Networks of atom-centered coordination octahedra commonly occur in inorganic and hybrid solid-state materials. Characterizing their spatial arrangements and characteristics is crucial for relating structures to properties for many materials families. The traditional method using case-by-case inspection becomes prohibitive for discovering trends and similarities in large datasets. Here, we operationalize chemical intuition to automate the geometric parsing, quantification, and classification of coordination octahedral networks. We find axis-resolved tilting trends in ABO$_{3}$ perovskite polymorphs, which assist in detecting oxidation state changes. Moreover, we develop a scale-invariant encoding scheme to represent these networks, which, combined with human-assisted unsupervised machine learning, allows us to taxonomize the inorganic framework polytypes in hybrid iodoplumbates (A$_x$Pb$_y$I$_z$). Consequently, we uncover a violation of Pauling's third rule and the design principles underpinning their topological diversity. Our results offer a glimpse into the vast design space of atomic octahedral networks and inform high-throughput, targeted screening of specific structure types.

Adversarial attacks on deep-learning models pose a serious threat to their reliability and security. Existing defense mechanisms are narrow addressing a specific type of attack or being vulnerable to sophisticated attacks. We propose a new defense mechanism that, while being focused on image-based classifiers, is general with respect to the cited category. It is rooted on hyperspace projection. In particular, our solution provides a pseudo-random projection of the original dataset into a new dataset. The proposed defense mechanism creates a set of diverse projected datasets, where each projected dataset is used to train a specific classifier, resulting in different trained classifiers with different decision boundaries. During testing, it randomly selects a classifier to test the input. Our approach does not sacrifice accuracy over legitimate input. Other than detailing and providing a thorough characterization of our defense mechanism, we also provide a proof of concept of using four optimization-based adversarial attacks (PGD, FGSM, IGSM, and C\&W) and a generative adversarial attack testing them on the MNIST dataset. Our experimental results show that our solution increases the robustness of deep learning models against adversarial attacks and significantly reduces the attack success rate by at least 89% for optimization attacks and 78% for generative attacks. We also analyze the relationship between the number of used hyperspaces and the efficacy of the defense mechanism. As expected, the two are positively correlated, offering an easy-to-tune parameter to enforce the desired level of security. The generality and scalability of our solution and adaptability to different attack scenarios, combined with the excellent achieved results, other than providing a robust defense against adversarial attacks on deep learning networks, also lay the groundwork for future research in the field.

Adversarial attack research in natural language processing (NLP) has made significant progress in designing powerful attack methods and defence approaches. However, few efforts have sought to identify which source samples are the most attackable or robust, i.e. can we determine for an unseen target model, which samples are the most vulnerable to an adversarial attack. This work formally extends the definition of sample attackability/robustness for NLP attacks. Experiments on two popular NLP datasets, four state of the art models and four different NLP adversarial attack methods, demonstrate that sample uncertainty is insufficient for describing characteristics of attackable/robust samples and hence a deep learning based detector can perform much better at identifying the most attackable and robust samples for an unseen target model. Nevertheless, further analysis finds that there is little agreement in which samples are considered the most attackable/robust across different NLP attack methods, explaining a lack of portability of attackability detection methods across attack methods.

"Foundation Models") have reshaped natural language processing (NLP) through their versatility in diverse downstream tasks. However, their potential extends far beyond NLP. This paper provides a software utility to help realize this potential, extending the applicability of GPTs to continuous-time sequences of complex events with internal dependencies, such as medical record datasets. Despite their potential, the adoption of foundation models in these domains has been hampered by the lack of suitable tools for model construction and evaluation. To bridge this gap, we introduce Event Stream GPT (ESGPT), an open-source library designed to streamline the end-to-end process for building GPTs for continuous-time event sequences. ESGPT allows users to (1) build flexible, foundation-model scale input datasets by specifying only a minimal configuration file, (2) leverage a Hugging Face compatible modeling API for GPTs over this modality that incorporates intra-event causal dependency structures and autoregressive generation capabilities, and (3) evaluate models via standardized processes that can assess few and even zero-shot performance of pre-trained models on user-specified fine-tuning tasks.

