cssi/SciSciGPT-SciSciNet huggingface dataset:
Constrained sloshing of liquid mercury in a flexible spherical tank

The mercury propellant tank system developed for use with solar electric propulsion was studied to analytically determine the resonant frequencies of the tank system and compare them with the anticipated control natural frequency of the spacecraft. The system consisted of a stainless steel spherical shell and a hemispherical elastomeric diaphram which separates the mercury propellant and the gaseous nitrogen pressurant. The major analytical tool used was the NASTRAN program. Six mathematical models, which represent various amounts of mercury in the tank system were developed. Resonant frequencies for six harmonics were obtained for each of the six models considered. The results show that the lowest resonant frequency for the tank system is about an order of magnitude greater than the anticipated control frequency of the spacecraft.
MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning

Exploration in reinforcement learning is a challenging problem: in the worst case, the agent must search for high-reward states that could be hidden anywhere in the state space. Can we define a more tractable class of RL problems, where the agent is provided with examples of successful outcomes? In this problem setting, the reward function can be obtained automatically by training a classifier to categorize states as successful or not. If trained properly, such a classifier can provide a well-shaped objective landscape that both promotes progress toward good states and provides a calibrated exploration bonus. In this work, we show that an uncertainty aware classifier can solve challenging reinforcement learning problems by both encouraging exploration and provided directed guidance towards positive outcomes. We propose a novel mechanism for obtaining these calibrated, uncertainty-aware classifiers based on an amortized technique for computing the normalized maximum likelihood (NML) distribution. To make this tractable, we propose a novel method for computing the NML distribution by using meta-learning. We show that the resulting algorithm has a number of intriguing connections to both count-based exploration methods and prior algorithms for learning reward functions, while also providing more effective guidance towards the goal. We demonstrate that our algorithm solves a number of challenging navigation and robotic manipulation tasks which prove difficult or impossible for prior methods.
Contextual search in the presence of irrational agents

We study contextual search, a generalization of binary search in higher dimensions, which captures settings such as feature-based dynamic pricing. Standard game-theoretic formulations of this problem assume that agents act in accordance with a specific behavioral model. In practice, some agents may not subscribe to the dominant behavioral model or may act in ways that are seemingly arbitrarily irrational. Existing algorithms heavily depend on the behavioral model being (approximately) accurate for all agents and have poor performance even with a few arbitrarily irrational agents. We initiate the study of contextual search when some of the agents can behave in ways inconsistent with the underlying behavioral model. In particular, we provide two algorithms, one based on multidimensional binary search methods and one based on gradient descent. Our techniques draw inspiration from learning theory, game theory, high-dimensional geometry, and convex analysis.
Identification of observer/Kalman filter Markov parameters - Theory and experiments

An algorithm to compute Markov parameters of an observer or Kalman filter from experimental input and output data is discussed. The Markov parameters can then be used for identification of a state space representation, with associated Kalman gain or observer gain, for the purpose of controller design. The algorithm is a non-recursive matrix version of two recursive algorithms developed in previous works for different purposes. The relationship between these other algorithms is developed. The new matrix formulation here gives insight into the existence and uniqueness of solutions of certain equations and gives bounds on the proper choice of observer order. It is shown that if one uses data containing noise, and seeks the fastest possible deterministic observer, the deadbeat observer, one instead obtains the Kalman filter, which is the fastest possible observer in the stochastic environment. Results are demonstrated in numerical studies and in experiments on an ten-bay truss structure.
Biofilm self-patterning: mechanical forces drive a reorientation cascade

In growing active matter systems, a large collection of engineered or living autonomous units metabolize free energy and create order at different length scales as they proliferate and migrate collectively. One such example is bacterial biofilms, which are surface-attached aggregates of bacterial cells embedded in an extracellular matrix. However, how bacterial growth coordinates with cell-surface interactions to create distinctive, long-range order in biofilms remains elusive. Here we report a collective cell reorientation cascade in growing Vibrio cholerae biofilms, leading to a differentially ordered, spatiotemporally coupled core-rim structure reminiscent of a blooming aster. Cell verticalization in the core generates differential growth that drives radial alignment of the cells in the rim, while the radially aligned rim in turn generates compressive stresses that expand the verticalized core. Such self-patterning disappears in adhesion-less mutants but can be restored through opto-manipulation of growth. Agent-based simulations and two-phase active nematic modeling reveal the strong interdependence of the driving forces for the differential ordering. Our findings provide insight into the collective cell patterning in bacterial communities and engineering of phenotypes and functions of living active matter.
Analysis of a Solar Space Cooling System Using Liquid Desiccants

