cssi/SciSciGPT-SciSciNet huggingface dataset:
Learning and real-time classification of hand-written digits with spiking neural networks

We describe a novel spiking neural network (SNN) for automated, real-time handwritten digit classification and its implementation on a GP-GPU platform. Information processing within the network, from feature extraction to classification is implemented by mimicking the basic aspects of neuronal spike initiation and propagation in the brain. The feature extraction layer of the SNN uses fixed synaptic weight maps to extract the key features of the image and the classifier layer uses the recently developed NormAD approximate gradient descent based supervised learning algorithm for spiking neural networks to adjust the synaptic weights. On the standard MNIST database images of handwritten digits, our network achieves an accuracy of 99.80% on the training set and 98.06% on the test set, with nearly 7x fewer parameters compared to the state-of-the-art spiking networks. We further use this network in a GPU based user-interface system demonstrating real-time SNN simulation to infer digits written by different users. On a test set of 500 such images, this real-time platform achieves an accuracy exceeding 97% while making a prediction within an SNN emulation time of less than 100 ms.
Bsmooth: Learning from user feedback to disambiguate query terms in interactive data retrieval

Abstract There is great interest in supporting imprecise queries (e.g., keyword search or natural language queries) over databases today. To support such queries, the database system is typically required to disambiguate parts of the user-specified query against the database, using whatever resources are intrinsically available to it (the database schema, data values distributions, natural language models etc.). Often, systems will also have a user-interaction log available, which can serve as an extrinsic resource to supplement their model based on their own intrinsic resources. This leads to a problem of how best to combine the system's prior ranking with insight derived from the user-interaction log. Statistical inference techniques such as maximum likelihood or Bayesian updates from a subjective prior turn out not to apply in a straightforward way due to possible noise from user search behavior and to encoding biases endemic to the system's models. In this paper, we address such learning problem in interactive data retrieval, with specific focus on type classification for user-specified query terms. We develop a novel Bayesian smoothing algorithm, Bsmooth , which is simple, fast, flexible and accurate. We analytically establish some desirable properties and show, through experiments against an independent benchmark, that the addition of such a learning layer performs much better than standard methods.
Fenchel-Nielsen coordinates on upper bounded pants decompositions

Let $X_0$ be an infinite genus hyperbolic surface (whose boundary components, if any, are closed geodesics or punctures) which has an upper bounded pants decomposition. The length spectrum Teichm\"uller space $T_{ls}(X_0)$ consists of all surfaces $X$ homeomorphic to $X_0$ such that the ratios of the corresponding simple closed geodesics are uniformly bounded from below and from above. Alessandrini, Liu, Papadopoulos and Su described the Fenchel-Nielsen coordinates for $T_{ls}(X_0)$ and using these coordinates they proved that $T_{ls}(X_0)$ is path connected. We use the Fenchel-Nielsen coordinates for $T_{ls}(X_0)$ to induce a locally biLipschitz homeomorphism between $l^{\infty}$ and $T_{ls}(X_0)$ (which extends analogous results by Fletcher and by Allessandrini, Liu, Papadopoulos, Su and Sun for the unreduced and the reduced $T_{qc}(X_0)$). Consequently, $T_{ls}(X_0)$ is contractible. We also characterize the closure in the length spectrum metric of the quasiconformal Teichm\"uller space $T_{qc}(X_0)$ in $T_{ls}(X_0)$.
SQL-Rank: A Listwise Approach to Collaborative Ranking

In this paper, we propose a listwise approach for constructing user-specific rankings in recommendation systems in a collaborative fashion. We contrast the listwise approach to previous pointwise and pairwise approaches, which are based on treating either each rating or each pairwise comparison as an independent instance respectively. By extending the work of (Cao et al. 2007), we cast listwise collaborative ranking as maximum likelihood under a permutation model which applies probability mass to permutations based on a low rank latent score matrix. We present a novel algorithm called SQL-Rank, which can accommodate ties and missing data and can run in linear time. We develop a theoretical framework for analyzing listwise ranking methods based on a novel representation theory for the permutation model. Applying this framework to collaborative ranking, we derive asymptotic statistical rates as the number of users and items grow together. We conclude by demonstrating that our SQL-Rank method often outperforms current state-of-the-art algorithms for implicit feedback such as Weighted-MF and BPR and achieve favorable results when compared to explicit feedback algorithms such as matrix factorization and collaborative ranking.
Pluriclosed flow on manifolds with globally generated bundles

We show global existence and convergence results for the pluriclosed flow on manifolds for which certain naturally associated tensor bundles are globally generated.
Friendly Artificial Intelligence : The Physics Challenge

