Building datasets of creative text, such as humor, is quite challenging. We introduce FunLines, a competitive game where players edit news headlines to make them funny, and where they rate the funniness of headlines edited by others. FunLines makes the humor generation process fun, interactive, collaborative, rewarding and educational, keeping players engaged and providing humor data at a very low cost compared to traditional crowdsourcing approaches. FunLines offers useful performance feedback, assisting players in getting better over time at generating and assessing humor, as our analysis shows. This helps to further increase the quality of the generated dataset. We show the effectiveness of this data by training humor classification models that outperform a previous benchmark, and we release this dataset to the public.

HiPlot, a visualization tool for plotting high-dimensional data as is used in machine learning tasks, was released by Facebook AI.

Democrats say they'll keep investigating Trump; reaction and analysis on'The Five.' The hosts of "The Five" dismissed Sunday's claim by Rep. Adam Schiff, D-Calif., that Democrats "proved" their case against the president in the Senate impeachment trial and he would not have done anything differently. "Look, there's nothing that I can see that we could have done differently, because as the senators have already admitted, we've proved our case," Schiff said on CBS News' "Face The Nation." "[Schiff] has to say that, but I'm sure he regrets it," co-host Jesse Watters said. "I mean, if they had done it properly and not started it out in a secret basement with no lawyers present, maybe they would have gone differently. Maybe they would have build a stronger case. Maybe they would have gone to a judge to compel witness testimony and maybe delivered. "They could have argued in a more convincing fashion, but they wanted to do a rush job to fit a political calendar," Watters added. "They didn't really care about making a really strong constitutional case so they can continue to investigate the president." Co-host Dana Perino called Trump's eventual acquittal a "loss" for Democrats and said it only emboldens the president. "Acquittal is a loss," Perino said. "And then whoever wins in a fight like this gets to write the history." "President Trump is like... 'Pac-Man,'" Perino said, comparing him to the popular video game character from the 1980s. "You go along, ding, ding, ding, and then you eat the fruit and you get bigger and stronger and you get another man, like, that's President Trump." Co-host Greg Gutfeld predicted that Democrats will keep the proceedings "running," with congressional investigations becoming "as mundane as living next to an airport "You know, we used to think planes were interesting," he said.

Lightning strokes create powerful electromagnetic pulses that routinely cause very low frequency (VLF) waves to propagate across hemispheres along geomagnetic field lines. VLF antenna receivers can be used to detect these whistler waves generated by these lightning strokes. The particular time/frequency dependence of the received whistler wave enables the estimation of electron density in the plasmasphere region of the magnetosphere. Therefore the identification and characterisation of whistlers are important tasks to monitor the plasmasphere in real time and to build large databases of events to be used for statistical studies. The current state of the art in detecting whistler is the Automatic Whistler Detection (A WD) method developed by Lichtenberger (2009) [1]. This method is based on image correlation in 2 dimensions and requires significant computing hardware situated at the VLF receiver antennas (e.g. in Antarctica). The aim of this work is to develop a machine learning based model capable of automatically detecting whistlers in the data provided by the VLF receivers. The approach is to use a combination of image classification and localisation on the spectrogram data generated by the VLF receivers to identify and localise each whistler. The data at hand has around 2300 events identified by A WD at SANAE and Marion and will be used as training, validation, and testing data. Three detector designs have been proposed. The first one using a similar method to A WD, the second using image classification on regions of interest extracted from a spectrogram, and the last one using YOLO, the current state of the art in object detection. It has been shown that these detectors can achieve a misdetection and false alarm of less than 15% on Marion's dataset. 1 Introduction Lightning strokes create powerful electromagnetic pulses that result in Very Low Frequency (VLF) waves propagating along the magnetic field lines of the earth. Due to the dipole shape of the geomagnetic field, these waves travel upward from the stroke location out through portions of the plasmasphere and back to the Earth's surface at the field line foot point in the opposite hemisphere. VLF antenna receivers set up at various high and middle latitude locations can detect whistler waves generated by these lightning strokes. The propagation time delay of these waves is dependent on the plasma density along the propagation path. This enables the use of whistler wave observations for characterising the plasmasphere in terms of particle number and energy density. The dynamics of energetic particle populations in the plasmasphere are an important factor in characterising the risk to spacecraft in orbit around Earth. Annual global lightning flash rates are on the order of 45 flash/s [2].

Abstract: Deep Reinforcement Learning (DRL) has shown impressive performance on domains with visual inputs, in particular various games. However, the agent is usually trained on a fixed environment, e.g. a fixed number of levels. A growing mass of evidence suggests that these trained models fail to generalize to even slight variations of the environments they were trained on. This paper advances the hypothesis that the lack of generalization is partly due to the input representation, and explores how rotation, cropping and translation could increase generality. We show that a cropped, translated and rotated observation can get better generalization on unseen levels of a two- dimensional arcade game. The generality of the agent is evaluated on a set of human-designed levels.

Abstract: Few-shot classification aims to recognize novel categories with only few labeled images in each class. Existing metric-based few-shot classification algorithms predict categories by comparing the feature embeddings of query images with those from a few labeled images (support examples) using a learned metric function. While promising performance has been demonstrated, these methods often fail to generalize to unseen domains due to large discrepancy of the feature distribution across domains. In this work, we address the problem of few-shot classification under domain shifts for metric-based methods. Our core idea is to use feature-wise transformation layers for augmenting the image features using affine transforms to simulate various feature distributions under different domains in the training stage.

Hey i would be very interested in asking you a few questions if i could get the chance about breaking into this field. My girlfriend is currently working in london for a data analysis company called quant. Im trying to remember the specifics of what i know from what she has told me because i dont work in this field. They look at key performance indicators and produce dashboards etc. She has a masters in data science and undergrad in buisness.