A cognitive radar is a constrained utility maximizer that adapts its sensing mode in response to a changing environment. If an adversary can estimate the utility function of a cognitive radar, it can determine the radar's sensing strategy and mitigate the radar performance via electronic countermeasures (ECM). This paper discusses how a cognitive radar can {\em hide} its strategy from an adversary that detects cognition. The radar does so by transmitting purposefully designed sub-optimal responses to spoof the adversary's Neyman-Pearson detector. We provide theoretical guarantees by ensuring the Type-I error probability of the adversary's detector exceeds a pre-defined level for a specified tolerance on the radar's performance loss. We illustrate our cognition masking scheme via numerical examples involving waveform adaptation and beam allocation. We show that small purposeful deviations from the optimal strategy of the radar confuse the adversary by significant amounts, thereby masking the radar's cognition. Our approach uses novel ideas from revealed preference in microeconomics and adversarial inverse reinforcement learning. Our proposed algorithms provide a principled approach for system-level electronic counter-countermeasures (ECCM) to mask the radar's cognition, i.e., hide the radar's strategy from an adversary. We also provide performance bounds for our cognition masking scheme when the adversary has misspecified measurements of the radar's response.

Elon Musk's brain implant company Neuralink is now hiring a clinical trial director, an indication that the company's longstanding goal of implanting chips in human brains is coming closer. The trial director position would oversee the startup's long-promised human trials of its medical device, according to the listing. Neuralink's brain implant -- which Musk has said already allows monkeys to play video games with their thoughts alone -- is intended to help treat a variety of neurological disorders, such as paralysis. The job description for the position, based in Fremont, California, promises that the applicant will "work closely with some of the most innovative doctors and top engineers" as well as with "Neuralink's first Clinical Trial participants." It also indicates that the job will mean leading and building "the team responsible for enabling Neuralink's clinical research activities," as well as adhering to regulations.

For most branching algorithms in Boolean logic "branching" means "variable-wise branching". We present the apparently novel technique of clause-wise branching, which is used to solve the ALLSAT problem for arbitrary Boolean functions in CNF format. Specifically, it converts a CNF into an orthogonal DNF, i.e. into an exclusive sum of products. Our method is enhanced by two ingredients: The use of a good SAT-solver and wildcards beyond the common don't-care symbol.

We provide a scheme for inferring causal relations from uncontrolled statistical data based on tools from computational algebraic geometry, in particular, the computation of Groebner bases. We focus on causal structures containing just two observed variables, each of which is binary. We consider the consequences of imposing different restrictions on the number and cardinality of latent variables and of assuming different functional dependences of the observed variables on the latent ones (in particular, the noise need not be additive). We provide an inductive scheme for classifying functional causal structures into distinct observational equivalence classes. For each observational equivalence class, we provide a procedure for deriving constraints on the joint distribution that are necessary and sufficient conditions for it to arise from a model in that class. We also demonstrate how this sort of approach provides a means of determining which causal parameters are identifiable and how to solve for these. Prospects for expanding the scope of our scheme, in particular to the problem of quantum causal inference, are also discussed.

Multi-agent pathfinding is an important challenge that relates to combinatorial search and has many applications, such as warehouse management, robotics and computer games. Finding an optimal solution is NP-hard and raises scalability issues for optimal solvers. Interestingly, however, it takes linear time to check the feasibility of an instance. These linear-time feasibility tests can be extended to provide path planners but to the best of the authors’ knowledge no such solver has been provided for general graphs. This work first describes a path planner that is inspired by a linear-time feasibility test for multi-agent pathfinding on general graphs. Initial experiments indicated reasonable scalability but worse path quality relative to existing suboptimal solutions. This led to the development of an algorithm that achieves both efficient running time and path quality relative to the alternatives and which finds a solution on available benchmarks. The paper outlines the relation of the final method to the feasibility tests and existing suboptimal planners. Experimental results evaluate the different algorithms, including an optimal solver.