cssi/SciSciGPT-SciSciNet huggingface dataset:
How Can Embedded Criminologists, Police Pracademics, and Crime Analysts Help Increase Police-Led Program Evaluations? A Survey of Authors Cited in the Evidence-Based Policing Matrix

Evidence-based policing emphasizes the evaluation of interventions to create a catalog of effective programs and practices. Program evaluation has primarily been considered the purview of academic researchers, with police agencies typically uninvolved in the evaluation of their own interventions. Scholars have recently advocated for police to take more ownership over program evaluation, often arguing for an increased role of three primary entities: embedded criminologists, police pracademics, and crime analysts. While an emerging body of literature has explored these entities individually, research has yet to explore the unique contributions each can make to police-led science. The current study is a survey of scholars who authored or co-authored one or more studies included in the Evidence-Based Policing Matrix. The authors explore four distinct research questions pertaining to police-led science. Findings suggest that embedded criminologists, police pracademics, and crime analysts may each have a unique role to play in promoting police-led science.
Infrared Spectrum of the Hyponitrite Dianion, N2O22-, Isolated and Insulated from Stabilizing Metal Cations in Solid Neon

Co-deposition of laser-ablated metals with NO in excess neon at 4 K produced new 1060.8 ′ 0.4 and 1052.6 ′ 0.2 cm - 1 absorptions, which almost disappear on annealing to 9 K and reappear in part on ultraviolet irradiation. These absorptions shift with 1 5 N 1 6 O and 1 5 N 1 8 O substitution as fundamental N-O vibrations and exhibit 1:2:1 triplet bands with 1 4 N 1 6 O + 1 5 N 1 6 O and 1 5 N 1 6 O + 1 5 N 1 8 O mixtures, which demonstrates the involvement of two equivalent NO oscillators. The above absorptions are assigned to N 2 O 2 2 - isolated and insulated from metal stabilizing cations in two solid neon matrix configurations. The blue shift from the 1028.5 cm - 1 solid argon band position for this species is larger than found for most small molecules and anions, which is consistent with a dianion. The photochemistry in laser-ablated gold experiments suggests the involvement of N 2 O 2 - in electron-transfer processes to form N 2 O 2 2 - .
Bankruptcy Prediction Models and the Cost of Debt

Financial institutions and academic researchers utilize bank - ruptcy prediction models to assess distress risk. However, predicting default can be problematic since 1) few firms actually experience default in any one year, 2) the lag between practical and actual default can vary significantly, 3) firms can strategically default, 4) firms can rework their obligations outside of bankruptcy, and 5) default frequency varies significantly over economic life cycles. Thus, relying on bankruptcy data alone to calibrate and validate these models can be problematic. In this article, the authors take an alternative approach by relying on the firm’s cost of debt as a market proxy for risk. They assess the validity of four widely used distress measures including two accounting-based models (Altman’s Z-Score and Ohlson’s O-Score), one reduced-form model (by Campbell, Hilscher, and Szilagyi, CHS), and one structural distance to default model (Merton-DD). The authors find dramatically different assessment of risk based on the models used. The CHS model has the most significant impact on the cost of debt followed by the Merton model. The accounting-based approaches of Altman’s Z-Score and Ohlson’s O-Score are highly ineffective. They caution researchers using Z- and O-Scores. The Merton distance to default model is superior to both accountingbased models but the authors recommend the use of the CHS model in research studies.
ON THE INTERNATIONAL FINANCIAL ARCHITECTURE: INSURING EMERGING MARKETS

In spite of significant institutional and macroeconomic reforms over the last decade or two, capital flows to developing economies remain highly volatile. In 1996, net private capital flows to emerging markets reached US$230 billions; by 1997 these flows had been cut in half; by 1998 halved again; and after a mild recovery during 1999, flows fell in 2000 and 2001 to slightly over one-tenth the level of 1996. These reversals in capital flows have enormous economic and social costs for developing economies. For well behaved' countries, a significant share of these fluctuations is triggered by events that are outside their direct control, and often outside the control of emerging markets as a whole. Building on this observation, this paper highlights some of the desirable features of insurance and hedging instruments against capital flow volatility, and discusses steps to facilitate the creation of these markets.
Signaling value of quality certification: Financing under asymmetric information

Abstract This study investigates signaling value of internationally-recognized quality certification in improving firm’s access to external finance for 39,638 mostly small and medium-sized firms in 137 countries. We find that certified firms have better access to external finance as compared to otherwise equal uncertified firms. Certified firms use more bank finance and equity capital while uncertified firms rely more on informal sources of finance. Quality certification helps firms to credibly signal their unobserved quality to creditors and investors, reduce informational asymmetry, and have better access to external finance. We also find that the signaling value of quality certification is stronger in presence of greater information asymmetry. Smaller firms and firms in less developed economies and financial systems benefit more from certifications by gaining better access to external finance.
Minimizing the usage of hardware counters for collective communication using triggered operations

