cssi/SciSciGPT-SciSciNet huggingface dataset:
Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex-Valued Convolutional Networks

The (inverse) discrete Fourier transform (DFT/ IDFT) is often perceived as essential to orthogonal frequency-division multiplexing (OFDM) systems. In this paper, a deep complex-valued convolutional network (DCCN) is developed to recover bits from time-domain OFDM signals without relying on any explicit DFT/IDFT. The DCCN can exploit the cyclic prefix (CP) of OFDM waveform for increased SNR by replacing DFT with a learned linear transform, and has the advantage of combining CP-exploitation, channel estimation, and intersymbol interference (ISI) mitigation, with a complexity of ${\mathcal O}(N^{2})$ . Numerical tests show that the DCCN receiver can outperform the legacy channel estimators based on ideal and approximate linear minimum mean square error (LMMSE) estimation and a conventional CP-enhanced technique in Rayleigh fading channels with various delay spreads and mobility. The proposed approach benefits from the expressive nature of complex-valued neural networks, which, however, currently lack support from popular deep learning platforms. In response, guidelines of exact and approximate implementations of a complex-valued convolutional layer are provided for the design and analysis of convolutional networks for wireless PHY. Furthermore, a suite of novel training techniques are developed to improve the convergence and generalizability of the trained model in fading channels. This work demonstrates the capability of deep neural networks in processing OFDM waveforms and the results suggest that the FFT processor in OFDM receivers can be replaced by a hardware AI accelerator.
Log-concave polynomials IV: approximate exchange, tight mixing times, and near-optimal sampling of forests

We prove tight mixing time bounds for natural random walks on bases of matroids, determinantal distributions, and more generally distributions associated with log-concave polynomials. For a matroid of rank k on a ground set of n elements, or more generally distributions associated with log-concave polynomials of homogeneous degree k on n variables, we show that the down-up random walk, started from an arbitrary point in the support, mixes in time O(klogk). Our bound has no dependence on n or the starting point, unlike the previous analyses of Anari et al. (STOC 2019), Cryan et al. (FOCS 2019), and is tight up to constant factors. The main new ingredient is a property we call approximate exchange, a generalization of well-studied exchange properties for matroids and valuated matroids, which may be of independent interest. In particular, given a distribution µ over size-k subsets of [n], our approximate exchange property implies that a simple local search algorithm gives a kO(k)-approximation of maxS µ(S) when µ is generated by a log-concave polynomial, and that greedy gives the same approximation ratio when µ is strongly Rayleigh. As an application, we show how to leverage down-up random walks to approximately sample random forests or random spanning trees in a graph with n edges in time O(nlog2 n). The best known result for sampling random forest was a FPAUS with high polynomial runtime recently found by Anari et al. (STOC 2019), Cryan et al. (FOCS 2019). For spanning tree, we improve on the almost-linear time algorithm by Schild (STOC 2018). Our analysis works on weighted graphs too, and is the first to achieve nearly-linear running time for these problems. Our algorithms can be naturally extended to support approximately sampling from random forests of size between k1 and k2 in time O(n log2 n), for fixed parameters k1, k2.
1H, 15N and 13C sequence specific backbone assignment of the MAP Kinase Binding Domain of the Dual Specificity Phosphatase 1 and its interaction with the MAPK p38

The sequence-specific backbone assignment of the mitogen-activated protein kinase (MAPK) binding domain of the dual-specificity phosphatase 1 (DUSP1) has been accomplished using a uniformly [13C, 15N]-labeled protein. These assignments will facilitate further studies of DUSP1 in the presence of inhibitors/ligands to target MAPK associated diseases and provide further insights into the function of dual-specificity phosphatase 1 in MAPK regulation.
Resin selection criteria for 'tough' composite structures

Resin selection criteria are derived using a structured methodology consisting of an upward integrated mechanistic theory and its inverse (top-down structured theory). These criteria are expressed in a "criteria selection space" which are used to identify resin bulk properties for improved composite "toughness". The resin selection criteria correlate with a variety of experimental data including laminate strength, elevated temperature effects and impact resistance.
Cooperative Exploration for Multi-Agent Deep Reinforcement Learning

Exploration is critical for good results in deep reinforcement learning and has attracted much attention. However, existing multi-agent deep reinforcement learning algorithms still use mostly noise-based techniques. Very recently, exploration methods that consider cooperation among multiple agents have been developed. However, existing methods suffer from a common challenge: agents struggle to identify states that are worth exploring, and hardly coordinate exploration efforts toward those states. To address this shortcoming, in this paper, we propose cooperative multi-agent exploration (CMAE): agents share a common goal while exploring. The goal is selected from multiple projected state spaces via a normalized entropy-based technique. Then, agents are trained to reach this goal in a coordinated manner. We demonstrate that CMAE consistently outperforms baselines on various tasks, including a sparse-reward version of the multiple-particle environment (MPE) and the Starcraft multi-agent challenge (SMAC).
Composite Structure Modeling and Analysis of Advanced Aircraft Fuselage Concepts

