By Emma Grimaldi, Data Scientist and Mechanical Engineer

The penultimate Le Monde mathematical puzzle  competition problem is once again anti-climactic and not worth its points:

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Most Retweeted, Favorited, Viewed & Clicked:Building a Question-Answering System from Scratch https://t.co/0QpE52XE8g https://t.co/D7uEgDAlif

AWS recently released Model Server for Apache MXNet (MMS) v1.0, featuring a new API for managing the state of the service, which includes the ability to dynamically load models during runtime, to lower latency, and to have higher throughput. In this post, we will explore the new features and showcase the performance gains of the MMS v1.0.

Distributed Coordinated Multi-Agent Bidding (DCMAB) Real-time advertising allows advertisers to bid for each impression for a visiting user. To optimize a specific goal such as maximizing the revenue led by ad placements, advertisers not only need to estimate the relevance between the ads and user’s interests, but most importantly require a strategic response with respect to other advertisers bidding in the market. In this paper, we formulate bidding optimization with multi-agent reinforcement learning. To deal with a large number of advertisers, we propose a clustering method and assign each cluster with a strategic bidding agent. A practical Distributed Coordinated Multi-Agent Bidding (DCMAB) has been proposed and implemented to balance the tradeoff between the competition and cooperation among advertisers. The empirical study on our industry-scaled real-world data has demonstrated the effectiveness of our modeling methods. Our results show that a cluster based bidding would largely outperform single-agent and bandit approaches, and the coordinated bidding achieves better overall objectives than the purely self-interested bidding agents. …

Generating a description of an image is called image captioning. Image captioning requires to recognize the important objects, their attributes and their relationships in an image. It also needs to generate syntactically and semantically correct sentences. Deep learning-based techniques are capable of handling the complexities and challenges of image captioning. In this survey paper, we aim to present a comprehensive review of existing deep learning-based image captioning techniques. We discuss the foundation of the techniques to analyze their performances, strengths and limitations. We also discuss the datasets and the evaluation metrics popularly used in deep learning based automatic image captioning. A Comprehensive Study of Deep Learning for Image Captioning

We’ve just released a sweet package to save you stress from the hassle of unnamed chunks in R Markdown! namer will name all your chunks, so you can quickly debug in future. More details in this post!

Automatic License Plate Recognition (ALPR) Automatic License Plate Recognition (ALPR) has been a frequent topic of research due to many practical applications. However, many of the current solutions are still not robust in real-world situations, commonly depending on many constraints. This paper presents a robust and efficient ALPR system based on the state-of-the-art YOLO object detection. The Convolutional Neural Networks (CNNs) are trained and fine-tuned for each ALPR stage so that they are robust under different conditions (e.g., variations in camera, lighting, and background). Specially for character segmentation and recognition, we design a two-stage approach employing simple data augmentation tricks such as inverted License Plates (LPs) and flipped characters. The resulting ALPR approach achieved impressive results in two datasets. First, in the SSIG dataset, composed of 2,000 frames from 101 vehicle videos, our system achieved a recognition rate of 93.53% and 47 Frames Per Second (FPS), performing better than both Sighthound and OpenALPR commercial systems (89.80% and 93.03%, respectively) and considerably outperforming previous results (81.80%). Second, targeting a more realistic scenario, we introduce a larger public dataset, called UFPR-ALPR dataset, designed to ALPR. This dataset contains 150 videos and 4,500 frames captured when both camera and vehicles are moving and also contains different types of vehicles (cars, motorcycles, buses and trucks). In our proposed dataset, the trial versions of commercial systems achieved recognition rates below 70%. On the other hand, our system performed better, with recognition rate of 78.33% and 35 FPS. …

Multiple comparisons of group-level means is a tricky problem in statistical inference. A standard practice is to adjust the threshold for statistical significance according to the number of pairwise tests performed. For example, according to the widely-known Bonferonni method, if we have 3 different groups for which we want to compare the means of a given variable, we would divide the standard significance level (.05 by convention) by the number of tests performed (3 in this case), and only conclude that a given comparison is statistically significant if its p-value falls below .017 (e.g. .05/3).