Search

The Quantum-Enhanced Space-Time (QUEST) experiment consists of a pair of co-located Power Recycled Michelson Interferometers, each designed to have a broadband, shot-noise limited displacement sensitivity of $2\times10^{-19}$ $\mathrm{m/\sqrt{Hz}}$ from 1 to 200 MHz. Here we present the first results of QUEST that set new upper limits on correlated length fluctuations from 13 to 80 MHz, constituting the first broadband constraints for a stochastic gravitational wave background at these frequencies. In a coincident observing run of $10^{4}$ s the averaging of the cross-correlation spectra between the two interferometer signals resulted in a strain sensitivity of $3\times10^{-20}$ $\mathrm{1/\sqrt{Hz}}$ at 40 MHz, making QUEST the most sensitive table-top interferometric system to date.

Speckle patterns manifesting from the interaction of coherent X-rays with matter offer a glimpse into the dynamics of nanoscale domains that underpin many emergent phenomena in quantum materials. While the dynamics of the average structure can be followed with time-resolved X-ray diffraction, the ultrafast evolution of local structures in nonequilibrium conditions have thus far eluded detection due to experimental limitations, such as insufficient X-ray coherent flux. Here we demonstrate a nonequilibrium speckle visibility experiment using a split-and-delay setup at an X-ray free-electron laser. Photoinduced electronic domain fluctuations of the magnetic model material Fe$_{3}$O$_{4}$ reveal changes of the trimeron network configuration due to charge dynamics that exhibit liquid-like fluctuations, analogous to a supercooled liquid phase. This suggests that ultrafast dynamics of electronic heterogeneities under optical stimuli are fundamentally different from thermally-driven ones.

In Data Assimilation, observations are fused with simulations to obtain an accurate estimate of the state and parameters for a given physical system. Combining data with a model, however, while accurately estimating uncertainty, is computationally expensive and infeasible to run in real-time for complex systems. Here, we present a novel particle filter methodology, the Deep Latent Space Particle filter or D-LSPF, that uses neural network-based surrogate models to overcome this computational challenge. The D-LSPF enables filtering in the low-dimensional latent space obtained using Wasserstein AEs with modified vision transformer layers for dimensionality reduction and transformers for parameterized latent space time stepping. As we demonstrate on three test cases, including leak localization in multi-phase pipe flow and seabed identification for fully nonlinear water waves, the D-LSPF runs orders of magnitude faster than a high-fidelity particle filter and 3-5 times faster than alternative methods while being up to an order of magnitude more accurate. The D-LSPF thus enables real-time data assimilation with uncertainty quantification for physical systems.

Resonate-and-Fire (RF) neurons are an interesting complementary model for integrator neurons in spiking neural networks (SNNs). Due to their resonating membrane dynamics they can extract frequency patterns within the time domain. While established RF variants suffer from intrinsic shortcomings, the recently proposed balanced resonate-and-fire (BRF) neuron marked a significant methodological advance in terms of task performance, spiking and parameter efficiency, as well as, general stability and robustness, demonstrated for recurrent SNNs in various sequence learning tasks. One of the most intriguing result, however, was an immense improvement in training convergence speed and smoothness, overcoming the typical convergence dilemma in backprop-based SNN training. This paper aims at providing further intuitions about how and why these convergence advantages emerge. We show that BRF neurons, in contrast to well-established ALIF neurons, span a very clean and smooth - almost convex - error landscape. Furthermore, empirical results reveal that the convergence benefits are predominantly coupled with a divergence boundary-aware optimization, a major component of the BRF formulation that addresses the numerical stability of the time-discrete resonator approximation. These results are supported by a formal investigation of the membrane dynamics indicating that the gradient is transferred back through time without loss of magnitude.

To make sense of their surroundings, intelligent systems must transform complex sensory inputs to structured codes that are reduced to task-relevant information such as object category. Biological agents achieve this in a largely autonomous manner, presumably via self-supervised learning. Whereas previous attempts to model the underlying mechanisms were largely discriminative in nature, there is ample evidence that the brain employs a generative model of the world. Here, we propose that eye movements, in combination with the focused nature of primate vision, constitute a generative, self-supervised task of predicting and revealing visual information. We construct a proof-of-principle model starting from the framework of masked image modeling (MIM), a common approach in deep representation learning. To do so, we analyze how core components of MIM such as masking technique and data augmentation influence the formation of category-specific representations. This allows us not only to better understand the principles behind MIM, but to then reassemble a MIM more in line with the focused nature of biological perception. We find that MIM disentangles neurons in latent space without explicit regularization, a property that has been suggested to structure visual representations in primates. Together with previous findings of invariance learning, this highlights an interesting connection of MIM to latent regularization approaches for self-supervised learning. The source code is available under https://github.com/RobinWeiler/FocusMIM

The GQuEST (Gravity from the Quantum Entanglement of Space-Time) experiment uses tabletop-scale Michelson laser interferometers to probe for fluctuations in space-time. We present a practicable interferometer design featuring a novel photon counting readout method that provides unprecedented sensitivity, as it is not subject to the interferometric standard quantum limit. We evaluate the potential of this design to measure space-time fluctuations motivated by recent `geontropic' quantum gravity models. The accelerated accrual of Fisher information offered by the photon counting readout enables GQuEST to detect the predicted quantum gravity phenomena within measurement times at least 100 times shorter than equivalent conventional interferometers. The GQuEST design thus enables a fast and sensitive search for signatures of quantum gravity in a laboratory-scale experiment.