NASA Environmentally Responsible Aviation (ERA) project and the Boeing Company are collabrating to advance the unitized damage arresting composite airframe technology with application to the Hybrid-Wing-Body (HWB) aircraft. The testing of a HWB fuselage section with Pultruded Rod Stitched Efficient Unitized Structure (PRSEUS) construction is presently being conducted at NASA Langley. Based on lessons learned from previous HWB structural design studies, improved finite-element models (FEM) of the HWB multi-bay and bulkhead assembly are developed to evaluate the performance of the PRSEUS construction. In order to assess the comparative weight reduction benefits of the PRSEUS technology, conventional cylindrical skin-stringer-frame models of a cylindrical and a double-bubble section fuselage concepts are developed. Stress analysis with design cabin-pressure load and scenario based case studies are conducted for design improvement in each case. Alternate analysis with stitched composite hat-stringers and C-frames are also presented, in addition to the foam-core sandwich frame and pultruded rod-stringer construction. The FEM structural stress, strain and weights are computed and compared for relative weight/strength benefit assessment. The structural analysis and specific weight comparison of these stitched composite advanced aircraft fuselage concepts demonstrated that the pressurized HWB fuselage section assembly can be structurally as efficient as the conventional cylindrical fuselage section with composite stringer-frame and PRSEUS construction, and significantly better than the conventional aluminum construction and the double-bubble section concept.
Using Meta Reinforcement Learning to Bridge the Gap between Simulation and Experiment in Energy Demand Response

Our team is proposing to run a full-scale energy demand response experiment in an office building. Although this is an exciting endeavor which will provide value to the community, collecting training data for the reinforcement learning agent is costly and will be limited. In this work, we apply a meta-learning architecture to warm start the experiment with simulated tasks, to increase sample efficiency. We present results that demonstrate a similar a step up in complexity still corresponds with better learning.
Integrated structure/control law design by multilevel optimization

A new approach to integrated structure/control law design based on multilevel optimization is presented. This new approach is applicable to aircraft and spacecraft and allows for the independent design of the structure and control law. Integration of the designs is achieved through use of an upper level coordination problem formulation within the multilevel optimization framework. The method requires the use of structure and control law design sensitivity information. A general multilevel structure/control law design problem formulation is given, and the use of Linear Quadratic Gaussian (LQG) control law design and design sensitivity methods within the formulation is illustrated. Results of three simple integrated structure/control law design examples are presented. These results show the capability of structure and control law design tradeoffs to improve controlled system performance within the multilevel approach.
The infinite line pressure probe

The infinite line pressure probe provides a means for measuring high frequency fluctuating pressures in difficult environments. A properly designed infinite line probe does not resonate; thus its frequency response is not limited by acoustic resonance in the probe tubing, as in conventional probes. The characteristics of infinite line pressure probes are reviewed and some applications in turbine engine research are described. A probe with a flat-oval cross section, permitting a constant-impedance pressure transducer installation, is described. Techniques for predicting the frequency response of probes with both circular and flat-oval cross sections are also cited.
Distributed Mirror Descent with Integral Feedback: Asymptotic Convergence Analysis of Continuous-time Dynamics

This work addresses distributed optimization, where a network of agents wants to minimize a global strongly convex objective function. The global function can be written as a sum of local convex functions, each of which is associated with an agent. We propose a continuous-time distributed mirror descent algorithm that uses purely local information to converge to the global optimum. Unlike previous work on distributed mirror descent, we incorporate an integral feedback in the update, allowing the algorithm to converge with a constant step-size when discretized. We establish the asymptotic convergence of the algorithm using Lyapunov stability analysis. We further illustrate numerical experiments that verify the advantage of adopting integral feedback for improving the convergence rate of distributed mirror descent.
Surveilling Surveillance: Estimating the Prevalence of Surveillance Cameras with Street View Data

The use of video surveillance in public spaces--both by government agencies and by private citizens--has attracted considerable attention in recent years, particularly in light of rapid advances in face-recognition technology. But it has been difficult to systematically measure the prevalence and placement of cameras, hampering efforts to assess the implications of surveillance on privacy and public safety. Here we present a novel approach for estimating the spatial distribution of surveillance cameras: applying computer vision algorithms to large-scale street view image data. Specifically, we build a camera detection model and apply it to 1.6 million street view images sampled from 10 large U.S. cities and 6 other major cities around the world, with positive model detections verified by human experts. After adjusting for the estimated recall of our model, and accounting for the spatial coverage of our sampled images, we are able to estimate the density of surveillance cameras visible from the road. Across the 16 cities we consider, the estimated number of surveillance cameras per linear kilometer ranges from 0.1 (in Seattle) to 0.9 (in Seoul). In a detailed analysis of the 10 U.S. cities, we find that cameras are concentrated in commercial, industrial, and mixed zones, and in neighborhoods with higher shares of non-white residents---a pattern that persists even after adjusting for land use. These results help inform ongoing discussions on the use of surveillance technology, including its potential disparate impacts on communities of color.
Ketamine Produces a Long-Lasting Enhancement of CA1 Neuron Excitability

