cssi/SciSciGPT-SciSciNet huggingface dataset:
MATERIALS FOR DIODE PUMPED SOLID STATE LASERS

The advantages of semiconductor diode lasers and laser arrays as pump sources for solid state lasers are reviewed. The properties desired for solid state laser media for various diode pumping applications are discussed, and the characteristics of several promising media are summarized. 13 refs., 4 tabs.
Mitigate Parasitic Resistance in Resistive Crossbar-based Convolutional Neural Networks

Traditional computing hardware often encounters on-chip memory bottleneck on large-scale Convolution Neural Networks (CNN) applications. With its unique in-memory computing feature, resistive crossbar-based computing attracts researchers’ attention as a promising solution to the memory bottleneck issue in von Neumann architectures. However, the parasitic resistances in crossbar deviate its behavior from the ideal weighted summation operation. In large-scale implementations, the impact of parasitic resistances must be carefully considered and mitigated to ensure circuits’ functionality. In this work, we implemented and simulated CNNs on resistive crossbar circuits with consideration of parasitic resistances. Moreover, we carried out a new mapping scheme for high utilization of crossbar arrays on convolution, and a mitigation algorithm to mitigate parasitic resistances in CNN applications. The mitigation algorithm considers parasitic resistances as well as data/kernel patterns of each layer to minimize the computing error in crossbar-based convolutions of CNNs. We demonstrated the proposed methods with implementations of a 4-layer CNN on MNIST, and residual neural network (ResNet) (20, 32, and 56) on CIFAR-10. Simulation results show the proposed methods well mitigate the parasitic resistances in crossbars. With our methods, modern CNNs on crossbars can preserve ideal (software) level classification accuracy with 6-bit ADCs and DACs implementation.
Disability and mortgage delinquency

In this study, we examine the effect of disability on mortgage delinquency. Using 2007–2017 data from the Panel Study of Income Dynamics (PSID), we study the impact of disability on ex-post delinqu...
Scalable Lattice Influence Maximization

Influence maximization is the task of finding $k$ seed nodes in a social network such that the expected number of activated nodes in the network (under certain influence propagation model), referred to as the influence spread, is maximized. Lattice influence maximization (LIM) generalizes influence maximization such that, instead of selecting $k$ seed nodes, one selects a vector $\mathbf {x}=(x_{1}, \ldots, x_{d})$ from a discrete space $\mathcal {X}$ called a lattice, where $x_{j}$ corresponds to the $j$ th marketing strategy and $\mathbf {x}$ represents a marketing strategy mix. Each strategy mix $\mathbf {x}$ has probability $h_{u}(\mathbf {x})$ to activate a node $u$ as a seed. LIM is the task of finding a strategy mix under the constraint $\sum _{j} x_{j} \le k$ such that its influence spread is maximized. We adopt the reverse influence sampling (RIS) approach and design scalable algorithms for LIM. We explore two complementary design choices: one algorithm IMM-PRR is based on partial coverage on reverse-reachable sets, and the other IMM-VSN is based on incorporating virtual strategy nodes. IMM-PRR can be applied as a general solution to LIM, and we further improve its efficiency for a large family of models where each strategy independently activates seed nodes. IMM-VSN is explicitly designed for the case of independent strategy activation, and it uses virtual nodes to represent strategies to reduce LIM back to the original influence maximization problem. We prove that both IMM-PRR and IMM-VSN guarantee $1-1/e-\varepsilon $ approximation for small $\varepsilon > 0$ . We further extend LIM to the partitioned budget case where strategies are partitioned into groups, each of which has a separate budget, and show that a minor variation of our algorithms would achieve $1/2 -\varepsilon $ approximation ratio with the same time complexity. Empirically, through extensive tests, we demonstrate that IMM-VSN runs faster than IMM-PRR and much faster than other baseline algorithms while providing the same level of influence spread. We conclude that IMM-VSN is the best one for models with independent strategy activations, while IMM-PRR works for general modes without this assumption.
AutoCkt: deep reinforcement learning of analog circuit designs

Domain specialization under energy constraints in deeply-scaled CMOS has been driving the need for agile development of Systems on a Chip (SoCs). While digital subsystems have design flows that are conducive to rapid iterations from specification to layout, analog and mixed-signal modules face the challenge of a long human-in-the-middle iteration loop that requires expert intuition to verify that post-layout circuit parameters meet the original design specification. Existing automated solutions that optimize circuit parameters for a given target design specification have limitations of being schematic-only, inaccurate, sample-inefficient or not generalizable. This work presents AutoCkt, a machine learning optimization framework trained using deep reinforcement learning that not only finds post-layout circuit parameters for a given target specification, but also gains knowledge about the entire design space through a sparse subsampling technique. Our results show that for multiple circuit topologies, AutoCkt is able to converge and meet all target specifications on at least 96.3% of tested design goals in schematic simulation, on average 40× faster than a traditional genetic algorithm. Using the Berkeley Analog Generator, AutoCkt is able to design 40 LVS passed operational amplifiers in 68 hours, 9.6× faster than the state-of-the-art when considering layout parasitics.
Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions.

