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Littérature scientifique sur le sujet « Latency variation »

Auteur : Grafiati

Publié le 11 janvier 2025

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Articles de revues sur le sujet "Latency variation"

Schmid, Andreas, David Halbhuber, Thomas Fischer, Raphael Wimmer et Niels Henze. « Small Latency Variations Do Not Affect Player Performance in First-Person Shooters ». Proceedings of the ACM on Human-Computer Interaction 7, CHI PLAY (29 septembre 2023) : 197–216. http://dx.doi.org/10.1145/3611027.

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In interactive systems high latency affects user performance and experience. This is especially problematic in video games. A large number of studies on this topic investigated the effects of constant, high latency. However, in practice, latency is never constant but varies by up to 100 ms due to variations in processing time and delays added by polling between system components. In a large majority of studies, these variations in latency are neither controlled for nor reported. Thus, it is unclear to which degree small, continuous variations in latency affect user performance. If these unreported variations had a significant impact, this might cast into doubt the findings of some studies. To investigate how latency variation affects player performance and experience in games, we conducted an experiment with 28 participants playing a first-person shooter. Participants played with two levels of base latency (50 ms vs. 150 ms) and variation (0 ms vs. 50 ms). As expected, high base latency significantly reduces player performance and experience. However, we found strong evidence that small variations in latency in the order of 50 ms, do not affect player performance significantly. Thus, our findings mitigate concerns that previous latency studies might have systematically ignored a confounding effect.

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Wu, Weixin, Yujie Dong et Adam Hoover. « Measuring Digital System Latency from Sensing to Actuation at Continuous 1-ms Resolution ». Presence : Teleoperators and Virtual Environments 22, n^o 1 (février 2013) : 20–35. http://dx.doi.org/10.1162/pres_a_00131.

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This paper describes a new method for measuring the end-to-end latency between sensing and actuation in a digital computing system. Compared to previous works, which generally measured the latency at 10–33-ms intervals or at discrete events separated by hundreds of ms, our new method measures the latency continuously at 1-ms resolution. This allows for the observation of variations in latency over sub 1-s periods, instead of relying upon averages of measurements. We have applied our method to two systems, the first using a camera for sensing and an LCD monitor for actuation, and the second using an orientation sensor for sensing and a motor for actuation. Our results show two interesting findings. First, a cyclical variation in latency can be seen based upon the relative rates of the sensor and actuator clocks and buffer times; for the components we tested, the variation was in the range of 15–50 Hz with a magnitude of 10–20 ms. Second, orientation sensor error can look like a variation in latency; for the sensor we tested, the variation was in the range of 0.5–1.0 Hz with a magnitude of 20–100 ms. Both of these findings have implications for robotics and virtual reality systems. In particular, it is possible that the variation in apparent latency caused by orientation sensor error may have some relation to simulator sickness.

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Gupta, Saket, et Sachin S. Sapatnekar. « Variation-Aware Variable Latency Design ». IEEE Transactions on Very Large Scale Integration (VLSI) Systems 22, n^o 5 (mai 2014) : 1106–17. http://dx.doi.org/10.1109/tvlsi.2013.2265662.

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Chang, Kevin K., Abhijith Kashyap, Hasan Hassan, Saugata Ghose, Kevin Hsieh, Donghyuk Lee, Tianshi Li, Gennady Pekhimenko, Samira Khan et Onur Mutlu. « Understanding Latency Variation in Modern DRAM Chips ». ACM SIGMETRICS Performance Evaluation Review 44, n^o 1 (30 juin 2016) : 323–36. http://dx.doi.org/10.1145/2964791.2901453.

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White, A., S. Tatam, D. Linares et A. Holcombe. « Visuomotor compensation for variation in perceptual latency ». Journal of Vision 9, n^o 8 (3 septembre 2010) : 841. http://dx.doi.org/10.1167/9.8.841.

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Fagius, Jan, Göran Sundlöf et B. Gunnar Wallin. « Variation of sympathetic reflex latency in man ». Journal of the Autonomic Nervous System 21, n^o 2-3 (décembre 1987) : 157–65. http://dx.doi.org/10.1016/0165-1838(87)90018-x.

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Chichibu, Shiko, Atsushi Chiba, Takahide Sugiyama et Takashi Kurita. « Latency variation and habituation in cremasteric reflex ». Neuroscience Research Supplements 9 (janvier 1989) : 43. http://dx.doi.org/10.1016/0921-8696(89)90590-2.

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Lee, Donghyuk, Samira Khan, Lavanya Subramanian, Saugata Ghose, Rachata Ausavarungnirun, Gennady Pekhimenko, Vivek Seshadri et Onur Mutlu. « Design-Induced Latency Variation in Modern DRAM Chips ». ACM SIGMETRICS Performance Evaluation Review 45, n^o 1 (18 septembre 2017) : 54. http://dx.doi.org/10.1145/3143314.3078533.

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Lee, Donghyuk, Samira Khan, Lavanya Subramanian, Saugata Ghose, Rachata Ausavarungnirun, Gennady Pekhimenko, Vivek Seshadri et Onur Mutlu. « Design-Induced Latency Variation in Modern DRAM Chips ». Proceedings of the ACM on Measurement and Analysis of Computing Systems 1, n^o 1 (13 juin 2017) : 1–36. http://dx.doi.org/10.1145/3084464.

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Jacobsen, Frederick M., Adriana Dreizzen et Thomas A. Wehr. « Diurnal mood variation and REM latency in depression ». Biological Psychiatry 22, n^o 8 (août 1987) : 1045–47. http://dx.doi.org/10.1016/0006-3223(87)90020-5.

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Thèses sur le sujet "Latency variation"

Pal, Asmita. « Split Latency Allocator : Process Variation-Aware Register Access Latency Boost in a Near-Threshold Graphics Processing Unit ». DigitalCommons@USU, 2018. https://digitalcommons.usu.edu/etd/7155.

