We propose SUMART, a method for summarizing and compressing the volume of verbose subtitle translations. SUMART is designed for understanding translated captions (e.g., interlingual conversations via subtitle translation or when watching movies in foreign language audio and translated captions). SUMART is intended for users who want a big-picture and fast understanding of the conversation, audio, video content, and speech in a foreign language. During the training data collection, when a speaker makes a verbose statement, SUMART employs a large language model on-site to compress the volume of subtitles. This compressed data is then stored in a database for fine-tuning purposes. Later, SUMART uses data pairs from those non-compressed ASR results and compressed translated results for fine-tuning the translation model to generate more concise translations for practical uses. In practical applications, SUMART utilizes this trained model to produce concise translation results. Furthermore, as a practical application, we developed an application that allows conversations using subtitle translation in augmented reality spaces. As a pilot study, we conducted qualitative surveys using a SUMART prototype and a survey on the summarization model for SUMART. We envision the most effective use case of this system is where users need to consume a lot of information quickly (e.g., Speech, lectures, podcasts, Q&A in conferences).

The authors lay an ambitious groundwork for studying the multitasking capability of general neural network architectures. They prove a variety of theorems concerning behavior of their defined criteria--the multitasking capacity \alpha--and how it relates to graph connectivity and size via graph matchings and degree. Pros: The problem being studied is quite clearly motivated, and the authors present a genuinely novel new measure for attempting to understand the multitasking capability of networks. The authors fairly systematically calculate bounds on their parameter in a variety of different graph-theoretic scenarios. Cons: It's unclear how practically useful the parameter \alpha will be for real networks currently in use.

In this work, we proposed a new style-diverse dataset for the domain of motion style transfer. The motion dataset uses an industrial-standard human bone structure and thus is industry-ready to be plugged into 3D characters for many projects. We claim the challenges in motion style transfer and encourage future work in this domain by releasing the proposed motion dataset both to the public and the market. We conduct a comprehensive study on motion style transfer in the experiment using the state-of-the-art method, and the results show the proposed dataset's validity for the motion style transfer task.

When it comes to artificial intelligence, Dr. Cornelius James thinks media and pop culture too often portray it as something dangerous that shouldn't be trusted. "One of the challenges is that people go to extremes," says James, a primary care physician and clinical assistant professor at Michigan Medicine. He says that people think of The Terminator and other villainous portrayals. In reality, artificial intelligence is much less harmful than we imagine -- in fact, it's quite helpful, particularly in medicine. The hard part is convincing others that's the case.

In traditional astronomies across the world, groups of stars in the night sky were linked into constellations -- symbolic representations rich in meaning and with practical roles. In some sky cultures, constellations are represented as line (or connect-the-dot) figures, which are spatial networks drawn over the fixed background of stars. We analyse 1802 line figures from 56 sky cultures spanning all continents, in terms of their network, spatial, and brightness features, and ask what associations exist between these visual features and culture type or sky region. First, an embedded map of constellations is learnt, to show clusters of line figures. We then form the network of constellations (as linked by their similarity), to study how similar cultures are by computing their assortativity (or homophily) over the network. Finally, we measure the diversity (or entropy) index for the set of constellations drawn per sky region. Our results show distinct types of line figures, and that many folk astronomies with oral traditions have widespread similarities in constellation design, which do not align with cultural ancestry. In a minority of sky regions, certain line designs appear universal, but this is not the norm: in the majority of sky regions, the line geometries are diverse.

Using only a few seconds of brain activity data, the AI guesses what a person has heard. It lists the correct answer in its top 10 possibilities up to 73 percent of the time, researchers found in a preliminary study. The AI's "performance was above what many people thought was possible at this stage," says Giovanni Di Liberto, a computer scientist at Trinity College Dublin who was not involved in the research. Thank you for signing up! There was a problem signing you up.

"AI is performing well in back-end processing of security events, allowing for automation and speed of use-case development," says Doug Saylors, partner and cybersecurity co-lead with global technology research and advisory firm ISG. This is crucial because "in modern environments, ephemeral cloud assets turn on and off in minutes, work-from-home devices are hidden from view, and data centers are full of dusty corners," says Rosiek. "In what's being called XDR, AI/ML is just another tool in the toolbox to find anomalies -- methods of attack that aren't caught by traditional defense-in-depth technologies," says Patrick Orzechowski, vice president and distinguished engineer at managed cybersecurity vendor Deepwatch. "In cybersecurity, this is best reflected in areas such as intrusion detection and network monitoring -- it's fairly safe for administrators to allow AI to discover activity that is an outlier and may be malicious in these cases" says Sean O'Brien, founder and lead researcher at Privacy Lab at Yale and CSO at privacy-focused chat company Panquake. Cyber AI is "very hard," warns Aaron Sant-Miller, chief data scientist at consulting firm Booz Allen Hamilton, but it is key to building effective defenses.

The shortcomings of artificial intelligence tools in the cybersecurity world have drawn a lot of attention. But does the bad press mean that AI isn't working? Or is AI just getting slammed for failing to meet overinflated expectations? It's time to take a hard look at what AI is accomplishing before kicking it to the curb. There's never been a superhero who hasn't gone to the dark side or fallen off their pedestal.

Epilepsy is one of the most occurring neurological diseases. The main characteristic of this disease is a frequent seizure, which is an electrical imbalance in the brain. It is generally accompanied by shaking of body parts and even leads (fainting). In the past few years, many treatments have come up. These mainly involve the use of anti-seizure drugs for controlling seizures. But in 70% of cases, these drugs are not effective, and surgery is the only solution when the condition worsens. So patients need to take care of themselves while having a seizure and be safe. Wearable electroencephalogram (EEG) devices have come up with the development in medical science and technology. These devices help in the analysis of brain electrical activities. EEG helps in locating the affected cortical region. The most important is that it can predict any seizure in advance on-site. This has resulted in a sudden increase in demand for effective and efficient seizure prediction and diagnosis systems. A novel approach to epileptic seizure prediction and diagnosis system EpilNet is proposed in the present paper. It is a one-dimensional (1D) convolution neural network. EpilNet gives the testing accuracy of 79.13% for five classes, leading to a significant increase of about 6-7% compared to related works. The developed Web API helps in bringing EpilNet into practical use. Thus, it is an integrated system for both patients and doctors. The system will help patients prevent injury or accidents and increase the efficiency of the treatment process by doctors in the hospitals.

With Japan's first commercial quantum computer going into operation last month, more global competitors are entering the race to gain an advantage by mastering the next-generation technology, with Germany emerging as a strong contender. In Kawasaki, a city in the Tokyo metropolitan area, sits a commercial quantum computer made by IBM at the Kawasaki Business Incubation Center. Toyota Motor, Hitachi and Toshiba are among the companies that are using the device. Quantum computers are expected to break the limitations of conventional computing. In 2019, Google startled the world by using the technology to solve a problem in 3 minutes and 20 seconds that would have required 10,000 years by a conventional computer.