Due to the rapid development of autonomous driving, the Internet of Things and streaming services, modern communication systems have to cope with varying channel conditions and a steadily rising number of users and devices. This, and the still rising bandwidth demands, can only be met by intelligent network automation, which requires highly flexible and blind transceiver algorithms. To tackle those challenges, we propose a novel adaptive equalization scheme, which exploits the prosperous advances in deep learning by training an equalizer with an adversarial network. The learning is only based on the statistics of the transmit signal, so it is blind regarding the actual transmit symbols and agnostic to the channel model. The proposed approach is independent of the equalizer topology and enables the application of powerful neural network based equalizers. In this work, we prove this concept in simulations of different -- both linear and nonlinear -- transmission channels and demonstrate the capability of the proposed blind learning scheme to approach the performance of non-blind equalizers. Furthermore, we provide a theoretical perspective and highlight the challenges of the approach.

It is a common practice to think of a video as a sequence of images (frames), and re-use deep neural network models that are trained only on images for similar analytics tasks on videos. In this paper, we show that this leap of faith that deep learning models that work well on images will also work well on videos is actually flawed. We show that even when a video camera is viewing a scene that is not changing in any human-perceptible way, and we control for external factors like video compression and environment (lighting), the accuracy of video analytics application fluctuates noticeably. These fluctuations occur because successive frames produced by the video camera may look similar visually, but these frames are perceived quite differently by the video analytics applications. We observed that the root cause for these fluctuations is the dynamic camera parameter changes that a video camera automatically makes in order to capture and produce a visually pleasing video. The camera inadvertently acts as an unintentional adversary because these slight changes in the image pixel values in consecutive frames, as we show, have a noticeably adverse impact on the accuracy of insights from video analytics tasks that re-use image-trained deep learning models. To address this inadvertent adversarial effect from the camera, we explore the use of transfer learning techniques to improve learning in video analytics tasks through the transfer of knowledge from learning on image analytics tasks. In particular, we show that our newly trained Yolov5 model reduces fluctuation in object detection across frames, which leads to better tracking of objects(40% fewer mistakes in tracking). Our paper also provides new directions and techniques to mitigate the camera's adversarial effect on deep learning models used for video analytics applications.

Oscar-winning director Pedro Almodóvar says that he is withdrawing from his first English-language feature, "A Manual for Cleaning Women" produced by and starring Cate Blanchett. Almodóvar, 72, told entertainment news website Deadline Hollywood that he was unable to handle the commitment. "It has been a very painful decision for me," Almodóvar told Deadline Hollywood. "I have dreamt of working with Cate for such a long time. Dirty Films has been so generous with me this whole time and I was blinded by excitement, but unfortunately, I no longer feel able to fully realize this film."

With artificial intelligence (AI) text-to-image generators exploding in popularity right now, it sometimes feels like photography is facing its most serious threat yet. In the last few months, DALL-E has been used to create shockingly realistic portraits of people who do not exist. Meanwhile, a Midjourney user even won a fine art competition using a picture he created with the software. With AI systems like DALL-E and Midjourney effortlessly churning out photo-realistic images, it may seem like there is no hope left for photography. With the havoc AI-generated art could wreck on the field of photography, for some people, this may bring to mind the way the invention of photography devastated painters in the nineteenth century.

The European Space Agency's Solar Orbiter spacecraft has captured the reversal of the Sun's magnetic field on camera for the first time. These reversals, known as magnetic switchbacks, have previously been hypothesised, but until now have not been observed directly. The new observation provides a full view of the structure and confirms that magnetic switchbacks have an S-shaped character. ESA hopes the footage will help to unravel the mystery of how their physical formation mechanism might help accelerate solar winds. Scientists develop a'recipe' for parents to stop babies crying Meghan Markle's handshake is ignored by member of the public Kremlin journalist admits Russia is losing'huge number of people' Thousands gather for arrival of Queen's coffin at Buckingham Palace The European Space Agency's Solar Orbiter spacecraft has captured the reversal of the Sun's magnetic field on camera for the first time.

Researchers have shown that machine learning models can mimic the basic structure of the fundamental laws of nature.

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A piece of digital art that won a prize at the Colorado State Fair has reignited an old furor over what art is and how artists create in an authentic way. Jason Allen used an artificial intelligence program to generate an image titled, "Théâtre D'opéra Spatial," that evokes an opera scene in a science fiction setting. Several Bloomington-Normal artists do not seem to view the debate in an especially sanguinary way. The AI program artist in game designer Allen used doesn't involve any hands-on technique at all. The AI operates by turning words and text into image.

There is a long history of an effort made to explore musical elements with the entities and spaces around us, such as musique concr\`ete and ambient music. In the context of computer music and digital art, interactive experiences that concentrate on the surrounding objects and physical spaces have also been designed. In recent years, with the development and popularization of devices, an increasing number of works have been designed in Extended Reality to create such musical experiences. In this paper, we describe MR4MR, a sound installation work that allows users to experience melodies produced from interactions with their surrounding space in the context of Mixed Reality (MR). Using HoloLens, an MR head-mounted display, users can bump virtual objects that emit sound against real objects in their surroundings. Then, by continuously creating a melody following the sound made by the object and re-generating randomly and gradually changing melody using music generation machine learning models, users can feel their ambient melody "reincarnating".

Chances are you've already seen the headline (or some variation thereof): "AI won an art contest, and artists are furious" Here's what happened: A Colorado man entered an art competition at the Colorado State Fair Fine Arts Competition in the category of "digital arts/digitally-manipulated photography". The problem, however, was that he produced the image using Midjourney, an online AI program that produces images based on user text input. He entered the piece using the name "Jason M. Allen via Midjourney", thus disclosing the use of the AI and meeting all competition rules. The judges were not initially aware that AI was used, yet later admitted that they still would have awarded Allen the prize even if they had. As the headline above attests, many artists the world over were not pleased with this outcome.