cssi/SciSciGPT-SciSciNet huggingface dataset:
Symmetric peaks and symmetric valleys in Dyck paths

Abstract The notion of symmetric and asymmetric peaks in Dyck paths was introduced by Florez and Rodriguez, who counted the total number of such peaks over all Dyck paths of a given length. In this paper we generalize their results by giving multivariate generating functions that keep track of the number of symmetric peaks and the number of asymmetric peaks, as well as the widths of these peaks. We recover a formula of Denise and Simion as a special case of our results. We also consider the analogous but more intricate notion of symmetric valleys. We find a continued fraction expression for the generating function of Dyck paths with respect to the number of symmetric valleys and the sum of their widths, which provides an unexpected connection between symmetric valleys and statistics on ordered rooted trees. Finally, we enumerate Dyck paths whose peak or valley heights satisfy certain monotonicity and unimodality conditions, using a common framework to recover some known results, and relating our questions to the enumeration of certain classes of column-convex polyominoes.
On the computational complexity of finding a sparse Wasserstein barycenter

The discrete Wasserstein barycenter problem is a minimum-cost mass transport problem for a set of probability measures with finite support. In this paper, we show that finding a barycenter of sparse support is hard, even in dimension 2 and for only 3 measures. We prove this claim by showing that a special case of an intimately related decision problem SCMP—does there exist a measure with a non-mass-splitting transport cost and support size below prescribed bounds? Is NP-hard for all rational data. Our proof is based on a reduction from planar 3-dimensional matching and follows a strategy laid out by Spieksma and Woeginger (Eur J Oper Res 91:611–618, 1996) for a reduction to planar, minimum circumference 3-dimensional matching. While we closely mirror the actual steps of their proof, the arguments themselves differ fundamentally due to the complex nature of the discrete barycenter problem. Containment of SCMP in NP will remain open. We prove that, for a given measure, sparsity and cost of an optimal transport to a set of measures can be verified in polynomial time in the size of a bit encoding of the measure. However, the encoding size of a barycenter may be exponential in the encoding size of the underlying measures.
The Effect of Civilian Casualties on Wartime Informing: Evidence from the Iraq War:

Scholars of civil war and insurgency have long posited that insurgent organizations and their state enemies incur costs for the collateral damage they cause. We provide the first direct quantitativ...
Simple production of cellulose nanofibril microcapsules and the rheology of their suspensions

Microcapsules are commonly used in applications ranging from therapeutics to personal care products due to their ability to deliver encapsulated species through their porous shells. Here, we demonstrate a simple and scalable approach to fabricate microcapsules with porous shells by interfacial complexation of cellulose nanofibrils and oleylamine, and investigate the rheological properties of suspensions of the resulting microcapsules. The suspensions of neat capsules are viscous liquids whose viscosity increases with volume fraction according to a modified Kreiger-Dougherty relation with a maximum packing fraction of 0.74 and an intrinsic viscosity of 4.1. When polyacrylic acid (PAA) is added to the internal phase of the microcapsules, however, the suspensions become elastic and display yield stresses with power-law dependencies on capsule volume fraction and PAA concentration. The elasticity appears to originate from associative microcapsule interactions induced by PAA that is contained within and incorporated into the microcapsule shell. These results demonstrate that it is possible to tune the rheological properties of microcapsule suspensions by changing only the composition of the internal phase, thereby providing a novel method to tailor complex fluid rheology.
LocusZoom.js: Interactive and embeddable visualization of genetic association study results.

LocusZoom.js is a JavaScript library for creating interactive web-based visualizations of genetic association study results. It can display one or more traits in the context of relevant biological data (such as gene models and other genomic annotation), and allows interactive refinement of analysis models (by selecting linkage disequilibrium reference panels, identifying sets of likely causal variants, or comparisons to the GWAS catalog). It can be embedded in web pages to enable data sharing and exploration. Views can be customized and extended to display other data types such as phenome-wide association study (PheWAS) results, chromatin co-accessibility, or eQTL measurements. A new web upload service harmonizes datasets, adds annotations, and makes it easy to explore user-provided result sets. Availability LocusZoom.js is open-source software under a permissive MIT license. Code and documentation are available at: https://github.com/statgen/locuszoom/. Installable packages for all versions are also distributed via NPM. Additional features are provided as standalone libraries to promote reuse. Use with your own GWAS results at https://my.locuszoom.org/. Supplementary information Supplementary data are available at Bioinformatics online.
A Theoretical Computer Science Perspective on Consciousness

The quest to understand consciousness, once the purview of philosophers and theologians, is now actively pursued by scientists of many stripes. This paper studies consciousness from the perspective...
S0-2 Star, G1- and G2-objects, and Flaring Activity of the Milky Way's Galactic Center Black Hole in 2019

