A NASA space capsule carrying the largest soil sample ever collected from the surface of an asteroid has landed in the Utah desert seven years after the mission's launch. Flight Control announced on Sunday. The gumdrop-shaped capsule, released from the robotic spacecraft OSIRIS-REx as the mothership passed within 108,000km (67,000 miles) of Earth hours earlier, touched down within a designated landing zone west of Salt Lake City on the United States military's vast Utah Test and Training Range. The samples will be flown on Monday to a new lab at NASA's Johnson Space Center in Houston. The building already houses nearly 400kg (842lb) of moon rocks gathered by the Apollo astronauts more than a half-century ago.

A space capsule carrying a sample of rocky material removed from the surface of an asteroid three years ago is expected to make a parachute landing in the Utah desert on Sunday. If successful, the OSIRIS-REx mission, a joint effort between NASA and scientists at the University of Arizona, would mark the third asteroid sample, and by far the largest, ever returned to Earth for analysis. OSIRIS-REx collected its samples from Bennu, a carbon-rich asteroid, before embarking on a 1.9-billion-km (1.2-billion-mile) journey back to Earth in May 2021. The Origins, Spectral Interpretation, Resource Identification, Security-Regolith Explorer (OSIRIS-REx) is an unmanned spacecraft from NASA that was sent to collect samples from Bennu. The spacecraft was equipped with cameras to capture images that were essential to collecting samples from the asteroid during the mission.

What could be more delightful than a trip to Greece to meet Yanis Varoufakis, the charismatic leftwing firebrand who tried to stick it to the man, AKA the IMF, EU and entire global financial order? The mental imagery I have before the visit is roughly two parts Zorba the Greek to one part an episode of BBC series Holiday from the Jill Dando era: blue skies, blue sea, maybe some plate breaking in a jolly taverna. What I'm not expecting is a wall of flames rippling across a hillside next to the highway from the airport and a plume of black smoke billowing across the carriageway. Because even a modernist villa on a hillside on the island of Aegina – a fast ferry ride from the port of Piraeus and the summer bolthole of chic Athenians – is not the sanctuary from the modern world that it might once have been. The house is where Varoufakis and his wife, landscape artist Danae Stratou, live, year round since the pandemic, but in August 2023 at the end of a summer of heatwaves and extreme weather conditions across the world, it feels more than a little apocalyptic. The sun is a dim orange orb struggling to shine through a haze of smoke while a shower of fine ash falls invisibly from the sky.

Cybersecurity has become a significant issue in the digital era as a result of the growth in everyday computer use. Cybercriminals now engage in more than virus distribution and computer hacking. Cyberwarfare has developed as a result because it has become a threat to a nation's survival. Malware analysis serves as the first line of defence against an attack and is a significant component of cybercrime. Every day, malware attacks target a large number of computer users, businesses, and governmental agencies, causing billions of dollars in losses. Malware may evade multiple AV software with a very minor, cunning tweak made by its designers, despite the fact that security experts have a variety of tools at their disposal to identify it. To address this challenge, a new dataset called the Obfuscated Malware Dataset (OMD) has been developed. This dataset comprises 40 distinct malware families having 21924 samples, and it incorporates obfuscation techniques that mimic the strategies employed by malware creators to make their malware variations different from the original samples. The purpose of this dataset is to provide a more realistic and representative environment for evaluating the effectiveness of malware analysis techniques. Different conventional machine learning algorithms including but not limited to Support Vector Machine (SVM), Random Forrest (RF), Extreme Gradient Boosting (XGBOOST) etc are applied and contrasted. The results demonstrated that XGBoost outperformed the other algorithms, achieving an accuracy of f 82%, precision of 88%, recall of 80%, and an F1-Score of 83%.

Recently, there has been a growing focus and interest in applying machine learning (ML) to the field of cybersecurity, particularly in malware detection and prevention. Several research works on malware analysis have been proposed, offering promising results for both academic and practical applications. In these works, the use of Generative Adversarial Networks (GANs) or Reinforcement Learning (RL) can aid malware creators in crafting metamorphic malware that evades antivirus software. In this study, we propose a mutation system to counteract ensemble learning-based detectors by combining GANs and an RL model, overcoming the limitations of the MalGAN model. Our proposed FeaGAN model is built based on MalGAN by incorporating an RL model called the Deep Q-network anti-malware Engines Attacking Framework (DQEAF). The RL model addresses three key challenges in performing adversarial attacks on Windows Portable Executable malware, including format preservation, executability preservation, and maliciousness preservation. In the FeaGAN model, ensemble learning is utilized to enhance the malware detector's evasion ability, with the generated adversarial patterns. The experimental results demonstrate that 100\% of the selected mutant samples preserve the format of executable files, while certain successes in both executability preservation and maliciousness preservation are achieved, reaching a stable success rate.