Abstract Triggered operations and counting events or counters are building blocks used by communication libraries, such as MPI, to offload collective operations to the Host Fabric Interface (HFI) or Network Interface Card (NIC). Triggered operations can be used to schedule a network or arithmetic operation to occur in the future, when a trigger counter reaches a specified threshold. On completion of the operation, the value of a completion counter increases by one. With this mechanism, it is possible to create a chain of dependent operations, so that the execution of an operation is triggered when all the operations it depends on have completed its execution. Triggered operations rely on hardware counters on the HFI and are a limited resource. Thus, if the number of required counters exceeds the number of hardware counters, a collective needs to stall until a previous collective completes and counters are released. In addition, if the HFI has a counter cache, utilizing a large number of counters can cause cache thrashing and provide poor performance. Therefore, it is important to reduce the number of counters, especially when running on a large supercomputer or when an application uses non-blocking collectives and multiple collectives can run concurrently. Moreover, counters being a scarce resource, it is important for the MPI library to be able to estimate the number of counters required by a collective so that it can fallback to the software implementation when the number of available counters is less than the required number. In this paper, we propose an algorithm to optimize the number of hardware counters used when offloading collectives with triggered operations. With our algorithm, different operations can share and re-use trigger and completion counters based on the dependences among them and their topological orderings. We have also proposed models to estimate the number of counters required by different collectives when using the optimization algorithm. While the proposed counter optimization algorithm assumes that the dependences among various operations in a collective are represented using a Directed Acyclic Graph (DAG), there might be cases when no DAGs are provided for the collective. In this paper, we also discuss how we can optimize the usage of counters for such cases. Our experimental results show that our proposed algorithm significantly reduces the number of counters over a naive approach that does not consider the dependences among the operations.
A divisive model of evidence accumulation explains uneven weighting of evidence over time

Divisive normalization has long been used to account for computations in various neural processes and behaviours. The model proposes that inputs into a neural system are divisively normalized by the system’s total activity. More recently, dynamical versions of divisive normalization have been shown to account for how neural activity evolves over time in value-based decision making. Despite its ubiquity, divisive normalization has not been studied in decisions that require evidence to be integrated over time. Such decisions are important when the information is not all available at once. A key feature of such decisions is how evidence is weighted over time, known as the integration kernel. Here, we provide a formal expression for the integration kernel in divisive normalization, and show that divisive normalization quantitatively accounts for 133 human participants’ perceptual decision making behaviour, performing as well as the state-of-the-art Drift Diffusion Model, the predominant model for perceptual evidence accumulation. Divisive normalization is thought to be a ubiquitous computation in the brain, but has not been studied in decisions that require integrating evidence over time. Here, the authors show in humans that dynamic divisive normalization accounts for the uneven weighting of perceptual evidence over time.
Thresholds of quasi-supercontinuum interband quantum dot lasers

We present the analysis of threshold conditions that produces wideband- stimulated emission in semiconductor quantum-dot laser. Uur theoretical model reveals critical occurrence of broadband lasing when the energy spacing between quantized energy states (ΔE) is comparable to the inhomogeneous broadening of quantum dot nanostructures.
COUNTRY FUNDS AND ASYMMETRIC INFORMATION

Using data on country funds, the authors study how differential access to information affects international investment. They find that past changes in net asset values (NAVs) and discounts predict current country fund prices more commonly than prices and discounts predict NAVs. The price (NAV) adjustment coefficients are low and negatively correlated with the local (foreign) market variability -- but not with the fund price (NAV) variability. NAVs seem to be closer to local information. They are the asset prices that react first to local news. Later the country fund holders receive the information and those prices react after NAVs have reacted. The 1995 Mexican crisis and the 1997 Asian crisis are two examples of this type of behavior. These findings are consistent with the hypothesis of asymmetric information, according to which the holders of the underlying assets have more information about local assets than the country fund holders do. The authors empirically test the asymmetric information hypothesis against the noise traders hypothesis. A theoretical model is presented in the appendix.
Formal synthesis of (-)-apicularen A.

A formal synthesis of (−)-apicularen A has been completed. The synthesis features a cyanohydrin acetonide coupling as a convergent approach to the C9−C18 segment and an intramolecular Diels−Alder addition sequence to create both the 10-membered macrocycle and the aromatic ring.
ZerNet: Convolutional Neural Networks on Arbitrary Surfaces Via Zernike Local Tangent Space Estimation

In this paper, we propose a novel formulation to extend CNNs to two-dimensional (2D) manifolds using orthogonal basis functions, called Zernike polynomials. In many areas, geometric features play a key role in understanding scientific phenomena. Thus, an ability to codify geometric features into a mathematical quantity can be critical. Recently, convolutional neural networks (CNNs) have demonstrated the promising capability of extracting and codifying features from visual information. However, the progress has been concentrated in computer vision applications where there exists an inherent grid-like structure. In contrast, many geometry processing problems are defined on curved surfaces, and the generalization of CNNs is not quite trivial. The difficulties are rooted in the lack of key ingredients such as the canonical grid-like representation, the notion of consistent orientation, and a compatible local topology across the domain. In this paper, we prove that the convolution of two functions can be represented as a simple dot product between Zernike polynomial coefficients; and the rotation of a convolution kernel is essentially a set of 2-by-2 rotation matrices applied to the coefficients. As such, the key contribution of this work resides in a concise but rigorous mathematical generalization of the CNN building blocks.
An aptamer-based magnetic flow cytometer using matched filtering.

