Academic literature on the topic 'Temporal delay invariance'

Humans can effortlessly recognize others’ actions in the presence of complex transformations, such as changes in viewpoint. Several studies have located the regions in the brain involved in invariant action recognition; however, the underlying neural computations remain poorly understood. We use magnetoencephalography decoding and a data set of well-controlled, naturalistic videos of five actions (run, walk, jump, eat, drink) performed by different actors at different viewpoints to study the computational steps used to recognize actions across complex transformations. In particular, we ask when the brain discriminates between different actions, and when it does so in a manner that is invariant to changes in 3D viewpoint. We measure the latency difference between invariant and noninvariant action decoding when subjects view full videos as well as form-depleted and motion-depleted stimuli. We were unable to detect a difference in decoding latency or temporal profile between invariant and noninvariant action recognition in full videos. However, when either form or motion information is removed from the stimulus set, we observe a decrease and delay in invariant action decoding. Our results suggest that the brain recognizes actions and builds invariance to complex transformations at the same time and that both form and motion information are crucial for fast, invariant action recognition. NEW & NOTEWORTHY The human brain can quickly recognize actions despite transformations that change their visual appearance. We use neural timing data to uncover the computations underlying this ability. We find that within 200 ms action can be read out of magnetoencephalography data and that this representation is invariant to changes in viewpoint. We find form and motion are needed for this fast action decoding, suggesting that the brain quickly integrates complex spatiotemporal features to form invariant action representations.

To localize low-frequency sounds, humans rely on an interaural comparison of the temporally encoded sound waveform after peripheral filtering. This process can be compared with cross-correlation. For a broadband stimulus, after filtering, the correlation function has a damped oscillatory shape where the periodicity reflects the filter's center frequency and the damping reflects the bandwidth (BW). The physiological equivalent of the correlation function is the noise delay (ND) function, which is obtained from binaural cells by measuring response rate to broadband noise with varying interaural time delays (ITDs). For monaural neurons, delay functions are obtained by counting coincidences for varying delays across spike trains obtained to the same stimulus. Previously, we showed that BWs in monaural and binaural neurons were similar. However, earlier work showed that the damping of delay functions differs significantly between these two populations. Here, we address this paradox by looking at the role of sensitivity to changes in interaural correlation. We measured delay and correlation functions in the cat inferior colliculus (IC) and auditory nerve (AN). We find that, at a population level, AN and IC neurons with similar characteristic frequencies (CF) and BWs can have different responses to changes in correlation. Notably, binaural neurons often show compression, which is not found in the AN and which makes the shape of delay functions more invariant with CF at the level of the IC than at the AN. We conclude that binaural sensitivity is more dependent on correlation sensitivity than has hitherto been appreciated and that the mechanisms underlying correlation sensitivity should be addressed in future studies.

The temporal behavior of quantum mechanical systems is reviewed. We mainly focus our attention on the time development of the so-called “survival” probability of those systems that are initially prepared in eigenstates of the unperturbed Hamiltonian, by assuming that the latter has a continuous spectrum. The exponential decay of the survival probability, familiar, for example, in radioactive decay phenomena, is representative of a purely probabilistic character of the system under consideration and is naturally expected to lead to a master equation. This behavior, however, can be found only at intermediate times, for deviations from it exist both at short and long times and can have significant consequences. After a short introduction to the long history of the research on the temporal behavior of such quantum mechanical systems, the short-time behavior and its controversial consequences when it is combined with von Neumann’s projection postulate in quantum measurement theory are critically overviewed from a dynamical point of view. We also discuss the so-called quantum Zeno effect from this standpoint. The behavior of the survival amplitude is then scrutinized by investigating the analytic properties of its Fourier and Laplace transforms. The analytic property that there is no singularity except a branch cut running along the real energy axis in the first Riemannian sheet is an important reflection of the time-reversal invariance of the dynamics governing the whole process. It is shown that the exponential behavior is due to the presence of a simple pole in the second Riemannian sheet, while the contribution of the branch point yields a power behavior for the amplitude. The exponential decay form is cancelled at short times and dominated at very long times by the branch-point contributions, which give a Gaussian behavior for the former and a power behavior for the latter. In order to realize the exponential law in quantum theory, it is essential to take into account a certain kind of macroscopic nature of the total system, since the exponential behavior is regarded as a manifestation of a complete loss of coherence of the quantum subsystem under consideration. In this respect, a few attempts at extracting the exponential decay form on the basis of quantum theory, aiming at the master equation, are briefly reviewed, including van Hove’s pioneering work and his well-known “λ2T” limit. In the attempt to further clarify the mechanism of the appearance of a purely probabilistic behavior without resort to any approximation, a solvable dynamical model is presented and extensively studied. The model describes an ultrarelativistic particle interacting with N two-level systems (called “spins”) and is shown to exhibit an exponential behavior at all times in the weak-coupling, macroscopic limit. Furthermore, it is shown that the model can even reproduce the short-time Gaussian behavior followed by the exponential law when an appropriate initial state is chosen. The analysis is exact and no approximation is involved. An interpretation for the change of the temporal behavior in quantum systems is drawn from the results obtained. Some implications for the quantum measurement problem are also discussed, in particular in connection with dissipation.