For tropical countries, solar space cooling is an attractive proposition. Dehumidification of air in hot, humid climates is almost as important as cooling. Removal of moisture from the air is much easier to achieve than cooling the air. The proposed cooling system operates on the ventilation mode. The ambient air is dehumidified using liquid desiccants followed by adiabatic evaporative cooling. The desiccant soon becomes saturated with the water extracted from the air and can be regenerated by using solar energy. For this system, a simple expression is derived in this paper to predict the amount of heat removed from the space to be conditioned in terms of known initial parameters through a simplified vapor pressure correlation and effectiveness of the dehumidifier and the heat exchanger. The effect of ambient air conditions, solution concentration, the cooling water temperature and the effectiveness of the dehumidifier and the heat exchanger on the performance of the cooling system are also discussed in this paper.
A new technique for measuring sputtering yields at high energies

The use of thin, self-supporting carbon catcher foils allows one to measure sputtering yields in a broad range of materials with high sensitivity. Analyzing the foils with Rutherford forward scattering, sputtered Al, Si and P surface densities down to 5 x 10 to the 13th per sq cm with uncertainties of about 20 percent have been measured.
The Dirichlet problem for elliptic operators having a BMO anti-symmetric part

The present paper establishes the first result on the absolute continuity of elliptic measure with respect to the Lebesgue measure for a divergence form elliptic operator with non-smooth coefficients that have a $${{\,\mathrm{BMO}\,}}$$ anti-symmetric part. In particular, the coefficients are not necessarily bounded. We prove that the Dirichlet problem for elliptic equation $$\mathrm{div}(A\nabla u)=0$$ in the upper half-space $$(x,t)\in {\mathbb {R}}^{n+1}_+$$ is uniquely solvable when $$n\ge 2$$ and the boundary data is in $$L^p({\mathbb {R}}^n,dx)$$ for some $$p\in (1,\infty )$$ . This result is equivalent to saying that the elliptic measure associated to L belongs to the $$A_\infty $$ class with respect to the Lebesgue measure dx, a quantitative version of absolute continuity.
Inferring serial correlation with dynamic backgrounds

Sequential data with serial correlation and an unknown, unstructured, and dynamic background is ubiquitous in neuroscience, psychology, and econometrics. Inferring serial correlation for such data is a fundamental challenge in statistics. We propose a total variation constrained least square estimator coupled with hypothesis tests to infer the serial correlation in the presence of unknown and unstructured dynamic background. The total variation constraint on the dynamic background encourages a piece-wise constant structure, which can approximate a wide range of dynamic backgrounds. The tuning parameter is selected via the Ljung-Box test to control the bias-variance trade-off. We establish a non-asymptotic upper bound for the estimation error through variational inequalities. We also derive a lower error bound via Fano's method and show the proposed method is near-optimal. Numerical simulation and a real study in psychology demonstrate the excellent performance of our proposed method compared with the state-of-the-art.
Social boldness correlates with brain gene expression in male green anoles.

Abstract Within populations, some individuals tend to exhibit a bold or shy social behavior phenotype relative to the mean. The neural underpinnings of these differing phenotypes – also described as syndromes, personalities, and coping styles – is an area of ongoing investigation. Although a social decision-making network has been described across vertebrate taxa, most studies examining activity within this network do so in relation to exhibited differences in behavioral expression. Our study instead focuses on constitutive gene expression in bold and shy individuals by isolating baseline gene expression profiles that influence social boldness predisposition, rather than those reflecting the results of social interaction and behavioral execution. We performed this study on male green anole lizards (Anolis carolinensis), an established model organism for behavioral research, which provides a crucial comparison group to investigations of birds and mammals. After identifying subjects as bold or shy through repeated reproductive and agonistic behavior testing, we used RNA sequencing to compare gene expression profiles between these groups within various forebrain, midbrain, and hindbrain regions. The ventromedial hypothalamus had the largest group differences in gene expression, with bold males having increased expression of neuroendocrine and neurotransmitter receptor and calcium channel genes compared to shy males. Conversely, shy males express more integrin alpha-10 in the majority of examined regions. There were no significant group differences in physiology or hormone levels. Our results highlight the ventromedial hypothalamus as an important center of behavioral differences across individuals and provide novel candidates for investigations into the regulation of individual variation in social behavior phenotype.
Application of Transformer Impedance Correction Tables in Power Flow Studies

Phase Shifting Transformers (PST) are used to control or block certain flows of real power through phase angle regulation across the device. Its functionality is crucial to special situations such as eliminating loop flow through an area and balancing real power flow between parallel paths. Impedance correction tables are used to model that the impedance of phase shifting transformers often vary as a function of their phase angle shift. The focus of this paper is to consider the modeling errors if the impact of this changing impedance is ignored. The simulations are tested through different scenarios using a 37-bus test case and a 10,000-bus synthetic power grid. The results verify the important role of impedance correction factor to get more accurate and optimal power solutions.
Fair and Efficient Online Allocations with Normalized Valuations.