Ketamine is a clinical anesthetic and antidepressant. Although ketamine is a known NMDA receptor antagonist, the mechanisms contributing to antidepression are unclear. This present study examined the loci and duration of ketamine’s actions, and the involvement of NMDA receptors. Local field potentials were recorded from the CA1 region of mouse hippocampal slices. Ketamine was tested at antidepressant and anesthetic concentrations. Effects of NMDA receptor antagonists APV and MK-801, GABA receptor antagonist bicuculline, and a potassium channel blocker TEA were also studied. Ketamine decreased population spike amplitudes during application, but a long-lasting increase in amplitudes was seen during washout. Bicuculline reversed the acute effects of ketamine, but the washout increase was not altered. This long-term increase was statistically significant, sustained for >2 h, and involved postsynaptic mechanisms. A similar effect was produced by MK-801, but was only partially evident with APV, demonstrating the importance of the NMDA receptor ion channel block. TEA also produced a lasting excitability increase, indicating a possible involvement of potassium channel block. This is this first report of a long-lasting increase in excitability following ketamine exposure. These results support a growing literature that increased GABA inhibition contributes to ketamine anesthesia, while increased excitatory transmission contributes to its antidepressant effects.
First experimental results of the fast atmospheric self-coherent camera technique on the Santa Cruz extreme adaptive optics laboratory testbed: demonstration of high speed focal plane wavefront control of residual atmospheric speckles

Current and future high contrast imaging instruments aim to detect exoplanets at closer orbital separations, lower masses, and/or older ages than their predecessors, with the eventual goal of directly detecting terrestrial-mass habitable-zone exoplanets. However, continually evolving speckles in the coronagraphic science image still limit state-of-the-art ground-based exoplanet imaging instruments to contrasts at least two orders of magnitude worse than what is needed to achieve this goal. For ground-based adaptive optics (AO) instruments it remains challenging for most speckle suppression techniques to attenuate both the dynamic atmospheric and quasi-static instrumental speckles. We have proposed a focal plane wavefront sensing and control algorithm to address this challenge, called the Fast Atmospheric Self-coherent camera (SCC) Technique (FAST), which enables the SCC to operate down to millisecond timescales even when only a few photons are detected per speckle. Here we present preliminary experimental results of FAST on the Santa Cruz Extreme AO Laboratory (SEAL) testbed, demonstrating closed-loop focal plane wavefront control on millisecond-timescales.
Violating Kirchhoff’s Law of Thermal Radiation in Semitransparent Structures

Kirchhoff's law of thermal radiation imposes a constraint on photon-based energy harvesting processes since part of the incident energy flux is inevitably emitted back to the source. By breaking the reciprocity of the system, it is possible to overcome this restriction and improve the efficiency of energy harvesting. Here, we design and analyze a semitransparent emitter that fully absorbs normally incident energy from a given direction with zero backward and unity forward emissivity. The nearly ideal performance with wavelength-scale thickness is achieved due to the magneto-optical effect and the guided-mode resonance engineered in the emitter structure. We derive the general requirements for the nonreciprocal emitter using the temporal coupled mode theory and the symmetry considerations. Finally, we provide a realistic emitter design based on a photonic crystal slab consisting of a magnetic Weyl semimetal and silicon.
Euler Equations on General Planar Domains

We obtain a general sufficient condition on the geometry of possibly singular planar domains that guarantees global uniqueness for any weak solution to the Euler equations on them whose vorticity is bounded and initially constant near the boundary. While similar existing results require domains that are $$C^{1,1}$$ except at finitely many convex corners, our condition involves much less domain smoothness, being only slightly more restrictive than the exclusion of corners with angles greater than $$\pi $$ . In particular, it is satisfied by all convex domains. The main ingredient in our approach is showing that constancy of the vorticity near the boundary is preserved for all time because Euler particle trajectories on these domains, even for general bounded solutions, cannot reach the boundary in finite time. We then use this to show that no vorticity can be created by the boundary of such possibly singular domains for general bounded solutions. We also show that our condition is essentially sharp in this sense by constructing domains that come arbitrarily close to satisfying it, and on which particle trajectories can reach the boundary in finite time. In addition, when the condition is satisfied, we find sharp bounds on the asymptotic rate of the fastest possible approach of particle trajectories to the boundary.
Sensor-Assisted Fault Mitigation in Quantum Computation

