Document worth reading: “Let’s Push Things Forward: A Survey on Robot Pushing”

As robot make their way out of factories into human environments, outer space, and beyond, they require the skill to manipulate their environment in multifarious, unforeseeable circumstances. With this regard, pushing is an essential motion primitive that dramatically extends a robot’s manipulation repertoire. In this work, we review the robotic pushing literature. While focusing on work concerned with predicting the motion of pushed objects, we also cover relevant applications of pushing for planning and control. Beginning with analytical approaches, under which we also subsume physics engines, we then proceed to discuss work on learning models from data. In doing so, we dedicate a separate section to deep learning approaches which have seen a recent upsurge in the literature. Concluding remarks and further research perspectives are given at the end of the paper. Let’s Push Things Forward: A Survey on Robot Pushing


What’s going on on PyPI

Scanning all new published packages on PyPI I know that the quality is often quite bad. I try to filter out the worst ones and list here the ones which might be worth a look, being followed or inspire you in some way.

propythia – automated platform for the classification of peptides/proteins using machine learning

Bayesian network learning with the Gurobi MIP solver

A python binding of C++ package for stochastic simulations of spatially extended systems

Python implementation of the R package synthpop

spaCy ANN Linker, a pipeline component for generating spaCy KnowledgeBase Alias Candidates for Entity Linking.

Watson Machine Learning online scoring integration in IBM Streams topology applications

PyTorch-based knowledge distillation toolkit for natural language processing tasks

Gaussian process methods in tensorflow

Bayesian Optimization with GPflow

Python stream processing for humans


A collection of utility scripts mainly for translating content from Excel spreadsheets into OWL ontologies

Document worth reading: “Automated Machine Learning in Practice: State of the Art and Recent Results”

A main driver behind the digitization of industry and society is the belief that data-driven model building and decision making can contribute to higher degrees of automation and more informed decisions. Building such models from data often involves the application of some form of machine learning. Thus, there is an ever growing demand in work force with the necessary skill set to do so. This demand has given rise to a new research topic concerned with fitting machine learning models fully automatically – AutoML. This paper gives an overview of the state of the art in AutoML with a focus on practical applicability in a business context, and provides recent benchmark results on the most important AutoML algorithms. Automated Machine Learning in Practice: State of the Art and Recent Results

If you did not already know

Scaling Up Secure Approximate k-Nearest Neighbors Search (SANNS) google
We present new secure protocols for approximate $k$-nearest neighbor search ($k$-NNS) over the Euclidean distance in the semi-honest model. Our implementation is able to handle massive datasets efficiently. On the algorithmic front, we show a new circuit for the approximate top-$k$ selection from $n$ numbers that is built from merely $O(n + \mathrm{poly}(k))$ comparators. Using this circuit as a subroutine, we design new approximate $k$-NNS algorithms and two corresponding secure protocols: 1) optimized linear scan; 2) clustering-based sublinear time algorithm. Our secure protocols utilize a combination of additively-homomorphic encryption, garbled circuit and Oblivious RAM. Along the way, we introduce various optimizations to these primitives, which drastically improve concrete efficiency. We evaluate the new protocols empirically and show that they are able to handle datasets that are significantly larger than in the prior work. For instance, running on two standard Azure instances within the same availability zone, for a dataset of $96$-dimensional descriptors of $10\,000\,000$ images, we can find $10$ nearest neighbors with average accuracy $0.9$ in under $10$ seconds improving upon prior work by at least two orders of magnitude. …

Apache Knox Gateway google
The Apache Knox Gateway is an Application Gateway for interacting with the REST APIs and UIs of Apache Hadoop deployments. The Knox Gateway provides a single access point for all REST and HTTP interactions with Apache Hadoop clusters. …

Computation Graph google
Structural equation modeling (SEM) is evolving as available data is becoming more complex, reaching the limits of what traditional estimation approaches can achieve. As SEM expands to ever larger, more complex applications, the estimation challenge grows and currently available methods will be insufficient. To overcome this challenge in SEM, we see an opportunity to use existing solutions from the field of deep learning, which has been pioneering methods for estimation of complex models for decades. To this end, this paper introduces computation graphs, a flexible method of specifying objective functions. When combined with state-of-the-art optimizers, we argue that our computation graph approach is capable not only of estimating SEM models, but also of rapidly extending them — without the need of bespoke software development for each new extension. We show that several SEM improvements follow naturally from our approach; not only existing extensions such as least absolute deviation estimation and penalized regression models, but also novel extensions such as spike-and-slab penalties for sparse factor analysis. By applying computation graphs to SEM, we hope to greatly accelerate the process of developing SEM techniques, paving the way for novel applications. The accompanying R package tensorsem is under active development. …