Relentless progress in artificial intelligence (AI) is increasingly raising concerns that machines will replace humans on the job market, and perhaps altogether. Eliezer Yudkowski and others have explored the possibility that a promising future for humankind could be guaranteed by a superintelligent "Friendly AI" , designed to safeguard humanity and its values. I will argue that, from a physics perspective where everything is simply an arrangement of elementary particles, this might be even harder than it appears. Indeed, it may require thinking rigorously about the meaning of life: What is "meaning" in a particle arrangement? What is "life"? What is the ultimate ethical imperative, i.e., how should we strive to rearrange the particles of our Universe and shape its future? If we fail to answer the last question rigorously, this future is unlikely to contain humans.
On the homogenization of a new class of locally periodic microstructures in linear elasticity with residual stress

Many biological and engineering materials have nonperiodic microstructures for which classical periodic homogenization results do not apply. Certain nonperiodic microstructures may be approximated by locally periodic microstructures for which homogenization techniques are available. Motivated by the consideration that such materials are often anisotropic and can possess residual stresses, a broad class of locally periodic microstructures are considered and the resulting effective macroscopic equations are derived. The effective residual stress and effective elasticity tensor are determined by solving unit cell problems at each point in the domain. However, it is found that for a certain class of locally periodic microstructures, solving the unit cell problems at only one point in the domain completely determines the effective elasticity tensor.
Simpler Specifications and Easier Proofs of Distributed Algorithms Using History Variables

This paper studies specifications and proofs of distributed algorithms when only message history variables are used, using Basic Paxos and Multi-Paxos for distributed consensus as precise case studies. We show that not using and maintaining other state variables yields simpler specifications that are more declarative and easier to understand. It also allows easier proofs to be developed by needing fewer invariants and facilitating proof derivations. Furthermore, the proofs are mechanically checked more efficiently.
The Nuisance Principle in Infinite Settings

Neo-Fregeans have been troubled by the Nuisance Principle (NP), an abstraction principle that is consistent but not jointly (second-order) satisfiable with the favored abstraction principle HP. We show that logically this situation persists if one looks at joint (second-order) consistency rather than satisfiability: under a modest assumption about infinite concepts, NP is also inconsistent with HP.
A comparison of preconditioned Krylov subspace methods for large-scale nonsymmetric linear systems

Preconditioned Krylov subspace (KSP) methods are widely used for solving large-scale sparse linear systems arising from numerical solutions of partial differential equations (PDEs). These linear systems are often nonsymmetric due to the nature of the PDEs, boundary or jump conditions, or discretization methods. While implementations of preconditioned KSP methods are usually readily available, it is unclear to users which methods are the best for different classes of problems. In this work, we present a comparison of some KSP methods, including GMRES, TFQMR, BiCGSTAB, and QMRCGSTAB, coupled with three classes of preconditioners, namely Gauss-Seidel, incomplete LU factorization (including ILUT, ILUTP, and multilevel ILU), and algebraic multigrid (including BoomerAMG and ML). Theoretically, we compare the mathematical formulations and operation counts of these methods. Empirically, we compare the convergence and serial performance for a range of benchmark problems from numerical PDEs in 2D and 3D with up to millions of unknowns and also assess the asymptotic complexity of the methods as the number of unknowns increases. Our results show that GMRES tends to deliver better performance when coupled with an effective multigrid preconditioner, but it is less competitive with an ineffective preconditioner due to restarts. BoomerAMG with proper choice of coarsening and interpolation techniques typically converges faster than ML, but both may fail for ill-conditioned or saddle-point problems while multilevel ILU tends to succeed. We also show that right preconditioning is more desirable. This study helps establish some practical guidelines for choosing preconditioned KSP methods and motivates the development of more effective preconditioners.
GeV Gamma Rays from Supernova Remnants Interacting with Molecular Clouds

We report the current status of the observations of supernova remnants (SNRs) with the LargeArea Telescope (LAT) aboard the Fermi Gamma-ray Space Telescope, focusing on middle-agedSNRs that appear to be interacting with molecular clouds. Observations with the Fermi LAT in anenergy range from 0.2 GeV to ∼100 GeV have unveiled the presence of luminous GeV gamma-ray emission in middle-aged SNRs, providing a new insight into the shock-acceleration theoryand the origin of galactic cosmic rays. The middle-aged SNRsdetected by the Fermi LAT aregenerally much brighter in GeV than in TeV in terms of energy ﬂ ux, which emphasizes the im-portanceof the GeV observations. Spectral steepeningin the Fermi-LATbandis commonlyfoundfor the GeV-luminous SNRs. Remarkably, most (if not all) of the GeV-luminous SNRs are knownto be interacting with molecular clouds, and they are also the strong sources of synchrotron radioemission. We discuss possible scenarios to explain the enhanced GeV gamma-ray emission in thecloud-interacting SNRs. Particular emphasis is placed on ascenario in which shock-acceleratedcosmic-rays are adiabatically compressed and energized asresult of radiative cooling behind thecloud shock.25th Texas Symposium on Relativistic AstrophysicsDecember 6-10, 2010Heidelberg, Germany
Bakry–Émery meet Villani