A plethora of problems in AI, engineering and the sciences are naturally formalized as inference in discrete probabilistic models. Exact inference is often prohibitively expensive, as it may require evaluating the (unnormalized) target density on its entire domain. Here we consider the setting where only a limited budget of calls to the unnormalized density oracle is available, raising the challenge of where in the domain to allocate these function calls in order to construct a good approximate solution. We formulate this problem as an instance of sequential decision-making under uncertainty and leverage methods from reinforcement learning for probabilistic inference with budget constraints. In particular, we propose the TreeSample algorithm, an adaptation of Monte Carlo Tree Search to approximate inference. This algorithm caches all previous queries to the density oracle in an explicit search tree, and dynamically allocates new queries based on a "best-first" heuristic for exploration, using existing upper confidence bound methods. Our non-parametric inference method can be effectively combined with neural networks that compile approximate conditionals of the target, which are then used to guide the inference search and enable generalization across multiple target distributions. We show empirically that TreeSample outperforms standard approximate inference methods on synthetic factor graphs.
A microscopy-based kinetic analysis of yeast vacuolar protein sorting

Saccharomyces cerevisiae is amenable to studying membrane traffic by live-cell fluorescence microscopy. We used this system to explore two aspects of cargo protein traffic through prevacuolar endosome (PVE) compartments to the vacuole. First, at what point during Golgi maturation does a biosynthetic vacuolar cargo depart from the maturing cisternae? To address this question, we modified a regulatable fluorescent secretory cargo by adding a vacuolar targeting signal. Traffic of the vacuolar cargo requires the GGA clathrin adaptors, which arrive during the early-to-late Golgi transition. Accordingly, the vacuolar cargo begins to exit the Golgi near the midpoint of maturation, significantly before exit of a secretory cargo. Second, how are cargoes delivered from PVE compartments to the vacuole? To address this question, we tracked biosynthetic and endocytic cargoes after they had accumulated in PVE compartments. The results suggest that stable PVE compartments repeatedly deliver material to the vacuole by a kiss-and-run mechanism.
A new bound for smooth spline spaces

For a planar simplicial complex Delta contained in R^2, Schumaker proved that a lower bound on the dimension of the space C^r_k(Delta) of planar splines of smoothness r and polynomial degree at most k on Delta is given by a polynomial P_Delta(r,k), and Alfeld-Schumaker showed this polynomial gives the correct dimension when k >= 4r+1. Examples due to Morgan-Scott, Tohaneanu, and Yuan show that the equality dim C^r_k(Delta) = P_Delta(r,k) can fail when k = 2r or 2r+1. We prove that the equality dim C^r_k(Delta)= P_Delta(r,k) cannot hold in general for k <= (22r+7)/10.
Evaluation of BMI Metrics to Assess Change in Adiposity in Children with Overweight and Moderate and Severe Obesity.

OBJECTIVE This study aimed to examine the relation between change in different BMI metrics and change in adiposity over a 12-month weight management intervention. METHODS Baseline and 12-month weights and heights from 399 children aged 2 to 12 years with BMI ≥ 85th percentile were used to calculate BMI, %BMIp95, %BMIp50, BMI z-score (BMIz), and modified BMI z-score (BMImz). Changes (Δ) in these measures were compared with changes in body fat percent (Δfat%) from bioelectrical impedance assessment. Correlation and regression models predicting associations between ΔBMI metrics and Δfat% were examined. RESULTS A total of 89% of the cohort was Hispanic, and 34% had class 2 or 3 obesity. In models predicting Δfat% adjusting for age, sex, and weight category, R2 for ΔBMI, ΔBMIz, BMImz, Δ%BMIp95, and Δ%BMIp50 were 0.53, 0.38, 0.45, 0.53, and 0.54, respectively (all P < 0.001). Only the ΔBMIz model had an interaction with weight status. Among the models with the highest R2, age group and sex interacted with the Δ%BMIp95 model but not ΔBMI or ΔBMIp50 models. CONCLUSIONS Longitudinal analyses demonstrate the utility of several BMI metrics other than z-score in capturing adiposity change consistently across a range of obesity severity. Characteristics of studied groups and interpretability could influence metric choice.
Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception.

In this paper, we introduce and tackle the simultaneous enhancement and super-resolution (SESR) problem for underwater robot vision and provide an efficient solution for near real-time applications. We present Deep SESR, a residual-in-residual network-based generative model that can learn to restore perceptual image qualities at 2x, 3x, or 4x higher spatial resolution. We supervise its training by formulating a multi-modal objective function that addresses the chrominance-specific underwater color degradation, lack of image sharpness, and loss in high-level feature representation. It is also supervised to learn salient foreground regions in the image, which in turn guides the network to learn global contrast enhancement. We design an end-to-end training pipeline to jointly learn the saliency prediction and SESR on a shared hierarchical feature space for fast inference. Moreover, we present UFO-120, the first dataset to facilitate large-scale SESR learning; it contains over 1500 training samples and a benchmark test set of 120 samples. By thorough experimental evaluation on the UFO-120 and other standard datasets, we demonstrate that Deep SESR outperforms the existing solutions for underwater image enhancement and super-resolution. We also validate its generalization performance on several test cases that include underwater images with diverse spectral and spatial degradation levels, and also terrestrial images with unseen natural objects. Lastly, we analyze its computational feasibility for single-board deployments and demonstrate its operational benefits for visually-guided underwater robots. The model and dataset information will be available at: this https URL.
Europe's Raison D'Etre

Distinguished Lecture delivered on the occasion of the XXIII European Union Law course of the Academy of European Law, on 2 July 2012.
Altered Hippocampal Place Cell Representation and Theta Rhythmicity following Moderate Prenatal Alcohol Exposure.

