pwc-archive/papers-with-abstracts huggingface dataset:
Detection and Localization of Drosophila Egg Chambers in Microscopy Images.
Authors: Jiří Borovec, Jan Kybic, Rodrigo Nava
Date: 2017-09-07 00:00:00
Proceeding: International Workshop on Machine Learning in Medical Imaging 2017 9
Tasks: Clustering, Image Segmentation, Segmentation, Semantic Segmentation

Drosophila melanogaster is a well-known model organism that can be used for studying oogenesis (egg chamber development) including gene expression patterns. Standard analysis methods require manual segmentation of individual egg chambers, which is a difficult and time-consuming task. We present an image processing pipeline to detect and localize Drosophila egg chambers that consists of the following steps: (i) superpixel-based image segmentation into relevant tissue classes; (ii) detection of egg center candidates using label histograms and ray features; (iii) clustering of center candidates and; (iv) area-based maximum likelihood ellipse model fitting. Our proposal is able to detect 96% of human-expert annotated egg chambers at relevant developmental stages with less than 1% false-positive rate, which is adequate for the further analysis.
Region growing using superpixels with learned shape prior.
Authors: Jiří Borovec, Jan Kybic, Akihiro Sugimoto
Date: 2017-11-06 00:00:00
Proceeding: SPIE Journal of Electronic Imaging 2017 11
Tasks: Image Segmentation, Semantic Segmentation, Superpixels

Region growing is a classical image segmentation method based on hierarchical region aggregation using local similarity rules. Our proposed method differs from classical region growing in three important aspects. First, it works on the level of superpixels instead of pixels, which leads to a substantial speed-up. Second, our method uses learned statistical shape properties that encourage plausible shapes. In particular, we use ray features to describe the object boundary. Third, our method can segment multiple objects and ensure that the segmentations do not overlap. The problem is represented as an energy minimization and is solved either greedily or iteratively using graph cuts. We demonstrate the performance of the proposed method and compare it with alternative approaches on the task of segmenting individual eggs in microscopy images of Drosophila ovaries.
Binary Pattern Dictionary Learning for Gene Expression Representation in Drosophila Imaginal Discs.
Authors: Jiří Borovec, Jan Kybic
Date: 2016-11-23 00:00:00
Proceeding: Asian Conference on Computer Vision 2016 11
Tasks: Dictionary Learning

We present an image processing pipeline which accepts a large number of images, containing spatial expression information for thousands of genes in Drosophila imaginal discs. We assume that the gene activations are binary and can be expressed as a union of a small set of non-overlapping spatial patterns, yielding a compact representation of the spatial activation of each gene. This lends itself well to further automatic analysis, with the hope of discovering new biological relationships. Traditionally, the images were labeled manually, which was very time consuming. The key part of our work is a binary pattern dictionary learning algorithm, that takes a set of binary images and determines a set of patterns, which can be used to represent the input images with a small error. We also describe the preprocessing phase, where input images are segmented to recover the activation images and spatially aligned to a common reference. We compare binary pattern dictionary learning to existing alternative methods on synthetic data and also show results of the algorithm on real microscopy images of the Drosophila imaginal discs.
Automatic analysis and 3D-modelling of Hi-C data using TADbit reveals structural features of the fly chromatin colors
Authors: François Serra, Davide Baù, Mike Goodstadt, David Castillo, Guillaume J. Filion, Marc A. Marti-Renom
Date: 2017-07-19 00:00:00
Proceeding: Plos Computational Biology 2017 7