Motion recognition has received increasing attention in recent years owing to heightened demand for computer vision in many domains, including the surveillance system, multimodal human computer interface, and traffic control system. Most conventional approaches classify the motion recognition task into partial feature extraction and time-domain recognition subtasks. However, the information of motion resides in the space-time domain instead of the time domain or space domain independently, implying that fusing the feature extraction and classification in the space and time domains into a single framework is preferred. Based on this notion, this work presents a novel Space-Time Delay Neural Network (STDNN) capable of handling the space-time dynamic information for motion recognition. The STDNN is unified structure, in which the low-level spatiotemporal feature extraction and high-level space-time-domain recognition are fused. The proposed network possesses the spatiotemporal shift-invariant recognition ability that is inherited from the time delay neural network (TDNN) and space displacement neural network (SDNN), where TDNN and SDNN are good at temporal and spatial shift-invariant recognition, respectively. In contrast to multilayer perceptron (MLP), TDNN, and SDNN, STDNN is constructed by vector-type nodes and matrix-type links such that the spatiotemporal information can be accurately represented in a neural network. Also evaluated herein is the performance of the proposed STDNN via two experiments. The moving Arabic numerals (MAN) experiment simulates the object's free movement in the space-time domain on image sequences. According to these results, STDNN possesses a good generalization ability with respect to the spatiotemporal shift-invariant recognition. In the lipreading experiment, STDNN recognizes the lip motions based on the inputs of real image sequences. This observation confirms that STDNN yields a better performance than the existing TDNN-based system, particularly in terms of the generalization ability. In addition to the lipreading application, the STDNN can be applied to other problems since no domain-dependent knowledge is used in the experiment.

The timescale-invariant recognition of temporal stimulus sequences is vital for many species and poses a challenge for their sensory systems. Here we present a simple mechanistic model to address this computational task, based on recent observations in insects that use rhythmic acoustic communication signals for mate finding. In the model framework, feedforward inhibition leads to burst-like response patterns in one neuron of the circuit. Integrating these responses over a fixed time window by a readout neuron creates a timescale-invariant stimulus representation. Only two additional processing channels, each with a feature detector and a readout neuron, plus one final coincidence detector for all three parallel signal streams, are needed to account for the behavioral data. In contrast to previous solutions to the general time-warp problem, no time delay lines or sophisticated neural architectures are required. Our results suggest a new computational role for feedforward inhibition and underscore the power of parallel signal processing.

Even though statics processing is well advanced and is routinely applied, interpreters are still required to evaluate seismic data that has either no statics corrections or poor statics corrections. It is a critical interpretation skill to recognize uncorrected statics and to correct for their effects. Poor decisions and dry holes are often the result of failure to do this. Since multifold seismic data has become the norm, statics are no longer time invariant. The method of flattening stacked data on a shallow reflector that worked so well for single fold data will show anomalous structure and isochron variation caused by the time‐variant effects of uncorrected statics. Uncorrected statics can be identified by anomalous undulations in a shallow reflector, by the time‐variant effects with increasing reflection time, and by the variations in optimal stacking velocities, all of which have been documented in the literature. Knowledge of these properties from stacked data observations can lead to robust estimates of the surface position delay profile necessary for all statics corrections. Although prestack analysis and correction provide the best solutions, full spatial and temporal corrections can be calculated easily and applied to the interpretation of stacked data on most work stations to enable quicker, less expensive decision making. Prestack reprocessing is only one action that may result from a quality poststack analysis. Stacked data time picks can be fully corrected with a statics term and a velocity term. Partial corrections, applied only to the center trace of a static‐causing body can be made without knowledge of the exact surface position delay model. The ratio of central trace delays of two reflectors is approximately equal to the ratio of effective spread lengths used to stack the two reflectors. This method is applied to a real data example published last year in Geophysics where the anomalous isochron thinning is accurately predicted.

