cssi/SciSciGPT-SciSciNet huggingface dataset:
Lrp4 Mediates Bone Homeostasis and Mechanotransduction through Interaction with Sclerostin In Vivo

Summary Wnt signaling plays a key role in regulating bone remodeling. In vitro studies suggest that sclerostin's inhibitory action on Lrp5 is facilitated by the membrane-associated receptor Lrp4. We generated an Lrp4 R1170W knockin mouse model (Lrp4KI), based on a published mutation in patients with high bone mass (HBM). Lrp4KI mice have an HBM phenotype (assessed radiographically), including increased bone strength and formation. Overexpression of a Sost transgene had osteopenic effects in Lrp4-WT but not Lrp4KI mice. Conversely, sclerostin inhibition had blunted osteoanabolic effects in Lrp4KI mice. In a disuse-induced bone wasting model, Lrp4KI mice exhibit significantly less bone loss than wild-type (WT) mice. In summary, mice harboring the Lrp4-R1170W missense mutation recapitulate the human HBM phenotype, are less sensitive to altered sclerostin levels, and are protected from disuse-induced bone loss. Lrp4 is an attractive target for pharmacological targeting aimed at increasing bone mass and preventing bone loss due to disuse.
Measuring Information Preferences

Advances in medical testing and widespread access to the internet have made it easier than ever to obtain information. Yet, when it comes to some of the most important decisions in life, people oft...
Making Lock-free Data Structures Verifiable with Artificial Transactions

Among all classes of parallel programming abstractions, lock-free data structures are considered one of the most scalable and efficient thanks to their fine-grained style of synchronization. However, they are also challenging for developers and tools to verify because of the huge number of possible interleavings that result from finegrained synchronizations.This paper addresses this fundamental problem between performance and verifiability of lock-free data structure implementations. We present TXIT, a system that greatly reduces the set of possible interleavings by inserting transactions into the implementation of a lock-free data structure. We leverage hardware transactional memory support from Intel Haswell processors to enforce these artificial transactions. Evaluation on six popular lock-free data structure libraries shows that TXIT makes it easy to verify lock-free data structures while incurring acceptable runtime overhead. Further analysis shows that two inefficiencies in Haswell are the largest contributors to this overhead.
A Rare Opportunist, Morganella morganii, Decreases Severity of Polymicrobial Catheter-Associated Urinary Tract Infection.

ABSTRACT Catheter-associated urinary tract infections (CAUTIs) are common hospital-acquired infections and frequently polymicrobial, which complicates effective treatment. However, few studies experimentally address the consequences of polymicrobial interactions within the urinary tract, and the clinical significance of polymicrobial bacteriuria is not fully understood. Proteus mirabilis is one of the most common causes of monomicrobial and polymicrobial CAUTI and frequently cocolonizes with Enterococcus faecalis, Escherichia coli, Providencia stuartii, and Morganella morganii. P. mirabilis infections are particularly challenging due to its potent urease enzyme, which facilitates formation of struvite crystals, catheter encrustation, blockage, and formation of urinary stones. We previously determined that interactions between P. mirabilis and other uropathogens can enhance P. mirabilis urease activity, resulting in greater disease severity during experimental polymicrobial infection. Our present work reveals that M. morganii acts on P. mirabilis in a contact-independent manner to decrease urease activity. Furthermore, M. morganii actively prevents urease enhancement by E. faecalis, P. stuartii, and E. coli. Importantly, these interactions translate to modulation of disease severity during experimental CAUTI, predominantly through a urease-dependent mechanism. Thus, products secreted by multiple bacterial species in the milieu of the catheterized urinary tract can directly impact prognosis.
R&D and the Rising Foreign Profitability of U.S. Multinational Corporations

We investigate how R&D contributes to rising foreign profitability in U.S. multinational corporations through wage and tax incentives. Our results suggest that wage savings increase foreign profit ...
Recent advances in coupled oscillator theory

We review the theory of weakly coupled oscillators for smooth systems. We then examine situations where application of the standard theory falls short and illustrate how it can be extended. Specific examples are given to non-smooth systems with applications to the Izhikevich neuron. We then introduce the idea of isostable reduction to explore behaviours that the weak coupling paradigm cannot explain. In an additional example, we show how bifurcations that change the stability of phase-locked solutions in a pair of identical coupled neurons can be understood using the notion of isostable reduction. This article is part of the theme issue 'Coupling functions: dynamical interaction mechanisms in the physical, biological and social sciences'.
Switched-Capacitor Voltage Boost Converter for Electric and Hybrid Electric Vehicle Drives

This article presents a switched-capacitor (SC) voltage boost converter and its control methods for implementing dc–ac and ac–dc power conversion. The SC converter employs an SC circuit augmented with the main converter circuit to the power source, thus providing unique features that cannot be attained by the traditional voltage-source inverter (VSI) or boost VSI. The additional features include doubling the area of the linear modulation region and eliminating both the large inductor in the boost dc–dc stage and the large filtering capacitor, which leads to a higher energy density and a lower cost. The SC converter concept can be applied to all dc–ac, ac–dc, ac–ac, and dc–dc power conversions. To describe the operating principle and the control, we focus on one example: a bidirectional SC converter for dc–ac and ac–dc power conversion in electric and hybrid electric vehicles. This article is accompanied by a video demonstrating the change of the modulation functions with the value of $A$ .
When Do Introspection Axioms Matter for Multi-Agent Epistemic Reasoning?