The sequence of a genome is insufficient to understand all genomic processes carried out in the cell nucleus. To achieve this, the knowledge of its three-dimensional architecture is necessary. Advances in genomic technologies and the development of new analytical methods, such as Chromosome Conformation Capture (3C) and its derivatives, provide unprecedented insights in the spatial organization of genomes. Here we present TADbit, a computational framework to analyze and model the chromatin fiber in three dimensions. Our package takes as input the sequencing reads of 3C-based experiments and performs the following main tasks: (i) pre-process the reads, (ii) map the reads to a reference genome, (iii) filter and normalize the interaction data, (iv) analyze the resulting interaction matrices, (v) build 3D models of selected genomic domains, and (vi) analyze the resulting models to characterize their structural properties. To illustrate the use of TADbit, we automatically modeled 50 genomic domains from the fly genome revealing differential structural features of the previously defined chromatin colors, establishing a link between the conformation of the genome and the local chromatin composition. TADbit provides three-dimensional models built from 3C-based experiments, which are ready for visualization and for characterizing their relation to gene expression and epigenetic states. TADbit is an open-source Python library available for download from https://github.com/3DGenomes/tadbit.
ETE 3: Reconstruction, Analysis, and Visualization of Phylogenomic Data
Authors: Jaime Huerta-Cepas, François Serra, Peer Bork
Date: 2016-02-26 00:00:00
Proceeding: Molecular Biology and Evolution 2016 2

The Environment for Tree Exploration (ETE) is a computational framework that simplifies the reconstruction, analysis, and visualization of phylogenetic trees and multiple sequence alignments. Here, we present ETE v3, featuring numerous improvements in the underlying library of methods, and providing a novel set of standalone tools to perform common tasks in comparative genomics and phylogenetics. The new features include (i) building gene-based and supermatrix-based phylogenies using a single command, (ii) testing and visualizing evolutionary models, (iii) calculating distances between trees of different size or including duplications, and (iv) providing seamless integration with the NCBI taxonomy database. ETE is freely available at http://etetoolkit.org
Derivative-Free Optimization of Neural Networks using Local Search
Authors: Ahmed Aly, Gianluca Guadagni, Joanne Bechta Dugan
Date: 2019-10-15 00:00:00
Proceeding: IEEE UEMCON 2019 2019 10
Tasks: Reinforcement Learning

Deep Neural Networks have received a great deal of attention in the past few years. Applications of Deep Learning broached areas of different domains such as Reinforcement Learning and Computer Vision. Despite their popularity and success, training neural networks can be a challenging process. This paper presents a study on derivative-free, single-candidate optimization of neural networks using Local Search (LS). LS is an algorithm where constrained noise is iteratively applied to subsets of the search space. It is coupled with a Score Decay mechanism to enhance performance. LS is a subsidiary of the Random Search family. Experiments were conducted using a setup that is both suitable for an introduction of the algorithm and representative of modern deep learning tasks, based on the FashionMNIST dataset. Training of a 5-Million parameter CNN was done in several scenarios, including Stochastic Gradient Descent (SGD) coupled with Backpropagation (BP) for comparison. Results reveal that although LS was not competitive in terms of convergence speed, it was actually able to converge to a lower loss than SGD. In addition, LS trained the CNN using Accuracy rather than Loss as a learning signal, though to a lower performance. In conclusion, LS presents a viable alternative in cases where SGD fails or is not suitable. The simplicity of LS can make it attractive to non-experts who would want to try neural nets for the first-time or on novel, non-differentiable tasks.
Examining Hyperparameters of Neural Networks Trained Using Local Search
Authors: Ahmed Aly, Gianluca Guadagni, Joanne Bechta Dugan
Date: 2019-12-10 00:00:00
Proceeding: IEEE ICICIS 2019 2019 12

Deep neural networks (DNNs) have been found useful for many applications. However, training and designing those networks can be challenging and is considered more of an art or an engineering process than rigorous science. In this regard, the important process of choosing hyperparameters is relevant. In addition, training neural networks with derivative-free methods is somewhat understudied. Particularly, with regards to hyperparameter selection. The paper presents a small-scale study of 3 hyperparam-eters choice for convolutional neural networks (CNNs). The networks were trained with two single-candidate optimization algorithms: Stochastic Gradient Descent (derivative-based) and Local Search (derivative-free). The CNN is trained on a subset of the FashionMNIST dataset. Experimental results show that hyperparameter selection can be detrimental for Local Search, especially regarding network parametrization. Moreover, the best hyperparameter choices didn't match for both algorithms. Future investigation into the training dynamics of Local Search is likely needed.
Clustering Approaches for Global Minimum Variance Portfolio
Authors: Jinwoo Park
Date: 2020-01-09 00:00:00
Tasks: Clustering

