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Impact of hydrodynamic interactions on the kinetic pathway of protein folding, march 27, 2024.

Local pressure field of the solvent upon volume shrinking of a protein domain.

Jiaxing Yuan and Hajime Tanaka Phys. Rev. Lett. 132 , 138402 (2024)

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Shielding quantum light in space and time, april 3, 2024.

A way to create single photons whose spatiotemporal shapes do not expand during propagation could limit information loss in future photonic quantum technologies.

Viewpoint on: Jianmin Wang et al. Phys. Rev. Lett. 132 , 143601 (2024)

Making Sense of Handedness on a Lattice

April 2, 2024.

David Kaplan has developed a lattice model for particles that are left- or right-handed, offering a firmer foundation for the theory of weak interactions.

Feature on: David B. Kaplan Phys. Rev. Lett. 132 , 141603 (2024)

David B. Kaplan and Srimoyee Sen Phys. Rev. Lett. 132 , 141604 (2024)

EDITORS' SUGGESTION

Large- n principal chiral model in arbitrary external fields.

An exact analytic solution for the vacuum state of the two-dimensional principal chiral model at any set of chemical potentials in the limit of large number of particle species exhibits an emergent third dimension and also supports the potential for a dual string description in a specific double-scaled limit.

Vladimir Kazakov, Evgeny Sobko, and Konstantin Zarembo Phys. Rev. Lett. 132 , 141602 (2024)

A Counterintuitive Set of Tunneling Effects Observed at Last

Graphene is the setting for the first demonstration of relativistic electrons’ paradoxical ability to whiz through a barrier, provided the barrier is high enough.

Synopsis on: Mirza M. Elahi et al. Phys. Rev. Lett. 132 , 146302 (2024)

Effects of Linking Topology on the Shear Response of Connected Ring Polymers: Catenanes and Bonded Rings Flow Differently

The dynamics of ring polymers under shear is strongly dependent on whether they are chemically bonded or mechanically linked

Reyhaneh A. Farimani, Zahra Ahmadian Dehaghani, Christos N. Likos, and Mohammad Reza Ejtehadi Phys. Rev. Lett. 132 , 148101 (2024)

Microscopic Origin of the Entropy of Astrophysical Black Holes

A theoretical proposal presents a framework for microscopic explanation of the entropy of astrophysical black holes.

Vijay Balasubramanian, Albion Lawrence, Javier M. Magán, and Martin Sasieta Phys. Rev. Lett. 132 , 141501 (2024)

Vacancy Spectroscopy of Non-Abelian Kitaev Spin Liquids

A proposed approach to detect Majorana fermions in Kitaev spin liquids by using scanning tunneling microscopy could lead to the unambiguous confirmation of both the spin-liquid state and its Majorana zero modes.

Wen-Han Kao, Gábor B. Halász, and Natalia B. Perkins Phys. Rev. B 109 , 125150 (2024)

Wen-Han Kao, Natalia B. Perkins, and Gábor B. Halász Phys. Rev. Lett. 132 , 136503 (2024)

A New Source for Quantum Light

March 29, 2024.

A new device consisting of a semiconductor ring produces pairs of entangled photons that could be used in a photonic quantum processor.

Focus story on: Hong Zeng et al. Phys. Rev. Lett. 132 , 133603 (2024)

Gravitational Raman Scattering in Effective Field Theory: A Scalar Tidal Matching at O ( G 3 )

A theoretical framework is presented for the computation of the gravitational analog of the Raman effect.

Mikhail M. Ivanov, Yue-Zhou Li, Julio Parra-Martinez, and Zihan Zhou Phys. Rev. Lett. 132 , 131401 (2024)

Experimental Realization of Heralded Three-Photon GHZ Entangled State

A heralded three-photon GHZ entangled state is generated based on the interference of six single photons from a quantum dot source.

Si Chen et al . Phys. Rev. Lett. 132 , 130603 (2024)

H. Cao et al. Phys. Rev. Lett. 132 , 130604 (2024)

Scaling and Localization in Multipole-Conserving Diffusion

Systems that conserve both total particle number and total dipole moment display unique dynamical exponents and nonuniform particle distributions in their steady state.

Jung Hoon Han, Ethan Lake, and Sunghan Ro Phys. Rev. Lett. 132 , 137102 (2024)

First Results from a Broadband Search for Dark Photon Dark Matter in the 44 to 52     μ eV Range with a Coaxial Dish Antenna

A proof-of-principle search for dark-photon dark-matter particles surpasses existing constraints for a mass ~50 μeV by about two orders of magnitude.

Stefan Knirck et al. (BREAD Collaboration) Phys. Rev. Lett. 132 , 131004 (2024)

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PRL Nobel Prize winning research

The Physical Review journals are home to the most Nobel-winning physics papers in the world. Over 65% of the Nobel-Prize-winning research published in the last four decades are included in Physical Review journals. Read more about these papers in the APS Newsroom .

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Measurement of the Earth Tides with a Diamagnetic-Levitated Micro-Oscillator at Room Temperature Yingchun Leng et al . Phys. Rev. Lett. 132 , 123601 (2024)

Current-Induced Creation of Topological Vortex Rings in a Magnetic Nanocylinder Yizhou Liu and Naoto Nagaosa Phys. Rev. Lett. 132 , 126701 (2024)

Dark Matter Induced Power in Quantum Devices Anirban Das, Noah Kurinsky, and Rebecca K. Leane Phys. Rev. Lett. 132 , 121801 (2024)

Observation of WWγ Production and Search for Hγ Production in Proton-Proton Collisions at √s=13TeV A. Hayrapetyan et al . (CMS Collaboration) Phys. Rev. Lett. 132 , 121901 (2024)

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Shape Matters in Self-Assembly

A theoretical study of self-assembly finds that hexagon-shaped building blocks can form large structures faster than triangular or square blocks. Read More »

Shielding Quantum Light in Space and Time

A way to create single photons whose spatiotemporal shapes do not expand during propagation could limit information loss in future photonic quantum technologies. Read More »

Making Sense of Handedness on a Lattice

David Kaplan has developed a lattice model for particles that are left- or right-handed, offering a firmer foundation for the theory of weak interactions. Read More »

A Counterintuitive Set of Tunneling Effects Observed at Last

Graphene is the setting for the first demonstration of relativistic electrons’ paradoxical ability to whiz through a barrier, provided the barrier is high enough. Read More »

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The Neuron vs the Synapse: Which One Is in the Driving Seat?

