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It seems these robots could be used to spy on you from home. 😃


A team of researchers demonstrated that popular robotic household vacuum cleaners can be remotely hacked to act as microphones.

The researchers—including Nirupam Roy, an assistant professor in the University of Maryland’s Department of Computer Science—collected information from the laser-based in a popular vacuum robot and applied and deep learning techniques to recover speech and identify playing in the same room as the device.

The research demonstrates the potential for any device that uses light detection and ranging (Lidar) technology to be manipulated for collecting , despite not having a microphone. This work, which is a collaboration with assistant professor Jun Han at the University of Singapore was presented at the Association for Computing Machinery’s Conference on Embedded Networked Sensor Systems (SenSys 2020) on November 18, 2020.

Summary: Astrocytes are involved in regulating inhibitory synapses by binding to neurons through the NrCAM adhesion molecule.

Source: Duke University

In the orchestra of the brain, the firing of each neuron is controlled by two notes–excitatory and inhibitory– that come from two distinct forms of a cellular structure called synapses. Synapses are essentially the connections between neurons, transmitting information from one cell to the other. The synaptic harmonies come together to create the most exquisite music–at least most of the time.

New device puts music in your head — no headphones required



LONDON (AP) — Imagine a world where you move around in your own personal sound bubble. You listen to your favorite tunes, play loud computer games, watch a movie or get navigation directions in your car — all without disturbing those around you.

That’s the possibility presented by “sound beaming,” a new futuristic audio technology from Noveto Systems, an Israeli company. On Friday it will debut a desktop device that beams sound directly to a listener without the need for headphones.

The company provided The Associated Press with an exclusive demo of the desktop prototype of its SoundBeamer 1.0 before its launch Friday.

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I highly recommend checking this fantastic look at Deep Blue and the fascinating role chess has played in the ongoing development of artificial intelligence.


After an electrical engineer enters the field of computer chess, his creation captures the attention of the world as he attempts to defeat the world chess champion.

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Transformer model, a deep learning framework, has achieved state-of-the-art results across diverse domains, including natural language, conversation, images, and even music. The core block of any Transformer architecture is the attention module, which computes similarity scores for all pairs of positions in an input sequence. Since it requires quadratic computation time and quadratic memory size of the storing matrix, with the increase in the input sequence’s length, its efficiency decreases.

Thus, for long-range attention, one of the most common methods is sparse attention. It reduces the complexity by computing selective similarity scores from the sequence, based on various methods. There are still certain limitations like unavailability of efficient sparse-matrix multiplication operations on all accelerators, lack of theoretical guarantees, insufficiency to address the full range of problems, etc.