Abstract We revisit Villani's approach to the study of hypocoercive diffusion operators by applying a variant of the Bakry–Emery machinery. The method relies on a generalized Bakry–Emery type criterion that applies to Kolmogorov type operators. Our approach includes as a special case the kinetic Fokker–Planck equation and allows, in that case, to recover and somehow improve hypocoercive estimates first obtained by Villani.
Generators of reductions of ideals in a local Noetherian ring with finite residue field

Let $(R,\mathfrak{m})$ be a local Noetherian ring with residue field $k$. While much is known about the generating sets of reductions of ideals of $R$ if $k$ is infinite, the case in which $k$ is finite is less well understood. We investigate the existence (or lack thereof) of proper reductions of an ideal of $R$ and the number of generators needed for a reduction in the case $k$ is a finite field. When $R$ is one-dimensional, we give a formula for the smallest integer $n$ for which every ideal has an $n$-generated reduction. It follows that in a one-dimensional local Noetherian ring every ideal has a principal reduction if and only if the number of maximal ideals in the normalization of the reduced quotient of $R$ is at most $|k|$. In higher dimensions, we show that for any positive integer, there exists an ideal of $R$ that does not have an $n$-generated reduction and that if $n \geq \dim R$ this ideal can be chosen to be $\mathfrak{m}$-primary. In the case where $R$ is a two-dimensional regular local ring, we construct an example of an integrally closed $\mathfrak{m}$-primary ideal that does not have a $2$-generated reduction and thus answer in the negative a question raised by Heinzer and Shannon.
A modular Szemerédi–Trotter theorem for hyperbolas

Abstract We establish a Szemeredi–Trotter type result for hyperbolas in F p × F p .
One-bit Compressive Sensing with partial support

The Compressive Sensing framework maintains relevance even when the available measurements are subject to extreme quantization, as is exemplified by the so-called one-bit compressed sensing framework which aims to recover a signal from measurements reduced to only their sign-bit. In applications, it is often the case that we have some knowledge of the structure of the signal beforehand, and thus would like to leverage it to attain more accurate and efficient recovery. This work explores avenues for incorporating such partial-support information into the one-bit setting. Experimental results demonstrate that newly proposed methods of this work yield improved signal recovery even for varying levels of accuracy in the prior information. This work is thus the first to provide recovery mechanisms that efficiently use prior signal information in the one-bit reconstruction setting.
Generalized proportional allocation mechanism design for multi-rate multicast service on the Internet

In multicast transmission on the Internet, agents are divided into multicast groups based on the content they demand. In addition, when multi-rate transmission is used, each user in the same multicast group may request different quality of service for the same content. With multi-rate multicast transmission, each link on the network carries only the highest quality content of each multicast group passing through this link, thus resulting in substantial resource savings compared to unicast transmission. In this paper a mechanism is constructed that fully implements social welfare maximising allocation in Nash equilibria for the case of multi-rate multicast service under the assumption of strategic agents for whom utilities are private information. The emphasis of this work is on full implementation, which means that all pure strategy Nash equilibria of the induced game result in the optimal allocations of the centralised allocation problem. The mechanism, which is constructed in a quasi-systematic way starting from the dual of the centralized problem, has a number of additional useful properties. Specifically, the proposed mechanism results in feasible allocation (in fact in Pareto optimal allocation) even off-equilibrium. Finally, in the extended version of this paper it is shown how strong budget balance at equilibrium can be added to the proposed mechanism in a straightforward manner.
Properties of the promotion Markov chain on linear extensions

The Tsetlin library is a very well-studied model for the way an arrangement of books on a library shelf evolves over time. One of the most interesting properties of this Markov chain is that its spectrum can be computed exactly and that the eigenvalues are linear in the transition probabilities. This result has been generalized in different ways by various people. In this work, we investigate one of the generalizations given by the extended promotion Markov chain on linear extensions of a poset P introduced by Ayyer et al. (J Algebr Comb 39(4):853–881, 2014). They showed that if the poset P is a rooted forest, the transition matrix of this Markov chain has eigenvalues that are linear in the transition probabilities and described their multiplicities. We show that the same property holds for a larger class of posets for which we also derive convergence to stationarity results.
Affine varieties, singularities and the growth rate of wrapped Floer cohomology

In this paper, we give partial answers to the following questions: Which contact manifolds are contactomorphic to links of isolated complex singularities? Which symplectic manifolds are symplectomo...
Energy Conversion via Metal Nanolayers

Current approaches for electric power generation from nanoscale conducting or semiconducting layers in contact with moving aqueous droplets are promising as they show efficiencies of around 30%, yet even the most successful ones pose challenges regarding fabrication and scaling. Here, we report stable, all-inorganic single-element structures synthesized in a single step that generate electrical current when alternating salinity gradients flow along its surface in a liquid flow cell. Nanolayers of iron, vanadium, or nickel, 10 to 30 nm thin, produce open-circuit potentials of several tens of millivolt and current densities of several microA cm^(−2) at aqueous flow velocities of just a few cm s^(−1). The principle of operation is strongly sensitive to charge-carrier motion in the thermal oxide nanooverlayer that forms spontaneously in air and then self-terminates. Indeed, experiments suggest a role for intraoxide electron transfer for Fe, V, and Ni nanolayers, as their thermal oxides contain several metal-oxidation states, whereas controls using Al or Cr nanolayers, which self-terminate with oxides that are redox inactive under the experimental conditions, exhibit dramatically diminished performance. The nanolayers are shown to generate electrical current in various modes of application with moving liquids, including sliding liquid droplets, salinity gradients in a flowing liquid, and in the oscillatory motion of a liquid without a salinity gradient.
Self-derivation through memory integration under low surface similarity conditions: The case of multiple languages.