The resonate-and-fire (RF) neuron, introduced over two decades ago, is a simple, efficient, yet biologically plausible spiking neuron model, which can extract frequency patterns within the time domain due to its resonating membrane dynamics. However, previous RF formulations suffer from intrinsic shortcomings that limit effective learning and prevent exploiting the principled advantage of RF neurons. Here, we introduce the balanced RF (BRF) neuron, which alleviates some of the intrinsic limitations of vanilla RF neurons and demonstrates its effectiveness within recurrent spiking neural networks (RSNNs) on various sequence learning tasks. We show that networks of BRF neurons achieve overall higher task performance, produce only a fraction of the spikes, and require significantly fewer parameters as compared to modern RSNNs. Moreover, BRF-RSNN consistently provide much faster and more stable training convergence, even when bridging many hundreds of time steps during backpropagation through time (BPTT). These results underscore that our BRF-RSNN is a strong candidate for future large-scale RSNN architectures, further lines of research in SNN methodology, and more efficient hardware implementations., Comment: Accepted at ICML 2024, https://proceedings.mlr.press/v235/higuchi24a.html

We report on a direct search for scalar field dark matter using data from LIGO's third observing run. We analyse the coupling of size oscillations of the interferometer's beamsplitter and arm test masses that may be caused by scalar field dark matter. Using new efficient search methods to maximise sensitivity for signatures of such oscillations, we set new upper limits for the coupling constants of scalar field dark matter as a function of its mass, which improve upon bounds from previous direct searches by several orders of magnitude in a frequency band from 10 Hz to 180 Hz., Comment: 6 pages (including references), 3 figures

The most massive black holes in our Universe form binaries at the centre of merging galaxies. The recent evidence for a gravitational-wave (GW) background from pulsar timing may constitute the first observation that these supermassive black hole binaries (SMBHBs) merge. Yet, the most massive SMBHBs are out of reach of interferometric {GW} detectors and are exceedingly difficult to resolve individually with pulsar timing. These limitations call for unexplored strategies to detect individual SMBHBs in the uncharted frequency band $\lesssim10^{-5}\,\rm Hz$ in order to establish their abundance and decipher the coevolution with their host galaxies. Here we show that SMBHBs imprint detectable long-term modulations on GWs from stellar-mass binaries residing in the same galaxy. We determine that proposed deci-Hz GW interferometers sensitive to numerous stellar-mass binaries could uncover modulations from $\sim\mathscr{O}(10^{-1}$ - $10^4)$ SMBHBs with masses $\sim\mathscr{O}(10^7$ - $10^8)\,\rm M_\odot$ out to redshift $z\sim3.5$. This offers a unique opportunity to map the population of SMBHBs through cosmic time, which might remain inaccessible otherwise., Comment: 20 pages, 6 figures. Nature Astronomy (2024)

Resonant driving of electronic transitions with coherent laser sources creates quantum coherent superpositions of the involved electronic states. Most time-resolved studies have focused on gases or isolated subsystems embedded in insulating solids, aiming for applications in quantum information. Here, we demonstrate coherent control of orbital wavefunctions in pyrochlore $Tb_{2}Ti_{2}O_{7}$, which forms an interacting spin liquid ground state. We show that resonant excitation with a strong THz pulse creates a coherent superposition of the lowest energy Tb 4f states before the magnetic interactions eventually dephase them. The coherence manifests itself as a macroscopic oscillating magnetic dipole, which is detected by ultrafast resonant x-ray diffraction. The induced quantum coherence demonstrates coherent control of orbital wave functions, a new tool for the ultrafast manipulation and investigation of quantum materials.

We present a formalism to discern the effects of fluctuations of the spacetime metric on electromagnetic radiation. The formalism works via the measurement of electromagnetic field correlations, while allowing a clear assessment of the assumptions involved. As an application of the formalism, we present a model of spacetime fluctuations that appear as random fluctuations of the refractive index of the vacuum in single, and two co-located Michelson interferometers. We compare an interferometric signal predicted using this model to experimental data from the Holometer and aLIGO. We show that if the signal manifests at a frequency at which the interferometers are sensitive, the strength and scale of possible spacetime fluctuations can be constrained. The bounds, thus obtained, on the strength and scale of the spacetime fluctuations, are also shown to be more stringent than the bounds obtained previously using astronomical observation at optical frequencies. The formalism enables us to evaluate proposed experiments such as QUEST for constraining quantum spacetime fluctuations and to design new ones., Comment: 21 pages, 3 figures, 2 tables