In 2019, the Galactic center black hole Sgr A* produced an unusually high number of bright near-infrared flares, including the brightest-ever detected flare (Do et al 2019, Gravity Collaboration 2020). We propose that this activity was triggered by the near simultaneous infall of material shed by G1 and G2 objects due to their interaction with the background accretion flow. We discuss mechanisms by which S-stars and G-objects shed material, and estimate both the quantity of material and the infall time to reach the black hole.
Characterization of suspended membrane waveguides towards a photonic atom trap integrated platform

We demonstrate an optical waveguide device, capable of supporting the high, in-vacuum, optical power necessary for trapping a single atom or a cold atom ensemble with evanescent fields. Our photonic integrated platform, with suspended membrane waveguides, successfully manages optical powers of 6 mW (500 μm span) to nearly 30 mW (125 μm span) over an un-tethered waveguide span. This platform is compatible with laser cooling and magneto-optical traps (MOTs) in the vicinity of the suspended waveguide, called the membrane MOT and the needle MOT, a key ingredient for efficient trap loading. We evaluate two novel designs that explore critical thermal management features that enable this large power handling. This work represents a significant step toward an integrated platform for coupling neutral atom quantum systems to photonic and electronic integrated circuits on silicon.
A new family of weighted one-parameter flux reconstruction schemes

Abstract The flux reconstruction (FR) approach offers a flexible framework for describing a range of high-order numerical schemes; including nodal discontinuous Galerkin and spectral difference schemes. This is accomplished through the use of so-called correction functions. In this study we employ a weighted Sobolev norm to define a new extended family of FR correction functions, the stability of which is affirmed through Fourier analysis. Several of the schemes within this family are found to exhibit reduced dissipation and dispersion overshoot. Moreover, many of the new schemes possess higher CFL limits whilst maintaining the expected rate of convergence. Numerical experiments with homogeneous linear convection and Burgers’ turbulence are undertaken, and the results observed to be in agreement with the theoretical findings.
Do Financing Constraints Lead to Incremental Tax Planning? Evidence from the Pension Protection Act of 2006*

Over the last three decades, academic research has sought to understand how cash shortfalls impact a firm’s ability to take all available value-increasing investment projects. We investigate whether firms facing greater financing constraints turn to tax strategies that generate lower cash effective tax rates (ETRs) to mitigate the adverse effect of these financing constraints. We use the Pension Protection Act of 2006 (PPA 2006) as an exogenous shock to financing constraints for pension firms, but not for other firms. Using a difference-in-difference research design, we predict and find that pension firms experience a decrease in their cash effective tax rates by 1.8 to 2.4 percentage points after the PPA 2006, relative to other firms. These cash tax savings mitigate the investment shortfall brought about by financing constraints by 19 percent. We also predict and find that the decline in cash ETRs is greater among firms more adversely affected by the PPA 2006. Our paper sheds light on the direction, causality, and economic magnitude of the association between financing constraints and tax planning activities. We also provide insight into the role of tax planning activities within firms’ broader corporate business strategies in responding to financing constraints.
Detection and Evaluation of Human and Machine Generated Speech in Spoofing Attacks on Automatic Speaker Verification Systems

Automatic speaker verification (ASV) systems utilize the biometric information in human speech to verify the speaker’s identity. The techniques used for performing speaker verification are often vulnerable to malicious attacks that attempt to induce the ASV system to return wrong results, allowing an impostor to bypass the system and gain access. Attackers use a multitude of spoofing techniques for this, such as voice conversion, audio replay, speech synthesis, etc. In recent years, easily available tools to generate deepfaked audio have increased the potential threat to ASV systems. In this paper, we compare the potential of human impersonation (voice disguise) based attacks with attacks based on machinegenerated speech, on black-box and white-box ASV systems. We also study countermeasures by using features that capture the unique aspects of human speech production, under the hypothesis that machines cannot emulate many of the finelevel intricacies of the human speech production mechanism. We show that fundamental frequency sequence-related entropy, spectral envelope, and aperiodic parameters are promising candidates for robust detection of deepfaked speech generated by unknown methods.
Quantitative combinatorial geometry for concave functions

Abstract We prove several exact quantitative versions of Helly's and Tverberg's theorems, which guarantee that a finite family of convex sets in R d has a large intersection. Our results characterize conditions that are sufficient for the intersection of a family of convex sets to contain a “witness set” which is large under some concave or log-concave measure. The possible witness sets include ellipsoids, zonotopes, and H-convex sets. Our results show that several new optimization problems can be solved with algorithms for LP-type problems. We obtain colorful and fractional variants of all our Helly-type theorems.
Proof-of-Execution: Reaching Consensus through Fault-Tolerant Speculation.

