On August 8th, 2021, Spanish police and customs agents intercepted the cargo ship NATALIA on suspicion of narcotics trafficking. The ship was en route from Lebanon via Iskenderun, Turkey, to Lagos, Nigeria, and hidden on board was nearly 20 tons of hashish worth $470 million. That may sound like the opening scene of an action flick, but it's the kind of occurrence that happens more frequently than you might expect on the high seas. Drug smuggling, illegal fishing, and piracy are constant threats. Following a number of recent piracy incidents in the Gulf of Aden, Iran, Russia, and China recently began naval and air drills seeking to counter maritime piracy.

With more than 1.2 million fans expected in the country for the World Cup, Qatar has set up a tech hub that uses artificial intelligence to keep an eye on the spectators, predict crowd swells and even control stadium temperature. More than 100 technicians will be working around the clock at the Aspire Command and Control Center, closely monitoring images flashing across their screens via 200,000 integrated units, from 22,000 security cameras spread across all eight World Cup stadiums. It is from here that they can operate entry gates, ensure there is running water and keep the air conditioners humming smoothly. Facial recognition technology will enable the crew to zoom in on each of the 80,000 seats at Lusail Stadium, which is set to host 10 matches, including the final. Experts from cybersecurity to anti-terrorism to transport will be stationed at the centre, along with Qatari and FIFA officials.

To apply, you must have the authorization to work within the United States and be returning to school in the fall. We have the vision of a world where each day is safer and more secure than the one before. These aren't easy goals to accomplish – but we're not here for easy. We are a company built on the foundation of challenging and disrupting the way things are done, and we're looking for innovators who are as committed to shaping the future of cybersecurity as we are. Palo Alto Networks is evolving to meet the needs of our employees now and in the future through FLEXWORK, our approach to how we work. And because it FLEXes around each individual employee based on their individual choices, employees are empowered to push boundaries and help us all evolve, together.

Conditional Independence (CI) graphs are a type of probabilistic graphical models that are primarily used to gain insights about feature relationships. Each edge represents the partial correlation between the connected features which gives information about their direct dependence. In this survey, we list out different methods and study the advances in techniques developed to recover CI graphs. We cover traditional optimization methods as well as recently developed deep learning architectures along with their recommended implementations. To facilitate wider adoption, we include preliminaries that consolidate associated operations, for example techniques to obtain covariance matrix for mixed datatypes. It is often beneficial to know which features are directly correlated to which other features. This can help us understand the input data better by giving a feature inter-dependence overview and also assist in taking system design decisions.

Statistical analysis of tensor-valued data has largely used the tensor-variate normal (TVN) distribution that may be inadequate when data comes from distributions with heavier or lighter tails. We study a general family of elliptically contoured (EC) tensor-variate distributions and derive its characterizations, moments, marginal and conditional distributions, and the EC Wishart distribution. We describe procedures for maximum likelihood estimation from data that are (1) uncorrelated draws from an EC distribution, (2) from a scale mixture of the TVN distribution, and (3) from an underlying but unknown EC distribution, where we extend Tyler's robust estimator. A detailed simulation study highlights the benefits of choosing an EC distribution over the TVN for heavier-tailed data. We develop tensor-variate classification rules using discriminant analysis and EC errors and show that they better predict cats and dogs from images in the Animal Faces-HQ dataset than the TVN-based rules. A novel tensor-on-tensor regression and tensor-variate analysis of variance (TANOVA) framework under EC errors is also demonstrated to better characterize gender, age and ethnic origin than the usual TVN-based TANOVA in the celebrated Labeled Faces of the Wild dataset.

