Is Einstein more agreeable and less neurotic than Hitler? Abstract Recent progress in distributed semantic models (DSM) offers new ways to estimate personality traits of both fictive and real people. In this exploratory study we applied an extended version of the algorithm developed in Jacobs (2019) to compute the likeability scores, emotional figure profiles and BIG5 personality traits for 100 historical persons from the arts, politics or science domains whose names are rather unique (e.g., Einstein, Kahlo, Picasso). We compared the results produced by static (word2vec) and dynamic (BERT) language model representations in four studies. The results show both the potential and limitations of such DSM-based computations of personality profiles and point ways to further develop this approach to become a useful tool in data science, psychology or computational and neurocognitive poetics (Jacobs, 2015).

Both performance and efficiency are crucial factors for sequence labeling tasks in many real-world scenarios. Although the pre-trained models (PTMs) have significantly improved the performance of various sequence labeling tasks, their computational cost is expensive. To alleviate this problem, we extend the recent successful early-exit mechanism to accelerate the inference of PTMs for sequence labeling tasks. However, existing early-exit mechanisms are specifically designed for sequence-level tasks, rather than sequence labeling. In this paper, we first propose a simple extension of sentence-level early-exit for sequence labeling tasks. To further reduce the computational cost, we also propose a token-level early-exit mechanism that allows partial tokens to exit early at different layers. Considering the local dependency inherent in sequence labeling, we employed a window-based criterion to decide for a token whether or not to exit. The token-level early-exit brings the gap between training and inference, so we introduce an extra self-sampling fine-tuning stage to alleviate it. The extensive experiments on three popular sequence labeling tasks show that our approach can save up to 66%-75% inference cost with minimal performance degradation. Compared with competitive compressed models such as DistilBERT, our approach can achieve better performance under the same speed-up ratios of 2X, 3X, and 4X.

It is well known that modern deep neural networks are powerful enough to memorize datasets even when the labels have been randomized. Recently, Vershynin (2020) settled a long standing question by Baum (1988), proving that \emph{deep threshold} networks can memorize $n$ points in $d$ dimensions using $\widetilde{\mathcal{O}}(e^{1/\delta^2}+\sqrt{n})$ neurons and $\widetilde{\mathcal{O}}(e^{1/\delta^2}(d+\sqrt{n})+n)$ weights, where $\delta$ is the minimum distance between the points. In this work, we improve the dependence on $\delta$ from exponential to almost linear, proving that $\widetilde{\mathcal{O}}(\frac{1}{\delta}+\sqrt{n})$ neurons and $\widetilde{\mathcal{O}}(\frac{d}{\delta}+n)$ weights are sufficient. Our construction uses Gaussian random weights only in the first layer, while all the subsequent layers use binary or integer weights. We also prove new lower bounds by connecting memorization in neural networks to the purely geometric problem of separating $n$ points on a sphere using hyperplanes.

Adeva partners with companies to scale engineering teams on-demand. AgentFire - Hyper local real estate websites powered by Wordpress. Aha! - Aha! is roadmapping software for PMs who want their mojo back. AirTreks - Multi-stop international flight planner with a distributed team. We are strategists, researchers, designers, and developers who craft custom digital experiences for publishers, nonprofit institutions, museums, and brands. ALICE empowers the world's best hotels to deliver a remarkable guest experience. Makes software that helps teachers make e-learning courses. AT&T - Nearly 20% of the eligible workforce works remotely. Authentic F & F - Independent design and technology studio based in Denver and Minnesota Aurity - 100% remote company, specializing in React and React Native.

New media can be defined as a highly interactive digital technology which allows people to interact anywhere anytime. This has evolved as a non-tangible channel for communication on the preset of growth in Information Technology. The ability to transform content to a digitized format allowed new-age media to take shape within the internet. Accessibility through hand-held devices like mobile platforms, personal computers, digital devices, and virtual computing machines has aided the growth of new-age media. The medium of new media is not just restricted to social networking platforms, blogs, online newspapers, digital games and virtual reality, but any aspect of communication that can be communicated real-time, processed, stored and delivered in formats of data instantaneously.