Abstract A primary objective of development is to build a knowledge base. To accumulate knowledge over time and experiences, learners must engage in productive processes, going beyond what is explicitly given to generate new knowledge. Although these processes are important to accumulating knowledge, they are also easily disrupted. Individuals often depend on surface-level similarities, such as visual features, to recognize the relation between learning episodes. When the surface-level similarity is low, performance on tasks that depend on productive processes, such as self-derivation through integration of new knowledge, suffers. The major purpose of the current research was to examine whether presentation of related information in different languages poses a challenge to memory integration and self-derivation due to low levels of surface similarity between episodes of learning through different languages. In Study 1, 62 children (Grade 2; mean age = 8 years 1 month) listened to story passages containing novel facts that could be integrated to self-derive new knowledge. Related passages were presented either through the same language or through two different languages (cross-language condition; Spanish and English). There were no significant differences between presentation conditions. In Study 2, 100 children (Grades 3 and 4; mean age = 9.7 years) heard novel facts in single sentences, again presented in either a same-language or cross-language condition. Whereas third-grade cross-language performance suffered compared with same-language English controls, fourth-grade performance did not. Results suggest that in addition to language proficiency, rich contextual support and experience in a bilingual environment facilitate cross-language integration.
A Competitive Algorithm for Online Multi-Robot Exploration of a Translating Plume

In this paper, we study the problem of exploring a translating plume with a team of aerial robots. The shape and the size of the plume are unknown to the robots. The objective is to find a tour for each robot such that they collectively explore the plume. Specifically, the tours must be such that each point in the plume must be visible from the field-of-view of some robot along its tour. We propose a recursive Depth-First Search (DFS)-based algorithm that yields a constant competitive ratio for the exploration problem. The competitive ratio is $\frac{2(S_{r}+S_{p})(R+\lfloor\log R\rfloor)}{(S_{r}-S_{p})(1+\lfloor\log R\rfloor)}$ where R is the number of robots, and S r and S p are the robot speed and the plume speed, respectively. We also consider a more realistic scenario where the plume shape is not restricted to grid cells but an arbitrary shape. We show our algorithm has $\frac{2(S_{r}+S_{p})(18R+\lfloor\log R\rfloor)}{(S_{r}-S_{p})(1+\lfloor\log R\rfloor)}$ competitive ratio under the fat condition. We empirically verify our algorithm using simulations.
Learning Probabilistic Multi-Modal Actor Models for Vision-Based Robotic Grasping

Many previous works approach vision-based robotic grasping by training a value network that evaluates grasp proposals. These approaches require an optimization process at run-time to infer the best action from the value network. As a result, the inference time grows exponentially as the dimension of action space increases. We propose an alternative method, by directly training a neural density model to approximate the conditional distribution of successful grasp poses from the input images. We construct a neural network that combines Gaussian mixture and normalizing flows, which is able to represent multi-modal, complex probability distributions. We demonstrate on both simulation and real robot that the proposed actor model achieves similar performance compared to the value network using the Cross-Entropy Method (CEM) for inference, on top-down grasping with a 4 dimensional action space. Our actor model reduces the inference time by 3 times compared to the state-of-the-art CEM method. We believe that actor models will play an important role when scaling up these approaches to higher dimensional action spaces.
Scalable Gaussian Process Computations Using Hierarchical Matrices

We present a kernel-independent method that applies hierarchical matrices to the problem of maximum likelihood estimation for Gaussian processes. The proposed approximation provides natural and sca...
Varying crosslinking motifs drive the mesoscale mechanics of actin-microtubule composites.

The cytoskeleton precisely tunes its mechanics by altering interactions between semiflexible actin filaments, rigid microtubules, and crosslinking proteins. We use optical tweezers microrheology and confocal microscopy to characterize how varying crosslinking motifs impact the mesoscale mechanics and mobility of actin-microtubule composites. We show that, upon subtle changes in crosslinking patterns, composites can exhibit two distinct classes of force response – primarily elastic versus more viscous. For example, a composite in which actin and microtubules are crosslinked to each other but not to themselves is markedly more elastic than one in which both filaments are independently crosslinked. Notably, this distinction only emerges at mesoscopic scales in response to nonlinear forcing, whereas varying crosslinking motifs have little impact on the microscale mechanics and mobility. Our unexpected scale-dependent results not only inform the physics underlying key cytoskeleton processes and structures, but, more generally, provide valuable perspective to materials engineering endeavors focused on polymer composites.
A Low-Power Domino Logic Architecture for Memristor-Based Neuromorphic Computing

We propose a domino logic architecture for memristor-based neuromorphic computing. The design uses the delay of memristor RC circuits to represent synaptic computations and a simple binary neuron activation function. Synchronization schemes are proposed for communicating information between neural network layers, and a simple linear power model is developed to estimate the design's energy efficiency for a particular network size. Results indicate that the proposed architecture can achieve 0.61 fJ per classification per component (neurons and synapses) and outperforms other designs in terms of energy per % accuracy.
Disfluencies and Human Speech Transcription Errors.