The early literature on epistemic logic in philosophy focused on reasoning about the knowledge or belief of a single agent, especially on controversies about "introspection axioms" such as the 4 and 5 axioms. By contrast, the later literature on epistemic logic in computer science and game theory has focused on multi-agent epistemic reasoning, with the single-agent 4 and 5 axioms largely taken for granted. In the relevant multi-agent scenarios, it is often important to reason about what agent A believes about what agent B believes about what agent A believes; but it is rarely important to reason just about what agent A believes about what agent A believes. This raises the question of the extent to which single-agent introspection axioms actually matter for multi-agent epistemic reasoning. In this paper, we formalize and answer this question. To formalize the question, we first define a set of multi-agent formulas that we call agent-alternating formulas, including formulas like Box_a Box_b Box_a p but not formulas like Box_a Box_a p. We then prove, for the case of belief, that if one starts with multi-agent K or KD, then adding both the 4 and 5 axioms (or adding the B axiom) does not allow the derivation of any new agent-alternating formulas -- in this sense, introspection axioms do not matter. By contrast, we show that such conservativity results fail for knowledge and multi-agent KT, though they hold with respect to a smaller class of agent-nonrepeating formulas.
Inferactive data analysis

We describe inferactive data analysis, so‐named to denote an interactive approach to data analysis with an emphasis on inference after data analysis. Our approach is a compromise between Tukey's exploratory and confirmatory data analysis allowing also for Bayesian data analysis. We see this as a useful step in concrete providing tools (with statistical guarantees) for current data scientists. The basis of inference we use is (a conditional approach to) selective inference, in particular its randomized form. The relevant reference distributions are constructed from what we call a DAG‐DAG—a Data Analysis Generative DAG, and a selective change of variables formula is crucial to any practical implementation of inferactive data analysis via sampling these distributions. We discuss a canonical example of an incomplete cross‐validation test statistic to discriminate between black box models, and a real HIV dataset example to illustrate inference after making multiple queries on data.
The Competitive Ratio of Threshold Policies for Online Unit-density Knapsack Problems

We study an online knapsack problem where the items arrive sequentially and must be either immediately packed into the knapsack or irrevocably discarded. Each item has a different size and the objective is to maximize the total size of items packed. We focus on the class of randomized algorithms which initially draw a threshold from some distribution, and then pack every fitting item whose size is at least that threshold. Threshold policies satisfy many desiderata including simplicity, fairness, and incentive-alignment. We derive two optimal threshold distributions, the first of which implies a competitive ratio of 0.432 relative to the optimal offline packing, and the second of which implies a competitive ratio of 0.428 relative to the optimal fractional packing. We also consider the generalization to multiple knapsacks, where an arriving item has a different size in each knapsack and must be placed in at most one. This is equivalent to the AdWords problem where item truncation is not allowed. We derive a randomized threshold algorithm for this problem which is 0.214-competitive. We also show that any randomized algorithm for this problem cannot be more than 0.461-competitive, providing the first upper bound which is strictly less than 0.5. This online knapsack problem finds applications in many areas, like supply chain ordering, online advertising, and healthcare scheduling, refugee integration, and crowdsourcing. We show how our optimal threshold distributions can be naturally implemented in the warehouses for a Latin American chain department store. We run simulations on their large-scale order data, which demonstrate the robustness of our proposed algorithms.
Sparse Combinatorial Group Testing

