cssi/SciSciGPT-SciSciNet huggingface dataset:
Odor modulates the temporal dynamics of fear memory consolidation

Systems consolidation (SC) theory proposes that recent, contextually rich memories are stored in the hippocampus (HPC). As these memories become remote, they are believed to rely more heavily on cortical structures within the prefrontal cortex (PFC), where they lose much of their contextual detail and become schematized. Odor is a particularly evocative cue for intense remote memory recall and despite these memories being remote, they are highly contextual. In instances such as posttraumatic stress disorder (PTSD), intense remote memory recall can occur years after trauma, which seemingly contradicts SC. We hypothesized that odor may shift the organization of salient or fearful memories such that when paired with an odor at the time of encoding, they are delayed in the de-contextualization process that occurs across time, and retrieval may still rely on the HPC, where memories are imbued with contextually rich information, even at remote time points. We investigated this by tagging odor- and non-odor-associated fear memories in male c57BL/6 mice and assessed recall and c-Fos expression in the dorsal CA1 (dCA1) and prelimbic cortex (PL) 1 or 21 d later. In support of SC, our data showed that recent memories were more dCA1-dependent whereas remote memories were more PL-dependent. However, we also found that odor influenced this temporal dynamic, biasing the memory system from the PL to the dCA1 when odor cues were present. Behaviorally, inhibiting the dCA1 with activity-dependent DREADDs had no effect on recall at 1 d and unexpectedly caused an increase in freezing at 21 d. Together, these findings demonstrate that odor can shift the organization of fear memories at the systems level.
Extreme MRI: Large‐scale volumetric dynamic imaging from continuous non‐gated acquisitions

Purpose To develop a framework to reconstruct large-scale volumetric dynamic MRI from rapid continuous and non-gated acquisitions, with applications to pulmonary and dynamic contrast-enhanced (DCE) imaging. Theory and methods The problem considered here requires recovering 100 gigabytes of dynamic volumetric image data from a few gigabytes of k-space data, acquired continuously over several minutes. This reconstruction is vastly under-determined, heavily stressing computing resources as well as memory management and storage. To overcome these challenges, we leverage intrinsic three-dimensional (3D) trajectories, such as 3D radial and 3D cones, with ordering that incoherently cover time and k-space over the entire acquisition. We then propose two innovations: (a) A compressed representation using multiscale low-rank matrix factorization that constrains the reconstruction problem, and reduces its memory footprint. (b) Stochastic optimization to reduce computation, improve memory locality, and minimize communications between threads and processors. We demonstrate the feasibility of the proposed method on DCE imaging acquired with a golden-angle ordered 3D cones trajectory and pulmonary imaging acquired with a bit-reversed ordered 3D radial trajectory. We compare it with "soft-gated" dynamic reconstruction for DCE and respiratory-resolved reconstruction for pulmonary imaging. Results The proposed technique shows transient dynamics that are not seen in gating-based methods. When applied to datasets with irregular, or non-repetitive motions, the proposed method displays sharper image features. Conclusions We demonstrated a method that can reconstruct massive 3D dynamic image series in the extreme undersampling and extreme computation setting.
A Mathematical Model of the Rat Kidney. II. Antidiuresis

Kidney water conservation requires a hypertonic medullary interstitium, NaCl in the outer medulla and NaCl and urea in the inner medulla, plus a vascular configuration that protects against washout. In this work, a multisolute model of the rat kidney is revisited to examine its capacity to simulate antidiuresis. The first step was to streamline model computation by parallelizing its Jacobian calculation, thus allowing finer medullary spatial resolution and more extensive examination of model parameters. It is found that outer medullary NaCl is modestly increased when transporter density in ascending Henle limbs from juxtamedullary nephrons is scaled to match the greater juxtamedullary solute flow. However, higher NaCl transport produces greater CO2 generation and, by virtue of countercurrent vascular flows, establishment of high medullary Pco2. This CO2 gradient can be mitigated by assuming that a fraction of medullary transport is powered anaerobically. Reducing vascular flows or increasing vessel permeabilities does little to further increase outer medullary solute gradients. In contrast to medullary models of others, vessels in this model have solute reflection coefficients close to zero; increasing these coefficients provides little enhancement of solute profiles but does generate high interstitial pressures, which distort tubule architecture. Increasing medullary urea delivery via entering vasa recta increases inner medullary urea, although not nearly to levels found in rats. In summary, 1) medullary Na+ and urea gradients are not captured by the model and 2) the countercurrent architecture that provides antidiuresis also produces exaggerated Pco2 profiles and is an unappreciated constraint on models of medullary function.
Spanning 2-forests and resistance distance in 2-connected graphs

