Analysis of somatic mutation profiles from cancer patients is essential in the development of cancer research. However, the low frequency of most mutations and the varying rates of mutations across patients makes the data extremely challenging to statistically analyze as well as difficult to use in classification problems, for clustering, visualization or for learning useful information. Thus, the creation of low dimensional representations of somatic mutation profiles that hold useful information about the DNA of cancer cells will facilitate the use of such data in applications that will progress precision medicine. In this paper, we talk about the open problem of learning from somatic mutations, and present Flatsomatic: a solution that utilizes variational autoencoders (VAEs) to create latent representations of somatic profiles. The work done in this paper shows great potential for this method, with the VAE embeddings performing better than PCA for a clustering task, and performing equally well to the raw high dimensional data for a classification task. We believe the methods presented herein can be of great value in future research and in bringing data-driven models into precision oncology.

The transfer learning toolkit wraps the codes of 17 transfer learn ing models and provides integrated interfaces, allowing users to use those models by calling a simple function. It is easy for primary researchers to use this toolkit and to choose proper models for real-world applica tions. The toolkit is written in Python and distributed under MIT open source license. In this pape r, the current state of this toolkit is described and the necessary environment setting and usage are in troduced. Keywords: Transfer Learning, Toolkit 1. Introduction Transfer learning is a promising and important direction in machine lear ning, which attempts to leverage the knowledge contained in a source domain to improve the le arning performance or minimize the number of labeled samples required in a target domain.

This paper presents the application of a newly developed nature-inspired metaheuristic optimization method, namely the Adaptive Wind Driven Optimization (AWDO), to the training of feedforward artificial neural networks (NN) and presents a discussion into the future research of AWDO implementation in Deep Learning (DL). Application example of digit classification with MNIST dataset reveals interesting behavior of the derivative-free AWDO method compared to steepest descent method where results and future work on the implementation of AWDO in deep neural networks are discussed.

We address the efficiency issue for the construction of a deep graph neural network (GNN). The approach exploits the idea of representing each input graph as a fixed point of a dynamical system (implemented through a recurrent neural network), and leverages a deep architectural organization of the recurrent units. Efficiency is gained by many aspects, including the use of small and very sparse networks, where the weights of the recurrent units are left untrained under the stability condition introduced in this work. This can be viewed as a way to study the intrinsic power of the architecture of a deep GNN, and also to provide insights for the set-up of more complex fully-trained models. Through experimental results, we show that even without training of the recurrent connections, the architecture of small deep GNN is surprisingly able to achieve or improve the state-of-the-art performance on a significant set of tasks in the field of graphs classification.

--We consider the problem of simultaneous reduction of acoustic echo, reverberation and noise. In real scenarios, these distortion sources may occur simultaneously and reducing them implies combining the corresponding distortion-specific filters. As these filters interact with each other, they must be jointly optimized. We propose to model the target and residual signals after linear echo cancellation and dereverberation using a multichannel Gaussian modeling framework and to jointly represent their spectra by means of a neural network. We develop an iterative block-coordinate ascent algorithm to update all the filters. We evaluate our system on real recordings of acoustic echo, reverberation and noise acquired with a smart speaker in various situations. The proposed approach outperforms in terms of overall distortion a cascade of the individual approaches and a joint reduction approach which does not rely on a spectral model of the target and residual signals. Index T erms--Acoustic echo, reverberation, background noise, joint distortion reduction, expectation-maximization, recurrent neural network. The near-end speaker can be a few meters away from the microphones and the interactions can be subject to several distortion sources such as background noise, acoustic echo and near-end reverberation. Each of these distortion sources degrades speech quality, intelligibility and listening comfort, and must be reduced. Single-and multichannel filters have been used to reduce each of these distortion sources independently. They can be categorized into short nonlinear filters that vary quickly over time and long linear filters that are time-invariant (or slowly time-varying). Short nonlinear filters are generally used for noise reduction [1]. They are robust to the fluctuations and nonlinearities inherent to real signals. Long linear filters can be required for dereverberation [2] and echo reduction [3].

European satellite missions Sentinel-1 (S1) and Sentinel-2 (S2) provide at highspatial resolution and high revisit time, respectively, radar and optical imagesthat support a wide range of Earth surface monitoring tasks such as LandUse/Land Cover mapping. A long-standing challenge in the remote sensingcommunity is about how to efficiently exploit multiple sources of information and leverage their complementary. In this particular case, get the most out ofradar and optical satellite image time series (SITS). Here, we propose to dealwith land cover mapping through a deep learning framework especially tailoredto leverage the multi-source complementarity provided by radar and opticalSITS. The proposed architecture is based on an extension of Recurrent NeuralNetwork (RNN) enriched via a customized attention mechanism capable to fitthe specificity of SITS data. In addition, we propose a new pretraining strategythat exploits domain expert knowledge to guide the model parameter initial-ization. Thorough experimental evaluations involving several machine learningcompetitors, on two contrasted study sites, have demonstrated the suitabilityof our new attention mechanism combined with the extend RNN model as wellas the benefit/limit to inject domain expert knowledge in the neural networktraining process.