The only input to attain the portfolio weights of global minimum variance portfolio (GMVP) is the covariance matrix of returns of assets being considered for investment. Since the population covariance matrix is not known, investors use historical data to estimate it. Even though sample covariance matrix is an unbiased estimator of the population covariance matrix, it includes a great amount of estimation error especially when the number of observed data is not much bigger than number of assets. As it is difficult to estimate the covariance matrix with high dimensionality all at once, clustering stocks is proposed to come up with covariance matrix in two steps: firstly, within a cluster and secondly, between clusters. It decreases the estimation error by reducing the number of features in the data matrix. The motivation of this dissertation is that the estimation error can still remain high even after clustering, if a large amount of stocks is clustered together in a single group. This research proposes to utilize a bounded clustering method in order to limit the maximum cluster size. The result of experiments shows that not only the gap between in-sample volatility and out-of-sample volatility decreases, but also the out-of-sample volatility gets reduced. It implies that we need a bounded clustering algorithm so that maximum clustering size can be precisely controlled to find the best portfolio performance.
Secrecy Rate Maximization for Intelligent Reflecting Surface Assisted Multi-Antenna Communications
Authors: Hong Shen;Wei Xu;Shulei Gong;Zhenyao He;Chunming Zhao
Date: 2019-09-10 00:00:00
Proceeding: journal 2019 9

We investigate transmission optimization for intelligent reflecting surface (IRS) assisted multi-antenna systems from the physical-layer security perspective. The design goal is to maximize the system secrecy rate subject to the source transmit power constraint and the unit modulus constraints imposed on phase shifts at the IRS. To solve this complicated non-convex problem, we develop an efficient alternating algorithm where the solutions to the transmit covariance of the source and the phase shift matrix of the IRS are achieved in closed form and semi-closed forms, respectively. The convergence of the proposed algorithm is guaranteed theoretically. Simulations results validate the performance advantage of the proposed optimized design.
SirenLess: reveal the intention behind news
Authors: Xumeng Chen, Leo Yu-Ho Lo, Huamin Qu
Date: 2020-01-08 00:00:00
Tasks: Articles, Decision Making

News articles tend to be increasingly misleading nowadays, preventing readers from making subjective judgments towards certain events. While some machine learning approaches have been proposed to detect misleading news, most of them are black boxes that provide limited help for humans in decision making. In this paper, we present SirenLess, a visual analytical system for misleading news detection by linguistic features. The system features article explorer, a novel interactive tool that integrates news metadata and linguistic features to reveal semantic structures of news articles and facilitate textual analysis. We use SirenLess to analyze 18 news articles from different sources and summarize some helpful patterns for misleading news detection. A user study with journalism professionals and university students is conducted to confirm the usefulness and effectiveness of our system.
Latent Factor Analysis of Gaussian Distributions under Graphical Constraints
Authors: Md Mahmudul Hasan, Shuangqing Wei, Ali Moharrer
Date: 2020-01-08 00:00:00

We explore the algebraic structure of the solution space of convex optimization problem Constrained Minimum Trace Factor Analysis (CMTFA), when the population covariance matrix $\Sigma_x$ has an additional latent graphical constraint, namely, a latent star topology. In particular, we have shown that CMTFA can have either a rank $ 1 $ or a rank $ n-1 $ solution and nothing in between. The special case of a rank $ 1 $ solution, corresponds to the case where just one latent variable captures all the dependencies among the observables, giving rise to a star topology. We found explicit conditions for both rank $ 1 $ and rank $n- 1$ solutions for CMTFA solution of $\Sigma_x$. As a basic attempt towards building a more general Gaussian tree, we have found a necessary and a sufficient condition for multiple clusters, each having rank $ 1 $ CMTFA solution, to satisfy a minimum probability to combine together to build a Gaussian tree. To support our analytical findings we have presented some numerical demonstrating the usefulness of the contributions of our work.
RangeNet++: Fast and Accurate LiDAR Semantic Segmentation
Authors: Andres Milioto, Ignacio Vizzo, Jens Behley, Cyrill Stachniss
Date: 2019-11-04 00:00:00
Proceeding: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2019 11
Tasks: 3D Semantic Segmentation, Autonomous Vehicles, GPU, LIDAR Semantic Segmentation, Robust 3D Semantic Segmentation, Semantic Segmentation