A set of divisible resources becomes available over a sequence of rounds and needs to be allocated immediately and irrevocably. Our goal is to distribute these resources to maximize fairness and efficiency. Achieving any non-trivial guarantees in an adversarial setting is impossible. However, we show that normalizing the agent values, a very common assumption in fair division, allows us to escape this impossibility. Our main result is an online algorithm for the case of two agents that ensures the outcome is fair while guaranteeing 91.6% of the optimal social welfare. We also show that this is near-optimal: there is no fair algorithm that guarantees more than 93.3% of the optimal social welfare.
The N-Terminal and Central Domains of CoV-2 nsp1 Play Key Functional Roles in Suppression of Cellular Gene Expression and Preservation of Viral Gene Expression

Nonstructural protein 1 (nsp1) is the first viral protein synthesized during coronavirus (CoV) infection and is a key virulence factor that dampens the innate immune response. It restricts cellular gene expression through a combination of inhibiting translation by blocking the mRNA entry channel of the 40S ribosomal subunit and by promoting mRNA degradation. We performed a detailed structure-guided mutational analysis of CoV-2 nsp1 coupled with in vitro and cell-based functional assays, revealing insight into how it coordinates these activities against host but not viral mRNA. We found that residues in the N-terminal and central regions of nsp1 not involved in docking into the 40S mRNA entry channel nonetheless stabilize its association with the ribosome and mRNA, thereby enhancing its restriction of host gene expression. These residues are also critical for the ability of mRNA containing the CoV-2 leader sequence to escape translational repression. Notably, we identify CoV-2 nsp1 mutants that gain the ability to repress translation of viral leader-containing transcripts. These data support a model in which viral mRNA binding functionally alters the association of nsp1 with the ribosome, which has implications for drug targeting and understanding how engineered or emerging mutations in CoV-2 nsp1 could attenuate the virus. Funding Information: This project was funded by a COVID19 Excellence in Research Award from the Laboratory of Genomics Research to BAG, JHC and NTI and an Emergent Ventures COVID19 Fast Grant #2155 to BAG, who is also an investigator of the Howard Hughes Medical Institute. ML is funded by NSERC Predoctoral Fellowship PGSD3-516787-2018. Declaration of Interests: No competing interests to declare. Ethics Approval Statement: N/A; no animal or human subjects work in this manuscript.
Characterizing and Comparing COVID-19 Misinformation across Languages, Countries and Platforms

Misinformation/disinformation about COVID-19 has been rampant on social media around the world. In this study, we investigate COVID-19 misinformation/ disinformation on social media in multiple languages/countries: Chinese (Mandarin)/China, English/USA, and Farsi (Persian)/Iran;and on multiple platforms such as Twitter, Facebook, Instagram, WhatsApp, Weibo, WeChat and TikTok. Misinformation, especially about a global pandemic, is a global problem yet it is common for studies of COVID-19 misinformation on social media to focus on a single language, like English, a single country, like the USA, or a single platform, like Twitter. We utilized opportunistic sampling to compile 200 specific items of viral and yet debunked misinformation across these languages, countries and platforms emerged between January 1 and August 31. We then categorized this collection based both on the topics of the misinformation and the underlying roots of that misinformation. Our multi-cultural and multi-linguistic team observed that the nature of COVID-19 misinformation on social media varied in substantial ways across different languages/countries depending on the cultures, beliefs/religions, popularity of social media, types of platforms, freedom of speech and the power of people versus governments. We observe that politics is at the root of most of the collected misinformation across all three languages in this dataset. We further observe the different impact of government restrictions on platforms and platform restrictions on content in China, Iran, and the USA and their impact on a key question of our age: how do we control misinformation without silencing the voices we need to hold governments accountable?. © 2021 ACM.
Towards Extremely Compact RNNs for Video Recognition with Fully Decomposed Hierarchical Tucker Structure

Recurrent Neural Networks (RNNs) have been widely used in sequence analysis and modeling. However, when processing high-dimensional data, RNNs typically require very large model sizes, thereby bringing a series of deployment challenges. Although various prior works have been proposed to reduce the RNN model sizes, executing RNN models in the resource-restricted environments is still a very challenging problem. In this paper, we propose to develop extremely compact RNN models with fully decomposed hierarchical Tucker (FDHT) structure. The HT decomposition does not only provide much higher storage cost reduction than the other tensor decomposition approaches, but also brings better accuracy performance improvement for the compact RNN models. Meanwhile, unlike the existing tensor decomposition-based methods that can only decompose the input-to-hidden layer of RNNs, our proposed fully decomposition approach enables the comprehensive compression for the entire RNN models with maintaining very high accuracy. Our experimental results on several popular video recognition datasets show that, our proposed fully decomposed hierarchical tucker-based LSTM (FDHT-LSTM) is extremely compact and highly efficient. To the best of our knowledge, FDHT-LSTM, for the first time, consistently achieves very high accuracy with only few thousand parameters (3,132 to 8,808) on different datasets. Compared with the state-of-the-art compressed RNN models, such as TT-LSTM, TR-LSTM and BT-LSTM, our FDHT-LSTM simultaneously enjoys both order-of-magnitude (3,985× to 10,711×) fewer parameters and significant accuracy improvement (0.6% to 12.7%).
Binary Scoring Rules that Incentivize Precision