This paper explores contexts associated with errors in transcrip-tion of spontaneous speech, shedding light on human perceptionof disfluencies and other conversational speech phenomena. Anew version of the Switchboard corpus is provided with disfluency annotations for careful speech transcripts, together with results showing the impact of transcription errors on evaluation of automatic disfluency detection.
Improved global well-posedness for defocusing sixth-order Boussinesq equations

Abstract This article studies the global well-posedness (GWP) for a class of defocusing, generalized sixth-order Boussinesq equations, extending a previous result obtained by Wang-Esfahani (Wang and Esfahani, 2014) for the case when the nonlinear term is cubic.
Enumeration of cyclic permutations in vector grid classes

A grid class consists of permutations whose pictorial depiction can be partitioned into increasing and decreasing parts as determined by a given matrix. In this paper, we introduce a method for enumerating cyclic permutations in vector grid classes by establishing a bijective relationship with certain necklaces. We use this method to complete the enumeration of cyclic permutations in the length 3 vector grid classes. In addition, we define an analog of Wilf-equivalence between these sets. We conclude by discussing cyclic permutations in alternating grid classes.
Terahertz multispectral imaging of epitaxially grown semiconductors' lattice defects

Epitaxially grown SiGe and Ge layers on Si substrate have been analyzed by terahertz multispectral reconstructive 3D imaging technique. In particular, 3D images of both samples were generated via a non-contact and non-destructive route and were analyzed by utilizing the terahertz reconstructive imaging algorithm. It was found that the algorithm of “gridding with inverse distance to power equations” adopted herein for reconstructive imaging is capable of reproducing the results accurately as indicated by a good match with the TEM images of the same samples. Both the 3D and 2D images were analyzed by graphical means to determine the respective layers' thicknesses. The results were compared with the TEM images. Both the terahertz and TEM results were found to be in good agreement. Further, the imaging technique was also able to detect and quantify the size of small features of <1 nm present in the SiGe epi layer. In addition, lattice stacking fault and dislocations were also visualized and identified.
Effects of the textile-sensor interface on stitched strain sensor performance.

The influence of the textile substrate on the performance of a textile-based strain sensor has not been well understood or characterized in many wearable sensor evaluations. The underlying textile has its own anisotropic mechanical behaviors due to its woven or knit fabrication process, and introduces non-trivial structural influences on integrated wearable strain sensors. This study considers stitched strain sensors of two stitch geometries, fabricated on two different knit fabrics, with the sensor stitched in different orientations with respect to the knit structure. The resulting mechanical and electrical performance is characterized under cyclic extension, as the angle of extension (relative to the fabric) is also incrementally changed. The results illustrate a shift from linear to non-linear mechanical behavior as fabric stiffness increases, and variations in behavior between stitch geometries. Results show that force direction and sensor placement both introduce variability in calculated elastic modulus, which affect sensor modeling (e.g. predicting applied force from sensor response). A novel stitch geometry (the chainstitch sensor) is characterized as having a higher gauge force and lower transverse sensitivity factor than the coverstitch sensor. This work offers insight into the textile-sensor interface and design implications for development of textile-based sensors.
MixMatch: A Holistic Approach to Semi-Supervised Learning

Semi-supervised learning has proven to be a powerful paradigm for leveraging unlabeled data to mitigate the reliance on large labeled datasets. In this work, we unify the current dominant approaches for semi-supervised learning to produce a new algorithm, MixMatch, that guesses low-entropy labels for data-augmented unlabeled examples and mixes labeled and unlabeled data using MixUp. MixMatch obtains state-of-the-art results by a large margin across many datasets and labeled data amounts. For example, on CIFAR-10 with 250 labels, we reduce error rate by a factor of 4 (from 38% to 11%) and by a factor of 2 on STL-10. We also demonstrate how MixMatch can help achieve a dramatically better accuracy-privacy trade-off for differential privacy. Finally, we perform an ablation study to tease apart which components of MixMatch are most important for its success. Code is attached.
Acute vascular effects of the angiotensin II receptor antagonist olmesartan in normal subjects: relation to the renin-aldosterone system

