cssi/SciSciGPT-SciSciNet huggingface dataset:
High-Fidelity Computational Aerodynamics of Multi-Rotor Unmanned Aerial Vehicles

High-fidelity Computational Fluid Dynamics (CFD) simulations have been carried out for several multi-rotor Unmanned Aerial Vehicles (UAVs). Three vehicles have been studied: the classic quadcopter DJI Phantom 3, an unconventional quadcopter specialized for forward flight, the SUI Endurance, and an innovative concept for Urban Air Mobility (UAM), the Elytron 4S UAV. The three-dimensional unsteady Navier-Stokes equations are solved on overset grids using high-order accurate schemes, dual-time stepping, and a hybrid turbulence model. The DJI Phantom 3 is simulated with different rotors and with both a simplified airframe and the real airframe including landing gear and a camera. The effects of weather are studied for the DJI Phantom 3 quadcopter in hover. The SUI En- durance original design is compared in forward flight to a new configuration conceived by the authors, the hybrid configuration, which gives a large improvement in forward thrust. The Elytron 4S UAV is simulated in helicopter mode and in airplane mode. Understanding the complex flows in multi-rotor vehicles will help design quieter, safer, and more efficient future drones and UAM vehicles.
A Combinatorial Design for Cascaded Coded Distributed Computing on General Networks

Coding theoretic approaches have been developed to significantly reduce the communication load in modern distributed computing system. In particular, coded distributed computing (CDC) introduced by Li et al. can efficiently trade computation resources to reduce the communication load in MapReduce like computing systems. For the more general cascaded CDC, Map computations are repeated at $r$ nodes to significantly reduce the communication load among nodes tasked with computing $Q$ Reduce functions $s$ times. In this paper, we propose a novel low-complexity combinatorial design for cascaded CDC which 1) determines both input file and output function assignments, 2) requires significantly less number of input files and output functions, and 3) operates on heterogeneous networks where nodes have varying storage and computing capabilities. We provide an analytical characterization of the computation-communication tradeoff, from which we show the proposed scheme can outperform the state-of-the-art scheme proposed by Li et al. for the homogeneous networks. Further, when the network is heterogeneous, we show that the performance of the proposed scheme can be better than its homogeneous counterpart. In addition, the proposed scheme is optimal within a constant factor of the information theoretic converse bound while fixing the input file and the output function assignments.
mTVR: Multilingual Moment Retrieval in Videos

We introduce mTVR, a large-scale multilingual video moment retrieval dataset, containing 218K English and Chinese queries from 21.8K TV show video clips. The dataset is collected by extending the popular TVR dataset (in English) with paired Chinese queries and subtitles. Compared to existing moment retrieval datasets, mTVR is multilingual, larger, and comes with diverse annotations. We further propose mXML, a multilingual moment retrieval model that learns and operates on data from both languages, via encoder parameter sharing and language neighborhood constraints. We demonstrate the effectiveness of mXML on the newly collected mTVR dataset, where mXML outperforms strong monolingual baselines while using fewer parameters. In addition, we also provide detailed dataset analyses and model ablations. Data and code are publicly available at https://github.com/jayleicn/mTVRetrieval
Acoustic Efficiency of Azimuthal Modes in Jet Noise Using Chevron Nozzles

The link between azimuthal modes in jet turbulence and in the acoustic sound field has been examined in cold, round jets. Chevron nozzles, however, impart an azimuthal structure on the jet with a shape dependent on the number, length and penetration angle of the chevrons. Two particular chevron nozzles, with 3 and 4 primary chevrons respectively, and a round baseline nozzle are compared at both cold and hot jet conditions to determine how chevrons impact the modal structure of the flow and how that change relates to the sound field. The results show that, although the chevrons have a large impact on the azimuthal shape of the mean axial velocity, the impact of chevrons on the azimuthal structure of the fluctuating axial velocity is small at the cold jet condition and smaller still at the hot jet condition. This is supported by results in the azimuthal structure of the sound field, which also shows little difference in between the two chevron nozzles and the baseline nozzle in the distribution of energy across the azimuthal modes measured.
SSN: Soft Shadow Network for Image Compositing

We introduce an interactive Soft Shadow Network (SSN) to generates controllable soft shadows for image compositing. SSN takes a 2D object mask as input and thus is agnostic to image types such as painting and vector art. An environment light map is used to control the shadow’s characteristics, such as angle and softness. SSN employs an Ambient Occlusion Prediction module to predict an intermediate ambient occlusion map, which can be further refined by the user to provides geometric cues to modulate the shadow generation. To train our model, we design an efficient pipeline to produce diverse soft shadow training data using 3D object models. In addition, we propose an inverse shadow map representation to improve model training. We demonstrate that our model produces realistic soft shadows in real-time. Our user studies show that the generated shadows are often indistinguishable from shadows calculated by a physics-based renderer and users can easily use SSN through an interactive application to generate specific shadow effects in minutes.
Enhancing System Flexibility through Corrective Demand Response in Security-Constrained Unit Commitment

