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Abstract Slow earthquakes, including low-frequency earthquakes, tremor, and geodetically detected slow-slip events, have been widely detected, most commonly at depths of 40–60 km in active subduction zones around the Pacific Ocean Basin. Rocks exhumed from these depths allow us to search for structures that may initiate slow earthquakes. The evidence for high pore-fluid pressures in subduction zones suggests that they may be associated with hydraulic fractures (e.g., veins) and with metamorphic reactions that release or consume water. Loss of continuity and resulting slip at rates exceeding 10−4 m s–1 are required to produce the quasi-seismic signature of low-frequency earthquakes, but the subseismic displacement rates require that the slip rate is slowed by a viscous process, such as low permeability, limiting the rate at which fluid can access a propagating fracture. Displacements during individual low-frequency earthquakes are unlikely to exceed 1 mm, but they need to be more than 0.1 mm and act over an area of ~105 m2 to produce a detectable effective seismic moment. This limits candidate structures to those that have lateral dimensions of ~300 m and move in increments of <1 mm. Possible candidates include arrays of sheeted shear veins showing crack-seal structures; dilational arcs in microfold hinges that form crenulation cleavages; brittle-ductile shear zones in which the viscous component of deformation can limit the displacement rate during slow-slip events; slip surfaces coated with materials, such as chlorite or serpentine, that exhibit a transition from velocity-weakening to velocity-strengthening behavior with increasing slip velocity; and block-in-matrix mélanges. 

Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the ML community to join the global effort against climate change. 

In recent years, enterprises have been targeted by advanced adversaries who leverage creative ways to infiltrate their systems and move laterally to gain access to critical data. One increasingly common evasive method is to hide the malicious activity behind a benign program by using tools that are already installed on user computers. These programs are usually part of the operating system distribution or another user-installed binary, therefore this type of attack is called “Living-Off-The-Land”. Detecting these attacks is challenging, as adversaries may not create malicious files on the victim computers and anti-virus scans fail to detect them. We propose the design of an Active Learning framework called LOLAL for detecting Living-Off-the-Land attacks that iteratively selects a set of uncertain and anomalous samples for labeling by a human analyst. LOLAL is specifically designed to work well when a limited number of labeled samples are available for training machine learning models to detect attacks. We investigate methods to represent command-line text using word-embedding techniques, and design ensemble boosting classifiers to distinguish malicious and benign samples based on the embedding representation. We leverage a large, anonymized dataset collected by an endpoint security product and demonstrate that our ensemble classifiers achieve an average F1 score of 96% at classifying different attack classes. We show that our active learning method consistently improves the classifier performance, as more training data is labeled, and converges in less than 30 iterations when starting with a small number of labeled instances. 

Abstract Practical quantum computing will require error rates well below those achievable with physical qubits. Quantum error correction^1,2offers a path to algorithmically relevant error rates by encoding logical qubits within many physical qubits, for which increasing the number of physical qubits enhances protection against physical errors. However, introducing more qubits also increases the number of error sources, so the density of errors must be sufficiently low for logical performance to improve with increasing code size. Here we report the measurement of logical qubit performance scaling across several code sizes, and demonstrate that our system of superconducting qubits has sufficient performance to overcome the additional errors from increasing qubit number. We find that our distance-5 surface code logical qubit modestly outperforms an ensemble of distance-3 logical qubits on average, in terms of both logical error probability over 25 cycles and logical error per cycle ((2.914 ± 0.016)% compared to (3.028 ± 0.023)%). To investigate damaging, low-probability error sources, we run a distance-25 repetition code and observe a 1.7 × 10⁻⁶logical error per cycle floor set by a single high-energy event (1.6 × 10⁻⁷excluding this event). We accurately model our experiment, extracting error budgets that highlight the biggest challenges for future systems. These results mark an experimental demonstration in which quantum error correction begins to improve performance with increasing qubit number, illuminating the path to reaching the logical error rates required for computation.