All proper scoring rules incentivize an expert to predict accurately (report their true estimate), but not all proper scoring rules equally incentivize precision. Rather than treating the expert's belief as exogenously given, we consider a model where a rational expert can endogenously refine their belief by repeatedly paying a fixed cost, and is incentivized to do so by a proper scoring rule. Specifically, our expert aims to predict the probability that a biased coin flipped tomorrow will land heads, and can flip the coin any number of times today at a cost of c per flip. Our first main result defines an incentivization index for proper scoring rules, and proves that this index measures the expected error of the expert's estimate (where the number of flips today is chosen adaptively to maximize the predictor's expected payoff). Our second main result finds the unique scoring rule which optimizes the incentivization index over all proper scoring rules. We also consider extensions to minimizing the lth moment of error, and again provide an incentivization index and optimal proper scoring rule. In some cases, the resulting scoring rule is differentiable, but not infinitely differentiable. In these cases, we further prove that the optimum can be uniformly approximated by polynomial scoring rules. Finally, we compare common scoring rules via our measure, and include simulations confirming the relevance of our measure even in domains outside where it provably applies.
Energy-Optimal Goal Assignment of Multi-Agent System with Goal Trajectories in Polynomials

In this paper, we propose an approach for solving an energy-optimal goal assignment problem to generate the desired formation in multi-agent systems. Each agent solves a decentralized optimization problem with only local information about its neighboring agents and the goals. The optimization problem consists of two sub-problems. The first problem seeks to minimize the energy for each agent to reach certain goals, while the second problem entreats an optimal combination of goal and agent pairs that minimizes the energy cost. By assuming the goal trajectories are given in a polynomial form, we prove the solution to the formulated problem exists globally. We validate the effectiveness of the proposed approach through the simulation.
Overcoming Poor Word Embeddings with Word Definitions

Modern natural language understanding models depend on pretrained subword embeddings, but applications may need to reason about words that were never or rarely seen during pretraining. We show that examples that depend critically on a rarer word are more challenging for natural language inference models. Then we explore how a model could learn to use definitions, provided in natural text, to overcome this handicap. Our model’s understanding of a definition is usually weaker than a well-modeled word embedding, but it recovers most of the performance gap from using a completely untrained word.
Perceiving and remembering speech depend on multifractal nonlinearity in movements producing and exploring speech

Speech perception and memory for speech require active engagement. Gestural theories have emphasized mainly the effect of speaker's movements on speech perception. They fail to address the effects ...
Analysis of a time-delayed HIV/AIDS epidemic model with education campaigns

We consider a time-delayed HIV/AIDS epidemic model with education dissemination and study the asymptotic dynamics of solutions as well as the asymptotic behavior of the endemic equilibrium with respect to the amount of information disseminated about the disease. Under appropriate assumptions on the infection rates, we show that if the basic reproduction number is less than or equal to one, then the disease will be eradicated in the long run and any solution to the Cauchy problem converges to the unique disease-free equilibrium of the model. On the other hand, when the basic reproduction number is greater than one, we prove that the disease will be permanent but its impact on the population can be significantly minimized as the amount of education dissemination increases. In particular, under appropriate hypothesis on the model parameters, we establish that the size of the component of the infected population of the endemic equilibrium decreases linearly as a function of the amount of information disseminated. We also fit our model to a set of data on HIV/AIDS in order to estimate the infection, effective response, and information rates of the disease. We then use these estimates to present numerical simulations to illustrate our theoretical findings.
Ordering of Small Molecules on Hydrophobic Self-Assembled n-Alkanethiols: Delicate Balance of Interfacial and Intermolecular Interactions

Growth of molecular thin films with desired orders and orientations has become technologically relevant as the electronic industries seek new opportunities and applications. However, the delicate balance of interfacial and intermolecular forces and their complex influence on thin-film growths still require more understanding. Here, the effects of a hydrophobic self-assembled monolayer (SAM) surface on the crystallization of four common solvents—acetonitrile, ethanol, methanol, and water—are investigated. Despite the absence of significant substrate–molecule forces, unexpected oriented growth is observed for these molecules except water. Acetonitrile and ethanol form a sustaining vertical assembly order with long-range crystalline structures. Coincident epitaxy with small lattice mismatches is found to be essential to these orderings, which are energetically favored but without a dominant azimuthal orientation. In contrast, a preferred in-plane registry of methanol overlayers is observed for an ultrathin nominal thickness and becomes lost in slightly thicker films. Such thickness-dependent ordering of methanol assemblies can be explained with semi-commensurate epitaxy with a tensile strain of ~6.6% along hydrogen-bonded chains, whose quick relief results in the loss of order and even the phase. These rich observations suggest that SAM surfaces offer good opportunities for selective crystallization of molecular films worthy of further investigations.
A model for line absorption and emission from turbulent mixing layers