A new theoretical framework for plastic neural networks predicts dynamical regimes where synapses rather than neurons primarily drive the network’s behavior, leading to an alternative candidate mechanism for working memory in the brain. Read More »

Metamaterials for Analog Optical Computing

Novel metamaterial-based architectures offer a promising platform for building mass-producible, reprogrammable schemes that perform computing tasks with light. Read More »

Nuclear Physics from Particle Physics

A new theoretical analysis connects the results of high-energy particle experiments at the Large Hadron Collider with three-proton correlations inside nuclei. Read More »

A New Source for Quantum Light

A new device consisting of a semiconductor ring produces pairs of entangled photons that could be used in a photonic quantum processor. Read More »

One Ring to Measure Blood-Glucose Level

A proposed ring-shaped device could measure the concentration of glucose in a person’s blood to an accuracy sufficient to make it clinically useful. Read More »

Levitated Nanoresonator Breaks Quality-Factor Record

A nanoresonator trapped in ultrahigh vacuum features an exceptionally high quality factor, showing promise for applications in force sensors and macroscopic tests of quantum mechanics.  Read More »

Characterizing the “Knee” of High-Energy Cosmic Rays

Using observations made with an array of thousands of particle detectors, researchers have uncovered an important clue about cosmic rays that originate from outside of our Galaxy. Read More »

Precision Cosmology with a Ground-Based Telescope

The South Pole Telescope has analyzed a trove of its CMB data, finding results that confirm the general picture of the cosmos drawn from previous space-based experiments. Read More »

Gravity Measurement Based on a Levitating Magnet

A new gravimeter is compact and stable and can detect the daily solar and lunar gravitational oscillations that are responsible for the tides. Read More »

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A theoretical study of self-assembly finds that hexagon-shaped building blocks can form large structures faster than triangular or square blocks.

David Kaplan has developed a lattice model for particles that are left- or right-handed, offering a firmer foundation for the theory of weak interactions.

Graphene is the setting for the first demonstration of relativistic electrons’ paradoxical ability to whiz through a barrier, provided the barrier is high enough.

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How to read a scientific paper

When I started my PhD, I was quite intimidated by the prospect of reading scientific papers. I had read some literature during my master’s research, but for my doctorate, I felt I needed to develop a much better system for reading papers and retaining what I read. Over the last few years, I’ve honed my scientific paper reading skills and thought I’d share my own system. I’ll be covering how to find literature in next week’s post (edit: here is that post), so here I’ll just be focusing on what I do once I have a paper that I’ve chosen to read.

Reading a scientific paper is a bit of an art and it’s something that requires practice. It can be quite hard, especially if you’re entering a new field. There will be loads of new jargon that you need to learn- I certainly found myself looking up terms every other sentence when I started my doctorate. But the good news is that over time you’ll become more and more familiar with the terms, theory, and methods commonly used in your field which makes reading and comprehending things much easier.

I use the three-pass method for reading papers. It was mentioned in a doctoral college workshop I attended early on in my programme and I’ve used it ever since. This is a useful way to filter out papers that are not completely relevant to your study and it gives you some structure to follow. As well as this, I have a list of ‘questions for critical engagement’ which come into play during the second pass and help me engage with what I’m reading.

The first pass

The first pass is pretty straight forward and should take a few minutes. I use this for any paper whose title has caught my eye. This step consists of:

• Scanning the title, abstract, and introduction
• Looking at the figures and captions
• Noting the section headings to get an overview of the content of the paper
• Scanning the conclusion
• Checking the bibliography for papers you have already read

After doing this, I can decide whether it’s worth my while reading the paper in more depth. Part of the skill of reading papers is in being able to identify when a paper isn’t relevant enough so you don’t waste precious research time on it when you could be reading something much more pertinent. I still record the papers I do a first pass on as I might want to come back to them at a later date when I’ve moved onto a slightly different research question.

During the first pass, I note down details of the paper in a spreadsheet or database (excel or Notion work well for this). The aim of this is to set it up so that it’s easy to retrieve the paper at a later date. I also identify tags that will help me gather papers into categories when I come to writing a literature review. These tags consist of key terms, materials, standard methods, theory, etc. that are covered in the paper:

I’ll be covering the literature synthesis and review process in more detail next week so keep an eye out for that if you’re interested in learning more details about my literature databases.

The second pass

The second pass is where the main bulk of my reading happens. This is where I’m trying to get a good understanding of the paper and how it relates to my own research. I like to print off papers as reading from a screen tends to make my visual stress worse and I find that I absorb information better when reading a paper copy, but you can do whatever you find most convenient. There are loads of programs that allow you to highlight and make notes on digital files these days if that’s what you prefer to use. I like to read the paper using a highlighting system to pick out various aspects of the work. My highlighter key consists of these categories:

🟥Objectives and findings 🟧Key terms/acronyms 🟨Past work & theoretical background 🟦Materials, experimental parameters & equations used 🟪Future work & implications 🟩References to check

As well as highlighting things in the text, I’ll write anything that comes to mind in the margins and I’ll add new key terms and acronyms to a big document I have that acts like a bit of a dictionary/encyclopedia for my PhD.