Abstract A spanning 2-forest separating vertices u and v of an undirected connected graph is a spanning forest with 2 components such that u and v are in distinct components. Aside from their combinatorial significance, spanning 2-forests have an important application to the calculation of resistance distance or effective resistance. The resistance distance between vertices u and v in a graph representing an electrical circuit with unit resistance on each edge is the number of spanning 2-forests separating u and v divided by the number of spanning trees in the graph. There are also well-known matrix theoretic methods for calculating resistance distance, but the way in which the structure of the underlying graph determines resistance distance via these methods is not well understood. For any connected graph G with a 2-separator separating vertices u and v , we show that the number of spanning trees and spanning 2-forests separating u and v can be expressed in terms of these same quantities for the smaller separated graphs, which makes computation significantly more tractable. An important special case is the preservation of the number of spanning 2-forests if u and v are in the same smaller graph. In this paper we demonstrate that this method of calculating resistance distance is more suitable for certain structured families of graphs than the more standard methods. We apply our results to count the number of spanning 2-forests and calculate the resistance distance in a family of Sierpinski triangles and in the family of linear 2-trees with a single bend.
Spanning tree modulus for secure broadcast games

The theory of $p$-modulus provides a general framework for quantifying the richness of a family of objects on a graph. When applied to the family of spanning trees, $p$-modulus has an interesting probabilistic interpretation. In particular, the $2$-modulus problem in this case has been shown to be equivalent to the problem of finding a probability distribution on spanning trees that utilizes the edges of the graph as evenly as possible. In the present work, we use this fact to produce a game-theoretic interpretation of modulus by employing modulus to solve a secure broadcast game.
Intertwining operator superalgebras and vertex tensor categories for superconformal algebras, II

We construct the intertwining operator superalgebras and vertex tensor categories for the N = 2 superconformal unitary minimal models and other related models.
Instability of rectangular jets

The instability of rectangular jets is investigated using a vortex-sheet model. It is shown that such jets support four linearly independent families of instability waves. Within each family there are infinitely many modes. A way to classify these modes according to the characteristics of their mode shapes or eigenfunctions is proposed. It is demonstrated that the boundary element method can be used to calculate the dispersion relations and eigenfunctions of these instability wave modes. The method is robust and efficient. A parametric study of the instability wave characteristics has been carried out. A sample of the numerical results is reported here. It is found that the first and third modes of each instability wave family are corner modes. The pressure fluctuations associated with these instability waves are localized near the corners of the jet. The second mode, however, is a center mode with maximum fluctuations concentrated in the central portion of the jet flow. The center mode has the largest spatial growth rate. It is anticipated that as the instability waves propagate downstream the center mode would emerge as the dominant instability of the jet.
Cross Subsidies, External Financing Constraints, and the Contribution of the Internal Capital Market to Firm Value

We examine the link between the excess value of a diversified firm and the value of its internal capital market. Subsidies to small financially constrained segments with good relative investment opportunities significantly increase excess value, while transfers of resources from segments with good relative investment opportunities significantly decrease excess value. Of interest is that subsidies to small financially constrained segments with poor relative investment opportunities also significantly increase excess value. However, there is little evidence that this result depends on the diversity of a firm's investment opportunities. We conclude that financing constraints drive the relationship between the internal capital market and firm value. Copyright 2003, Oxford University Press.
Effects of Single Mutations on Protein Stability Are Gaussian Distributed.

Abstract The distribution of protein stability effects is known to be well approximated by a Gaussian distribution from previous empirical fits. Starting from first-principles statistical mechanics, we more rigorously motivate this empirical observation by deriving per-residue-position protein stability effects to be Gaussian. Our derivation requires the number of amino acids to be large, which is satisfied by the standard set of 20 amino acids found in nature. No assumption is needed on the number of residues in close proximity in space, in contrast to previous applications of the central limit theorem to protein energetics. We support our derivation results with computational and experimental data on mutant protein stabilities across all types of protein residues.
Chapter 7 – Renewal Phenomena

Publisher Summary This chapter reviews renewal phenomena. Renewal theory began with the study of stochastic systems whose evolution through time was interspersed with renewals or regeneration times when, in a statistical sense, the process began anew. A renewal (counting) process {N(t), t ≥ 0} is a nonnegative integer-valued stochastic process that registers the successive occurrences of an event during the time interval (0, t), where the time durations between consecutive events are positive, independent, identically distributed random variables. Presently, the subject is viewed as the study of general functions of independent, identically distributed, nonnegative random variables representing the successive intervals between renewals. The results are applicable in a wide variety of both theoretical and practical probability models. The Poisson process with parameter λ is a renewal process; the Poisson process is the only renewal process (in continuous time) whose renewal function is exactly linear. A large number of the functionals that have explicit expressions for Poisson renewal processes are far more difficult to compute for other renewal processes. There are, however, many simple formulas that describe the asymptotic behavior. Thereafter, a delayed renewal process will arise when the component in operation at time t = 0 is not new, but all subsequent replacements are new. The renewal theorem provides conditions under which the solution {Vn} to a renewal equation will converge as n grows large; certain periodic behavior must, thus, be precluded.
Are Supply Shocks Contractionary at the ZLB? Evidence from Utilization-Adjusted TFP Data