Abstract Facing unprecedented population-ageing, the management of noncommunicable diseases (NCDs) urgently needs a point-of-care (PoC) testing infrastructure. Magnetic flow cytometers are one such solution for rapid cancer cellular detection in a PoC setting. In this work, we report a giant magnetoresistive spin-valve (GMR SV) biosensor array with a multi-stripe sensor geometry and matched filtering to improve detection accuracy without compromising throughput. The carefully designed sensor geometry generates a characteristic signature when cells labeled with magnetic nanoparticles (MNPs) pass by thus enabling multi-parametric measurement like optical flow cytometers (FCMs). Enumeration and multi-parametric information were successfully measured across two decades of throughput (37 — 2730 cells/min). 10-μm polymer microspheres were used as a biomimetic model where MNPs and MNP-decorated polymer conjugates were flown over the GMR SV sensor array and detected with a signal-to-noise ratio (SNR) as low as 2.5 dB due to the processing gain afforded by the matched filtering. The performance was compared against optical observation, exhibiting a 92% detection efficiency. The system achieved a 95% counting accuracy for biomimetic models and 98% for aptamer-based pancreatic cancer cell detection. This system demonstrates the ability to perform reliable flow cytometry toward PoC diagnostics to benefit NCD control plans.
NeckSense: A Multi-Sensor Necklace for Detecting Eating Activities in Free-Living Conditions

We present the design, implementation, and evaluation of a multi-sensor, low-power necklace, NeckSense, for automatically and unobtrusively capturing fine-grained information about an individual's eating activity and eating episodes, across an entire waking day in a naturalistic setting. NeckSense fuses and classifies the proximity of the necklace from the chin, the ambient light, the Lean Forward Angle, and the energy signals to determine chewing sequences, a building block of the eating activity. It then clusters the identified chewing sequences to determine eating episodes. We tested NeckSense on 11 participants with and 9 participants without obesity, across two studies, where we collected more than 470 hours of data in a naturalistic setting. Our results demonstrate that NeckSense enables reliable eating detection for individuals with diverse body mass index (BMI) profiles, across an entire waking day, even in free-living environments. Overall, our system achieves an F1-score of 81.6% in detecting eating episodes in an exploratory study. Moreover, our system can achieve an F1-score of 77.1% for episodes even in an all-day-long free-living setting. With more than 15.8 hours of battery life, NeckSense will allow researchers and dietitians to better understand natural chewing and eating behaviors. In the future, researchers and dietitians can use NeckSense to provide appropriate real-time interventions when an eating episode is detected or when problematic eating is identified.
Spike-Timing Dependent Plasticity Effect on the Temporal Patterning of Neural Synchronization

Neural synchrony in the brain at rest is usually variable and intermittent, thus intervals of predominantly synchronized activity are interrupted by intervals of desynchronized activity. Prior studies suggested that this temporal structure of the weakly synchronous activity might be functionally significant: many short desynchronizations may be functionally different from few long desynchronizations even if the average synchrony level is the same. In this study, we used computational neuroscience methods to investigate the effects of spike-timing dependent plasticity (STDP) on the temporal patterns of synchronization in a simple model. We employed a small network of conductance-based model neurons that were connected via excitatory plastic synapses. The dynamics of this network was subjected to the time-series analysis methods used in prior experimental studies. We found that STDP could alter the synchronized dynamics in the network in several ways, depending on the time scale that plasticity acts on. However, in general, the action of STDP in the simple network considered here is to promote dynamics with short desynchronizations (i.e., dynamics reminiscent of that observed in experimental studies). Complex interplay of the cellular and synaptic dynamics may lead to the activity-dependent adjustment of synaptic strength in such a way as to facilitate experimentally observed short desynchronizations in the intermittently synchronized neural activity.
Semiorthogonal Decompositions on Total Spaces of Tautological Bundles

Let U be the tautological subbundle on the Grassmannian $\mathrm{Gr}(k, n)$. There is a natural morphism $\mathrm{Tot}(U) \to \mathbb{A}^n$. Using it, we give a semiorthogonal decomposition for the bounded derived category $D^b_{\mathrm{coh}}(\mathrm{Tot}(U))$ into several exceptional objects and several copies of $D^b_{\mathrm{coh}}(\mathbb{A}^n)$. We also prove a global version of this result: given a vector bundle $E$ with a regular section $s$, consider a subvariety of the relative Grassmannian $\mathrm{Gr}(k, E)$ of those subspaces which contain the value of $s$. The derived category of this subvariety admits a similar decomposition into copies of the base and the zero locus of $s$. This may be viewed as a generalization of the blow-up formula of Orlov, which is the case $k = 1$.
End-to-End Optimization of Scene Layout