Multi-party data management and blockchain systems require data sharing among participants. To provide resilient and consistent data sharing, transactions engines rely on Byzantine FaultTolerant consensus (BFT), which enables operations during failures and malicious behavior. Unfortunately, existing BFT protocols are unsuitable for high-throughput applications due to their high computational costs, high communication costs, high client latencies, and/or reliance on twin-paths and non-faulty clients. In this paper, we present the Proof-of-Execution consensus protocol (PoE) that alleviates these challenges. At the core of PoE are out-of-order processing and speculative execution, which allow PoE to execute transactions before consensus is reached among the replicas. With these techniques, PoE manages to reduce the costs of BFT in normal cases, while guaranteeing reliable consensus for clients in all cases. We envision the use of PoE in high-throughput multi-party data-management and blockchain systems. To validate this vision, we implement PoE in our efficient ResilientDB fabric and extensively evaluate PoE against several state-of-the-art BFT protocols. Our evaluation showcases that PoE achieves up-to-80% higher throughputs than existing BFT protocols in the presence of failures.
Rates of convergence to equilibrium for potlatch and smoothing processes

We analyze the local and global smoothing rates of the smoothing process and obtain convergence rates to stationarity for the dual process known as the potlatch process. For general finite graphs we connect the smoothing and convergence rates to the spectral gap of the associated Markov chain. We perform a more detailed analysis of these processes on the torus. Polynomial corrections to the smoothing rates are obtained. They show that local smoothing happens faster than global smoothing. These polynomial rates translate to rates of convergence to stationarity in L2-Wasserstein distance for the potlatch process on Zd.
Bounded Point Derivations and Functions of Bounded Mean Oscillation

Let X be a compact subset of the complex plane with the property that every relatively open subset of X has positive area and let $$A_0(X)$$ denote the space of VMO functions that are analytic on X. $$A_0(X)$$ is said to admit a bounded point derivation of order t at a point $$x_0 \in \partial X$$ if there exists a constant C such that $$|f^{(t)}(x_0)|\le C \Vert f\Vert _{{\text {BMO}}}$$ for all functions in $${\text {VMO}}(X)$$ that are analytic on X. In this paper, we give necessary and sufficient conditions in terms of lower 1-dimensional Hausdorff content for $$A_0(X)$$ to admit a bounded point derivation at $$x_0$$ . These conditions are similar to conditions for the existence of bounded point derivations on other functions spaces.
A drift kinetic model for the expander region of a magnetic mirror

We present a drift kinetic model for the free expansion of a thermal plasma out of a magnetic nozzle. This problem relates to plasma space propulsion systems, natural environments such as the solar wind, and end losses from the expander region of mirror magnetically confined fusion concepts such as the gas dynamic trap. The model incorporates trapped and passing orbit types encountered in the mirror expander geometry and maps to an upstream thermal distribution. This boundary condition and quasineutrality require the generation of an ambipolar potential drop of ∼ 5 T e / e, forming a thermal barrier for the electrons. The model for the electron and ion velocity distributions and fluid moments is confirmed with data from a fully kinetic simulation. Finally, the model is extended to account for a population of fast sloshing ions arising from neutral beam heating within a magnetic mirror, again resulting in good agreement with a corresponding kinetic simulation.
Memoryless Cumulative Sign Detector for Stealthy CPS Sensor Attacks

Abstract Stealthy false data injection attacks on cyber-physical systems introduce erroneous measurements onto sensors with the intent to degrade system performance. An intelligent attacker can design stealthy attacks with knowledge of the system model and noise characteristics to evade detection from state-of-the-art fault detectors by remaining within detection thresholds. However, during these hidden attacks, an attacker with the intention of hijacking a system will leave traces of non-random behavior that contradict with the expectation of the system model. Given these premises, in this paper we propose a run-time monitor called Cumulative Sign (CUSIGN) detector, for identifying stealthy falsified measurements by identifying if measurements are no longer behaving in a random manner. Specifically, our proposed CUSIGN monitor considers the changes in sign of the measurement residuals and their expected occurrence in order to detect if a sensor could be compromised. Moreover, our detector is designed to be a memoryless procedure, eliminating the need to store large sequences of data for attack detection. We characterize the detection capabilities of the proposed CUSIGN technique following the well-known X2 failure detection scheme. Additionally, we show the advantage of augmenting CUSIGN to the model-based Cumulative Sum (CUSUM) detector, which provides magnitude bounds on attacks, for enhanced detection of sensor spoofing attacks. Our approach is validated with simulations on an unmanned ground vehicle (UGV) during a navigation case study.
Generalized Single Index Models and Jensen Effects on Reproduction and Survival