Summary Prenatal alcohol exposure (PAE) leads to profound deficits in spatial memory and synaptic and cellular alterations to the hippocampus that last into adulthood. Neurons in the hippocampus called place cells discharge as an animal enters specific places in an environment, establish distinct ensemble codes for familiar and novel places, and are modulated by local theta rhythms. Spatial memory is thought to critically depend on the integrity of hippocampal place cell firing. Therefore, we tested the hypothesis that hippocampal place cell firing is impaired after PAE by performing in vivo recordings from the hippocampi (CA1 and CA3) of moderate PAE and control adult rats. Our results show that hippocampal CA3 neurons from PAE rats have reduced spatial tuning. Second, CA1 and CA3 neurons from PAE rats are less likely to orthogonalize their firing between directions of travel on a linear track and between changes in contextual stimuli in an open arena compared to control neurons. Lastly, reductions in the number of hippocampal place cells exhibiting significant theta rhythmicity and phase precession were observed, which may suggest changes to hippocampal microcircuit function. Together, the reduced spatial tuning and sensitivity to contextual changes provide a neural systems-level mechanism to explain spatial memory impairment after moderate PAE.
Stochastic Virtual Battery Modeling of Uncertain Electrical Loads Using Variational Autoencoder

Effective utilization of flexible loads for grid services, while satisfying end-user preferences and constraints, requires an accurate estimation of the aggregated predictive flexibility offered by the electrical loads. Virtual battery (VB) models are often used to quantify the predictive flexibility in thermostatic loads (e.g. residential air-conditioners, electric water-heaters), which model the temporal evolution of a (virtual) energy state via a first order dynamics including self-dissipation rate, and power and energy capacities as parameters. Uncertainties and lack of information regarding end-usage and equipment models render deterministic VB models impractical. In this paper, we introduce the notion of stochastic VB models, and propose a variational autoencoder-based deep learning algorithm to identify the probability distribution of the VB model parameters. Using available sensors and meters data, the proposed algorithm generates not only point estimates of the VB parameters, but also confidence intervals around those values. Effectiveness of the proposed frameworks is demonstrated on a collection of electric water-heater loads, whose operation is driven by uncertain water usage profiles.
Closed-loop Parameter Identification of Linear Dynamical Systems through the Lens of Feedback Channel Coding Theory

This paper considers the problem of closed-loop identification of linear scalar systems with Gaussian process noise, where the system input is determined by a deterministic state feedback policy. The regularized least-square estimate (LSE) algorithm is adopted, seeking to find the best estimate of unknown model parameters based on noiseless measurements of the state. We are interested in the fundamental limitation of the rate at which unknown parameters can be learned, in the sense of the D-optimality scalarization criterion subject to a quadratic control cost. We first establish a novel connection between a closed-loop identification problem of interest and a channel coding problem involving an additive white Gaussian noise (AWGN) channel with feedback and a certain structural constraint. Based on this connection, we show that the learning rate is fundamentally upper bounded by the capacity of the corresponding AWGN channel. Although the optimal design of the feedback policy remains challenging, we derive conditions under which the upper bound is achieved. Finally, we show that the obtained upper bound implies that super-linear convergence is unattainable for any choice of the policy.
An inverse problem of a simultaneous reconstruction of the dielectric constant and conductivity from experimental backscattering data

This report extends our recent progress in tackling a challenging 3D inverse scattering problem governed by the Helmholtz equation. Our target application is to reconstruct dielectric constants, el...
Guests with guns: public support for "no carry" defaults on private land

A nationally representative survey of 2000 American adults shows broad support for prohibiting gun-possession on private land without the landowner's explicit permission. Many states have laws whic...
Rapid path planning for Dubins vehicles under environmental currents

Abstract This paper presents a rapid (real time) solution to the minimum-time path planning problem for Dubins vehicles under environmental currents (wind or ocean currents). Real-time solutions are essential in time-critical situations (such as replanning under dynamically changing environments or tracking fast moving targets). Typically, Dubins problem requires to solve for six path types; however, due to the presence of currents, four of these path types require to solve the root-finding problem involving transcendental functions. Thus, the existing methods result in high computation times and their applicability for real-time applications is limited. In this regard, in order to obtain a real-time solution, this paper proposes a novel approach where only a subset of two Dubins path types ( L S L and R S R ) are used which have direct analytical solutions in the presence of currents. However, these two path types do not provide full reachability. We show that by extending the feasible range of circular arcs in the L S L and R S R path types from 2 π to 4 π : (1) full reachability of any goal pose is guaranteed, and (2) paths with lower time costs as compared to the corresponding 2 π -arc paths can be produced. Theoretical properties are rigorously established, supported by several examples, and evaluated in comparison to the Dubins solutions by extensive Monte-Carlo simulations.
Off the Beaten Sidewalk: Pedestrian Prediction in Shared Spaces for Autonomous Vehicles