Abstract The extent to which the clinical effects of angiotensin receptor blockers (ARB) are related to ambient renin system activity remains poorly defined. Therefore, we measured blood pressure (BP), large (C1) and small (C2) arterial compliance, systemic vascular resistance (SVR), plasma renin activity (PRA), and the 24-h urinary excretion of sodium (UNaV) and aldosterone before and 1, 2, 4, and 24 h after administration of single doses of placebo, and 5, 20, and 40 mg of the ARB olmesartan medoximil to 12 unmedicated normotensive subjects. In the basal state, SVR was inversely related to UNaV (r = −0.3, P = .04); the greater the UNaV, the more vasodilated the subject. Indices of arterial compliance, both C1 (r = −0.32, P = .03) and C2 (r = −0.35, P = .02) were inversely related to the basal PRA. Renin also predicted olmesartan-induced changes in C1 (r = 0.43, P = .004) and C2 (r = 0.33, P = .04). The greater the basal PRA, the less the arterial compliance, and the more compliance improved after olmesartan. Both systolic (P = .003) and diastolic (P We conclude that 1) the inverse relation of UNaV and SVR illustrates the reciprocal role of volume versus constrictor factors in maintaining normal BP; and 2) PRA is a physiologic determinant of arterial compliance in normal individuals and of the response to the ARB olmesartan. Measurement of PRA may help to predict clinical ARB responses in individual subjects.
Heuristic Thinking in Patient Care

This paper studies heuristic thinking and cognitive bias using a natural experiment from the field. The setting for the study is a set of acute care hospitals, where we examine the care process and...
Oxygen fugacities of extrasolar rocks: Evidence for an Earth-like geochemistry of exoplanets

Oxygen fugacity is a measure of rock oxidation that influences planetary structure and evolution. Most rocky bodies in the Solar System formed at oxygen fugacities approximately five orders of magnitude higher than a hydrogen-rich gas of solar composition. It is unclear whether this oxidation of rocks in the Solar System is typical among other planetary systems. We exploit the elemental abundances observed in six white dwarfs polluted by the accretion of rocky bodies to determine the fraction of oxidized iron in those extrasolar rocky bodies and therefore their oxygen fugacities. The results are consistent with the oxygen fugacities of Earth, Mars, and typical asteroids in the Solar System, suggesting that at least some rocky exoplanets are geophysically and geochemically similar to Earth.
Responsibility Analysis by Abstract Interpretation

Given a behavior of interest in the program, statically determining the corresponding responsible entity is a task of critical importance, especially in program security. Classical static analysis techniques (e.g. dependency analysis, taint analysis, slicing, etc.) assist programmers in narrowing down the scope of responsibility, but none of them can explicitly identify the responsible entity. Meanwhile, the causality analysis is generally not pertinent for analyzing programs, and the structural equations model (SEM) of actual causality misses some information inherent in programs, making its analysis on programs imprecise. In this paper, a novel definition of responsibility based on the abstraction of event trace semantics is proposed, which can be applied in program security and other scientific fields. Briefly speaking, an entity \(E_{R }\) is responsible for behavior \(\mathcal {B}\), if and only if \(E_{R }\) is free to choose its input value, and such a choice is the first one that ensures the occurrence of \(\mathcal {B}\) in the forthcoming execution. Compared to current analysis methods, the responsibility analysis is more precise. In addition, our definition of responsibility takes into account the cognizance of the observer, which, to the best of our knowledge, is a new innovative idea in program analysis.
A Local Relative Trace Formula for the Ginzburg-rallis Model: The Geometric Side

Following the method developed by Waldspurger and Beuzart-Plessis in their proofs of the local Gan-Gross-Prasad conjecture, we are able to prove the geometric side of a local relative trace formula for the Ginzburg-Rallis model. Then by applying such formula, we prove a multiplicity formula of the Ginzburg-Rallis model for the supercuspidal representations. Using that multiplicity formula, we prove the multiplicity one theorem for the Ginzburg-Rallis model over Vogan packets in the supercuspidal case.
Symbolic Analysis and Simulation for Power System Dynamic Performance Assessment

This panel contribution presents an integrated approach that combines symbolic and numeric computations to investigate dynamic analysis and simulation issues in power systems. Power system studies including load flow and local bifurcation analysis, require the numerical solution of non-linear equations using the Newton-Raphson-Seydel technique. This algorithm requires the computation of higher order Jacobian matrices, preferably in symbolic form. Symbolic programming procedures are implemented in the MATLAB-MAPLE environment. These include construction of the classical network model in symbolic form, symbolic computation of the Jacobian and 2nd order derivatives and conversion of symbolic equations into C source code that then is compiled as a MEX function callable from MATLAB. A software package, the voltage stability toolbox, has been designed to integrate symbolic and numeric computation with a graphical menu-driven interface based on MATLAB and its extended symbolic toolbox. This contribution demonstrates how this combined symbolic/numeric analysis and simulation tool can be used for dynamic system analysis and simulation including bifurcation studies and non-linear observability assessment. The integrated approach has been tested for various AC power systems including the IEEE 118-bus test systems as well as for AC/DC shipboard power system models
Systematic variation in marine dissolved organic matter stoichiometry and remineralization ratios as a function of lability