Once I’ve gone through the paper with my highlighters, I then fill out my literature matrix. This consists of writing a very brief summary of the paper and then noting down the objective, key results, the theory used, materials, methods, conclusions, future work suggested, and my critiques. I usually do this in bullet point format so it’s easy to read when I return to these notes at a later date. I then answer a series of questions about the paper:

Questions for critical engagement:

• What was the research question/problem?
• Is this title of possible value to my study?
• If so, why do I think so?
• What is the author of this paper attempting to persuade me of, and am I persuaded?
• If not, why not?
• Which part of this paper is of most interest to me and why that part?
• Why have they chosen this approach or theoretical framework?
• Do I agree with the author’s reasoning for this?
• What does the author think are the implications of their findings?
• What does this paper contribute to my own thoughts on my own research?

By doing this, I think about how the paper relates to my research and can then decide whether it is a ‘core’ paper for me. If it is, I’ll note down to do a third pass.

The third pass

I don’t get to this stage with many papers, but for the most seminal works in my field and studies that are very closely linked to my research, a third pass ensures that I fully understand prior work. To do this pass, you basically have to reimplement the paper. It forces you to think about how you would approach the same study. I usually do this by drawing out a paper outline and bullet pointing what I would do if I were carrying out the same work. This helps you identify where the authors were particularly innovative and where they are lacking. For example, I like to analyse the assumptions the authors have made in the study and ensure that their experimental and analytical techniques are sound. This often involves reading the supplementary material too.

What to do when you don’t understand a scientific paper

This happens more often than you might think so don’t worry if you find it hard to understand something that you’re reading. It may just be that it’s a poorly written paper (there are lots of those out there) or that you have a gap in your knowledge that you need to fill.

When this happens to me, I do a bit of research around the topic (usually using textbooks as they don’t assume as much knowledge as a lot of papers do) and I’ll also ask colleagues what their thoughts are on the paper. My supervisor and I have a little journal club meeting from time to time and I’ll often pick something I don’t quite get to cover in these so that I can discuss it with him and see what his take is on it. Reading around the subject and reaching out for help to discuss things with colleagues is all part of becoming a self-directed learner, which I spoke about a bit in last week’s post .

The other thing to do is just put the paper to one side for now. I did this a lot early on in my PhD and it’s a nice feeling returning to something you couldn’t get your head around to begin with, but now it makes more sense because you have a better understanding of the field. I always try to remember that as a researcher, it’s part of my job to read literature. The more I read, the better I become as a researcher because I know what makes a good study and a bad one which shapes how I do my own work.

I hope you found this post useful and happy reading!

If you like my work, I’d love your support!

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• Physics Online Information Resources

A brief overview of most useful resources for physics research online:

Most complete physics resource. Over 17 million records, international in scope, in all fields of physics, materials science, computer science, and engineering. PROS: • Much greater historical backlog (starting 1898) • Coverage much broader than arXiv • Coverage contains published works not found in GS CONS: • Basic searching not as “smart” as Google Search

Google Scholar

This service can be used to find articles from a wide variety of academic publishers, professional societies, preprint repositories and universities, as well as scholarly articles available across the web. It has the fastest, simplest interface. PROS: • Fewer clicks • Very smart, very flexible unstructured searching (you can copy and paste citations into the search box) • Shows multiple versions of each article in multiple publications (when available) • Includes non-peer-reviewed publications (including the arXiv) • Includes Google Books (some full text) CONS: • Less comprehensive than Inspec, especially for older materials

ADS (SMITHSONIAN/NASA ASTROPHYSICS DATA SYSTEM)

A Digital Library portal for research in Astronomy and Physics. PROS: • Highly customizable searching • Broad coverage of physics topics • Searches all of arXiv CONS: • May not include all the materials covered by Inspec

WEB OF SCIENCE (SCIENCE CITATION INDEX EXPANDED)

Indexes major journals in science disciplines. PROS: • Very good cited literature search features • Goes back to 1900 CONS: • Does not have the depth of coverage of INSPEC or ADS

Cutting-edge preprint server for many (but not all) major physics fields. PROS: • Most up-to-date resource • Preprints • Always full text • Always free • Shows today’s papers CONS: • No peer review • Quality control issues • Not all subfields upload regularly to the arXiv

High-Energy Physics Literature Database. PROS: • Very good search and citation statistics features CONS: • Limited to HEP subjects

AMERICAN JOURNAL OF PHYSICS

Primary journal resource for pedagogical and student-friendly physics articles. PROS: • Papers are highly readable. • Good for quickly getting background and context in a new topic • Good for coursework and teaching materials. CONS: • Not best source to find cutting-edge research

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The Deutsche Physikalische Gesellschaft (DPG) with a tradition extending back to 1845 is the largest physical society in the world with more than 61,000 members. The DPG sees itself as the forum and mouthpiece for physics and is a non-profit organisation that does not pursue financial interests. It supports the sharing of ideas and thoughts within the scientific community, fosters physics teaching and would also like to open a window to physics for all those with a healthy curiosity.

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Caroline Cohen et al 2015 New J. Phys. 17 063001

The conical shape of a shuttlecock allows it to flip on impact. As a light and extended particle, it flies with a pure drag trajectory. We first study the flip phenomenon and the dynamics of the flight and then discuss the implications on the game. Lastly, a possible classification of different shots is proposed.

Ran Finkelstein et al 2023 New J. Phys. 25 035001

This tutorial introduces the theoretical and experimental basics of electromagnetically induced transparency (EIT) in thermal alkali vapors. We first give a brief phenomenological description of EIT in simple three-level systems of stationary atoms and derive analytical expressions for optical absorption and dispersion under EIT conditions. Then we focus on how the thermal motion of atoms affects various parameters of the EIT system. Specifically, we analyze the Doppler broadening of optical transitions, ballistic versus diffusive atomic motion in a limited-volume interaction region, and collisional depopulation and decoherence. Finally, we discuss the common trade-offs important for optimizing an EIT experiment and give a brief 'walk-through' of a typical EIT experimental setup. We conclude with a brief overview of current and potential EIT applications.

Roger Bach et al 2013 New J. Phys. 15 033018

Double-slit diffraction is a corner stone of quantum mechanics. It illustrates key features of quantum mechanics: interference and the particle-wave duality of matter. In 1965, Richard Feynman presented a thought experiment to show these features. Here we demonstrate the full realization of his famous thought experiment. By placing a movable mask in front of a double-slit to control the transmission through the individual slits, probability distributions for single- and double-slit arrangements were observed. Also, by recording single electron detection events diffracting through a double-slit, a diffraction pattern was built up from individual events.