Pedestrians and drivers interact closely in a wide range of environments. Autonomous vehicles (AVs) correspondingly face the need to predict pedestrians' future trajectories in these same environments. Traditional model-based prediction methods have been limited to making predictions in highly structured scenes with signalized intersections, marked crosswalks, or curbs. Deep learning methods have instead leveraged datasets to learn predictive features that generalize across scenes, at the cost of model interpretability. This letter aims to achieve both widely applicable and interpretable predictions by proposing a risk-based attention mechanism to learn when pedestrians yield, and a model of vehicle influence to learn how yielding affects motion. A novel probabilistic method, Off the Sidewalk Predictions (OSP), uses these to achieve accurate predictions in both shared spaces and traditional scenes. Experiments on urban datasets demonstrate that the realtime method achieves state-of-the-art performance.
Functional and structural characterization of allosteric activation of phospholipase Cε by Rap1A

Phospholipase Ce (PLCe) is activated downstream of G protein–coupled receptors and receptor tyrosine kinases through direct interactions with small GTPases, including Rap1A and Ras. Although Ras has been reported to allosterically activate the lipase, it is not known whether Rap1A has the same ability or what its molecular mechanism might be. Rap1A activates PLCe in response to the stimulation of β-adrenergic receptors, translocating the complex to the perinuclear membrane. Because the C-terminal Ras association (RA2) domain of PLCe was proposed to the primary binding site for Rap1A, we first confirmed using purified proteins that the RA2 domain is indeed essential for activation by Rap1A. However, we also showed that the PLCe pleckstrin homology (PH) domain and first two EF hands (EF1/2) are required for Rap1A activation and identified hydrophobic residues on the surface of the RA2 domain that are also necessary. Small-angle X-ray scattering showed that Rap1A binding induces and stabilizes discrete conformational states in PLCe variants that can be activated by the GTPase. These data, together with the recent structure of a catalytically active fragment of PLCe, provide the first evidence that Rap1A, and by extension Ras, allosterically activate the lipase by promoting and stabilizing interactions between the RA2 domain and the PLCe core.
A Receding-Horizon MDP Approach for Performance Evaluation of Moving Target Defense in Networks

In this paper we study the problem of assessing the effectiveness of a proactive defense-by-detection policy with a network-based moving target defense. We model the network system using a probabilistic attack graph-a graphical security model. Given a network system with a proactive defense strategy an intelligent attacker needs to perform reconnaissance repeatedly to learn about the locations of intrusion detection systems and re- plan optimally to reach the target while avoiding detection. To compute the attacker's strategy for security evaluation we develop a receding-horizon planning algorithm using a risk-sensitive Markov decision process with a time-varying reward function. Finally we implement both defense and attack strategies in a synthetic network and analyze how the frequency of network randomization and the number of detection systems can influence the success rate of the attacker. This study provides insights for designing proactive defense strategies against online and multi-stage attacks by a resourceful attacker.
Spatial-And-Context aware (SpACe) "virtual biopsy" radiogenomic maps to target tumor mutational status on structural MRI

With growing emphasis on personalized cancer-therapies, radiogenomics has shown promise in identifying target tumor mutational status on routine imaging (i.e. MRI) scans. These approaches largely fall into two categories: (1) deep-learning/radiomics (context-based) that employ image features from the entire tumor to identify the gene mutation status, or (2) atlas (spatial)-based to obtain likelihood of gene mutation status based on population statistics. While many genes (i.e. EGFR, MGMT) are spatially variant, a significant challenge in reliable assessment of gene mutation status on imaging is the lack of available co-localized ground truth for training the models. We present Spatial-And-Context aware (SpACe) “virtual biopsy” maps that incorporate context-features from co-localized biopsy sites along with spatial-priors from population atlases, within a Least Absolute Shrinkage and Selection Operator (LASSO) regression model, to obtain a per-voxel likelihood of the presence of a mutation status (\(M^{+}\) vs \(M^{-}\)). We then use probabilistic pair-wise Markov model to improve the voxel-wise likelihood. We evaluate the efficacy of SpACe maps on MRI scans with co-localized ground truth obtained from biopsy, to predict the mutation status of 2 driver genes in Glioblastoma (GBM): (1) \(EGFR^{+}\) versus \(EGFR^{-}\), (n = 91), and (2) \(MGMT^{+}\) versus \(MGMT^{-}\), (n = 81). When compared against state-of-the-art deep-learning (DL) and radiomic models, SpACe maps obtained training and testing accuracies of 90% (n = 70) and 90.48% (n = 21) in identifying EGFR amplification status, compared to 80% and 71.4% via radiomics, and 74.28% and 65.5% via DL. For MGMT methylation status, training and testing accuracies using SpACe were 88.3% (n = 60) and 71.5% (n = 21), compared to 52.4% and 66.7% using radiomics, and 79.3% and 68.4% using DL. Following validation, SpACe maps could provide surgical navigation to improve localization of sampling sites for targeting of specific driver genes in cancer.
Webs of Influence: Secondary Stakeholder Actions and Cross-National Corporate Social Performance

