Lifeboat Foundation

Large-capacity wireless data transmission systems are demanded along with the development of multimedia services, video-based interactions, and cloud computing in the era of big data. Compared with radio-frequency communication systems, free-space optical (FSO) signal transmission technology has the merits of high data rate, great flexibility, less power consumption, high security, and large license-free bandwidths [1– 3], which has been widely applied in terrestrial transmission [4], last mile solutions [5], ground-to-satellite optical communication [6], disaster recovery [7], and so on. To date, up to 10 Gbit/s FSO communication system has been realized for transmission distance over 1,000 km of star-ground or inter-star communications [8], and 208 Gbit/s terrestrial communication is also reported at 55 m transmission distance [9]. Wavelength-division multiplexing (WDM) technology is commonly employed to improve data transmission capacity in fiber communication systems, which would be more effective in FSO communication systems benefitting from very weak non-linear cross talk between different frequency channels in free space. Based on a simulation platform, a WDM FSO communication system could boost the signal transmission capacity to 1.28 Tbit/s by modulating 32 optical channels with dual-polarization 16 quadrature amplitude modulation signals [10]. To date, beyond 10 Tbit/s FSO communication systems have been experimentally demonstrated recently using WDM technology [11,12]. However, a WDM communication system becomes power-hungry and bulky with the increase of transmission channels while traditional distributed feedback lasers are used as optical carriers. In addition, more rigorous requirement is imposed on the frequency tolerance of carrier lasers to avoid channel overlap with the decrease of channel frequency interval.

The invention of microresonator-based optical frequency combs provides novel integrated optical laser sources with the natural characteristic of equi-spaced frequency intervals which can overcome the challenge of massive parallel carrier generation [13 – 19]. In particular, the spontaneously organized solitons in continuous-wave (CW)-driven microresonators provide a route to low-noise ultra-short pulses with a repetition rate from 10 GHz to beyond terahertz. Soliton microcombs (SMCs) are typical stable laser sources where the double balances of non-linearity and dispersion as well as dissipation and gain are reached in microcavities. Meanwhile, the linewidth of the comb lines is similar with the pump laser, which enables low power consumption and costs multiwavelength narrow-linewidth carriers for a wide range of applications. Through designing the scale of microresonators, the repetition rate of SMCs could be compatible with dense wavelength-division multiplexing (DWDM) communication standard. To date, several experiments have demonstrated the potential capacity for ultra-high-speed fiber communication systems using SMCs as multiwavelength laser sources [20 – 30]. For instance, a coherent fiber communication system has improved the transmission capacity up to 55 Tbit/s using single bright SMCs as optical carriers and a local oscillator [20]. And dark solitons and soliton crystals are also employed as multiwavelength laser sources for WDM communication systems [27 – 30]. However, few studies have carried out massive parallel FSO communication systems using the integrated SMCs as laser sources.

In this paper, we experimentally demonstrate a massive parallel FSO communication system using an SMC as a multiple optical carrier generator. 102 comb lines are modulated by 10 Gbit/s differential phase shift keying (DPSK) signals to boost the FSO transmission rate up to beyond 1 Tbit/s. The transmitter and receiver terminals are installed in two buildings at a distance of ∼1 km, respectively. Using a CW laser as reference, the influence of optical signal-to-noise ratios (OSNRs) on the bit error rate (BER) performance is experimentally analyzed. Our results show an effective solution for large-capacity spatial signal transmission using an integrated SMC source which has potential applications in future satellite-to-ground communication systems.

SPEAKING at the University of Cambridge in 1980, Stephen Hawking considered the possibility of a theory of everything that would unite general relativity and quantum mechanics – our two leading descriptions of reality – into one neat, all-encompassing equation. We would need some help, he reckoned, from computers. Then he made a provocative prediction about these machines’ growing abilities. “The end might not be in sight for theoretical physics,” said Hawking. “But it might be in sight for theoretical physicists.”

Artificial intelligence has achieved much since then, yet physicists have been slow to use it to search for new and deeper laws of nature. It isn’t that they fear for their jobs. Indeed, Hawking may have had his tongue firmly in his cheek. Rather, it is that the deep-learning algorithms behind AIs spit out answers that amount to a “what” rather than a “why”, which makes them about as useful for a theorist as saying the answer to the question of life, the universe and everything is 42.

The information security landscape is rapidly changing in response to quantum computing technology, which is capable of cracking modern encryption techniques in minutes, but a promising US government encryption algorithm for the post-quantum world was just cracked in less than an hour thanks to a decades-old math theorem.

In July 2022, the US National Institute of Standards and Technology (NIST) chose a set of encryption algorithms that it hoped would stand up to the encryption-cracking power of quantum computers and tasked researchers with probing them for vulnerabilities, offering a $50,000 prize for anyone who was able to break the encryption.

Astronomers at Caltech have used a machine learning algorithm to classify 1,000 supernovae completely autonomously. The algorithm was applied to data captured by the Zwicky Transient Facility, or ZTF, a sky survey instrument based at Caltech’s Palomar Observatory.