Currently, system operators implement demand response by dispatching controllable loads for economic reasons in day-ahead scheduling. Particularly, demand shifting from peak hours when the cost of electricity is higher to non-peak hours to maintain system reliability by flattening the load profile. However, the system flexibility and economic benefits of such action in post-contingency scenarios are not explicitly considered in short-term operations. Hence, this paper highlights the benefits of demand response as a corrective action for potential post-contingency emergencies in day-ahead scheduling. A security-constrained unit commitment (SCUC) model which considers the flexibility offered through corrective demand response (CDR) to maintain system reliability when a line or generator outage occurs is proposed. The proposed model was tested on IEEE 24-bus system where simulation results point to significant total cost savings in daily operations. Moreover, the results point to better long-term reliability of generators along with the ability to use existing system flexibility and serve higher critical demands in base-case when CDR is implemented.
Simulating Many-Body Systems with a Projective Quantum Eigensolver

A reformulation of a variational quantum algorithm enables investigation of strongly correlated electronic systems with one to two orders of magnitude fewer quantum measurements than the state-of-the-art.
An ordinary state-based peridynamic elastoplastic 2D model consistent with J2 plasticity

Abstract A new ordinary state-based peridynamic model (OSB-PD) in 2D consistent with J2 plasticity using a novel decomposition for force and extension states is presented. A novel strategy for testing the consistency of an OSB-PD formulation for elastoplasticity is introduced. In contrast with other similar models, the new elasto-plastic OSB-PD model is objective and works for large rotations. Two rate-independent yield functions equivalent to J2 plasticity with associated flow rules are formulated based on force states and on the strain energy density. An efficient return mapping algorithm for peridynamic formulations in plasticity is proposed. The model is verified against results obtained with Abaqus for the classical formulation using two examples, one featuring plastic deformations under large rotations. The evolution of the plastic zone computed with the new model is found to match that from the classical model computed with Abaqus. With a simple damage model, a demonstrative plane stress example for shear localization and full rupture captures the same failure mechanism as observed experimentally: an initial almost symmetrical shear banding, evolves into a single, predominant shear band, followed by full rupture.
Model-based testing of networked applications

We present a principled automatic testing framework for application-layer protocols. The key innovation is a domain-specific embedded language for writing nondeterministic models of the behavior of networked servers. These models are defined within the Coq interactive theorem prover, supporting a smooth transition from testing to formal verification. Given a server model, we show how to automatically derive a tester that probes the server for unexpected behaviors. We address the uncertainties caused by both the server's internal choices and the network delaying messages nondeterministically. The derived tester accepts server implementations whose possible behaviors are a subset of those allowed by the nondeterministic model. We demonstrate the effectiveness of this framework by using it to specify and test a fragment of the HTTP/1.1 protocol, showing that the automatically derived tester can capture RFC violations in buggy server implementations, including the latest versions of Apache and Nginx.
Exponential Absolute Stability of Discrete Systems

Fur die absolute exponentielle Stabilitat nichtlinearer diskreter Systeme vom Lurje-Typ werden hinreichende Bedingungen formuliert. Durch einen Grenzprozes werden sie auf diejenigen fur die absolute exponentielle Stabilitat stetiger Systeme zuruckgefuhrt. Sufficient conditions are formulated under which nonlinear discrete systems of the Lur'e type are exponentially absolutely stable. By a limiting process the obtained conditions are reduced to those for exponential absolute stability of continuous systems.
Can Existing Theory Predict the Response of Tropical Cyclone Intensity to Idealized Landfall

Tropical cyclones cause significant inland hazards, including wind damage and freshwater flooding, that depend strongly on how storm intensity evolves at and after landfall. Existing theoretical predictions for the time-dependent and equilibrium response of storm intensity have been tested over the open ocean but not yet to be applied to storms after landfall. Recent work examined the transient response of the tropical cyclone low-level wind field to instantaneous surface roughening or drying in idealized axisymmetric f-plane simulations. Here, experiments testing combined surface roughening and drying with varying magnitudes of each are used to test theoretical predictions for the intensity response. The transient response to combined surface forcings can be reproduced by the product of their individual responses, in line with traditional potential intensity theory. Existing intensification theory is generalized to weakening and found capable of reproducing the time-dependent inland intensity decay. The initial (0-10min) rapid decay of near-surface wind caused by surface roughening is not captured by existing theory but can be reproduced by a simple frictional spin-down model, where the decay rate is a function of surface roughness. Finally, the theory is shown to compare well with the prevailing empirical decay model for real-world storms. Overall, results indicate the potential for existing theory to predict how tropical cyclone intensity evolves after landfall.
Coupling Right- and Left-Handed Photons Differently to Charged Matter