We propose an end-to-end variational generative model for scene layout synthesis conditioned on scene graphs. Unlike unconditional scene layout generation, we use scene graphs as an abstract but general representation to guide the synthesis of diverse scene layouts that satisfy relationships included in the scene graph. This gives rise to more flexible control over the synthesis process, allowing various forms of inputs such as scene layouts extracted from sentences or inferred from a single color image. Using our conditional layout synthesizer, we can generate various layouts that share the same structure of the input example. In addition to this conditional generation design, we also integrate a differentiable rendering module that enables layout refinement using only 2D projections of the scene. Given a depth and a semantics map, the differentiable rendering module enables optimizing over the synthesized layout to fit the given input in an analysis-by-synthesis fashion. Experiments suggest that our model achieves higher accuracy and diversity in conditional scene synthesis and allows exemplar-based scene generation from various input forms.
Dynamic locomotion for passive-ankle biped robots and humanoids using whole-body locomotion control:

Whole-body control (WBC) is a generic task-oriented control method for feedback control of loco-manipulation behaviors in humanoid robots. The combination of WBC and model-based walking controllers...
$$tZ'$$ production at hadron colliders

We study the production of a single top quark in association with a heavy extra $$Z'$$ at hadron colliders in new physics models with and without flavor-changing neutral-current (FCNC) couplings. We use QCD soft-gluon resummation and threshold expansions to calculate higher-order corrections for the total cross section and transverse-momentum distributions for $$t Z'$$ production. The impact of the uncertainties due to the structure of the proton and scale dependence is also analyzed.
On variational iterative methods for semilinear problems

Abstract In this article, an iterative method suitable for inverting semilinear problems is presented. The method employs a two-way parametrization that is able to reduce semilinear boundary-value problems into simpler and linear systems governed by the Laplacian. In contrast to Newton–Picard iterations, the method prescribes a well-defined procedure for constructing initial iterates, and thus, does not suffer from lack of global convergence. With rectangular domains and periodic boundary conditions, the technique also allows fast Poisson solvers to be engaged. Theoretical justifications are provided and supported by several experiments. Numerical results show that the method yields approximations which are comparable to reference solutions.
Optimizing Offer Sets in Sub-Linear Time

Personalization and recommendations are now accepted as core competencies in just about every online setting, ranging from media platforms to e-commerce to social networks. While the challenge of estimating user preferences has garnered significant attention, the operational problem of using such preferences to construct personalized offer sets to users is largely still open, particularly in modern settings where a massive number of items and a millisecond response time requirement mean that even enumerating all of the items is impossible. Faced with such settings, existing techniques are either (a) entirely heuristic with no principled justification, or (b) theoretically sound, but simply too slow to work. Thus motivated, we propose an algorithm for personalized offer set optimization that runs in time sub-linear in the number of items while enjoying a uniform performance guarantee. Our algorithm works for an extremely general class of problems and models of user choice that includes the mixture MNL model as a special case. We achieve a sub-linear runtime by leveraging the dimensionality reduction from learning an accurate latent factor model, along with existing sub-linear time approximate near neighbor algorithms. Our algorithm can be entirely data-driven, relying on samples of the user, where a 'sample' refers to the user interaction data typically collected by firms. We evaluate our approach on a massive content discovery dataset from Outbrain that includes millions of advertisements. Results show that our implementation indeed runs on the order of milliseconds, with increased performance relative to existing fast heuristics.
ClonArch: visualizing the spatial clonal architecture of tumors.

MOTIVATION Cancer is caused by the accumulation of somatic mutations that lead to the formation of distinct populations of cells, called clones. The resulting clonal architecture is the main cause of relapse and resistance to treatment. With decreasing costs in DNA sequencing technology, rich cancer genomics datasets with many spatial sequencing samples are becoming increasingly available, enabling the inference of high-resolution tumor clones and prevalences across different spatial coordinates. While temporal and phylogenetic aspects of tumor evolution, such as clonal evolution over time and clonal response to treatment, are commonly visualized in various clonal evolution diagrams, visual analytics methods that reveal the spatial clonal architecture are missing. RESULTS This article introduces ClonArch, a web-based tool to interactively visualize the phylogenetic tree and spatial distribution of clones in a single tumor mass. ClonArch uses the marching squares algorithm to draw closed boundaries representing the presence of clones in a real or simulated tumor. ClonArch enables researchers to examine the spatial clonal architecture of a subset of relevant mutations at different prevalence thresholds and across multiple phylogenetic trees. In addition to simulated tumors with varying number of biopsies, we demonstrate the use of ClonArch on a hepatocellular carcinoma tumor with ∼280 sequencing biopsies. ClonArch provides an automated way to interactively examine the spatial clonal architecture of a tumor, facilitating clinical and biological interpretations of the spatial aspects of intra-tumor heterogeneity. AVAILABILITY AND IMPLEMENTATION https://github.com/elkebir-group/ClonArch.
The 2-adic analysis of Stirling numbers of the second kind via higher order Bernoulli numbers and polynomials

