288,683 research outputs found
Fully Unleashing the Power of Paying Multiplexing Only Once in Stochastic Network Calculus
The stochastic network calculus (SNC) holds promise as a versatile and
uniform framework to calculate probabilistic performance bounds in networks of
queues. A great challenge to accurate bounds and efficient calculations are
stochastic dependencies between flows due to resource sharing inside the
network. However, by carefully utilizing the basic SNC concepts in the network
analysis the necessity of taking these dependencies into account can be
minimized. To that end, we fully unleash the power of the pay multiplexing only
once principle (PMOO, known from the deterministic network calculus) in the SNC
analysis. We choose an analytic combinatorics presentation of the results in
order to ease complex calculations. In tree-reducible networks, a subclass of
general feedforward networks, we obtain a perfect analysis in terms of avoiding
the need to take internal flow dependencies into account. In a comprehensive
numerical evaluation, we demonstrate how this unleashed PMOO analysis can
reduce the known gap between simulations and SNC calculations significantly,
and how it favourably compares to state-of-the art SNC calculations in terms of
accuracy and computational effort. Motivated by these promising results, we
also consider general feedforward networks, when some flow dependencies have to
be taken into account. To that end, the unleashed PMOO analysis is extended to
the partially dependent case and a case study of a canonical example topology,
known as the diamond network, is provided, again displaying favourable results
over the state of the art
On the discovery of social roles in large scale social systems
The social role of a participant in a social system is a label
conceptualizing the circumstances under which she interacts within it. They may
be used as a theoretical tool that explains why and how users participate in an
online social system. Social role analysis also serves practical purposes, such
as reducing the structure of complex systems to rela- tionships among roles
rather than alters, and enabling a comparison of social systems that emerge in
similar contexts. This article presents a data-driven approach for the
discovery of social roles in large scale social systems. Motivated by an
analysis of the present art, the method discovers roles by the conditional
triad censuses of user ego-networks, which is a promising tool because they
capture the degree to which basic social forces push upon a user to interact
with others. Clusters of censuses, inferred from samples of large scale network
carefully chosen to preserve local structural prop- erties, define the social
roles. The promise of the method is demonstrated by discussing and discovering
the roles that emerge in both Facebook and Wikipedia. The article con- cludes
with a discussion of the challenges and future opportunities in the discovery
of social roles in large social systems
Robust, automated sleep scoring by a compact neural network with distributional shift correction.
Studying the biology of sleep requires the accurate assessment of the state of experimental subjects, and manual analysis of relevant data is a major bottleneck. Recently, deep learning applied to electroencephalogram and electromyogram data has shown great promise as a sleep scoring method, approaching the limits of inter-rater reliability. As with any machine learning algorithm, the inputs to a sleep scoring classifier are typically standardized in order to remove distributional shift caused by variability in the signal collection process. However, in scientific data, experimental manipulations introduce variability that should not be removed. For example, in sleep scoring, the fraction of time spent in each arousal state can vary between control and experimental subjects. We introduce a standardization method, mixture z-scoring, that preserves this crucial form of distributional shift. Using both a simulated experiment and mouse in vivo data, we demonstrate that a common standardization method used by state-of-the-art sleep scoring algorithms introduces systematic bias, but that mixture z-scoring does not. We present a free, open-source user interface that uses a compact neural network and mixture z-scoring to allow for rapid sleep scoring with accuracy that compares well to contemporary methods. This work provides a set of computational tools for the robust automation of sleep scoring
Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey
Time Series Classification and Extrinsic Regression are important and
challenging machine learning tasks. Deep learning has revolutionized natural
language processing and computer vision and holds great promise in other fields
such as time series analysis where the relevant features must often be
abstracted from the raw data but are not known a priori. This paper surveys the
current state of the art in the fast-moving field of deep learning for time
series classification and extrinsic regression. We review different network
architectures and training methods used for these tasks and discuss the
challenges and opportunities when applying deep learning to time series data.