Remineralization of organic matter by heterotrophic organisms regulates the biological sequestration of carbon, thereby mediating atmospheric CO2. While surface nutrient supply impacts the elemental ratios of primary production, stoichiometric control by remineralization remains unclear. Here we develop a mechanistic description of remineralization and its stoichiometry in a marine microbial ecosystem model. The model simulates the observed elemental plasticity of phytoplankton and the relatively constant, lower C:N of heterotrophic biomass. In addition, the model captures the observed decreases in DOC:DON and the C:N remineralization ratio with depth for more labile substrates, which are driven by a switch in the dominant source of labile DOM from phytoplankton to heterotrophic biomass. Only a model version with targeted remineralization of N-rich components when DOM concentrations are high is able to simulate the observed profiles of preferential remineralization of DON relative to DOC and the elevated C:N of bulk DOM. Thus the model suggests that consumption of more labile substrates by C-limited heterotrophs does not result in preferential remineralization. Rather, preferential remineralization of DON is associated with growth that is limited by the processing rate of DOM. Critically, our results imply that the specific form of organic carbon produced or excreted by phytoplankton determines the extent to which a proportional increase in carbon production resulting from changes in phytoplankton stoichiometry will increase the efficiency of the biological pump. This emphasizes the importance of understanding the physiology of both phytoplankton and heterotrophs for anticipating changes in biologically driven ocean carbon storage.
Voltage Stability and Voltage Recovery: Load Dynamics and Dynamic VAR Sources

Two of the most frequent causes of power system voltage instability are voltage recovery following faults, and voltage collapse during transient swings. We propose methods for the identification and mitigation of these phenomena by appropriate usage of control devices such as dynamic VAr sources. We also propose a method for the reliable simulation of these phenomena, called "the time continuation method". This method combines the efficiency of power flow techniques and the capabilities of transient stability models. It is based on a quadratic model of the power system, including generators, voltage regulators and dynamic loads consisting mainly of induction motors. This model explains recently observed voltage stability and voltage recovery phenomena. Numerical experiments with visualizations are presented for the purpose of quantifying the phenomena and identifying the major affecting parameters. It is shown that proper combinations of static and dynamic reactive power sources can alleviate the risk of voltage instability. Finally, we propose a methodology for selecting the location and size and mix of static and dynamic VAr resources for the purpose of alleviating the risk of voltage instability and slow voltage recovery
TxForest: A DSL for Concurrent Filestores

Many systems use ad hoc collections of files and directories to store persistent data. For consumers of this data, the process of properly parsing, using, and updating these filestores using conventional APIs is cumbersome and error-prone. Making matters worse, most filestores are too big to fit in memory, so applications must process the data incrementally while managing concurrent accesses by multiple users. This paper presents Transactional Forest (TxForest), which builds on earlier work on Forest to provide a simpler, more powerful API for managing filestores, including a mechanism for managing concurrent accesses using serializable transactions. Under the hood, TxForest implements an optimistic concurrency control scheme using Huet’s zippers to track the data associated with filestores. We formalize TxForest in a core calculus, develop a proof of serializability, and describe our OCaml prototype, which we have used to build several practical applications.
Interlaced Greedy Algorithm for Maximization of Submodular Functions in Nearly Linear Time

A deterministic approximation algorithm is presented for the maximization of non-monotone submodular functions over a ground set of size $n$ subject to cardinality constraint $k$; the algorithm is based upon the idea of interlacing two greedy procedures. The algorithm uses interlaced, thresholded greedy procedures to obtain tight ratio $1/4 - \epsilon$ in $O \left( \frac{n}{\epsilon} \log \left( \frac{k}{\epsilon} \right) \right)$ queries of the objective function, which improves upon both the ratio and the quadratic time complexity of the previously fastest deterministic algorithm for this problem. The algorithm is validated in the context of two applications of non-monotone submodular maximization, on which it outperforms the fastest deterministic and randomized algorithms in prior literature.
The Way the Wind Blows: Direction of Airflow Energizes Consumers and Fuels Creative Engagement

Abstract Retail spaces contain copious sensory information that can affect consumers’ shopping behavior. This research investigates a novel, yet ubiquitous, retail atmospheric variable: airflow direction. We examine how the sensory experience of frontal (vs. dorsal) airflow energizes consumers in retail spaces and influences creative engagement. Five studies demonstrate that frontal airflow (air blowing on the front of the body) boosts energetic activation and fuels enhanced performance on creative tasks, compared to dorsal airflow (air blowing on the back of the body). Study 1 establishes the link between frontal (vs. dorsal) airflow and energetic activation in a laboratory setting. Study 2 tests the full model in a laboratory setting to provide initial evidence that frontal (vs. dorsal) airflow enhances creativity and that energetic activation drives this effect. Using a visualization task and an online setting, study 3 conceptually replicates airflow direction’s effect on creativity and the mediating role of energetic activation, while study 4 shows evidence of the mediating role of energetic activation via a moderation design. Study 5, an outdoor field study, provides further support for the predicted relationship between airflow direction and creative engagement. The theoretical and practical implications of these findings for retailing are also discussed.
Estimating Rates of Progression and Predicting Future Visual Fields in Glaucoma Using a Deep Variational Autoencoder.