We propose a method to assist fault mitigation in quantum computation through the use of sensors co-located near physical qubits. Specifically, we consider using transition edge sensors co-located on silicon substrates hosting superconducting qubits to monitor for energy injection from ionizing radiation, which has been demonstrated to increase decoherence in transmon qubits. We generalize from these two physical device concepts and explore the potential advantages of co-located sensors to assist fault mitigation in quantum computation. In the simplest scheme, co-located sensors beneficially assist rejection of calculations potentially affected by environmental disturbances. Investigating the potential computational advantage further required development of an extension to the standard formulation of quantum error correction. In a specific case of the standard three-qubit, bit-flip quantum error correction code, we show that given a 20% overall error probability per qubit, approximately 90% of repeated calculation attempts are correctable. However, when sensor-detectable errors account for 45% of overall error probability, the use of co-located sensors uniquely associated with independent qubits boosts the fraction of correct final-state calculations to 96%, at the cost of rejecting 7% of repeated calculation attempts.
Evaluation of solar reflective surfaces for dish concentrators

The technology for evaluating solar reflective surfaces is presented in this paper with special application to parabolic dishes. Commercial, second-surface glass mirrors are emphasized but aluminum and metallized polymeric films are included also. Criteria for metallization of solar mirrors to prevent environmental degradation are presented. Important factors concerning the performance of the mirrors are summarized and typical costs are treated. Degradation effects from recent experiments are treated briefly.
Resource-diversity relationships in bacterial communities reflect the network structure of microbial metabolism.

The relationship between the number of available nutrients and community diversity is a central question in ecological research that remains unanswered. Here we studied the assembly of hundreds of soil-derived microbial communities on a wide range of well-defined resource environments, from single carbon sources to combinations of up to 16. We found that, while single resources supported multispecies communities varying from 8 to 40 taxa, mean community richness increased only one-by-one with additional resources. Cross-feeding could reconcile these seemingly contrasting observations, with the metabolic network seeded by the supplied resources explaining the changes in richness due to both the identity and the number of resources, as well as the distribution of taxa across different communities. By using a consumer–resource model incorporating the inferred cross-feeding network, we provide further theoretical support to our observations and a framework to link the type and number of environmental resources to microbial community diversity. The authors conduct experiments with soil microbes grown in communities with increasing numbers of available carbon sources, each of which can support variable numbers of species. The results show that each additional resource enables only one to two additional species to grow, lower than expectations.
Naturalness and the muon magnetic moment

We study a predictive model for explaining the apparent deviation of the muon anomalous magnetic moment from the Standard Model expectation. There are no new scalars and hence no new hierarchy puzzles beyond those associated with the Higgs; the only new particles at the TeV scale are vector-like singlet and doublet leptons. Interestingly, this simple model provides a calculable example violating the Wilsonian notion of naturalness: despite the absence of any symmetries prohibiting its generation, the coefficient of the naively leading dimension-six operator for (g − 2) vanishes at one-loop. While effective field theorists interpret this either as a surprising UV cancellation of power divergences, or as a delicate cancellation between matching UV and calculable IR corrections to (g − 2) from parametrically separated scales, there is a simple explanation in the full theory: the loop integrand is a total derivative of a function vanishing in both the deep UV and IR. The leading contribution to (g − 2) arises from dimension-eight operators, and thus the required masses of new fermions are lower than naively expected, with a sizeable portion of parameter space already covered by direct searches at the LHC. The viable parameter space free of fine-tuning for the muon mass will be fully covered by future direct LHC searches, and all of the parameter space can be probed by precision measurements at planned future lepton colliders.
James‐Stein estimation of the first principal component †

The Stein paradox has played an influential role in the field of high dimensional statistics. This result warns that the sample mean, classically regarded as the "usual estimator", may be suboptimal in high dimensions. The development of the James-Stein estimator, that addresses this paradox, has by now inspired a large literature on the theme of "shrinkage" in statistics. In this direction, we develop a James-Stein type estimator for the first principal component of a high dimension and low sample size data set. This estimator shrinks the usual estimator, an eigenvector of a sample covariance matrix under a spiked covariance model, and yields superior asymptotic guarantees. Our derivation draws a close connection to the original James-Stein formula so that the motivation and recipe for shrinkage is intuited in a natural way.
Private Classical Communication over Quantum Multiple-Access Channels

We study private classical communication over quantum multiple-access channels. For an arbitrary number of transmitters, we derive a regularized expression of the capacity region. In the case of degradable channels, we establish a single-letter expression for the best achievable sum-rate and prove that this quantity also corresponds to the best achievable sum-rate for quantum communication over degradable quantum multiple-access channels. Our achievability result decouples the reliability and privacy constraints, which are handled via distributed source coding with quantum side information at the receiver and distributed hashing, respectively. As a by-product of independent interest, we derive a distributed leftover hash lemma against quantum side information that ensures privacy in our achievability result.
Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation

