The war between Russia and Ukraine has highlighted a number of potential weaknesses from energy security and in supply chains of chip set supplies and commodities such as food, to the exploitation of IT, through cyber-attacks and cyber-espionage. The latter can be a strength too, as new technologies have been tested in the conflict by, for example, Ukraine, to oust Russian forces from their territories. These include autonomous and unmanned aerial vehicles (UAVs), the use of artificial intelligence for data gathering to enable "precise strikes and effective surveillance and reconnaissance, which can – at least in part – be attributed to the knowledge which the Ministry of Defence of Ukraine is receiving from the western allies"; and the use of satellites such as Elon Musk's Starlink by Ukraine to attack Russian positions. Key to the success in the conflict is Positioning, Navigation and Timing (PNT). "The ongoing Russia-Ukraine war has exposed the importance of resilient PNT for a nation's safety and security. It has opened up doors to discussions on the fragility of GPS signals that can cost millions of human lives, when data is available to an enemy entity", writes Nibedita Mohanta for Geospatial World in her article, 'Why PNT is vital for national security?'.

Artificial intelligence is the latest tech flavor of the month. Industry giants Google, Microsoft and China's Baidu have all had big AI announcements in recent days, as ChatGPT bot mania is taking the corporate world by storm. All of this AI news has helped boost shares of Baidu (BIDU), Microsoft (MSFT) and Google owner Alphabet (GOOGL) this year. However, Alphabet (GOOGL) tumbled Wednesday following a rocky demo of Bard, its rival to ChatGPT. Traders have also been bidding up the stocks of much smaller, unprofitable companies that are trying to make a name for themselves in the AI arms race.

Earth's changing climate poses an increased risk of drought due to global warming. Since 1880, the global temperature has increased 1.01 C. Since 1993, sea levels have risen 102.5 millimeters. Since 2002, the land ice sheets in Antarctica have been losing mass at a rate of 151.0 billion metric tons per year. In 2022, the Earth's atmosphere contains more than 400 parts per million of carbon dioxide, which is 50% more than it had in 1750. While these numbers might seem removed from our daily lives, the Earth has been warming at an unprecedented rate over the past 10,000 years [1].

An animal-welfare organization said it plans to ask a U.S. government agency on Thursday to investigate Elon Musk's brain-implant company Neuralink over records it said show potentially illegal movement of hazardous pathogens. The Physicians Committee of Responsible Medicine (PCRM) said in a letter to the U.S. Department of Transportation, which was shared with Reuters, that it has obtained emails and other documents that suggest unsafe packaging and movement of implants removed from the brains of monkeys. These implants may have carried infectious diseases in violation of federal law, PCRM said. The letter said records that the group obtained showed instances of pathogens, such as antibiotic-resistant staphylococcus and herpes B virus, that may have been transported without proper containment measures. PCRM's letter adds to the scrutiny facing Neuralink, which is developing a brain implant it hopes will help paralyzed people walk again and cure other neurological ailments.

Google's unveiling of a rival to ChatGPT had an expensively embarrassing stumble on Wednesday when it emerged that promotional material showed the chatbot giving an incorrect response to a question. A video demo of the Google chatbot, Bard, contained a reply suggesting Nasa's James Webb space telescope was used to take the very first pictures of a planet outside the Earth's solar system, or exoplanets. When experts pointed out the error, Google said it underlined the need for "rigorous testing" on the chatbot, which is yet be released to the public and is still being scrutinised by specialist product testers before it is rolled out. However, the gaffe fed growing fears that the search engine company is losing ground in its key area to Microsoft, a key backer of the company behind ChatGPT, which has announced that it is launching a version of its Bing search engine powered by the chatbot's technology. Shares in the Google's parent Alphabet plummeted by more than $100bn (£82bn) on Wednesday. So what exactly went wrong with the Bard demo and what does it say about hopes for AI to revolutionise the search market?

Quantum computers are believed to have the ability to process huge data sizes which can be seen in machine learning applications. In these applications, the data in general is classical. Therefore, to process them on a quantum computer, there is a need for efficient methods which can be used to map classical data on quantum states in a concise manner. On the other hand, to verify the results of quantum computers and study quantum algorithms, we need to be able to approximate quantum operations into forms that are easier to simulate on classical computers with some errors. Motivated by these needs, in this paper we study the approximation of matrices and vectors by using their tensor products obtained through successive Schmidt decompositions. We show that data with distributions such as uniform, Poisson, exponential, or similar to these distributions can be approximated by using only a few terms which can be easily mapped onto quantum circuits. The examples include random data with different distributions, the Gram matrices of iris flower, handwritten digits, 20newsgroup, and labeled faces in the wild. And similarly, some quantum operations such as quantum Fourier transform and variational quantum circuits with a small depth also may be approximated with a few terms that are easier to simulate on classical computers. Furthermore, we show how the method can be used to simplify quantum Hamiltonians: In particular, we show the application to randomly generated transverse field Ising model Hamiltonians. The reduced Hamiltonians can be mapped into quantum circuits easily and therefore can be simulated more efficiently.