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Over the last decade, Graphics Processing Units (GPUs) have been used extensively in gaming consoles, mobile phones, workstations and data centers, as they have exhibited immense performance improvement over CPUs, in graphics intensive applications. Due to their highly parallel architecture, general purpose GPUs (GPGPUs) have gained the foreground in applications where large data blocks can be processed in parallel. However, the performance improvement is constrained by a large power consumption. Likewise, Near Threshold Computing (NTC) has emerged as an energy-efficient design paradigm. Hence, operating GPUs at NTC seems like a plausible solution to counteract the high energy consumption. This work investigates the challenges associated with NTC operation of GPUs and proposes a low-power GPU design, Split Latency Allocator, to sustain the performance of GPGPU applications.

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Channe, Gowda Anushree. « Latency and Jitter Control in 5G Ethernet Fronthaul Network ». Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/17651/.

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With 5G technology, networks are expected to offer high speed with ultra-low latency among different users. Maintaining the current network architecture will lead to an unsustainable transport delay and jitters increase. Limiting the transport delay and the jitters have become a necessity for mobile network operators. The main requirement in 5G networks is the demand of limiting the transport delay. This, thesis proposes a novel mechanism to minimize packet delay and delay variation in 5G Ethernet fronthaul network. The goal is to achieve bounded delay aggregation of traffic ,suitable for application in fronthaul transport. Hybrid switching technology can be adopted to provide efficient fronthaul in 5G. Hybrid switches allows to multiplex traffics with different characteristics over the same wavelengths, thus increasing the network resource utilization. This thesis proposes a scheduling mechanism for hybrid switches to aggregate streams from the network, the Bypass traffic (BP), and the traffic from the fronthaul links, the ADD traffic, using an algorithm which looks for the time gaps in the BP stream for the insertion of the ADD traffic. The proposed strategy minimizes the delay of packets by making use of the available gaps during the transmission to limit the network latency. The size of the required time gaps, the time window, is suitably reduced by dividing the timeout time duration with number of intervals (N) with the Window reduction mechanism so that the delay variation or jitter of both aggregated streams are bounded. The results demonstrate that the aforementioned requirements are can be achieved by suitably tuning the parameters of the algorithm inputs, mainly the window reduction factor, timeout time duration and the number of intervals, resulting in values of packet delay and delay variation bounded at 10 microseconds or even lower up to 85-90percent carried load of aggregated flows. Hence, we show their suitability for delay sensitive future applications in 5G networking.

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Aukštuolienė, Eglė. « Herpes simplex virus sequence variation in the promoter of the latency associated gene and correlation with clinical features ». Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2013. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2013~D_20130327_100916-38563.

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Herpes simplex virus (HSV) causes recurrent orofacial and genital infections and establishes latent infection in sensory neurons. During latency all virus genes are supressed except the latency associated transcripts which are transcribed from latency associated gene (LAT). It is established that HSV LAT promoter mutants have lower levels of spontaneous reactivation rates in small animal models compared to wild virus. However, the variation in the LAT promoter has not been studied in viruses from clinical samples in humans. The aim of the sudy was to evaluate the sequence variation in herpes simplex virus latency associated gene promoter from clinical samples by developing and applying molecular methods and correlate with herpes infection clinical features. In this study a new PCR method specific for HSV LAT promoter was developed and HSV LAT promoter DNA sequences from Lithuanian and Swedish mucocutaneous and cerebrospinal fluid clinical samples were analyzed. HSV type 2 was found to be the main cause of genital herpes in the population of the Lithuanian patients. All cases of orofacial herpes simplex infection were caused by HSV type 1. The structure of the LAT promoter region was studied in 145 HSV clinical samples. HSV LAT promoter was found to be G+C rich and contained variable homopolimer tracts. An inter- and intrastrain variability of homopolimer tracts in the promoter region was detected, potentially giving rise to a large variation at the protein level, leading to... [to full text]
Herpes simplex virusas sukelia recidyvuojančią burnos-veido ir lytinių organų infekciją. Latentinėje būklėje šis virusas glūdi sensoriniuose ganglijuose. Latencijos metu visi HSV genai yra supresuoti, išskyrus su latencija susijusį geną (LAT). Tyrimais nustatyta, kad tarp HSV LAT promotoriaus mutantų reaktyvacijos dažnis laboratorinių gyvūnėlių modeliuose yra mažesnis nei laukinių virusų. Nėra atlikta tyrimų, kurie nagrinėtų LAT promotoriaus sekų variaciją herpes simplex virusuose, išskirtuose iš žmonių klinikinių mėginių. Šio tyrimo tikslas buvo įvertinti herpes simplex viruso LAT promotoriaus sekų įvairovę molekulinės diagnostikos metodais bei palyginti su infekcijos klinikiniais požymiais. Tuo tikslu buvo sukurtas PGR metodas HSV LAT promotoriaus analizei atlikti. Buvo ištirta Lietuvos ir Švedijos klinikiniuose odos-gleivinių bei cerebrospinalinio skysčio mėginiuose rasto herpes simplex viruso promotoriaus DNR sekų įvairovė. Tyrimo metu rasta, kad 2 tipo herpes simplex virusas buvo pagrindinė lytinių organų HSV infekcijos priežastis tarp Lietuvos pacientų. Visuose veido srities bėrimuose rasta 1 tipo HSV. HSV LAT promotoriaus sekos ištirtos 145 klinikiniuose mėginiuose. Nustatyta, kad HSV LAT promotoriaus sekos yra gausios GC ir turi variabilias homopolimerinių nukleotidų sritis, kurios varijuoja tarp viruso padermių ir pačių padermių viduje. Ši variacija gali turėti įtakos baltymų sintezei, o drauge ir fenotipo pokyčiams. Nenustatytas ryšys tarp HSV LAT promotoriaus... [toliau žr. visą tekstą]

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Soudais, Guillaume. « End-to-End Service Guarantee for High-Speed Optical Networks ». Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAT027.