Abstract The basic New Keynesian model predicts that positive supply shocks are less expansionary at the zero lower bound (ZLB) compared to periods of active monetary policy. We test this prediction empirically using Fernald's (2014) utilization-adjusted total factor productivity series, which we take as a measure of exogenous productivity. In contrast to the predictions of the model, positive productivity shocks are estimated to be more expansionary at the ZLB compared to normal times. We find that there is no significant difference in the response of expected inflation to a productivity shock at the ZLB compared to normal times.
MANAGING PHOSPHOROUS SOIL DYNAMICS OVER SPACE AND TIME

Understanding the relationship between soil fertility dynamics and crop response is conceptually appealing. Even more appealing is comprehension of the spatial and temporal heterogeneity of these connections over a production surface and across seasons. But gaining knowledge about these interactions is difficult because nutrient carryover dynamics and crop response to inputs are determined simultaneously on the one hand, and sequentially on the other. A second problem enters when crops are rotated, for example, the corn [Zea mays (L.) Merr.]–soybean [Glycine max (L.) Merr.] system commonly practiced in the U.S. Corn Belt. This article examines nutrient carryover–crop response dynamics using data from a corn-soybean, variable rate nitrogen (N), and phosphorous (P) experiment conducted over five years in Minnesota, USA. Site-specific corn response to N and P and soybean response to P is estimated with a P carryover equation. Estimates are used in a dynamic programming model to determine site-specific optimal N and P fertilizer policies, soil P evolution, and profitability. The net present value of managing N and P site-specifically is compared to a strategy in which these inputs are managed uniformly following Extension guidelines. The results suggest that when P carryover is accounted for in determining optimal P fertilizer rates, returns to the variable rate strategies are higher than returns to a uniform or whole-field management strategy.
Regulation of Insurance with Adverse Selection and Switching Costs: Evidence from Medicare Part D.

Abstract I take advantage of regulatory and pricing dynamics in Medicare Part D to explore interactions among adverse selection, inertia, and regulation. I first document novel evidence of adverse selection and switching frictions within Part D using detailed administrative data. I then estimate a contract choice and pricing model that quantifies the importance of inertia for risk sorting. I find that in Part D switching costs help sustain an adversely-selected equilibrium. I also estimate that active decision making in the existing policy environment could lead to a substantial gain in annual consumer surplus of on average $400–$600 per capita—20 percent to 30 percent of average annual spending. (JEL D82, G22, H51, I13, I18)
Market Size Matters

THIS PAPER EXAMINES EMPIRICALLY the effects of market size on producers’ sizes in industries with many producers. A robust prediction of oligopoly theory is that larger markets are more competitive and have lower price-cost markups. Because producers in more competitive markets must recover their fixed costs by selling more at a lower markup, our estimated market size effects indicate whether or not this prediction of oligopoly theory carries over to large-group competition. Our analysis uses observations from thirteen narrowly-defined retail trade industries in 225 metropolitan statistical areas (MSAs), each of which we identify with a separate market. In all of the industries we consider, almost all of these MSAs contain a large number of establishments. Our primary data source is the 1992 Census of Retail Trade (CRT), from which we calculate establishments’ average sales and employment in each market. We supplement these measures with observations of the empirical c.d.f. of establishments’ sizes from the 1992 County Business Patterns (CBP). We regress these statistics from the size distribution against the MSA’s market size and a set of control variables. The control variables account for differences in MSAs’ factor prices and demographics, which covary with market size and can by themselves affect retailers’ sizes.
Chapter 1 – What Is Pharmacology?

In this chapter, the relevance of Pharmacology to new drug discovery is presented as well as the two most important properties of drugs used to quantify drug activity; affinity and efficacy. The groundwork for the rest of the volume is completed with discussions of the receptor concepts, different biological drug targets and the major tool used in Pharmacological experimentation (dose-response curves).
Automatic Stabilizers in a Low-Rate Environment

With interest rates persistently low or even negative in advanced countries, policymakers have barely any room to ease monetary policy when the next recession hits. Fiscal policy will have to play a major and likely dominant role in stimulating the economy, requiring policymakers to fundamentally reconsider fiscal policy. Blanchard and Summers argue for the introduction of what they call “semiautomatic” stabilizers. Unlike purely automatic stabilizers (mechanisms built into government budgets that automatically—without discretionary government action or explicit triggers—increase spending or decrease taxes when the economy slows or enters a recession), semiautomatic stabilizers are targeted tax or spending measures that are triggered if, say, the output growth rate declines or the unemployment rate increases beyond a specified threshold. The authors argue that the trigger should be changes in unemployment rather than changes in output, and the design of semiautomatic stabilizers, whether they focus on mechanisms that rely primarily on income or on intertemporal substitution effects (changing the timing of consumption), depends crucially on the design of discretionary policy.
Structural disruption of exonic stem–loops immediately upstream of the intron regulates mammalian splicing