We consider a modification of electrodynamics in which rightand left-circularly polarized photons are coupled to charged sources differently. Even though photon helicity is a Lorentz invariant quantity, such a modification breaks Lorentz symmetry, as well as locality. The modified theory includes novel magnetic forces between perpendicular currents. Existing data can be used to constrain the modification at approximately a 2× 10−3 level. baltschu@physics.sc.edu
Influence of Diffusion and Convective Transport on Dendritic Growth in Dilute Alloys

Extensive experimentation has been carried out in which the kinetics and morphology of dendritic growth were measured as a function of thermal supercooling, solute concentration, and spatial orientation of the dendritic growth axis. The crystal growth system studied is succinonitrile [NC(CH2)2CN] with additions of argon (up to 0.1 mole %). This system is especially useful as a model for alloy studies because kinetic data are available for high purity (7–9's) succinonitrile. The addition of argon provides a simple, controllable dilute solute that now permits the first comparably detailed dendritic growth studies on binary alloys.One dramatic influence of the solute, at fixed thermal supercooling, is to increase the growth velocity (to a maximum) and correspondingly decrease intrinsic crystal dimensions (tip radius). Morphological measurements will be described in detail relating tip size, perturbation wavelength, supercooling, and solute concentration. The analysis of these effects based on morphological stability theory will also be discussed. Finally, experiments permitting the separation of convective and diffusive heat transport during crystal growth of succinonitrile will be described briefly. These studies clearly underscore the importance of gravitationally-induced buoyancy effects on crystal growth kinetics and morphology.
Learning to Generate Fair Clusters from Demonstrations

Fair clustering is the process of grouping similar entities together, while satisfying a mathematically well-defined fairness metric as a constraint. Due to the practical challenges in precise model specification, the prescribed fairness constraints are often incomplete and act as proxies to the intended fairness requirement. Clustering with proxies may lead to biased outcomes when the system is deployed. We examine how to identify the intended fairness constraint for a problem based on limited demonstrations from an expert. Each demonstration is a clustering over a subset of the data. We present an algorithm to identify the fairness metric from demonstrations and generate clusters using existing off-the-shelf clustering techniques, and analyze its theoretical properties. To extend our approach to novel fairness metrics for which clustering algorithms do not currently exist, we present a greedy method for clustering. Additionally, we investigate how to generate interpretable solutions using our approach. Empirical evaluation on three real-world datasets demonstrates the effectiveness of our approach in quickly identifying the underlying fairness and interpretability constraints, which are then used to generate fair and interpretable clusters.
Promise and Challenges of a Data-Driven Approach for Battery Lifetime Prognostics

Recent data-driven approaches have shown great potential in early prediction of battery cycle life by utilizing features from the discharge voltage curve. However, these studies caution that data-driven approaches must be combined with specific design of experiments in order to limit the range of aging conditions, since the expected life of Li-ion batteries is a complex function of various aging factors. In this work, we investigate the performance of the data-driven approach for battery lifetime prognostics with Li-ion batteries cycled under a variety of aging conditions, in order to determine when the data-driven approach can successfully be applied. Results show a correlation between the variance of the discharge capacity difference and the end-of-life for cells aged under a wide range of charge/discharge C-rates and operating temperatures. This holds despite the different conditions being used not only to cycle the batteries but also to obtain the features: the features are calculated directly from cycling data without separate slow characterization cycles at a controlled temperature. However, the correlation weakens considerably when the voltage data window for feature extraction is reduced, or when features from the charge voltage curve instead of discharge are used. As deep constant-current discharges rarely happen in practice, this imposes new challenges for applying this method in a realworld system.
Towards understanding the effect of leak in Spiking Neural Networks

Abstract Spiking Neural Networks (SNNs) are being explored to emulate the astounding capabilities of human brain that can learn to perform robust and efficient computations with noisy spikes. A variety of spiking neuron models have been proposed to resemble biological neuronal functionalities. The simplest and most commonly used among these SNNs are leaky-integrate-and-fire (LIF), which contain a leak path in their membrane potential and integrate-and-fire (IF), where the leakage path is absent. While the LIF models have been argued as more bio-plausible, a comparative analysis between models with and without leak from a purely computational point of view demands attention, which we try to address in this paper. Our results reveal that LIF model provides improved robustness and better generalization compared to IF. Frequency domain analysis demonstrates that leak aids in eliminating high-frequency components from the input, thus enhancing noise-robustness of SNNs. Additionally, we compare the sparsity of computation between these models. In general, for the same input, the LIF model would be expected to achieve higher sparsity compared to IF due to the layer-wise decay of spikes caused by membrane potential leak with time. However, contrary to this expectation, we observe that leak decreases the sparsity of computation. Therefore, there exists a trade-off between robustness and energy-efficiency in SNNs which can be optimized through suitable choice of amount of leak in the models.
Boost-R: Gradient boosted trees for recurrence data

Recurrence data arise from multi-disciplinary domains spanning reliability, cyber security, healthcare, online retailing, etc. This paper investigates an additive-tree-based approach, known as Boos...
Quantum Computing with Circular Rydberg Atoms

A proposal for preparing Rydberg arrays containing hundreds of circular Rydberg atoms and comparatively small two-qubit gate errors is presented, enabling significant improvement to quantum computing and simulation with neutral atoms.
Superior photo-carrier diffusion dynamics in organic-inorganic hybrid perovskites revealed by spatiotemporal conductivity imaging.

