Electronics engineers worldwide are trying to improve the performance of devices, while also lowering their power consumption. Tunnel field-effect transistors (TFETs), an experimental class of transistors with a unique switching mechanism, could be a particularly promising solution for developing low-power electronics.
Despite their potential, most TFETs based on silicon and III-V heterojunctions exhibit low on-current densities and on/off current ratios in some modes of operation. Fabricating these transistors using 2D materials could help to improve electrostatic control, potentially increasing their on-current densities and on/off ratios.
Researchers at University of Pennsylvania, the Chinese Academy of Sciences, the National Institute of Standards and Technology, and the Air Force Research Laboratory have recently developed new heterojunction tunnel triodes based on van der Waals heterostructures formed from 2D metal selenide and 3D silicon. These triodes, presented in a paper published in Nature Electronics, could outperform other TFETs presented in the past in terms of on-current densities and on/off ratios.
Turing’s machine should sound familiar for another reason. It’s similar to the way ribosomes read genetic code on ribbons of RNA to construct proteins.
Cellular factories are a kind of natural Turing machine. What Leigh’s team is after would work the same way but go beyond biochemistry. These microscopic Turing machines, or molecular computers, would allow engineers to write code for some physical output onto a synthetic molecular ribbon. Another molecule would travel along the ribbon, read (and one day write) the code, and output some specified action, like catalyzing a chemical reaction.
Now, Leigh’s team says they’ve built the first components of a molecular computer: A coded molecular ribbon and a mobile molecular reader of the code.
Their quantum computing processors can store information up to two milliseconds.
Researchers from the University of New South Wales have broken new ground in quantum computing by demonstrating that ‘spin qubits’- qubits where the information is stored in the spin momentum of an electron-can store data for up to two milliseconds, 100 times longer than previous benchmarks in the same quantum processor.
Classical computers work with bits—consisting of ones and zeroes—but a quantum computer uses quantum bits or qubits, which, on top of the ones and zeroes, also has a superposition where it can be a one and a zero at the same time.
Hh5800/iStock.
The time that qubits can be manipulated in increasingly complex calculations is known as ‘coherence time.’
Researchers have assembled the world’s smallest flying structure, a tiny microchip that travels like wind-dispersed seeds with onboard technology to track air pollution and airborne diseases.
Solar cells that are stretchable, flexible and wearable won the day and the best poster award from a pool of 215 at Research Expo 2016 April 14 at the University of California San Diego. The winning nanoengineering researchers aim to manufacture small, flexible devices that can power watches, LEDs and wearable sensors. The ultimate goal is to design and build much bigger flexible solar cells that could be used as power sources and shelter in natural disasters and other emergencies.
Research Expo is an annual showcase of top graduate research projects for the Jacobs School of Engineering at UC San Diego. During the poster session, graduate students are judged on the quality of their work and how well they articulate the significance of their research to society. Judges from industry, who often are alumni, pick the winners for each department. A group of faculty judges picks the overall winner from the six department winners.
This year, in addition to solar cells, judges recognized efforts to develop 3D skeletal muscle on a chip; a better way to alleviate congestion in data center networks; a nano-scale all-optical sensor; fiber optic strain sensors for structural health monitoring; and a way to predict earthquake damage in freestanding structural systems.
Large Language Models have the ability to store vast amounts of facts about the world. But little is known, how these models actually do this. This paper aims at discovering the mechanism and location of storage and recall of factual associations in GPT models, and then proposes a mechanism for the targeted editing of such facts, in form of a simple rank-one update to a single MLP layer. This has wide implications both for how we understand such models’ inner workings, and for our ability to gain greater control over such models in the future.
OUTLINE: 0:00 — Introduction. 1:40 — What are the main questions in this subfield? 6:55 — How causal tracing reveals where facts are stored. 18:40 — Clever experiments show the importance of MLPs. 24:30 — How do MLPs store information? 29:10 — How to edit language model knowledge with precision? 36:45 — What does it mean to know something? 39:00 — Experimental Evaluation & the CounterFact benchmark. 45:40 — How to obtain the required latent representations? 51:15 — Where is the best location in the model to perform edits? 58:00 — What do these models understand about language? 1:02:00 — Questions for the community.
Abstract: We analyze the storage and recall of factual associations in autoregressive transformer language models, finding evidence that these associations correspond to localized, directly-editable computations. We first develop a causal intervention for identifying neuron activations that are decisive in a model’s factual predictions. This reveals a distinct set of steps in middle-layer feed-forward modules that mediate factual predictions while processing subject tokens. To test our hypothesis that these computations correspond to factual association recall, we modify feed-forward weights to update specific factual associations using Rank-One Model Editing (ROME). We find that ROME is effective on a standard zero-shot relation extraction (zsRE) model-editing task, comparable to existing methods. To perform a more sensitive evaluation, we also evaluate ROME on a new dataset of counterfactual assertions, on which it simultaneously maintains both specificity and generalization, whereas other methods sacrifice one or another. Our results confirm an important role for mid-layer feed-forward modules in storing factual associations and suggest that direct manipulation of computational mechanisms may be a feasible approach for model editing. The code, dataset, visualizations, and an interactive demo notebook are available at this https URL
Authors: Kevin Meng, David Bau, Alex Andonian, Yonatan Belinkov.
After six decades of examining signals from space, why have we yet to discover evidence of extra-terrestrial life? Keith’s book “The Contact Paradox: Challenging our Assumptions in the Search for Extraterrestrial Intelligence” is available now — https://geni.us/JFpy.
For the past six decades a small cadre of researchers have been on a quest, as part of SETI, to search for extraterrestrial intelligence. So far, SETI has found no evidence of extraterrestrial life, but with more than a hundred billion stars in our Galaxy alone to search, the odds of quick success are stacked against us.
Keith Cooper explores how far SETI has come since its modest beginnings, where it’s going and the assumptions that we make in our search for extraterrestrial life.
Keith Cooper is a freelance science journalist and editor. Since 2006 Keith has been the Editor of Astronomy Now, and he is also the Editor of Astrobiology Magazine. In addition he has written on numerous space-and physics-related topics, from exploding stars to quantum computers, for Centauri Dreams, New Scientist, Physics World, physicsworld.com and Sky and Telescope. He holds a BSc in Physics with Astrophysics from the University of Manchester.
This talk was filmed in the Ri on 22 November 2019.