“We needed a helping hand, and we knew that once we trained our computers to do the job, they would take a big load off our backs,” says Christoffer Fremling, a staff astronomer at Caltech and the mastermind behind the new algorithm, dubbed SNIascore. “SNIascore classified its first supernova in April 2021, and, a year and a half later, we are hitting a nice milestone of 1,000 supernovae.”

Continue reading “Machine learning tools autonomously classify 1,000 supernovae” »

Ground News Black Friday Sale: Compare news coverage. Spot media bias. Avoid algorithms. Download the free Ground News app to get 40% off a Ground News Vantage membership by going to https://ground.news/isaacarthur.
As we head through hard times things can seem rather bleak, but there’s lots of amazing and beneficial technologies on the horizon.

Checkout the World’s Fair Posters: https://worldsfair.co/gallery.
Visit our Website: http://www.isaacarthur.net.
Support us on Patreon: https://www.patreon.com/IsaacArthur.
Support us on Subscribestar: https://www.subscribestar.com/isaac-arthur.
Facebook Group: https://www.facebook.com/groups/1583992725237264/
Reddit: https://www.reddit.com/r/IsaacArthur/
Twitter: https://twitter.com/Isaac_A_Arthur on Twitter and RT our future content.
SFIA Discord Server: https://discord.gg/53GAShE

Continue reading “Reasons To Be Optimistic About The Future” »

Deep Learning AI Specialization: https://imp.i384100.net/GET-STARTED
Nvidia unveils its new artificial intelligence 3D model maker for game design uses text or photo input to output a 3D mesh and can also edit and adjust 3D models with text descriptions. New video style transfer from Nvidia uses CLIP to convert the style of 3D models and photos. New differential equation-based neural network machine learning AI from MIT solves brain dynamics.

AI News Timestamps:
0:00 Nvidia AI Turns Text To 3D Model Better Than Google.
2:03 Nvidia 3D Object Style Transfer AI
4:56 New Machine Learning AI From MIT

#nvidia #ai #3D

https://www.youtube.com/watch?v=QrXnYHubFPc

Deep Learning AI Specialization: https://imp.i384100.net/GET-STARTED
AI News Timestamps:
0:00 New AI Robot Dog Beats Human Soccer Skills.
2:34 Breakthrough Humanoid Robotics & AI Tech.
5:21 Google AI Makes HD Video From Text.
8:41 New OpenAI DALL-E Robotics.
11:31 Elon Musk Reveals Tesla Optimus AI Robot.
16:49 Machine Learning Driven Exoskeleton.
19:33 Google AI Makes Video Game Objects From Text.
22:12 Breakthrough Tesla AI Supercomputer.
25:32 Underwater Drone Humanoid Robot.
29:19 Breakthrough Google AI Edits Images With Text.
31:43 New Deep Learning Tech With Light waves.
34:50 Nvidia General Robot Manipulation AI
36:31 Quantum Computer Breakthrough.
38:00 In-Vitro Neural Network Plays Video Games.
39:56 Google DeepMind AI Discovers New Matrices Algorithms.
45:07 New Meta Text To Video AI
48:00 Bionic Tech Feels In Virtual Reality.
53:06 Quantum Physics AI
56:40 Soft Robotics Gripper Learns.
58:13 New Google NLP Powered Robotics.
59:48 Ionic Chips For AI Neural Networks.
1:02:43 Machine Learning Interprets Brain Waves & Reads Mind.

Researchers have investigated the capability of known quantum computing algorithms for fault-tolerant quantum computing to simulate the laser-driven electron dynamics of excitation and ionization processes in small molecules. Their research is published in the Journal of Chemical Theory and Computation.

“These quantum computer algorithms were originally developed in a completely different context. We used them here for the first time to calculate electron densities of molecules, in particular their dynamic evolution after excitation by a light pulse,” says Annika Bande, who heads a group on theoretical chemistry at Helmholtz Association of German Research Centers (HZB). Bande and Fabian Langkabel, who is doing his doctorate with her, show in the study how well this works.

“We developed an algorithm for a fictitious, completely error-free quantum computer and ran it on a classical server simulating a quantum computer of ten qubits,” says Langkabel. The scientists limited their study to smaller molecules in order to be able to perform the calculations without a real quantum computer and to compare them with conventional calculations.

They then used QUARTZ to analyze retinal images from 7,411 more people, these aged 48 to 92, and combined this data with information about their health history (such as smoking, statin use, and previous heart attacks) to predict their risk of heart disease. Participants’ health was tracked for seven to nine years, and their outcomes were compared to Framingham risk score (FRS) predictions.

A common tool for estimating heart disease risk, the FRS looks at age, gender, total cholesterol, high density lipoprotein cholesterol, smoking habits, and systolic blood pressure to estimate the probability someone will develop heart disease within a given span of time, usually 10 to 30 years.

The QUARTZ team compared their data to 10-year FRS predictions and said the algorithm’s accuracy was on par with that of the conventional tool.