Characterizing quantum flakes is a critical step in quantum hardware engineering because the quality of these flakes directly influences qubit performance. Although computer vision methods for identifying two-dimensional quantum flakes have emerged, they still face significant challenges in estimating flake thickness. These challenges include limited data, poor generalization, sensitivity to domain shifts, and a lack of physical interpretability. In this paper, we introduce one of the first Physics-informed Adaptation Learning approaches to overcome these obstacles. We focus on two main issues, i.e., data scarcity and generalization. First, we propose a new synthetic data generation framework that produces diverse quantum flake samples across various materials and configurations, reducing the need for time-consuming manual collection. Second, we present $φ$-Adapt, a physics-informed adaptation method that bridges the performance gap between models trained on synthetic data and those deployed in real-world settings. Experimental results show that our approach achieves state-of-the-art performance on multiple benchmarks, outperforming existing methods. Our proposed approach advances the integration of physics-based modeling and domain adaptation. It also addresses a critical gap in leveraging synthesized data for real-world 2D material analysis, offering impactful tools for deep learning and materials science communities.

In quantum machine field, detecting two-dimensional (2D) materials in Silicon chips is one of the most critical problems. Instance segmentation can be considered as a potential approach to solve this problem. However, similar to other deep learning methods, the instance segmentation requires a large scale training dataset and high quality annotation in order to achieve a considerable performance. In practice, preparing the training dataset is a challenge since annotators have to deal with a large image, e.g 2K resolution, and extremely dense objects in this problem. In this work, we present a novel method to tackle the problem of missing annotation in instance segmentation in 2D quantum material identification. We propose a new mechanism for automatically detecting false negative objects and an attention based loss strategy to reduce the negative impact of these objects contributing to the overall loss function. We experiment on the 2D material detection datasets, and the experiments show our method outperforms previous works.

It is challenging to implement Kernel methods, if the data sources are distributed and cannot be joined at a trusted third party for privacy reasons. It is even more challenging, if the use case rules out privacy-preserving approaches that introduce noise. An example for such a use case is machine learning on clinical data. To realize exact privacy preserving computation of kernel methods, we propose FLAKE, a Federated Learning Approach for KErnel methods on horizontally distributed data. With FLAKE, the data sources mask their data so that a centralized instance can compute a Gram matrix without compromising privacy. The Gram matrix allows to calculate many kernel matrices, which can be used to train kernel-based machine learning algorithms such as Support Vector Machines. We prove that FLAKE prevents an adversary from learning the input data or the number of input features under a semi-honest threat model. Experiments on clinical and synthetic data confirm that FLAKE is outperforming the accuracy and efficiency of comparable methods. The time needed to mask the data and to compute the Gram matrix is several orders of magnitude less than the time a Support Vector Machine needs to be trained. Thus, FLAKE can be applied to many use cases.

Shiny tanks of molten chocolate stand guard over a factory floor where three production lines squirt, chill and fill festive treats into existence. Production of Hotel Chocolat's Christmas selection starts in June at its factory in Huntingdon, Cambridgeshire, and finishes several weeks before Christmas, when it switches to making Valentine's Day and Easter delicacies. Christmas is by far the busiest time of year for Hotel Chocolat's shops, where sales easily outstrip Easter, the traditional time for a chocolate binge. This year, robots have been shouldering a bigger share of the work in making peanut butter and jelly confectionery and batons of dark chocolate as the company copes with rising costs that led it to report an annual loss this year, after a bumper time during the coronavirus pandemic, when sales jumped by two-thirds over two years. On one production line, workers in hairnets and white coats sprinkle florentine and biscuit pieces into moulds for chocolate Christmas wreaths.