Graph Stream Sketch (GSS) google
A graph stream is a continuous sequence of data items, in which each item indicates an edge, including its two endpoints and edge weight. It forms a dynamic graph that changes with every item in the stream. Graph streams play important roles in cyber security, social networks, cloud troubleshooting systems and other fields. Due to the vast volume and high update speed of graph streams, traditional data structures for graph storage such as the adjacency matrix and the adjacency list are no longer sufficient. However, prior art of graph stream summarization, like CM sketches, gSketches, TCM and gMatrix, either supports limited kinds of queries or suffers from poor accuracy of query results. In this paper, we propose a novel Graph Stream Sketch (GSS for short) to summarize the graph streams, which has the linear space cost (O(|E|), E is the edge set of the graph) and the constant update time complexity (O(1)) and supports all kinds of queries over graph streams with the controllable errors. Both theoretical analysis and experiment results confirm the superiority of our solution with regard to the time/space complexity and query results’ precision compared with the state-of-the-art. …

What’s going on on PyPI

Scanning all new published packages on PyPI I know that the quality is often quite bad. I try to filter out the worst ones and list here the ones which might be worth a look, being followed or inspire you in some way.

A binary timeseries storage format, where the time axis is given via an expression.

Tools for Binary and Text data.

A tool for generating data and managing relational databases.

Package for Multiple Objective Decision Analysis

A Data Science project structure pip package

Eisen is a collection of tools to train neural networks for medical image analysis

Helpers utils for manage and track experiments.

A library for fair machine learning.

FlowETL is a collection of special purposes Airflow operators and sensors for use with FlowKit.

A python package that allows importing Jupyter notebooks as python modules

Python library for Natural Language Processing supporting the open annotation standard

Generates a navigable book-like structure to a collection of jupyter notebooks

If you did not already know

Multiset Dimension google
We introduce a variation of the metric dimension, called the multiset dimension. The representation multiset of a vertex $v$ with respect to $W$ (which is a subset of the vertex set of a graph $G$), $r_m (v|W)$, is defined as a multiset of distances between $v$ and the vertices in $W$ together with their multiplicities. If $r_m (u |W) \neq r_m(v|W)$ for every pair of distinct vertices $u$ and $v$, then $W$ is called a resolving set of $G$. If $G$ has a resolving set, then the cardinality of a smallest resolving set is called the multiset dimension of $G$, denoted by $md(G)$. If $G$ does not contain a resolving set, we write $md(G) = \infty$. We present basic results on the multiset dimension. We also study graphs of given diameter and give some sufficient conditions for a graph to have an infinite multiset dimension. …

Empirical Bayes Geometric Mean (EBGM) google
Adjusted estimate for the relative reporting ratio. Example: if EBGM=3.9 for acetaminophen-hepatic failure, then this drug-event combination occurred in the data 3.9 times more frequently than expected under the assumption of no association between the drug and the event. …

Jack the Reader (Jack) google
Many Machine Reading and Natural Language Understanding tasks require reading supporting text in order to answer questions. For example, in Question Answering, the supporting text can be newswire or Wikipedia articles; in Natural Language Inference, premises can be seen as the supporting text and hypotheses as questions. Providing a set of useful primitives operating in a single framework of related tasks would allow for expressive modelling, and easier model comparison and replication. To that end, we present Jack the Reader (Jack), a framework for Machine Reading that allows for quick model prototyping by component reuse, evaluation of new models on existing datasets as well as integrating new datasets and applying them on a growing set of implemented baseline models. Jack is currently supporting (but not limited to) three tasks: Question Answering, Natural Language Inference, and Link Prediction. It is developed with the aim of increasing research efficiency and code reuse. …

Jaya Optimisation Algorithm google
An Efficient Multi-core Implementation of the Jaya Optimisation Algorithm

Document worth reading: “Object Detection in 20 Years: A Survey”

Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Its development in the past two decades can be regarded as an epitome of computer vision history. If we think of today’s object detection as a technical aesthetics under the power of deep learning, then turning back the clock 20 years we would witness the wisdom of cold weapon era. This paper extensively reviews 400+ papers of object detection in the light of its technical evolution, spanning over a quarter-century’s time (from the 1990s to 2019). A number of topics have been covered in this paper, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speed up techniques, and the recent state of the art detection methods. This paper also reviews some important detection applications, such as pedestrian detection, face detection, text detection, etc, and makes an in-deep analysis of their challenges as well as technical improvements in recent years. Object Detection in 20 Years: A Survey

What’s going on on PyPI

Scanning all new published packages on PyPI I know that the quality is often quite bad. I try to filter out the worst ones and list here the ones which might be worth a look, being followed or inspire you in some way.

Fast CUDA C++ GMRES implementation for Toeplitz-like (Toeplitz, Hankel, Circulant) matrices and mixed (combinations of Diagonal ones and Toeplitz-like ones) matrices. We propose implementations of the Generalized Minimal Residual Method (GMRES) for solving linear systems based on dense, Toeplitz or mixed matrices. The software consists of a python module and a C++ library. The mixed matrices consist of the sum of the diag- onal and the Toeplitz matrices. The GMRES solver is parallelized for running on NVIDIA GPGPU accelerator. We report on the efficiency of the parallelization method applying GMRES to the Helmholtz linear system based on the use of Green’s Function Integral Equation Method (GFIEM) for computing electric field distribution in the design domain.