The outstanding performance of organic-inorganic metal trihalide solar cells benefits from the exceptional photo-physical properties of both electrons and holes in the material. Here, we directly probe the free-carrier dynamics in Cs-doped FAPbI3 thin films by spatiotemporal photoconductivity imaging. Using charge transport layers to selectively quench one type of carriers, we show that the two relaxation times on the order of 1 μs and 10 μs correspond to the lifetimes of electrons and holes in FACsPbI3, respectively. Strikingly, the diffusion mapping indicates that the difference in electron/hole lifetimes is largely compensated by their disparate mobility. Consequently, the long diffusion lengths (3~5 μm) of both carriers are comparable to each other, a feature closely related to the unique charge trapping and de-trapping processes in hybrid trihalide perovskites. Our results unveil the origin of superior diffusion dynamics in this material, crucially important for solar-cell applications. Spatiotemporal dynamic of charge carriers is commonly studied with optical or photoconductivity measurements, yet these techniques come with their own limitations. To circumvent these limits, the authors probe the free-carrier diffusion dynamics of microsecond lifetimes via laser-illuminated microwave impedance microscopy.
Discrete transformation elasticity: An approach to design lattice-based polar metamaterials

Abstract The transformation method is a powerful tool providing the constitutive parameters necessary for arbitrary geometric transformations of solution fields. These constitutive parameters, in elasticity, describe a constitutive law that, unlike conventional Hooke's law, is polar and chiral and that no known solids exhibit. This raises the question of whether polar and chiral elastic solids can be designed from the bottom up as architected lattice-based materials; this design task is a major challenge in the field of transformation elasticity. The present study aims to provide a theoretically justified design methodology based on a discrete transformation method. The key idea is to let the gradient of the geometric transformation operate not only on the elastic properties but on the underlying lattice-based architecture of the solids. As an outstanding application, we leverage the proposed design paradigm to construct a polar lattice metamaterial subsequently used for elastic carpet cloaking purposes. Numerical simulations are carried to show excellent cloaking performance under different static and dynamic mechanical loads and thus demonstrate the validity of the proposed designs. The approach presented herein could promote and accelerate new designs of lattice topologies for transformation elasticity in particular and can be extended to realize other emergent elastic properties and to unlock peculiar field-warping functions other than cloaking in static and dynamic contexts.
An approach for open multivariate analysis of integrated clinical and environmental exposures data

Abstract The Integrated Clinical and Environmental Exposures Service (ICEES) provides regulatory-compliant open access to sensitive patient data that have been integrated with public exposures data. ICEES was designed initially to support dynamic cohort creation and bivariate contingency tests. The objective of the present study was to develop an open approach to support multivariate analyses using existing ICEES functionalities and abiding by all regulatory constraints. We first developed an open approach for generating a multivariate table that maintains contingencies between clinical and environmental variables using programmatic calls to the open ICEES application programming interface. We then applied the approach to data on a large cohort (N = 22,365) of patients with asthma or related conditions and generated an eight-feature table. Due to regulatory constraints, data loss was incurred with the incorporation of each successive feature variable, from a starting sample size of N = 22,365 to a final sample size of N = 4,556 (20.4%), but data loss was
Asymptotic Distribution of Bernoulli Quadratic Forms

Consider the random quadratic form Tn=∑ 1≤u<v≤nauvXuXv, where ((auv))1≤u,v≤n is a {0,1}-valued symmetric matrix with zeros on the diagonal, and X1,X2,…,Xn are i.i.d. Ber(pn), with pn∈(0,1). In this paper, we prove various characterization theorems about the limiting distribution of Tn, in the sparse regime, where pn→0 such that E(Tn)=O(1). The main result is a decomposition theorem showing that distributional limits of Tn is the sum of three components: a mixture which consists of a quadratic function of independent Poisson variables; a linear Poisson mixture, where the mean of the mixture is itself a (possibly infinite) linear combination of independent Poisson random variables; and another independent Poisson component. This is accompanied with a universality result which allows us to replace the Bernoulli distribution with a large class of other discrete distributions. Another consequence of the general theorem is a necessary and sufficient condition for Poisson convergence, where an interesting second moment phenomenon emerges.
Benchmarking embedded chain breaking in quantum annealing

