The new sci-fi drama from Vince Gilligan posits an end-of-humanity scenario that everyone other than its protagonist can agree on. Even before her fellow-humans' contamination, Carol didn't seem to have much use for them. On the night that the world as we know it is destroyed, a novelist named Carol Sturka (played by Rhea Seehorn) sees cars and planes veer off course, an emergency room full of convulsing bodies, and her city, Albuquerque, on fire. The President dies under mysterious circumstances, and, more devastatingly for Carol, so does her live-in partner, Helen (Miriam Shor). Then, in less than an hour, the apocalypse cleans up after itself.

"I don't kill anyone who doesn't deserve it," says Sam (Ben Whishaw), the self-described "triggerman"--hit man--in the new Netflix spy thriller Black Doves. Sam, like the series' other main character, Helen (not her real name, played by Keira Knightley), works for Black Doves' eponymous organization. They are spies, more or less, but spies for hire, and when you get right down to it, most of Sam's gigs seem to be carrying out hits for drug dealers. Sam isn't the only hit man featured in a sleek, starry TV thriller this winter. On Peacock, Eddie Redmayne plays Alex in a new adaptation of Frederick Forsyth's 1971 novel The Day of the Jackal.

This work proposes a variational inference (VI) framework for hyperspectral unmixing in the presence of endmember variability (HU-EV). An EV-accounted noisy linear mixture model (LMM) is considered, and the presence of outliers is also incorporated into the model. Following the marginalized maximum likelihood (MML) principle, a VI algorithmic structure is designed for probabilistic inference for HU-EV. Specifically, a patch-wise static endmember assumption is employed to exploit spatial smoothness and to try to overcome the ill-posed nature of the HU-EV problem. The design facilitates lightweight, continuous optimization-based updates under a variety of endmember priors. Some of the priors, such as the Beta prior, were previously used under computationally heavy, sampling-based probabilistic HU-EV methods. The effectiveness of the proposed framework is demonstrated through synthetic, semi-real, and real-data experiments.

Theory of Mind (ToM) refers to the ability to attribute mental states, such as beliefs, desires, intentions, and knowledge, to oneself and others, and to understand that these mental states can differ from one's own and from reality. We investigate ToM in environments with multiple, distinct, independent AI agents, each possessing unique internal states, information, and objectives. Inspired by human false-belief experiments, we present an AI ('focal AI') with a scenario where its clone undergoes a human-centric ToM assessment. We prompt the focal AI to assess whether its clone would benefit from additional instructions. Concurrently, we give its clones the ToM assessment, both with and without the instructions, thereby engaging the focal AI in higher-order counterfactual reasoning akin to human mentalizing--with respect to humans in one test and to other AI in another. We uncover a discrepancy: Contemporary AI demonstrates near-perfect accuracy on human-centric ToM assessments. Since information embedded in one AI is identically embedded in its clone, additional instructions are redundant. Yet, we observe AI crafting elaborate instructions for their clones, erroneously anticipating a need for assistance. An independent referee AI agrees with these unsupported expectations. Neither the focal AI nor the referee demonstrates ToM in our 'silico-centric' test.

Click-Through Rate (CTR) prediction holds paramount significance in online advertising and recommendation scenarios. Despite the proliferation of recent CTR prediction models, the improvements in performance have remained limited, as evidenced by open-source benchmark assessments. Current researchers tend to focus on developing new models for various datasets and settings, often neglecting a crucial question: What is the key challenge that truly makes CTR prediction so demanding? In this paper, we approach the problem of CTR prediction from an optimization perspective. We explore the typical data characteristics and optimization statistics of CTR prediction, revealing a strong positive correlation between the top hessian eigenvalue and feature frequency. This correlation implies that frequently occurring features tend to converge towards sharp local minima, ultimately leading to suboptimal performance. Motivated by the recent advancements in sharpness-aware minimization (SAM), which considers the geometric aspects of the loss landscape during optimization, we present a dedicated optimizer crafted for CTR prediction, named Helen. Helen incorporates frequency-wise Hessian eigenvalue regularization, achieved through adaptive perturbations based on normalized feature frequencies. Empirical results under the open-source benchmark framework underscore Helen's effectiveness. It successfully constrains the top eigenvalue of the Hessian matrix and demonstrates a clear advantage over widely used optimization algorithms when applied to seven popular models across three public benchmark datasets on BARS. Our code locates at github.com/NUS-HPC-AI-Lab/Helen.

One night in 1966, a twenty-three-year-old graduate student named Nicholas Humphrey was working in a darkened psychology lab at the University of Cambridge. An anesthetized monkey sat before him; glowing targets moved across a screen in front of the animal, and Humphrey, using an electrode, recorded the activity of nerve cells in its superior colliculus, an ancient brain area involved in visual processing. The superior colliculus predates the more advanced visual cortex, which enables conscious sight in mammals. Although the monkey was not awake, the cells in its superior colliculus were firing anyway, their activation registering as a series of crackles issuing from a loudspeaker. Humphrey seemed to be listening to the brain cells "seeing."

With technology being her first love, Helen has spent a lifetime obsessed over helping customers solve their complex problems using automation technologies as the key to driving business intelligence. Her engineering background, vast expertise in product development, and robust entrepreneurial selling techniques have brought her to Inpixon in the role of Senior Enterprise Account Executive. Throughout her career, many organizations have invited her to speak at their events to share her knowledge and passion for tech. Full of tenacity and curiosity, when Helen is not reading or trying to solve world problems with technology, she spends time with her family, cooking, biking, and Geocaching.

My denim bikini has been replaced with exposed, pale pink nipples – and a smooth, hairless crotch. I zoom in on the image, attempting to gauge what, if anything, could reveal the truth behind it. There's the slight pixilation around part of my waist, but that could be easily fixed with amateur Photoshopping. Although the image isn't exactly what I see staring back at me in the mirror in real life, it's not a million miles away either. And hauntingly, it would take just two clicks of a button for someone to attach it to an email, post it on Twitter or mass distribute it to all of my contacts. Or upload it onto a porn site, leaving me spending the rest of my life fearful that every new person I meet has seen me naked. Because this image, despite looking realistic, is a fake.

Digital Transformation moved at a relatively slow pace for the past ten years, mainly focusing on improving products, employee experience and processes. But then, after COVID – 19 hit, IT decision-makers were forced to prioritize their IT initiatives in order to increase digital investments. According to IDC, over the next four years, worldwide Digital Transformation technology investment is set to reach at least $7.4 trillion and will be the first time that DX will account for the majority of IT spending – predicted to be a huge 53% of budgets. Digital transformation is a set of methodologies and tools which are used by modern companies to optimize their operational activities, such as increasing their reach power, providing differentiated service and increasing performance. However, digital transformation is not just a new department in the firm, but it is definitely a game-changer in technology's role in the corporate environment. That's why it is increasingly being seen as the 4th Industrial Revolution. "Think of digital transformation less as a technology project to be finished than as a state of perpetual agility, always ready to evolve for whatever customers want next, and you'll be pointed down the right path."-

Furthermore, as the photos were not shared with Helen directly, nor did the intention seem to be to cause distress to Helen, the second element is not fulfilled - even though it did, evidently, cause distress. The other potential criminal offence would be harassment, but given the perpetrator here did not direct it at Helen herself, this didn't apply either.