In combinatorial group testing, the primary objective is to fully identify the set of at most d defective items from a pool of n items using as few tests as possible. The celebrated result for the combinatorial group testing problem is that the number of tests, denoted by $t$ , can be made logarithmic in n when ${d} = {O}(\text {poly}(\log {n}))$ . However, state-of-the-art group testing codes require the items to be tested ${w} = \Omega \left ({\frac {{d} \log {n}}{\log {d} + \log \log {n}} }\right)$ times and tests to include $\rho = \Omega \left ({\frac {{n}}{{d} \log _{{d}} {n}}}\right)$ items. In many emerging applications, items can only participate in a limited number of tests and tests are constrained to include a limited number of items. In this paper, we study the “sparse” regime for the group testing problem where we restrict the number of tests each item can participate in by ${w}_{\max }$ or the number of items each test can include by $\rho _{\max }$ in both noiseless and noisy settings. These constraints lead to a largely unexplored regime where t is a fractional power of n , rather than logarithmic in n as in the classical setting. Our results characterize the number of tests t needed in this regime as a function of ${w}_{\max }$ or $\rho _{\max }$ and show, for example, that t decreases drastically when ${w}_{\max }$ is increased beyond a bare minimum. In particular, in the noiseless case it can be shown that if ${w}_{\max } \leq {d}$ , then we must have ${t}={n}$ , i.e., testing every item individually is optimal. We show that if ${w}_{\max }={d}+1$ , the number of tests decreases suddenly from ${t}={n}$ to ${t} = \Theta ({d} \sqrt {{n}})$ . The order-optimal construction is obtained via a modification of the classical Kautz-Singleton construction, which is known to be suboptimal for the classical group testing problem. For the more general case, when ${w}_{\max }={{ ld}}+1$ for integer ${l}>1$ , the modified Kautz-Singleton construction requires ${t} = \Theta \left ({{d} {n}^{\frac {1}{{l}+1}}}\right)$ tests, which we prove to be near order-optimal. We also show that our constructions have a favorable encoding and decoding complexity, i.e. they can be decoded in $({poly}({d}) + {O}({t}))$ -time and each entry in any codeword can be computed in ${poly}(\log {n})$ memory space. We finally discuss an application of our results to the construction of energy-limited random access schemes for Internet of Things networks, which provided the initial motivation for our work.
Error analysis of an accelerated interpolative decomposition for 3D Laplace problems

Abstract In fast direct solvers for integral equations with the Laplace kernel, a hierarchical compression process needs to compute interpolative decompositions of off-diagonal block rows of the discretized integral operators. This computation can be dramatically accelerated by a technique called the proxy surface method, which is motivated by potential theory. We present a long overdue, rigorous error analysis of this acceleration technique. The analysis provides theoretical guidance for the discretization of the proxy surface used in the technique.
Cascade R-CNN: High Quality Object Detection and Instance Segmentation

In object detection, the intersection over union (IoU) threshold is frequently used to define positives/negatives. The threshold used to train a detector defines its quality . While the commonly used threshold of 0.5 leads to noisy (low-quality) detections, detection performance frequently degrades for larger thresholds. This paradox of high-quality detection has two causes: 1) overfitting, due to vanishing positive samples for large thresholds, and 2) inference-time quality mismatch between detector and test hypotheses. A multi-stage object detection architecture, the Cascade R-CNN, composed of a sequence of detectors trained with increasing IoU thresholds, is proposed to address these problems. The detectors are trained sequentially, using the output of a detector as training set for the next. This resampling progressively improves hypotheses quality, guaranteeing a positive training set of equivalent size for all detectors and minimizing overfitting. The same cascade is applied at inference, to eliminate quality mismatches between hypotheses and detectors. An implementation of the Cascade R-CNN without bells or whistles achieves state-of-the-art performance on the COCO dataset, and significantly improves high-quality detection on generic and specific object datasets, including VOC, KITTI, CityPerson, and WiderFace. Finally, the Cascade R-CNN is generalized to instance segmentation, with nontrivial improvements over the Mask R-CNN.
A Stabilizing Control Algorithm for Asynchronous Parallel Quadratic Programming via Dual Decomposition

This paper proposes a control algorithm for stable implementation of asynchronous parallel quadratic programming (PQP) through dual decomposition technique. In general, distributed and parallel optimization requires synchronization of data at each iteration step due to the interdependency of data. The synchronization latency may incur a large amount of waiting time caused by an idle process during computation. We aim to mitigate this synchronization penalty in PQP problems by implementing asynchronous updates of the dual variable. The price to pay for adopting asynchronous computing algorithms is the unpredictability of the solution, resulting in a tradeoff between speedup and accuracy. In the worst case, the state of interest may become unstable owing to the stochastic behavior of asynchrony. We investigate the stability condition of asynchronous PQP problems by employing the switched system framework. A formal algorithm is provided to ensure the asymptotic stability of dual variables. Further, it is shown that the implementation of the proposed algorithm guarantees the uniqueness of optimal solutions, irrespective of asynchronous behavior. To verify the validity of the proposed methods, simulation results are presented.
PBS3 and EPS1 Complete Salicylic Acid Biosynthesis from Isochorismate in Arabidopsis.

