Different linguistic expressions can conceptualize the same event from different viewpoints by emphasizing certain participants over others. Here, we investigate a case where this has social consequences: how do linguistic expressions of gender-based violence (GBV) influence who we perceive as responsible? We build on previous psycholinguistic research in this area and conduct a large-scale perception survey of GBV descriptions automatically extracted from a corpus of Italian newspapers. We then train regression models that predict the salience of GBV participants with respect to different dimensions of perceived responsibility. Our best model (fine-tuned BERT) shows solid overall performance, with large differences between dimensions and participants: salient _focus_ is more predictable than salient _blame_, and perpetrators' salience is more predictable than victims' salience. Experiments with ridge regression models using different representations show that features based on linguistic theory similarly to word-based features. Overall, we show that different linguistic choices do trigger different perceptions of responsibility, and that such perceptions can be modelled automatically. This work can be a core instrument to raise awareness of the consequences of different perspectivizations in the general public and in news producers alike.

Disney has a seemingly endless selection of Marvel movies and plenty of Star Wars and Pixar fare, too. Problem is, there's so much stuff, it's hard to know where to begin. WIRED is here to help. Below are our picks for the best movies on Disney right now. For more viewing ideas, try our guides to the best movies on Netflix and the best movies on Amazon Prime. This content can also be viewed on the site it originates from. Sam Raimi's sequel to 2016's Doctor Strange isn't the beloved director's first superhero movie, but it is his first foray into the Marvel Cinematic Universe style of making movies, which ultimately proves to be both a blessing and a curse. On the plus side, the movie is probably the closest thing the Marvel franchise has gotten to a straight-up horror film, and it's full of Raimi's signature practical effects (plus the ever-important Bruce Campbell cameo). Yet, because the MCU is such a box office powerhouse, the movie never goes full Raimi--which is understandable, but somewhat disappointing for fans of The Evil Dead maestro.

Conventional automatic doors cannot distinguish between people wishing to pass through the door and people passing by the door, so they often open unnecessarily. This leads to the need to adopt new systems in both commercial and non-commercial environments: smart doors. In particular, a smart door system predicts the intention of people near the door based on the social context of the surrounding environment and then makes rational decisions about whether or not to open the door. This work proposes the first position paper related to smart doors, without bells and whistles. We first point out that the problem not only concerns reliability, climate control, safety, and mode of operation. Indeed, a system to predict the intention of people near the door also involves a deeper understanding of the social context of the scene through a complex combined analysis of proxemics and scene reasoning. Furthermore, we conduct an exhaustive literature review about automatic doors, providing a novel system formulation. Also, we present an analysis of the possible future application of smart doors, a description of the ethical shortcomings, and legislative issues.

Islamic and capitalist economies have several differences, the most fundamental being that the Islamic economy is characterized by the prohibition of interest (riba) and speculation (gharar) and the enforcement of Shariah-compliant profit-loss sharing (mudaraba, murabaha, salam, etc.) and wealth redistribution (waqf, sadaqah, and zakat). In this study, I apply new econophysics models of wealth exchange and redistribution to quantitatively compare these characteristics to those of capitalism and evaluate wealth distribution and disparity using a simulation. Specifically, regarding exchange, I propose a loan interest model representing finance capitalism and riba and a joint venture model representing shareholder capitalism and mudaraba; regarding redistribution, I create a transfer model representing inheritance tax and waqf. As exchanges are repeated from an initial uniform distribution of wealth, wealth distribution approaches a power-law distribution more quickly for the loan interest than the joint venture model; and the Gini index, representing disparity, rapidly increases. The joint venture model's Gini index increases more slowly, but eventually, the wealth distribution in both models becomes a delta distribution, and the Gini index gradually approaches 1. Next, when both models are combined with the transfer model to redistribute wealth in every given period, the loan interest model has a larger Gini index than the joint venture model, but both converge to a Gini index of less than 1. These results quantitatively reveal that in the Islamic economy, disparity is restrained by prohibiting riba and promoting reciprocal exchange in mudaraba and redistribution through waqf. Comparing Islamic and capitalist economies provides insights into the benefits of economically embracing the ethical practice of mutual aid and suggests guidelines for an alternative to capitalism.

