Adversarial examples in the digital domain against deep learning-based computer vision models allow for perturbations that are imperceptible to human eyes. However, producing similar adversarial examples in the physical world has been difficult due to the non-differentiable image distortion functions in visual sensing systems. The existing algorithms for generating physically realizable adversarial examples often loosen their definition of adversarial examples by allowing unbounded perturbations, resulting in obvious or even strange visual patterns. In this work, we make adversarial examples imperceptible in the physical world using a straight-through estimator (STE, a.k.a. BPDA). We employ STE to overcome the non-differentiability -- applying exact, non-differentiable distortions in the forward pass of the backpropagation step, and using the identity function in the backward pass. Our differentiable rendering extension to STE also enables imperceptible adversarial patches in the physical world. Using printout photos, and experiments in the CARLA simulator, we show that STE enables fast generation of $\ell_\infty$ bounded adversarial examples despite the non-differentiable distortions. To the best of our knowledge, this is the first work demonstrating imperceptible adversarial examples bounded by small $\ell_\infty$ norms in the physical world that force zero classification accuracy in the global perturbation threat model and cause near-zero ($4.22\%$) AP50 in object detection in the patch perturbation threat model. We urge the community to re-evaluate the threat of adversarial examples in the physical world.

This paper explores how automation and artificial intelligence (AI) are transforming U.S. cyber diplomacy. Leveraging these technologies helps the U.S. manage the complexity and urgency of cyber diplomacy, improving decision-making, efficiency, and security. As global inter connectivity grows, cyber diplomacy, managing national interests in the digital space has become vital. The ability of AI and automation to quickly process vast data volumes enables timely responses to cyber threats and opportunities. This paper underscores the strategic integration of these tools to maintain U.S. competitive advantage and secure national interests. Automation enhances diplomatic communication and data processing, freeing diplomats to focus on strategic decisions. AI supports predictive analytics and real time decision making, offering critical insights and proactive measures during high stakes engagements. Case studies show AIs effectiveness in monitoring cyber activities and managing international cyber policy. Challenges such as ethical concerns, security vulnerabilities, and reliance on technology are also addressed, emphasizing human oversight and strong governance frameworks. Ensuring proper ethical guidelines and cybersecurity measures allows the U.S. to harness the benefits of automation and AI while mitigating risks. By adopting these technologies, U.S. cyber diplomacy can become more proactive and effective, navigating the evolving digital landscape with greater agility.

This article presents a digital twin (DT)-enhanced reinforcement learning (RL) framework aimed at optimizing performance and reliability in network resource management, since the traditional RL methods face several unified challenges when applied to physical networks, including limited exploration efficiency, slow convergence, poor long-term performance, and safety concerns during the exploration phase. To deal with the above challenges, a comprehensive DT-based framework is proposed to enhance the convergence speed and performance for unified RL-based resource management. The proposed framework provides safe action exploration, more accurate estimates of long-term returns, faster training convergence, higher convergence performance, and real-time adaptation to varying network conditions. Then, two case studies on ultra-reliable and low-latency communication (URLLC) services and multiple unmanned aerial vehicles (UAV) network are presented, demonstrating improvements of the proposed framework in performance, convergence speed, and training cost reduction both on traditional RL and neural network based Deep RL (DRL). Finally, the article identifies and explores some of the research challenges and open issues in this rapidly evolving field.

Adversarial attacks against deep learning-based object detectors (ODs) have been studied extensively in the past few years. These attacks cause the model to make incorrect predictions by placing a patch containing an adversarial pattern on the target object or anywhere within the frame. However, none of prior research proposed a misclassification attack on ODs, in which the patch is applied on the target object. In this study, we propose a novel, universal, targeted, label-switch attack against the state-of-the-art object detector, YOLO. In our attack, we use (i) a tailored projection function to enable the placement of the adversarial patch on multiple target objects in the image (e.g., cars), each of which may be located a different distance away from the camera or have a different view angle relative to the camera, and (ii) a unique loss function capable of changing the label of the attacked objects. The proposed universal patch, which is trained in the digital domain, is transferable to the physical domain. We performed an extensive evaluation using different types of object detectors, different video streams captured by different cameras, and various target classes, and evaluated different configurations of the adversarial patch in the physical domain.