Dominic Horsman et al 2012 New J. Phys. 14 123011

In recent years, surface codes have become a leading method for quantum error correction in theoretical large-scale computational and communications architecture designs. Their comparatively high fault-tolerant thresholds and their natural two-dimensional nearest-neighbour (2DNN) structure make them an obvious choice for large scale designs in experimentally realistic systems. While fundamentally based on the toric code of Kitaev, there are many variants, two of which are the planar- and defect-based codes. Planar codes require fewer qubits to implement (for the same strength of error correction), but are restricted to encoding a single qubit of information. Interactions between encoded qubits are achieved via transversal operations, thus destroying the inherent 2DNN nature of the code. In this paper we introduce a new technique enabling the coupling of two planar codes without transversal operations, maintaining the 2DNN of the encoded computer. Our lattice surgery technique comprises splitting and merging planar code surfaces, and enables us to perform universal quantum computation (including magic state injection) while removing the need for braided logic in a strictly 2DNN design, and hence reduces the overall qubit resources for logic operations. Those resources are further reduced by the use of a rotated lattice for the planar encoding. We show how lattice surgery allows us to distribute encoded GHZ states in a more direct (and overhead friendly) manner, and how a demonstration of an encoded CNOT between two distance-3 logical states is possible with 53 physical qubits, half of that required in any other known construction in 2D.

Jarrod R McClean et al 2016 New J. Phys. 18 023023

Many quantum algorithms have daunting resource requirements when compared to what is available today. To address this discrepancy, a quantum-classical hybrid optimization scheme known as 'the quantum variational eigensolver' was developed (Peruzzo et al 2014 Nat. Commun. 5 4213 ) with the philosophy that even minimal quantum resources could be made useful when used in conjunction with classical routines. In this work we extend the general theory of this algorithm and suggest algorithmic improvements for practical implementations. Specifically, we develop a variational adiabatic ansatz and explore unitary coupled cluster where we establish a connection from second order unitary coupled cluster to universal gate sets through a relaxation of exponential operator splitting. We introduce the concept of quantum variational error suppression that allows some errors to be suppressed naturally in this algorithm on a pre-threshold quantum device. Additionally, we analyze truncation and correlated sampling in Hamiltonian averaging as ways to reduce the cost of this procedure. Finally, we show how the use of modern derivative free optimization techniques can offer dramatic computational savings of up to three orders of magnitude over previously used optimization techniques.

L S Liebovitch et al 2019 New J. Phys. 21 073022

Peace is not merely the absence of war and violence, rather 'positive peace' is the political, economic, and social systems that generate and sustain peaceful societies. Our international and multidisciplinary group is using physics inspired complex systems analysis methods to understand the factors and their interactions that together support and maintain peace. We developed causal loop diagrams and from them ordinary differential equation models of the system needed for sustainable peace. We then used that mathematical model to determine the attractors in the system, the dynamics of the approach to those attractors, and the factors and connections that play the most important role in determining the final state of the system. We used data science ('big data') methods to measure quantitative values of the peace factors from structured and unstructured (social media) data. We also developed a graphical user interface for the mathematical model so that social scientists or policy makers, can by themselves, explore the effects of changing the variables and parameters in these systems. These results demonstrate that complex systems analysis methods, previously developed and applied to physical and biological systems, can also be productively applied to analyze social systems such as those needed for sustainable peace.

Shinsei Ryu et al 2010 New J. Phys. 12 065010

Antonio Acín et al 2018 New J. Phys. 20 080201

Within the last two decades, quantum technologies (QT) have made tremendous progress, moving from Nobel Prize award-winning experiments on quantum physics (1997: Chu, Cohen-Tanoudji, Phillips; 2001: Cornell, Ketterle, Wieman; 2005: Hall, Hänsch-, Glauber; 2012: Haroche, Wineland) into a cross-disciplinary field of applied research. Technologies are being developed now that explicitly address individual quantum states and make use of the 'strange' quantum properties, such as superposition and entanglement. The field comprises four domains: quantum communication, where individual or entangled photons are used to transmit data in a provably secure way; quantum simulation, where well-controlled quantum systems are used to reproduce the behaviour of other, less accessible quantum systems; quantum computation, which employs quantum effects to dramatically speed up certain calculations, such as number factoring; and quantum sensing and metrology, where the high sensitivity of coherent quantum systems to external perturbations is exploited to enhance the performance of measurements of physical quantities. In Europe, the QT community has profited from several EC funded coordination projects, which, among other things, have coordinated the creation of a 150-page QT Roadmap ( http://qurope.eu/h2020/qtflagship/roadmap2016 ). This article presents an updated summary of this roadmap.

Guo-Xiang Zhi et al 2024 New J. Phys. 26 033030

Carlos L Benavides-Riveros et al 2024 New J. Phys. 26 033020

Computing excited-state properties of molecules and solids is considered one of the most important near-term applications of quantum computers. While many of the current excited-state quantum algorithms differ in circuit architecture, specific exploitation of quantum advantage, or result quality, one common feature is their rooting in the Schrödinger equation. However, through contracting (or projecting) the eigenvalue equation, more efficient strategies can be designed for near-term quantum devices. Here we demonstrate that when combined with the Rayleigh–Ritz variational principle for mixed quantum states, the ground-state contracted quantum eigensolver (CQE) can be generalized to compute any number of quantum eigenstates simultaneously. We introduce two excited-state (anti-Hermitian) CQEs that perform the excited-state calculation while inheriting many of the remarkable features of the original ground-state version of the algorithm, such as its scalability. To showcase our approach, we study several model and chemical Hamiltonians and investigate the performance of different implementations.