In this manuscript we develop a deep learning algorithm to improve estimation of rates of progression and prediction of future patterns of visual field loss in glaucoma. A generalized variational auto-encoder (VAE) was trained to learn a low-dimensional representation of standard automated perimetry (SAP) visual fields using 29,161 fields from 3,832 patients. The VAE was trained on a 90% sample of the data, with randomization at the patient level. Using the remaining 10%, rates of progression and predictions were generated, with comparisons to SAP mean deviation (MD) rates and point-wise (PW) regression predictions, respectively. The longitudinal rate of change through the VAE latent space (e.g., with eight dimensions) detected a significantly higher proportion of progression than MD at two (25% vs. 9%) and four (35% vs 15%) years from baseline. Early on, VAE improved prediction over PW, with significantly smaller mean absolute error in predicting the 4th, 6th and 8th visits from the first three (e.g., visit eight: VAE8: 5.14 dB vs. PW: 8.07 dB; P < 0.001). A deep VAE can be used for assessing both rates and trajectories of progression in glaucoma, with the additional benefit of being a generative technique capable of predicting future patterns of visual field damage.
Learning from errors is attributable to episodic recollection rather than semantic mediation.

Abstract Many recent studies have shown that memory for correct answers is enhanced when an error is committed and then corrected, as compared to when the correct answer is provided without intervening error commission. The fact that the kind of errors that produced such a benefit, in past research, were those that were semantically related to the correct answer suggested that the effect may occur because the error provides a semantic stepping stone to the correct answer: the Semantic Mediation hypothesis. This hypothesis seems at odds with the finding that amnesicsgenerate answers, again including those studied by Tulving and his colleagues–who purportedly have spared semantic/implicit memory–experience enormous difficulties when they commit errors. Accordingly, the present experiments investigated whether the error-generation benefit seen in typicals was attributable Semantic Mediation or to Episodic Recollection. In Experiment 1, we used polysemous materials to create Congruent (e.g., wrist-palm) and Incongruent (e.g., tree-palm) cues for target words (e.g., HAND). In the Congruent condition, participants generated errors that were semantically related to the target (e.g., finger), and which could have provided a semantic mediator. In the Incongruent condition they generated errors that were unrelated to the target (e.g., coconut), and which, therefore, should not have provided a semantic mediator. The Congruent and Incongruent conditions both produced an error-generation benefit–contradicting the Semantic Mediation hypothesis. Experiment 2 showed that the error-generation benefit only occurred when the original error was also recollected on the final memory test. Indeed, in the Incongruent condition, when the error was not, itself, recalled, error generation resulted in a deficit in memory for the correct response. These results point to episodic/explicit, rather than semantic/implicit memory, as the locus of the ‘learning from errors’ benefits.
Sinkhorn limits in finitely many steps

Abstract Applied to a nonnegative m × n matrix with a nonzero σ-diagonal, the sequence of matrices constructed by alternate row and column scaling converges to a doubly stochastic matrix. It is proved that if this sequence converges after only a finite number of scalings, then it converges after at most two scalings.
The Effect of Hydriding on the Physical Structure of Palladium and on the Release of Contained Tritium

The behavior of tritium released from a contaminated palladium cathode is determined and compared with the pattern found in cells claimed to produce tritium by a cold fusion reaction. Void space is...
Mixed field theory

We consider scalar field theory defined over a direct product of the real and p-adic numbers. An adjustable dynamical scaling exponent z enters into the microscopic lagrangian, so that the Gaussian theories provide a line of fixed points. We argue that at z = 1/3, a branch of Wilson-Fisher fixed points joins onto the line of Gaussian theories. We compute standard critical exponents at the Wilson-Fisher fixed points in the region where they are perturbatively accessible, including a loop correction to the dynamical critical exponent. We show that the classical propagator contains oscillatory behavior in the real direction, though the amplitude of these oscillations can be made exponentially small without fine-tuning parameters of the theory. Similar oscillatory behavior emerges in Fourier space from two-loop corrections, though again it can be highly suppressed. We also briefly consider compact p-adic extra dimensions, showing in non-linear, classical, scalar field theories that a form of consistent truncation allows us to retain only finitely many Kaluza-Klein modes in an effective theory formulated on the non-compact directions.
Do (Some) University Endowments Earn Alpha

The authors analyzed the returns earned by U.S. educational endowments using style attribution models. For the average endowment, models with only public stock and bond benchmarks explain virtually all the time-series variation in returns, yield no alpha, and generate sensible factor loadings. Elite institutions perform better than public stock and bond benchmarks because of large allocations to alternative investments. The authors found no evidence that manager selection, market timing, and tactical asset allocation generate alpha.