Search results for "ENCODE"
showing 10 items of 91 documents
Employing artificial neural networks to find reaction coordinates and pathways for self-assembly
2021
Capturing the autonomous self-assembly of molecular building blocks in computer simulations is a persistent challenge, requiring to model complex interactions and to access long time scales. Advanced sampling methods allow to bridge these time scales but typically require to construct accurate low-dimensional representations of the transition pathways. In this work, we demonstrate for the self-assembly of two single-stranded DNA fragments into a ring-like structure how autoencoder architectures based on unsupervised neural networks can be employed to reliably expose transition pathways and to provide a suitable low-dimensional representation. The assembly occurs as a two-step process throug…
Including invariances in SVM remote sensing image classification
2012
This paper introduces a simple method to include invariances in support vector machine (SVM) for remote sensing image classification. We rely on the concept of virtual support vectors, by which the SVM is trained with both the selected support vectors and synthetic examples encoding the invariance of interest. The algorithm is very simple and effective, as demonstrated in two particularly interesting examples: invariance to the presence of shadows and to rotations in patchbased image segmentation. The improved accuracy (around +6% both in OA and Cohen's κ statistic), along with the simplicity of the approach encourage its use and extension to encode other invariances and other remote sensin…
An Ensemble Learning Method for Emotion Charting Using Multimodal Physiological Signals
2022
Emotion charting using multimodal signals has gained great demand for stroke-affected patients, for psychiatrists while examining patients, and for neuromarketing applications. Multimodal signals for emotion charting include electrocardiogram (ECG) signals, electroencephalogram (EEG) signals, and galvanic skin response (GSR) signals. EEG, ECG, and GSR are also known as physiological signals, which can be used for identification of human emotions. Due to the unbiased nature of physiological signals, this field has become a great motivation in recent research as physiological signals are generated autonomously from human central nervous system. Researchers have developed multiple methods for …
TRACX2: a RAAM -like autoencoder modeling graded chunking in infant visual -sequence learning
2017
International audience; Even newborn infants are able to extract structure from a stream of sensory inputs and yet, how this is achieved remains largely a mystery. We present a connectionist autoencoder model, TRACX2, that learns to extract sequence structure by gradually constructing chunks, storing these chunks in a distributed manner across its synaptic weights, and recognizing these chunks when they re-occur in the input stream. Chunks are graded rather than all-or-none in nature and during learning their component parts become ever more tightly bound together. TRACX2 successfully models data from four experiments from the infant visual statistical-learning literature, including tasks i…
3D high definition video coding on a GPU-based heterogeneous system
2013
H.264/MVC is a standard for supporting the sensation of 3D, based on coding from 2 (stereo) to N views. H.264/MVC adopts many coding options inherited from single view H.264/AVC, and thus its complexity is even higher, mainly because the number of processing views is higher. In this manuscript, we aim at an efficient parallelization of the most computationally intensive video encoding module for stereo sequences. In particular, inter prediction and its collaborative execution on a heterogeneous platform. The proposal is based on an efficient dynamic load balancing algorithm and on breaking encoding dependencies. Experimental results demonstrate the proposed algorithm's ability to reduce the…
Event generation and statistical sampling for physics with deep generative models and a density information buffer
2021
Simulating nature and in particular processes in particle physics require expensive computations and sometimes would take much longer than scientists can afford. Here, we explore ways to a solution for this problem by investigating recent advances in generative modeling and present a study for the generation of events from a physical process with deep generative models. The simulation of physical processes requires not only the production of physical events, but to also ensure that these events occur with the correct frequencies. We investigate the feasibility of learning the event generation and the frequency of occurrence with several generative machine learning models to produce events l…
"Master-Slave" Biological Network Alignment
2010
Performing global alignment between protein-protein interaction (PPI) networks of different organisms is important to infer knowledge about conservation across species. Known methods that perform this task operate symmetrically, that is to say, they do not assign a distinct role to the input PPI networks. However, in most cases, the input networks are indeed distinguishable on the basis of how well the corresponding organism is biologically well-characterized. For well-characterized organisms the associated PPI network supposedly encode in a sound manner all the information about their proteins and associated interactions, which is far from being the case for not well characterized ones. He…
TAF-ChIP: An ultra-low input approach for genome wide chromatin immunoprecipitation assay
2018
Chromatin immunoprecipitation (ChIP) followed by next generation sequencing is an invaluable and powerful technique to understand transcriptional regulation. However, ChIP is currently limited by the requirement of large amount of starting material. This renders studying rare cell populations very challenging, or even impossible. Here, we present a tagmentation-assisted fragmentation ChIP (TAF-ChIP) and sequencing method to generate high-quality datasets from low cell numbers. The method relies on Tn5 transposon activity to fragment the chromatin that is immunoprecipitated, thus circumventing the need for sonication or MNAse digestion to fragment. Furthermore, Tn5 adds the sequencing adapto…
High-accuracy approximation of piecewise smooth functions using the Truncation and Encode approach
2017
Abstract In the present work, we analyze a technique designed by Geraci et al. in [1,11] named the Truncate and Encode (TE) strategy. It was presented as a non-intrusive method for steady and non-steady Partial Differential Equations (PDEs) in Uncertainty Quantification (UQ), and as a weakly intrusive method in the unsteady case. We analyze the TE algorithm applied to the approximation of functions, and in particular its performance for piecewise smooth functions. We carry out some numerical experiments, comparing the performance of the algorithm when using different linear and non-linear interpolation techniques and provide some recommendations that we find useful in order to achieve a hig…
Asymptotic observer of the link states of flexible joint robots with motor-side sensing
2015
This paper proposes an approach for observing the link states, i.e. angular position and velocity, of the robotic joints with elasticities, when only the motor-side sensing is available. By separating the state-dependent nonlinearities of the rigid manipulator dynamics a linear sub-model of the elastic joint robot is obtained in an observable state-space form. The standard asymptotic Luenberger state observer is then designed for the given motor position signal which is measured by encoder and rectified from the nonlinear contribution. The configurable poles of observer allow shaping the estimated states dynamics to be fast enough in relation to the actual resonant joint behavior. Therefore…