Radio jamming systems apparently thwarted an attempted presidential assassination with improvised drone bombs in Venezuela. On Saturday 4th August, President Nicolas Maduro's speech at an outdoor rally was interrupted by two explosions. Seven soldiers on parade were injured, three critically. Others scattered while bodyguards rushed to protect the president with bulletproof shields. Witnesses reported seeing two multicopter drones which crashed into a nearby apartment building and exploded.

CARACAS – Venezuela's all-powerful constituent assembly was to launch proceedings Wednesday to try opposition lawmakers over a failed "attack" on President Nicolas Maduro, who also accused exiled opposition leader Julio Borges over the incident. Constituent Assembly chief Diosdado Cabello called the session to strip the lawmakers of their parliamentary immunity so they could face trial for the alleged and failed bid to kill the president. "When justice comes, it hits hard," Cabello said. Maduro and his government said the president had been targeted by two flying drones each carrying 1 kilogram (2.2 pounds) of powerful C4 plastic explosives. But details of Saturday's incident remain unclear, with conflicting information coming from various sources. The Maduro administration said Colombia -- including ex-President Juan Manuel Santos, who ended his term Tuesday -- had collaborated on the attack with the "ultra-far-right" Venezuelan opposition, and it was financed by unnamed figures in Florida.

President Nicolas Maduro has accused two opposition legislators of having roles in the drone attack that Venezuelan officials have called an assassination attempt on the leader, and his allies are moving against the accused.

CARACAS, Venezuela – President Nicolas Maduro went on television Tuesday night to accuse one of Venezuela's most prominent opposition leaders of being linked to a weekend assassination attempt using drones. Maduro said statements by several of the six suspects already arrested pointed to involvement by Julio Borges, an opposition leader living in exile in Colombia. "Several of the declarations indicated Julio Borges. The investigations point to him," Maduro said, though he provided no details on Borges' alleged role. Critics of Maduro's socialist government had said immediately following Saturday evening's attack that they feared the unpopular leader would use the incident as an excuse to round up opposition politicians amid widespread unrest over Venezuela's devastating economic collapse.

CARACAS, Venezuela – President Nicolas Maduro has accused two opposition legislators of having roles in the drone attack that Venezuelan officials have called an assassination attempt on the leader, and his allies are moving against the accused. The head of Venezuela's pro-government constitutional assembly said he would have the body take up a proposal Wednesday to strip the lawmakers of their immunity from prosecution. During a national television broadcast Tuesday night, Maduro said statements from some of the six suspects already arrested in the weekend attack pointed to key financiers and others, including Julio Borges, one of the country's most prominent opposition leaders who is a lawmaker but is living in exile in Colombia. "Several of the declarations indicated Julio Borges. The investigations point to him," Maduro said, though he provided no details on Borges' alleged role.

Critics of Maduro's socialist government had said immediately following Saturday evening's attack that they feared the unpopular leader would use the incident as an excuse to round up opposition politicians as he seeks to dampen spreading discontent over Venezuela's devastating economic collapse.

BOGOTA/CARACAS – A former Venezuelan municipal police chief and anti-government activist says he helped organize an operation to launch armed drones over a military rally on Saturday that President Nicolas Maduro has called an assassination attempt. In an interview, Salvatore Lucchese, a Venezuelan activist who was previously imprisoned for his role in past protests, told Reuters he orchestrated the attack with a loose association of anti-Maduro militants known generally in Venezuela as the "resistance." The "resistance" referred to by Lucchese is a diffuse collection of street activists, student organizers and former military officers. It has little formal structure, but is known in the country mostly for organizing protests in recent years in which demonstrators have clashed with police and soldiers. Reuters could not independently verify Lucchese's claims about the attack, in which drones flew over the rally in central Caracas.

In this paper, we study the problem of compressed sensing using binary measurement matrices, and $\ell_1$-norm minimization (basis pursuit) as the recovery algorithm. We derive new upper and lower bounds on the number of measurements to achieve robust sparse recovery with binary matrices. We establish sufficient conditions for a column-regular binary matrix to satisfy the robust null space property (RNSP), and show that the sparsity bounds for robust sparse recovery obtained using the RNSP are better by a factor of $(3 \sqrt{3})/2 \approx 2.6$ compared to the restricted isometry property (RIP). Next we derive universal lower bounds on the number of measurements that any binary matrix needs to have in order to satisfy the weaker sufficient condition based on the RNSP, and show that bipartite graphs of girth six are optimal. Then we display two classes of binary matrices, namely parity check matrices of array codes, and Euler squares, that have girth six and are nearly optimal in the sense of almost satisfying the lower bound. In principle randomly generated Gaussian measurement matrices are "order-optimal." So we compare the phase transition behavior of the basis pursuit formulation using binary array code and Gaussian matrices, and show that (i) there is essentially no difference between the phase transition boundaries in the two cases, and (ii) the CPU time of basis pursuit with binary matrices is hundreds of times faster than with Gaussian matrices, and the storage requirements are less. Therefore it is suggested that binary matrices are a viable alternative to Gaussian matrices for compressed sensing using basis pursuit.

Many machine learning classifiers are vulnerable to adversarial attacks, inputs with perturbations designed to intentionally trigger misclassification. Modern adversarial methods either directly alter pixel colors, or "paint" colors onto a 3D shapes. We propose novel adversarial attacks that directly alter the geometry of 3D objects and/or manipulate the lighting in a virtual scene. We leverage a novel differentiable renderer that is efficient to evaluate and analytically differentiate. Our renderer generates images realistic enough for correct classification by common pre-trained models, and we use it to design physical adversarial examples that consistently fool these models. We conduct qualitative and quantitate experiments to validate our adversarial geometry and adversarial lighting attack capabilities.

Quantum computers can offer dramatic improvements over classical devices for data analysis tasks such as prediction and classification. However, less is known about the advantages that quantum computers may bring in the setting of reinforcement learning, where learning is achieved via interaction with a task environment. Here, we consider a special case of reinforcement learning, where the task environment allows quantum access. In addition, we impose certain "naturalness" conditions on the task environment, which rule out the kinds of oracle problems that are studied in quantum query complexity (and for which quantum speedups are well-known). Within this framework of quantum-accessible reinforcement learning environments, we demonstrate that quantum agents can achieve exponential improvements in learning efficiency, surpassing previous results that showed only quadratic improvements. A key step in the proof is to construct task environments that encode well-known oracle problems, such as Simon's problem and Recursive Fourier Sampling, while satisfying the above "naturalness" conditions for reinforcement learning. Our results suggest that quantum agents may perform well in certain game-playing scenarios, where the game has recursive structure, and the agent can learn by playing against itself.