Recognition of highly degenerate mammalian splice sites by the core spliceosomal machinery is regulated by several protein factors that predominantly bind exonic splicing motifs. These are postulated to be single-stranded in order to be functional, yet knowledge of secondary structural features that regulate the exposure of exonic splicing motifs across the transcriptome is not currently available. Using transcriptome-wide RNA structural information we show that retained introns in mouse are commonly flanked by a short (≲70 nucleotide), highly base-paired segment upstream and a predominantly single-stranded exonic segment downstream. Splicing assays with select pre-mRNA substrates demonstrate that loops immediately upstream of the introns contain pre-mRNA-specific splicing enhancers, the substitution or hybridization of which impedes splicing. Additionally, the exonic segments flanking the retained introns appeared to be more enriched in a previously identified set of hexameric exonic splicing enhancer (ESE) sequences compared to their spliced counterparts, suggesting that base-pairing in the exonic segments upstream of retained introns could be a means for occlusion of ESEs. The upstream exonic loops of the test substrate promoted recruitment of splicing factors and consequent pre-mRNA structural remodeling, leading up to assembly of the early spliceosome. These results suggest that disruption of exonic stem-loop structures immediately upstream (but not downstream) of the introns regulate alternative splicing events, likely through modulating accessibility of splicing factors.
Randomized CP Tensor Decomposition

The CANDECOMP/PARAFAC (CP) tensor decomposition is a popular dimensionality-reduction method for multiway data. Dimensionality reduction is often sought after since many high-dimensional tensors have low intrinsic rank relative to the dimension of the ambient measurement space. However, the emergence of `big data' poses significant computational challenges for computing this fundamental tensor decomposition. By leveraging modern randomized algorithms, we demonstrate that coherent structures can be learned from a smaller representation of the tensor in a fraction of the time. Thus, this simple but powerful algorithm enables one to compute the approximate CP decomposition even for massive tensors. The approximation error can thereby be controlled via oversampling and the computation of power iterations. In addition to theoretical results, several empirical results demonstrate the performance of the proposed algorithm.
TuckerMPI: A Parallel C++/MPI Software Package for Large-scale Data Compression via the Tucker Tensor Decomposition

Our goal is compression of massive-scale grid-structured data, such as the multi-terabyte output of a high-fidelity computational simulation. For such data sets, we have developed a new software package called TuckerMPI, a parallel C++/MPI software package for compressing distributed data. The approach is based on treating the data as a tensor, i.e., a multidimensional array, and computing its truncated Tucker decomposition, a higher-order analogue to the truncated singular value decomposition of a matrix. The result is a low-rank approximation of the original tensor-structured data. Compression efficiency is achieved by detecting latent global structure within the data, which we contrast to most compression methods that are focused on local structure. In this work, we describe TuckerMPI, our implementation of the truncated Tucker decomposition, including details of the data distribution and in-memory layouts, the parallel and serial implementations of the key kernels, and analysis of the storage, communication, and computational costs. We test the software on 4.5 and 6.7 terabyte data sets distributed across 100 s of nodes (1,000 s of MPI processes), achieving compression ratios between 100 and 200,000×, which equates to 99--99.999% compression (depending on the desired accuracy) in substantially less time than it would take to even read the same dataset from a parallel file system. Moreover, we show that our method also allows for reconstruction of partial or down-sampled data on a single node, without a parallel computer so long as the reconstructed portion is small enough to fit on a single machine, e.g., in the instance of reconstructing/visualizing a single down-sampled time step or computing summary statistics. The code is available at https://gitlab.com/tensors/TuckerMPI.
Measure what matters: Counts of hospitalized patients are a better metric for health system capacity planning for a reopening.