Salicylic acid (SA) is an important phytohormone mediating both local and systemic defense responses in plants. Despite over half a century of research, how plants biosynthesize SA remains unresolved. In Arabidopsis, a major part of SA is derived from isochorismate, a key intermediate produced by the isochorismate synthase, which is reminiscent of SA biosynthesis in bacteria. Whereas bacteria employ an isochorismate pyruvate lyase (IPL) that catalyzes the turnover of isochorismate to pyruvate and SA, plants do not contain an IPL ortholog and generate SA from isochorismate through an unknown mechanism. Combining genetic and biochemical approaches, we delineated the SA biosynthetic pathway downstream of isochorismate in Arabidopsis. We found that PBS3, a GH3 acyl adenylase-family enzyme important for SA accumulation, catalyzes ATP- and Mg2+-dependent conjugation of L-glutamate primarily to the 8-carboxyl of isochorismate and yields the key SA biosynthetic intermediate, isochorismoyl-glutamate A. Moreover, we discovered that EPS1, a BAHD acyltransferase-family protein with a previously implicated role in SA accumulation upon pathogen attack, harbors a noncanonical active site and an unprecedented isochorismoyl-glutamate A pyruvoyl-glutamate lyase activity that produces SA from the isochorismoyl-glutamate A substrate. Together, PBS3 and EPS1 form a two-step metabolic pathway to produce SA from isochorismate in Arabidopsis, which is distinct from how SA is biosynthesized in bacteria. This study closes a major knowledge gap in plant SA metabolism and would help develop new strategies for engineering disease resistance in crop plants.
Three Formulations of the Kuramoto Model as a System of Polynomial Equations

We compare three formulations of stationary equations of the Kuramoto model as systems of polynomial equations. In the comparison, we present bounds on the numbers of real equilibria based on the work of Bernstein, Kushnirenko, and Khovanskii, and performance of methods for the optimisation over the set of equilibria based on the work of Lasserre, both of which could be of independent interest.
GenDICE: Generalized Offline Estimation of Stationary Values

An important problem that arises in reinforcement learning and Monte Carlo methods is estimating quantities defined by the stationary distribution of a Markov chain. In many real-world applications, access to the underlying transition operator is limited to a fixed set of data that has already been collected, without additional interaction with the environment being available. We show that consistent estimation remains possible in this scenario, and that effective estimation can still be achieved in important applications. Our approach is based on estimating a ratio that corrects for the discrepancy between the stationary and empirical distributions, derived from fundamental properties of the stationary distribution, and exploiting constraint reformulations based on variational divergence minimization. The resulting algorithm, GenDICE, is straightforward and effective. We prove the consistency of the method under general conditions, provide a detailed error analysis, and demonstrate strong empirical performance on benchmark tasks, including off-line PageRank and off-policy policy evaluation.
Dynamics on the Laminar-Turbulent Boundary and the Origin of the Maximum Drag Reduction Asymptote

Dynamical trajectories on the boundary in state space between laminar and turbulent plane channel flow-edge states-are computed for Newtonian and viscoelastic fluids. Viscoelasticity has a negligible effect on the properties of these solutions, and, at least at a low Reynolds number, their mean velocity profiles correspond closely to experimental observations for polymer solutions in the maximum drag reduction regime. These results confirm the existence of weak turbulence states that cannot be suppressed by polymer additives, explaining the fact that there is an upper limit for polymer-induced drag reduction.
LTR_FINDER_parallel: parallelization of LTR_FINDER enabling rapid identification of long terminal repeat retrotransposons.

Annotation of plant genomes is still a challenging task due to the abundance of repetitive sequences, especially long terminal repeat (LTR) retrotransposons. LTR_FINDER is a widely used program for the identification of LTR retrotransposons but its application on large genomes is hindered by its single-threaded processes. Here we report an accessory program that allows parallel operation of LTR_FINDER, resulting in up to 8500X faster identification of LTR elements. It takes only 72 min to process the 14.5 Gb bread wheat (Triticum aestivum) genome in comparison to 1.16 years required by the original sequential version. LTR_FINDER_parallel is freely available at https://github.com/oushujun/LTR_FINDER_parallel.
Simulation of non-resonant stellarator divertor

An efficient numerical method of studying nonresonant stellarator divertors was introduced in Boozer and Punjabi [Phys. Plasmas 25, 092505 (2018)]. This method is used in this paper to study a different magnetic field model of a nonresonant divertor. The most novel and interesting finding of this study is that diffusive magnetic field lines can be distinguished from lines that exit through the primary and the secondary turnstile, and that below some diffusive velocity, all lines exit through only the primary turnstile. The footprints of each family are stellarator symmetric and have a fixed location on the wall for all velocities. The probability exponent of the primary turnstile is d1 = 9/4 and that of the secondary turnstile is d2 = −3/2. This study also addresses the issues of an inadequate separation of the chamber walls from the outermost confining magnetic surface and a marginal step size of the numerical integrations that could compromise the interpretation of the earlier results [Boozer and Punjabi, Phys. Plasmas 25, 092505 (2018)]. The previous value of d1 = 2 is within the error bar of d1 = 9/4 estimated here.
Nonlinear, tunable, and active optical metasurface with liquid film