Across all industries, organizations are now managing more data, nearly 14 petabytes on average, according to Dell Technologies' 2020 Global Data Protection Index (1 petabyte is just over 1 million gigabytes). In healthcare, providers and patients want to see more done with all that data. Some 75 percent of healthcare consumers want to work together with providers on wellness goals, according to Deloitte research, and 85 percent of physicians expect interoperability and data sharing to become standardized. The pandemic has highlighted the value of innovative technologies to gather, manage and gain insights from the vast stores of data that hospitals collect, guiding them toward improved care and adaptive clinical workflows. "The pandemic has been a huge validation of the path we were on and the investments we've made in data management," Lamm says.

Autodesk's Will Harris, Foundry's Mathieu Mazerolle and Unity Technologies' Brian Gaffney will discuss how their companies are incorporating machine learning into software tools to make higher quality and more realistic visual effects and boost production speed. Visual Effects Supervisor Ryan Laney will describe the novel way artificial intelligence and machine learning were used to mask the identities of interview subjects in the award-winning HBO documentary Welcome to Chechnya. "Machine learning is poised to transform visual effects production, accelerating workflows and paving the way for a new generation of astonishingly real visual effects," says Barry Goch, who will moderate the discussion. "Will Harris, Mathieu Mazerolle and Brian Gaffney will demonstrate game-changing technologies. Ryan Laney will share his experience in applying machine learning to a real-world production."

Machine-learning (ML) solutions are proliferating across a wide variety of industries, but the overwhelming majority of the commercial implementations still rely on digital logic for their solution. With the exception of in-memory computing, analog solutions mostly have been restricted to universities and attempts at neuromorphic computing. However, that's starting to change. "Everyone's looking at the fact that deep neural networks are so energy-intensive when you implement them in digital, because you've got all these multiply-and-accumulates, and they're so deep, that they can suck up enormous amounts of power," said Elias Fallon, software engineering group director for the Custom IC & PCB Group at Cadence. Some suggest we're reaching a limit with digital. "Digital architectural approaches have hit the wall to solve the deep neural network MAC (multiply-accumulate) operations," said Sumit Vishwakarma, product manager at Siemens EDA. "As the size of the DNN increases, weight access operations result in huge energy consumption." The current analog approaches aren't attempting to define an entirely new ML paradigm. "The last 50 years have all been focused on digital processing, and for good reason," said Thomas Doyle, CEO and co-founder of Aspinity.

Tucked away in an office on a quiet Los Angeles street, past hallways chockablock with miniature props and movie posters, is a cavernous motion-capture studio. And in that studio is the National Mall in Washington, D.C., in 1963, on the day Martin Luther King Jr. delivered his "I Have a Dream" speech. Or rather, it was inside that room that the visual-effects studio Digital Domain captured the expressions, movements and spirit of King, so that he could appear digitally in The March, a virtual reality experience that TIME has produced in partnership with the civil rights leader's estate. The experience, which is executive–produced and narrated by actor Viola Davis, draws on more than a decade of research in machine learning and human anatomy to create a visually striking re-creation of the country's National Mall circa 1963--and of King himself. When work on the project began more than three years ago, a big question needed answering.

Meet DigiDoug, the first digital human to give a TED talk in real time. DigiDoug is the virtual version of Dr. Doug Roble, senior director of Software R&D at Digital Domain, the award-winning visual effects studio behind the characters and visual effects for movies like The Curious Case of Benjamin Button, Maleficent, Disney's The Beauty and the Beast and Avengers: Endgame. Roble and Digital Domain's Digital Human Group have presented DigiDoug at multiple events, showcasing their state-of-the-art digital human technology that's driven by an inertial motion-capture suit and a single camera capture for facial animation. But to capture and recreate emotions and actions in real time, the Los Angeles-based studio turned to more powerful and advanced technology: machine learning and real-time rendering. With NVIDIA RTX technology and Unreal Engine from Epic Games, Digital Domain is bringing photorealistic digital humans to life -- all in real time.

Meet DigiDoug, the first digital human to give a TED talk in real time. DigiDoug is the virtual version of Dr. Doug Roble, senior director of Software R&D at Digital Domain, the award-winning visual effects studio behind the characters and visual effects for movies like The Curious Case of Benjamin Button, Maleficent, Disney's The Beauty and the Beast and Avengers: Endgame. Roble and Digital Domain's Digital Human Group have presented DigiDoug at multiple events, showcasing their state-of-the-art digital human technology that's driven by an inertial motion-capture suit and a single camera capture for facial animation. But to capture and recreate emotions and actions in real time, the Los Angeles-based studio turned to more powerful and advanced technology: machine learning and real-time rendering. With NVIDIA RTX technology and Unreal Engine from Epic Games, Digital Domain is bringing photorealistic digital humans to life -- all in real time.