Many functionally relevant ferroic phenomena in quantum materials can be manipulated by driving the lattice coherently with optical and terahertz pulses. New physical phenomena and non-equilibrium phases that have no equilibrium counterpart have been discovered following these protocols. The underlying structural dynamics has been mostly studied by recording the average atomic position along dynamical structural coordinates with elastic scattering methods. However, crystal lattice fluctuations, which are known to influence phase transitions in equilibrium, are also expected to determine these dynamics but have rarely been explored. Here, we study the driven dynamics of the quantum paraelectric SrTiO3, in which mid-infrared drives have been shown to induce a metastable ferroelectric state. Crucial in these physics is the competition between the polar instability and antiferrodistortive rotations, which in equilibrium frustrate the formation of long-range ferroelectricity. We make use of high intensity mid-infrared optical pulses to resonantly drive a Ti-O stretching mode at 17 THz, and we measure the resulting change in lattice fluctuations using time-resolved x-ray diffuse scattering at a free electron laser. After a prompt increase, we observe a long-lived quench in R-point antiferrodistortive lattice fluctuations. The enhancement and reduction in lattice fluctuations are explained theoretically by considering fourth-order nonlinear phononic interactions and third-order coupling to the driven optical phonon and to lattice strain, respectively. These observations provide a number of new and testable hypotheses for the physics of light-induced ferroelectricity.

Gravitational waves modulate the apparent frequencies of other periodic signals. Low-frequency gravitational waves could therefore be detected by observing frequency modulations in signals from higher-frequency sources, e.g., those from binary white dwarfs detected with the LISA gravitational-wave detector. We propose a concrete method to extract these modulations by coherently adding the cross-spectra of a large number of well-resolved quasi-monochromatic signals. We apply this method to the case of LISA, and find this method would enable the detection of background gravitational wave strain amplitudes of, e.g., $A\simeq10^{-10}$ at a frequency $F\simeq10^{-8}\,\rm Hz$, given current projections for the number and properties of Galactic binary white dwarfs and the sensitivity of the instrument. We also estimate (to within an order of magnitude) that this method could potentially compete with that of current Pulsar Timing Arrays when using signals from binary neutron stars such as those expected to be observed with proposed detectors like DECIGO. Our results show that gravitational-wave detectors could be sensitive at frequencies outside of their designed bandwidth using the same infrastructure, which has the potential to open up unexplored and otherwise inaccessible parts of the gravitational wave spectrum., Comment: 12 pages, 6 figures, 1 table

In this work, we propose polarimetry experiments to search for low-mass (sub-eV) bosonic field dark matter, including axions and axion-like particles. We show that a polarimetry configuration consisting of a thick birefringent solid inside a Fabry-P\'erot cavity is exceptionally sensitive to scalar field dark matter, which may cause oscillatory variations in the solid's thickness and refractive index. In addition, we show that a reconfiguration of this polarimetry experiment, in which two quarter-wave plates are placed inside the Fabry-P\'erot cavity instead of a thick birefringent solid, is very sensitive to axion-like particles. We investigate the possibility of using cross-correlation of twin polarimeters to increase the sensitivity of the experiment, which in turn could allow us to explore unexplored parts of the parameter space and potentially detect a signal in either dark matter scenario.

The event-driven and sparse nature of communication between spiking neurons in the brain holds great promise for flexible and energy-efficient AI. Recent advances in learning algorithms have demonstrated that recurrent networks of spiking neurons can be effectively trained to achieve competitive performance compared to standard recurrent neural networks. Still, as these learning algorithms use error-backpropagation through time (BPTT), they suffer from high memory requirements, are slow to train, and are incompatible with online learning. This limits the application of these learning algorithms to relatively small networks and to limited temporal sequence lengths. Online approximations to BPTT with lower computational and memory complexity have been proposed (e-prop, OSTL), but in practice also suffer from memory limitations and, as approximations, do not outperform standard BPTT training. Here, we show how a recently developed alternative to BPTT, Forward Propagation Through Time (FPTT) can be applied in spiking neural networks. Different from BPTT, FPTT attempts to minimize an ongoing dynamically regularized risk on the loss. As a result, FPTT can be computed in an online fashion and has fixed complexity with respect to the sequence length. When combined with a novel dynamic spiking neuron model, the Liquid-Time-Constant neuron, we show that SNNs trained with FPTT outperform online BPTT approximations, and approach or exceed offline BPTT accuracy on temporal classification tasks. This approach thus makes it feasible to train SNNs in a memory-friendly online fashion on long sequences and scale up SNNs to novel and complex neural architectures., Comment: 12 pages, 4 figures

Low-mass (sub-eV) scalar field dark matter may induce apparent oscillations of fundamental constants, resulting in corresponding oscillations of the size and the index of refraction of solids. Laser interferometers are highly sensitive to changes in the size and index of refraction of the main beamsplitter. Using cross-correlated data of the Fermilab Holometer instrument, which consists of twin co-located 40-m arm length power-recycled interferometers, we investigate the possible existence of scalar field dark matter candidates in the mass range between 1.6$\cdot$10$^{-12}$ eV and 1.0$\cdot$10$^{-7}$ eV. We set new upper limits for the coupling parameters of scalar field dark matter, improving on limits from previous direct searches by up to three orders of magnitude.