We also summarize two critical applications of time series classification and
extrinsic regression, human activity recognition and satellite earth
observation
Insights into Cellular Evolution: Temporal Deep Learning Models and Analysis for Cell Image Classification
Understanding the temporal evolution of cells poses a significant challenge in developmental biology. This study embarks on a comparative analysis of various machine-learning techniques to classify cell colony images across different timestamps, thereby aiming to capture dynamic transitions of cellular states. By performing Transfer Learning with state-of-the-art classification networks, we achieve high accuracy in categorizing single-timestamp images. Furthermore, this research introduces the integration of temporal models, notably LSTM (Long Short Term Memory Network), R-Transformer (Recurrent Neural Network enhanced Transformer) and ViViT (Video Vision Transformer), to undertake this classification task to verify the effectiveness of incorporating temporal features into the classification through a comprehensive comparative analysis of these models compare to non-temporal models. This investigation not only benchmarks the efficacy of different machine-learning approaches in understanding cellular forms but also sets a precedent for future research aimed at enriching our comprehension of cellular developments through enhanced computational methodologies. The insights and methodologies derived from this study promise to contribute significantly to the advancement of computational techniques in the realm of biological research, paving the way for deeper insights into the intricacies of cellular behavior and evolution
Identifying Associations Between Brain Imaging Phenotypes and Genetic Factors via A Novel Structured SCCA Approach
Brain imaging genetics attracts more and more attention since it can reveal associations between genetic factors and the structures or functions of human brain. Sparse canonical correlation analysis (SCCA) is a powerful bi-multivariate association identification technique in imaging genetics. There have been many SCCA methods which could capture different types of structured imaging genetic relationships. These methods either use the group lasso to recover the group structure, or employ the graph/network guided fused lasso to find out the network structure. However, the group lasso methods have limitation in generalization because of the incomplete or unavailable prior knowledge in real world. The graph/network guided methods are sensitive to the sign of the sample correlation which may be incorrectly estimated. We introduce a new SCCA model using a novel graph guided pairwise group lasso penalty, and propose an efficient optimization algorithm. The proposed method has a strong upper bound for the grouping effect for both positively and negatively correlated variables. We show that our method performs better than or equally to two state-of-the-art SCCA methods on both synthetic and real neuroimaging genetics data. In particular, our method identifies stronger canonical correlations and captures better canonical loading profiles, showing its promise for revealing biologically meaningful imaging genetic associations
Using Psychophysical Methods to Understand Mechanisms of Face Identification in a Deep Neural Network
Deep Convolutional Neural Networks (CNNs) have been one of the most influential recent developments in computer vision, particularly for categorization [20]. The promise of CNNs is at least two-fold. First, they represent the best engineering solution to successfully tackle the foundational task of visual categorization with a performance level that even exceeds that of humans [19, 27]. Second, for computational neuroscience, CNNs provide a testable modelling platform for visual categorizations inspired by the multi-layered organization of visual cortex [7]. Here, we used a 3D generative model to control the variance of information learned to identify 2,000 face identities in one CNN architecture (10-layer ResNet [9]). We generated 25M face images to train the network by randomly sampling intrinsic (i.e. face morphology, gender, age, expression and ethnicity) and extrinsic factors of face variance (i.e. 3D pose, illumination, scale and 2D translation). At testing, the network performed with 99% generalization accuracy for face identity across variations of intrinsic and extrinsic factors. State-of-the-art information mapping techniques from psychophysics (i.e. Representational Similarity Analysis [18] and Bubbles [8]) revealed respectively the network layer at which factors of variance are resolved and the face features that are used for identity. By explicitly controlling the generative factors of face information, we provide an alternative framework based on human psychophysics to understand information processing in CNNs
Initial specification of the evaluation tasks "Use cases to bridge validation and benchmarking" PROMISE Deliverable 2.1
Evaluation of multimedia and multilingual information access systems needs to be performed from a usage oriented perspective. This document outlines use cases from the three use case domains of the PROMISE project and gives some initial pointers to how their respective characteristics can be extrapolated to determine and guide evaluation activities, both with respect to benchmarking and to validation of the usage hypotheses. The use cases will be developed further during the course of the evaluation activities and workshops projected to occur in coming CLEF conferences
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