In the past decade the mathematical theory of machine learning has lagged far behind the triumphs of deep neural networks on practical challenges. However, the gap between theory and practice is gradually starting to close. In this paper I will attempt to assemble some pieces of the remarkable and still incomplete mathematical mosaic emerging from the efforts to understand the foundations of deep learning. The two key themes will be interpolation and its sibling over-parametrization. Interpolation corresponds to fitting data, even noisy data, exactly. Over-parametrization enables interpolation and provides flexibility to select a suitable interpolating model. As we will see, just as a physical prism separates colours mixed within a ray of light, the figurative prism of interpolation helps to disentangle generalization and optimization properties within the complex picture of modern machine learning. This article is written in the belief and hope that clearer understanding of these issues will bring us a step closer towards a general theory of deep learning and machine learning.
A Structural Causal Model for MR Images of Multiple Sclerosis

Precision medicine involves answering counterfactual questions such as “Would this patient respond better to treatment A or treatment B?” These types of questions are causal in nature and require the tools of causal inference to be answered, e.g., with a structural causal model (SCM). In this work, we develop an SCM that models the interaction between demographic information, disease covariates, and magnetic resonance (MR) images of the brain for people with multiple sclerosis. Inference in the SCM generates counterfactual images that show what an MR image of the brain would look like if demographic or disease covariates are changed. These images can be used for modeling disease progression or used for downstream image processing tasks where controlling for confounders is necessary.
Improved Bounds for Coloring Locally Sparse Hypergraphs

We show that, for every k ≥ 2, every k-uniform hypergaph of degree Δ and girth at least 5 is efficiently (1+o(1))(k-1) (Δ / ln Δ)^{1/(k-1)}-list colorable. As an application we obtain the currently best deterministic algorithm for list-coloring random hypergraphs of bounded average degree.
Global Solutions of the Two-Dimensional Kuramoto–Sivashinsky Equation with a Linearly Growing Mode in Each Direction

We consider the Kuramoto–Sivashinsky equation in two space dimensions. We establish the first proof of global existence of solutions in the presence of a linearly growing mode in both spatial directions for sufficiently small data. We develop a new method to this end, categorizing wavenumbers as low (linearly growing modes), intermediate (linearly decaying modes that serve as energy sinks for the low modes), and high (strongly linearly decaying modes). The low and intermediate modes are controlled by means of a Lyapunov function, while the high modes are controlled with operator estimates in function spaces based on the Wiener algebra.
Spontaneous Cyclogenesis without Radiative and Surface-Flux Feedbacks

Tropical cyclones (TCs) are among the most intense and feared storms in the world. What physical processes lead to cyclogenesis remains the most mysterious aspect of TC physics. Here, we study spontaneous TC genesis in rotating radiative-convective equilibrium using cloud-resolving simulations over an f-plane with constant sea-surface temperature. Previous studies proposed that spontaneous TC genesis requires either radiative or surface-flux feedbacks. To test this hypothesis, we perform mechanism-denial experiments, in which we switch off both feedback processes in numerical simulations. We find that TCs can self-emerge even without radiative and surface-flux feedbacks. Although these feedbacks accelerate the genesis and impact the size of the TCs, TCs in the experiments without them can reach similar intensities as those in the control experiment. We show that TC genesis is associated with an increase in the Available Potential Energy (APE); and that convective heating dominates the APE production. Our result suggests that spontaneous TC genesis may result from a cooperative interaction between convection and circulation, and that radiative and surface-flux feedbacks accelerate the process. Furthermore, we find that increasing the planetary rotation favors spontaneous TC genesis.
An explicitly solvable energy-conserving algorithm for pitch-angle scattering in magnetized plasmas

Abstract We develop an Explicitly Solvable Energy-Conserving (ESEC) algorithm for the Stochastic Differential Equation (SDE) describing the pitch-angle scattering process in magnetized plasmas. The Cayley transform is used to calculate both the deterministic gyromotion and stochastic scattering, affording the algorithm to be explicitly solvable and exactly energy conserving. An unusual property of the SDE for pitch-angle scattering is that its coefficients diverge at the zero velocity and do not satisfy the global Lipschitz condition. Consequently, when standard numerical methods, such as the Euler-Maruyama (EM), are applied, numerical convergence is difficult to establish. For the proposed ESEC algorithm, its energy-preserving property enables us to overcome this obstacle. We rigorously prove that the ESEC algorithm is order 1/2 strongly convergent. This result is confirmed by detailed numerical studies. For the case of pitch-angle scattering in a magnetized plasma with a constant magnetic field, the numerical solution is benchmarked against the analytical solution, and excellent agreements are found.
Multi-period portfolio optimization using model predictive control with mean-variance and risk parity frameworks