We analyze the relationship between the actions and interactions of secondary stakeholders with an interest in corporate social performance (CSP) and variation in firm-level CSP across countries. O...
Measuring Global Mean Sea Level Changes With Surface Drifting Buoys

Combining ocean model data and in-situ Lagrangian data, I show that an array of surface drifting buoys tracked by a Global Navigation Satellite System (GNSS), such as the Global Drifter Program, could provide estimates of global mean sea level (GMSL) and its changes, including linear decadal trends. For a sustained array of 1250 globally distributed buoys with a standardized design, I demonstrate that GMSL decadal linear trend estimates with an uncertainty less than 0.3 mm yr$^{-1}$ could be achieved with GNSS daily random error of 1.6 m or less in the vertical direction. This demonstration assumes that controlled vertical position measurements could be acquired from drifting buoys, which is yet to be demonstrated. Development and implementation of such measurements could ultimately provide an independent and resilient observational system to infer natural and anthropogenic sea level changes, augmenting the on-going tide gauge and satellites records.
A Spiking Neural Network Emulating the Structure of the Oculomotor System Requires No Learning to Control a Biomimetic Robotic Head

Robotic vision introduces requirements for real-time processing of fast-varying, noisy information in a continuously changing environment. In a real-world environment, convenient assumptions, such as static camera systems and deep learning algorithms devouring high volumes of ideally slightlyvarying data are hard to survive. Leveraging on recent studies on the neural connectome associated with eye movements, we designed a neuromorphic oculomotor controller and placed it at the heart of our in-house biomimetic robotic head prototype. The controller is unique in the sense that (1) all data are encoded and processed by a spiking neural network (SNN), and (2) by mimicking the associated brain areas’ topology, the SNN is biologically interpretable and requires no training to operate. Here, we report the robot’s target tracking ability, demonstrate that its eye kinematics are similar to those reported in human eye studies and show that a biologically-constrained learning, although not required for the SNN’s function, can be used to further refine its performance. This work aligns with our ongoing effort to develop energy-efficient neuromorphic SNNs and harness their emerging intelligence to control biomimetic robots with versatility and robustness.
Environmental Regulation and Productivity: Evidence from Oil Refineries

We examine the effect of air quality regulation on productivity in some of the most heavily regulated manufacturing plants in the United States, the oil refineries of the Los Angeles (South Coast) Air Basin. We use direct measures of local air pollution regulation to estimate their effects on abatement investment. Refineries not subject to these regulations are used as a comparison group. We study a period of sharply increased regulation between 1979 and 1992. Initial compliance with each regulation cost $3 million per plant and a further $5 million to comply with increased stringency. We construct measures of total factor productivity using Census of Manufacturers output and materials data that report physical quantities of inputs and outputs for the entire population of refineries. Despite high costs associated with the local regulations, productivity in the Los Angeles Air Basin refineries rose sharply between 1987 and 1992, which was a period of decreased refinery productivity in other regions. We con...
A multiplex microsphere IgG assay for SARS-CoV-2 using ACE2-mediated inhibition as a surrogate for neutralization.

The COVID-19 pandemic has highlighted the challenges inherent to the serological detection of a novel pathogen such as SARS-CoV-2. Serological tests can be used diagnostically and for surveillance, but their usefulness depends on their throughput, sensitivity and specificity. Here, we describe a multiplex fluorescent microsphere-based assay, 3Flex, that can detect antibodies to three major SARS-CoV-2 antigens-spike (S) protein, the spike ACE2 receptor-binding domain (RBD), and nucleocapsid (NP). Specificity was assessed using 213 pre-pandemic samples. Sensitivity was measured and compared to the Abbottâ ARCHITECTâ SARS-CoV-2 IgG assay using serum samples from 125 unique patients equally binned (n = 25) into 5 time intervals (≤5, 6 to 10, 11 to 15, 16 to 20, and ≥21 days from symptom onset). With samples obtained at ≤5 days from symptom onset, the 3Flex assay was more sensitive (48.0% vs. 32.0%), but the two assays performed comparably using serum obtained ≥21 days from symptom onset. A larger collection (n = 534) of discarded sera was profiled from patients (n = 140) whose COVID-19 course was characterized through chart review. This revealed the relative rise, peak (S, 23.8; RBD, 23.6; NP, 16.7; in days from symptom onset), and decline of the antibody response. Considerable interperson variation was observed with a subset of extensively sampled ICU patients. Using soluble ACE2, inhibition of antibody binding was demonstrated for S and RBD, and not for NP. Taken together, this study described the performance of an assay built on a flexible and high-throughput serological platform that proved adaptable to the emergence of a novel infectious agent.
Quantifying Color Vision Changes Associated With Cataracts Using Cone Contrast Thresholds