Several new estimates for the 2-adic valuations of Stirling numbers of the second kind are proved. These estimates, together with criteria for when they are sharp, lead to improvements in several k...
Analytical Study of the Effects of Wind Tunnel Turbulence on Turbofan Rotor Noise

An analytical study of the effects of wind tunnel turbulence on turbofan rotor noise was carried out to evaluate the effectiveness of the NASA Ames 40 by 80-foot wind tunnel in simulating flight levels of fan noise. A previously developed theory for predicting rotor/turbulence interaction noise, refined and extended to include first-order effects of inlet turbulence anisotropy, was employed to carry out a parametric study of the effects of fan size, blade number, and operating line for outdoor test stand, NASA Ames wind tunnel, and flight inlet turbulence conditions. A major result of this study is that although wind tunnel rotor/turbulence noise levels are not as low as flight levels, they are substantially lower than the outdoor test stand levels and do not mask other sources of fan noise.
A novel fully automated MRI-based deep-learning method for classification of 1p/19q co-deletion status in brain gliomas

Background One of the most important recent discoveries in brain glioma biology has been the identification of the isocitrate dehydrogenase (IDH) mutation and 1p/19q co-deletion status as markers for therapy and prognosis. 1p/19q co-deletion is the defining genomic marker for oligodendrogliomas and confers a better prognosis and treatment response than gliomas without it. Our group has previously developed a highly accurate deep-learning network for determining IDH mutation status using T2-weighted (T2w) MRI only. The purpose of this study was to develop a similar 1p/19q deep-learning classification network. Methods Multiparametric brain MRI and corresponding genomic information were obtained for 368 subjects from The Cancer Imaging Archive and The Cancer Genome Atlas. 1p/19 co-deletions were present in 130 subjects. Two-hundred and thirty-eight subjects were non-co-deleted. A T2w image-only network (1p/19q-net) was developed to perform 1p/19q co-deletion status classification and simultaneous single-label tumor segmentation using 3D-Dense-UNets. Three-fold cross-validation was performed to generalize the network performance. Receiver operating characteristic analysis was also performed. Dice scores were computed to determine tumor segmentation accuracy. Results 1p/19q-net demonstrated a mean cross-validation accuracy of 93.46% across the 3 folds (93.4%, 94.35%, and 92.62%, SD = 0.8) in predicting 1p/19q co-deletion status with a sensitivity and specificity of 0.90 ± 0.003 and 0.95 ± 0.01, respectively and a mean area under the curve of 0.95 ± 0.01. The whole tumor segmentation mean Dice score was 0.80 ± 0.007. Conclusion We demonstrate high 1p/19q co-deletion classification accuracy using only T2w MR images. This represents an important milestone toward using MRI to predict glioma histology, prognosis, and response to treatment.
Sparsity-Adaptive Beamspace Channel Estimation for 1-Bit mmWave Massive MIMO Systems

We propose sparsity-adaptive beamspace channel estimation algorithms that improve accuracy for 1-bit data converters in all-digital millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) basestations. Our algorithms include a tuning stage based on Stein’s unbiased risk estimate (SURE) that automatically selects optimal denoising parameters depending on the instantaneous channel conditions. Simulation results with line-of-sight (LoS) and non-LoS mmWave massive MIMO channel models show that our algorithms improve channel estimation accuracy with 1-bit measurements in a computationally-efficient manner.
RL-Based Interference Mitigation in Uncoordinated Networks with Partially Overlapping Tones

Partially-overlapping tones (POT) are known to help mitigate co-channel interference in uncoordinated multi-carrier networks by introducing intentional frequency offsets (FOs) to the transmitted signals. In this paper, we explore the use of POT with reinforcement learning (RL) in dense networks where multiple links access time-frequency resources simultaneously. We propose a novel framework based on Q-learning, to obtain the FO for the multi-carrier waveform used for each link. In particular, we consider filtered multi-tone (FMT) systems that utilize Gaussian, root-raised-cosine (RRC), and isotropic orthogonal transform algorithm (IOTA) based prototype filters. Our simulation results show that the proposed scheme enhances the capacity of the links by at least 30% in additive white Gaussian noise (AWGN) channel at high signal-to-noise ratio (SNR), and even more so in the presence of severe multi-path fading. For a wide range of interfering link densities, we demonstrate substantial improvements in the outage probability and multi-user efficiency facilitated by POT, with the Gaussian filter outperforming the other two filters.
Specification-Guided Verification and Abstraction Refinement of Mixed Monotone Stochastic Systems