Abstract. We have determined the locations of over 6000 individual auroral kilometric radiation (AKR) bursts between July 2002 and May 2003 using a very long baseline interferometer (VLBI) array. Burst locations were determined by triangulation using differential delays from cross-correlated Cluster WBD waveforms. Typical position uncertainties are 200-400km in the plane normal to the source-spacecraft line, but much larger along this line. The AKR bursts are generally located above the auroral zone with a strong preference for the evening sector (22:00 MLT±2h). However, a few epochs imaged during the austral summer have loci in the daytime sector, especially near 15:00 MLT. There is marginal evidence for a small N-S hemispheric asymmetry in mean MLT and invariant latitude.

High-density electrocorticogram (ECoG) electrodes are capable of recording neurophysiological data with high temporal resolution with wide spatial coverage. These recordings are a window to understanding how the human brain processes information and subsequently behaves in healthy and pathologic states. Here, we describe and implement delay differential analysis (DDA) for the characterization of ECoG data obtained from human patients with intractable epilepsy. DDA is a time-domain analysis framework based on embedding theory in nonlinear dynamics that reveals the nonlinear invariant properties of an unknown dynamical system. The DDA embedding serves as a low-dimensional nonlinear dynamical basis onto which the data are mapped. This greatly reduces the risk of overfitting and improves the method's ability to fit classes of data. Since the basis is built on the dynamical structure of the data, preprocessing of the data (e.g., filtering) is not necessary. We performed a large-scale search for a DDA model that best fit ECoG recordings using a genetic algorithm to qualitatively discriminate between different cortical states and epileptic events for a set of 13 patients. A single DDA model with only three polynomial terms was identified. Singular value decomposition across the feature space of the model revealed both global and local dynamics that could differentiate electrographic and electroclinical seizures and provided insights into highly localized seizure onsets and diffuse seizure terminations. Other common ECoG features such as interictal periods, artifacts, and exogenous stimuli were also analyzed with DDA. This novel framework for signal processing of seizure information demonstrates an ability to reveal unique characteristics of the underlying dynamics of the seizure and may be useful in better understanding, detecting, and maybe even predicting seizures.

Des modèles spécifiques de gravitation quantique suggèrent l’existence d’une Violation de l’Invariance de Lorentz (LIV en anglais) à l’échelle de Planck. Une des signatures de cette violation est la modification de la propagation des photons dans le vide, induisant des décalages temporels dépendant de l’énergie des photons observés sur Terre. De tels décalages dans le temps d’arrivée de rayons γ sont recherchés avec l’expérience H.E.S.S. (High Energy Stereoscopic System), grâce aux émissions de très hautes énergies en provenance de sources lointaines telles que les blazars. Néanmoins, l’origine du décalage temporel doit être comprise en détails. En effet, un décalage intrinsèque à la source pourrait venir biaiser les contraintes sur les modèles de gravitation quantique. Cette thèse propose dans un premier temps de s’intéresser à la modélisation temporelle des éruptions de blazars, pour étudier les possibles décalages intrinsèques liés aux processus d’émissions de ces éruptions. Grâce à l’élaboration d’un modèle simple, cette étude met en relief les différentes caractéristiques de ces décalages intrinsèques sur les scénarios d’éruptions de blazar afin d’essayer de les distinguer des décalages potentiellement dus à un effet de LIV et aussi de proposer de nouvelles contraintes basées sur ces décalages temporels. Dans un deuxième temps, la méthode de recherche de décalages temporels dépendant de l’énergie avec H.E.S.S. est présentée ainsi qu’une application sur l’éruption du blazar Markarian 501 ayant eu lieu en juillet 2014. Cette analyse a permis d’établir la meilleure limite obtenue sur le terme quadratique de la signature de la LIV avec l’utilisation d’éruption de blazars
Specific models of quantum gravity suggest the existence of a Lorentz Invariance Violation (LIV) at the Planck scale. One signature of that violation is a modification the propagation of photons in vacuum which induces energy dependent delays in the arrival time of photons on Earth. The H.E.S.S. (High Energy Stereoscopic System) experiment can search for such delays in the arrival time of gamma rays, thanks to the very high energy emission coming from distant blazars. However, the time delay origin have to be fully understood. Indeed, an intrinsic time delay coming from the source can bias the constraints made on quantum gravity models. In the first part of this thesis, a time dependent blazar flare model is considered to search for the presence of intrinsic time delays related to the emission mechanisms of flares. With the elaboration of a simple scenario, this study highlights the different characteristics of intrinsic time delays in order to investigate how to disentangle them from delays due to LIV as well as to provide new constraints on blazar modeling. In the second part of this thesis, the method used to search for LIV signatures in blazar light curves at very high energy is presented as well as an application to the flare of Markarian 501 which occurred in July 2014. This analysis provides in particular the best upper limit on the quadratic term of LIV signature

