It looks like fabric, feels like metal, and is as light as rubber. Any watch fan looking to tick all of the above boxes would normally expect to be a dab hand with a spring bar removal tool to experience all the above individually, but a new strap developed by Malaysian independent brand Ming appears to now offer the best of all worlds. The one strap to rule them all has been dubbed the Polymesh, and is 3D-printed from grade five titanium, and comprises 1,693 interconnected pieces (including the buckle) held together without any pins or screws. The only additional parts requiring assembly are the quick-release spring bars at each end that attach it to the watch--the articulated pin buckle is also formed in the same process. Ming says that the strap, which is made up from rows of 15 equilateral triangles, meshed together and bookended by larger end pieces, "has more motion engineered into the radial axis than the lateral one," leading to a supple end result that drapes like fabric yet retains the strength of titanium.

--Previous work has shown that the addition of haptic feedback to the hands can improve awareness of tool-tissue interactions and enhance performance of teleoperated tasks in robot-assisted minimally invasive surgery. However, hand-based haptic feedback occludes direct interaction with the manipulanda of surgeon console in teleoperated surgical robots. We propose relocating haptic feedback to the wrist using a wearable haptic device so that haptic feedback mechanisms do not need to be integrated into the manipulanda. However, it is unknown if such feedback will be effective, given that it is not co-located with the finger movements used for manipulation. T o test if relocated haptic feedback improves force application during teleoperated tasks using da Vinci Research Kit (dVRK) surgical robot, participants learned to palpate a phantom tissue to desired forces. Participants performed the palpation task with and without wrist-worn haptic feedback and were evaluated for the accuracy of applied forces. Participants demonstrated statistically significant lower force error when wrist-worn haptic feedback was provided. Participants also performed the palpation task with longer movement times when provided wrist-worn haptic feedback, indicating that the haptic feedback may have caused participants to operate at a different point in the speed-accuracy tradeoff curve.

Conversational assistants are increasingly popular across diverse real-world applications, highlighting the need for advanced multimodal speech modeling. Speech, as a natural mode of communication, encodes rich user-specific characteristics such as speaking rate and pitch, making it critical for effective interaction. Our work introduces a data-centric customization approach for efficiently enhancing multimodal understanding in conversational speech modeling. Central to our contributions is a novel multi-task learning paradigm that involves designing auxiliary tasks to utilize a small amount of speech data. Our approach achieves state-of-the-art performance on the Spoken-SQuAD benchmark, using only 10% of the training data with open-weight models, establishing a robust and efficient framework for audio-centric conversational modeling. We also introduce ASK-QA, the first dataset for multi-turn spoken dialogue with ambiguous user requests and dynamic evaluation inputs. Code and data forthcoming.

If the recent Jumanji film reboots have taught us nothing else – and they haven't – they have demonstrated how much fun there is to be had with dropping characters from our familiar world into outlandish fantasy settings. Forspoken has the same potential: its protagonist Frey is accustomed to the harsh realities of surviving in the scruffier corners of Hell's Kitchen, New York, and her problems gravitate around harbouring debts to petty criminals and remembering to feed her cat. So when Frey puts on a bracelet in an abandoned tenement for reasons best not examined too closely and she's transported into the quasi-medieval world of Athia, you think to yourself: here we go. It's a great fish-out-of-water setup that allows for witty observations about video game fantasy worlds and the bizarre tropes within them that we generally don't bat an eyelash at. And with a writing team including Uncharted's Amy Hennig and Rogue One co-writer Gary Whitta, expectations are pitched high.

The use of artificial intelligence is fast spreading in the digital marketing industry including social media where Instagram belongs. Businesses are looking for ways to automate tasks, boost productivity, generate more leads, and make more sales. According to a report, the market of AI in social media is expected to hit $2.2 billion in 2023. This is to tell you that social media marketers are investing in AI to make smart decisions and achieve higher ROI. Are you skeptical about leveraging AI in Instagram marketing?

Language models can be augmented with a context retriever to incorporate knowledge from large external databases. By leveraging retrieved context, the neural network does not have to memorize the massive amount of world knowledge within its internal parameters, leading to better parameter efficiency, interpretability and modularity. In this paper we examined a simple yet effective architecture for incorporating external context into language models based on decoupled Encoder-Decoder architecture. We showed that such a simple architecture achieves competitive results on auto-regressive language modeling and open domain question answering tasks. We also analyzed the behavior of the proposed model which performs grounded context transfer. Finally we discussed the computational implications of such retrieval augmented models.

Wearable activity trackers that monitor changes in skin temperature and heart and breathing rates, combined with artificial intelligence (AI), might be used to pick up COVID-19 infection days before symptoms start, suggests preliminary research published in the open access journal BMJ Open. The researchers base their findings on wearers of the AVA bracelet, a regulated and commercially available fertility tracker that monitors breathing rate, heart rate, heart rate variability, wrist skin temperature and blood flow, as well as sleep quantity and quality. Typical COVID-19 symptoms may take several days after infection before they appear during which time an infected person can unwittingly spread the virus. Attention has started to focus on the potential of activity trackers and smartwatches to detect all stages of COVID-19 infection in the body from incubation to recovery, with the aim of facilitating early isolation and testing of those with the infection. The researchers therefore wanted to see if physiological changes, monitored by an activity tracker, could be used to develop a machine learning algorithm to detect COVID-19 infection before the start of symptoms. Participants (1163 all under the age of 51) were drawn from the GAPP study between March 2020 and April 2021.

Wearable activity trackers that monitor changes in skin temperature and heart and breathing rates, combined with artificial intelligence (AI), might be used to pick up COVID-19 infection days before symptoms start, suggests preliminary research published in the open access journal BMJ Open. The researchers base their findings on wearers of the AVA bracelet, a regulated and commercially available fertility tracker that monitors breathing rate, heart rate, heart rate variability, wrist skin temperature and blood flow, as well as sleep quantity and quality. Typical COVID-19 symptoms may take several days after infection before they appear during which time an infected person can unwittingly spread the virus. Attention has started to focus on the potential of activity trackers and smartwatches to detect all stages of COVID-19 infection in the body from incubation to recovery, with the aim of facilitating early isolation and testing of those with the infection. The researchers therefore wanted to see if physiological changes, monitored by an activity tracker, could be used to develop a machine learning algorithm to detect COVID-19 infection before the start of symptoms.

Health trackers worn on the wrist could be used to spot Covid-19 days before any symptoms appear, according to researchers. Growing numbers of people worldwide use the devices to monitor changes in skin temperature, heart and breathing rates. Now a new study shows that this data could be combined with artificial intelligence (AI) to diagnose Covid-19 even before the first tell-tale signs of the disease appear. "Wearable sensor technology can enable Covid-19 detection during the presymptomatic period," the researchers concluded. The findings were published in the journal BMJ Open.

Mike Kisch, Aktiia CEO, told MailOnline that having constant blood pressure measurements in all settings was a'game changer' for doctors and patients That will be for doctors, allowing them to remotely gauge the progress of patients, even see what time of day medication should be taken. 'Right now, after they do the initial diagnosis and prescribe medication, they don't get a lot of data from the patient, so the likelihood that the first time it will work is low, so now they get ongoing data to see if they need to modify treatment. 'That is a game changer for the physician,' explains Mr Kisch. Data gathered by this device is fed into large scale cohort studies, with nine currently running or about to run around the world. One is about the way patient engagement in hypertension management programmes increase when using these products and how a doctors decision making process improves.