Optical metamaterials and metasurfaces, which emerged in the course of the last few decades, have revolutionized our understanding of light and light–matter interaction. While solid materials are naturally employed as key building elements for construction of optical metamaterials mainly due to their structural stability, practically no attention was given to study of liquid-made optical two-dimensional (2-D) metasurfaces and the underlying interaction regimes between surface optical modes and liquids. We theoretically demonstrate that surface plasmon polaritons and slab waveguide modes that propagate within a thin liquid dielectric film trigger optical self-induced interaction facilitated by surface tension effects, which leads to the formation of 2-D optical liquid-made lattices/metasurfaces with tunable symmetry and can be leveraged for tuning of lasing modes. Furthermore, we show that the symmetry breaking of the 2-D optical liquid lattice leads to phase transition and tuning of its topological properties, which allows the formation, destruction, and movement of Dirac-points in the k-space. Our results indicate that optical liquid lattices support extremely low lasing threshold relative to solid dielectric films and have the potential to serve as configurable analogous computation platform.
Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models

Tensor methods have become a promising tool to solve high-dimensional problems in the big data era. By exploiting possible low-rank tensor factorization, many high-dimensional model-based or data-driven problems can be solved to facilitate decision making or machine learning. In this paper, we summarize the recent applications of tensor computation in obtaining compact models for uncertainty quantification and deep learning. In uncertainty analysis where obtaining data samples is expensive, we show how tensor methods can significantly reduce the simulation or measurement cost. To enable the deployment of deep learning on resource-constrained hardware platforms, tensor methods can be used to significantly compress an over-parameterized neural network model or directly train a small-size model from scratch via optimization or statistical techniques. Recent Bayesian tensorized neural networks can automatically determine their tensor ranks in the training process.
Splittings of Mapping Tori of Free Group Automorphisms

We present necessary and sufficient conditions for the existence of a splitting over ℤ of the mapping torus \(M_\Phi = F \times _\Phi {\mathbb{Z}}\) of a free group automorphism Φ.
A revision game of experimentation on a common threshold

Abstract A revision game of experimentation is examined in which players search for an unknown threshold. Each player encounters individual random opportunities to revise her own action. Lower action saves flow cost, but the player whose action falls below the threshold suffers a costly breakdown and exits the game. The set of symmetric pure-strategy Markov equilibria has a simple characterization. The difference between these equilibria vanishes as the revision opportunity becomes infinitely frequent. In all such equilibria, players revise actions gradually over time and, absent breakdowns, settle asymptotically. The asymptotic level of actions decreases with the patience of the players, and the speed of decline in actions decreases with the number of players. In equilibrium, the endogenous arrival rate of breakdown is decreasing over time. The model extends to incorporate collateral damage from breakdowns, where two competing externalities jointly shape the dynamics.
A Century of the American Woman Voter: Sex Gaps in Political Participation, Preferences, and Partisanship Since Women’s Enfranchisement

This year marks the centennial of the Nineteenth Amendment, which provided American women a constitutional guarantee to the franchise. We assemble data from a variety of sources to document and explore trends in women's political participation, issue preferences, and partisanship since that time. We show that in the early years following enfranchisement, women voted at much lower rates than men and held distinct issue preferences, despite splitting their votes across parties similarly to men. But by the dawn of the twenty-first century, women not only voted more than men, but also voted differently, systematically favoring the Democratic party. We find that the rise in women's relative voter turnout largely reflects cross-cohort changes in voter participation and coincided with increasing rates of high school completion. By contrast, women's relative shift toward the Democratic party permeates all cohorts and appears to owe more to changes in how parties have defined themselves than to changes in issue preferences. The findings suggest that a confluence of factors have led to the unique place women currently occupy in the American electorate, one where they are arguably capable of exerting more political influence than ever before.
Locating the Source of Events in Power Distribution Systems Using Micro-PMU Data

A novel method is proposed to locate the source of events in power distribution systems by using distribution-level phasor measurement units, a.k.a., micro-PMUs. An event in this paper is defined rather broadly to include any major change in any component across the distribution feeder. The goal is to enhance situational awareness in distribution grid by keeping track of the operation (or misoperation) of various grid equipment, assets, distribution energy resources (DERs), loads, etc. The proposed method is built upon the compensation theorem in circuit theory to generate an equivalent circuit to represent the event by using voltage and current synchrophasors that are captured by micro-PMUs. Importantly, this method makes critical use of not only magnitude but also synchronized phase angle measurements, thus, it justifies the need to use microPMUs, as opposed to ordinary RMS based voltage and current sensors. The proposed method can work with data from as few as only two micro-PMUs. The effectiveness of the developed method is demonstrated through computer simulations on the IEEE 123- bus test system, and also on micro-PMUs measurements from a real-life 12.47 kV test feeder in Riverside, CA. The results verify that the proposed method is accurate and robust in locating the source of different types of events on power distribution systems.
Phase Retrieval by Alternating Minimization With Random Initialization

We consider the phase retrieval problem, where the goal is to reconstruct an $n$ -dimensional complex vector from its phaseless scalar products with $m$ sensing vectors, independently sampled from complex normal distributions. We show that, if ${m}\geq Mn^{3/2}\log ^{7/2}n$ for some $M>0$ , then the classical algorithm of alternating minimization with random initialization succeeds with high probability as $n,m\rightarrow \infty $ . This is a step toward proving the conjecture in, which conjectures that the algorithm succeeds when $m=O(n)$ . The analysis depends on an approach that enables the decoupling of the dependency between the algorithmic iterates and the sensing vectors.
Evoked and spontaneous pain assessment during tooth pulp injury