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Realistic nuclear reaction cross-section models are an essential ingredient of reliable heavy-ion transport codes. Such codes are used for risk evaluation of manned space exploration missions as well as for ion-beam therapy dose calculations and treatment planning. Therefore, in this study, a collection of total nuclear reaction cross-section data has been generated within a GSI-ESA-NASA collaboration. The database includes the experimentally measured total nucleus–nucleus reaction cross-sections. The Tripathi, Kox, Shen, Kox–Shen, and Hybrid-Kurotama models are systematically compared with the collected data. Details about the implementation of the models are given. Literature gaps are pointed out and considerations are made about which models fit best the existing data for the most relevant systems to radiation protection in space and heavy-ion therapy.

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Title: realm: reference resolution as language modeling.

Abstract: Reference resolution is an important problem, one that is essential to understand and successfully handle context of different kinds. This context includes both previous turns and context that pertains to non-conversational entities, such as entities on the user's screen or those running in the background. While LLMs have been shown to be extremely powerful for a variety of tasks, their use in reference resolution, particularly for non-conversational entities, remains underutilized. This paper demonstrates how LLMs can be used to create an extremely effective system to resolve references of various types, by showing how reference resolution can be converted into a language modeling problem, despite involving forms of entities like those on screen that are not traditionally conducive to being reduced to a text-only modality. We demonstrate large improvements over an existing system with similar functionality across different types of references, with our smallest model obtaining absolute gains of over 5% for on-screen references. We also benchmark against GPT-3.5 and GPT-4, with our smallest model achieving performance comparable to that of GPT-4, and our larger models substantially outperforming it.

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Large language models use a surprisingly simple mechanism to retrieve some stored knowledge

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Large language models, such as those that power popular artificial intelligence chatbots like ChatGPT, are incredibly complex. Even though these models are being used as tools in many areas, such as customer support, code generation, and language translation, scientists still don’t fully grasp how they work.

In an effort to better understand what is going on under the hood, researchers at MIT and elsewhere studied the mechanisms at work when these enormous machine-learning models retrieve stored knowledge.

They found a surprising result: Large language models (LLMs) often use a very simple linear function to recover and decode stored facts. Moreover, the model uses the same decoding function for similar types of facts. Linear functions, equations with only two variables and no exponents, capture the straightforward, straight-line relationship between two variables.

The researchers showed that, by identifying linear functions for different facts, they can probe the model to see what it knows about new subjects, and where within the model that knowledge is stored.

Using a technique they developed to estimate these simple functions, the researchers found that even when a model answers a prompt incorrectly, it has often stored the correct information. In the future, scientists could use such an approach to find and correct falsehoods inside the model, which could reduce a model’s tendency to sometimes give incorrect or nonsensical answers.

“Even though these models are really complicated, nonlinear functions that are trained on lots of data and are very hard to understand, there are sometimes really simple mechanisms working inside them. This is one instance of that,” says Evan Hernandez, an electrical engineering and computer science (EECS) graduate student and co-lead author of a paper detailing these findings .

Hernandez wrote the paper with co-lead author Arnab Sharma, a computer science graduate student at Northeastern University; his advisor, Jacob Andreas, an associate professor in EECS and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL); senior author David Bau, an assistant professor of computer science at Northeastern; and others at MIT, Harvard University, and the Israeli Institute of Technology. The research will be presented at the International Conference on Learning Representations.

Finding facts

Most large language models, also called transformer models, are neural networks . Loosely based on the human brain, neural networks contain billions of interconnected nodes, or neurons, that are grouped into many layers, and which encode and process data.

Much of the knowledge stored in a transformer can be represented as relations that connect subjects and objects. For instance, “Miles Davis plays the trumpet” is a relation that connects the subject, Miles Davis, to the object, trumpet.

As a transformer gains more knowledge, it stores additional facts about a certain subject across multiple layers. If a user asks about that subject, the model must decode the most relevant fact to respond to the query.

If someone prompts a transformer by saying “Miles Davis plays the. . .” the model should respond with “trumpet” and not “Illinois” (the state where Miles Davis was born).

“Somewhere in the network’s computation, there has to be a mechanism that goes and looks for the fact that Miles Davis plays the trumpet, and then pulls that information out and helps generate the next word. We wanted to understand what that mechanism was,” Hernandez says.

The researchers set up a series of experiments to probe LLMs, and found that, even though they are extremely complex, the models decode relational information using a simple linear function. Each function is specific to the type of fact being retrieved.

For example, the transformer would use one decoding function any time it wants to output the instrument a person plays and a different function each time it wants to output the state where a person was born.

The researchers developed a method to estimate these simple functions, and then computed functions for 47 different relations, such as “capital city of a country” and “lead singer of a band.”

While there could be an infinite number of possible relations, the researchers chose to study this specific subset because they are representative of the kinds of facts that can be written in this way.

They tested each function by changing the subject to see if it could recover the correct object information. For instance, the function for “capital city of a country” should retrieve Oslo if the subject is Norway and London if the subject is England.

Functions retrieved the correct information more than 60 percent of the time, showing that some information in a transformer is encoded and retrieved in this way.

“But not everything is linearly encoded. For some facts, even though the model knows them and will predict text that is consistent with these facts, we can’t find linear functions for them. This suggests that the model is doing something more intricate to store that information,” he says.

Visualizing a model’s knowledge

They also used the functions to determine what a model believes is true about different subjects.

In one experiment, they started with the prompt “Bill Bradley was a” and used the decoding functions for “plays sports” and “attended university” to see if the model knows that Sen. Bradley was a basketball player who attended Princeton.

“We can show that, even though the model may choose to focus on different information when it produces text, it does encode all that information,” Hernandez says.

They used this probing technique to produce what they call an “attribute lens,” a grid that visualizes where specific information about a particular relation is stored within the transformer’s many layers.

Attribute lenses can be generated automatically, providing a streamlined method to help researchers understand more about a model. This visualization tool could enable scientists and engineers to correct stored knowledge and help prevent an AI chatbot from giving false information.

In the future, Hernandez and his collaborators want to better understand what happens in cases where facts are not stored linearly. They would also like to run experiments with larger models, as well as study the precision of linear decoding functions.