Abstract--We develop an end-to-end deep learning-based anomaly detection model for temporal data in transportatio n networks. The proposed EVT -LSTM model is derived from the popular LSTM (Long Short-T erm Memory) network and adopts an objective function that is based on fundamental results f rom EVT (Extreme V alue Theory). We compare the EVT -LSTM model with some established statistical, machine learning, and hybrid deep learning baselines. Experiments on seven diver se real-world data sets demonstrate the superior anomaly dete ction performance of our proposed model over the other models considered in the comparison study. The increasing availability of large-scale traffic data set s provides an opportunity to explore them for knowledge discovery in ITS (Intelligent Transportation Systems). The av - enues for exploration are numerous, ranging from uncoverin g traffic patterns [1], city dynamics [2], driving directions [3], discovering hot spots in a city [4], finding vacant taxis arou nd a city [5], predicting taxi demand [6], taxi operation patte rns [7], to detecting anomalies [8], among others. V arious verticals of ITS have always received active research attention in the past. However, the recent emergence of deep learning techniques and their applicability in tran s-portation systems has resulted in a heightened interest in t his area [9]. Consequently, traditional machine learning mode ls in many applications are now being replaced by deep learning techniques, which is reshaping the landscape of intelligen t transport networks. Out of the several applications of ITS, the area of anomaly detection has benefited significantly from th e application of deep learning-based techniques [10]. Anoma ly detection aims to find those patterns which are not normally expected from the data. Typical observations from traffic da ta demonstrate strong spatiotemporal patterns, showing per iod-icity and strong correlations between adjacent observatio ns.

Distributed stochastic gradient descent (SGD) algorithms are widely deployed in training large-scale deep learning models, while the communication overhead among workers becomes the new system bottleneck. Recently proposed gradient sparsification techniques, especially Top-$k$ sparsification with error compensation (TopK-SGD), can significantly reduce the communication traffic without an obvious impact on the model accuracy. Some theoretical studies have been carried out to analyze the convergence property of TopK-SGD. However, existing studies do not dive into the details of Top-$k$ operator in gradient sparsification and use relaxed bounds (e.g., exact bound of Random-$k$) for analysis; hence the derived results cannot well describe the real convergence performance of TopK-SGD. To this end, we first study the gradient distributions of TopK-SGD during the training process through extensive experiments. We then theoretically derive a tighter bound for the Top-$k$ operator. Finally, we exploit the property of gradient distribution to propose an approximate top-$k$ selection algorithm, which is computing-efficient for GPUs, to improve the scaling efficiency of TopK-SGD by significantly reducing the computing overhead. Codes are available at: \url{https://github.com/hclhkbu/GaussianK-SGD}.

-- There are many award - winning pre - trained Convolutional Neural Network (CNN), which have a common phenomen on of increasing depth in convolutional layers. However, I inspect on VGG network, which is one of the famous model submitted to ILSVRC - 2014, to show that slight modification in the basic architecture can enhance the accuracy result of the image classification task. In this paper, We present two improv e architectures of pre - trained VGG - 16 and VGG - 19 networks that appl y transfer learning when trained on a different dataset. I report a series of experimental result on various modification of the primary VGG networks and achieved sign ificant out - performance on image classification task by: (1) freezing the first two blocks of the convolutional layers to prevent over - fitting and (2) applying different combination of dilation rate in the last three blocks of convolutional layer to reduce image resolution for feature extraction. Both the proposed architecture achieve s a competitive result on CIFAR - 10 and CIFAR - 100 dataset. Keywords -- CNN, VGG - 16, VGG - 19, Dilated Convolution, transfer learning I. INTRODUCTION Convolutional networks (ConvNets) have achieved excellent success in the large - scale image and video recognition, which has become feasible before large public image repositories such as ImageNet [1] and high - performance computi ng system s such as GPUs or large - scale distributed clusters. These advancements were largely motivated by strong baseline schema s, such as semantic segmentation [2], object recognition [3], image capt ioning [4], and human pose estimation[4].

ABSTRACT In this paper, we present a new open source toolkit for automatic speech recognition (ASR), named CA T (CRF-based ASR Toolkit). A key feature of CA T is discriminative training in the framework of conditional random field (CRF), particularly with connectionist temporal classification (CTC) inspired state topology. CA T contains a full-fledged implementation of CTC-CRF and provides a complete workflow for CRF-based end-to-end speech recognition. Evaluation results on Chinese and English benchmarks such as Switchboard and Aishell show that CA T obtains the state-of-the-art results among existing end-to-end models with less parameters, and is competitive compared with the hybrid DNN-HMM models. Towards flexibility, we show that i-vector based speaker-adapted recognition and latency control mechanism can be explored easily and effectively in CA T. We hope CA T, especially the CRF-based framework and software, will be of broad interest to the community, and can be further explored and improved. Index T erms-- speech recognition, open source toolkit, conditional random field, end-to-end 1. INTRODUCTION In addition to theories and algorithms, open source toolkits make substantial contributions to automatic speech recognition (ASR) technologies.