With the use of twin, co-located, 3D interferometers, Cardiff University's Gravity Exploration Institute aims to observe quantum fluctuations of space-time as predicted by some theories of quantum gravity. Our design displacement sensitivity exceeds that of previous similar experiments, which have constrained the magnitudes of the fluctuations in the 1-25 MHz band. The increased sensitivity comes in large part from the comparably higher circulating power we aim to achieve, which reduces the overall shot noise. One complication of higher circulating power is an increase in contrast defect light, which includes higher-order modes. We will use the DC-readout scheme, whose dark-fringe offset must sufficiently dominate the contrast defect in order to detect faint signals. However, too much total output power risks saturating the high-bandwidth photodetectors. Suppressing the higher-order mode content of the contrast defect is a key strategy to realising the high circulating power and eliminating non-signal-carrying power that contributes to shot noise. For this, the inclusion of an output mode cleaner, whose design is described, is required., Comment: 6 pages, 3 figures, 1 table, Contribution to the 2021 Gravitation session of the 55th Rencontres de Moriond

Inspired by more detailed modeling of biological neurons, Spiking neural networks (SNNs) have been investigated both as more biologically plausible and potentially more powerful models of neural computation, and also with the aim of extracting biological neurons' energy efficiency; the performance of such networks however has remained lacking compared to classical artificial neural networks (ANNs). Here, we demonstrate how a novel surrogate gradient combined with recurrent networks of tunable and adaptive spiking neurons yields state-of-the-art for SNNs on challenging benchmarks in the time-domain, like speech and gesture recognition. This also exceeds the performance of standard classical recurrent neural networks (RNNs) and approaches that of the best modern ANNs. As these SNNs exhibit sparse spiking, we show that they theoretically are one to three orders of magnitude more computationally efficient compared to RNNs with comparable performance. Together, this positions SNNs as an attractive solution for AI hardware implementations., Comment: 11 pages

The nature of dark matter remains unknown to date; several candidate particles are being considered in a dynamically changing research landscape. Scalar field dark matter is a prominent option that is being explored with precision instruments, such as atomic clocks and optical cavities. Here we report on the first direct search for scalar field dark matter utilising a gravitational-wave detector, which operates beyond the quantum shot-noise limit. We set new upper limits for the coupling constants of scalar field dark matter as a function of its mass, by excluding the presence of signals that would be produced through the direct coupling of this dark matter to the beamsplitter of the GEO$\,$600 interferometer. The new constraints improve upon bounds from previous direct searches by more than six orders of magnitude, and are in some cases more stringent than limits obtained in tests of the equivalence principle by up to four orders of magnitude. Our work demonstrates that scalar field dark matter can be probed or constrained with direct searches using gravitational-wave detectors, and highlights the potential of quantum-enhanced interferometry for dark matter detection.

We present a novel reduced order model (ROM) approach for parameterized time-dependent PDEs based on modern learning. The ROM is suitable for multi-query problems and is nonintrusive. It is divided into two distinct stages: A nonlinear dimensionality reduction stage that handles the spatially distributed degrees of freedom based on convolutional autoencoders, and a parameterized time-stepping stage based on memory aware neural networks (NNs), specifically causal convolutional and long short-term memory NNs. Strategies to ensure generalization and stability are discussed. The methodology is tested on the heat equation, advection equation, and the incompressible Navier-Stokes equations, to show the variety of problems the ROM can handle.

Theories of quantum gravity based on the holographic principle predict the existence of quantum fluctuations of distance measurements that accumulate and exhibit correlations over macroscopic distances. This paper models an expected signal due to this phenomenology, and details the design and estimated sensitivity of co-located twin table-top 3D interferometers being built to measure or constrain it. The experiment is estimated to be sensitive to displacements $\sim10^{-19}\,\rm{m}/\sqrt{\rm{Hz}}$ in a frequency band between 1 and 250 MHz, surpassing previous experiments and enabling the possible observation of quantum gravity phenomena. The experiment will also be sensitive to MHz gravitational waves and various dark matter candidates., Comment: Accepted for publication in Classical and Quantum Gravity

The emergence of brain-inspired neuromorphic computing as a paradigm for edge AI is motivating the search for high-performance and efficient spiking neural networks to run on this hardware. However, compared to classical neural networks in deep learning, current spiking neural networks lack competitive performance in compelling areas. Here, for sequential and streaming tasks, we demonstrate how a novel type of adaptive spiking recurrent neural network (SRNN) is able to achieve state-of-the-art performance compared to other spiking neural networks and almost reach or exceed the performance of classical recurrent neural networks (RNNs) while exhibiting sparse activity. From this, we calculate a $>$100x energy improvement for our SRNNs over classical RNNs on the harder tasks. To achieve this, we model standard and adaptive multiple-timescale spiking neurons as self-recurrent neural units, and leverage surrogate gradients and auto-differentiation in the PyTorch Deep Learning framework to efficiently implement backpropagation-through-time, including learning of the important spiking neuron parameters to adapt our spiking neurons to the tasks., Comment: 11 pages,5 figures