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Poussée par un besoin croissant de bande passante et de performance, le réseau informatique s’est développé de telle sorte que les réseaux OT et de télécommunications cherchent à exploiter cette infrastructure pour leur expansion. Ces trois secteurs ont historiquement été séparés en raison de différentes exigences entre autres en matière de latence, de sa variation et de fiabilité. Pour répondre aux besoins des applications temps critiques, le groupe de travail Time Sensitive Network a développé de nouveaux ensembles de protocoles qui commencent à être mis en œuvre dans des produits commerciaux. D’autres groupes ont proposé des architectures novatrices avec contrôle du temps pour permettre des performances garanties, entre et à l’intérieur des centres de données périphériques. Dans ma thèse, je propose une solution pour transporter les applications temps critiques dans les réseaux existants, car elle ne nécessite pas de changer toute l’architecture. Je montre les avantages de sa mise en œuvre dans les réseaux TSN pour une solution pérenne avec une utilisation améliorée des ressources. Pour transporter le trafic temps critique dans les réseaux existants, je propose de créer un chemin en isolant et en planifiant le trafic temps critique sur un canal avec une latence garantie. Avec cette construction, je développe un algorithme pour effectuer une compensation de la variation de latence, permettant une transmission à la latence constante pour le trafic temps critique. Dans un second temps, je propose une méthode de synchronisation et mets en œuvre un réseau de mesure principalement utilise ici pour la mesure de la latence, m’aidant à obtenir des informations sur la distribution de la latence que mon protocole créé. Enfin, avec un algorithme de compensation de variation de latence amélioré, je démontre de meilleures performances en matière de gigue et étudie le temps de mise en service de notre protocole, permettant l’utilisation des ressources uniquement lorsque le trafic temps critique est présent. Dans ma thèse, je démontre, avec une implémentation FPGA, la réduction de la variation de latence, permettant aux applications des réseaux OT et de télécommunications de fonctionner sur les réseaux existants et augmentés par des normes TSN
Driven by an ever-growing bandwidth and performance need, the IT network has grown such that OT and telecommunications networks are looking to exploit this infrastructure for their expansion. These three sectors have historically been separated due to different requirements, on latency, its variation and on reliability. To answer to time critical application needs, the Time Sensitive Network taskforce has developed new sets of protocols that are starting to be implemented in commercial products. Other groups have proposed novel architecture with time control to enable guaranteed performances between and inside edge datacenters. In my PhD I propose a solution to carry time critical application in legacy networks as it does not require to change the whole architecture. I show the benefits of its implementation in TSN networks for a future-proof solution with improved resource usage. To carry time critical traffic in legacy I propose to create a path by isolating and scheduling time critical traffic on a channel with guaranteed latency. With this construction, I build an algorithm to perform latency variation compensation enabling a constant latency transmission for time critical traffic. In a second time, propose a synchronization scheme and implemented a monitoring network primarily used here for latency monitoring, helping me gain insights on the distribution of latency that my protocol creates. Lastly, with an improved latency compensation algorithm, I demonstrate better jitter performances and study the turn-up time for our protocol enabling resource usage only when time-critical traffic is present. In my PhD I demonstrate, with an FPGA implementation and commercial product, latency variation reduction enabling OT and telecommunications network applications to run on legacy and TSN augmented network

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Challis, E. A. L. « Variational approximate inference in latent linear models ». Thesis, University College London (University of London), 2013. http://discovery.ucl.ac.uk/1414228/.

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Latent linear models are core to much of machine learning and statistics. Specific examples of this model class include Bayesian generalised linear models, Gaussian process regression models and unsupervised latent linear models such as factor analysis and principal components analysis. In general, exact inference in this model class is computationally and analytically intractable. Approximations are thus required. In this thesis we consider deterministic approximate inference methods based on minimising the Kullback-Leibler (KL) divergence between a given target density and an approximating `variational' density. First we consider Gaussian KL (G-KL) approximate inference methods where the approximating variational density is a multivariate Gaussian. Regarding this procedure we make a number of novel contributions: sufficient conditions for which the G-KL objective is differentiable and convex are described, constrained parameterisations of Gaussian covariance that make G-KL methods fast and scalable are presented, the G-KL lower-bound to the target density's normalisation constant is proven to dominate those provided by local variational bounding methods. We also discuss complexity and model applicability issues of G-KL and other Gaussian approximate inference methods. To numerically validate our approach we present results comparing the performance of G-KL and other deterministic Gaussian approximate inference methods across a range of latent linear model inference problems. Second we present a new method to perform KL variational inference for a broad class of approximating variational densities. Specifically, we construct the variational density as an affine transformation of independently distributed latent random variables. The method we develop extends the known class of tractable variational approximations for which the KL divergence can be computed and optimised and enables more accurate approximations of non-Gaussian target densities to be obtained.

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Hedges, Stephanie Nicole. « A Latent Class Analysis of American English Dialects ». BYU ScholarsArchive, 2017. https://scholarsarchive.byu.edu/etd/6480.

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Research on the dialects of English spoken within the United States shows variation regarding lexical, morphological, syntactic, and phonological features. Previous research has tended to focus on one linguistic variable at a time with variation. To incorporate multiple variables in the same analysis, this thesis uses a latent class analysis to perform a cluster analysis on results from the Harvard Dialect Survey (2003) in order to investigate what phonetic variables from the Harvard Dialect Survey are most closely associated with each dialect. This thesis also looks at how closely the latent class analysis results correspond to the Atlas of North America (Labov, Ash & Boberg, 2005b) and how well the results correspond to Joshua Katz's heat maps (Business Insider, 2013; Byrne, 2013; Huffington Post, 2013; The Atlantic, 2013). The results from the Harvard Dialect Survey generally parallel the findings of the Linguistic Atlas of North American English, providing support for six basic dialects of American English. The variables with the highest probability of occurring in the North dialect are ‘pajamas: /æ/’, ‘coupon: /ju:/’, ‘Monday, Friday: /e:/’ ‘Florida: /ɔ/’, and ‘caramel: 2 syllables’. For the South dialect, the top variables are ‘handkerchief: /ɪ/’, ‘lawyer: /ɒ/’, ‘pajamas: /ɑ/’, and ‘poem’ as 2 syllables. The top variables in the West dialect include ‘pajamas: /ɑ/’, ‘Florida: /ɔ/’, ‘Monday, Friday: /e:/’, ‘handkerchief: /ɪ/’, and ‘lawyer: /ɔj/’. For the New England dialect, they are ‘Monday, Friday: /e:/’, ‘route: /ru:t/’, ‘caramel: 3 syllables’, ‘mayonnaise: /ejɑ/’, and ‘lawyer: /ɔj/’. The top variables for the Midland dialect are ‘pajamas: /æ/’, ‘coupon: /u:/’, ‘Monday, Friday: /e:/’, ‘Florida: /ɔ/’, and ‘lawyer: /ɔj/’ and for New York City and the Mid-Atlantic States, they are ‘handkerchief: /ɪ/’, ‘Monday, Friday: /e:/’, ‘pajamas: /ɑ/’, ‘been: /ɪ/’, ‘route: /ru:t/’, ‘lawyer: /ɔj/’, and ‘coupon: /u:/’. One major discrepancy between the results from the latent class analysis and the linguistic atlas is the region of the low back merger. In the latent class analysis, the North dialect has a low probability of the ‘cot/caught’ low back vowel distinction, whereas the linguistic atlas found this to be a salent variable of the North dialect. In conclusion, these results show that the latent class analysis corresponds with current research, as well as adding additional information with multiple variables.