Author(s): Mehta, Urmi; Diep, Anna; Nguyen, Kevin; Le, Bryan; Yuh, Clara; Frambach, Caroline; Doan, John; Wei, Ang; Palma, Anton M; Farid, Marjan; Garg, Sumit; Kedhar, Sanjay; Wade, Matthew; Marshall, Kailey A; Jameson, Kimberly A; Cristina Kenney, M; Browne, Andrew W | Abstract: PurposeTo evaluate effects of age and simulated and real cataractous changes on color vision as measured by the high-definition cone contrast test (CCT).MethodsTwenty-four healthy volunteers from two cohort studies performed CCT using best-corrected visual acuity, filters, mydriasis, and pinhole correction. Retrospective cross-sectional study of patients seen in eye clinics evaluated the relationship between age and color vision, and age and lens status in 355 eyes. Last, 25 subjects underwent CCT before and after cataract surgery.ResultsCCT scores were most reliable in the nonmydriatic condition without pinhole correction. Progressively dense brown filters produced small decreases in S-cone sensitivity. Linear regression analysis of phakic subjects showed a decline for all cone classes with age. Rate of decline was greater for S-cones (slope = -1.09; 95% confidence interval [CI], -1.30 to 0.86) than M-cones (slope = -0.80; 95% CI, -1.03 to -0.58) and L-cones (slope = -0.66; 95% CI, -0.88 to -0.44). CCT scores increased for S-cones but reduced for L- and M-cones in pseudophakic subjects compared with phakic patients. CCT scores after cataract surgery increased for S-cones, M-cones, and L-cones by 33.0 (95% CI, 8.6 to 57.4), 24.9 (95% CI, 3.8 to 46.0), and 22.0 (95% CI, -3.2 to 47.3), respectively.ConclusionsCCT assessment allows for clinically practical quantitation of color and contrast vision improvement after cataract surgery and aging patients who note poor vision despite good visual acuity.Translational relevanceCCT testing, which quantifies hereditary and acquired color deficiency, can also quantify the degree of cataract severity and, combined with other parameters, can provide more precise guidance for cataract extraction to optimize patient care.
Permutations Unlabeled Beyond Sampling Unknown

A recent unlabeled sampling result by Unnikrishnan, Haghighatshoar, and Vetterli states that with probability one over Gaussian random matrices A with iid entries, any $x$ can be uniquely recovered from an unknown permutation of $y = A x$ as soon as $A$ has at least twice as many rows as columns. We show that this condition on $A$ implies something much stronger: that an unknown vector $x$ can be recovered from measurements $y = T A x$ , when the unknown $T$ belongs to an arbitrary set of invertible, diagonalizable linear transformations $\mathcal {T}$ . The set $\mathcal {T}$ can be finite or countably infinite. When it is the set of $m \times m$ permutation matrices, we have the classical unlabeled sampling problem. We show that for almost all $A$ with at least twice as many rows as columns, all $x$ can be recovered either uniquely, or up to a scale depending on $\mathcal {T}$ , and that the condition on the size of $A$ is necessary. Our proof is based on vector space geometry. Specializing to permutations, we obtain a simplified proof of the uniqueness result of Unnikrishnan, Haghighatshoar, and Vetterli. In this letter, we are only concerned with uniqueness; stability and algorithms are left for future work.
On the image of complex conjugation in certain Galois representations

We compute the image of any choice of complex conjugation on the Galois representations associated to regular algebraic cuspidal automorphic representations and to torsion classes in the cohomology of locally symmetric spaces for over a totally real field .
DETECTION AND CHARACTERIZATION OF EXOPLANETS AND DISKS USING PROJECTIONS ON KARHUNEN-LOÈVE EIGENIMAGES

We describe a new method to achieve point-spread function (PSF) subtractions for high-contrast imaging using principal component analysis that is applicable to both point sources or extended objects (disks). Assuming a library of reference PSFs, a Karhunen–Lo` eve transform of these references is used to create an orthogonal basis of eigenimages on which the science target is projected. For detection this approach provides comparable suppression to the Locally Optimized Combination of Images (LOCI) algorithm, albeit with increased robustness to the algorithm parameters and speed enhancement. For characterization of detected sources, the method enables forward modeling of astrophysical sources. This alleviates the biases in the astrometry and photometry of discovered faint sources, which are usually associated with LOCI-based PSF subtractions schemes. We illustrate the algorithm performance using archival Hubble Space Telescope images, but the approach may also be considered for ground-based data acquired with angular differential imaging or integral-field spectrographs.
STEAP: simultaneous trajectory estimation and planning

We present a unified probabilistic framework for simultaneous trajectory estimation and planning. Estimation and planning problems are usually considered separately, however, within our framework we show that solving them simultaneously can be more accurate and efficient. The key idea is to compute the full continuous-time trajectory from start to goal at each time-step. While the robot traverses the trajectory, the history portion of the trajectory signifies the solution to the estimation problem, and the future portion of the trajectory signifies a solution to the planning problem. Building on recent probabilistic inference approaches to continuous-time localization and mapping and continuous-time motion planning, we solve the joint problem by iteratively recomputing the maximum a posteriori trajectory conditioned on all available sensor data and cost information. Our approach can contend with high-degree-of-freedom trajectory spaces, uncertainty due to limited sensing capabilities, model inaccuracy, the stochastic effect of executing actions, and can find a solution in real-time. We evaluate our framework empirically in both simulation and on a mobile manipulator.
Prospects for observing the magnetorotational instability in the plasma Couette experiment