Extracting and binding salient information from different sensory modalities to determine common features in the environment is a significant challenge in robotics. Here we present MuPNet (Multi-modal Predictive Coding Network), a biologically plausible network architecture for extracting joint latent features from visuo-tactile sensory data gathered from a biomimetic mobile robot. In this study we evaluate MuPNet applied to place recognition as a simulated biomimetic robot platform explores visually aliased environments. The F1 scores demonstrate that its performance over prior hand-crafted sensory feature extraction techniques is equivalent under controlled conditions, with significant improvement when operating in novel environments., Comment: Submitted to ICRA 2020. 6+1 Pages with 5 figures

We demonstrate full control of acoustic and thermal periodic deformations at solid surfaces down to sub-nanosecond time scales and few-micrometer length scales via independent variation of the temporal and spatial phase of two optical transient grating (TG) excitations. For this purpose, we introduce an experimental setup that exerts control of the spatial phase of subsequent time-delayed TG excitations depending on their polarization state. Specific exemplary coherent control cases are discussed theoretically and corresponding experimental data are presented in which time-resolved x-ray reflectivity measures the spatiotemporal surface distortion of nanolayered heterostructures. Finally, we discuss examples where the application of our method may enable the control of functional material properties via tailored spatiotemporal strain fields., Comment: 10 pages, 4 Figures

While modern convolutional neural networks achieve outstanding accuracy on many image classification tasks, they are, compared to humans, much more sensitive to image degradation. Here, we describe a variant of Batch Normalization, LocalNorm, that regularizes the normalization layer in the spirit of Dropout while dynamically adapting to the local image intensity and contrast at test-time. We show that the resulting deep neural networks are much more resistant to noise-induced image degradation, improving accuracy by up to three times, while achieving the same or slightly better accuracy on non-degraded classical benchmarks. In computational terms, LocalNorm adds negligible training cost and little or no cost at inference time, and can be applied to already-trained networks in a straightforward manner., Comment: 14 pages, 17 figures

In this paper we study the generalization capabilities of fully-connected neural networks trained in the context of time series forecasting. Time series do not satisfy the typical assumption in statistical learning theory of the data being i.i.d. samples from some data-generating distribution. We use the input and weight Hessians, that is the smoothness of the learned function with respect to the input and the width of the minimum in weight space, to quantify a network's ability to generalize to unseen data. While such generalization metrics have been studied extensively in the i.i.d. setting of for example image recognition, here we empirically validate their use in the task of time series forecasting. Furthermore we discuss how one can control the generalization capability of the network by means of the training process using the learning rate, batch size and the number of training iterations as controls. Using these hyperparameters one can efficiently control the complexity of the output function without imposing explicit constraints.

This paper proposes a data-driven approach, by means of an Artificial Neural Network (ANN), to value financial options and to calculate implied volatilities with the aim of accelerating the corresponding numerical methods. With ANNs being universal function approximators, this method trains an optimized ANN on a data set generated by a sophisticated financial model, and runs the trained ANN as an agent of the original solver in a fast and efficient way. We test this approach on three different types of solvers, including the analytic solution for the Black-Scholes equation, the COS method for the Heston stochastic volatility model and Brent's iterative root-finding method for the calculation of implied volatilities. The numerical results show that the ANN solver can reduce the computing time significantly.

We report a benchmark experiment that demonstrates shortening of hard x-ray pulses in a synchrotron-based optical pump - x-ray probe experiment. The pulse shortening device, a picosecond Bragg switch, reduces the temporal resolution of an incident x-ray pulse to 7.5 ps. We employ the Bragg switch to monitor propagating sound waves in nanometer-thin epitaxial films. With the experimental data we infer pulse duration, diffraction efficiency and switching contrast of the device. A detailed efficiency analysis shows, that the switch can deliver up to 1010 photons/sec in high-repetition rate synchrotron experiments.

Vision research has been shaped by the seminal insight that we can understand the higher-tier visual cortex from the perspective of multiple functional pathways with different goals. In this paper, we try to give a computational account of the functional organization of this system by reasoning from the perspective of multi-task deep neural networks. Machine learning has shown that tasks become easier to solve when they are decomposed into subtasks with their own cost function. We hypothesize that the visual system optimizes multiple cost functions of unrelated tasks and this causes the emergence of a ventral pathway dedicated to vision for perception, and a dorsal pathway dedicated to vision for action. To evaluate the functional organization in multi-task deep neural networks, we propose a method that measures the contribution of a unit towards each task, applying it to two networks that have been trained on either two related or two unrelated tasks, using an identical stimulus set. Results show that the network trained on the unrelated tasks shows a decreasing degree of feature representation sharing towards higher-tier layers while the network trained on related tasks uniformly shows high degree of sharing. We conjecture that the method we propose can be used to analyze the anatomical and functional organization of the visual system and beyond. We predict that the degree to which tasks are related is a good descriptor of the degree to which they share downstream cortical-units., Comment: 16 pages, 5 figures