Abstract We employ model predictive control for a multi-period portfolio optimization problem. In addition to the mean-variance objective, we construct a portfolio whose allocation is given by model predictive control with a risk-parity objective, and provide a successive convex program algorithm that provides 30 times faster and robust solutions in the experiments. We provide a comprehensive comparison of the models in regard of planning horizon, parameter estimation, as well as objective function choice. Computational results on a multi-asset universe show that multi-period models perform better than their single period counterparts in out-of-sample period, 2006–2020, in the presence of market impact costs. The out-of-sample risk-adjusted performance of both mean-variance and risk-parity formulations beat the fix-mix benchmarks, and achieve Sharpe ratio of 0.64 and 0.97, respectively. We also include tests on different asset universes (Fama French industry portfolios) and alternative parameter estimation methods (Bayes-Stein and Black-Litterman) with consistent findings.
Cell and circuit origins of fast network oscillations in the mammalian main olfactory bulb

Neural synchrony generates fast network oscillations throughout the brain, including the main olfactory bulb (MOB), the first processing station of the olfactory system. Identifying the mechanisms synchronizing neurons in the MOB will be key to understanding how network oscillations support the coding of a high-dimensional sensory space. Here, using paired recordings and optogenetic activation of glomerular sensory inputs in MOB slices, we uncovered profound differences in principal mitral cell (MC) vs. tufted cell (TC) spike-time synchrony: TCs robustly synchronized across fast- and slow-gamma frequencies, while MC synchrony was weaker and concentrated in slow-gamma frequencies. Synchrony among both cell types was enhanced by shared glomerular input but was independent of intraglomerular lateral excitation. Cell-type differences in synchrony could also not be traced to any difference in the synchronization of synaptic inhibition. Instead, greater TC than MC synchrony paralleled the more periodic firing among resonant TCs than MCs and emerged in patterns consistent with densely synchronous network oscillations. Collectively, our results thus reveal a mechanism for parallel processing of sensory information in the MOB via differential TC vs. MC synchrony, and further contrast mechanisms driving fast network oscillations in the MOB from those driving the sparse synchronization of irregularly firing principal cells throughout cortex.
Online Trajectory Optimization for Dynamic Aerial Motions of a Quadruped Robot

This work presents a two part framework for online planning and execution of dynamic aerial motions on a quadruped robot. Motions are planned via a centroidal momentum-based nonlinear optimization that is general enough to produce rich sets of novel dynamic motions based solely on the user-specified contact schedule and desired launch velocity of the robot. Since this nonlinear optimization is not tractable for real-time receding horizon control, motions are planned once via nonlinear optimization in preparation of an aerial motion and then tracked continuously using a variational-based optimal controller that offers robustness to the uncertainties that exist in the real hardware such as modeling error or disturbances. Motion planning typically takes between 0.05-0.15 s, while the optimal controller finds stabilizing feedback inputs at 500 Hz. Experimental results on the MIT Mini Cheetah demonstrate that the framework can reliably produce successful aerial motions such as jumps onto and off of platforms, spins, flips, barrel rolls, and running jumps over obstacles.
Heterogeneity, state dependence and health

Summary We investigate the evolution of health over the life-cycle. We allow for two sources of persistence: unobserved heterogeneity and state dependence. Estimation indicates that there is a large degree of heterogeneity. For half the population, there are modest degrees of state dependence. For the other half of the population, the degree of state dependence is near unity. However, this may be the result of a high frequency of people in our data who never exit healthy states, potentially resulting in a failure to pin down the state dependence parameter for this segment of the population. We conclude that individual characteristics that trace back to early adulthood and before can have far reaching effects on health.
Classifying intrinsically linked tournaments by score sequence

A tournament on 8 or more vertices may be intrinsically linked as a directed graph. We begin the classification of intrinsically linked tournaments by examining their score sequences. While many distinct tournaments may have the same score sequence, there exist score sequences $S$ such that any tournament with score sequence $S$ has an embedding with no nonsplit consistently oriented link. We call such score sequences $\textit{linkless}$, and we show that the vast majority of score sequences for 8 vertex tournaments are linkless. We also extend these results to $n$ vertex tournaments and are able to classify many longer score sequences as well. We show that for any $n$, there exist at least $O(n)$ linkless score sequences, but we conjecture that the fraction of score sequences of length $n$ that are linkless goes to 0 as $n$ becomes large.
Data-level Linkage of Multiple Surveys for Improved Understanding of Global Health Challenges

Data-driven approaches can provide more enhanced insights for domain experts in addressing critical global health challenges, such as newborn and child health, using surveys (e.g., Demographic Health Survey). Though there are multiple surveys on the topic, data-driven insight extraction and analysis are often applied on these surveys separately, with limited efforts to exploit them jointly, and hence results in poor prediction performance of critical events, such as neonatal death. Existing machine learning approaches to utilise multiple data sources are not directly applicable to surveys that are disjoint on collection time and locations. In this paper, we propose, to the best of our knowledge, the first detailed work that automatically links multiple surveys for the improved predictive performance of newborn and child mortality and achieves cross-study impact analysis of covariates.
Denial and punishment in war

Formal models of war termination have been developed along two major approaches: in one, war is interpreted as a series of battles, where nations exchange denials that aim to destroy enemy forces; ...
Visual design intuition: predicting dynamic properties of beams from raw cross-section images.

