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A trio of researchers has found a way to pick an ordinary physical lock using a smartphone with special software. The three, Soundarya Ramesh, Harini Ramprasad, and Jun Han, gave a talk at a workshop called HotMobile 2020 at this year’s International Workshop on Mobile Computing Systems and Applications, outlining their work.

With traditional locks, such as those found on the front doors of most homes, a person inserts the proper (metal) key and then turns it. Doing so pushes up a series of pins in the lock by a certain amount based on the ridges on the key. When the pins are pushed in a way that matches a preset condition, the tumbler can turn, retracting the metal piece of the assembly from its berth, allowing the door to open. In this new effort, the researchers have found that it is possible to record the sounds made as the key comes into contact with the pins and then as the pins move upward, and use to recreate the conditions that produce the same noises. Those conditions can be used to fabricate a metal key to unlock the door. The result is a system the team calls SpiKey, which involves use of a smartphone to record lock clicks, decipher them and then create a key signature for use in creating a new key.

The researchers acknowledged in their presentation that the weak link in their system is recording the key unlocking the door. Because of its nature, they assume that the recording would have to be done secretly so as to not alert a homeowner that their lock is being picked. They suggest that several possible options for wrongdoers, including walking past while holding a microphone, hiding a microphone nearby, or installing software on the victim’s phone. Each has its own risks, they note, which would minimize the likelihood of run-of-the-mill burglars using such an approach. But for high-profile victims, the effort might be worth the risk. They say that they next plan to investigate ways to foil such attacks by modifying traditional locks.

As best we can guess, life started on planet Earth about 3.5 billion years ago. Unfortunately, so did death. And the reaper remains undefeated.

About 99 percent of all species that ever lived are now extinct. There’s almost no scientific reason to believe humans won’t join them in a relatively insignificant amount of time. I say almost because, if we try really hard, we can conceive of a theoretical, science-based intervention for death. Let’s call it a “quantum respawn.”

We’re not the first generation to imagine immortality. But we are the first one to have access to this really cool research paper from physicists working at the University of Rochester in New York, and Purdue University in Indiana.

Controlling temperature is crucial for the functioning of electronic devices. It’s even more so for highly complex quantum computers that rely on the ability to control quantum bits (also called qubits) in order to achieve processing capabilities far above the most powerful classical computer.

For a quantum computer to maintain its prowess, it must be cooled to a temperature close to absolute zero (−273.15oC) to keep the qubits in a state of coherence. However, keeping a quantum computer’s core temperature near absolute zero is not a simple feat and poses a major roadblock to the advancement of quantum computing. Often quantum computer producers keep the machines cool by using liquid helium as a refrigerant delivered in multiple stages. Nonetheless, this system is cumbersome and elaborate, and is not user-friendly.

Researchers have fashioned ultrathin silicon nanoantennas that trap and redirect light, for applications in quantum computing, LIDAR and even the detection of viruses.

Light is notoriously fast. Its speed is crucial for rapid information exchange, but as light zips through materials, its chances of interacting and exciting atoms and molecules can become very small. If scientists can put the brakes on light particles, or photons, it would open the door to a host of new technology applications.

Now, in a paper published on August 17, 2020, in Nature Nanotechnology, Stanford scientists demonstrate a new approach to slow light significantly, much like an echo chamber holds onto sound, and to direct it at will. Researchers in the lab of Jennifer Dionne, associate professor of materials science and engineering at Stanford, structured ultrathin silicon chips into nanoscale bars to resonantly trap light and then release or redirect it later. These “high-quality-factor” or “high-Q” resonators could lead to novel ways of manipulating and using light, including new applications for quantum computing, virtual reality and augmented reality; light-based WiFi; and even the detection of viruses like SARS-CoV-2.

Molecular engineers at the University of Chicago have found a way to extend the quantum state of a qubit to 22 milliseconds, representing a huge improvement and a window some say will make quantum computers far more feasible. The secret is an alternating magnetic field, which they say is scientifically “intricate” but easy to apply.

🤯 You like quantum. So do we. Let’s nerd out over it together.

“Using microfluidics, computer modeling and other techniques, they found that about half of the cells age through a gradual decline in the stability of the nucleolus, a region of nuclear DNA where key components of protein-producing “factories” are synthesized,” a press release announcing the research explains. “In contrast, the other half age due to dysfunction of their mitochondria, the energy production units of cells.”


Researchers studying aging have discovered that cells tend to follow one of two aging pathways. The way each individual cell ages is determined early on, and scientists can predict how a cell will age based on early observations.

Circa 2017


Positron emission tomography (PET) image synthesis plays an important role, which can be used to boost the training data for computer aided diagnosis systems. However, existing image synthesis methods have problems in synthesizing the low resolution PET images. To address these limitations, we propose multi-channel generative adversarial networks (M-GAN) based PET image synthesis method. Different to the existing methods which rely on using low-level features, the proposed M-GAN is capable to represent the features in a high-level of semantic based on the adversarial learning concept. In addition, M-GAN enables to take the input from the annotation (label) to synthesize the high uptake regions e.g., tumors and from the computed tomography (CT) images to constrain the appearance consistency and output the synthetic PET images directly.