Biological neurons communicate with a sparing exchange of pulses - spikes. It is an open question how real spiking neurons produce the kind of powerful neural computation that is possible with deep artificial neural networks, using only so very few spikes to communicate. Building on recent insights in neuroscience, we present an Adapting Spiking Neural Network (ASNN) based on adaptive spiking neurons. These spiking neurons efficiently encode information in spike-trains using a form of Asynchronous Pulsed Sigma-Delta coding while homeostatically optimizing their firing rate. In the proposed paradigm of spiking neuron computation, neural adaptation is tightly coupled to synaptic plasticity, to ensure that downstream neurons can correctly decode upstream spiking neurons. We show that this type of network is inherently able to carry out asynchronous and event-driven neural computation, while performing identical to corresponding artificial neural networks (ANNs). In particular, we show that these adaptive spiking neurons can be drop in replacements for ReLU neurons in standard feedforward ANNs comprised of such units. We demonstrate that this can also be successfully applied to a ReLU based deep convolutional neural network for classifying the MNIST dataset. The ASNN thus outperforms current Spiking Neural Networks (SNNs) implementations, while responding (up to) an order of magnitude faster and using an order of magnitude fewer spikes. Additionally, in a streaming setting where frames are continuously classified, we show that the ASNN requires substantially fewer network updates as compared to the corresponding ANN., Comment: 14 pages, 9 figures

Recent experimental work has suggested that the neural firing rate can be interpreted as a fractional derivative, at least when signal variation induces neural adaptation. Here, we show that the actual neural spike-train itself can be considered as the fractional derivative, provided that the neural signal is approximated by a sum of power-law kernels. A simple standard thresholding spiking neuron suffices to carry out such an approximation, given a suitable refractory response. Empirically, we find that the online approximation of signals with a sum of power-law kernels is beneficial for encoding signals with slowly varying components, like long-memory self-similar signals. For such signals, the online power-law kernel approximation typically required less than half the number of spikes for similar SNR as compared to sums of similar but exponentially decaying kernels. As power-law kernels can be accurately approximated using sums or cascades of weighted exponentials, we demonstrate that the corresponding decoding of spike-trains by a receiving neuron allows for natural and transparent temporal signal filtering by tuning the weights of the decoding kernel., Comment: 13 pages, 5 figures, in Advances in Neural Information Processing 2010

We analyse the SM97 partial wave amplitudes for nucleon--nucleon (NN) scattering to 2.5 GeV, in which resonance and meson production effects are evident for energies above pion production threshold. Our analyses are based upon boson exchange or quantum inversion potentials with which the sub-threshold data are fit perfectly. Above 300 MeV they are extrapolations, to which complex short ranged Gaussian potentials are added in the spirit of the optical models of nuclear physics and of diffraction models of high energy physics. The data to 2.5 GeV are all well fit. The energy dependences of these Gaussians are very smooth save for precise effects caused by the known $\Delta$ and N$^\star$ resonances. With this approach, we confirm that the geometrical implications of the profile function found from diffraction scattering are pertinent in the regime 300 MeV to 2.5 GeV and that the overwhelming part of meson production comes from the QCD sector of the nucleons when they have a separation of their centres of 1 to 1.2 fm. This analysis shows that the elastic NN scattering data above 300 MeV can be understood with a local potential operator as well as has the data below 300 MeV., Comment: 49 pages, including 23 figures, LaTeX2e/RevTeX/ps file

Low energy scattering and bound state properties of the \pi N, \pi\pi and K\pi systems are studied as coupled channel problems using inversion potentials of phase shift data. In a first step we apply the potential model to explain recent measurements of pionic hydrogen shift and width. Secondly, predictions of the model for pionium lifetime and shift confirm a well known and widely used effective range expression. Thirdly, as extension of this confirmation, we predict an unexpected medium effect of the pionium lifetime which shortens by several orders of magnitude. The \sigma meson shows a narrow resonance structure as a function of the medium modified mass with the implication of being essentially energy independent. Similarly, we see this medium resonance effect realized for the K\pi system. To support our findings we present also results for the \rho meson and the \Delta(1232) resonance., Comment: 42 pages, 17 PS figures, REFTeX, epsfig.sty needed, submitted to Phys. Rev

Two-body interactions of elementary particles are useful in particle and nuclear physics to describe qualitatively and quantitatively few- and many-body systems. We are extending for this purpose the quantum inversion approach for systems consisting of nucleons and mesons. From the wide range of experimentally studied two-body systems we concentrate here on $\pi N$, $\pi\pi$, $K^+N$, $K \pi$ and $K\bar{K}$. As input we require results of phase shift analyses. Quantum inversion Gelfand-Levitan and Marchenko single and coupled channel algorithms are used for Schr\"odinger type wave equations in partial wave decomposition. The motivation of this study comes from our two approaches: to generate and investigate potentials directly from data by means of inversion and alternatively use linear and nonlinear boson exchange models. The interesting results of inversion are coordinate space informations about radial ranges, strengths, long distance behaviors, resonance characteristics, threshold effects, scattering lengths and bound state properties., Comment: Contribution to the International Conference on Inverse and Algebraic Scattering Theory, Lake Balaton '96, to appear in Springer LNP, 22 pages RevTeX, incl. 7 figures (needs epsfig)