Injury of the tooth pulp is excruciatingly painful and yet the receptors and neural circuit mechanisms that transmit this form of pain remain poorly defined in both the clinic and preclinical rodent models. Easily quantifiable behavioral assessment in the mouse orofacial area remains a major bottleneck in uncovering molecular mechanisms that govern inflammatory pain in the tooth. In this study we sought to address this problem using the Mouse Grimace Scale and a novel approach to the application of mechanical Von Frey hair stimuli. We use a dental pulp injury model that exposes the pulp to the outside environment, a procedure we have previously shown produces inflammation. Using RNAscope technology, we demonstrate an upregulation of genes that contribute to the pain state in the trigeminal ganglia of injured mice. We found that mice with dental pulp injury have greater Mouse Grimace Scores than sham within 24 hours of injury, suggestive of spontaneous pain. We developed a scoring system of mouse refusal to determine thresholds for mechanical stimulation of the face with Von Frey filaments. This method revealed that mice with a unilateral dental injury develop bilateral mechanical allodynia that is delayed relative to the onset of spontaneous pain. This work demonstrates that tooth pain can be quantified in freely behaving mice using approaches common for other types of pain assessment. Harnessing these assays in the orofacial area during gene manipulation should assist in uncovering mechanisms for tooth pulp inflammatory pain and other forms of trigeminal pain.
Finite gap conditions and small dispersion asymptotics for the classical periodic Benjamin–Ono equation

In this paper we characterize the Nazarov-Sklyanin hierarchy for the classical periodic Benjamin-Ono equation in two complementary degenerations: for the multi-phase initial data (the periodic multi-solitons) at fixed dispersion and for bounded initial data in the limit of small dispersion. First, we express this hierarchy in terms of a piecewise-linear function of an auxiliary real variable which we call a dispersive action profile and whose regions of slope $\pm 1$ we call gaps and bands, respectively. Our expression uses Kerov's theory of profiles and Kre\uin's spectral shift functions. Next, for multi-phase initial data, we identify Baker-Akhiezer functions in Dobrokhotov-Krichever and Nazarov-Sklyanin and prove that multi-phase dispersive action profiles have finitely-many gaps determined by the singularities of their Dobrokhotov-Krichever spectral curves. Finally, for bounded initial data independent of the coefficient of dispersion, we show that in the small dispersion limit, the dispersive action profile concentrates weakly on a convex profile which encodes the conserved quantities of the dispersionless equation. To establish the weak limit, we reformulate Szegő's first theorem for Toeplitz operators using spectral shift functions. To illustrate our results, we identify the dispersive action profile of sinusoidal initial data with a profile found by Nekrasov-Pestun-Shatashvili and its small dispersion limit with the convex profile found by Vershik-Kerov and Logan-Shepp.
Dust Devils on Titan

Conditions on Saturn's moon Titan suggest dust devils, which are convective, dust-laden plumes, may be active. Although the exact nature of dust on Titan is unclear, previous observations confirm an active aeolian cycle, and dust devils may play an important role in Titan's aeolian cycle, possibly contributing to regional transport of dust and even production of sand grains. The Dragonfly mission to Titan will document dust devil and convective vortex activity and thereby provide a new window into these features, and our analysis shows that associated winds are likely to be modest and pose no hazard to the mission.
Worst-Case Optimal Data-Driven Estimators for Switched Discrete-Time Linear Systems

This paper proposes a data-driven framework to address the worst-case estimation problem for switched discrete-time linear systems based solely on the measured data (input & output) and an l ∞ bound over the noise. We start with the problem of designing a worst-case optimal estimator for a single system and show that this problem can be recast as a rank minimization problem and efficiently solved using standard relaxations of rank. Then we extend these results to the switched case. Our main result shows that, when the mode variable is known, the problem can be solved proceeding in a similar manner. To address the case where the mode variable is unmeasurable, we impose the hybrid decoupling constraint(HDC) in order to reformulate the original problem as a polynomial optimization which can be reduced to a tractable convex optimization using moments-based techniques.
Deconstructing Generative Adversarial Networks