Complex conversation settings such as persuasion involve communicating changes in attitude or behavior, so users' perspectives need to be addressed, even when not directly related to the topic. In this work, we contribute a novel modular dialogue system framework that seamlessly integrates factual information and social content into persuasive dialogue. Our framework is generalizable to any dialogue tasks that have mixed social and task contents. We conducted a study that compared user evaluations of our framework versus a baseline end-to-end generation model. We found our framework was evaluated more favorably in all dimensions including competence and friendliness, compared to the end-to-end model which does not explicitly handle social content or factual questions.

Recently a multi-agent variant of the classical multi-armed bandit was proposed to tackle fairness issues in online learning. Inspired by a long line of work in social choice and economics, the goal is to optimize the Nash social welfare instead of the total utility. Unfortunately previous algorithms either are not efficient or achieve sub-optimal regret in terms of the number of rounds $T$. We propose a new efficient algorithm with lower regret than even previous inefficient ones. For $N$ agents, $K$ arms, and $T$ rounds, our approach has a regret bound of $\tilde{O}(\sqrt{NKT} + NK)$. This is an improvement to the previous approach, which has regret bound of $\tilde{O}( \min(NK, \sqrt{N} K^{3/2})\sqrt{T})$. We also complement our efficient algorithm with an inefficient approach with $\tilde{O}(\sqrt{KT} + N^2K)$ regret. The experimental findings confirm the effectiveness of our efficient algorithm compared to the previous approaches.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Could AI text-to-image generators like DALL-E inspire new designs for iconic candies like M&Ms or Skittles? As a candy-packed Halloween approaches, it seemed like an obvious question to ask the head of AI and machine learning at Mars Inc. -- who over the past century has overseen a slew of popular confectionery brands from M&Ms to Milky Way and Snickers; grown into a CPG behemoth that includes brands such as Dove, Pedigree and Whiskas; and now claims to care for half the world's pets through nutrition, health and services businesses including Banfield Pet Hospitals and Anicura. While Shubham Mehrish, global vice president of digital strategy at Mars Inc., wouldn't say whether an AI-designed M&M was on the horizon, he did sound bullish on DALL-E and other AI art tools for idea generation at Mars. "The DALL-E team has been stingy in giving access, but we have a few of our AI scientists already playing with it," he said.

While AI is making a big impact in pretty much every business area, it is also important to note some of the ways it is helping to save our planet. Conservationists are increasingly turning to AI as an innovative solution to overcome various biodiversity crises. It helps protect a diverse set of species and assists law enforcement agents who are often short-staffed, and it is almost impossible for them to cover a vast stretch of land, such as a national park. This is one of the reasons why AI is so useful because it can take a lot of the time-consuming work off the shoulders of human workers, such as constantly monitoring surveillance data. In this article, we will talk about some of the interesting ways AI is being used to protect endangered species and the data annotation that is required to create it.

The assessment of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) associated with brain changes remains a challenging task. Recent studies have demonstrated that combination of multi-modality imaging techniques can better reflect pathological characteristics and contribute to more accurate diagnosis of AD and MCI. In this paper, we propose a novel tensor-based multi-modality feature selection and regression method for diagnosis and biomarker identification of AD and MCI from normal controls. Specifically, we leverage the tensor structure to exploit high-level correlation information inherent in the multi-modality data, and investigate tensor-level sparsity in the multilinear regression model. We present the practical advantages of our method for the analysis of ADNI data using three imaging modalities (VBM- MRI, FDG-PET and AV45-PET) with clinical parameters of disease severity and cognitive scores. The experimental results demonstrate the superior performance of our proposed method against the state-of-the-art for the disease diagnosis and the identification of disease-specific regions and modality-related differences. The code for this work is publicly available at https://github.com/junfish/BIOS22.

Neural models are known to be over-parameterized, and recent work has shown that sparse text-to-speech (TTS) models can outperform dense models. Although a plethora of sparse methods has been proposed for other domains, such methods have rarely been applied in TTS. In this work, we seek to answer the question: what are the characteristics of selected sparse techniques on the performance and model complexity? We compare a Tacotron2 baseline and the results of applying five techniques. We then evaluate the performance via the factors of naturalness, intelligibility and prosody, while reporting model size and training time. Complementary to prior research, we find that pruning before or during training can achieve similar performance to pruning after training and can be trained much faster, while removing entire neurons degrades performance much more than removing parameters. To our best knowledge, this is the first work that compares sparsity paradigms in text-to-speech synthesis.