Turbulent mixing layers (TMLs) are ubiquitous in multiphase gas. They can potentially explain observations of high ions such as O VI, which have significant observed column densities despite short cooling times. Previously, we showed that global mass, momentum and energy transfer between phases mediated by TMLs is not sensitive to details of thermal conduction or numerical resolution (Tan et al. 2021). By contrast, we show here that observables such as temperature distributions, column densities and line ratios are sensitive to such considerations. We explain the reason for this difference. We develop a prescription for applying a simple 1D conductive-cooling front model which quantitatively reproduces 3D hydrodynamic simulation results for column densities and line ratios, even when the TML has a complex fractal structure. This enables sub-grid absorption and emission line predictions in large scale simulations. The predicted line ratios are in good agreement with observations, while observed column densities require numerous mixing layers to be pierced along a line of sight.
5 Reasons to Expect an 8 MeV Line in the SN 1987A Neutrino Spectrum

Evidence was previously reported for an 8 MeV neutrino line associated with SN 1987A based on an analysis of 997 events recorded in the Kamiokande-II detector on the day of the supernova. That claimed line, however, occurred at the peak of the background spectrum, and both had a similar shape, making the claim tenuous at best. Here the claim is buttressed by providing five reasons to expect such an 8 MeV neutrino line. A final section of the paper concerns the ongoing KATRIN experiment to find the neutrino mass, which might provide additional support for the line, should it validate a controversial 3 + 3 model of theneutrino masses, including a tachyonic \((m^2 < 0)\) mass.
Review of Learning-Based Longitudinal Motion Planning for Autonomous Vehicles: Research Gaps Between Self-Driving and Traffic Congestion:

Self-driving technology companies and the research community are accelerating the pace of use of machine learning longitudinal motion planning (mMP) for autonomous vehicles (AVs). This paper review...
Tunable unidirectional surface plasmon-polaritons at the interface between gyrotropic and isotropic conductors

The propagation of SPPs in solid state systems is usually reciprocal, which means that a forward-traveling SPP wave can be easily backscattered by disorders. Since such backscattering is detrimental for SPP-based information processing applications, the development of nonreciprocal plasmonic platforms that enforce unidirectional SPP propagations is of great importance.
Hitting time of rapid intensification onset in hurricane-like vortices

Predicting tropical cyclone (TC), rapid intensification (RI) is an important yet challenging task in current weather forecast due to our incomplete understanding of TC nonlinear processes. This study examines the variability of RI onset, including the probability of RI occurrence and the timing of RI onset, using a low-order stochastic model for TC development. Defining RI onset as the first hitting time for a given subset in the TC-scale state space, we quantify the probability of the occurrence of RI onset and the distribution of the timing of RI onset for a range of initial conditions and model parameters. Based on asymptotic analysis for stochastic differential equations, our results show that RI onset occurs later, along with a larger variance of RI onset timing, for weaker vortex initial condition and stronger noise amplitude. In the small noise limit, RI onset probability approaches one and the RI onset timing has less uncertainty (i.e., a smaller variance), consistent with observation of TC development under idealized environment. Our theoretical results are also verified against Monte Carlo simulations and compared with explicit results for a general one-dimensional system, thus providing new insights into the variability of RI onset and helping better quantify the uncertainties of RI variability for practical applications.
The oblateness of Uranus at the 1-microbar level

Atmospheric occultation half-light times and scale heights, together with updated ring-orbit model parameters, are presently used to ascertain the planetary limb shape and thereby derive the radius and oblateness of Uranus at the 1-microbar level from stellar occultation observations obtained on March 10, 1977, and March 23, 1983. Assuming that the planet is in hydrostatic equilibrium, the inferred rotation period is 17.7 + or - 0.6 hr in the -30 to +26 deg latitude range of these observations. These results are noted to be consistent with those based by Lindal et al. (1987) on Voyager 2 observations, where the period at 5 deg latitude is of the order of 18.0 + or - 0.3 hr.
Q-space Conditioned Translation Networks for Directional Synthesis of Diffusion Weighted Images from Multi-modal Structural MRI

Current deep learning approaches for diffusion MRI modeling circumvent the need for densely-sampled diffusion-weighted images (DWIs) by directly predicting microstructural indices from sparsely-sampled DWIs. However, they implicitly make unrealistic assumptions of static q-space sampling during training and reconstruction. Further, such approaches can restrict downstream usage of variably sampled DWIs for usages including the estimation of microstructural indices or tractography. We propose a generative adversarial translation framework for high-quality DWI synthesis with arbitrary q-space sampling given commonly acquired structural images (e.g., B0, T1, T2). Our translation network linearly modulates its internal representations conditioned on continuous q-space information, thus removing the need for fixed sampling schemes. Moreover, this approach enables downstream estimation of high-quality microstructural maps from arbitrarily subsampled DWIs, which may be particularly important in cases with sparsely sampled DWIs. Across several recent methodologies, the proposed approach yields improved DWI synthesis accuracy and fidelity with enhanced downstream utility as quantified by the accuracy of scalar microstructure indices estimated from the synthesized images. Code is available at https://github.com/mengweiren/q-space-conditioned-dwi-synthesis.
Multi-Sensor Spatial Association Using Joint Range-Doppler Features

