a two-stage parachute system to recover a space telescope weighing up to 2000 pounds is described. The system consists of a 15-ft dia ribbon parachute reefed to 50% for 10 seconds and a 73-ft dia paraform or cross second stage reefed to 10% for 10 seconds. The results of eight drop tests and one operational rocket launched flight and recovery are presented. A successful operational recovery of a 1600-lb NASA space telescope was conducted. The payload was launched by a second stage Minuteman rocket to an altitude of about 300 miles above sea level.
Ramp-to-threshold dynamics in a hindbrain population controls the timing of spontaneous saccades

Organisms have the capacity to make decisions based solely on internal drives. However, it is unclear how neural circuits form decisions in the absence of sensory stimuli. Here we provide a comprehensive map of the activity patterns underlying the generation of saccades made in the absence of visual stimuli. We perform calcium imaging in the larval zebrafish to discover a range of responses surrounding spontaneous saccades, from cells that display tonic discharge only during fixations to neurons whose activity rises in advance of saccades by multiple seconds. When we lesion cells in these populations we find that ablation of neurons with pre-saccadic rise delays saccade initiation. We analyze spontaneous saccade initiation using a ramp-to-threshold model and are able to predict the times of upcoming saccades using pre-saccadic activity. These findings suggest that ramping of neuronal activity to a bound is a critical component of self-initiated saccadic movements.
Periodic Excitation for Jet Vectoring and Enhanced Spreading

The effects of periodic excitation on the evolution of a turbulent jet were studied experimentally. A short, wide-angle diffuser was attached to the jet exit, and excitation was introduced at the junction between the jet exit and the diffuser inlet. The introduction of high-amplitude periodic excitation at the jet exit enhances the mixing and promotes attachment of the jet shear layer to the diffuser wall. Vectoring is achieved by applying the excitation over a fraction of the circumference of the circular jet, enhancing its spreading rate on the excited side and its tendency to reattach to that side. Static-deflection studies demonstrate that the presence of the wide-angle diffuser increases the effectiveness of the added periodic momentum caused by a favorable interaction between the excitation, the jet shear layer, and the diffuser wall
Maximal spacelike surfaces in a certain homogeneous Lorentzian 3-manifold

The 2-parameter family of certain homogeneous Lorentzian 3-manifolds, which includes Minkowski 3-space and anti-de Sitter 3-space, is considered. Each homogeneous Lorentzian 3-manifold in the 2-parameter family has a solvable Lie group structure with left invariant metric. A generalized integral representation formula for maximal spacelike surfaces in the homogeneous Lorentzian 3-manifolds is obtained. The normal Gaus map of maximal spacelike surfaces and its harmonicity are discussed.
Decelerator Fabric Constants Required by the Generalized Form of Hooke's Law

The availability of the required elastic constants for use in structural analyses of fabric structures is exceedingly poor in comparison with the current situation for the common structural materials. Experiments with nylon-polyurethane-coated fabric have indicated highly nonlinear characteristics of the elastic constants of Hooke's law, a strong dependency of these coefficients on all three membrane loads, and a lack of symmetry in the Hookian relationships. A complete set of the nine coefficients of the generalized form of Hooke's law are furnished and analyzed for the test fabric.
FLCD: A Flexible Low Complexity Design of Coded Distributed Computing

We propose a flexible low complexity design (FLCD) of coded distributed computing (CDC) with empirical evaluation on Amazon Elastic Compute Cloud (Amazon EC2). CDC can expedite MapReduce like computation by trading increased map computations to reduce communication load and shuffle time. A main novelty of FLCD is to utilize the design freedom in defining map and reduce functions to develop asymptotic homogeneous systems to support varying intermediate values (IV) sizes under a general MapReduce framework. Compared to existing designs with constant IV sizes, FLCD offers greater flexibility in adapting to network parameters and significantly reduces the implementation complexity by requiring fewer input files and shuffle groups. The FLCD scheme is the first proposed low-complexity CDC design that can operate on a network with an arbitrary number of nodes and computation load. We perform empirical evaluations of the FLCD by executing the TeraSort algorithm on an Amazon EC2 cluster. This is the first time that theoretical predictions of the CDC shuffle time are validated by empirical evaluations. The evaluations demonstrate a 2.0 to 4.24 speedup compared to conventional uncoded MapReduce, a 12% to 52% reduction in total time, and a wider range of operating network parameters compared to existing CDC schemes.
An abstract critical point theorem with applications to elliptic problems with combined nonlinearities

We prove an abstract critical point theorem based on a cohomological index theory that produces pairs of nontrivial critical points with nontrivial higher critical groups. This theorem yields pairs of nontrivial solutions that are neither local minimizers nor of mountain pass type for problems with combined nonlinearities. Applications are given to subcritical and critical p-Laplacian problems, Kirchhoff type nonlocal problems, and critical fractional p-Laplacian problems.
Point-to-point free-space quantum cryptography over 1/2 km