Wikidata is a knowledge graph increasingly adopted by many communities for diverse applications. Wikidata statements are annotated with qualifier-value pairs that are used to depict information, such as the validity context of the statement, its causality, provenances, etc. Handling the qualifiers in reasoning is a challenging problem. When defining inference rules (in particular, rules on ontological properties (x subclass of y, z instance of x, etc.)), one must consider the qualifiers, as most of them participate in the semantics of the statements. This poses a complex problem because a) there is a massive number of qualifiers, and b) the qualifiers of the inferred statement are often a combination of the qualifiers in the rule condition. In this work, we propose to address this problem by a) defining a categorization of the qualifiers b) formalizing the Wikidata model with a many-sorted logical language; the sorts of this language are the qualifier categories. We couple this logic with an algebraic specification that provides a means for effectively handling qualifiers in inference rules. Using Wikidata ontological properties, we show how to use the MSL and specification to reason on qualifiers. Finally, we discuss the methodology for practically implementing the work and present a prototype implementation. The work can be naturally extended, thanks to the extensibility of the many-sorted algebraic specification, to cover more qualifiers in the specification, such as uncertain time, recurring events, geographic locations, and others.

We develop an open-source tool (EmTract) that extracts emotions from social media text tailed for financial context. To do so, we annotate ten thousand short messages from a financial social media platform (StockTwits) and combine it with open-source emotion data. We then use a pre-tuned NLP model, DistilBERT, augment its embedding space by including 4,861 tokens (emojis and emoticons), and then fit it first on the open-source emotion data, then transfer it to our annotated financial social media data. Our model outperforms competing open-source state-of-the-art emotion classifiers, such as Emotion English DistilRoBERTa-base on both human and chatGPT annotated data. Compared to dictionary based methods, our methodology has three main advantages for research in finance. First, our model is tailored to financial social media text; second, it incorporates key aspects of social media data, such as non-standard phrases, emojis, and emoticons; and third, it operates by sequentially learning a latent representation that includes features such as word order, word usage, and local context. Using EmTract, we explore the relationship between investor emotions expressed on social media and asset prices. We show that firm-specific investor emotions are predictive of daily price movements. Our findings show that emotions and market dynamics are closely related, and we provide a tool to help study the role emotions play in financial markets.

The U.S. Coast Guard said Tuesday afternoon that there around 40-41 hours of "breathable air" left on the OceanGate Titan submersible that disappeared en route to the Titanic wreckage in the North Atlantic. Deep-sea vehicle industry insiders say the combination of the enormous depth, the lack of communication and the rapidly dwindling window of opportunity make rescue of the five people aboard the OceanGate highly unlikely. The OceanGate was bringing its passengers to see the wreckage of the Titanic, about 12,500 below sea level, when it went missing on Sunday. The OceanGate has about 40 hours of oxygen remaining, the Coast Guard reported Tuesday afternoon, assuming the vehicle did not suffer an instant catastrophic explosion, as some experts have said they fear. "The math is not great," one career expert in autonomous underwater vehicles, more commonly known as drones, told Fox News Digital.

Microsoft, Google, OpenAI and other major tech companies are rushing to develop new AI tools and push them out to millions of people. The companies have been lobbying in Washington and to other governments around the world, suggesting potential regulation while stressing the importance of allowing them to continue develop the tech. Critics have warned that the companies are focused on profit, and are trying to head-off strict government controls, or have a hand in shaping them to their own benefit.

Russia has accused Ukraine of planning to attack annexed Crimea with long-range United States and British missiles and warned it would retaliate if that happened. Russian Defence Minister Sergey Shoigu told a meeting of military officials on Tuesday that Moscow possesses information that Ukraine plans to strike Crimea with US-supplied HIMARS long-range rocket systems and British-supplied Storm Shadow cruise missiles. "The use of these missiles outside the zone of our special military operation would mean that the United States and Britain would be fully dragged into the conflict and would entail immediate strikes on decision-making centres in Ukraine," Shoigu said. Russia annexed Ukraine's Crimean Peninsula in 2014 and considers it to be outside the scope of its invasion – which is focused in eastern and southern Ukraine, where Ukraine is fighting to retake territory. Kyiv, which says it is battling for its survival in a war of colonial conquest, said it wants to reclaim all of its territory, including Crimea, the home of Russia's Black Sea naval base.

The president speaks after meeting with AI experts in effort to manage its risks. President Biden is expected to discuss artificial intelligence Tuesday afternoon in San Francisco in his first public speech since son Hunter Biden signed a plea deal on federal tax charges. Hunter Biden will plead guilty to two misdemeanor counts of willful failure to pay federal income tax, Fox News learned Tuesday. "Despite owing in excess of $100,000 in federal income taxes each year, he did not pay the income tax due for either year," the U.S. Attorney for the District of Delaware David C. Weiss' office said. He will also enter into a pretrial diversion agreement regarding a separate felony charge of possession of a firearm by a person who is an unlawful user of or addicted to a controlled substance.