A routine check-up at the dentist ends with bad news: tooth decay has left a large cavity, and the tooth needs a crown. The treatment requires several follow-up appointments. During the first appointment, the dentist removes the decay, fills the cavity and prepares the tooth for the crown. She then takes an impression and fits a temporary crown. The permanent crown is produced based on the impression and can only be placed at a later appointment.

A tiny dental robot called MIR could automate crown preparation and reduce dental visits. The University of Basel prototype uses digital dentistry for tooth preparation.

The tiny robo-dentist will see you now. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Its motors and control system are located outside the robot and connected to it via flexible drive shafts, cables, and tubes. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

Limitations One limitation of our model is its potential for data bias. KOSMOS-1 is trained on a2 web-scale multimodal corpus, which means that it is likely to be biased towards the data that it was3 trained on. This could lead to the model generating text that is biased towards certain demographics4 or viewpoints.5 Another limitation of KOSMOS-1 is its relatively small size compared to other large language models.6 This means that the model may not be able to learn as complex relationships between different7 modalities. This could lead to the model making mistakes when it is asked to perform tasks that8 require a deep understanding of multiple modalities.9 Finally, KOSMOS-1 only supports vision modality.

Finding minimum distortion of adversarial examples and thus certifying robustness in neural networks classifiers is known to be a challenging problem. Nevertheless, recently it has been shown to be possible to give a non-trivial certified lower bound of minimum distortion, and some recent progress has been made towards this direction by exploiting the piece-wise linear nature of ReLU activations. However, a generic robustness certification for \textit{general} activation functions still remains largely unexplored. To address this issue, in this paper we introduce CROWN, a general framework to certify robustness of neural networks with general activation functions. The novelty in our algorithm consists of bounding a given activation function with linear and quadratic functions, hence allowing it to tackle general activation functions including but not limited to the four popular choices: ReLU, tanh, sigmoid and arctan. In addition, we facilitate the search for a tighter certified lower bound by \textit{adaptively} selecting appropriate surrogates for each neuron activation. Experimental results show that CROWN on ReLU networks can notably improve the certified lower bounds compared to the current state-of-the-art algorithm Fast-Lin, while having comparable computational efficiency. Furthermore, CROWN also demonstrates its effectiveness and flexibility on networks with general activation functions, including tanh, sigmoid and arctan.

Finding minimum distortion of adversarial examples and thus certifying robustness in neural networks classifiers is known to be a challenging problem. Nevertheless, recently it has been shown to be possible to give a non-trivial certified lower bound of minimum distortion, and some recent progress has been made towards this direction by exploiting the piece-wise linear nature of ReLU activations. However, a generic robustness certification for \textit{general} activation functions still remains largely unexplored. To address this issue, in this paper we introduce CROWN, a general framework to certify robustness of neural networks with general activation functions. The novelty in our algorithm consists of bounding a given activation function with linear and quadratic functions, hence allowing it to tackle general activation functions including but not limited to the four popular choices: ReLU, tanh, sigmoid and arctan. In addition, we facilitate the search for a tighter certified lower bound by \textit{adaptively} selecting appropriate surrogates for each neuron activation. Experimental results show that CROWN on ReLU networks can notably improve the certified lower bounds compared to the current state-of-the-art algorithm Fast-Lin, while having comparable computational efficiency. Furthermore, CROWN also demonstrates its effectiveness and flexibility on networks with general activation functions, including tanh, sigmoid and arctan.

Muskan Sharma stood up to men who tried to bully her over her clothes - and went on to win hearts and a beauty pageant. The 23-year-old, who was crowned Miss Rishikesh 2025 last week in the northern Indian state of Uttarakhand, told the BBC that even though it was a small local pageant, it made me feel like Miss Universe. Sharma's win has made headlines in India as it came after a viral video that showed her spiritedly arguing with a man who barged into their rehearsals just a day before the 4 October contest. Sharma, who wanted to be a model and participate in a pageant since I was in school, said the intruders came in just as they broke for lunch. We were sitting around, chilling, having a laugh when they walked in, she said.