Many astrophysical disks, such as protoplanetary disks, are in a regime where non-ideal, plasma-specific magnetohydrodynamic (MHD) effects can significantly influence the behaviour of the magnetorotational instability (MRI). The possibility of studying these effects in the plasma Couette experiment (PCX) is discussed. An incompressible, dissipative global stability analysis is developed to include plasma-specific two-fluid effects and neutral collisions, which are inherently absent in analyses of Taylor–Couette flows (TCFs) in liquid metal experiments. It is shown that with boundary driven flows, a ion-neutral collision drag body force significantly affects the azimuthal velocity profile, thus limiting the flows to regime where the MRI is not present. Electrically driven flow (EDF) is proposed as an alternative body force flow drive in which the MRI can destabilize at more easily achievable plasma parameters. Scenarios for reaching MRI relevant parameter space and necessary hardware upgrades are described.
Converting Instance Checking to Subsumption: A Rethink for Object Queries over Practical Ontologies.

Efficiently querying Description Logic (DL) ontologies is becoming a vital task in various data-intensive DL applications. Considered as a basic service for answering object queries over DL ontologies, instance checking can be realized by using the most specific concept (MSC) method, which converts instance checking into subsumption problems. This method, however, loses its simplicity and efficiency when applied to large and complex ontologies, as it tends to generate very large MSCs that could lead to intractable reasoning. In this paper, we propose a revision to this MSC method for DL SHI , allowing it to generate much simpler and smaller concepts that are specific enough to answer a given query. With independence between computed MSCs, scalability for query answering can also be achieved by distributing and parallelizing the computations. An empirical evaluation shows the efficacy of our revised MSC method and the significant efficiency achieved when using it for answering object queries.
Z -structures on product groups

A Z –structure on a group G , defined by M Bestvina, is a pair . y X ;Z/ of spaces such that y X is a compact ER, Z is a Z –set in y X , G acts properly and cocompactly on X D y XnZ and the collection of translates of any compact set in X forms a null sequence in y X . It is natural to ask whether a given group admits a Z –structure. In this paper, we show that if two groups each admit a Z –structure, then so do their free and direct products.
High-Resolution Optical Rotation Curves of Low-Luminosity Spiral Galaxies

We present optical long-slit spectroscopic observations of 21 low-luminosity, extreme late-type spiral galaxies. Our sample is comprised of Sc-Sm Local Supercluster spirals with moderate-to-low optical surface brightnesses and with luminosities at the low end for spiral disk galaxies (MV ≥ -18.8). For each galaxy we have measured high spatial resolution position-velocity (P-V) curves using the Hα emission line, and for 15 of the galaxies we also derive major-axis rotation curves. In ~50% of our sample, the P-V curves show significant asymmetries in shape, extent, and/or amplitude on the approaching and receding sides of the disk. A number of the P-V curves are still rising to the last measured point or reach a clear turnover on only one side. In most instances we find good agreement between the kinematic centers of extreme late-type spirals as defined by the global H I emission profile and by their optical continuum, although in a few cases we see evidence of possible real offsets. In spite of their shallow central gravitational potentials, at least six of the galaxies in our sample possess semi-stellar nuclei that appear to be compact nuclear star clusters; in five of these cases we see kinematic signatures in the P-V curves at the location of the nucleus. Finally, we find that like giant spirals, our sample galaxies have higher specific angular momenta than predicted by current cold dark matter models.
A Survey for New Stars and Brown Dwarfs in the Ophiuchus Star-forming Complex

We have performed a survey for new members of the Ophiuchus cloud complex using high-precision astrometry from the second data release of Gaia, proper motions measured with multi-epoch images from the Spitzer Space Telescope, and color-magnitude diagrams constructed with photometry from various sources. Through spectroscopy of candidates selected with those data, we have identified 155 new young stars. Based on available measurements of kinematics, we classify 102, 47, and six of those stars as members of Ophiuchus, Upper Sco, and other populations in Sco-Cen, respectively. We have also assessed the membership of all other stars in the vicinity of Ophiuchus that have spectroscopic evidence of youth from previous studies, arriving at a catalog of 373 adopted members of the cloud complex. For those adopted members, we have compiled mid-IR photometry from Spitzer and the Wide-field Infrared Survey Explorer and have used mid-IR colors to identify and classify circumstellar disks. We find that 210 of the members show evidence of disks, including 48 disks that are in advanced stages of evolution. Finally, we have estimated the relative median ages of the populations near the Ophiuchus clouds and the surrounding Upper Sco association using absolute K-band magnitudes (MK ) based on Gaia parallaxes. If we adopt an age 10 Myr for Upper Sco, then the relative values of MK imply median ages of ~ 2 Myr for L1689 and embedded stars in L1688, 3-4 Myr for low-extinction stars near L1688, and ~ 6 Myr for the group containing ρ Oph.
DAMPING OF ELECTRON DENSITY STRUCTURES AND IMPLICATIONS FOR INTERSTELLAR SCINTILLATION