Environmental variability often has substantial impacts on natural populations and communities through its effects on the performance of individuals. Because organisms’ responses to environmental conditions are often nonlinear (e.g., decreasing performance on both sides of an optimal temperature), the mean response is often different from the response in the mean environment. Ye et al. (Ann Appl Stat 14(3):1326–12341, 2020) proposed testing for the presence of such variance effects on individual or population growth rates by estimating the “Jensen Effect”, the difference in average growth rates under varying versus fixed environments, in functional single index models for environmental effects on growth. In this paper, we extend this analysis to effects of environmental variance on reproduction and survival, which have count and binary outcomes. In the standard generalized linear models used to analyze such data the direction of the Jensen Effect is tacitly assumed a priori by the model’s link function. Here we extend the methods of Ye et al. (2020) using a generalized single index model to test whether this assumed direction is contradicted by the data. We show that our test has reasonable power under mild alternatives, but requires sample sizes that are larger than are often available. We demonstrate our methods on a long-term time series of plant ground cover in Idaho sagebrush steppe.
Similar masking effects of natural backgrounds on detection performances in humans, macaques, and macaque-V1 population responses.

We measured macaque detection performance in natural images and compared their performance to the detection sensitivity of neurophysiological responses recorded in the primary visual cortex (V1), a...
Quick Streaming Algorithms for Maximization of Monotone Submodular Functions in Linear Time

We consider the problem of monotone, submodular maximization over a ground set of size $n$ subject to cardinality constraint $k$. For this problem, we introduce the first deterministic algorithms with linear time complexity; these algorithms are streaming algorithms. Our single-pass algorithm obtains a constant ratio in $\lceil n / c \rceil + c$ oracle queries, for any $c \ge 1$. In addition, we propose a deterministic, multi-pass streaming algorithm with a constant number of passes that achieves nearly the optimal ratio with linear query and time complexities. We prove a lower bound that implies no constant-factor approximation exists using $o(n)$ queries, even if queries to infeasible sets are allowed. An empirical analysis demonstrates that our algorithms require fewer queries (often substantially less than $n$) yet still achieve better objective value than the current state-of-the-art algorithms, including single-pass, multi-pass, and non-streaming algorithms.
Outreach Strategies for Vaccine Distribution: A Multi-period Stochastic Modeling Approach

Vaccination has been proven to be the most effective method to prevent infectious diseases. However, in many low- and middle-income countries with geographically dispersed and nomadic populations, last-mile vaccine delivery can be extremely complex. Because newborns in remote population centers often do not have direct access to clinics and hospitals, they face significant risk from diseases and infections. An approach known as outreach is typically utilized to raise immunization rates in these situations. A set of these remote locations is chosen, and over an appropriate planning period, teams of clinicians and support personnel are sent from a depot to set up mobile clinics at these locations to vaccinate people there and in the immediate surrounding area. In this paper, we model the problem of optimally designing outreach efforts as a mixed integer program that is a combination of a set covering problem and a vehicle routing problem. In addition, because elements relevant to outreach (such as populations and road conditions) are often unstable and unpredictable, we address uncertainty and determine the worst-case solutions. This is done using a multi-period stochastic modeling approach that considers updated model parameter estimates and revised plans for subsequent planning periods. We also conduct numerical experiments to provide insights on how demographic characteristics affect outreach planning and where outreach planners should focus their attention when gathering data.
Two-Stage Adaptive Pooling with RT-QPCR for Covid-19 Screening

We propose two-stage adaptive pooling schemes, 2-STAP and 2-STAMP, for detecting COVID-19 using real-time reverse transcription quantitative polymerase chain reaction (RT-qPCR) test kits. Similar to the Tapestry scheme of Ghosh et al., the proposed schemes leverage soft information from the RT-qPCR process about the total viral load in the pool. This is in contrast to conventional group testing schemes where the measurements are Boolean. The proposed schemes provide higher testing throughput than the popularly used Dorfman’s scheme. They also provide higher testing throughput, sensitivity and specificity than the state-of-the-art non-adaptive Tapestry scheme. The number of pipetting operations is lower than the Tapestry scheme, and is higher than that for the Dorfman’s scheme. The proposed schemes can work with substantially smaller group sizes than non-adaptive schemes and are simple to describe. Monte-Carlo simulations using the statistical model in the work of Ghosh et al. (Tapestry) show that 10 infected people in a population of size 961 can be identified with 70.86 tests on the average with a sensitivity of 99.50% and specificity of 99.62%. This is 13.5x, 4.24x, and 1.3x the testing throughput of individual testing, Dorfman’s testing, and the Tapestry scheme, respectively.
Audio-Visual Event Recognition Through the Lens of Adversary