This article addresses the problem of verifying discrete-time stochastic systems against omega-regular specifications using finite-state abstractions. Omega-regular properties allow specifying complex behavior and encompass, for example, linear temporal logic. We focus on a class of systems with mixed monotone dynamics. This class is shown to be amenable to efficient reachable set computation and models a wide range of physically relevant systems. In general, finite-state abstractions of continuous state stochastic systems give rise to augmented Markov chains wherein the probabilities of transition between states are restricted to an interval. We present a procedure to compute a finite-state interval-valued Markov chain (IMC) abstraction of discrete-time, mixed monotone stochastic systems subject to affine disturbances given a rectangular partition of the state space. Then, we suggest an algorithm for performing verification against omega-regular properties in IMCs. Specifically, we aim to compute bounds on the probability of satisfying a specification from any initial state in the IMC. This is achieved by solving a reachability problem on the sets of so-called winning and losing components in the Cartesian product between the IMC and a Rabin automaton representing the specification. Next, the verification of IMCs may yield a set of states whose acceptance status is undecided with respect to the specification, requiring a refinement of the abstraction. We describe a specification-guided approach that compares the best and worst case behaviors of accepting paths in the IMC and targets the appropriate states accordingly. Finally, we show a case study.
Risk-adjusted output feedback receding horizon control of constrained linear parameter varying systems

In the past few years, control of linear parameter varying (LPV) systems has been the object of considerable attention, as a way of formalizing the intuitively appealing idea of gain scheduling control for nonlinear systems. However, currently available LPV techniques are both computationally demanding and (potentially) very conservative. In this paper, we propose to address these difficulties by combining receding horizon and risk-adjusted techniques. The resulting controllers are guaranteed to stabilize the plant and have computational complexity that increases polynomially, rather than exponentially, with the prediction horizon. Copyright © 2007 John Wiley & Sons, Ltd.
Evasive Active Hypothesis Testing

We consider an active hypothesis testing scenario in which an adversary obtains observations while legitimate parties engage in a sequential adaptive control policy to estimate an unknown parameter. The objective is for the legitimate parties to evade the adversary by controlling the risk of their test while minimizing the detection ability of the adversary, measured in terms of its error exponent. We develop bounds on the adversary’s error exponent that offer insight into how legitimate adversaries can best evade the adversary’s detection. We illustrate the results in a wireless transmission detection example.
Nearly Optimal Embeddings of Flat Tori.

We show that for any $n$-dimensional lattice $\mathcal{L} \subseteq \mathbb{R}^n$, the torus $\mathbb{R}^n/\mathcal{L}$ can be embedded into Hilbert space with $O(\sqrt{n\log n})$ distortion. This improves the previously best known upper bound of $O(n\sqrt{\log n})$ shown by Haviv and Regev (APPROX 2010) and approaches the lower bound of $\Omega(\sqrt{n})$ due to Khot and Naor (FOCS 2005, Math. Annal. 2006).
Bridging the Gap Between Safety and Real-Time Performance in Receding-Horizon Trajectory Design for Mobile Robots

To operate with limited sensor horizons in unpredictable environments, autonomous robots use a receding-horizon strategy to plan trajectories, wherein they execute a short plan while creating the n...
E-cigarette, combustible, and smokeless tobacco product use combinations among youth in the united states, 2014-2019.

Abstract Introduction Youth e-cigarette use has been rising, however U.S. prevalence data are generally reported without disaggregating by individuals’ use of other tobacco products. It is not clear how the proportion of youth e-cigarette users naive to all combustible tobacco is changing. Methods Annual prevalence estimates of ever and current (defined as past 30-day use) tobacco use prevalence by school type are reported using the 2014–2019 National Youth Tobacco Surveys (NYTS) with mutually exclusive categories of e-cigarette, smokeless tobacco, and/or combustible tobacco product use. T-tests were used to compare annual estimates with the preceding year. The annual percent change (APC) for each category from 2014 to 2018 were analyzed using JoinPoint regression. Data for 2019 were reported separately due to the change in survey format from paper to electronic. Results Current use of only e-cigarettes among HS students who never used combustible tobacco increased significantly from 2014 to 2018 (APC = +42.4%, 95% CI: 0.7, 101.3); by 2019, prevalence peaked at 9.2% (95% CI: 8.2, 10.2) among never combustible users and 8.3% (95% CI: 7.3, 9.3) among former combustible users. This coincided with significant declines in use of only combustible tobacco (APC = −14.5%, 95% CI: −18.3, −10.5). Conclusions Use of only e-cigarettes among US youth with no history of combustible tobacco use has increased substantially over time, even as combustible tobacco use continues to decline. Of the 17.5% (95% CI: 15.7, 19.0) of HS students who currently used only e-cigarettes (but not other tobacco) in 2019, more than half have no history of combustible tobacco use.
Three-photon imaging of synthetic dyes in deep layers of the neocortex

Multiphoton microscopy has emerged as the primary imaging tool for studying the structural and functional dynamics of neural circuits in brain tissue, which is highly scattering to light. Recently, three-photon microscopy has enabled high-resolution fluorescence imaging of neurons in deeper brain areas that lie beyond the reach of conventional two-photon microscopy, which is typically limited to ~ 450 µm. Three-photon imaging of neuronal calcium signals, through the genetically-encoded calcium indicator GCaMP6, has been used to successfully record neuronal activity in deeper neocortical layers and parts of the hippocampus in rodents. Bulk-loading cells in deeper cortical layers with synthetic calcium indicators could provide an alternative strategy for labelling that obviates dependence on viral tropism and promoter penetration, particularly in non-rodent species. Here we report a strategy for visualized injection of a calcium dye, Oregon Green BAPTA-1 AM (OGB-1 AM), at 500-600 µm below the surface of the mouse visual cortex in vivo. We demonstrate successful OGB-1 AM loading of cells in cortical layers 5-6 and subsequent three-photon imaging of orientation- and direction- selective visual responses from these cells.
Assessing Influenza Vaccination Behaviors Among Medically Underserved Obstetric Patients.