In this work we aim to mimic the human ability to acquire the intuition to estimate the performance of a design from visual inspection and experience alone. We study the ability of convolutional ne...
"Average" Approximates "First Principal Component"? An Empirical Analysis on Representations from Neural Language Models.

Contextualized representations based on neural language models have furthered the state of the art in various NLP tasks. Despite its great success, the nature of such representations remains a mystery. In this paper, we present an empirical property of these representations—”average” approximates “first principal component”. Specifically, experiments show that the average of these representations shares almost the same direction as the first principal component of the matrix whose columns are these representations. We believe this explains why the average representation is always a simple yet strong baseline. Our further examinations show that this property also holds in more challenging scenarios, for example, when the representations are from a model right after its random initialization. Therefore, we conjecture that this property is intrinsic to the distribution of representations and not necessarily related to the input structure. We realize that these representations empirically follow a normal distribution for each dimension, and by assuming this is true, we demonstrate that the empirical property can be in fact derived mathematically.
Smoothness-Adaptive Contextual Bandits

We study a non-parametric multi-armed bandit problem with stochastic covariates, where a key complexity driver is the smoothness of payoff functions with respect to covariates. Previous studies have focused on deriving minimax-optimal algorithms in cases where it is a priori known how smooth the payoff functions are. In practice, however, the smoothness of payoff functions is typically not known in advance, and misspecification of smoothness may severely deteriorate the performance of existing methods. In this work, we consider a framework where the smoothness of payoff functions is not known, and study when and how algorithms may adapt to unknown smoothness. First, we establish that designing algorithms that adapt to unknown smoothness of payoff functions is, in general, impossible. However, under a self-similarity condition (which does not reduce the minimax complexity of the dynamic optimization problem at hand), we establish that adapting to unknown smoothness is possible, and further devise a general policy for achieving smoothness-adaptive performance. Our policy infers the smoothness of payoffs throughout the decision-making process, while leveraging the structure of off-the-shelf non-adaptive policies. We establish that for problem settings with either differentiable or non-differentiable payoff functions, this policy matches (up to a logarithmic scale) the regret rate that is achievable when the smoothness of payoffs is known a priori.
Mushroom Body Input Connections Form Independently of Sensory Activity in Drosophila melanogaster

Associative brain centers, such as the insect mushroom body, need to represent sensory information in an efficient manner. In Drosophila melanogaster, the Kenyon cells of the mushroom body integrate inputs from a random set of olfactory projection neurons, but some projection neurons — namely those activated by a few ethologically meaningful odors — connect to Kenyon cells more frequently than others. This biased and random connectivity pattern is conceivably advantageous, as it enables the mushroom body to represent a large number of odors as unique activity patterns while prioritizing the representation of a few specific odors. How this connectivity pattern is established remains largely unknown. Here, we test whether the mechanisms patterning the connections between Kenyon cells and projection neurons depend on sensory activity or whether they are hardwired. We mapped a large number of mushroom body input connections in anosmic flies — flies lacking the obligate odorant co-receptor Orco — and in wildtype flies. Statistical analyses of these datasets reveal that the random and biased connectivity pattern observed between Kenyon cells and projection neurons forms normally in the absence of most olfactory sensory activity. This finding supports the idea that even comparatively subtle, population-level patterns of neuronal connectivity can be encoded by fixed genetic programs and are likely to be the result of evolved prioritization of ecologically and ethologically salient stimuli.
Extracting Fine-Grained Knowledge Graphs of Scientific Claims: Dataset and Transformer-Based Results.