As audio/visual classification models are widely deployed for sensitive tasks like content filtering at scale, it is critical to understand their robustness along with improving the accuracy. This work aims to study several key questions related to multimodal learning through the lens of adversarial noises: 1) The trade-off between early/middle/late fusion affecting its robustness and accuracy 2) How does different frequency/time domain features contribute to the robustness? 3) How does different neural modules contribute against the adversarial noise? In our experiment, we construct adversarial examples to attack state-of-the-art neural models trained on Google AudioSet.[1]1 We compare how much attack potency in terms of adversarial perturbation of size using different L p norms we would need to "deactivate" the victim model. Using adversarial noise to ablate multimodal models, we are able to provide insights into what is the best potential fusion strategy to balance the model parameters/accuracy and robustness trade-off, and distinguish the robust features versus the non-robust features that various neural networks model tend to learn.
Should information be sold separately? Evidence from MiFID II

Abstract Information production is key to the efficiency of financial markets. Does selling information separately improve its production? We investigate this question using MiFID II, a European regulation that unbundles research from transactions. We show that unbundling causes fewer research analysts to cover a firm. This decrease does not come from small- or mid-cap firms but is concentrated in large firms. Surprisingly, the reduction in analyst coverage is accompanied by a decrease in forecast error. Further analyses suggest that analyst competition enhancement could drive the results: inaccurate analysts drop out, and analysts who stay produce more accurate research.
Double-Janus linear sigma models and generalized reciprocity for Gauss sums

We study the supersymmetric partition function of a 2d linear σ-model whose target space is a torus with a complex structure that varies along one worldsheet direction and a Kahler modulus that varies along the other. This setup is inspired by the dimensional reduction of a Janus configuration of 4d $$ \mathcal{N} $$ = 4 U(1) Super-Yang-Mills theory compactified on a mapping torus (T2 fibered over S1) times a circle with an SL(2, ℤ) duality wall inserted on S1, but our setup has minimal supersymmetry. The partition function depends on two independent elements of SL(2, ℤ), one describing the duality twist, and the other describing the geometry of the mapping torus. It is topological and can be written as a multivariate quadratic Gauss sum. By calculating the partition function in two different ways, we obtain identities relating different quadratic Gauss sums, generalizing the Landsberg-Schaar relation. These identities are a subset of a collection of identities discovered by F. Deloup. Each identity contains a phase which is an eighth root of unity, and we show how it arises as a Berry phase in the supersymmetric Janus-like configuration. Supersymmetry requires the complex structure to vary along a semicircle in the upper half-plane, as shown by Gaiotto and Witten in a related context, and that semicircle plays an important role in reproducing the correct Berry phase.
Discount Schemes for the Preemptible Service of a Cloud Platform with Unutilized Capacity

Rapid growth in the cloud industry not only provides tremendous opportunities to cloud providers who have invested heavily in computing capacities, but also leads to low utilization of capacities a...
A Pipeline for Vision-Based On-Orbit Proximity Operations Using Deep Learning and Synthetic Imagery

Deep learning has become the gold standard for image processing over the past decade. Simultaneously, we have seen growing interest in orbital activities such as satellite servicing and debris removal that depend on proximity operations between spacecraft. However, two key challenges currently pose a major barrier to the use of deep learning for vision-based on-orbit proximity operations. Firstly, efficient implementation of these techniques relies on an effective system for model development that streamlines data curation, training, and evaluation. Secondly, a scarcity of labeled training data (images of a target spacecraft) hinders creation of robust deep learning models. This paper presents an open-source deep learning pipeline, developed specifically for on-orbit visual navigation applications, that addresses these challenges. The core of our work consists of two custom software tools built on top of a cloud architecture that interconnects all stages of the model development process. The first tool leverages Blender, an open-source 3D graphics toolset, to generate labeled synthetic training data with configurable model poses (positions and orientations), lighting conditions, backgrounds, and commonly observed in-space image aberrations. The second tool is a plugin-based framework for effective dataset curation and model training; it provides common functionality like metadata generation and remote storage access to all projects while giving complete independence to project-specific code. Time-consuming, graphics-intensive processes such as synthetic image generation and model training run on cloud-based computational resources which scale to any scope and budget and allow development of even the largest datasets and models from any machine. The presented system has been used in the Texas Spacecraft Laboratory with marked benefits in development speed and quality. Remote development, scalable compute, and automatic organization of data and artifacts have dramatically decreased iteration time while increasing reproducibility and system comprehension. Diverse, high-fidelity synthetic images that more closely replicate the real environment have improved model performance against real-world data. These results demonstrate that the presented pipeline offers tangible benefits to the application of deep learning for vision-based on-orbit proximity operations.
Examining Passenger Vehicle Miles Traveled and Carbon Emissions in the Boston Metropolitan Area