Designing an optimum portfolio for allocating suitable weights to its constituent assets so that the return and risk associated with the portfolio are optimized is a computationally hard problem. The seminal work of Markowitz that attempted to solve the problem by estimating the future returns of the stocks is found to perform sub-optimally on real-world stock market data. This is because the estimation task becomes extremely challenging due to the stochastic and volatile nature of stock prices. This work illustrates three approaches to portfolio design minimizing the risk, optimizing the risk, and assigning equal weights to the stocks of a portfolio. Thirteen critical sectors listed on the National Stock Exchange (NSE) of India are first chosen. Three portfolios are designed following the above approaches choosing the top ten stocks from each sector based on their free-float market capitalization. The portfolios are designed using the historical prices of the stocks from Jan 1, 2017, to Dec 31, 2022. The portfolios are evaluated on the stock price data from Jan 1, 2022, to Dec 31, 2022. The performances of the portfolios are compared, and the portfolio yielding the higher return for each sector is identified.

The widespread adoption of large language models (LLMs) makes it important to recognize their strengths and limitations. We argue that in order to develop a holistic understanding of these systems we need to consider the problem that they were trained to solve: next-word prediction over Internet text. By recognizing the pressures that this task exerts we can make predictions about the strategies that LLMs will adopt, allowing us to reason about when they will succeed or fail. This approach - which we call the teleological approach - leads us to identify three factors that we hypothesize will influence LLM accuracy: the probability of the task to be performed, the probability of the target output, and the probability of the provided input. We predict that LLMs will achieve higher accuracy when these probabilities are high than when they are low - even in deterministic settings where probability should not matter. To test our predictions, we evaluate two LLMs (GPT-3.5 and GPT-4) on eleven tasks, and we find robust evidence that LLMs are influenced by probability in the ways that we have hypothesized. In many cases, the experiments reveal surprising failure modes. For instance, GPT-4's accuracy at decoding a simple cipher is 51% when the output is a high-probability word sequence but only 13% when it is low-probability. These results show that AI practitioners should be careful about using LLMs in low-probability situations. More broadly, we conclude that we should not evaluate LLMs as if they are humans but should instead treat them as a distinct type of system - one that has been shaped by its own particular set of pressures.

Empirical risk minimization (ERM) and distributionally robust optimization (DRO) are popular approaches for solving stochastic optimization problems that appear in operations management and machine learning. Existing generalization error bounds for these methods depend on either the complexity of the cost function or dimension of the random perturbations. Consequently, the performance of these methods can be poor for high-dimensional problems with complex objective functions. We propose a simple approach in which the distribution of random perturbations is approximated using a parametric family of distributions. This mitigates both sources of complexity; however, it introduces a model misspecification error. We show that this new source of error can be controlled by suitable DRO formulations. Our proposed parametric DRO approach has significantly improved generalization bounds over existing ERM and DRO methods and parametric ERM for a wide variety of settings. Our method is particularly effective under distribution shifts and works broadly in contextual optimization. We also illustrate the superior performance of our approach on both synthetic and real-data portfolio optimization and regression tasks.

Learning from label proportions (LLP) is a weakly supervised classification problem where data points are grouped into bags, and the label proportions within each bag are observed instead of the instance-level labels. The task is to learn a classifier to predict the individual labels of future individual instances. Prior work on LLP for multi-class data has yet to develop a theoretically grounded algorithm. In this work, we provide a theoretically grounded approach to LLP based on a reduction to learning with label noise, using the forward correction (FC) loss of \citet{Patrini2017MakingDN}. We establish an excess risk bound and generalization error analysis for our approach, while also extending the theory of the FC loss which may be of independent interest. Our approach demonstrates improved empirical performance in deep learning scenarios across multiple datasets and architectures, compared to the leading existing methods.

California governor Gavin Newsom worked late last night, vetoing a law that would have banned self-driving trucks without a human aboard from state roads until the early 2030s. State lawmakers had voted through the law with wide margins, backed by unions that argued autonomous trucks are a safety risk and threaten jobs. The bill would have seen California, which in 2012 became the first state to clear a regulatory path for autonomous vehicles, turn against self-driving technology just as driverless taxis are starting to serve the public. Autonomous truck developers now hope the freight-heavy state--home to two of the largest US ports--will one day become a critical link in an autonomous trucking network spanning the US. Companies developing the technology say it will save freight shippers money by enabling trucks to run loads on highways 24 hours a day, and by eliminating the dangers of distracted human driving, which could bring down insurance costs.