Summary form only given. We have demonstrated a point-to-point single photon quantum cryptographic key distribution over a free-space optical path of /spl sim/475 m (>1,000 times farther than any reported point-to-point demonstration, and >6 times the previous daylight transmission distance), under both daylight and nighttime conditions. Within the next few months, we expect to extend the daylight range to 2 km. A brief description of the system is given here.
Quantum Annealing for Large MIMO Downlink Vector Perturbation Precoding

In a multi-user system with multiple antennas at the base station, precoding techniques in the downlink broadcast channel allow users to detect their respective data in a non-cooperative manner. Vector Perturbation Precoding (VPP) is a non-linear variant of transmit-side channel inversion that perturbs user data to achieve full diversity order. While promising, finding an optimal perturbation in VPP is known to be an NP-hard problem, demanding heavy computational support at the base station and limiting the feasibility of the approach to small MIMO systems. This work proposes a radically different processing architecture for the downlink VPP problem, one based on Quantum Annealing (QA), to enable the applicability of VPP to large MIMO systems. Our design reduces VPP to a quadratic polynomial form amenable to QA, then refines the problem coefficients to mitigate the adverse effects of QA hardware noise. We evaluate our proposed QA based VPP (QAVP) technique on a real Quantum Annealing device over a variety of design and machine parameter settings. With existing hardware, QAVP can achieve a BER of 10−4 with 100µs compute time, for a 6 × 6 MIMO system using 64 QAM modulation at 32 dB SNR.
Quantification of Carbon Sequestration in Urban Forests

Vegetation, trees in particular, sequester carbon by absorbing carbon dioxide from the atmosphere. However, the lack of efficient quantification methods of carbon stored in trees renders it difficult to track the process. We present an approach to estimate the carbon storage in trees based on fusing multi-spectral aerial imagery and LiDAR data to identify tree coverage, geometric shape, and tree species -- key attributes to carbon storage quantification. We demonstrate that tree species information and their three-dimensional geometric shapes can be estimated from aerial imagery in order to determine the tree's biomass. Specifically, we estimate a total of $52,000$ tons of carbon sequestered in trees for New York City's borough Manhattan.
Feature representation under crowding in V1 and V4 neuronal populations

Visual perception depends strongly on spatial context. A profound example is visual crowding, whereby the presence of nearby stimuli impairs discriminability of object features. Despite extensive work on both perceptual crowding and the spatial integrative properties of visual cortical neurons, the link between these two aspects of visual processing remains unclear. To understand better the neural basis of crowding, we recorded simultaneously from neuronal populations in V1 and V4 of fixating macaque monkeys. We assessed the information about the orientation of a visual target available from the measured responses, both for targets presented in isolation and amid distractors. Both single neuron and population responses had less information about target orientation when distractors were present. Information loss was moderate in V1 and more substantial in V4. Information loss could be traced to systematic divisive and additive changes in neuronal tuning. Tuning changes were more severe in V4; in addition, tuning exhibited greater context-dependent distortions in V4, further restricting the ability of a fixed sensory readout strategy to extract accurate feature information across changing environments. Our results provide a direct test of crowding effects at different stages of the visual hierarchy, reveal how these effects alter the spiking activity of cortical populations by which sensory stimuli are encoded, and connect these changes to established mechanisms of neuronal spatial integration.
Parallel physics-informed neural networks via domain decomposition

Abstract We develop a distributed framework for the physics-informed neural networks (PINNs) based on two recent extensions, namely conservative PINNs (cPINNs) and extended PINNs (XPINNs), which employ domain decomposition in space and in time-space, respectively. This domain decomposition endows cPINNs and XPINNs with several advantages over the vanilla PINNs, such as parallelization capacity, large representation capacity, efficient hyperparameter tuning, and is particularly effective for multi-scale and multi-physics problems. Here, we present a parallel algorithm for cPINNs and XPINNs constructed with a hybrid programming model described by MPI + X, where X ∈ { CPUs , GPUs } . The main advantage of cPINN and XPINN over the more classical data and model parallel approaches is the flexibility of optimizing all hyperparameters of each neural network separately in each subdomain. We compare the performance of distributed cPINNs and XPINNs for various forward problems, using both weak and strong scalings. Our results indicate that for space domain decomposition, cPINNs are more efficient in terms of communication cost but XPINNs provide greater flexibility as they can also handle time-domain decomposition for any differential equations, and can deal with any arbitrarily shaped complex subdomains. To this end, we also present an application of the parallel XPINN method for solving an inverse diffusion problem with variable conductivity on the United States map, using ten regions as subdomains.
Spin-textured Chern bands in AB-stacked transition metal dichalcogenide bilayers.