This paper presents our research on leveraging social media Big Data and AI to support hurricane disaster emergency response. The current practice of hurricane emergency response for rescue highly relies on emergency call centres. The more recent Hurricane Harvey event reveals the limitations of the current systems. We use Hurricane Harvey and the associated Houston flooding as the motivating scenario to conduct research and develop a prototype as a proof-of-concept of using an intelligent agent as a complementary role to support emergency centres in hurricane emergency response. This intelligent agent is used to collect real-time streaming tweets during a natural disaster event, to identify tweets requesting rescue, to extract key information such as address and associated geocode, and to visualize the extracted information in an interactive map in decision supports. Our experiment shows promising outcomes and the potential application of the research in support of hurricane emergency response.

Perception of the lane boundaries is crucial for the tasks related to autonomous trajectory control. In this paper, several methodologies for lane detection are discussed with an experimental illustration: Hough transformation, Blob analysis, and Bird's eye view. Following the abstraction of lane marks from the boundary, the next approach is applying a control law based on the perception to control steering and speed control. In the following, a comparative analysis is made between an open-loop response, PID control, and a neural network control law through graphical statistics. To get the perception of the surrounding a wireless streaming camera connected to Raspberry Pi is used. After pre-processing the signal received by the camera the output is sent back to the Raspberry Pi that processes the input and communicates the control to the motors through Arduino via serial communication.

Attention mechanisms have shown promising results in sequence modeling tasks that require long-term memory. Recent work investigated mechanisms to reduce the computational cost of preserving and storing memories. However, not all content in the past is equally important to remember. We propose Expire-Span, a method that learns to retain the most important information and expire the irrelevant information. This forgetting of memories enables Transformers to scale to attend over tens of thousands of previous timesteps efficiently, as not all states from previous timesteps are preserved. We demonstrate that Expire-Span can help models identify and retain critical information and show it can achieve strong performance on reinforcement learning tasks specifically designed to challenge this functionality. Next, we show that Expire-Span can scale to memories that are tens of thousands in size, setting a new state of the art on incredibly long context tasks such as character-level language modeling and a frame-by-frame moving objects task. Finally, we analyze the efficiency of Expire-Span compared to existing approaches and demonstrate that it trains faster and uses less memory.

Randomized Smoothing (RS) is a promising method for obtaining robustness certificates by evaluating a base model under noise. In this work we: (i) theoretically motivate why ensembles are a particularly suitable choice as base models for RS, and (ii) empirically confirm this choice, obtaining state of the art results in multiple settings. The key insight of our work is that the reduced variance of ensembles over the perturbations introduced in RS leads to significantly more consistent classifications for a given input, in turn leading to substantially increased certifiable radii for difficult samples. We also introduce key optimizations which enable an up to 50-fold decrease in sample complexity of RS, thus drastically reducing its computational overhead. Experimentally, we show that ensembles of only 3 to 10 classifiers consistently improve on the strongest single model with respect to their average certified radius (ACR) by 5% to 21% on both CIFAR-10 and ImageNet. On the latter, we achieve a state-of-the-art ACR of 1.11. We release all code and models required to reproduce our results upon publication.

Commonsense reasoning research has so far been limited to English. We aim to evaluate and improve popular multilingual language models (ML-LMs) to help advance commonsense reasoning (CSR) beyond English. We collect the Mickey Corpus, consisting of 561k sentences in 11 different languages, which can be used for analyzing and improving ML-LMs. We propose Mickey Probe, a language-agnostic probing task for fairly evaluating the common sense of popular ML-LMs across different languages. In addition, we also create two new datasets, X-CSQA and X-CODAH, by translating their English versions to 15 other languages, so that we can evaluate popular ML-LMs for cross-lingual commonsense reasoning. To improve the performance beyond English, we propose a simple yet effective method -- multilingual contrastive pre-training (MCP). It significantly enhances sentence representations, yielding a large performance gain on both benchmarks.