With spatial analytic, econometric, and visualization tools, this book chapter investigates greenhouse gas emissions for the on-road passenger vehicle transport sector in the Boston metropolitan area in 2014. It compares greenhouse gas emission estimations from both the production-based and consumption-based perspectives with two large-scale administrative datasets—the vehicle odometer readings (from individual vehicle annual inspection) and the road inventory data (containing road-segment level geospatial and traffic information). Based on spatial econometric models that examine socioeconomic and built environment factors contributing to the vehicle miles traveled (VMT) at the census tract level, it offers insights to help cities reduce VMT and carbon footprint for passenger vehicle travel. Finally, it recommends a pathway for cities and towns in the Boston metropolitan area to curb VMT and mitigate carbon emissions to achieve climate goals of carbon neutrality.
Tracking the Evolution of Magmas from Heterogeneous Mantle Sources to Eruption

This contribution reviews the effects of source heterogeneities, melt-rock reactions and intracrustal differentiation on magma chemistry across mid-ocean ridges, intraplate settings and subduction zones using experimental studies and natural data. We compare melting behaviors of pyroxenites and peridotites and their relative contributions to magmas as functions of composition, mantle potential temperatures and lithospheric thickness. We also discuss the fate of chemically distinct melts derived from heterogeneities as they travel through a peridotitic mantle. Using nearly 60,000 natural major element compositions of volcanic rocks, melt inclusions, and crystalline cumulates, we assess broad petrogenetic trends in as large of a global dataset as possible. Consistent with previous studies, major element chemistry of mid-ocean ridge basalts (MORBs) and their cumulates favor a first-order control of intracrustal crystal-liquid segregation, while trace element studies emphasize the role of melt-rock reactions, highlighting the decoupling between the two. Ocean island basalts (OIB) show a larger compositional variability than MORB, partly attributed to large variations of pyroxenite proportions in the mantle source. However, the estimated proportions vary considerably with heterogeneity composition, melting model and thermal structure of the mantle. For arcs, we highlight current views on the role of the downgoing slab into the source of primary arc magmas, and the role of the overriding lithosphere as a magmatic chemical filter and as the repository of voluminous arc cumulates. Our approach of simultaneously looking at a large database of volcanic + deep crustal rocks across diverse tectonic settings underscores the challenge of deciphering the source signal versus intracrustal/lithospheric processes.
Termination analysis without the tears

Determining whether a given program terminates is the quintessential undecidable problem. Algorithms for termination analysis may be classified into two groups: (1) algorithms with strong behavioral guarantees that work in limited circumstances (e.g., complete synthesis of linear ranking functions for polyhedral loops), and (2) algorithms that are widely applicable, but have weak behavioral guarantees (e.g., Terminator). This paper investigates the space in between: how can we design practical termination analyzers with useful behavioral guarantees? This paper presents a termination analysis that is both compositional (the result of analyzing a composite program is a function of the analysis results of its components) and monotone (“more information into the analysis yields more information out”). The paper has two key contributions. The first is an extension of Tarjan’s method for solving path problems in graphs to solve infinite path problems. This provides a foundation upon which to build compositional termination analyses. The second is a collection of monotone conditional termination analyses based on this framework. We demonstrate that our tool ComPACT (Compositional and Predictable Analysis for Conditional Termination) is competitive with state-of-the-art termination tools while providing stronger behavioral guarantees.
Tones Encountered with a Coannular Nozzle and a Method for Their Suppression

With multistream coannular exhaust flows, sometimes tones occur that may fail the nozzle hardware meeting noise regulation standards. A two-stream nozzle was studied experimentally and numerically ...
Fractional Parts Of Non-Integer Powers Of Primes. II

We continue to study the distribution of prime numbers $p$, satisfying the condition $\{ p^{\alpha} \} \in I \subset [0; 1)$, in arithmetic progressions. In the paper, we prove an analogue of Bombieri-Vinogradov theorem for $0 < \alpha < 1/9$ with the level of distribution $\theta = 2/5 - (3/5) \alpha$, which improves the previous result corresponding to $\theta \leq 1/3$.
A Survey on Silicon Photonics for Deep Learning

Deep learning has led to unprecedented successes in solving some very difficult problems in domains such as computer vision, natural language processing, and general pattern recognition. These achi...
Efficiency Assessment of a Residential DC Nanogrid with Low and High Distribution Voltages Using Realistic Data

Direct Current (DC) power distribution has gained attention in the Residential Nanogrids (RNGs) due to the substantial increase in the number of roof-top Photovoltaic (PV) systems and internally DC appliances used in buildings. Using DC distribution improves the efficiency of the RNGs compared to AC distribution. This paper investigated the efficiency of a DC RNG for low and high distribution voltage levels by exploring reasons for power losses. The studied DC RNG consisted of various types of local loads, on-site PV generation, and battery storage systems. The realistic load, PV profiles, and converter efficiency curves were used to make the analysis more accurate. In addition, three load profiles with low, medium, and high power consumptions were considered to study the load impacts on the overall system efficiency.
Recreational cannabis legalization and alcohol purchasing: a difference-in-differences analysis.