The notion of interacting elementary particles for low and medium energy nuclear physics is associated with definitions of potential operators. In principle, this potential carries the rich substructure consisting of quarks and gluons and thus may be deduced from some microscopic model. In this spirit we propose a boson exchange potential from a nonlinear quantum field theory. Essentially, the meson propagators and form factors of conventional models are replaced by amplitudes derived from the dynamics of self-interacting mesons in terms of solitary fields. Contrary to deduction, we position the inversion approach. Using Gel'fand-Levitan and Marchenko inversion we compute local, energy-independent potentials from experimental phase shifts for various partial waves. Both potential models give excellent results for on-shell NN scattering data. In the off-shell domain we study both potential models in PP Bremsstrahlung, elastic nucleon-nucleus scattering and triton binding energy calculations. It remains surprising that for all observables the inversion and microscopic meson exchange potentials are equivalent in their reproduction of data. Finally, we look for another realm of elementary interactions where inversion and meson exchange models can be applied with the hope to find more sensitivity to discern substructure dynamics., Comment: 23 pages REVTeX, 4 Postscript figures, Contribution to the International Conference on Inverse and Algebraic Quantum Scattering Theory, Lake Balaton '96

The sensitivity of nucleon-nucleus elastic scattering to the off-shell behavior of realistic nucleon-nucleon interactions is investigated when on-shell equivalent nucleon-nucleon potentials are used. The study is based on applications of the full-folding optical model potential for an explicit treatment of the off-shell behavior of the nucleon-nucleon effective interaction. Applications were made at beam energies between 40 and 500 MeV for proton scattering from 40Ca and 208Pb. We use the momentum-dependent Paris potential and its local on-shell equivalent as obtained with the Gelfand-Levitan and Marchenko inversion formalism for the two nucleon Schroedinger equation. Full-folding calculations for nucleon-nucleus scattering show small fluctuations in the corresponding observables. This implies that off-shell features of the NN interaction cannot be unambiguously identified with these processes. Inversion potentials were also constructed directly from NN phase-shift data (SM94) in the 0-1.3 GeV energy range. Their use in proton-nucleus scattering above 200 MeV provide a superior description of the observables relative to those obtained from current realistic NN potentials. Limitations and scope of our findings are presented and discussed., Comment: 17 pages tightened REVTeX, 8 .ps figures, submitted to Phys. Rev. C

Characteristic properties of pionium $A_{2\pi}$ and associated low energy s--wave cross sections $\sigma(\pi^0\pi^0\to \pi^0\pi^0)$, $\sigma(\pi^+\pi^-\to\pi^0\pi^0)$ and $\sigma(\pi^0\pi^0\to \pi^+\pi^-)$ are investigated with a coupled channels potential model. Some experimental results and conclusions are to be reconsidered., Comment: 10 Pages REVTeX using epsfig.sty. Submitted to PRC

NN and Pi-N partial wave radial potentials have been generated using the latest SM94-VPI(NN) and FA93-VPI(Pi-N) phase shifts. The potentials are used to determine the deuteron properties and to compute the 3H and 3He binding energies. The e_1 mixing angles of SM94-VPI and NY93-Nijmegen differ significantly and inversion potentials yield a P_d of 6.37% and 5.78%, respectively. Underbinding of 3H and 3He is enhanced by SM94, which signals more nonlocality and/or three-body potential effects than predicted from Nijmegen phase shifts and boson exchange models. The local pion-nucleon S_31, P_31 and P_33 channel potentials have been generated for guidance and to obtain a quantitative impression in r-space of what the Pi-N FA93-VPI phase shifts imply., Comment: Presented at the International Conference on Physics with GeV Particle Beams, August 22.-25., 1994, Juelich, Germany. 4 Pages LaTeX, 6 Pictures (you need epsfig.sty)

We examined arterial baroreflex control of muscle sympathetic nerve activity (MSNA) during abrupt decreases in mean arterial pressure (MAP) and evaluated whether endurance training alters baroreflex function. Acute hypotension was induced nonpharmacologically in 14 healthy subjects, of which 7 were of high fitness (HF) and 7 were of average fitness (AF), by releasing a unilateral arterial thigh cuff after 9 min of resting ischemia under two conditions: control, which used aortic and carotid baroreflex (ABR and CBR, respectively) deactivation; and suction, which used ABR deactivation alone. The application of neck suction to counteract changes in carotid sinus transmural pressure during cuff release significantly attenuated the MSNA response (which increased 134 +/- 32 U/14 s) compared with control (which increased 195 +/- 43 U/14 s) and caused a greater decrease in MAP (19 +/- 2 vs. 15 +/- 2 mmHg; P < 0.05). Furthermore, during both trials, the HF subjects exhibited a greater decrease in MAP compared with AF subjects despite an augmented baroreflex control of MSNA. These data indicate that the CBR contributes importantly to the MSNA response during acute systemic hypotension. Additionally, we suggest that an impaired control of vascular reactivity hinders blood pressure regulation in HF subjects.