Background: Despite recommendations, maternal influenza vaccine acceptance has stagnated around 50%. Materials and Methods: A prospective cohort study was conducted of pregnant women seen in the clinic from September 2018 to April 2019. Primary outcomes included influenza vaccine uptake and reasons for vaccine refusal, categorized based on the Health Belief Model. We compared characteristics between three vaccination groups (never refused, refused and vaccinated, and refused and not vaccinated) by using chi-square and one-way analysis of variance. We used multivariate logistic regression to calculate adjusted odds ratios (aORs) and 95% confidence intervals (CIs) for associations between patient characteristics and vaccine acceptance. Mixed-effects logistic regression models were used to explore the impact of provider-patient race concordance on influenza vaccine uptake. Results: Among 1666 women, 902 (54.1%) were vaccinated. Of these, 183 (20.3%) initially refused. Those who refused and were never vaccinated were more likely to be non-Hispanic black (aOR: 1.64, 95% CI: 1.05-2.56) and less likely to be Hispanic (aOR: 0.44, 95% CI: 0.24-0.81). Overall, perceived barriers were the most common reason for refusal (52.4%). Women who refused consistently were more likely to cite reasons related to perceived benefits (38.5% vs. 7.6%). Those who eventually accepted were more likely to cite cue to action (22.4% vs. 12.6%). Women who were race discordant with their provider were more likely to be vaccinated compared with those who were race concordant (57.9% vs. 52.9%, aOR: 1.16, 95% CI: 1.07-1.27). Conclusions: Women who refuse influenza vaccination in pregnancy may later choose to be vaccinated. Continued promotion of vaccination throughout pregnancy is crucial for vaccine uptake.
Sudo RM -RF: Efficient Networks for Universal Audio Source Separation

In this paper, we present an efficient neural network for end-to-end general purpose audio source separation. Specifically, the backbone structure of this convolutional network is the SUccessive DOwnsampling and Resampling of Multi-Resolution Features (SuDoRM-RF) as well as their aggregation which is performed through simple one-dimensional convolutions. In this way, we are able to obtain high quality audio source separation with limited number of floating point operations, memory requirements, number of parameters and latency. Our experiments on both speech and environmental sound separation datasets show that SuDoRM - RF performs comparably and even surpasses various state-of-the-art approaches with significantly higher computational resource requirements.
Where the Action Could Be: Speakers Look at Graspable Objects and Meaningful Scene Regions when Describing Potential Actions

The world is visually complex, yet we can efficiently describe it by extracting the information that is most relevant to convey. How do the properties of real-world scenes help us decide where to look and what to say? Image salience has been the dominant explanation for what drives visual attention and production as we describe displays, but new evidence shows scene meaning predicts attention better than image salience. Here we investigated the relevance of one aspect of meaning, graspability (the grasping interactions objects in the scene afford), given that affordances have been implicated in both visual and linguistic processing. We quantified image salience, meaning, and graspability for real-world scenes. In 3 eyetracking experiments, native English speakers described possible actions that could be carried out in a scene. We hypothesized that graspability would preferentially guide attention due to its task-relevance. In 2 experiments using stimuli from a previous study, meaning explained visual attention better than graspability or salience did, and graspability explained attention better than salience. In a third experiment we quantified image salience, meaning, graspability, and reach-weighted graspability for scenes that depicted reachable spaces containing graspable objects. Graspability and meaning explained attention equally well in the third experiment, and both explained attention better than salience. We conclude that speakers use object graspability to allocate attention to plan descriptions when scenes depict graspable objects within reach, and otherwise rely more on general meaning. The results shed light on what aspects of meaning guide attention during scene viewing in language production tasks. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Symplectically replacing plumbings with Euler characteristic $2 \: 4$‑manifolds

We introduce new symplectic cut-and-paste operations that generalize the rational blowdown. In particular, we will define $k$-replaceable plumbings to be those that, heuristically, can be symplectically replaced by Euler characteristic $k$ 4-manifolds. We will then classify 2-replaceable linear plumbings, construct 2-replaceable plumbing trees, and use one such tree to construct a symplectic exotic $\mathbb{C}P^2\#6\overline{\mathbb{C}P^2}$.
Scalable Solutions for Automated Single Pulse Identification and Classification in Radio Astronomy