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Khan, Mohammad. « Variational learning for latent Gaussian model of discrete data ». Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/43640.

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This thesis focuses on the variational learning of latent Gaussian models for discrete data. The learning is difficult since the discrete-data likelihood is not conjugate to the Gaussian prior. Existing methods to solve this problem are either inaccurate or slow. We consider a variational approach based on evidence lower bound optimization. We solve the following two main problems of the variational approach: the computational inefficiency associated with the maximization of the lower bound and the intractability of the lower bound. For the first problem, we establish concavity of the lower bound and design fast learning algorithms using concave optimization. For the second problem, we design tractable and accurate lower bounds, some of which have provable error guarantees. We show that these lower bounds not only make accurate variational learning possible, but can also give rise to algorithms with a wide variety of speed-accuracy trade-offs. We compare various lower bounds, both theoretically and experimentally, giving clear design guidelines for variational algorithms. Through application to real-world data, we show that the variational approach can be more accurate and faster than existing methods.

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Toyinbo, Peter Ayo. « Additive Latent Variable (ALV) Modeling : Assessing Variation in Intervention Impact in Randomized Field Trials ». Scholar Commons, 2009. http://scholarcommons.usf.edu/etd/3673.

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In order to personalize or tailor treatments to maximize impact among different subgroups, there is need to model not only the main effects of intervention but also the variation in intervention impact by baseline individual level risk characteristics. To this end a suitable statistical model will allow researchers to answer a major research question: who benefits or is harmed by this intervention program? Commonly in social and psychological research, the baseline risk may be unobservable and have to be estimated from observed indicators that are measured with errors; also it may have nonlinear relationship with the outcome. Most of the existing nonlinear structural equation models (SEM’s) developed to address such problems employ polynomial or fully parametric nonlinear functions to define the structural equations. These methods are limited because they require functional forms to be specified beforehand and even if the models include higher order polynomials there may be problems when the focus of interest relates to the function over its whole domain. To develop a more flexible statistical modeling technique for assessing complex relationships between a proximal/distal outcome and 1) baseline characteristics measured with errors, and 2) baseline-treatment interaction; such that the shapes of these relationships are data driven and there is no need for the shapes to be determined a priori. In the ALV model structure the nonlinear components of the regression equations are represented as generalized additive model (GAM), or generalized additive mixed-effects model (GAMM). Replication study results show that the ALV model estimates of underlying relationships in the data are sufficiently close to the true pattern. The ALV modeling technique allows researchers to assess how an intervention affects individuals differently as a function of baseline risk that is itself measured with error, and uncover complex relationships in the data that might otherwise be missed. Although the ALV approach is computationally intensive, it relieves its users from the need to decide functional forms before the model is run. It can be extended to examine complex nonlinearity between growth factors and distal outcomes in a longitudinal study.

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Christmas, Jacqueline. « Robust spatio-temporal latent variable models ». Thesis, University of Exeter, 2011. http://hdl.handle.net/10036/3051.

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Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA) are widely-used mathematical models for decomposing multivariate data. They capture spatial relationships between variables, but ignore any temporal relationships that might exist between observations. Probabilistic PCA (PPCA) and Probabilistic CCA (ProbCCA) are versions of these two models that explain the statistical properties of the observed variables as linear mixtures of an alternative, hypothetical set of hidden, or latent, variables and explicitly model noise. Both the noise and the latent variables are assumed to be Gaussian distributed. This thesis introduces two new models, named PPCA-AR and ProbCCA-AR, that augment PPCA and ProbCCA respectively with autoregressive processes over the latent variables to additionally capture temporal relationships between the observations. To make PPCA-AR and ProbCCA-AR robust to outliers and able to model leptokurtic data, the Gaussian assumptions are replaced with infinite scale mixtures of Gaussians, using the Student-t distribution. Bayesian inference calculates posterior probability distributions for each of the parameter variables, from which we obtain a measure of confidence in the inference. It avoids the pitfalls associated with the maximum likelihood method: integrating over all possible values of the parameter variables guards against overfitting. For these new models the integrals required for exact Bayesian inference are intractable; instead a method of approximation, the variational Bayesian approach, is used. This enables the use of automatic relevance determination to estimate the model orders. PPCA-AR and ProbCCA-AR can be viewed as linear dynamical systems, so the forward-backward algorithm, also known as the Baum-Welch algorithm, is used as an efficient method for inferring the posterior distributions of the latent variables. The exact algorithm is tractable because Gaussian assumptions are made regarding the distribution of the latent variables. This thesis introduces a variational Bayesian forward-backward algorithm based on Student-t assumptions. The new models are demonstrated on synthetic datasets and on real remote sensing and EEG data.

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Dahl, Joakim. « Analysis of the effect of latent dimensions on disentanglement in Variational Autoencoders ». Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-291614.