Generative Adversarial Networks (GANs) are a thriving unsupervised machine learning technique that has led to significant advances in various fields such as computer vision, natural language processing, among others. However, GANs are known to be difficult to train and usually suffer from mode collapse and the discriminator winning problem. To interpret the empirical observations of GANs and design better ones, we deconstruct the study of GANs into three components and make the following contributions. • Formulation: we propose a perturbation view of the population target of GANs. Building on this interpretation, we show that GANs can be connected to the robust statistics framework, and propose a novel GAN architecture, termed as Cascade GANs , to provably recover meaningful low-dimensional generator approximations when the real distribution is high-dimensional and corrupted by outliers. • Generalization: given a population target of GANs, we design a systematic principle, projection under admissible distance , to design GANs to meet the population requirement using only finite samples. We implement our principle in three cases to achieve polynomial and sometimes near-optimal sample complexities: (1) learning an arbitrary generator under an arbitrary pseudonorm; (2) learning a Gaussian location family under total variation distance, where we utilize our principle to provide a new proof for the near-optimality of the Tukey median viewed as GANs; (3) learning a low-dimensional Gaussian approximation of a high-dimensional arbitrary distribution under Wasserstein distance. We demonstrate a fundamental trade-off in the approximation error and statistical error in GANs, and demonstrate how to apply our principle in practice with only empirical samples to predict how many samples would be sufficient for GANs in order not to suffer from the discriminator winning problem. • Optimization: we demonstrate alternating gradient descent is provably not locally asymptotically stable in optimizing the GAN formulation of PCA. We found that the minimax duality gap being non-zero might be one of the causes, and propose a new GAN architecture whose duality gap is zero, where the value of the game is equal to the previous minimax value (not the maximin value). We prove the new GAN architecture is globally asymptotically stable in solving PCA under alternating gradient descent.
Science objectives and early results of the DEEP2 Redshift Survey

The DEIMOS spectrograph has now been installed on the Keck-II telescope and commissioning is nearly complete. The DEEP2 Redshift Survey, which will take approximately 120 nights at the Keck Observatory over a three year period and has been designed to utilize the power of DEIMOS, began in the summer of 2002. The multiplexing power and high efficiency of DEIMOS enables us to target 1000 faint galaxies per clear night. Our goal is to gather high-quality spectra of ≈ 60,000 galaxies with z>0.75 in order to study the properties and large scale clustering of galaxies at z ≈ 1. The survey will be executed at high spectral resolution, R=λ/Δλ ≈ 5000, allowing us to work between the bright OH sky emission lines and to infer linewidths for many of the target galaxies (for several thousand objects, we will obtain rotation curves as well). The linewidth data will facilitate the execution of the classical redshift-volume cosmological test, which can provide a precision measurement of the equation of state of the Universe. This talk reviews the project, summarizes our science goals and presents some early DEIMOS data.
Robust and Optimal Single-loop Voltage Controller for Grid-forming Voltage Source Inverters

Design and implementation of an optimal and robust single-loop voltage controller is proposed for single-phase grid-forming voltage source inverter (VSI). The objective of the proposed controller is to have good reference tracking and disturbance rejection. Uncertain nature of loads can significantly alter system’s behavior, especially during heavily loaded conditions, and impose an uncertainty in the dynamic model. This deteriorates the robustness of the controller severely. ℋ ∞ -based controller design is proposed in this article to address this issue. The required objectives of the optimal controller are formulated after modeling the loads as uncertain element of the control system. Time-domain MATLAB/SIMULINK-based simulation study substantiates the fact that the resulting controller exhibits superior robustness in performance during varying loading conditions of VSI than that of the conventional multi-loop control architecture. OPAL-RT based controller hardware-in-the-loop (CHIL) simulations with low-cost controller are conducted to validate the computational footprint of the resulting controller.
Treatment of a traumatic aortic bifurcation injury with an iliac branch endoprosthesis.

Abstract We present the case of a 62-year-old man who sustained a traumatic distal aortic injury associated with an adjacent lumbar vertebral body fracture resulting from a 20-ft fall. Given the site of injury, an iliac artery branched endograft was deployed off-label to preserve the aortic bifurcation and cover a limited amount of healthy aorta to preserve the collaterals. The procedure was successful, with no intraoperative complications or evidence of an endoleak. The aortic bifurcation and distal iliac arteries remained widely patent by computed tomography angiography at the follow-up examination without evidence of sequelae.
Machine learning for nonintrusive model order reduction of the parametric inviscid transonic flow past an airfoil

Fluid flow in the transonic regime finds relevance in aerospace engineering, particularly in the design of commercial air transportation vehicles. Computational fluid dynamics models of transonic flow for aerospace applications are computationally expensive to solve because of the high degrees of freedom as well as the coupled nature of the conservation laws. While these issues pose a bottleneck for the use of such models in aerospace design, computational costs can be significantly minimized by constructing special, structure-preserving surrogate models called reduced-order models. In this work, we propose a machine learning method to construct reduced-order models via deep neural networks and we demonstrate its ability to preserve accuracy with a significantly lower computational cost. In addition, our machine learning methodology is physics-informed and constrained through the utilization of an interpretable encoding by way of proper orthogonal decomposition. Application to the inviscid transonic flow past the RAE2822 airfoil under varying freestream Mach numbers and angles of attack, as well as airfoil shape parameters with a deforming mesh, shows that the proposed approach adapts to high-dimensional parameter variation well. Notably, the proposed framework precludes the knowledge of numerical operators utilized in the data generation phase, thereby demonstrating its potential utility in the fast exploration of design space for diverse engineering applications. Comparison against a projection-based nonintrusive model order reduction method demonstrates that the proposed approach produces comparable accuracy and yet is orders of magnitude computationally cheap to evaluate, despite being agnostic to the physics of the problem.
Chapter 8 – Basic Equations and Solution Procedure

