This paper presents various automatic detection methods to extract so called tortured phrases from scientific papers. These tortured phrases, e.g. flag to clamor instead of signal to noise, are the results of paraphrasing tools used to escape plagiarism detection. We built a dataset and evaluated several strategies to flag previously undocumented tortured phrases. The proposed and tested methods are based on language models and either on embeddings similarities or on predictions of masked token. We found that an approach using token prediction and that propagates the scores to the chunk level gives the best results. With a recall value of .87 and a precision value of .61, it could retrieve new tortured phrases to be submitted to domain experts for validation.

The thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023), held in New Orleans, drew to a close on Saturday 16 December. In this post, we take a look at what participants got up to during the last few days of the conference. You can find part one of our round-up here. This is my attempt to convey how unreasonable and unimaginably large #NeurIPS2023 is. So excited to be part of the Intrinsically Motivated Open-Ended Learning workshop at #NeurIPS2023!

In September 1998, Google was created in a garage by two Stanford University students – a humble beginning to say the least. Who would have thought a quarter of century later it would be the world's most used search engine with unprecedented access to our personal data? To mark its 25 birthday, Google has packed its site with Easter Eggs, including a new Google Doodle – a temporary alteration of its logo – with '25' in place of where the two Os would normally be. Meanwhile, if you enter'Google 25th birthday' into the search bar, colourful confetti will rain down over your results. The tech giant has also brought back its'birthday surprise spinner', which lets you play interactive games from its archives, including Pac-man, Snake and Tic-tac-toe.

I've been of the opinion lately that, if they weren't born into privilege, every writer in the third world needs to quit their job. On my end, that entails giving up a 15-year career that's given me everything I could've possibly hoped for: close friends, a remarkable portfolio, a diverse network of clients, and several lifetimes' worth of memories, from staging sex comedies at the CCP to getting a grant for a childhood dream. At the same time, however, all that experience includes having been on several sinking ships. I feel like I've gotten fairly good at spotting one that's about to go under. These days, that ship's name is Writing Itself, and AI's got an eye on the hull and a drill in hand.

The kitchenware company's head engineer, Mack Mor, had dug through the archives to find some product prototypes to help me understand how OXO designs and develops gadgets. Now, sitting on a table in the employee break room, amid jury-rigged cherry pitters and spiralizers constructed from sawed-apart water bottles, was a large, baby blue Tiffany box, of the sort in which you might expect to see encased a sparkling wedding present. Mor opened the box--and revealed the company's very first salad spinner. OXO revolutionized the salad spinner, to be sure. But to see this humble prototype--Frankenstein'd out of a child's toy top and some hand-carved plastic, dull with age--swaddled inside a gorgeous Tiffany box made me laugh. OXO, with its embrace of dutiful, functional design and every-cook utility, certainly wasn't Tiffany. Maybe not, but don't tell that to the people who love OXO.

In a neural network, the ReLU activation function is responsible for transubstantiating the added weighted input from the knot into the activation of the knot or affair for that input. The remedied direct activation function or ReLU for short is a piecewise direct function that will affair the input directly if it's positive, else, it'll affair zero. It has come the dereliction activation function for numerous types of neural networks because a model that uses it's easier to train and frequently achieves better performance. In this tutorial, you'll discover the remedied direct activation function for deep literacy neural networks. Limitations of Sigmoid and Tanh Activation Functions A neural network is comprised of layers of bumps and learns to collude exemplifications of inputs to labors.

Since content is king on the World Wide Web, you need to have the right equipment to help you make the best content. The right content management is essential to mark your presence online. It does not perish and can remain immortal while creating consequences that can make or break you. There is no denying the fact that nothing can beat the human mind as the ultimate tool behind good-quality content writing. However, it has its limitations such as the time and costs involved. This is where Artificial Intelligence (AI) enters the stage. With the help of Artificial Intelligence, it is now possible for articles to be written, texts to be summarized, blog posts to be published and content to be created more quickly.

For example, it is of clinical interest to understand associations between: (a) alcoholism and the electrical activity of different brain regions over time collected from electroencephalography (EEG) (Li et al., 2010); (b) cognitive function and three-dimensional white-matter structure data collected from diffusion tensor imaging (DTI) (Goldsmith et al., 2014) for patients with multiple sclerosis (MS); and (c) cognitive impairment and brain's metabolic activity data collected from three-dimensional positron emission tomography (PET) imaging (Wang et al., 2014). Our work focuses on the problem of identifying brain network connections that are associated with neurocognitive measures for HIVinfected individuals. The outcome (response) is a continuous variable and the predictors are matrix representations of functional connectivity between the brain's cortical regions. Biophysical considerations motivate our interest in estimating a matrix of regression coefficients that has the following two properties: (i) it should be relatively sparse, since we aim to identify connections that most strongly predict the outcome; and more importantly, (ii) the response-related connections form clusters, since brain activity networks are known to consist of densely connected regions. These two properties translate to the coefficient matrix having relatively small clusters, or blocks of nonzero entries, which implies that it is low-rank. Hence, we aim to solve the matrix regression problem by estimating a coefficient matrix that is both sparse and low-rank. To further illustrate our approach, consider the three matrices in Figure 1. The one in the left panel is sparse, but full-rank, the one on the right panel is low-rank, but not sparse, while the one in the middle panel is both low-rank and sparse, which is the structure we are interested in. To find such a solution, we propose a regularization method called SParsity Inducing Nuclear Norm EstimatoR (SpINNEr).

Digital trends are hard to understand. An average person would have never imagined the internet to go crazy at fidget spinners. Random people become memes and got free viral marketing and no one knows why that happened. What if you get to know what's going to be hot on the internet and act on it before anyone else? Well, Signum is that astrologer for digital trends. Signum is a subscription-based mailing list where you receive bi-weekly emails with a report on emerging hot trends before everyone else learns about them.

Back in 2002, movie director Steven Spielberg and automaker Lexus worked together to create a vehicle that predicted what cars might be like in the year 2054. That car, the Lexus CS 2054, was "driven" in Minority Report by actor Tom Cruise; driven in quote marks because the car actually drove itself. But while such vehicles weren't expected until the middle of this century, a research project undertaken by Leasing Options, a British vehicle-leasing company, says that Lexus CS 2054-like cars will be on the road by 2027. "Who would have thought that 2027, just eight short years away, could be the year we see the Lexus 2054 from Minority Report become commercially available," the company said in its news release last month. "That's a whole 27 years earlier than Spielberg had predicted, seeing as the film was set in 2054."