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Disentanglement is a subcategory to Representaton learning where we, apart from believing that useful properties can be extracted from the data in a more compact form, also envision that the data itself is constituted from a lower-dimensional subset of explanatory factors. Explanatory factors are an ambiguous concept and what they portray varies with the dataset. A dataset constituted of flowers may have stem size and color as explanatory factors, while for another dataset it may be location or position. The explanatory factors are themselves often nested in a complex interaction in order to generate the data. Disentanglement can be summarized as to breaking the potentially complex interaction between the explanatory factors to liberate them from one another. The liberated explanatory factors can then constitute the foundation of the representations, a procedure that is believed to enhance downstream machine learning tasks. Disentangling the explanatory factors in an unsupervised environment has proven to be a difficult task for many reasons, perhaps most notably the lack of knowledge of how many they are and what they reflect. To be able to evaluate the degree of disentanglement attained, we will consider a dataset annotated for us with target labels corresponding to the explanatory factors that generated the data. Knowing the number of explanatory factors gives an indication of what dimensionality the representation should have to at least be able to capture all of the explanatory factors. Many of the empirical studies that have been considered in this paper treat the dimensionality of the representations as a constant when evaluating the degree of disentanglement achieved. The purpose of this paper is to extend the discussion regarding disentanglement by treating the dimensionality of the representations as a variable to be alternated and investigate how this impacts the degree of disentanglement achieved. The experiments performed in this paper do however suggest that the visual inspection of the disentanglement attained in a high dimensional representation space are difficult to interpret and evaluate for the human eye. One is therefore even further reliant on the disentanglement scores, which does not require any human interaction for the evaluation. The disentanglement scores seem to exhibit a static behaviour, not changing as much as one would believe given the visual inspection. Therefore, investigating how the representation dimensionality affect the disentanglement attained among the representations is a delicate matter. Many of the empirical studies considered in this paper suggest that mostly the regularization is impacting the disentanglement. It does however seem like there are far more parameters than originally was expected that need further evaluation to deduce their impact with respect to disentanglement.
Utnästling är en underkategori till Representations inlärning där vi inte bara tror att nyttiga egenskaper av datan kan utvinnas i en mer kompakt form, utan också att datan själv är bestående av en lågdimensionell delmängd av förklarande faktorer. Förklarande faktorer är ett tvetydigt begrepp och vad dessa poträtterar är varierande med datasetet i fråga. Ett dataset bestående av blommor kan ha stjälk storlek och färg som förklarande faktorer, medan för ett annat dataset kan detta vara plats eller position. Dem förklarande faktorerna är ofta nästlade i en komplex interaktion för att generera datan. Utnästling kan summeras som att bryta ner denna potentiellt komplexa interaktion mellan dem förklarande faktorerna för att frigöra dem från varandra. Dem frigivna förklarande faktorerna utgör därefter själva grunden till representationerna, en procedur som är betrodd att förbättra nedströms maskininlärningsuppgifter. Att nästla ut dem förklarande faktorerna i en oövervakad omgivning har visat sig vara en svår uppgift av många anledningar, kanske den allra viktigaste är att vi saknar vetskap om hur många dem är och vad dem reflekterar. För att kunna utvärdera graden av utnästling som vi har uppnått så kommer vi att betrakta ett dataset som är annoterat med de förklarande faktorer som genererade datan. Att veta hur många de förklarande faktorerna är ger en indikation av vilken dimensionalitet representationerna bör ha för att åtminstone kunna fånga alla de förklarande faktorerna. Många av de empiriska studier som betraktats i detta papper behandlar dimensionaliteten av representationerna som en konstant när vi utvärderar utnästlingen som uppnåtts. Syftet med detta papper är att förlänga diskussionen runt utnästling genom att behandla dimensionaliteten av representationerna som en variabel att alternera och undersöka hur detta påverkar graden av utnästling som uppnåtts. Experimenten som utförts i detta papper indikerar att visuell inspektion av den uppnådda utnästlingen i ett högdimensionellt representationsrum är svårt att tolka och utvärdera för ett mänskligt öga. Därav är vi ofta beroende av utnästlingspoäng, som inte behöver någon mänsklig interaktion för utvärderingen. Utnästlingspoängen uppvisar dock ett statiskt beteende, och förändras inte till den grad som den visuella inspektionen indikerar. Av just denna anledning är utvärderingen av hur representations dimensionaliteten påverkar utnästlingen uppnådd hos representationerna ett känsligt ämne. Många av de empiriska studier som betraktats i detta papper föreslår att regulariseringen är det som mestadels påverkar utnästlingen. Det verkar huruvida som att där är betydligt fler parametrar än vad som tidigare misstänkts som behöver utvärderas, och i synnerhet deras påverkan på utnästlingen.

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Livres sur le sujet "Latency variation"

Evans, Stephen T. Latent functions of employment : Variations according to employment status and labour market. London : Social Statistics Research Unit, City University, 1990.

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Yi, Chao, et United States. National Aeronautics and Space Administration., dir. Evolution dynamics of tropical ocean-atmosphere annual cycle variability. [Washington, DC : National Aeronautics and Space Administration, 1996.

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Deen, Jason F., et Karen K. Stout. Causes and diagnosis of valvular problems. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0158.

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While valvular heart disease encountered in developing countries is primarily rheumatic in aetiology, in industrialized countries it is largely comprised of degenerative valvular disease. Although less prevalent than ischaemic heart disease, its prevalence increases with older age and increased life expectancy, and therefore represents significant disease burden in aging populations. Transthoracic echocardiography remains the imaging modality of choice for timely delineation of the anatomy and severity of the lesion,although, once identified, may not correlate with symptoms due to clinical latency of disease onset to disease manifestation. Variations of disease severity, which may not meet criteria for intervention, lead to chronicity of disease, while clinically silent lesions may remain undiagnosed—both of these situations may lead to acute illness requiring intensive care management. Stabilization through medical intervention may be required, although many patients with severe disease will need emergent surgical repair, therefore collaborative involvement between intensivists, cardiologists, and cardiovascular surgeons is needed to minimize patient mortality and morbidity.

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Allern, Elin Haugsgjerd, et Tània Verge. Still Connecting with Society ? Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198758631.003.0005.

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How parties structure their interaction with social groups is a key determinant of their capacity to provide linkage between the institutions of government and the public at large. This chapter investigates the extent to which modern political parties use formal measures to connect to relevant societal interests and strengthen their anchorage in society. The analysis centres on parties’ use of formal rules governing affiliation and representation to link with externally organized interests and parties’ establishment of sub-organizations with representation rights within the party. The chapter authors develop and test several hypotheses concerning cross-country and within-country sources of variation in formal linkage and test them empirically. In addition, they examine whether formal status and representation rights shape parties’ ability to represent descriptively the associated latent social interests focusing on the case of women and ethnic minorities.