“This is an exciting work that reveals a missing piece in our understanding of how large language models recall factual knowledge during inference. Previous work showed that LLMs build information-rich representations of given subjects, from which specific attributes are being extracted during inference. This work shows that the complex nonlinear computation of LLMs for attribute extraction can be well-approximated with a simple linear function,” says Mor Geva Pipek, an assistant professor in the School of Computer Science at Tel Aviv University, who was not involved with this work.

This research was supported, in part, by Open Philanthropy, the Israeli Science Foundation, and an Azrieli Foundation Early Career Faculty Fellowship.

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Researchers at MIT have found that large language models mimic intelligence using linear functions, reports Kyle Wiggers for  TechCrunch . “Even though these models are really complicated, nonlinear functions that are trained on lots of data and are very hard to understand, there are sometimes really simple mechanisms working inside them,” writes Wiggers. 

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• Evan Hernandez
• Jacob Andreas
• Language and Intelligence Group
• Computer Science and Artificial Intelligence Laboratory
• Department of Electrical Engineering and Computer Science

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• Computer science and technology
• Artificial intelligence
• Human-computer interaction
• Computer Science and Artificial Intelligence Laboratory (CSAIL)
• Electrical Engineering & Computer Science (eecs)

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Apple Researchers Reveal New AI System That Can Beat GPT-4

Apple researchers have developed an artificial intelligence system named ReALM (Reference Resolution as Language Modeling) that aims to radically enhance how voice assistants understand and respond to commands.

Reference resolution is an important part of natural language understanding, enabling users to use pronouns and other indirect references in conversation without confusion. For digital assistants, this capability has historically been a significant challenge, limited by the need to interpret a wide range of verbal cues and visual information. Apple's ReALM system seeks to address this by converting the complex process of reference resolution into a pure language modeling problem. In doing so, it can comprehend references to visual elements displayed on a screen and integrate this understanding into the conversational flow.

ReALM reconstructs the visual layout of a screen using textual representations. This involves parsing on-screen entities and their locations to generate a textual format that captures the screen's content and structure. Apple researchers found that this strategy, combined with specific fine-tuning of language models for reference resolution tasks, significantly outperforms traditional methods, including the capabilities of OpenAI's GPT-4.

ReALM could enable users to interact with digital assistants much more efficiently with reference to what is currently displayed on their screen without the need for precise, detailed instructions. This has the potential to make voice assistants much more useful in a variety of settings, such as helping drivers navigate infotainment systems while driving or assisting users with disabilities by providing an easier and more accurate means of indirect interaction.

Apple has now published several AI research papers. Last month, the company revealed a new method for training large language models that seamlessly integrates both text and visual information. Apple is widely expected to unveil an array of AI features at WWDC in June.

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Top Rated Comments

enabling users to use pronouns and other indirect references in conversation without confusion.

It's good if AI understands "Can you repeat that?" properly. /thread

[HEADING=2]Apple Researchers Reveal[/HEADING]

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Collection  31 March 2021

Top 100 in Physics

This collection highlights our most downloaded* physics papers published in 2020. Featuring authors from around the world, these papers showcase valuable research from an international community.

*Data obtained from SN Insights which is based on Digital Science's Dimensions.

Excitation of Faraday-like body waves in vibrated living earthworms

• Ivan S. Maksymov
• Andrey Pototsky

Design and characterization of an acoustic composite lens with high-intensity and directionally controllable focusing

Swarm Hunting and Cluster Ejections in Chemically Communicating Active Mixtures

• Jens Grauer
• Hartmut Löwen
• Benno Liebchen

Deposits from giant floods in Gale crater and their implications for the climate of early Mars

• J. F. Schroeder
• A. G. Fairén

Quantum random number generators with entanglement for public randomness testing

• Janusz E. Jacak
• Witold A. Jacak
• Lucjan Jacak

Magnetometer with nitrogen-vacancy center in a bulk diamond for detecting magnetic nanoparticles in biomedical applications

• Akihiro Kuwahata
• Takahiro Kitaizumi
• Masaki Sekino

Generation of megatesla magnetic fields by intense-laser-driven microtube implosions

• M. Murakami
• J. J. Honrubia
• S. V. Bulanov

Quantum double-double-slit experiment with momentum entangled photons

• Manpreet Kaur
• Mandip Singh

Amoeba-inspired analog electronic computing system integrating resistance crossbar for solving the travelling salesman problem

• Kenta Saito
• Masashi Aono
• Seiya Kasai

Acoustic levitation with optimized reflective metamaterials

• Spyros Polychronopoulos
• Gianluca Memoli

The Chaotic Terrains of Mercury Reveal a History of Planetary Volatile Retention and Loss in the Innermost Solar System

• J. Alexis P. Rodriguez
• Gregory J. Leonard

Information theory and dimensionality of space

• Subhash Kak

Impact of Spin-Orbit Torque on Spin-Transfer Torque Switching in Magnetic Tunnel Junctions

• Sachin Pathak
• Chanyoung Youm
• Jongill Hong

Narrow-band hard-x-ray lasing with highly charged ions

• Chunhai Lyu
• Stefano M. Cavaletto
• Zoltán Harman

Answering Mermin’s challenge with conservation per no preferred reference frame

• W. M. Stuckey
• Michael Silberstein

Co-appearance of superconductivity and ferromagnetism in a Ca 2 RuO 4 nanofilm crystal

• Hiroyoshi Nobukane
• Kosei Yanagihara
• Satoshi Tanda

An In-situ and Direct Confirmation of Super-Planckian Thermal Radiation Emitted From a Metallic Photonic-Crystal at Optical Wavelengths

• Shawn-Yu Lin
• Mei-Li Hsieh
• Shankar Narayanan

Identification of networking quantum teleportation on 14-qubit IBM universal quantum computer

• Ni-Ni Huang
• Wei-Hao Huang
• Che-Ming Li

The nature of the diffuse light near cities detected in nighttime satellite imagery