Objective Responding to the COVID-19 pandemic requires accurate forecasting of health system capacity requirements using readily available inputs. We examined whether testing and hospitalization data could help quantify the anticipated burden on the health system given shelter-in-place (SIP) order. Materials and methods 16,103 SARS-CoV-2 RT-PCR tests were performed on 15,807 patients at Stanford facilities between March 2 and April 11, 2020. We analyzed the fraction of tested patients that were confirmed positive for COVID-19, the fraction of those needing hospitalization, and the fraction requiring ICU admission over the 40 days between March 2nd and April 11th 2020. Results We find a marked slowdown in the hospitalization rate within ten days of SIP even as cases continued to rise. We also find a shift towards younger patients in the age distribution of those testing positive for COVID-19 over the four weeks of SIP. The impact of this shift is a divergence between increasing positive case confirmations and slowing new hospitalizations, both of which affects the demand on health systems. Conclusion Without using local hospitalization rates and the age distribution of positive patients, current models are likely to overestimate the resource burden of COVID-19. It is imperative that health systems start using these data to quantify effects of SIP and aid reopening planning.
Accurate Tracking of Aggressive Quadrotor Trajectories Using Incremental Nonlinear Dynamic Inversion and Differential Flatness

Autonomous unmanned aerial vehicles (UAVs) that can execute aggressive (i.e., high-speed and high-acceleration) maneuvers have attracted significant attention in the past few years. This article focuses on accurate tracking of aggressive quadcopter trajectories. We propose a novel control law for tracking of position and yaw angle and their derivatives of up to fourth order, specifically velocity, acceleration, jerk, and snap along with yaw rate and yaw acceleration. Jerk and snap are tracked using feedforward inputs for angular rate and angular acceleration based on the differential flatness of the quadcopter dynamics. Snap tracking requires direct control of body torque, which we achieve using closed-loop motor speed control based on measurements from optical encoders attached to the motors. The controller utilizes incremental nonlinear dynamic inversion (INDI) for robust tracking of linear and angular accelerations despite external disturbances, such as aerodynamic drag forces. Hence, prior modeling of aerodynamic effects is not required. We rigorously analyze the proposed control law through response analysis and demonstrate it in experiments. The controller enables a quadcopter UAV to track complex 3-D trajectories, reaching speeds up to 12.9 m/s and accelerations up to 2.1 g, while keeping the root-mean-square tracking error down to 6.6 cm, in a flight volume that is roughly 18 m $\times 7$ m and 3-m tall. We also demonstrate the robustness of the controller by attaching a drag plate to the UAV in flight tests and by pulling on the UAV with a rope during hover.
Faster Random k-CNF Satisfiability

We describe an algorithm to solve the problem of Boolean CNF-Satisfiability when the input formula is chosen randomly. We build upon the algorithms of Schoning 1999 and Dantsin et al. in 2002. The Schoning algorithm works by trying many possible random assignments, and for each one searching systematically in the neighborhood of that assignment for a satisfying solution. Previous algorithms for this problem run in time O(2^(n (1- Ω(1)/k))). Our improvement is simple: we count how many clauses are satisfied by each randomly sampled assignment, and only search in the neighborhoods of assignments with abnormally many satisfied clauses. We show that assignments like these are significantly more likely to be near a satisfying assignment. This improvement saves a factor of 2^(n Ω(lg² k)/k), resulting in an overall runtime of O(2^(n (1- Ω(lg² k)/k))) for random k-SAT.
Tocilizumab for treatment of mechanically ventilated patients with COVID-19.

Background Severe coronavirus disease 2019 (COVID-19) can manifest in rapid decompensation and respiratory failure with elevated inflammatory markers, consistent with cytokine release syndrome for which IL-6 blockade is an approved treatment.
Monitoring and competing principals: A double-edged sword

Abstract Do monitoring technologies increase a principal's profits if she has to compete with others for an agent? While monitoring improves the risk-incentive tradeoff, it also reduces the costs for a rivaling principal to offer a more attractive contract. We show in a two-action, two-outcome model that when the derivative of the agent's risk tolerance is smaller than one, equilibrium profits are lower when monitoring is available if there is some competition. When the derivative is larger than one, equilibrium profits are higher when monitoring is available. Conversely, the agent benefits from monitoring when the competition is intense but can be hurt when it is mild.
Adolescent Intermittent Ethanol Exposure Effects on Kappa Opioid Receptor Mediated Dopamine Transmission: Sex and Age of Exposure Matter.

Underage alcohol drinking increases the risk of developing alcohol use disorder (AUD). In rodents, adolescent ethanol exposure augments ethanol consumption and anxiety-like behavior while reducing social interaction. However, the underlying mechanisms driving these adaptations are unclear. The dopamine and kappa opioid receptor (KOR) systems in the nucleus accumbens (NAc) are implicated in affective disorders, including AUD, with studies showing augmented KOR function and reduced dopamine transmission in ethanol-dependent adult animals. Thus, here we examine the impact of adolescent intermittent ethanol (AIE) exposure on dopamine transmission and KOR function in the NAc. Rats were exposed to water or ethanol (4 g/kg, intragastrically) every other day during early (postnatal day (PD) 25–45) or late (PD 45–65) adolescence. While AIE exposure during early adolescence (early-AIE) did not alter dopamine release in male and female rats, AIE exposure during late adolescence (late-AIE) resulted in greater dopamine release in males and lower dopamine release in females. To determine the impact of AIE on KOR function, we measured the effect of KOR activation using U50,488 (0.01–1.00 µM) on dopamine release. Early-AIE exposure potentiated KOR-mediated inhibition of dopamine release in females, while late-AIE exposure attenuated this effect in males. Interestingly, no differences in KOR function were observed in early-AIE exposed males and late-AIE exposed females. Together, these data suggest that AIE exposure impact on neural processes is dependent on sex and exposure timing. These differences likely arise from differential developmental timing in males and females. This is the first study to show changes in KOR function following AIE exposure.
Exploiting an Adversary’s Intentions in Graphical Coordination Games