High-level Regression Utilities

robotframework-sqless is a SQL abstraction library for Robot Framework

State-of-the-art Computer Vision and Object Detection for TensorFlow.

Scorebased Neural Architecture REduction

Search space optimization via gradient boosting regression

A Python algorithms used to perform machine learning.

A Library for Homomorphic Encryption Operations on Tensors

Generate C code for microcontrollers from Tensorflow models

A python package capable of performing and illustrating a few clustering methods.

Automated Time Series in Python

A deep learning approach for mapping and dating burned areas using temporal sequences of satellite images

If you did not already know

Gather-Excite google
While the use of bottom-up local operators in convolutional neural networks (CNNs) matches well some of the statistics of natural images, it may also prevent such models from capturing contextual long-range feature interactions. In this work, we propose a simple, lightweight approach for better context exploitation in CNNs. We do so by introducing a pair of operators: gather, which efficiently aggregates feature responses from a large spatial extent, and excite, which redistributes the pooled information to local features. The operators are cheap, both in terms of number of added parameters and computational complexity, and can be integrated directly in existing architectures to improve their performance. Experiments on several datasets show that gather-excite can bring benefits comparable to increasing the depth of a CNN at a fraction of the cost. For example, we find ResNet-50 with gather-excite operators is able to outperform its 101-layer counterpart on ImageNet with no additional learnable parameters. We also propose a parametric gather-excite operator pair which yields further performance gains, relate it to the recently-introduced Squeeze-and-Excitation Networks, and analyse the effects of these changes to the CNN feature activation statistics. …

Generalized k-Nearest Neighbor (GkNN) google
Three methods of temporal data upscaling, which may collectively be called the generalized k-nearest neighbor (GkNN) method, are considered. The accuracy of the GkNN simulation of month by month yield is considered (where the term yield denotes the dependent variable). The notion of an eventually well distributed time series is introduced and on the basis of this assumption some properties of the average annual yield and its variance for a GkNN simulation are computed. The total yield over a planning period is determined and a general framework for considering the GkNN algorithm based on the notion of stochastically dependent time series is described and it is shown that for a sufficiently large training set the GkNN simulation has the same statistical properties as the training data. An example of the application of the methodology is given in the problem of simulating yield of a rainwater tank given monthly climatic data. …

Proximal Policy Optimization (PPO) google
We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a ‘surrogate’ objective function using stochastic gradient ascent. Whereas standard policy gradient methods perform one gradient update per data sample, we propose a novel objective function that enables multiple epochs of minibatch updates. The new methods, which we call proximal policy optimization (PPO), have some of the benefits of trust region policy optimization (TRPO), but they are much simpler to implement, more general, and have better sample complexity (empirically). Our experiments test PPO on a collection of benchmark tasks, including simulated robotic locomotion and Atari game playing, and we show that PPO outperforms other online policy gradient methods, and overall strikes a favorable balance between sample complexity, simplicity, and wall-time. …

Residual Hourglass Recurrent Neural Network (RHR-Net) google
Most current speech enhancement models use spectrogram features that require an expensive transformation and result in phase information loss. Previous work has overcome these issues by using convolutional networks to learn long-range temporal correlations across high-resolution waveforms. These models, however, are limited by memory-intensive dilated convolution and aliasing artifacts from upsampling. We introduce an end-to-end fully-recurrent hourglass-shaped neural network architecture with residual connections for waveform-based single-channel speech enhancement. Our model can efficiently capture long-range temporal dependencies by reducing the features resolution without information loss. Experimental results show that our model outperforms state-of-the-art approaches in six evaluation metrics. …

Document worth reading: “Recent Advances in Deep Learning for Object Detection”

Object detection is a fundamental visual recognition problem in computer vision and has been widely studied in the past decades. Visual object detection aims to find objects of certain target classes with precise localization in a given image and assign each object instance a corresponding class label. Due to the tremendous successes of deep learning based image classification, object detection techniques using deep learning have been actively studied in recent years. In this paper, we give a comprehensive survey of recent advances in visual object detection with deep learning. By reviewing a large body of recent related work in literature, we systematically analyze the existing object detection frameworks and organize the survey into three major parts: (i) detection components, (ii) learning strategies, and (iii) applications & benchmarks. In the survey, we cover a variety of factors affecting the detection performance in detail, such as detector architectures, feature learning, proposal generation, sampling strategies, etc. Finally, we discuss several future directions to facilitate and spur future research for visual object detection with deep learning. Keywords: Object Detection, Deep Learning, Deep Convolutional Neural Networks Recent Advances in Deep Learning for Object Detection