Whether recreational cannabis legalization is associated with changes in alcohol consumption (suggesting a potential substitution or complementary relationship) is a key question as cannabis policy evolves, particularly given the adverse health and social effects of alcohol use. Relatively little research has explored this question. This study examined the association between recreational cannabis legalization and alcohol purchasing in the USA using an interrupted time series design. We used data from the Nielsen Consumer Panel (2004–2017) from 69,761 households in all 50 states to calculate monthly milliliters of pure ethanol purchased for four beverage categories (beer, wine, spirits, and all alcohol products). We used difference-in-differences models and robust cluster standard errors to compare changes in milliliters of pure ethanol purchased. We fit models for each beverage category, comparing three “policy” states that have legalized recreational cannabis (Colorado, Oregon, and Washington) to states that had not legalized recreational cannabis. In one set of models, a single control state was selected that matched pre-policy purchasing trends in the policy states. In another set, policy states were compared to all states that had not legalized recreational cannabis. Compared to all other states that did not legalize recreational cannabis, Colorado households showed a 13% average monthly decrease in purchases of all alcoholic products combined (estimate, 0.87; CI, 0.77, 0.98) and a 6% decrease in wine (0.94; CI, 0.89, 0.99). Estimates in Washington were suggestive of an increase in spirits purchased in both the unrestricted (1.24; CI, 1.12, 1.37) and restricted sample (1.18; CI, 1.02, 1.36). Oregon showed a significant decrease in monthly spirits purchased when compared to its selected comparator state (0.87; CI, 0.77, 0.99) and to all other states without legalized recreational cannabis (0.85; CI, 0.77, 0.95). Results suggest that alcohol and cannabis are not clearly substitutes nor complements to one-another. Future studies should examine additional states as more time passes and more post-legalization data becomes available, use cannabis purchase data and consider additional methods for control selection in quasi-experimental studies.
Stacking-sequence optimization for buckling of laminated plates by integer programming

Integer-programming formulations for the design of symmetric and balanced laminated plates under biaxial compression are presented. Both maximization of buckling load for a given total thickness and the minimization of total thickness subject to a buckling constraint are formulated. The design variables that define the stacking sequence of the laminate are zero-one integers. It is shown that the formulation results in a linear optimization problem that can be solved on readily available software. This is in contrast to the continuous case, where the design variables are the thicknesses of layers with specified ply orientations, and the optimization problem is nonlinear. Constraints on the stacking sequence such as a limit on the number of contiguous plies of the same orientation and limits on in-plane stiffnesses are easily accommodated. Examples are presented for graphite-epoxy plates under uniaxial and biaxial compression using a commercial software package based on the branch-and-bound algorithm.
Modified kernel MLAA using autoencoder for PET-enabled dual-energy CT.

Combined use of PET and dual-energy CT provides complementary information for multi-parametric imaging. PET-enabled dual-energy CT combines a low-energy X-ray CT image with a high-energy -ray CT (G...
Measurement of Visual Reaction Times using Hand-held Mobile Devices

Modern mobile devices provide a convenient platform for collecting research data in the field. But,because the working of these devices is often cloaked behind multiple layers of proprietary system software, it can bedifficult to assess the accuracy of the data they produce, particularly in the case of timing. We have been collecting datain a simple visual reaction time experiment, as part of a fatigue testing protocol known as the Psychomotor Vigilance Test (PVT). In this protocol, subjects run a 5-minute block consisting of a sequence of trials in which a visual stimulus appears after an unpredictable variable delay. The subject is required to tap the screen as soon as possible after the appearance of the stimulus. In order to validate the reaction times reported by our program, we had subjects perform the task while a high-speed video camera recorded both the display screen, and a side view of the finger (observed in a mirror). Simple image-processing methods were applied to determine the frames in which the stimulus appeared and disappeared, and in which the finger made and broke contact with the screen. The results demonstrate a systematic delay between the initial contact by the finger and the detection of the touch by the software, having a value of 80 +- 20 milliseconds.
Reflections on Termination of Linear Loops