How does information regarding an adversary's intentions affect optimal system design? This paper addresses this question in the context of graphical coordination games where an adversary can indirectly influence the behavior of agents by modifying their payoffs. We study a situation in which a system operator must select a graph topology in anticipation of the action of an unknown adversary. The designer can limit her worst-case losses by playing a security strategy, effectively planning for an adversary which intends maximum harm. However, fine-grained information regarding the adversary's intention may help the system operator to fine-tune the defenses and obtain better system performance. In a simple model of adversarial behavior, this paper asks how much a system operator can gain by fine-tuning a defense for known adversarial intent. We find that if the adversary is weak, a security strategy is approximately optimal for any adversary type; however, for moderately-strong adversaries, security strategies are far from optimal.
On the efficacy of optimized exit rule for mean reversion trading

We investigate the effect of using an optimized exit rule on pairs trading. For every asset pair, we optimize the positions so that resulting intraday portfolio value is best fitted to an Ornstein–...
A Connection between Feedback Capacity and Kalman Filter for Colored Gaussian Noises

In this paper, we establish a connection between the feedback capacity of additive colored Gaussian noise channels and the Kalman filters with additive colored Gaussian noises. In light of this, we are able to provide lower bounds on feedback capacity of such channels with finite-order auto-regressive moving average colored noises, and the bounds are seen to be consistent with various existing results in the literature; particularly, the bound is tight in the case of first-order auto-regressive moving average colored noises. On the other hand, the Kalman filtering systems, after certain equivalence transformations, can be employed as recursive coding schemes/algorithms to achieve the lower bounds. In general, our results provide an alternative perspective while pointing to potentially tighter bounds for the feedback capacity problem.
Longer time accuracy for incompressible Navier-Stokes simulations with the EMAC formulation

Abstract In this paper, we consider the recently introduced EMAC formulation for the incompressible Navier–Stokes (NS) equations, which is the only known NS formulation that conserves energy, momentum and angular momentum when the divergence constraint is only weakly enforced. Since its introduction, the EMAC formulation has been successfully used for a wide variety of fluid dynamics problems. We prove that discretizations using the EMAC formulation are potentially better than those built on the commonly used skew-symmetric formulation, by deriving a better longer time error estimate for EMAC: while the classical results for schemes using the skew-symmetric formulation have Gronwall constants dependent on exp ( C ⋅ R e ⋅ T ) with R e the Reynolds number, it turns out that the EMAC error estimate is free from this explicit exponential dependence on the Reynolds number. Additionally, it is demonstrated how EMAC admits smaller lower bounds on its velocity error, since incorrect treatment of linear momentum, angular momentum and energy induces lower bounds for L 2 velocity error, and EMAC treats these quantities more accurately. Results of numerical tests for channel flow past a cylinder and 2D Kelvin–Helmholtz instability are also given, both of which show that the advantages of EMAC over the skew-symmetric formulation increase as the Reynolds number gets larger and for longer simulation times.
A Unified Sparse Optimization Framework to Learn Parsimonious Physics-Informed Models From Data

Machine learning (ML) is redefining what is possible in data-intensive fields of science and engineering. However, applying ML to problems in the physical sciences comes with a unique set of challenges: scientists want physically interpretable models that can (i) generalize to predict previously unobserved behaviors, (ii) provide effective forecasting predictions (extrapolation), and (iii) be certifiable. Autonomous systems will necessarily interact with changing and uncertain environments, motivating the need for models that can accurately extrapolate based on physical principles (e.g. Newton's universal second law for classical mechanics, F = ma). Standard ML approaches have shown impressive performance for predicting dynamics in an interpolatory regime, but the resulting models often lack interpretability and fail to generalize. We build on a sparse regression framework that discovers governing dynamical systems models from data, selecting relevant terms in the dynamics from a library of possible functions. Our critically enabling innovation introduces a relaxed version of a sparse optimization framework that allows the use of non-convex sparsity promoting regularization functions and addresses three open challenges in scientific problems and data sets: (i) robust handling of outliers and corrupt data within noisy sensor measurements, (ii) parametric dependencies in candidate library functions, and (iii) the imposition of physical constraints. By explicitly addressing these open challenges, the integrated and unified algorithm developed provides a significant advancement over current state-of-the-art sparse model discovery methods. We show that the approach discovers parsimonious dynamical models on several example systems. This flexible approach can be tailored to the unique challenges associated with a wide range of applications and data sets, providing a powerful ML-based framework for learning governing models for physical systems from data.
Modeling and Predicting Trust Dynamics in Human–Robot Teaming: A Bayesian Inference Approach