Quantum annealing solves combinatorial optimization problems by ﬁnding the energetic ground states of an embedded Hamiltonian. However, quantum annealing dynamics under the embedded Hamiltonian may violate the principles of adiabatic evolution and generate excitations that correspond to errors in the computed solution. Here we empirically benchmark the probability of chain breaks and identify sweet spots for solving a suite of embedded Hamiltonians. We further correlate the physical location of chain breaks in the quantum annealing hardware with the underlying embedding technique and use these localized rates in a tailored post-processing strategies. Our results demonstrate how to use characterization of the quantum annealing hardware to tune the embedded Hamiltonian and remove computational errors.
All-optical synthesis of an arbitrary linear transformation using diffractive surfaces

Spatially-engineered diffractive surfaces have emerged as a powerful framework to control light-matter interactions for statistical inference and the design of task-specific optical components. Here, we report the design of diffractive surfaces to all-optically perform arbitrary complex-valued linear transformations between an input (Ni) and output (No), where Ni and No represent the number of pixels at the input and output fields-of-view (FOVs), respectively. First, we consider a single diffractive surface and use a matrix pseudoinverse-based method to determine the complex-valued transmission coefficients of the diffractive features/neurons to all-optically perform a desired/target linear transformation. In addition to this data-free design approach, we also consider a deep learning-based design method to optimize the transmission coefficients of diffractive surfaces by using examples of input/output fields corresponding to the target transformation. We compared the all-optical transformation errors and diffraction efficiencies achieved using data-free designs as well as data-driven (deep learning-based) diffractive designs to all-optically perform (i) arbitrarily-chosen complex-valued transformations including unitary, nonunitary, and noninvertible transforms, (ii) 2D discrete Fourier transformation, (iii) arbitrary 2D permutation operations, and (iv) high-pass filtered coherent imaging. Our analyses reveal that if the total number (N) of spatially-engineered diffractive features/neurons is ≥Ni × No, both design methods succeed in all-optical implementation of the target transformation, achieving negligible error. However, compared to data-free designs, deep learning-based diffractive designs are found to achieve significantly larger diffraction efficiencies for a given N and their all-optical transformations are more accurate for N < Ni × No. These conclusions are generally applicable to various optical processors that employ spatially-engineered diffractive surfaces.
On the Security of Proofs of Sequential Work in a Post-Quantum World.

A Proof of Sequential Work (PoSW) allows a prover to convince a resource-bounded verifier that the prover invested a substantial amount of sequential time to perform some underlying computation. PoSWs have many applications including time-stamping, blockchain design, and universally verifiable CPU benchmarks. Mahmoody, Moran, and Vadhan (ITCS 2013) gave the first construction of a PoSW in the random oracle model though the construction relied on expensive depth-robust graphs. In a recent breakthrough, Cohen and Pietrzak (EUROCRYPT 2018) gave an efficient PoSW construction that does not require expensive depth-robust graphs. In the classical parallel random oracle model, it is straightforward to argue that any successful PoSW attacker must produce a long $\mathcal{H}$-sequence and that any malicious party running in sequential time $T-1$ will fail to produce an $\mathcal{H}$-sequence of length $T$ except with negligible probability. In this paper, we prove that any quantum attacker running in sequential time $T-1$ will fail to produce an $\mathcal{H}$-sequence except with negligible probability -- even if the attacker submits a large batch of quantum queries in each round. The proof is substantially more challenging and highlights the power of Zhandry's recent compressed oracle technique (CRYPTO 2019). We further extend this result to establish post-quantum security of a non-interactive PoSW obtained by applying the Fiat-Shamir transform to Cohen and Pietrzak's efficient construction (EUROCRYPT 2018).
Optimizing pulsed-laser ablation production of AlCl molecules for laser cooling.

Aluminum monochloride (AlCl) has been proposed as a promising candidate for laser cooling to ultracold temperatures, and recent spectroscopy results support this prediction. It is challenging to produce large numbers of AlCl molecules because it is a highly reactive open-shell molecule and must be generated in situ. Here we show that pulsed-laser ablation of stable, non-toxic mixtures of Al with alkali or alkaline earth chlorides, denoted XCln, can provide a robust and reliable source of cold AlCl molecules. Both the chemical identity of XCln and the Al : XCln molar ratio are varied, and the yield of AlCl is monitored using absorption spectroscopy in a cryogenic gas. For KCl, the production of Al and K atoms was also monitored. We model the AlCl production in the limits of nonequilibrium recombination dominated by first-encounter events. The non-equilibrium model is in agreement with the data and also reproduces the observed trend with different XCln precursors. We find that AlCl production is limited by the solid-state densities of Al and Cl atoms and the recondensation of Al atoms in the ablation plume. We suggest future directions for optimizing the production of cold AlCl molecules using laser ablation.
Move2Hear: Active Audio-Visual Source Separation

