Motivation: Predicting Drug-Target Interaction (DTI) is a well-studied topic in bioinformatics due to its relevance in the fields of proteomics and pharmaceutical research. Although many machine learning methods have been successfully applied in this task, few of them aim at leveraging the inherent heterogeneous graph structure in the DTI network to address the challenge. For better learning and interpreting the DTI topological structure and the similarity, it is desirable to have methods specifically for predicting interactions from the graph structure. Results: We present an end-to-end framework, DTI-GAT (Drug-Target Interaction prediction with Graph Attention networks) for DTI predictions. DTI-GAT incorporates a deep neural network architecture that operates on graph-structured data with the attention mechanism, which leverages both the interaction patterns and the features of drug and protein sequences. DTI-GAT facilitates the interpretation of the DTI topological structure by assigning different attention weights to each node with the self-attention mechanism. Experimental evaluations show that DTI-GAT outperforms various state-of-the-art systems on the binary DTI prediction problem. Moreover, the independent study results further demonstrate that our model can be generalized better than other conventional methods. Availability: The source code and all datasets are available at https://github.com/Haiyang-W/DTI-GRAPH

Common neural network architectures are susceptible to attack by adversarial samples. Neural network architectures are commonly thought of as divided into low-level feature extraction layers and high-level classification layers; susceptibility of networks to adversarial samples is often thought of as a problem related to classification rather than feature extraction. We test this idea by selectively retraining different portions of VGG and ResNet architectures on CIFAR-10, Imagenette and ImageNet using non-adversarial and adversarial data. Our experimental results show that susceptibility to adversarial samples is associated with low-level feature extraction layers. Therefore, retraining high-level layers is insufficient for achieving robustness. This phenomenon could have two explanations: either, adversarial attacks yield outputs from early layers that are indistinguishable from features found in the attack classes, or adversarial attacks yield outputs from early layers that differ statistically from features for non-adversarial samples and do not permit consistent classification by subsequent layers. We test this question by large-scale non-linear dimensionality reduction and density modeling on distributions of feature vectors in hidden layers and find that the feature distributions between non-adversarial and adversarial samples differ substantially. Our results provide new insights into the statistical origins of adversarial samples and possible defenses.

Recent advancements in artificial neural networks that focus on reconstructing 3D meshes from input 2D images show great potential and significant practical value in a multitude of GIS applications. This series of posts describes our experiments with one such neural network architecture that we applied to reconstruct 3D building shells from various types of remotely sensed data. This post, the first of the series, describes extracting buildings from elevation rasters, specifically, normalized digital surface model rasters. Modern municipal governments and national mapping agencies are evolving their traditional 2D geographic datasets into 3D interactive and realistically looking digital twins to optimize results from planning an analysis projects. For example, some local governments that are responsible for urban design, public events planning, safety, pollution monitoring, solar radiation potential assessment, etc. rely more and more on this kind of data.

When gravitational waves were first detected in 2015 by the advanced Laser Interferometer Gravitational-Wave Observatory (LIGO), they sent a ripple through the scientific community, as they confirmed another of Einstein's theories and marked the birth of gravitational wave astronomy. Five years later, numerous gravitational wave sources have been detected, including the first observation of two colliding neutron stars in gravitational and electromagnetic waves. As LIGO and its international partners continue to upgrade their detectors' sensitivity to gravitational waves, they will be able to probe a larger volume of the universe, thereby making the detection of gravitational wave sources a daily occurrence. This discovery deluge will launch the era of precision astronomy that takes into consideration extrasolar messenger phenomena, including electromagnetic radiation, gravitational waves, neutrinos and cosmic rays. Realizing this goal, however, will require a radical re-thinking of existing methods used to search for and find gravitational waves.

To implement General Secretary XI Jinping's important remarks and crucial instructions concerning artificial intelligence, and to implement the work requirements of Ministry of Science and Technology and the construction of Shanghai National New-Generation Artificial Intelligence Innovation and Development Pilot Zone, under the guidance of Department of Strategic Planning & Department of High New Technology of Ministry of Science and Technology and Science and Technology Commission of Shanghai Municipality, "Artificial Intelligence Governance Forum" will be themed under "People-oriented, Application-concerned" and explore methods analyzing the impact of social application of artificial intelligence technology, promote "artificial intelligence governance enabling'People-oriented' concept", take stock of "Shanghai Experiences" and "Shanghai's Image", in which artificial intelligence technology application is contributing to responsible development and exhibiting demonstrative effects, so as to serve the modernization of artificial intelligence governance system and governance capacity building. To implement General Secretary XI Jinping's important remarks and crucial instructions concerning artificial intelligence, and to implement the work requirements of Ministry of Science and Technology and the construction of Shanghai National New-Generation Artificial Intelligence Innovation and Development Pilot Zone, under the guidance of Department of Strategic Planning & Department of High New Technology of Ministry of Science and Technology and Science and Technology Commission of Shanghai Municipality, "Artificial Intelligence Governance Forum" will be themed under "People-oriented, Application-concerned" and explore methods analyzing the impact of social application of artificial intelligence technology, promote "artificial intelligence governance enabling'People-oriented' concept", take stock of "Shanghai Experiences" and "Shanghai's Image", in which artificial intelligence technology application is contributing to responsible development and exhibiting demonstrative effects, so as to serve the modernization of artificial intelligence governance system and governance capacity building.