• Alejandro Sanchez de Miguel
• Christopher C. M. Kyba
• Kevin J. Gaston

A discrete core-shell-like micro-light-emitting diode array grown on sapphire nano-membranes

• Seungmin Lee
• Jongmyeong Kim
• Euijoon Yoon

Methanogenic Archaea Can Produce Methane in Deliquescence-Driven Mars Analog Environments

• Deborah Maus
• Jacob Heinz
• Dirk Schulze-Makuch

Estimating survival probability using the terrestrial extinction history for the search for extraterrestrial life

• Kohji Tsumura

Reinforcement Quantum Annealing: A Hybrid Quantum Learning Automata

• Ramin Ayanzadeh
• Milton Halem

Thermoelectric properties of graphene-like nanoribbon studied from the perspective of symmetry

• Xue-Feng Wang

Machine-Learning Studies on Spin Models

• Kenta Shiina
• Hiroyuki Mori
• Hwee Kuan Lee

Intrinsically p-type cuprous iodide semiconductor for hybrid light-emitting diodes

• Joonyeon Chang

A Galactic centre gravitational-wave Messenger

• Marek Abramowicz
• Michał Bejger
• Odele Straub

Optically tunable terahertz chiral metasurface based on multi-layered graphene

• Maxim Masyukov
• Anna Vozianova
• Mikhail Khodzitsky

Hybrid image sensor of small molecule organic photodiode on CMOS – Integration and characterization

• Himanshu Shekhar
• Amos Fenigstein
• Nir Tessler

Position and Direction Tracking of a Magnetic Object Based on an M x -Atomic Magnetometer

• Asieh Soheilian
• Maliheh Ranjbaran
• Mohammad Mehdi Tehranchi

Breaking limitation of quantum annealer in solving optimization problems under constraints

• Masayuki Ohzeki

A quantum algorithm for evolving open quantum dynamics on quantum computing devices

• Rongxin Xia

Theory of relaxor-ferroelectricity

• Li-Li Zhang
• Yi-Neng Huang

Quantum thermodynamics of single particle systems

• Md. Manirul Ali
• Wei-Ming Huang
• Wei-Min Zhang

Broadband microwave coding metamaterial absorbers

• Manh Cuong Tran
• Van Hai Pham
• Dinh Lam Vu

From LaH 10 to room–temperature superconductors

• M. Kostrzewa
• K. M. Szczęśniak
• R. Szczęśniak

High mobility approaching the intrinsic limit in Ta-doped SnO 2 films epitaxially grown on TiO 2 (001) substrates

• Michitaka Fukumoto
• Shoichiro Nakao
• Tetsuya Hasegawa

Damage-free Metal Electrode Transfer to Monolayer Organic Single Crystalline Thin Films

• Tatsuyuki Makita
• Akifumi Yamamura

Forecasting high-dimensional dynamics exploiting suboptimal embeddings

• Shunya Okuno
• Kazuyuki Aihara
• Yoshito Hirata

Separating the two polarities of the POLO contacts of an 26.1%-efficient IBC solar cell

• C. Hollemann

Prime factorization algorithm based on parameter optimization of Ising model

• Baonan Wang

CVD-Grown Carbon Nanotube Branches on Black Silicon Stems for Ultrahigh Absorbance in Wide Wavelength Range

• Thanh Luan Phan
• Woo Jong Yu

Energy-Dispersive X-ray Absorption Spectroscopy with an Inverse Compton Source

• Juanjuan Huang
• Benedikt Günther
• Franz Pfeiffer

A percolation model for the emergence of the Bitcoin Lightning Network

• Silvia Bartolucci
• Fabio Caccioli
• Pierpaolo Vivo

A thin film efficient pn-junction thermoelectric device fabricated by self-align shadow mask

• Gilbert Kogo
• Messaoud Bahoura

Enhanced Kinetic Impactor for Deflecting Large Potentially Hazardous Asteroids via Maneuvering Space Rocks

Surface Dielectric Barrier Discharge plasma: a suitable measure against fungal plant pathogens

• Paolo F. Ambrico
• Milan Šimek
• Rita M. De Miccolis Angelini

Role of Molecular Orbital Energy Levels in OLED Performance

• Rohit Ashok Kumar Yadav
• Deepak Kumar Dubey
• Jwo-Huei Jou

Ionisation processes and laser induced periodic surface structures in dielectrics with mid-infrared femtosecond laser pulses

• George D. Tsibidis
• Emmanuel Stratakis

Phase estimation algorithm for the multibeam optical metrology

• V. V. Zemlyanov
• N. S. Kirsanov
• G. B. Lesovik

High Performance All Nonmetal SiN x Resistive Random Access Memory with Strong Process Dependence

• Albert Chin
• Vladimir Gritsenko

Unveiling the Physics of the Mutual Interactions in Paramagnets

• Lucas Squillante
• Isys F. Mello
• Mariano de Souza

NanoThermoMechanical AND and OR Logic Gates

• Ahmed Hamed

Modified Drude model for small gold nanoparticles surface plasmon resonance based on the role of classical confinement

• Asef Kheirandish
• Nasser Sepehri Javan
• Hosein Mohammadzadeh

An anomaly in quantum phases induced by borders

• Mike Guidry

Room Temperature Electrically Detected Nuclear Spin Coherence of NV Centres in Diamond

• H. Morishita
• S. Kobayashi
• N. Mizuochi

Cryogenic Memory Architecture Integrating Spin Hall Effect based Magnetic Memory and Superconductive Cryotron Devices

• Minh-Hai Nguyen
• Guilhem J. Ribeill
• Thomas A. Ohki

Self-Assembled Electret for Vibration-Based Power Generator

• Yuya Tanaka
• Noritaka Matsuura
• Hisao Ishii

System for the remote control and imaging of MW fields for spin manipulation in NV centers in diamond

• Giacomo Mariani
• Shuhei Nomoto
• Shintaro Nomura

Quantum State Optimization and Computational Pathway Evaluation for Gate-Model Quantum Computers