We investigate the problem of localizing multiple targets using a single set of measurements from a network of radar sensors. Such “single snapshot imaging” provides timely situational awareness, but can utilize neither platform motion, as in synthetic aperture radar, nor track targets across time, as in Kalman filtering and its variants. Associating measurements with targets becomes a fundamental bottleneck in this setting. In this paper, we present a computationally efficient method to extract 2D position and velocity of multiple targets using a linear array of FMCW radar sensors by identifying and exploiting inherent geometric features to drastically reduce the complexity of spatial association. The proposed framework is robust to detection anomalies, and achieves order of magnitude lower complexity compared to conventional methods. While our approach is compatible with conventional FFT-based range-Doppler processing, we show that more sophisticated techniques for range-Doppler estimation lead to reduced data association complexity as well as higher accuracy estimates of target positions and velocities.
Poisson geometry and representations of PI 4-dimensional Sklyanin algebras

Take S to be a 4-dimensional Sklyanin (elliptic) algebra that is module-finite over its center Z; thus, S is PI. Our first result is the construction of a Poisson Z-order structure on S such that the induced Poisson bracket on Z is non-vanishing. We also provide the explicit Jacobian structure of this bracket, leading to a description of the symplectic core decomposition of the maximal spectrum Y of Z. We then classify the irreducible representations of S by combining (1) the geometry of the Poisson order structures, with (2) algebro-geometric methods for the elliptic curve attached to S, along with (3) representation-theoretic methods using line and fat point modules of S. Along the way, we improve results of Smith and Tate obtaining a description the singular locus of Y for such S. The classification results for irreducible representations are in turn used to determine the zero sets of the discriminants ideals of these algebras S.
Constraining f ( R ) gravity with a k -cut cosmic shear analysis of the Hyper Suprime-Cam first-year data

Using Subaru Hyper Suprime-Cam (HSC) year 1 data, we perform the first $k$-cut cosmic shear analysis constraining both $\Lambda$CDM and $f(R)$ Hu-Sawicki modified gravity. To generate the $f(R)$ cosmic shear theory vector, we use the matter power spectrum emulator trained on COLA (COmoving Lagrangian Acceleration) simulations. The $k$-cut method is used to significantly down-weight sensitivity to small scale ($k > 1 \ h {\rm Mpc }^{-1}$) modes in the matter power spectrum where the emulator is less accurate, while simultaneously ensuring our results are robust to baryonic feedback model uncertainty. We have also developed a test to ensure that the effects of poorly modeled small scales are nulled as intended. For $\Lambda$CDM we find $S_8 = \sigma_8 (\Omega_m / 0.3) ^ {0.5} = 0.789 ^{+0.039}_{-0.022}$, while the constraints on the $f(R)$ modified gravity parameters are prior dominated. In the future, the $k$-cut method could be used to constrain a large number of theories of gravity where computational limitations make it infeasible to model the matter power spectrum down to extremely small scales.
Weak independence of events and the converse of the Borel–Cantelli Lemma

Abstract The converse of the Borel–Cantelli Lemma states that if { A i } i = 1 ∞ is a sequence of independent events such that ∑ P ( A i ) = ∞ , then almost surely infinitely many of these events will occur. Erdős and Renyi proved that it is sufficient to weaken the condition of independence to pairwise independence. Later, several other weakenings of the condition appeared in the literature. The aim of this paper is to provide a collection of conditions, all of which imply that almost surely infinitely many of the events occur, and determine the complete implicational relationship between them. Many of these results are known, or follow from known results, however, they are not widely known among non-specialists. Yet, the results can be extremely useful for areas outside of probability theory, as evidenced by the original motivation of this paper emerging from infinite combinatorics. Our proofs are aimed to be accessible to a general mathematical audience.
Wave manipulation using a bistable chain with reversible impurities

We systematically study linear and nonlinear wave propagation in a chain composed of piecewise-linear bistable springs. Such bistable systems are ideal test beds for supporting nonlinear wave dynamical features including transition and (supersonic) solitary waves. We show that bistable chains can support the propagation of subsonic wave packets which in turn can be trapped by a low-energy phase to induce energy localization. The spatial distribution of these energy foci strongly affects the propagation of linear waves, typically causing scattering, but, in special cases, leading to a reflectionless mode analogous to the Ramsauer-Townsend effect. Furthermore, we show that the propagation of nonlinear waves can spontaneously generate or remove additional foci, which act as effective ``impurities.'' This behavior serves as a new mechanism for reversibly programming the dynamic response of bistable chains.
A Sensitivity-based Data Augmentation Framework for Model Predictive Control Policy Approximation