We introduce the active audio-visual source separation problem, where an agent must move intelligently in order to better isolate the sounds coming from an object of interest in its environment. The agent hears multiple audio sources simultaneously (e.g., a person speaking down the hall in a noisy household) and must use its eyes and ears to automatically separate out the sounds originating from the target object within a limited time budget. Towards this goal, we introduce a reinforcement learning approach that trains movement policies controlling the agent's camera and microphone placement over time, guided by the improvement in predicted audio separation quality. We demonstrate our approach in scenarios motivated by both augmented reality (system is already co-located with the target object) and mobile robotics (agent begins arbitrarily far from the target object). Using state-of-the-art realistic audio-visual simulations in 3D environments, we demonstrate our model's ability to find minimal movement sequences with maximal payoff for audio source separation. Project: this http URL.
LTO: Lazy Trajectory Optimization with Graph-Search Planning for High DOF Robots in Cluttered Environments

Although Trajectory Optimization (TO) is one of the most powerful motion planning tools, it suffers from expensive computational complexity as a time horizon increases in cluttered environments. It can also fail to converge to a globally optimal solution. In this paper, we present Lazy Trajectory Optimization (LTO) that unifies local short-horizon TO and global Graph-Search Planning (GSP) to generate a long-horizon global optimal trajectory. LTO solves TO with the same constraints as the original long-horizon TO with improved time complexity. We also propose a TO-aware cost function that can balance both solution cost and planning time. Since LTO solves many nearly identical TO in a roadmap, it can provide an informed warm-start for TO to accelerate the planning process. We also present proofs of the computational complexity and optimality of LTO. Finally, we demonstrate LTO’s performance on motion planning problems for a 2 DOF free-flying robot and a 21 DOF legged robot, showing that LTO outperforms existing algorithms in terms of its runtime and reliability.
Realization of active metamaterials with odd micropolar elasticity.

Materials made from active, living, or robotic components can display emergent properties arising from local sensing and computation. Here, we realize a freestanding active metabeam with piezoelectric elements and electronic feed-forward control that gives rise to an odd micropolar elasticity absent in energy-conserving media. The non-reciprocal odd modulus enables bending and shearing cycles that convert electrical energy into mechanical work, and vice versa. The sign of this elastic modulus is linked to a non-Hermitian topological index that determines the localization of vibrational modes to sample boundaries. At finite frequency, we can also tune the phase angle of the active modulus to produce a direction-dependent bending modulus and control non-Hermitian vibrational properties. Our continuum approach, built on symmetries and conservation laws, could be exploited to design others systems such as synthetic biofilaments and membranes with feed-forward control loops. Mechanical metamaterials can be engineered with properties not possible in ordinary materials. Here the authors demonstrate and study an active metamaterial with self-sensing characteristics that enables odd elastic properties not observed in passive media.
Moons are planets: Scientific usefulness versus cultural teleology in the taxonomy of planetary science

Abstract We argue that taxonomical concept development is vital for planetary science as in all branches of science, but its importance has been obscured by unique historical developments. The literature shows that the concept of planet developed by scientists during the Copernican Revolution was theory-laden and pragmatic for science. It included both primaries and satellites as planets due to their common intrinsic, geological characteristics. About two centuries later the non-scientific public had just adopted heliocentrism and was motivated to preserve elements of geocentrism including teleology and the assumptions of astrology. This motivated development of a folk concept of planet that contradicted the scientific view. The folk taxonomy was based on what an object orbits, making satellites out to be non-planets and ignoring most asteroids. Astronomers continued to keep primaries and moons classed together as planets and continued teaching that taxonomy until the 1920s. The astronomical community lost interest in planets ca. 1910 to 1955 and during that period complacently accepted the folk concept. Enough time has now elapsed so that modern astronomers forgot this history and rewrote it to claim that the folk taxonomy is the one that was created by the Copernican scientists. Starting ca. 1960 when spacecraft missions were developed to send back detailed new data, there was an explosion of publishing about planets including the satellites, leading to revival of the Copernican planet concept. We present evidence that taxonomical alignment with geological complexity is the most useful scientific taxonomy for planets. It is this complexity of both primary and secondary planets that is a key part of the chain of origins for life in the cosmos.
On Stochastic Heating and Its Phase-space Signatures in Low-beta Kinetic Turbulence