• Laszlo Gyongyosi

Fluctuations in measured radioactive decay rates inside a modified Faraday cage: Correlations with space weather

• V. Milián-Sánchez
• F. Scholkmann

Optimizing High-Efficiency Quantum Memory with Quantum Machine Learning for Near-Term Quantum Devices

• Sandor Imre

Estimating the photo-fission yield of the Trinity Test

Simple analytical method for determining electrical resistivity and sheet resistance using the van der Pauw procedure

• F. S. Oliveira
• R. B. Cipriano
• C. A. M. dos Santos

Evaporation coefficient and condensation coefficient of vapor under high gas pressure conditions

• Kotaro Ohashi
• Kazumichi Kobayashi
• Masao Watanabe

Radiative MHD Casson Nanofluid Flow with Activation energy and chemical reaction over past nonlinearly stretching surface through Entropy generation

• Wejdan Deebani

The influence of AlN buffer layer on the growth of self-assembled GaN nanocolumns on graphene

• Andreas Liudi Mulyo
• Mohana K. Rajpalke
• Bjørn-Ove Fimland

Skewed electronic band structure induced by electric polarization in ferroelectric BaTiO 3

• Norihiro Oshime
• Hitoshi Osawa

Integration of amorphous ferromagnetic oxides with multiferroic materials for room temperature magnetoelectric spintronics

• Humaira Taz
• Bhagwati Prasad
• Ramki Kalyanaraman

Experimental observation of drumhead surface states in SrAs 3

• M. Mofazzel Hosen
• Gyanendra Dhakal
• Madhab Neupane

Classifying superconductivity in Moiré graphene superlattices

• E. F. Talantsev
• R. C. Mataira
• W. P. Crump

Tuning interfacial Dzyaloshinskii-Moriya interactions in thin amorphous ferrimagnetic alloys

Rumor spreading model considering rumor credibility, correlation and crowd classification based on personality

• Xuelong Chen

Crystal-like order and defects in metazoan epithelia with spherical geometry

• Daria S. Roshal
• Karim Azzag
• Stephen Baghdiguian

The role of faceting and elongation on the magnetic anisotropy of magnetite Fe 3 O 4 nanocrystals

• Roberto Moreno
• Samuel Poyser
• Richard F. L. Evans

Fast camera spatial characterization of photonic polarization entanglement

• Christopher Ianzano
• Peter Svihra
• Eden Figueroa

Hinged-3D metamaterials with giant and strain-independent Poisson’s ratios

• Mohamed Shaat
• Ahmed Wagih

Spin-orbit Torque Switching of Perpendicular Magnetization in Ferromagnetic Trilayers

• Dong-Kyu Lee
• Kyung-Jin Lee

Surface Enhanced Visible Absorption of Dye Molecules in the Near-Field of Gold Nanoparticles

• H. Ishitobi

Effects of Oxygen Modification on the Structural and Magnetic Properties of Highly Epitaxial La 0.7 Sr 0.3 MnO 3 (LSMO) thin films

• Shalini Kumari
• Navid Mottaghi
• Mikel B. Holcomb

Single-shot all-optical switching of magnetization in Tb/Co multilayer-based electrodes

• L. Avilés-Félix
• I. L. Prejbeanu

Tuneable quantum spin Hall states in confined 1 T' transition metal dichalcogenides

• Biswapriyo Das
• Diptiman Sen
• Santanu Mahapatra

Molecular Orientation in a Variable-Focus Liquid Crystal Lens Induced by Ultrasound Vibration

• Yuki Harada
• Daisuke Koyama
• Mami Matsukawa

Topological transitions in an oscillatory driven liquid crystal cell

• Marcel G. Clerc
• Michał Kowalczyk
• Valeska Zambra

Electrical activity at the AlN/Si Interface: identifying the main origin of propagation losses in GaN-on-Si devices at microwave frequencies

• Damien Valente
• Yvon Cordier

Sub-lattice of Jahn-Teller centers in hexaferrite crystal

• V. V. Gudkov
• M. N. Sarychev
• I. B. Bersuker

Double power-law viscoelastic relaxation of living cells encodes motility trends

• J. S. de Sousa
• R. S. Freire
• C. L. N. Oliveira

Floquet-engineered quantum walks

• Haruna Katayama
• Noriyuki Hatakenaka
• Toshiyuki Fujii

A fibered laser system for the MIGA large scale atom interferometer

• D. O. Sabulsky

Circuit Depth Reduction for Gate-Model Quantum Computers

Formation and suppression of hydrogen blisters in tunnelling oxide passivating contact for crystalline silicon solar cells

• Sungjin Choi
• Ka-Hyun Kim

Experimental realization of controlled quantum teleportation of arbitrary qubit states via cluster states

• Abhijeet Kumar
• Saeed Haddadi
• Prasanta K. Panigrahi

Identifying vital nodes in complex networks by adjacency information entropy

Surface plasmon enhanced Organic color image sensor with Ag nanoparticles coated with silicon oxynitride

• Kyung-Bae Park

Investigating the biological potential of galactic cosmic ray-induced radiation-driven chemical disequilibrium in the Martian subsurface environment

• Dimitra Atri

Ultra-thin passivation layers in Cu(In,Ga)Se 2 thin-film solar cells: full-area passivated front contacts and their impact on bulk doping

• Florian Werner
• Boris Veith-Wolf
• Susanne Siebentritt

Analysis of Surface Current by Quantum Tunneling Effect of Thin Film Transistors with Topological Insulators

Acoustic waveguide with virtual soft boundary based on metamaterials

• Guang-Sheng Liu
• Jian-Chun Cheng

A caloritronics-based Mott neuristor

• Javier del Valle
• Pavel Salev
• Ivan K. Schuller

Upconversion photoluminescence of Ho 3+ -Yb 3+ doped barium titanate nanocrystallites: Optical tools for structural phase detection and temperature probing

• Manoj Kumar Mahata
• Tristan Koppe
• Ulrich Vetter

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