While transition-metal dichalcogenide (TMD)-based moire materials have been shown to host various correlated electronic phenomena, topological states have not been experimentally observed until now [T. Li et al., Quantum anomalous Hall effect from intertwined moire bands. arXiv [Preprint] (2021). https://arxiv.org/abs/2107.01796 (Accessed 5 July 2021)]. In this work, using first-principle calculations and continuum modeling, we reveal the displacement field-induced topological moire bands in AB-stacked TMD heterobilayer [Formula: see text]/[Formula: see text] Valley-contrasting Chern bands with nontrivial spin texture are formed from interlayer hybridization between [Formula: see text] and [Formula: see text] bands of nominally opposite spins. Our study establishes a recipe for creating topological bands in AB-stacked TMD bilayers in general, which provides a highly tunable platform for realizing quantum-spin Hall and interaction-induced quantum anomalous Hall effects.
Muon g − 2 in two-Higgs-doublet models with vectorlike leptons

We calculate contributions to the anomalous magnetic moment of the muon from heavy neutral and charged Higgs bosons and new leptons in two-Higgs-doublet models extended by vectorlike leptons. We present detailed predictions of two models with type-II couplings to standard model fermions, motivated by a ${Z}_{2}$ symmetry and supersymmetry. In addition, we compare the results with the standard model extended by vectorlike leptons. We find that the model motivated by a ${Z}_{2}$ symmetry can generate much larger contributions to the magnetic moment compared to the standard model, even by two orders of magnitude due to ${\mathrm{tan}}^{2}\ensuremath{\beta}$ enhancement, while satisfying current constraints. As a consequence, the standard model explanation of the anomaly requires much larger corrections to muon couplings making this model easier to probe at future precision machines. Additionally, we find that the model with couplings motivated by supersymmetry typically leads to much smaller contributions to the magnetic moment as a result of cancellations. We also identify interesting scenarios where contributions from the charged Higgs boson can fully explain the anomaly.
MobIntel: Sensing and analytics infrastructure for urban mobility intelligence

Abstract Mobility monitoring in urban environments provides valuable insights into pedestrian and vehicle movement. Understanding the causes and effects of changing mobility patterns can help city officials and businesses optimize operations and support economic development. In this paper, we introduce MobIntel, a privacy-centric alternative to visual surveillance for mobility monitoring. We present the design, implementation, and evaluation of the MobIntel sensor network, the associated cloud-based processing system, and the web-based visualization portal. We evaluate system capacity and accuracy based on real-life data collected from a deployment in downtown West Palm Beach, FL, spanning 14 months. We offer sample use cases where pedestrian activity patterns revealed by MobIntel can be helpful to the city government. Finally, we discuss several obstacles that must be overcome to transition from pedestrian counting to trajectory monitoring.
Simultaneous in vitro simulation of multiple antimicrobial agents with different elimination half-lives in a pre-clinical infection model

Abstract Combination therapy for treatment of multi-drug resistant bacterial infections is becoming common. In vitro testing of drug combinations under realistic pharmacokinetic conditions is needed before a corresponding combination is eventually put into clinical use. The current standard for design of such in vitro simulations for drugs with different half-lives is heuristic and limited to two drugs. To address that void, we develop a rigorous design method suitable for an arbitrary number of N drugs with different half-lives. The method developed offers substantial flexibility and produces novel designs even for two drugs. Explicit design equations are rigorously developed and are suitable for immediate use by experimenters. These equations were used in experimental verification using a combination of three antibiotics with distinctly different half-lives. In addition to antibiotics, the method is applicable to any anti-infective or anti-cancer drugs with distinct elimination pharmacokinetics.
A New Wave of Urban Sprawl: Influence of Autonomous Vehicles on the Policy Toolkit and Property Tax Revenue of Local Governments

Relatively inexpensive vehicles and public policies that favored a vehicle-centric infrastructure facilitated the movement of families from the confines of urban centers to the periphery of cities ...
Alphaproteobacteria facilitate Trichodesmium community trimethylamine utilization.

In the surface waters of the warm oligotrophic ocean, filaments and aggregated colonies of the nitrogen (N)-fixing cyanobacterium Trichodesmium create microscale nutrient-rich oases. These hotspots fuel primary productivity and harbour a diverse consortium of heterotrophs. Interactions with associated microbiota can affect the physiology of Trichodesmium, often in ways that have been predicted to support its growth. Recently, it was found that trimethylamine (TMA), a globally abundant organic N compound, inhibits N2 fixation in cultures of Trichodesmium without impairing growth rate, suggesting that Trichodesmium can use TMA as an alternate N source. In this study, 15 N-TMA DNA stable isotope probing (SIP) of a Trichodesmium enrichment was employed to further investigate TMA metabolism and determine whether TMA-N is incorporated directly or secondarily via cross-feeding facilitated by microbial associates. Herein, we identify two members of the marine Roseobacter clade (MRC) of Alphaproteobacteria as the likely metabolizers of TMA and provide genomic evidence that they converted TMA into a more readily available form of N, e.g., ammonium (NH4+ ), which was subsequently used by Trichodesmium and the rest of the community. The results implicate microbiome-mediated carbon (C) and N transformations in modulating N2 fixation and thus highlight the involvement of host-associated heterotrophs in global biogeochemical cycling.