Trust in automation, or more recently trust in autonomy, has received extensive research attention in the past three decades. The majority of prior literature adopted a “snapshot” view of trust and typically evaluated trust through questionnaires administered at the end of an experiment. This “snapshot” view, however, does not acknowledge that trust is a dynamic variable that can strengthen or decay over time. To fill the research gap, the present study aims to model trust dynamics when a human interacts with a robotic agent over time. The underlying premise of the study is that by interacting with a robotic agent and observing its performance over time, a rational human agent will update his/her trust in the robotic agent accordingly. Based on this premise, we develop a personalized trust prediction model and learn its parameters using Bayesian inference. Our proposed model adheres to three properties of trust dynamics characterizing human agents’ trust development process de facto and thus guarantees high model explicability and generalizability. We tested the proposed method using an existing dataset involving 39 human participants interacting with four drones in a simulated surveillance mission. The proposed method obtained a root mean square error of 0.072, significantly outperforming existing prediction methods. Moreover, we identified three distinct types of trust dynamics, the Bayesian decision maker, the oscillator, and the disbeliever, respectively. This prediction model can be used for the design of individualized and adaptive technologies.
LiDAR-enhanced Structure-from-Motion

Although Structure-from-Motion (SfM) as a maturing technique has been widely used in many applications, state-of-the-art SfM algorithms are still not robust enough in certain situations. For example, images for inspection purposes are often taken in close distance to obtain detailed textures, which will result in less overlap between images and thus decrease the accuracy of estimated motion. In this paper, we propose a LiDAR-enhanced SfM pipeline that jointly processes data from a rotating LiDAR and a stereo camera pair to estimate sensor motions. We show that incorporating LiDAR helps to effectively reject falsely matched images and significantly improve the model consistency in large-scale environments. Experiments are conducted in different environments to test the performance of the proposed pipeline and comparison results with the state-of-the-art SfM algorithms are reported.
Passive Quadrupedal Gait Synchronization for Extra Robotic Legs Using a Dynamically Coupled Double Rimless Wheel Model

The Extra Robotic Legs (XRL) system is a robotic augmentation worn by a human operator consisting of two articulated robot legs that walk with the operator and help bear a heavy backpack payload. It is desirable for the Human-XRL quadruped system to walk with the rear legs lead the front by 25% of the gait period, minimizing the energy lost from foot impacts while maximizing balance stability. Unlike quadrupedal robots, the XRL cannot command the human’s limbs to coordinate quadrupedal locomotion. Using a pair of Rimless Wheel models, it is shown that the systems coupled with a spring and damper converge to the desired 25% phase difference. A Poincare return map was generated using numerical simulation to examine the convergence properties to different coupler design parameters, and initial conditions. The Dynamically Coupled Double Rimless Wheel system was physically realized with a spring and dashpot chosen from the theoretical results, and initial experiments indicate that the desired synchronization properties may be achieved within several steps using this set of passive components alone.
High Dimensional Discrete Integration over the Hypergrid.

Recently Ermon et al. (2013) pioneered a way to practically compute approximations to large scale counting or discrete integration problems by using random hashes. The hashes are used to reduce the counting problem into many separate discrete optimization problems. The optimization problems then can be solved by an NP-oracle such as commercial SAT solvers or integer linear programming (ILP) solvers. In particular, Ermon et al. showed that if the domain of integration is $\{0,1\}^n$ then it is possible to obtain a solution within a factor of $16$ of the optimal (a 16-approximation) by this technique. In many crucial counting tasks, such as computation of partition function of ferromagnetic Potts model, the domain of integration is naturally $\{0,1,\dots, q-1\}^n, q>2$, the hypergrid. The straightforward extension of Ermon et al.'s method allows a $q^2$-approximation for this problem. For large values of $q$, this is undesirable. In this paper, we show an improved technique to obtain an approximation factor of $4+O(1/q^2)$ to this problem. We are able to achieve this by using an idea of optimization over multiple bins of the hash functions, that can be easily implemented by inequality constraints, or even in unconstrained way. Also the burden on the NP-oracle is not increased by our method (an ILP solver can still be used). We provide experimental simulation results to support the theoretical guarantees of our algorithms.
The virtual resolutions package for Macaulay2

We introduce the VirtualResolution package for the computer algebra system Macaulay2. This package has tools to construct, display, and study virtual resolutions for products of projective spaces. The package also has tools for generating curves in $\mathbb{P}^1\times\mathbb{P}^2$, providing sources for interesting virtual resolutions.
POLYNOMIAL INVARIANTS OF PSEUDO-ANOSOV MAPS