Perception in autonomous vehicles is often carried out through a suite of different sensing modalities. Given the massive amount of openly available labeled RGB data and the advent of high-quality deep learning algorithms for image-based recognition, high-level semantic perception tasks are pre-dominantly solved using high-resolution cameras. As a result of that, other sensor modalities potentially useful for this task are often ignored. In this paper, we push the state of the art in LiDAR-only semantic segmentation forward in order to provide another independent source of semantic information to the vehicle. Our approach can accurately perform full semantic segmentation of LiDAR point clouds at sensor frame rate. We exploit range images as an intermediate representation in combination with a Convolutional Neural Network (CNN) exploiting the rotating LiDAR sensor model. To obtain accurate results, we propose a novel post-processing algorithm that deals with problems arising from this intermediate representation such as discretization errors and blurry CNN outputs. We implemented and thoroughly evaluated our approach including several comparisons to the state of the art. Our experiments show that our approach outperforms state-of-the-art approaches, while still running online on a single embedded GPU. The code can be accessed at https://github.com/PRBonn/lidar-bonnetal
Multi-Objective Optimisation of Damper Placement for Improved Seismic Response in Dynamically Similar Adjacent Buildings
Authors: Mahesh B. Patil, Ramakrishna U., Mohan S. C
Date: 2019-12-29 00:00:00
Tasks: Evolutionary Algorithms

Multi-objective optimisation of damper placement in dynamically symmetric adjacent buildings is considered with identical viscoelastic dampers used for vibration control. First, exhaustive search is used to describe the solution space in terms of various quantities of interest such as maximum top floor displacement, maximum floor acceleration, base shear, and interstorey drift. With the help of examples, it is pointed out that the Pareto fronts in these problems contain a very small number of solutions. The effectiveness of two commonly used multi-objective evolutionary algorithms, viz., NSGA-II and MOPSO, is evaluated for a specific example.
Internal representation dynamics and geometry in recurrent neural networks
Authors: Stefan Horoi, Guillaume Lajoie, Guy Wolf
Date: 2020-01-09 00:00:00
Tasks: Attribute

The efficiency of recurrent neural networks (RNNs) in dealing with sequential data has long been established. However, unlike deep, and convolution networks where we can attribute the recognition of a certain feature to every layer, it is unclear what "sub-task" a single recurrent step or layer accomplishes. Our work seeks to shed light onto how a vanilla RNN implements a simple classification task by analysing the dynamics of the network and the geometric properties of its hidden states. We find that early internal representations are evocative of the real labels of the data but this information is not directly accessible to the output layer. Furthermore the network's dynamics and the sequence length are both critical to correct classifications even when there is no additional task relevant information provided.
Pruning Convolutional Neural Networks with Self-Supervision
Authors: Mathilde Caron, Ari Morcos, Piotr Bojanowski, Julien Mairal, Armand Joulin
Date: 2020-01-10 00:00:00