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Wiener, Jonathan. Precaution and Climate Change. Sous la direction de Kevin R. Gray, Richard Tarasofsky et Cinnamon Carlarne. Oxford University Press, 2016. http://dx.doi.org/10.1093/law/9780199684601.003.0008.

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This chapter examines the role of precaution as applied to climate change. Article 3 of the United Nations Framework Convention on Climate Change (UNFCCC) specifically calls for precautionary measures to anticipate, prevent, or minimize the causes of climate change and mitigate its adverse effects. Because climate change poses uncertain but serious and even catastrophic risks, and because greenhouse gas emissions have latent and long-lasting effects which last over decades and even centuries, precautionary action is widely urged as essential to preventing future climate change—rather than waiting to act after the damage is done, when it is too late to address the cause. The chapter describes the key features of the Precautionary Principle (PP), and illustrates the advance of the PP across the terrain of national and international law and also the variation among its proliferating versions established by various scholars, such as Per Sandin, David VanderZwaag, and Christopher Stone.

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Goswami, B. N., et Soumi Chakravorty. Dynamics of the Indian Summer Monsoon Climate. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228620.013.613.

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Lifeline for about one-sixth of the world’s population in the subcontinent, the Indian summer monsoon (ISM) is an integral part of the annual cycle of the winds (reversal of winds with seasons), coupled with a strong annual cycle of precipitation (wet summer and dry winter). For over a century, high socioeconomic impacts of ISM rainfall (ISMR) in the region have driven scientists to attempt to predict the year-to-year variations of ISM rainfall. A remarkably stable phenomenon, making its appearance every year without fail, the ISM climate exhibits a rather small year-to-year variation (the standard deviation of the seasonal mean being 10% of the long-term mean), but it has proven to be an extremely challenging system to predict. Even the most skillful, sophisticated models are barely useful with skill significantly below the potential limit on predictability. Understanding what drives the mean ISM climate and its variability on different timescales is, therefore, critical to advancing skills in predicting the monsoon. A conceptual ISM model helps explain what maintains not only the mean ISM but also its variability on interannual and longer timescales.The annual ISM precipitation cycle can be described as a manifestation of the seasonal migration of the intertropical convergence zone (ITCZ) or the zonally oriented cloud (rain) band characterized by a sudden “onset.” The other important feature of ISM is the deep overturning meridional (regional Hadley circulation) that is associated with it, driven primarily by the latent heat release associated with the ISM (ITCZ) precipitation. The dynamics of the monsoon climate, therefore, is an extension of the dynamics of the ITCZ. The classical land–sea surface temperature gradient model of ISM may explain the seasonal reversal of the surface winds, but it fails to explain the onset and the deep vertical structure of the ISM circulation. While the surface temperature over land cools after the onset, reversing the north–south surface temperature gradient and making it inadequate to sustain the monsoon after onset, it is the tropospheric temperature gradient that becomes positive at the time of onset and remains strongly positive thereafter, maintaining the monsoon. The change in sign of the tropospheric temperature (TT) gradient is dynamically responsible for a symmetric instability, leading to the onset and subsequent northward progression of the ITCZ. The unified ISM model in terms of the TT gradient provides a platform to understand the drivers of ISM variability by identifying processes that affect TT in the north and the south and influence the gradient.The predictability of the seasonal mean ISM is limited by interactions of the annual cycle and higher frequency monsoon variability within the season. The monsoon intraseasonal oscillation (MISO) has a seminal role in influencing the seasonal mean and its interannual variability. While ISM climate on long timescales (e.g., multimillennium) largely follows the solar forcing, on shorter timescales the ISM variability is governed by the internal dynamics arising from ocean–atmosphere–land interactions, regional as well as remote, together with teleconnections with other climate modes. Also important is the role of anthropogenic forcing, such as the greenhouse gases and aerosols versus the natural multidecadal variability in the context of the recent six-decade long decreasing trend of ISM rainfall.

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Chapitres de livres sur le sujet "Latency variation"

Hounsel, Austin, Paul Schmitt, Kevin Borgolte et Nick Feamster. « Can Encrypted DNS Be Fast ? » Dans Passive and Active Measurement, 444–59. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72582-2_26.

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AbstractIn this paper, we study the performance of encrypted DNS protocols and conventional DNS from thousands of home networks in the United States, over one month in 2020. We perform these measurements from the homes of 2,693 participating panelists in the Federal Communications Commission’s (FCC) Measuring Broadband America program. We found that clients do not have to trade DNS performance for privacy. For certain resolvers, DoT was able to perform faster than DNS in median response times, even as latency increased. We also found significant variation in DoH performance across recursive resolvers. Based on these results, we recommend that DNS clients (e.g., web browsers) should periodically conduct simple latency and response time measurements to determine which protocol and resolver a client should use. No single DNS protocol nor resolver performed the best for all clients.

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Wilkie, Bernard, Karla Muñoz Esquivel et Jamie Roche. « An LSTM Framework for the Effective Screening of Dementia for Deployment on Edge Devices ». Dans Communications in Computer and Information Science, 21–37. Cham : Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59080-1_2.

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AbstractDementia is a series of neurodegenerative disorders that affect 1 in 4 people over the age of 80 and can greatly reduce the quality of life of those afflicted. Alzheimer’s disease (AD) is the most common variation, accounting for roughly 60% of cases. The current financial cost of these diseases is an estimated $1.3 trillion per year. While treatments are available to help patients maintain their mental function and slow disease progression, many of those with AD are asymptomatic in the early stages, resulting in late diagnosis. The addition of the routine testing needed for an effective level of early diagnosis would put a costly burden on both patients and healthcare systems. This research proposes a novel framework for the modelling of dementia, designed for deployment in edge hardware. This work extracts a wide variety of thoroughly researched Electroencephalogram (EEG) features, and through extensive feature selection, model testing, tuning, and edge optimization, we propose two novel Long Short-Term Memory (LSTM) neural networks. The first, uses 4 EEG sensors and can classify AD and Frontotemporal Dementia from cognitively normal (CN) subjects. The second, requires 3 EEG sensors and can classify AD from CN subjects. This is achieved with optimisation that reduces the model size by 83×, latency by 3.7×, and performs with an accuracy of 98%. Comparative analysis with existing research shows this performance exceeds current less portable techniques. The deployment of this model in edge hardware could aid in routine testing, providing earlier diagnosis of dementia, reducing the strain on healthcare systems, and increasing the quality of life for those afflicted with the disease.