Approximating model predictive control (MPC) policy using expert-based supervised learning techniques requires labeled training data sets sampled from the MPC policy. This is typically obtained by sampling the feasible state-space and evaluating the control law by solving the numerical optimization problem offline for each sample. Although the resulting approximate policy can be cheaply evaluated online, generating large training samples to learn the MPC policy can be time consuming and prohibitively expensive. This is one of the fundamental bottlenecks that limit the design and implementation of MPC policy approximation. This technical note aims to address this challenge, and proposes a novel sensitivity-based data augmentation scheme for direct policy approximation. The proposed approach is based on exploiting the parametric sensitivities to cheaply generate additional training samples in the neighborhood of the existing samples.
The Absence of Attrition in a War of Attrition under Complete Information

Abstract We consider a two-player game of war of attrition under complete information. It is well-known that this class of games admits equilibria in pure, as well as mixed strategies, and much of the literature has focused on the latter. We show that if the players' payoffs whilst in “war” vary stochastically and their exit payoffs are heterogeneous, then the game admits Markov Perfect equilibria in pure strategies only. This is true irrespective of the degree of randomness and heterogeneity, thus highlighting the fragility of mixed-strategy equilibria to a natural perturbation of the canonical model. In contrast, when the players' flow payoffs are deterministic or their exit payoffs are homogeneous, the game admits equilibria in pure and mixed strategies.
Characterization of mouse Bmp5 regulatory injury element in zebrafish wound models.

Abstract Many key signaling molecules used to build tissues during embryonic development are re-activated at injury sites to stimulate tissue regeneration and repair. Bone morphogenetic proteins provide a classic example, but the mechanisms that lead to reactivation of BMPs following injury are still unknown. Previous studies have mapped a large “injury response element” (IRE) in the mouse Bmp5 gene that drives gene expression following bone fractures and other types of injury. Here we show that the large mouse IRE region is also activated in both zebrafish tail resection and mechanosensory hair cell injury models. Using the ability to test multiple constructs and image temporal and spatial dynamics following injury responses, we have narrowed the original size of the mouse IRE region by over 100 fold and identified a small 142 bp minimal enhancer that is rapidly induced in both mesenchymal and epithelial tissues after injury. These studies identify a small sequence that responds to evolutionarily conserved local signals in wounded tissues and suggest candidate pathways that contribute to BMP reactivation after injury.
The Stark Effect in Heterodimer Reaction Centers

The Stark-effect of the optical absorption of reaction centers (RC) of two mutants and the wild type of Rhodobacter (Rb.) capsulatus were measured. The two heterodimer mutants had Histidine M200, the axial ligand to the M-side bacteriochlorophyll (BChl), replaced with Leucine and Phenylalanine through site-directed mutagenesis [1]. These substitutions result in the replacement of the M-side BChl with bacteriopheo-phytin (BPh), forming a BChl-BPh heterodimer. Our data can be interpreted by the formation of a charge transfer state within the heterodimer. The angles between the direction of the change in dipole moment from the ground to the excited state and the Qy transition moment are consistent with X-ray structure data of the related RC of Rb. sphaeroides R26.
Need and Functionality Analysis

Generally the word “smart” is used synonymously with “intelligent,” but in reality the dictionary definition of “smartness” is “marked by often sharp forceful activity or vigorous strength,” that is, it is active. Thus, a “smartphone” can only wake you up with an alarm, but in reality it is smart only if it can “slap” you if you don’t wake up!
In the Spirit of Miyazawa: Multipliers and the Metropolis

This chapter begins with a brief description summary of the present condition of the American inner-city neighborhoods, highlighting those aspects that will be addressed in this contribution. The long-standing problems of the American inner-cities are well documented in the literature — see, for example, Goldsmith and Blakely (1991). The variety of strategies for reviving these locales range from allowing them to degenerate into brown fields which can then be redeveloped from scratch, to invoking targeted “territorial” strategies that use carefully selected leading sectors to force an economy-wide multiplier process. This chapter will explore some macro-economic aspects of the territorial approach — especially the size and origin of multiplier processes in inner city neighborhoods, and the role that these might play in a neighborhood’s revival.
Branch-and-Check: A Hybrid Framework Integrating Mixed Integer Programming and Constraint Logic Programming

We present Branch-and-Check, a hybrid framework integrating Mixed Integer Programming and Constraint Logic Programming, which encapsulates the traditional Benders Decomposition and Branch-and-Bound as special cases. In particular we describe its relation to Benders and the use of nogoods and linear relaxations. We give two examples of how problems can be modelled and solved using Branch-and-Check and present computational results demonstrating more than order-of-magnitude speedup compared to previous approaches. We also mention important future research issues such as hierarchical, dynamic and adjustable linear relaxations.
Consciousness, Quantum Physics, and Hermeneutical Phenomenology