We revisit the theory of stochastic heating of ions and investigate its phase-space signatures in kinetic turbulence of relevance to low-$\beta$ portions of the solar wind. We retain a full scale-dependent approach in our treatment, and consider the case in which electric-field fluctuations can be described by a generalized Ohm's law that includes Hall and thermo-electric effects. These two electric-field terms provide the dominant contributions to stochastic ion heating when the ion-Larmor scale is much smaller than the ion skin depth, $\rho_{\mathrm{i}}\ll d_{\mathrm{i}}$, which is the case at $\beta{\ll}1$. Employing well-known spectral scaling laws for Alfven-wave and kinetic-Alfven-wave turbulent fluctuations, we obtain scaling relations characterizing the field-perpendicular particle-energization rate and energy diffusion coefficient associated with stochastic heating in these two regimes. Phase-space signatures of ion heating are then investigated using 3D hybrid-kinetic simulations of continuously driven Alfvenic turbulence at low $\beta$. In these simulations, energization of ions parallel to the magnetic field is sub-dominant compared to its perpendicular counterpart ($Q_{\parallel,\mathrm{i}}\ll Q_{\perp,\mathrm{i}}$), and the fraction of turbulent energy that goes into ion heating is ${\approx}75$\% at $\beta_{\mathrm{i}}=0.3$ and ${\approx}40$\% at $\beta_{\mathrm{i}}{\simeq}0.1$. The phase-space signatures of ion energization are consistent with Landau-resonant collisionless damping and a ($\beta$-dependent) combination of ion-cyclotron and stochastic heating. We demonstrate good agreement between our theory and various signatures associated with the stochastic portion of the heating. We discuss the effect of intermittency on stochastic heating and the implications of our work for the interpretation of stochastic heating in solar-wind spacecraft data.
The Anticompetitive Nature of Brand-Name Firm Introduction of Generics before Patent Expiration

Brand name pharmaceutical firms in the current health care climate are faced with intense competition from generic firm manufacturers after the brand name version of the drug loses patent protection. Faced with this loss of revenue, as well as the ever present demand by public and private purchasers for cost concessions, some brand name firms have adopted a strategy of introducing their own generic form of their brand name drug before patent expiration. However, these activities can be seen to be anticompetitive under the policy articulated by the classic Areeda & Turner predatory pricing model. Further, economic analysis of these actions using first mover advantage, entry lag, and raising rivals' costs theories bolster this conclusion. Thus, to maintain a level competitive playing field for pharmaceuticals after patent expiration, some incentive structure adjustment for brand name manufacturers' pre-patent expiration is required; e.g., mandatory licensing at the time a generic form is introduced by the brand name firm if introduction is before patent expiration, or prohibition of brand name firm pre-patent expiration supply contracting that extends past the patent expiration date. Overall, introduction of generics by brand name firms before patent expiration may be anticompetitive and solutions that address this issue should ensure that brand name firms cannot utilize patent protection of their brand name drugs to leverage market share in the post-patent expiration generic drug market.
Maximum deconstructibility in module categories

Abstract We prove that Vopěnka's Principle implies that for every class X of modules over any ring, the class of X -Gorenstein Projective modules ( X - GP ) is a precovering class. In particular, it is not possible to prove (unless Vopěnka's Principle is inconsistent) that there is a ring over which the Ding Projectives ( DP ) or the Gorenstein Projectives ( GP ) do not form a precovering class (Saroch previously obtained this result for the class GP , using different methods). The key innovation is a new “top-down” characterization of deconstructibility, which is a well-known sufficient condition for a class to be precovering. We also prove that Vopěnka's Principle implies, in some sense, the maximum possible amount of deconstructibility in module categories.
A tumor suppressive long noncoding RNA, DRAIC, inhibits protein translation and induces autophagy by activating AMPK.

LncRNAs are long RNA transcripts that do not code for proteins and that have been shown to play a major role in cellular processes through diverse mechanisms. DRAIC, a lncRNA which is downregulated in castration-resistant advanced prostate cancer, inhibits the NF-kB pathway by inhibiting the IκB kinase. Decreased DRAIC expression predicted poor patient outcome in gliomas and seven other cancers. We now report that DRAIC suppresses invasion, migration, colony formation and xenograft growth of glioblastoma derived cell lines. DRAIC activates AMPK by downregulating the NF-κB target gene GLUT1, and thus represses mTOR, leading to downstream effects such as decrease in protein translation and increase in autophagy. DRAIC, therefore, has an effect on multiple signal transduction pathways that are important for oncogenesis: the NF-κB pathway and AMPK-mTOR-S6K/ULK1 pathway. The regulation of NF-κB, protein translation and autophagy by the same lncRNA explains the tumor suppressive role of DRAIC in different cancers and reinforces the importance of lncRNAs as emerging regulators of signal transduction pathways.
Learning Security Classifiers with Verified Global Robustness Properties

Many recent works have proposed methods to train classifiers with local robustness properties, which can provably eliminate classes of evasion attacks for most inputs, but not all inputs. Since data distribution shift is very common in security applications, e.g., often observed for malware detection, local robustness cannot guarantee that the property holds for unseen inputs at the time of deploying the classifier. Therefore, it is more desirable to enforce global robustness properties that hold for all inputs, which is strictly stronger than local robustness. In this paper, we present a framework and tools for training classifiers that satisfy global robustness properties. We define new notions of global robustness that are more suitable for security classifiers. We design a novel booster-fixer training framework to enforce global robustness properties. We structure our classifier as an ensemble of logic rules and design a new verifier to verify the properties. In our training algorithm, the booster increases the classifier's capacity, and the fixer enforces verified global robustness properties following counterexample guided inductive synthesis. We show that we can train classifiers to satisfy different global robustness properties for three security datasets, and even multiple properties at the same time, with modest impact on the classifier's performance. For example, we train a Twitter spam account classifier to satisfy five global robustness properties, with 5.4% decrease in true positive rate, and 0.1% increase in false positive rate, compared to a baseline XGBoost model that doesn't satisfy any property.
Age-related susceptibility of ferrets to SARS-CoV-2 infection.