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Zhu, Xinqi, Chang Xu et Dacheng Tao. « Learning Disentangled Representations with Latent Variation Predictability ». Dans Computer Vision – ECCV 2020, 684–700. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58607-2_40.

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Agakov, Felix, et David Barber. « Auxiliary Variational Information Maximization for Dimensionality Reduction ». Dans Subspace, Latent Structure and Feature Selection, 103–14. Berlin, Heidelberg : Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11752790_6.

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Ma, Yonghua, Zhimin Zhang et Guoliang Yang. « Analysis of Temperature Field Characteristics in Seasonal Frost Region Airport Pavement Subgrade ». Dans Lecture Notes in Civil Engineering, 47–54. Singapore : Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-4355-1_5.

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AbstractTo study the variation patterns of the temperature field in seasonal frost region airport subgrades, this research establishes a heat conduction equation with phase change latent heat as an internal heat source. During the freezing and thawing processes in seasonal frost regions, a numerical simulation model of airport subgrades is developed, with thermal characteristic parameters of the soil as research variables. The study focuses on investigating the characteristic changes in the temperature field of seasonal frost region airport subgrades, ultimately obtaining the variation patterns of shallow temperature within the natural subgrade and surface temperature.

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Lücke, Jörg, Zhenwen Dai et Georgios Exarchakis. « Truncated Variational Sampling for ‘Black Box’ Optimization of Generative Models ». Dans Latent Variable Analysis and Signal Separation, 467–78. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93764-9_43.

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Bentley, Peter J., Soo Ling Lim, Adam Gaier et Linh Tran. « Evolving Through the Looking Glass : Learning Improved Search Spaces with Variational Autoencoders ». Dans Lecture Notes in Computer Science, 371–84. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14714-2_26.

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AbstractNature has spent billions of years perfecting our genetic representations, making them evolvable and expressive. Generative machine learning offers a shortcut: learn an evolvable latent space with implicit biases towards better solutions. We present SOLVE: Search space Optimization with Latent Variable Evolution, which creates a dataset of solutions that satisfy extra problem criteria or heuristics, generates a new latent search space, and uses a genetic algorithm to search within this new space to find solutions that meet the overall objective. We investigate SOLVE on five sets of criteria designed to detrimentally affect the search space and explain how this approach can be easily extended as the problems become more complex. We show that, compared to an identical GA using a standard representation, SOLVE with its learned latent representation can meet extra criteria and find solutions with distance to optimal up to two orders of magnitude closer. We demonstrate that SOLVE achieves its results by creating better search spaces that focus on desirable regions, reduce discontinuities, and enable improved search by the genetic algorithm.

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Boutalbi, Rafika, Lazhar Labiod et Mohamed Nadif. « Latent Block Regression Model ». Dans Studies in Classification, Data Analysis, and Knowledge Organization, 73–81. Cham : Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09034-9_9.

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AbstractWhen dealing with high dimensional sparse data, such as in recommender systems,co-clusteringturnsouttobemorebeneficialthanone-sidedclustering,even if one is interested in clustering along one dimension only. Thereby, co-clusterwise is a natural extension of clusterwise. Unfortunately, all of the existing approaches do not consider covariates on both dimensions of a data matrix. In this paper, we propose a Latent Block Regression Model (LBRM) overcoming this limit. For inference, we propose an algorithm performing simultaneously co-clustering and regression where a linear regression model characterizes each block. Placing the estimate of the model parameters under the maximum likelihood approach, we derive a Variational Expectation–Maximization (VEM) algorithm for estimating the model’s parameters. The finality of the proposed VEM-LBRM is illustrated through simulated datasets.

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Lorenzi, Marco, Marie Deprez, Irene Balelli, Ana L. Aguila et Andre Altmann. « Integration of Multimodal Data ». Dans Machine Learning for Brain Disorders, 573–97. New York, NY : Springer US, 2012. http://dx.doi.org/10.1007/978-1-0716-3195-9_19.

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AbstractThis chapter focuses on the joint modeling of heterogeneous information, such as imaging, clinical, and biological data. This kind of problem requires to generalize classical uni- and multivariate association models to account for complex data structure and interactions, as well as high data dimensionality.Typical approaches are essentially based on the identification of latent modes of maximal statistical association between different sets of features and ultimately allow to identify joint patterns of variations between different data modalities, as well as to predict a target modality conditioned on the available ones. This rationale can be extended to account for several data modalities jointly, to define multi-view, or multi-channel, representation of multiple modalities. This chapter covers both classical approaches such as partial least squares (PLS) and canonical correlation analysis (CCA), along with most recent advances based on multi-channel variational autoencoders. Specific attention is here devoted to the problem of interpretability and generalization of such high-dimensional models. These methods are illustrated in different medical imaging applications, and in the joint analysis of imaging and non-imaging information, such as -omics or clinical data.

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Drefs, Jakob, Enrico Guiraud, Filippos Panagiotou et Jörg Lücke. « Direct Evolutionary Optimization of Variational Autoencoders with Binary Latents ». Dans Machine Learning and Knowledge Discovery in Databases, 357–72. Cham : Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26409-2_22.

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Actes de conférences sur le sujet "Latency variation"

Huang, Dong, Dan Feng, Qiankun Liu, Bo Ding, Wei Zhao, Xueliang Wei et Wei Tong. « A Read Latency Variation Aware Independent Read Scheme for QLC SSDs ». Dans 2024 Design, Automation & ; Test in Europe Conference & ; Exhibition (DATE), 1–6. IEEE, 2024. http://dx.doi.org/10.23919/date58400.2024.10546573.

