The administration of President Joe Biden entered office on January 20, 2021, pledging a broad-strokes overhaul of how Washington interacts with the world, promising to be a distinct counterpoint to the disruptive, go-it-alone posture of former President Donald Trump, and tying stability and prosperity at home to US interests abroad in his so-called "foreign policy for the middle class". As 2021 ends, the administration has indeed sought to re-up relations with key allies and position itself as a central player in combating global crises, but has faced criticism for failing to live up to vows of a human rights-leading foreign policy and for what some have described as an over-emphasis on sweeping ideological differences at a time when global cooperation -- particularly between superpowers -- is sorely needed. "2021 was a year of transition. President Biden replaced Trump's impetuousness with pragmatism and realism. There is a greater understanding of what US policy actually is," PJ Crowley, the former US assistant secretary of state for public affairs under President Barack Obama, told Al Jazeera.

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In time series analysis, when fitting an autoregressive model, one must solve a Toeplitz ordinary least squares problem numerous times to find an appropriate model, which can severely affect computational times with large data sets. Two recent algorithms (LSAR and Repeated Halving) have applied randomized numerical linear algebra (RandNLA) techniques to fitting an autoregressive model to big time-series data. We investigate and compare the quality of these two approximation algorithms on large-scale synthetic and real-world data. While both algorithms display comparable results for synthetic datasets, the LSAR algorithm appears to be more robust when applied to real-world time series data. We conclude that RandNLA is effective in the context of big-data time series.

Active Directory is the default security management system for Windows domain networks. We study the shortest path edge interdiction problem for defending Active Directory style attack graphs. The problem is formulated as a Stackelberg game between one defender and one attacker. The attack graph contains one destination node and multiple entry nodes. The attacker's entry node is chosen by nature. The defender chooses to block a set of edges limited by his budget. The attacker then picks the shortest unblocked attack path. The defender aims to maximize the expected shortest path length for the attacker, where the expectation is taken over entry nodes. We observe that practical Active Directory attack graphs have small maximum attack path lengths and are structurally close to trees. We first show that even if the maximum attack path length is a constant, the problem is still $W[1]$-hard with respect to the defender's budget. Having a small maximum attack path length and a small budget is not enough to design fixed-parameter algorithms. If we further assume that the number of entry nodes is small, then we derive a fixed-parameter tractable algorithm. We then propose two other fixed-parameter algorithms by exploiting the tree-like features. One is based on tree decomposition and requires a small tree width. The other assumes a small number of splitting nodes (nodes with multiple out-going edges). Finally, the last algorithm is converted into a graph convolutional neural network based heuristic, which scales to larger graphs with more splitting nodes.

Recent backscatter communication techniques enable ultra low power wireless devices that operate without batteries while interoperating directly with unmodified commodity wireless devices. Commodity devices cooperate in providing the unmodulated carrier that the battery-free nodes need to communicate while collecting energy from their environment to perform sensing, computation, and communication tasks. The optimal provision of the unmodulated carrier limits the size of the network because it is an NP-hard combinatorial optimization problem. Consequently, previous works either ignore carrier optimization altogether or resort to suboptimal heuristics, wasting valuable energy and spectral resources. We present DeepGANTT, a deep learning scheduler for battery-free devices interoperating with wireless commodity ones. DeepGANTT leverages graph neural networks to overcome variable input and output size challenges inherent to this problem. We train our deep learning scheduler with optimal schedules of relatively small size obtained from a constraint optimization solver. DeepGANTT not only outperforms a carefully crafted heuristic solution but also performs within ~3% of the optimal scheduler on trained problem sizes. Finally, DeepGANTT generalizes to problems more than four times larger than the maximum used for training, therefore breaking the scalability limitations of the optimal scheduler and paving the way for more efficient backscatter networks.

Clearview AI has been provided with the go-ahead signal for securing a federal patent for its facial recognition software, as reported by Politico. The US Patents and Trademarks Office sent a "notice of allowance" to Clearview on December 1, implying that the patent will be awarded once the organization pays the necessary administrative fees. The patent will, among other things, cover its method of obtaining information, specifically, its "automated web crawler" tool that scans social networking sites and the internet, the same process that has led the company to face extensive backlash from numerous privacy advocates. "There are other facial recognition patents out there -- that are methods of doing it -- but this is the first one around the use of large-scale internet data," Ton-That, the founder of Clearview AI, told Politico in an interview. Clearview AI has more than 10 billion photos in their database, which was confirmed by Ton-That, much more than the previous estimate of 3 billion images.

Artificial intelligence (AI) backed drug discovery company Insilico Medicine announced last week that it was dosing the first healthy volunteer in a microdose trial of ISM 001-005. Designed with the help of AI, the drug is a small-molecule inhibitor of a biological target that was discovered by Pharma.AI. The trial is being conducted in Australia. The AI-designed drug will be used to treat chronic lung disease idiopathic pulmonary fibrosis, or IPF. IPF usually leads to progressive and irreversible lung-function decline and affects 20 people out of over 100,000 globally.

Modern neural language models widely used in tasks across NLP risk memorizing sensitive information from their training data. As models continue to scale up in parameters, training data, and compute, understanding memorization in language models is both important from a learning-theoretical point of view, and is practically crucial in real world applications. An open question in previous studies of memorization in language models is how to filter out "common" memorization. In fact, most memorization criteria strongly correlate with the number of occurrences in the training set, capturing "common" memorization such as familiar phrases, public knowledge or templated texts. In this paper, we provide a principled perspective inspired by a taxonomy of human memory in Psychology. From this perspective, we formulate a notion of counterfactual memorization, which characterizes how a model's predictions change if a particular document is omitted during training. We identify and study counterfactually-memorized training examples in standard text datasets. We further estimate the influence of each training example on the validation set and on generated texts, and show that this can provide direct evidence of the source of memorization at test time.

The disruption of supplies during the Covid-19 crisis has led to shortages but has also shown the adaptability of some companies, which have succeeded in adapting their production chains quickly to produce goods experiencing shortages: hydroalcoholic gel, masks, and medical gowns. These productive jumps from product A to product B are feasible because of the know-how proximity between the two classes of products. The proximities were computed from the analysis of co-exports and resulted in the construction of the product space. Based on the product space, as well as the customer-supplier relationships resulting from the input-output matrices, we propose a recommender system for companies. The goal is to promote distributed manufacturing by recommending a list of local suppliers to each company. As there is not always a local supplier for a desired product class, we consider the proximity between products to identify, in the absence of a supplier, a substitute supplier able to adapt its production tools to provide the required product. Our experiments are based on French data, from which we build a graph of synergies illustrating the potential productive links between companies. Finally, we show that our approach offers new perspectives to determine the level of territories' industrial resilience considering potential productive jumps.