Films and TV programmes have long portrayed caveman as using grunts to communicate with one another. But a new study suggests that our ancient ancestors likely did not use sounds to communicate, and instead opted for hand gestures. Researchers from the University of Western Australia asked volunteers to attempt to describe words using only grunts or gestures. They found that gestures were far more effective in communicating meaning and were often similar between cultures. 'The universality of gesture means it is ideally suited to bootstrapping human communication among modern humans and therefore supports the hypothesis that gesture is the primary modality for language creation,' the researchers said in their study, published in Proceedings of the Royal Society B. Films and TV programmes have long portrayed caveman as using grunts to communicate with one another. Searching for a way to make your point?

Rice University chemists are employing machine learning to fine-tune its flash Joule heating process to make graphene. A flash signifies the creation of graphene from waste. Rice University scientists are using machine learning techniques to streamline the process of synthesizing graphene from waste through flash Joule heating. This flash Joule process has expanded beyond making graphene from various carbon sources, to extracting other materials, like metals, from urban waste. The technique is the same for all of the above: blasting a jolt of high energy through the source material to eliminate all but the desired product.

Machine learning is the common basis for modern artificial intelligence, using large amounts of data to build AI models that recognize patterns and make predictions when presented with new information. A new Seattle startup led by a former Microsoft distinguished engineer uses a different approach: machine teaching. "It's not extracting knowledge from data; it's extracting knowledge from the person," explained Patrice Simard, CEO and co-founder of Intelus, who oversaw Microsoft research groups in areas including machine learning, databases, graphics, vision and cryptography in more than 20 years at the Redmond company. Intelus emerged from stealth mode Tuesday to launch an open beta of its new machine teaching platform, Duet, which offers a graphical user interface to create AI models from unstructured data without writing code or requiring advanced data science tools. The models can then be used to classify and extract data from text.

Republican Arizona Sen. Jeff Flake has developed a reputation as someone who is willing to criticize Donald Trump and his party enablers in unusually blunt terms--but not generally willing to use his status as a potential swing vote in a narrowly divided Senate to initiate investigations into administration corruption, protect Robert Mueller's special counsel investigation, or achieve any of the other good-governance goals he might be expected to support given his feelings on Trumpism. That was Flake's reputation, at least, before he announced a dramatic effort last Friday to delay Brett Kavanaugh's confirmation vote until the FBI could conduct a weeklong review of sexual assault accusations against the Supreme Court nominee. When it became clear that fellow Trump-skeptic GOP senators Susan Collins and Lisa Murkowski supported Flake's position, the White House almost immediately agreed to order just such an investigation. Over the weekend, though, there was some grinding in the gears. NBC reported, and other outlets confirmed, that the White House had set narrow limits on which individuals the FBI was allowed to contact.

Anti-Trump Senator Jeff Flake, a member of the Senate Judiciary Committee, said in an interview Sunday evening that until he learns more about the sexual assault allegation regarding Brett Kavanaugh, he is "not comfortable voting yes" on Kavanaugh. It's Flakes last chance to poke President Trump and the country in the eye before he rides retires and likely finds a job in the liberal media. Jeff Flake becomes the first Republican senator to call for a pause on the Kavanaugh hearings until the Judiciary Committee hears from his accuser. Kavanaugh's accuser is a far left anti-Trump activist. Over the past few days, what appeared at first to be a merely token resistance to the nomination of Trump SCOTUS pick Brett Kavanaugh has morphed into something entirely more menacing.

Last Thursday, Senator Jeff Flake of Arizona introduced the following amendment to the U.S. Department of Defense appropriations bill currently in Congress: None of the amounts appropriated or otherwise made available by this Act may be obligated or expended for the development of a beerbot or other robot bartender. This sounds like a joke, but it's not: Legislation prohibiting Department of Defense funding of robot bartenders is on its way to becoming law. The reason why Senator Flake wants this to become law is based, at best, on a misunderstanding of how basic robotics research works. At worst, it's a deliberate decision to misrepresent the research for political gain. In 2015, MIT researchers presented a paper at the Robotics: Science and Systems (RSS) conference on "Policy Search for Multi-Robot Coordination under Uncertainty" [PDF].