The forms of electron density structures in kinetic Alfv´ en wave (KAW) turbulence are studied in connection with scintillation. The focus is on small scales L ∼ 10 8 –10 10 cm where the KAW regime is active in the interstellar medium, principally within turbulent Hii regions. Scales at 10 times the ion gyroradius and smaller are inferred to dominate scintillation in the theory of Boldyrev et al. From numerical solutions of a decaying KAW turbulence model, structure morphology reveals two types of localized structures, filaments and sheets, and shows that they arise in different regimes of resistive and diffusive damping. Minimal resistive damping yields localized current filaments that form out of Gaussian-distributed initial conditions. When resistive damping is large relative to diffusive damping, sheet-like structures form. In the filamentary regime, each filament is associated with a nonlocalized magnetic and density structure, circularly symmetric in cross section. Density and magnetic fields have Gaussian statistics (as inferred from Gaussian-valued kurtosis) while density gradients are strongly non-Gaussian, more so than current. This enhancement of non-Gaussian statistics in a derivative field is expected since gradient operations enhance small-scale fluctuations. The enhancement of density gradient kurtosis over current kurtosis is not obvious, yet it suggests that modest density fluctuations may yield large scintillation events during pulsar signal propagation. In the sheet regime the same statistical observations hold, despite the absence of localized filamentary structures. Probability density functions are constructed from statistical ensembles in both regimes, showing clear formation of long, highly non-Gaussian tails.
THE ROLE OF ICE COMPOSITIONS FOR SNOWLINES AND THE C/N/O RATIOS IN ACTIVE DISKS

The elemental compositions of planets define their chemistry, and could potentially be used as beacons for their formation location if the elemental gas and grain ratios of planet birth environments, i.e. protoplanetary disks, are well understood. In disks, the ratios of volatile elements, such as C/O and N/O, are regulated by the abundance of the main C, N, O carriers, their ice binding environment, and the presence of snowlines of major volatiles at different distances from the central star. We explore the effects of disk dynamical processes, molecular compositions and abundances, and ice compositions on the snowline locations of the main C, O and N carriers, and the C/N/O ratios in gas and dust throughout the disk. The gas-phase N/O ratio enhancement in the outer disk (exterior to the H2O snowline) exceeds the C/O ratio enhancement for all reasonable volatile compositions. Ice compositions and disk dynamics individually change the snowline location of N2, the main nitrogen carrier, by a factor of 2-3, and when considered together the range of possible N2 snowline locations is ~11- ~79 AU in a standard disk model. Observations that anchor snowline locations at different stages of planet formation are therefore key to develop C/N/O ratios as a probe of planet formation zones.
The Magnetic Field of Cloud 3 in L204

The L204 dark cloud complex is a nearby filamentary structure in Ophiuchus North that has no signs of active star formation. Past studies show that L204 is interacting with the nearby runaway O star, ζ Oph, and hosts a magnetic field that is coherent across parsec-length scales. Near-infrared H-band (1.6 μm) linear polarization measurements were obtained for 3896 background stars across a 1° × 15 region centered on the dense Cloud 3 in L204, using the Mimir near-infrared instrument on the 1.8 m Perkins Telescope. Analysis of these observations reveals both large-scale properties and small-scale changes in the magnetic field direction in Cloud 3. In the northern and western ζ Oph facing regions of the cloud, the magnetic field appears to be pushed up against the face of the cloud. This may indicate that the UV flux from ζ Oph has compressed the magnetic field on the western edge of L204. The plane-of-sky magnetic field strength is estimated to be ~11-26 μG using the Chandrasekhar-Fermi method. The polarimetry data also reveal that the polarization efficiency (PE ≡ P H/A V) steadily decreases with distance from ζ Oph (–0.09% ± 0.03% mag–1 pc–1). Additionally, power-law fits of PE versus A V for localized samples of probe stars show steeper negative indices with distance from ζ Oph. Both findings highlight the importance of external illumination, here from ζ Oph, in aligning dust grains to embedded magnetic fields.
Differential Dynamic Programming for time-delayed systems

Trajectory optimization considers the problem of deciding how to control a dynamical system to move along a trajectory which minimizes some cost function. Differential Dynamic Programming (DDP) is an optimal control method which utilizes a second-order approximation of the problem to find the control. It is fast enough to allow real-time control and has been shown to work well for trajectory optimization in robotic systems. Here we extend classic DDP to systems with multiple time-delays in the state. Being able to find optimal trajectories for time-delayed systems with DDP opens up the possibility to use richer models for system identification and control, including recurrent neural networks with multiple timesteps in the state. We demonstrate the algorithm on a two-tank continuous stirred tank reactor. We also demonstrate the algorithm on a recurrent neural network trained to model an inverted pendulum with position information only.
Quantification of the spin-Hall anti-damping torque with a resonance spectrometer

We present a simple technique using a cavity-based resonance spectrometer to quantify the anti-damping torque due to the spin Hall effect. Modification of ferromagnetic resonance is observed as a function of small DC current in sub-mm-wide strips of bilayers, consisting of magnetically soft FeGaB and strong spin-Hall metal Ta. From the detected current-induced linewidth change, we obtain an effective spin Hall angle of 0.08–0.09 independent of the magnetic layer thickness. Our results demonstrate that a sensitive resonance spectrometer can be a general tool to investigate spin Hall effects in various material systems, even those with vanishingly low conductivity and magnetoresistance.