The basic equations of solid and structural mechanics, the methods of formulating solid and structural mechanics problems, the formulation of finite element equations for static problems, and the nature of the resulting finite equations are discussed. The equilibrium equations of an infinitesimal element, Hooke's law, and strain-displacement relations of a solid body are derived. The number of available equations and the number of unknowns are given for three-, two-, and one-dimensional problems. Additional equations that need to be satisfied in order to find the correct or exact solution of the problem are also presented; these include the equilibrium equations of the overall body, boundary conditions of the body, and compatibility equations. The methods of formulating solid and structural mechanics problems using variational approaches, including the principle of minimum potential energy, principle of minimum complementary energy and principle of stationary Reissner energy for static problems, and Hamilton's principle for dynamic problems, are indicated. The general formulation of finite element equations for static analysis of a solid mechanics problem, in matrix form, is outlined based on the principle of minimum potential energy.
Chapter 7 – Circulatory Systems

In order to function, the cells of a multicellular animal must take up nutrients, discard metabolic wastes, and receive chemical messages from hormones. These substances can only pass through the cell membrane if they are dissolved in water, and for this reason, every living cell must be surrounded by an extracellular aqueous medium, the interstitial fluid. In vertebrates, interstitial fluid, or lymph, is supplied with oxygen, nutrients, and hormones from blood within the capillaries of the closed circulatory system and returns carbon dioxide and other waste materials back to the blood for elimination. Insects and other arthropods have an open circulatory system with no distinction between the blood and interstitial fluid, so the body fluid is termed hemolymph and circulates freely throughout the body cavity.
Chapter 3 – The Inverse

Publisher Summary This chapter illustrates inverse matrix. An inverse of an n × n matrix A is a n × n matrix β having the property that AB = BA = I. Here, B is called an inverse of A and is denoted by A-1. If a square matrix A has an inverse, then it is said to be invertible or nonsingular. If A does not possess an inverse, then it is said to be singular. However, inverses are only defined for square matrices. The inverse for a diagonal matrix D having only nonzero elements on its main diagonal is also a diagonal matrix whose diagonal elements are the reciprocals of the corresponding diagonal elements of D. A square matrix has an inverse if and only if reduction to row-reduced form by elementary row operations results in a matrix having all unity elements on the main diagonal. An n × n matrix has an inverse if and only if it has rank n.
Phase-response measurement technique for waveguide-grating filters

The recent emergence of many integrated and fiber-optical devices has led to an increased interest in waveguide-grating filters. Among these devices are dispersion-compensating filters that employ chirped gratings to counteract the dispersion of fiber communication systems.1,2 It is essential to measure both the amplitude and the phase response of grating filters in order to completely characterize their performance. However, making accurate phase measurements is not a trivial task, and thus most reports of these devices do not include such measurements.
Pathophysiology of Protracted Acute Renal Failure in Man

Postischemic acute renal failure (ARF) induced by cardiac surgery is commonly prolonged and may be irreversible. To examine whether persistence of postischemic, tubular cell injury accounts for delayed recovery from ARF, we studied 10 patients developing protracted (36 +/- 4 d) ARF after cardiac surgery. The differential clearance and excretion dynamics of probe solutes of graded size were determined. Inulin clearance was depressed (5.0 +/- 1.7 ml/min), while the fractional urinary clearance of dextrans (radii 17-30 A) were elevated above unity. Employing a model of conservation of mass, we calculated that 44% of filtered inulin was lost via transtubular backleak. The clearance and fractional backleak of technetium-labeled DTPA ([99mTc]DTPA, radius = 4 A) were identical to those of inulin (radius 15 A). The time at which inulin or DTPA excretion reached a maximum after an intravenous bolus injection was markedly delayed when compared with control subjects with ARF of brief duration, 102 vs. 11 min. Applying a three-compartment model of inulin/DTPA kinetics (which takes backleak into account) revealed the residence time of intravenously administered inulin/DTPA in the compartment occupied by tubular fluid and urine to be markedly prolonged, 20 vs. 6 min in controls, suggesting reduced velocity of tubular fluid flow. We conclude that protracted human ARF is characterized by transtubular backleak of glomerular ultrafiltrate, such that inulin clearance underestimates true glomerular filtration rate by approximately 50%, and by sluggish tubular fluid flow, which strongly suggests the existence of severe and generalized intraluminal tubular obstruction. Because all patients also exhibited extreme hyperreninemia (16 +/- 2 ng/ml per h) that was inversely related to inulin clearance (r value = -0.83) and urine flow (r value = -0.70), we propose that persistent, angiotensin II-mediated renal vasoconstriction may have delayed healing of the injured tubular epithelium.