We investigate the structure of the characteristic polynomial det(xI - T) of a transition matrix T that is associated to a train track representative of a pseudo-Anosov map [F] acting on a surface. As a result we obtain three new polynomial invariants of [F], one of them being the product of the other two, and all three being divisors of det(xI - T). The degrees of the new polynomials are invariants of [F] and we give simple formulas for computing them by a counting argument from an invariant train-track. We give examples of genus 2 pseudo-Anosov maps having the same dilatation, and use our invariants to distinguish them.
HELICITY CONDENSATION AS THE ORIGIN OF CORONAL AND SOLAR WIND STRUCTURE

Three of the most important and most puzzling features of the Sun's atmosphere are the smoothness of the closed-field corona (the so-called coronal loops), the accumulation of magnetic shear at photospheric polarity inversion lines (PILs; filament channels), and the complex dynamics of the slow wind. We propose that a single process, helicity condensation, is the physical mechanism giving rise to all three features. A simplified model is presented for how helicity is injected and transported in the closed corona by magnetic reconnection. With this model, we demonstrate that magnetic shear must accumulate at PILs and coronal hole boundaries, and estimate the rate of shear growth at PILs and the loss to the wind. Our results can account for many of the observed properties of the corona and wind.
WIYN Open Cluster Study. XLVIII. The Hard-binary Population of NGC?188

We present an in-depth study of the hard-binary population of the old (7 Gyr) open cluster NGC 188. Utilizing 85 spectroscopic binary orbits out of a complete sample of 129 detected binary members, we study the cluster binary frequency and the distributions of binary orbital elements among the main-sequence (MS), giant, and blue straggler (BS) populations. The results are derived from our ongoing radial velocity survey of the cluster, which spans in magnitude from the brightest stars in the cluster to V = 16.5 (about 1.1-0.9 M{sub Sun} ), and extends to a projected radius of 17 pc ({approx}13 core radii). Our detectable binaries have periods ranging from a few days to of order 10{sup 4} days, and thus are hard binaries that dynamically power the cluster. The MS solar-type hard binaries in NGC 188 are nearly indistinguishable from similar binaries in the Galactic field. We observe a global solar-type MS hard-binary frequency in NGC 188 of 23% {+-} 2%, which when corrected for incompleteness results in a frequency of 29% {+-} 3% for binaries with periods less than 10{sup 4} days. For MS hard binaries in the cluster, we observe a log-period distribution that rises toward our detection limit,more » a roughly Gaussian eccentricity distribution centered on e = 0.35 (for binaries with periods longer than the circularization period), and a secondary-mass distribution that rises toward lower-mass companions. Importantly, the NGC 188 BS binaries show significantly different characteristics than the solar-type MS binaries in NGC 188. We observe a BS hard-binary frequency of 76% {+-} 19%, three times that of the MS. The excess of this binary frequency over the normal MS binary frequency is valid at the >99% confidence level. Furthermore, the BS binary eccentricity-log-period distribution is distinct from that of the MS at the 99% confidence level, with the majority of the BS binaries having periods of order 1000 days and lower eccentricities. The secondary-mass distribution for these long-period BS binaries is narrow and peaked with a mean value of about 0.5 M{sub Sun }. Predictions for mass-transfer products are most closely consistent with the binary properties of these NGC 188 BSs, which comprise two-thirds of the BS population. Additionally, we compare the NGC 188 binaries to those evolved within the sophisticated Hurley et al. (2005) N-body open cluster simulation. The MS hard-binary population predicted by the simulation is significantly different from the MS hard-binary population observed in NGC 188, in frequency and distributions of period and eccentricity. Many of these differences result from the adopted initial binary population, while others reflect on the physics used in the simulation (e.g., tidal circularization). Additional simulations with initial conditions that are better motivated by observations are necessary to properly investigate the dynamical evolution of a rich binary population in open clusters like NGC 188.« less
Quartic Rings Associated to Binary Quartic Forms

We give a bijection between binary quartic forms and quartic rings with a monogenic cubic resolvent ring, relating the rings associated to binary quartic forms with Bhargava’s cubic resolvent rings. This gives a parametrization of quartic rings with monogenic cubic resolvents. We also give a geometric interpretation of this parametrization.
A Ray-Tracing Model of the Vela Pulsar

In the relativistic plasma surrounding a pulsar, a subluminal ordinary-mode electromagnetic wave will propagate along a magnetic field line. After some distance, it can break free of the field line and escape the magnetosphere to reach an observer. We describe a simple model of pulsar radio emission based on this scenario and find that applying this model to the case of the Vela pulsar reproduces qualitative characteristics of the observed Vela pulse profile.