Susceptibility to SARS-CoV-2 and the outcome of COVID-19 have been linked to underlying health conditions and the age of affected individuals. Here we assessed the effect of age on SARS-CoV-2 infection using a ferret model. For this, young (6-month-old) and aged (18-to-39-month-old) ferrets were inoculated intranasally with various doses of SARS-CoV-2. By using infectious virus shedding in respiratory secretions and seroconversion, we estimated that the infectious dose of SARS-CoV-2 in aged animals is ∼32 plaque forming units (PFU) per animal while in young animals it was estimated to be ∼100 PFU. We showed that viral replication in the upper respiratory tract and shedding in respiratory secretions is enhanced in aged ferrets when compared to young animals. Similar to observations in humans, this was associated with higher transcription levels of two key viral entry factors - ACE2 and TMPRSS2 - in the upper respiratory tract of aged ferrets. Importance In humans, ACE2 and TMPRSS2 are expressed in various cells and tissues, and a differential expression have been described in young and old people, with a higher level of expressing cells being detected in the nasal brushing of older people when compared to young individuals. We described the same pattern occurring in ferrets and we demonstrated that age affects susceptibility of ferrets to SARS-CoV-2. Aged animals were more likely to get infected when exposed to lower infectious dose of the virus when compared to young animals and the viral replication in the URT and shedding is enhanced in aged ferrets. Together these results suggest that the higher infectivity and enhanced ability of SARS-CoV-2 to replicate in aged individuals is associated - at least in part - with transcription levels of ACE2 and TMPRSS2 at the sites of virus entry. The young and aged ferret model developed here may represent a great platform to assess age-related differences in SARS-CoV-2 infection dynamics and replication.
Material Characterization of Heat Shrinkable Film

Shape memory polymers represent a class of polymers that display properties of shape change under stimulus. The presented research focused on material characterization of thermally stimulated shape memory polymers, more specifically heat shrinkable bands often used as packaging in the food and the medical industry. Increasingly, regulatory agencies are pushing for the use of non-PVC materials industry wide. This study represented an experimental approach for evaluation of polyester shrink bands (non-PVC) in comparison with PVC with regard to thermomechanical and shrinkage properties.
Decisions to Forgo Hospitalization in Advanced Dementia: A Nationwide Study

OBJECTIVES: To examine the prevalence and factors associated with decisions to forgo hospitalization in nursing home (NH) residents with advanced dementia. DESIGN: Cross-sectional study. SETTING: All Medicare- and Medicaid-certified NHs within the 48 contiguous U.S. states. PARTICIPANTS: NH residents with advanced dementia were identified using Minimum Data Set (MDS) assessments completed close to April 1, 2000 (N=91,521). MEASUREMENTS: Multilevel, multivariate logistic regression identified factors independently associated with having a do-not-hospitalize (DNH) directive. Independent variables included subject characteristics (MDS), facility factors (On-line Survey of Certification of Automated Records), and hospital referral region (HRR) features (Dartmouth Atlas). RESULTS: Nationwide, 7.1% (n=6,518) residents with advanced dementia had DNH orders (range 0.7% in Oklahoma to 25.9% in Rhode Island). Resident characteristics associated with having a DNH order were older age, white, living will, durable power of attorney for health care, and total functional dependence. Controlling for these factors, DNH orders were more likely in residents of facilities with the following features: not part of a chain, urban location, special care dementia unit, fewer black residents, nurse practitioner or physician assistant on staff, higher staffing ratios, and location in HRRs with fewer intensive care unit admissions during terminal hospitalizations. CONCLUSION: Directives to forgo hospitalization for U.S. NH residents with advanced dementia are uncommon and are associated with the organizational features of the facilities caring for them and the intensity of end-of-life care practiced in the region, as well as individual resident characteristics.
Parallel Computation of Invariant Measures

Let S:[0,1]→[0,1] be a nonsingular transformation and let P:L 1(0,1)→L 1(0,1) be the corresponding Frobenius–Perron operator. In this paper we propose a parallel algorithm for computing a fixed density of P, using Ulam's method and a modified Monte Carlo approach. Numerical results are also presented.
AutoCAD Land Development Desktop Release 2i

Land development projects require the skills of numerous design professionals: land and urban planners, surveyors, civil engineers. Whatever your contribution, whatever the size of your project, you need a CAD solution with topographic analysis functionality, real-world coordinate systems, volume totals, roadway geometry--all in an environment that promotes collaborative design and analysis. And you need to customize your CAD solution to fit the way you work. You can have it all with AutoCAD® Land Development Desktop Release 2i software--your platform for success.