Retired Navy SEAL Commander Dave Sears suggests Russia, China and Pakistan could face national security issues once U.S. troops leave Afghanistan. This is a rush transcript of "Your World with Neil Cavuto" on July 8, 2021. This copy may not be in its final form and may be updated. QUESTION: Do you trust the Taliban, Mr. President? Do you trust the Taliban, sir? JOE BIDEN, PRESIDENT OF THE UNITED STATES: Are you -- is that a serious question? QUESTION: It is absolutely a serious question. Do you trust the Taliban? BIDEN: No, I do not. BIDEN: No, I do not trust the Taliban. QUESTION: Is the U.S. responsible for the deaths that happen the Afghans after you leave the country? QUESTION: Mr. President, will you amplify that question, please? Will you amplify your answer, please, why you don't trust the Taliban? BIDEN: It is a silly question. Do I trust the Taliban? And it almost seemed like a Donald Trump press conference, with angry reporters trying to get a simple answer from the president, and their agitation showing, as the questions and the nonanswers went on, all of this at a time U.S. forces are moving rapidly ahead of schedule. Better than 90 percent now have left Afghanistan. And we could see them all out well before the 9/11 deadline that the president has set. But he says he's not going to change his mind. And he says that, after 20 years, Afghans must look after themselves. Jennifer Griffin has more from the Pentagon.

President Joe Biden's decision to elevate the director of the Office of Science and Technology Policy to a Cabinet-level position underscores the importance of artificial intelligence in America's future. His selection of Alondra Nelson to be deputy director of OSTP shows that unlocking AI's potential will be done with a focus on racial and gender equity. Nelson, a Black woman whose research focuses on the intersection of science, technology and social inequality, has said that technologies like AI "reveal and reflect even more about the complex and sometimes dangerous social architecture that lies beneath the scientific progress that we pursue." There's no doubt that ethics must be foundational to the design, development and acquisition of AI capabilities, and that government agencies should embed trustworthy AI as part of a holistic strategy to transform the way government operates. Agency leaders can start by identifying areas where AI can transform their internal operations and improve their public-facing mission services with minimal risk of bias.

It is no question that Artificial Intelligence ("AI") technologies have popped up in all aspects of society such as online shopping, music streaming, and social networking. The U.S. Patent and Trademark Office ("USPTO") has even reported that patents which incorporate AI has increased from under 5% in 1980 to over 20% in 2018. Among those organizations that utilize AI is the USPTO itself. The USPTO has recently incorporated AI technology into patent examination, specifically with prior art searching and patent classification. These changes may bring a variety of benefits to patent applicants even though their implementation is behind the scenes of the patent application examination process.

According to AIMResearch, Indian AI startups raised $836.3 million in 2020, the largest funding outlay in the last seven years at a 9.7% year-on-year growth. The same year, Indian government increased the outlay for Digital India to $477 million to boost AI, IoT, big data, cybersecurity, machine learning and robotics. In the 2019 Union Budget, Finance Minister Nirmala Sitharaman said the government would offer industry-relevant skill training for 10 million youth in India in technologies like AI, Big Data and robotics. To wit, India's AI ecosystem is seeing explosive growth with a lot of inventive startups entering the space. Analytics India Magazine comes with a list of 10 exceptional startups leading the AI race every year.

Take it from U.S. Air Force Captain Kyle McAlpin when he says that scheduling C-17 aircraft crews is a headache. An artificial intelligence research flight commander for the Department of Air Force–MIT AI Accelerator Program, McAlpin is also an experienced C-17 pilot. "You could have a mission change and spend the next 12 hours of your life rebuilding a schedule that works," he says. It's a pain point for crew of 52 squadrons who operate C-17s, the military cargo aircraft that transport troops and supplies globally. This year, the Air Force marked 4 million flight hours for its C-17 fleet, which comprises 275 U.S. and allied aircraft.