This paper shows how techniques for linear dynamical systems can be used to reason about the behavior of general loops. We present two main results. First, we show that every loop that can be expressed as a transition formula in linear integer arithmetic has a best model as a deterministic affine transition system. Second, we show that for any linear dynamical system f with integer eigenvalues and any integer arithmetic formula G, there is a linear integer arithmetic formula that holds exactly for the states of f for which G is eventually invariant. Combining the two, we develop a monotone conditional termination analysis for general loops.
Bayesian optimization for modular black-box systems with switching costs

Most existing black-box optimization methods assume that all variables in the system being optimized have equal cost and can change freely at each iteration. However, in many real-world systems, inputs are passed through a sequence of different operations or modules, making variables in earlier stages of processing more costly to update. Such structure induces a dynamic cost from switching variables in the early parts of a data processing pipeline. In this work, we propose a new algorithm for switch-cost-aware optimization called Lazy Modular Bayesian Optimization (LaMBO). This method efficiently identifies the global optimum while minimizing cost through a passive change of variables in early modules. The method is theoretically grounded which achieves a vanishing regret regularized with switching cost. We apply LaMBO to multiple synthetic functions and a three-stage image segmentation pipeline used in a neuroimaging task, where we obtain promising improvements over existing cost-aware Bayesian optimization algorithms. Our results demonstrate that LaMBO is an effective strategy for black-box optimization capable of minimizing switching costs.
Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective

In this work we study the problem of measuring the fairness of a machine learning model under noisy information. Focusing on group fairness metrics, we investigate the particular but common situation when the evaluation requires controlling for the confounding effect of covariate variables. In a practical setting, we might not be able to jointly observe the covariate and group information, and a standard workaround is to then use proxies for one or more of these variables. Prior works have demonstrated the challenges with using a proxy for sensitive attributes, and strong independence assumptions are needed to provide guarantees on the accuracy of the noisy estimates. In contrast, in this work we study using a proxy for the covariate variable and present a theoretical analysis that aims to characterize weaker conditions under which accurate fairness evaluation is possible. Furthermore, our theory identifies potential sources of errors and decouples them into two interpretable parts y and E. The first part y depends solely on the performance of the proxy such as precision and recall, whereas the second part E captures correlations between all the variables of interest. We show that in many scenarios the error in the estimates is dominated by y via a linear dependence, whereas the dependence on the correlations E only constitutes a lower order term. As a result we expand the understanding of scenarios where measuring model fairness via proxies can be an effective approach. Finally, we compare, via simulations, the theoretical upper-bounds to the distribution of simulated estimation errors and show that assuming some structure on the data, even weak, is key to significantly improve both theoretical guarantees and empirical results.
Piloted ignition delay of PMMA in space exploration atmospheres

Abstract In order to reduce the risk of decompression sickness associated with extra-vehicular activity (EVA), NASA is designing the next generation of exploration vehicles and habitats with a different cabin environment than used previously. The proposed environment uses a total cabin pressure of 52.7–58.6 kPa with an oxygen concentration of 30–34% by volume and was chosen with material flammability in mind. Because materials may burn differently under these conditions and there is little information on how this new environment affects the flammability of the materials onboard, it is important to conduct material flammability experiments at the intended exploration atmosphere. One method to evaluate material flammability is by its ease of ignition. To this end, piloted ignition delay tests were conducted in the Forced Ignition and Spread Test (FIST) apparatus subject to this new environment. In these tests, polymethylmethacylate (PMMA) was exposed to a range of oxidizer flow velocities and externally applied heat fluxes. Tests were conducted for a baseline case of normal pressure and oxygen concentration, low pressure (58.6 kPa) with normal oxygen (21%), and low pressure with 32% oxygen concentration conditions to determine the individual effect of pressure and the combined effect of pressure and oxygen concentration on the ignition delay. It was found that reducing the pressure while keeping the oxygen concentration at 21% reduced the ignition time by 17% on average. Increasing the oxygen concentration at low pressures reduced the ignition time by an additional 10%. It was also noted that the critical heat flux for ignition decreases at exploration atmospheres. These results show that tests conducted in standard atmospheric conditions will underpredict the ignition of materials intended for use on spacecraft and that, at these conditions, materials are more susceptible to ignition than at current spacecraft atmospheres.
Securing Mobile Multiuser Transmissions With UAVs in the Presence of Multiple Eavesdroppers

This paper analyzes the problem of securing the transmissions of multiple mobile users against eavesdropping attacks. We propose and optimize the deployment of a single unmanned aerial vehicle (UAV), which serves as an aerial relay (AR) between the user cluster and the base station. The focus is on maximizing the secrecy energy efficiency by jointly optimizing the uplink transmission powers of the ground users and the position of the UAV. The joint optimization problem is nonconvex; therefore we split it into two subproblems and solve them using an iterative algorithm.