La Habana Province
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The 'discombobulator': Did US use 'secret weapon' in Maduro abduction?
Why is the US Fed chair criminal probe causing alarm? Venezuela's defence minister has accused the United States of using the country as a "weapons laboratory" during the abduction of President Nicolas Maduro and his wife, Cilia Flores, on January 3. Vladimir Padrino Lopez said last week that the US had used Venezuela as a testing ground for "advanced military technologies" that rely on artificial intelligence and weaponry never used before, according to the Venezuelan newspaper El Universal. On Sunday, US President Donald Trump told the New York Post that US forces had indeed used a weapon he referred to as "the discombobulator". "I'm not allowed to talk about it," he said, adding that the weapon "made equipment not work" during the operation. Details of the US military mission to abduct Maduro have not been made public, but the US has been known to use weapons to disorient soldiers and guards or disable equipment and infrastructure in the past.
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The Download: spying on the spies, and promising climate tech
In April 2025, Ronald Deibert left all electronic devices at home in Toronto and boarded a plane. When he landed in Illinois, he bought a new laptop and iPhone. He wanted to reduce the risk of having his personal devices confiscated, because he knew his work made him a prime target for surveillance. "I'm traveling under the assumption that I am being watched, right down to exactly where I am at any moment," Deibert says. Deibert directs the Citizen Lab, a research center he founded in 2001 to serve as "counterintelligence for civil society." Housed at the University of Toronto, it's one of the few institutions that investigate cyberthreats exclusively in the public interest, and in doing so, it has exposed some of the most egregious digital abuses of the past two decades.
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Algorithms for Boolean Matrix Factorization using Integer Programming and Heuristics
Kolomvakis, Christos, Bobille, Thomas, Vandaele, Arnaud, Gillis, Nicolas
Boolean matrix factorization (BMF) approximates a given binary input matrix as the product of two smaller binary factors. Unlike binary matrix factorization based on standard arithmetic, BMF employs the Boolean OR and AND operations for the matrix product, which improves interpretability and reduces the approximation error. It is also used in role mining and computer vision. In this paper, we first propose algorithms for BMF that perform alternating optimization (AO) of the factor matrices, where each subproblem is solved via integer programming (IP). We then design different approaches to further enhance AO-based algorithms by selecting an optimal subset of rank-one factors from multiple runs. To address the scalability limits of IP-based methods, we introduce new greedy and local-search heuristics. We also construct a new C++ data structure for Boolean vectors and matrices that is significantly faster than existing ones and is of independent interest, allowing our heuristics to scale to large datasets. We illustrate the performance of all our proposed methods and compare them with the state of the art on various real datasets, both with and without missing data, including applications in topic modeling and imaging.
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