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Shi, Ye, et C. S. George Lee. « Uniform Transformation : Refining Latent Representation in Variational Autoencoders ». Dans 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE), 2786–92. IEEE, 2024. http://dx.doi.org/10.1109/case59546.2024.10711552.

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Helwani, Karim, Masahito Togami, Paris Smaragdis et Michael M. Goodwin. « Sound Source Separation Using Latent Variational Block-Wise Disentanglement ». Dans 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), 750–54. IEEE, 2024. http://dx.doi.org/10.1109/icasspw62465.2024.10626303.

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Fan, Yiming, Peiyuan Zhou, David Forrester, Brian Ju et Fotis Kopsaftopoulos. « Evaluation of Local and Global Diagnostics for the Integration of Stochastic Time Series Models and Variational Autoencoders : Experimental Assessment on a Full Scale Helicopter Blade ». Dans Vertical Flight Society 80th Annual Forum & Technology Display, 1–10. The Vertical Flight Society, 2024. http://dx.doi.org/10.4050/f-0080-2024-1371.

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In this work, a unified framework integrating global and local SHM methods for structural health monitoring (SHM) of rotorcraft structures is proposed. This framework integrates both "local" ultrasonic-guided wave-based and "global" vibration-based SHM schemes for tackling damage detection, identification, and quantification under uncertainty. The local SHM is completed by training a variation of variational auto-encoder (MMD-VAE) along with feed-forward neural networks (FFNN). The compressed latent space vector obtained during the training process is applied to achieve both signal reconstruction and state prediction. In terms of the global model, functionally pooled auto-regressive models with exogenous excitation (VFP-ARX) models are applied including to capture low-frequency vibrations. The complete experimental evaluation and assessment of the proposed framework are presented for an Airbus H125 helicopter blade under both low-frequency vibrations and ultrasonic guided waves for SHM.

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Navarrete, Sebastián, Jean-François Mangin, Cecilia Hernández et Pamela Guevara. « Fast White Matter Fiber Clustering Using Variational Autoencoder Latent Space ». Dans 2024 20th International Symposium on Medical Information Processing and Analysis (SIPAIM), 1–5. IEEE, 2024. https://doi.org/10.1109/sipaim62974.2024.10783627.

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Chatterjee, Abhiroop, Susmita Ghosh et Ashish Ghosh. « Stochastic Variations for Latent Data Representation (SVLDR) : A Probabilistic Generative Modeling Framework ». Dans 2024 IEEE International Conference on Computer Vision and Machine Intelligence (CVMI), 1–6. IEEE, 2024. https://doi.org/10.1109/cvmi61877.2024.10782000.

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Høiland-Jørgensen, Toke, Bengt Ahlgren, Per Hurtig et Anna Brunstrom. « Measuring Latency Variation in the Internet ». Dans CoNEXT '16 : The 12th International Conference on emerging Networking EXperiments and Technologies. New York, NY, USA : ACM, 2016. http://dx.doi.org/10.1145/2999572.2999603.

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Chang, Kevin K., Abhijith Kashyap, Hasan Hassan, Saugata Ghose, Kevin Hsieh, Donghyuk Lee, Tianshi Li, Gennady Pekhimenko, Samira Khan et Onur Mutlu. « Understanding Latency Variation in Modern DRAM Chips ». Dans SIGMETRICS '16 : SIGMETRICS/PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems. New York, NY, USA : ACM, 2016. http://dx.doi.org/10.1145/2896377.2901453.

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Bhattacharya, Abhishek, Zhenyu Yang et Deng Pan. « Multi-Source Latency Variation Synchronization for Collaborative Applications ». Dans GLOBECOM 2010 - 2010 IEEE Global Communications Conference. IEEE, 2010. http://dx.doi.org/10.1109/glocom.2010.5685219.

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Lee, Donghyuk, Samira Khan, Lavanya Subramanian, Saugata Ghose, Rachata Ausavarungnirun, Gennady Pekhimenko, Vivek Seshadri et Onur Mutlu. « Design-Induced Latency Variation in Modern DRAM Chips ». Dans SIGMETRICS '17 : ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems. New York, NY, USA : ACM, 2017. http://dx.doi.org/10.1145/3078505.3078533.

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Rapports d'organisations sur le sujet "Latency variation"

Worley, P. H., D. R. Mackay, A. C. Robinson et E. J. Barragy. A study of application sensitivity to variation in message passing latency and bandwidth. Office of Scientific and Technical Information (OSTI), juin 1996. http://dx.doi.org/10.2172/279676.

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Teh, Yee W., David Newman et Max Welling. A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation. Fort Belvoir, VA : Defense Technical Information Center, septembre 2007. http://dx.doi.org/10.21236/ada629956.

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Rachele, Henry, et Arnold Tunick. Sensitivity of C2 sub n to Random Variations of Windspeed, Sensible Heat Flux, and Latent Heat Flux. Fort Belvoir, VA : Defense Technical Information Center, mars 1992. http://dx.doi.org/10.21236/ada248696.

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Chronopoulos, Ilias, Katerina Chrysikou, George Kapetanios, James Mitchell et Aristeidis Raftapostolos. Deep Neural Network Estimation in Panel Data Models. Federal Reserve Bank of Cleveland, juillet 2023. http://dx.doi.org/10.26509/frbc-wp-202315.

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In this paper we study neural networks and their approximating power in panel data models. We provide asymptotic guarantees on deep feed-forward neural network estimation of the conditional mean, building on the work of Farrell et al. (2021), and explore latent patterns in the cross-section. We use the proposed estimators to forecast the progression of new COVID-19 cases across the G7 countries during the pandemic. We find significant forecasting gains over both linear panel and nonlinear time-series models. Containment or lockdown policies, as instigated at the national level by governments, are found to have out-of-sample predictive power for new COVID-19 cases. We illustrate how the use of partial derivatives can help open the "black box" of neural networks and facilitate semi-structural analysis: school and workplace closures are found to have been effective policies at restricting the progression of the pandemic across the G7 countries. But our methods illustrate significant heterogeneity and time variation in the effectiveness of specific containment policies.

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