Two hundred years ago Friedrich Schleiermacher (See Wellmon 2006) modified Kant’s notion of anthropology—‘hermeneutically,’ as he said—so as to make it inclusive of the tribes that Captain Cook found in the South Sea Islands. This paper honors the late Joseph J. Kockelmans for making a similar hermeneutic move to update Kant’s notion of natural science so as to make it inclusive of the phenomenological lifeworld (For ‘lifeworld,’ see Husserl’s The Crisis of European Sciences and Transcendental Philosophy, 1954, 121–148, and the ‘lifeworld’ theme throughout the Crisis.) syntheses of classical, relativity, and quantum physics. The new synthesis is in fact not alien to the views of some of the founders of quantum mechanics, notably Eugene Wigner, John von Neumann, Paul Dirac, Werner Heisenberg—possibly even Albert Einstein. In this hermeneutical move, the ‘observer’ is ‘embodied consciousness,’ and ‘measure-numbers’ represent ‘observable presence.’ The new theoretical synthesis of physics is a representation of a physical system as a dynamic Hilbert Vector Space; empirical ‘observables’ are represented by projection operators, each of which maps a subspace of definite measurable values. Among these projection operators, some pairs are ‘complementary’ and share a common subspace of the Hilbert Space where they can be precisely measured together in a common laboratory setting. Some pairs, however, are ‘non-complementary’ and do not share a common subspace; these lead to Uncertainty Principles of the quantum mechanical kind. The quantum notion of an “observable” introduces into the discursive language of physics the common sense lifeworld notion of “contextuality.” This analysis completes Husserl’s analysis of science in the Crisis, so well articulated and developed by Kockelmans (See Kockelmans’ contributions to the phenomenology of natural science in Kockelmans and Kisiel (1970)).
Immunoelectron microscopy of Tetrahymena.

Publisher Summary This chapter focuses on the immunogold labeling technique for transmission electron microscopy. Pre-embedding and post-embedding labeling protocols with a single primary antibody and a double labeling protocol with two different primary antibodies are described. The labeling protocols include a description of a morphometric analysis to quantitate colloidal gold distribution in labeling experiments. Colloidal gold-labeled antisera are widely used to map intracellular distribution of antigens. To use this technique, a primary antibody is bound to antigenic sites in situ , and a secondary antibody, coupled to colloidal gold and specific for the general class of the primary antibody, is bound to the primary antibody. Colloidal gold particles are readily visible as dense spheres in electron microscope images. Colloidal gold labeling has been used to map Tetrahymena proteins in the oral apparatus, membrane skeleton, and nucleolus. For double labeling, two different primary antibodies are used followed by secondary antibodies labeled with gold particles of different size.
Fokale Neuropathien durch physikalische Noxen, vaskuläre, infektiöse und immunologische Erkrankungen

Nervenlasionen haben vielfaltige Ursachen. Im Vordergrund stehen mechanisch bedingte Schaden der Nerven durch unmittelbare Verletzung oder Kompression. Letztere sind Themen dieses Buchs. Die Kenntnis anderer Ursachen einer Mononeuropathie ist aber unerlasslich, nicht zuletzt auch, um uberflussige chirurgische Eingriffe zu vermeiden. Nachfolgend werden mogliche Ursachen einer fokalen Neuropathie beschrieben.
Symptomatic hyperammonemia after lung transplantation: Lessons learnt

Hyperammonemia, post-orthotopic lung transplantation, is a rare but mostly fatal complication. Various therapies, including those to decrease ammonia generation, increase nitrogen excretion, and several dialytic methods for removing ammonia have been tried. We describe three lung transplant recipients who developed acute hyperammonemia early after transplantation. Two of the three patients survived after a multidisciplinary approach including discontinuation of drugs, which impair urea cycle, aggressive ammonia reduction with prolonged daily intermittent hemodialysis (HD), and overnight slow low-efficiency dialysis in conjunction with early weaning of steroids and other therapeutic measures. Our experience suggests that early initiation of dialysis, high dialysis dose, increased frequency, and HD preferably to less efficient modalities increases survival in these patients.
Unique double-ring structure of the peroxisomal Pex1/Pex6 ATPase complex revealed by cryo-electron microscopy

Members of the AAA family of ATPases assemble into hexameric double rings and perform vital functions, yet their molecular mechanisms remain poorly understood. Here, we report structures of the Pex1/Pex6 complex; mutations in these proteins frequently cause peroxisomal diseases. The structures were determined in the presence of different nucleotides by cryo-electron microscopy. Models were generated using a computational approach that combines Monte Carlo placement of structurally homologous domains into density maps with energy minimization and refinement protocols. Pex1 and Pex6 alternate in an unprecedented hexameric double ring. Each protein has two N-terminal domains, N1 and N2, structurally related to the single N domains in p97 and N-ethylmaleimide sensitive factor (NSF); N1 of Pex1 is mobile, but the others are packed against the double ring. The N-terminal ATPase domains are inactive, forming a symmetric D1 ring, whereas the C-terminal domains are active, likely in different nucleotide states, and form an asymmetric D2 ring. These results suggest how subunit activity is coordinated and indicate striking similarities between Pex1/Pex6 and p97, supporting the hypothesis that the Pex1/Pex6 complex has a role in peroxisomal protein import analogous to p97 in ER-associated protein degradation.