We study the average robustness notion in deep neural networks in (selected) wide and narrow, deep and shallow, as well as lazy and non-lazy training settings. We prove that in the under-parameterized setting, width has a negative effect while it improves robustness in the over-parameterized setting. The effect of depth closely depends on the initialization and the training mode. In particular, when initialized with LeCun initialization, depth helps robustness with the lazy training regime. In contrast, when initialized with Neural Tangent Kernel (NTK) and He-initialization, depth hurts the robustness. Moreover, under the non-lazy training regime, we demonstrate how the width of a two-layer ReLU network benefits robustness. Our theoretical developments improve the results by [Huang et al. NeurIPS21; Wu et al. NeurIPS21] and are consistent with [Bubeck and Sellke NeurIPS21; Bubeck et al. COLT21].

Interpretable machine learning offers insights into what factors drive a certain prediction of a black-box system. A large number of interpreting methods focus on identifying explanatory input features, which generally fall into two main categories: attribution and selection. A popular attribution-based approach is to exploit local neighborhoods for learning instance-specific explainers in an additive manner. The process is thus inefficient and susceptible to poorly-conditioned samples. However, they can only interpret single-class predictions and many suffer from inconsistency across different settings, due to a strict reliance on a pre-defined number of features selected. This work exploits the strengths of both methods and proposes a framework for learning local explanations simultaneously for multiple target classes. Our model explainer significantly outperforms additive and instance-wise counterparts on faithfulness with more compact and comprehensible explanations. We also demonstrate the capacity to select stable and important features through extensive experiments on various data sets and black-box model architectures. Black-box machine learning systems enjoy a remarkable predictive performance at the cost of interpretability. This trade-off has motivated a number of interpreting approaches for explaining the behavior of these complex models. Such explanations are particularly useful for high-stakes applications such as healthcare (Caruana et al., 2015; Rich, 2016), cybersecurity (Nguyen et al., 2021) or criminal investigation (Lipton, 2018). While model interpretation can be done in various ways (Mothilal et al., 2020; Bodria et al., 2021), our discussion will focus on feature importance or saliency-based approach - that is, to assign relative importance weights to individual features w.r.t the model's prediction on an input example. Features here refer to input components interpretable to humans; for high-dimensional data such as texts or images, features can be a bag of words/phrases or a group of pixels/super-pixels (Ribeiro et al., 2016). Explanations are generally made by selecting top K features with the highest weights, signifying K most important features to a black-box's decision.

This paper proposes an unsupervised classification method that partitions a set of files into non-overlapping dialects based upon their behaviors, determined by messages produced by a collection of programs that consume them. The pattern of messages can be used as the signature of a particular kind of behavior, with the understanding that some messages are likely to co-occur, while others are not. Patterns of messages can be used to classify files into dialects. A dialect is defined by a subset of messages, called the required messages. Once files are conditioned upon dialect and its required messages, the remaining messages are statistically independent. With this definition of dialect in hand, we present a greedy algorithm that deduces candidate dialects from a dataset consisting of a matrix of file-message data, demonstrate its performance on several file formats, and prove conditions under which it is optimal. We show that an analyst needs to consider fewer dialects than distinct message patterns, which reduces their cognitive load when studying a complex format.

Conflict prediction in communication is integral to the design of virtual agents that support successful teamwork by providing timely assistance. The aim of our research is to analyze discourse to predict collaboration success. Unfortunately, resource scarcity is a problem that teamwork researchers commonly face since it is hard to gather a large number of training examples. To alleviate this problem, this paper introduces a multi-feature embedding (MFeEmb) that improves the generalizability of conflict prediction models trained on dialogue sequences. MFeEmb leverages textual, structural, and semantic information from the dialogues by incorporating lexical, dialogue acts, and sentiment features. The use of dialogue acts and sentiment features reduces performance loss from natural distribution shifts caused mainly by changes in vocabulary. This paper demonstrates the performance of MFeEmb on domain adaptation problems in which the model is trained on discourse from one task domain and applied to predict team performance in a different domain. The generalizability of MFeEmb is quantified using the similarity measure proposed by Bontonou et al. (2021). Our results show that MFeEmb serves as an excellent domain-agnostic representation for meta-pretraining a few-shot model on collaborative multiparty dialogues.