Data collection for scientific applications is increasing exponentially and is forecasted to soon reach peta- and exabyte scales. Applications which process and analyze scientific data must be scalable and focus on execution performance to keep pace. In the field of radio astronomy, in addition to increasingly large datasets, tasks such as the identification of transient radio signals from extrasolar sources are computationally expensive. We present a scalable approach to radio pulsar detection written in Scala that parallelizes candidate identification to take advantage of in-memory task processing using Apache Spark on a YARN distributed system. Furthermore, we introduce a novel automated multiclass supervised machine learning technique that we combine with feature selection to reduce the time required for candidate classification. Experimental testing on a Beowulf cluster with 15 data nodes shows that the parallel implementation of the identification algorithm offers a speedup of up to 5X that of a similar multithreaded implementation. Further, we show that the combination of automated multiclass classification and feature selection speeds up the execution performance of the RandomForest machine learning algorithm by an average of 54% with less than a 2% average reduction in the algorithm's ability to correctly classify pulsars. The generalizability of these results is demonstrated by using two real-world radio astronomy data sets.
Invariant Measures for Dissipative Dynamical Systems: Abstract Results and Applications

In this work we study certain invariant measures that can be associated to the time averaged observation of a broad class of dissipative semigroups via the notion of a generalized Banach limit. Consider an arbitrary complete separable metric space X which is acted on by any continuous semigroup {S(t)}t ≥ 0. Suppose that {S(t)}t ≥ 0 possesses a global attractor \({\mathcal{A}}\). We show that, for any generalized Banach limit LIMT → ∞ and any probability distribution of initial conditions \({\mathfrak{m}_0}\), that there exists an invariant probability measure \({\mathfrak{m}}\), whose support is contained in \({\mathcal{A}}\), such that $$\int_{X} \varphi(x) {\rm d}\mathfrak{m}(x) = \underset{t \rightarrow \infty}{\rm LIM}\frac{1}{T} \int_0^T \int_X \varphi(S(t) x) {\rm d}\mathfrak{m}_0(x) {\rm d}t,$$ for all observables φ living in a suitable function space of continuous mappings on X.
Long-term Photometric Variability in Kepler Full-frame Images: Magnetic Cycles of Sun–like Stars

Photometry from the Kepler mission is optimized to detect small, short duration signals like planet transits at the expense of long-term trends. This long-term variability can be recovered in photometry from the Full Frame Images (FFIs), a set of calibration data collected approximately monthly during the Kepler mission. Here, we present f3, an open-source package to perform photometry on the Kepler FFIs in order to detect changes in the brightness of stars in the Kepler field of view over long time baselines. We apply this package to a sample of 4,000 Sun-like stars with measured rotation periods. We find $\approx 10\%$ of these targets have long-term variability in their observed flux. For the majority of targets we identify the luminosity variations are either correlated or anticorrelated with the short-term variability due to starspots on the stellar surface. We find a transition between anticorrelated (starspot-dominated) variability and correlated (facula-dominated) variability between rotation periods of 15 and 25 days, suggesting the transition between the two modes is complete for stars at the age of the Sun. We also identify a sample of stars with apparently complete cycles, as well as a collection of short-period binaries with extreme photometric variation over the Kepler mission.
Triggered Star Formation in Galaxy Pairs at z = 0.08-0.38

We measure the strength, frequency, and timescale of tidally triggered star formation at redshift z = 0.08-0.38 in a spectroscopically complete sample of galaxy pairs drawn from the magnitude-limited redshift survey of 9825 Smithsonian Hectospec Lensing Survey galaxies with R 1/5) pair galaxies with MR < ?20.8 in the redshift range z = 0.08-0.31. The size and completeness of the spectroscopic survey allow us to focus on regions of low local density. The spectrophotometric calibration enables the use of the 4000 ? break (Dn 4000), the H? specific star formation rate (SSFRH?), and population models to characterize the galaxies. We show that Dn 4000 is a useful population classification tool; it closely tracks the identification of emission line galaxies. The sample of major pair galaxies in regions of low local density with low Dn 4000 demonstrates the expected anti-correlation between pairwise projected separation and a set of star formation indicators explored in previous studies. We measure the frequency of triggered star formation by comparing the SSFRH? in the volume-limited sample in regions of low local density: 32% ? 7% of the major pair galaxies have SSFRH? at least double the median rate of the unpaired field galaxies. Comparison of stellar population models for pair and for unpaired field galaxies implies a timescale for triggered star formation of ~300-400?Myr.
Many, many more intrinsically knotted graphs

We list more than 200 new examples of minor minimal intrinsically knotted graphs and describe many more that are intrinsically knotted and likely minor minimal.
Deuterated silicon nitride photonic devices for broadband optical frequency comb generation.

We report and characterize low-temperature, plasma-deposited deuterated silicon nitride films for nonlinear integrated photonics. With a peak processing temperature less than 300°C, it is back-end compatible with complementary metal-oxide semiconductor substrates. We achieve microresonators with a quality factor of up to 1.6×106 at 1552 nm and >1.2×106 throughout λ=1510–1600 nm, without annealing or stress management (film thickness of 920 nm). We then demonstrate the immediate utility of this platform in nonlinear photonics by generating a 1 THz free-spectral-range, 900 nm bandwidth modulation-instability microresonator Kerr comb and octave-spanning, supercontinuum-broadened spectra.