Recent transformer-based approaches demonstrate promising results on relational scientific information extraction. Existing datasets focus on high-level description of how research is carried out. Instead we focus on the subtleties of how experimental associations are presented by building SciClaim, a dataset of scientific claims drawn from Social and Behavior Science (SBS), PubMed, and CORD-19 papers. Our novel graph annotation schema incorporates not only coarse-grained entity spans as nodes and relations as edges between them, but also fine-grained attributes that modify entities and their relations, for a total of 12,738 labels in the corpus. By including more label types and more than twice the label density of previous datasets, SciClaim captures causal, comparative, predictive, statistical, and proportional associations over experimental variables along with their qualifications, subtypes, and evidence. We extend work in transformer-based joint entity and relation extraction to effectively infer our schema, showing the promise of fine-grained knowledge graphs in scientific claims and beyond.
Geographic difference-in-discontinuities

A recent econometric literature has critiqued the use of regression discontinuities where administrative borders serve as the ‘cutoff’. Identification in this context is difficult since multiple tr...
Symmetry and isoperimetry for Riemannian surfaces

For a domain $$\Omega $$ in a geodesically convex surface, we introduce a scattering energy $$\mathcal {E}(\Omega )$$ , which measures the asymmetry of $$\Omega $$ by quantifying its incompatibility with an isometric circle action. We prove several sharp quantitative isoperimetric inequalities involving $$\mathcal {E}(\Omega )$$ and characterize the domains with vanishing scattering energy by their convexity and rotational symmetry. We also give a new of the sharp Sobolev inequality for Riemannian surfaces.
Responses of mammary cells to hormones.

Publisher Summary This chapter discusses the response of mammary cells to hormones. The mammary gland offers unique opportunities for studying the molecular mechanisms of hormone action, because it is an organ with exceptional biosynthetic capabilities and an organ that is perhaps the most complex multiple hormone target organ in the mammalian body. Studies with explants of mammary tissue fragments or the entire gland also reveal that the mammary epithelium in a culture medium responds to combinations of hormones similar to those needed in vivo. This responsiveness of the mammary cell to hormones in a serum-free culture medium provides a useful tool for elucidation of the molecular mechanisms involved in specific responses to the synergistic action of steroid and polypeptide hormones. The molecular responses of the mammary cells so far studied mainly measured the synthesis and cellular concentration of the macromolecules. Characterization of these hormone-responsive molecular events with respect to specific biological control mechanisms in the mammary gland remains to be accomplished. The link between such molecular events and the physiology of the gland has been so far studied using inhibitors of nucleic acids and protein synthesis.
Is a map more than a picture? the role of SDSS technology, subject characteristics, and problem complexity on map reading and problem solving 1

This research investigated how the use of a spatial decision support system (SDSS)-a type of geographic information system (GIS)-influenced the accuracy and efficiency of different types of problem solvers (i.e., professionals versus students) completing problems of varied complexity. This research-first to simultaneously study these variables-examined subjects who completed a problem involving spatially-referenced information. The experiment was guided by a research model synthesized from various perspectives, including the theory of cognitive fit, prior research on map reading and interpretation, and research examining subject expertise and experience. The results are largely supportive of the research model and demonstrate that SDSS, an increasingly important class of management decision-making technology, increased the efficiency of users working on more complex problems. Professionals were found to be more accurate but less efficient than students; however, professionals who used the SDSS were no more accurate than professionals using paper maps. Need for cognition, a construct that focuses on an individual's willingness to engage in problem solving tasks, was found to be marginally related to accuracy. The implications of these findings for researchers and practitioners are presented and discussed.
Embedded Automatic Speech Recognition and Text‐To‐Speech Synthesis

This chapter contains sections titled: Automatic speech recognition Mathematical formulation Acoustic parameterization Acoustic modeling Language modeling Modifications for embedded speech recognition Applications Text‐to‐speech synthesis Text to speech in a nutshell Front end Back end Embedded text‐to‐speech Evaluation Summary Bibliography
Flow and Transport through Unsaturated Fractured Rock: An Overview

Groundwater flow and contaminant transport through the unsaturated zone continue to receive attention as former nuclear weapons development sites are being characterized for determining decommissioning options, and geologic sites are being considered for deep disposal of nuclear and hazardous waste. The ability to make decisions with regard to cleanup, site suitability, and anticipated performance of disposal or storage facilities is dependent upon the characterization and modeling of unsaturated flow and transport features, events, and processes. Hazardous materials may be mobilized by infiltrating water and move downward to affect groundwater resources. These contaminants may also move upward or laterally by capillary movement or as vapor to the accessible environment. Substantial experience exists with respect to near-surface partially saturated porous media, traditionally the domain of the soil scientists and geotechnical engineers, but does not exist for fractured rock in either near-surface or deep, complex, unsaturated systems that are now being characterized and modeled. The ability to represent these complex systems composed of both a matrix and a fracture component is being tested through a variety of field experiments and comparisons to natural analogues. The present focus is on evaluating conceptual flow and transport models and quantifying the spatial and temporal parameters for these models using laboratory and field methods, and environmental tracer analyses. Modeling of these dynamic and transient systems has followed two paths, porous equivalency and discrete fracture models. In general, characterization methods and modeling are in the confirmation stage with the greatest lack of knowledge being the interaction between fracture and matrix properties for flow and transport.