Event argument extraction has long been studied as a sequential prediction problem with extractive-based methods, tackling each argument in isolation. Although recent work proposes generation-based methods to capture cross-argument dependency, they require generating and post-processing a complicated target sequence (template). Motivated by these observations and recent pretrained language models' capabilities of learning from demonstrations. We propose a retrieval-augmented generative QA model (R-GQA) for event argument extraction. It retrieves the most similar QA pair and augments it as prompt to the current example's context, then decodes the arguments as answers. Our approach outperforms substantially prior methods across various settings (i.e. fully supervised, domain transfer, and fewshot learning). Finally, we propose a clustering-based sampling strategy (JointEnc) and conduct a thorough analysis of how different strategies influence the few-shot learning performance. The implementations are available at https:// github.com/xinyadu/RGQA

Dependency focuses on the proximity of words in a sentence, and the hierarchical relations between words in the sentence are represented by a tree structure called the dependency tree of the sentence. Recently, an international collaboration project called Universal Dependency (UD) has created a standard annotation scheme for constructing dependency trees from sentences, and hundreds of UD treebanks of various languages have been made publicly available [7]. These datasets form key materials for syntax analysis, providing new opportunities for automated text processing and syntactic typology studies to name a few. Parallel Universal Dependency (PUD) treebanks are a class of UD treebanks consisting of dependency trees of 1,000 sentences and their translations to other languages [33]. The 1,000 sentences are randomly selected from the news domain and Wikipedia and are originally written in English, French, German, Italian or Spanish. At the time of writing, there are 20 PUD treebanks containing the dependency trees of the 1,000 sentences in 20 languages respectively. These UD treebanks have stimulated novel computational methods for syntax analysis and the development of quantitative measures for syntax similarity [19, 31, 32]. However, current methods describing dependency trees mainly focus on partial syntactic information recorded in the structures such as the order of words and the dependency distance [2, 3, 11, 18]. In this work, we introduce a comprehensive representation of dependency trees based on a tree distinguishing polynomial.

Empirical evaluation of deep learning models against adversarial attacks entails solving nontrivial constrained optimization problems. Popular algorithms for solving these constrained problems rely on projected gradient descent (PGD) and require careful tuning of multiple hyperparameters. Moreover, PGD can only handle $\ell_1$, $\ell_2$, and $\ell_\infty$ attack models due to the use of analytical projectors. In this paper, we introduce a novel algorithmic framework that blends a general-purpose constrained-optimization solver PyGRANSO, With Constraint-Folding (PWCF), to add reliability and generality to robustness evaluation. PWCF 1) finds good-quality solutions without the need of delicate hyperparameter tuning, and 2) can handle general attack models, e.g., general $\ell_p$ ($p \geq 0$) and perceptual attacks, which are inaccessible to PGD-based algorithms.

Physiological computing uses human physiological data as system inputs in real time. It includes, or significantly overlaps with, brain-computer interfaces, affective computing, adaptive automation, health informatics, and physiological signal based biometrics. Physiological computing increases the communication bandwidth from the user to the computer, but is also subject to various types of adversarial attacks, in which the attacker deliberately manipulates the training and/or test examples to hijack the machine learning algorithm output, leading to possible user confusion, frustration, injury, or even death. However, the vulnerability of physiological computing systems has not been paid enough attention to, and there does not exist a comprehensive review on adversarial attacks to them. This paper fills this gap, by providing a systematic review on the main research areas of physiological computing, different types of adversarial attacks and their applications to physiological computing, and the corresponding defense strategies. We hope this review will attract more research interests on the vulnerability of physiological computing systems, and more importantly, defense strategies to make them more secure.

US Citizens only1-on-1 mentorship, training and advice to help users land their next job. Pay only if you succeed in getting hired and start work at a high-paying job first. Ready to find your dream job? Use this free career assessment test to figure it out. These tools support mission planning, rehearsal, training and execution use cases for military commanders across echelons and services Our applications have been shown to deliver unique value to the warfighter due to the quality of generated COAs, User Experience/User Interface (UX/UI) design that makes it easy to fully comprehend complex COA results and to define mission inputs, as well as ease of deployment and integration with other systems with well-defined modern Application Program Interfaces (APIs)1-on-1 mentorship, training and advice to help users land their next job.