The future is coming at Croydon fast. It might not look like Britain's cutting edge but North End, a pedestrianised high street lined with the usual mix of pawn shops, fast-food outlets and branded clothing stores, is expected to be one of two roads to host the UK's first fixed facial recognition cameras. Digital photographs of passersby will be silently taken and processed to extract the measurements of facial features, known as biometric data. They will be immediately compared by artificial intelligence to images on a watchlist. Alerts can lead to arrests.

Kris Jenner gets compared to her daughters with new look; "Mormon Wives" stars' say fame tore their friendships apart. Welcome to the Fox News Entertainment Newsletter. Billy Joel cancels all shows after a rare brain disorder diagnosis. MUSIC ON HOLD - Billy Joel cancels all concerts due to brain disorder diagnosis. ROCK TRAGEDY - Music exec and rock drummer among those killed in San Diego plane crash.

Police believe live facial recognition cameras may become "commonplace" in England and Wales, according to internal documents, with the number of faces scanned having doubled to nearly 5m in the last year. A joint investigation by the Guardian and Liberty Investigates highlights the speed at which the technology is becoming a staple of British policing. Major funding is being allocated and hardware bought, while the British state is also looking to enable police forces to more easily access the full spread of its image stores, including passport and immigration databases, for retrospective facial recognition searches. Live facial recognition involves the matching of faces caught on surveillance camera footage against a police watchlist in real time, in what campaigners liken to the continual finger printing of members of the public as they go about their daily lives. Retrospective facial recognition software is used by the police to match images on databases with those caught on CCTV and other systems.

In less than a year-and-a-half, Frankie Johnson, a man incarcerated at the William E Donaldson prison outside Birmingham, Alabama, says he was stabbed around 20 times. In December of 2019, Johnson says, he was stabbed "at least nine times" in his housing unit. In March of 2020, an officer handcuffed him to a desk following a group therapy meeting, and left the unit, after which another prisoner came in and stabbed him five times. In November of the same year, Johnson says, he was handcuffed by an officer and brought to the prison yard, where another prisoner attacked him with an ice pick, stabbing him "five to six times", as two correctional officers looked on. According to Johnson, one of the officers had actually encouraged his attacker to carry out the assault in retaliation for a previous argument between Johnson and the officer.

In Gaza, the sound of drones can be heard everywhere. An analysis by Al Jazeera's digital investigations team, Sanad, has revealed that Israel is repurposing commercial drones to use as weapons of war in the Strip. And as drones become ever more accessible, the line between their civilian use and their military use is becoming increasingly blurred.

A.1 Multimodal Large Language Models Building on the foundational capabilities of groundbreaking Large Language Models (LLMs) such as GPT [3], PALM [6], Mistral [49], and LLama [108], which excel in language understanding and reasoning, recent innovations have integrated these models with other modalities (especially vision), leading to the development of Multimodal Large Language Models (MLLMs). These advanced MLLMs combine and process visual and textual data, demonstrating enhanced versatility in addressing both traditional vision tasks [21, 40, 42, 133] and complex multimodal challenges [34, 70, 136]. Among all MLLMs, proprietary models consistently perform well. OpenAI's GPT-4-Vision [82] pioneered this space by adeptly handling both text and image content. Anthropic's Claude 3 series [7] integrates advanced vision capabilities and multilingual support, enhancing its application across diverse cognitive and real-time tasks.

We only consider the feasible cases. Then we consider the sigm(...) function in Equation 1. For instance, say x is the parameter to be trained, we want x to satisfy sigm(x) = 1 or 0, we would apply gradient-descent which will cause x + / . Two basic blocks of fully connected + ReLU layer are utilized as backbone for all benchmark datasets, generating instance-level representations with a dimensionality of 512. We set one branch here for the binary-classification problems.

Having reviewed dozens of drones of all shapes, sizes, and prices, I'd recently come to the conclusion that smaller, lighter, and cheaper drones were the way to go for 90 percent of consumers. But then DJI launched its new premium-priced, jumbo-size flagship consumer drone, the Mavic 4 Pro, and made me fall in love all over again. Yes, this drone is seriously impressive. But before I deep-dive the phenomenally good camera and ridiculously long range, it's important to note that the Mavic 4 Pro will not be officially available in the US. As well as ongoing issues around flight restrictions and security, a DJI spokesperson told WIRED, "Like many global companies, we have had to adjust our market strategy as local conditions and the industry environment have evolved. While we do not have a timeline for when we can introduce the product to the US market, we are closely monitoring the situation and actively exploring every possible solution."

Anomaly detection in time series data is fundamental to the design, deployment, and evaluation of industrial control systems. Temporal modeling has been the natural focus of anomaly detection approaches for time series data. However, the focus on temporal modeling can obscure or dilute the spatial information that can be used to capture complex interactions in multivariate time series. In this paper, we propose SARAD, an approach that leverages spatial information beyond data autoencoding errors to improve the detection and diagnosis of anomalies. SARAD trains a Transformer to learn the spatial associations, the pairwise inter-feature relationships which ubiquitously characterize such feedback-controlled systems. As new associations form and old ones dissolve, SARAD applies subseries division to capture their changes over time. Anomalies exhibit association descending patterns, a key phenomenon we exclusively observe and attribute to the disruptive nature of anomalies detaching anomalous features from others. To exploit the phenomenon and yet dismiss non-anomalous descent, SARAD performs anomaly detection via autoencoding in the association space. We present experimental results to demonstrate that SARAD achieves state-of-the-art performance, providing robust anomaly detection and a nuanced understanding of anomalous events.

Recent progress in deep generative models has been fueled by two paradigms - autoregressive and adversarial models. We propose a combination of both approaches with the goal of learning generative models of text. Our method first produces a high-level sentence outline and then generates words sequentially, conditioning on both the outline and the previous outputs. We generate outlines with an adversarial model trained to approximate the distribution of sentences in a latent space induced by general-purpose sentence encoders. This provides strong, informative conditioning for the autoregressive stage. Our quantitative evaluations suggests that conditioning information from generated outlines is able to guide the autoregressive model to produce realistic samples, comparable to maximum-likelihood trained language models, even at high temperatures with multinomial sampling. Qualitative results also demonstrate that this generative procedure yields natural-looking sentences and interpolations.