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In experimental animals, systemic administration of nitric oxide synthase (NOS) inhibitors causes large increases in blood pressure that are in part sympathetically mediated. The aim of this study was to determine the extent to which these conclusions can be extrapolated to humans. In healthy normotensive humans, we measured blood pressure in response to two NOS inhibitors, NG-monomethyl-L-arginine (L-NMMA) and NG-nitro-L-arginine methyl ester (L-NAME), the latter of which recently became available for use in humans. The major new findings are 3-fold. First, L-NAME produced robust increases in blood pressure that were more than 2 times larger than those previously reported in humans with L-NMMA and approximated those seen in experimental animals. L-NAME (4 mg/kg) raised mean arterial pressure by 24+/-2 mm Hg (n=27, P<0.001), whereas in subjects who received both inhibitors, a 12-fold higher dose of L-NMMA (50 mg/kg) raised mean arterial pressure by 15+/-2 mm Hg (n=4, P<0.05 vs L-NAME). Second, the L-NAME-induced increases in blood pressure were caused specifically by NOS inhibition because they were reversed by L-arginine (200 mg/kg, n=12) but not D-arginine (200 mg/kg, n=6) and because NG-nitro-D-arginine methyl ester (4 mg/kg, n=5) had no effect on blood pressure. Third, in humans, there is an important sympathetic component to the blood pressure-raising effect of NOS inhibition. alpha-Adrenergic blockade with phentolamine (0.2 mg/kg, n=9) attenuated the L-NAME-induced increase in blood pressure by 40% (P<0.05). From these data, we conclude that pharmacological inhibition of NOS causes large increases in blood pressure that are in part sympathetically mediated in humans as well as experimental animals.

Nitric oxide is hypothesized to be an inhibitory modulator of central sympathetic nervous outflow, and deficient neuronal nitric oxide production to cause sympathetic overactivity, which then contributes to nitric-oxide-deficient hypertension. The biochemical and neuroanatomical basis for this concept revolves around nitric oxide modulation of glutamatergic neurotransmission within brainstem vasomotor centers. The functional consequence of neuronal nitric oxide in blood pressure regulation is, however, marked by an apparent conflict in the literature. On one hand, conscious animal studies using sympathetic blockade suggest a significant role for neuronal nitric oxide deficiency in the development of nitric-oxide-deficient hypertension, and on the other hand, there is evidence against such a role derived from 'knock-out' mice lacking nitric-oxide synthase 1, the major source of neuronal nitric oxide.

The present investigation is mainly concerned with a reexamination of the Spacelab payload process. The candidate missions are considered in connection with discipline payloads or laboratories. Attention is given to the Space Biomedical Laboratory (SBL), the Space Plasma Lab (SPL), the Astronomy Research Laboratory (Astro), the Shuttle High Energy Astrophysics Laboratory (SHEAL), the Shuttle Infrared Telescope Facility (SIRTF), the Solar Optical Telescope (SOT), the Materials Sciences Laboratory (MLS), the Environmental Observations Mission (EOM), the Shuttle Radar Lab (SRL), the International Microgravity Laboratory (IML), and the Payload of Opportunity Carrier (POOC). The POOC is a low cost means of placing experiments aboard the Shuttle in the cargo bay.

The history, current status, and future plans of the Spacelab program are reviewed, with a focus on the cooperative relationship between ESA and NASA. The initial decision to undertake the program and the three agreements signed to begin its implementation are examined, and the division of responsibilities and financial contributions is discussed insofar as it affected the management structure. Consideration is given to the major facilities, the 50-mission operational cycle, communications, the currently scheduled activities (through 1985), the prospective later uses, and the ten dedicated discipline laboratories. The importance of continuous mutual support during the planning and development phases is stressed. The program so far is considered a success, in terms of the goals set by the participants and in terms of the resolution of the problems inherent in international technological endeavors.

Mission configurations, instrumentation, and objectives for Spacelab sorties on board the Shuttle beginning in Sept. 1983 are reviewed. The first two flights will serve to verify the Spacelab systems and will be followed by operational status, including the fifth flight, which will be a reimbursible venture. Scientific investigations in the fields of atmospheric physics and environmental observation, space plasma physics, astronomy and solar physics, materials processing, and life sciences will be performed using the habitable long module, instrument pallets, and/or an instrument igloo mounted in the payload bay. Instrumentation, such as the imaging spectrometric observatory, which was developed in the U.S., will originate in either the U.S. or Europe. Details of the first four Spacelab flights are presented, noting that the OSS-3 through -7 missions will feature the first time that entire NASA payloads have returned to space.

Processing and storage of space science data from Mariner IV space probe

Processing technique for scientific data telemetered to Earth from Mariner IV Mars probe

The purpose of this paper is to describe the system for the display, processing, and production of image-data products created to support the Mariner 9 Television Experiment. Of necessity, the system was large in order to respond to the needs of a large team of scientists with a broad scope of experimental objectives. The desire to generate processed data products as rapidly as possible, coupled with the complexities introduced by the nature of the vidicon camera, greatly increased the scale of the ground-image processing effort. This paper describes the systems that carried out the processes and delivered the products necessary for real-time and near-real-time analyses. References are made to the computer algorithms used for the different levels of decalibration and analysis.