Convolutional neural networks trained without supervision come close to matching performance with supervised pre-training, but sometimes at the cost of an even higher number of parameters. Extracting subnetworks from these large unsupervised convnets with preserved performance is of particular interest to make them less computationally intensive. Typical pruning methods operate during training on a task while trying to maintain the performance of the pruned network on the same task. However, in self-supervised feature learning, the training objective is agnostic on the representation transferability to downstream tasks. Thus, preserving performance for this objective does not ensure that the pruned subnetwork remains effective for solving downstream tasks. In this work, we investigate the use of standard pruning methods, developed primarily for supervised learning, for networks trained without labels (i.e. on self-supervised tasks). We show that pruned masks obtained with or without labels reach comparable performance when re-trained on labels, suggesting that pruning operates similarly for self-supervised and supervised learning. Interestingly, we also find that pruning preserves the transfer performance of self-supervised subnetwork representations.
Identifying Distinct, Effective Treatments for Acute Hypotension with SODA-RL: Safely Optimized Diverse Accurate Reinforcement Learning
Authors: Joseph Futoma, Muhammad A. Masood, Finale Doshi-Velez
Date: 2020-01-09 00:00:00
Tasks: Reinforcement Learning

Hypotension in critical care settings is a life-threatening emergency that must be recognized and treated early. While fluid bolus therapy and vasopressors are common treatments, it is often unclear which interventions to give, in what amounts, and for how long. Observational data in the form of electronic health records can provide a source for helping inform these choices from past events, but often it is not possible to identify a single best strategy from observational data alone. In such situations, we argue it is important to expose the collection of plausible options to a provider. To this end, we develop SODA-RL: Safely Optimized, Diverse, and Accurate Reinforcement Learning, to identify distinct treatment options that are supported in the data. We demonstrate SODA-RL on a cohort of 10,142 ICU stays where hypotension presented. Our learned policies perform comparably to the observed physician behaviors, while providing different, plausible alternatives for treatment decisions.
Choosing the Sample with Lowest Loss makes SGD Robust
Authors: Vatsal Shah, Xiaoxia Wu, Sujay Sanghavi
Date: 2020-01-10 00:00:00
Tasks: regression

The presence of outliers can potentially significantly skew the parameters of machine learning models trained via stochastic gradient descent (SGD). In this paper we propose a simple variant of the simple SGD method: in each step, first choose a set of k samples, then from these choose the one with the smallest current loss, and do an SGD-like update with this chosen sample. Vanilla SGD corresponds to k = 1, i.e. no choice; k >= 2 represents a new algorithm that is however effectively minimizing a non-convex surrogate loss. Our main contribution is a theoretical analysis of the robustness properties of this idea for ML problems which are sums of convex losses; these are backed up with linear regression and small-scale neural network experiments
Data-Dependence of Plateau Phenomenon in Learning with Neural Network --- Statistical Mechanical Analysis
Authors: Yuki Yoshida, Masato Okada
Date: 2020-01-10 00:00:00
Proceeding: NeurIPS 2019 12

The plateau phenomenon, wherein the loss value stops decreasing during the process of learning, has been reported by various researchers. The phenomenon is actively inspected in the 1990s and found to be due to the fundamental hierarchical structure of neural network models. Then the phenomenon has been thought as inevitable. However, the phenomenon seldom occurs in the context of recent deep learning. There is a gap between theory and reality. In this paper, using statistical mechanical formulation, we clarified the relationship between the plateau phenomenon and the statistical property of the data learned. It is shown that the data whose covariance has small and dispersed eigenvalues tend to make the plateau phenomenon inconspicuous.
Entropy Regularized Power k-Means Clustering
Authors: Saptarshi Chakraborty, Debolina Paul, Swagatam Das, Jason Xu
Date: 2020-01-10 00:00:00
Tasks: Clustering

Despite its well-known shortcomings, $k$-means remains one of the most widely used approaches to data clustering. Current research continues to tackle its flaws while attempting to preserve its simplicity. Recently, the \textit{power $k$-means} algorithm was proposed to avoid trapping in local minima by annealing through a family of smoother surfaces. However, the approach lacks theoretical justification and fails in high dimensions when many features are irrelevant. This paper addresses these issues by introducing \textit{entropy regularization} to learn feature relevance while annealing. We prove consistency of the proposed approach and derive a scalable majorization-minimization algorithm that enjoys closed-form updates and convergence guarantees. In particular, our method retains the same computational complexity of $k$-means and power $k$-means, but yields significant improvements over both. Its merits are thoroughly assessed on a suite of real and synthetic data experiments.