Cette thèse porte sur deux sujets de vision par ordinateur axés sur l’analyse de mouvement dans une scène dynamique vue par une ou plusieurs caméras. En premier lieu, nous avons travaillé sur le problème de la capture de mouvement avec des caméras non synchronisées. Ceci entraı̂ne généralement des erreurs de mise en correspondance 2D et par la suite des erreurs de reconstruction 3D. En contraste avec les solutions matérielles déjà existantes qui essaient de minimiser voire annuler le délai temporel entre les caméras, nous avons proposé une solution qui assure une invariance aux délais. En d’autres termes, nous avons développé une méthode qui permet de trouver la bonne mise en correspondance entre les points à reconstruire indépendamment du délai temporel. En second lieu, nous nous sommes intéressés au problème du flux optique avec une approche différente des méthodes proposées dans l’état de l’art. Le flux optique est utilisé pour l’analyse de mouvement en temps réel. Il est donc important qu’il soit calculé rapidement. Généralement, les méthodes existantes de flux optique sont classées en deux principales catégories: ou bien à la fois denses et précises mais très exigeantes en calcul, ou bien rapides mais moins denses et moins précises. Nous avons proposé une alternative qui tient compte à la fois du temps de calcul et de la précision du résultat. Nous avons proposé d’utiliser les isocontours d’intensité et de les mettre en correspondance afin de retrouver le flux optique en question. Ces travaux ont amené à deux principales contributions intégrées dans les chapitres de la thèse.
In this thesis we focused on two computer vision subjects. Both of them concern motion analysis in a dynamic scene seen by one or more cameras. The first subject concerns motion capture using unsynchronised cameras. This causes many correspondence errors and 3D reconstruction errors. In contrast with existing material solutions trying to minimize the temporal delay between the cameras, we propose a software solution ensuring an invariance to the existing temporal delay. We developed a method that finds the good correspondence between points regardless of the temporal delay. It solves the resulting spatial shift and finds the correct position of the shifted points. In the second subject, we focused on the optical flow problem using a different approach than the ones in the state of the art. In most applications, optical flow is used for real-time motion analysis. It is then important to be performed in a reduced time. In general, existing optical flow methods are classified into two main categories: either precise and dense but computationally intensive, or fast but less precise and less dense. In this work, we propose an alternative solution being at the same time, fast and precise. To do this, we propose extracting intensity isocontours to find corresponding points representing the related optical flow. By addressing these problems we made two major contributions.

Critical, power-law behavior in space and/or time manifests in a large variety of complex systems [12] within physics and, nowadays, more conspicuously in other fields, such as biology, ecology, geophysics, and economics. Universality, the same power law holding for completely different systems, is a consequence of the characteristic self-similar, scale-invariant property of criticality, and can be understood in terms of basins of attraction of the renormalization-group (RG) fixed points. However, the guiding quality of a variatkmal approach has been seemingly lacking in the theoretical studies of critical phenomena. Here we give an account of entropy extrema associated with fixed points of RG transformations. As illustrations, we consider simple one-dimensional models of random walks and nonlinear dynamical systems. In describing these systems we consider distribution and/or time relaxation functions with power-law decay that may have infinite first- or second- and higher-order moments. When these moments diverge, we observe the emergence of nonexponential or non-Gaussian fractal properties that can be measured by the nonextensive Tsallis entropy index q. We note that the presence of nonextensive properties may signal situations of hindered movement among the system's possible configurations. Some representative applications within physics, but with suggested or recognized connections to other fields, are critical behavior in fluids and magnets, anomalous diffusion processes, transitions to chaos in nonlinear systems, and relaxation properties of supercooled liquids near the glass formation. Two prototypical model systems serve to illustrate the development of critical states characterized by power laws from generic states described by exponential behavior. These are random walks and nonlinear iterated maps that we discuss below in some detail. Random walks [18] are suitable, for example, for representing Brownian motion (molecular thermal motion under the microscope), but also for many types of data originating from diverse disciplines. One type is that which comes in the form of a "time series," a temporal sequence of measured values, for instance, stock market prices in economics or electroencephalographic potentials in medicine.