Full-time editorial positions – Physical Review journals

The Physical Review journals currently have two full-time editorial openings. Both positions are "remote-first," meaning that the successful applicant may hold the position while residing anywhere in the USA.

Associate or Senior Associate Editor, Quantum Information

The editorial teams of PRX Quantum and Physical Review Applied are looking for someone with a deep understanding of the research and academic publishing landscape in quantum, a keen eye for detail, and excellent communication skills. A full job description can be found here. APS follows a remote-first environment within the USA and sponsors visa applications.

This full-time position is an excellent opportunity for someone with a strong publication record and referee experience, who has decided to shift gears and pursue a different path outside of academia. Postdoctoral experience in the broad field of quantum information is required. Individuals with editorial experience can apply for a senior position.

Associate Editor or Senior Associate Editor, Physical Review Letters

Would you like to join our close-knit team of editors running the world’s leading physics journal? As an Associate Editor of Physical Review Letters, you would handle all phases of the peer review process and ultimately decide which papers we publish. For this important work, we seek, for two open positions, dynamic and personable individuals with a strong scientific background in either condensed matter and materials science, or physics of fluids, polymer physics, chemical physics, geophysics, or complex systems.

Our editors stay engaged with the physics community and abreast of the latest research by attending meetings and visiting research institutions around the world. In addition, editors participate in various editorial initiatives and cross-departmental APS projects.

No editorial experience is required, though we do expect familiarity with the review process as an author and referee. We will train you to develop the editorial skills needed to be part of our team. Candidates with considerable editorial experience in handling manuscripts for peer-reviewed journals, and demonstrated impact in that role, may qualify for the senior position. 

The application deadline is April 30th, 2023.

Large language models for everyone

ChatGPT's release late last year attracted a surge in interest -- and investment -- in anticipation of numerous monetization opportunities offered by the new and improved large language model. At the time there were no serious competitors - everyone had to use OpenAI's service, which is now pay to play.

As I wrote last month, competing models such as LLaMA have been released with downloadable weights, allowing end-users to run them locally (on high-end GPUs or even CPUs after discretization).

Researchers from Stanford University have released Alpaca, a fine-tuned version of LLaMA, showing how fine-tuning of language models for more specialized applications could be carried out relatively inexpensively provided one has access to a sufficiently powerful foundation model. 

However, LLaMA (and therefore its derivatives) were released under a restrictive license, in principle limiting them to non-commercial research purposes only. Nevertheless, students have been free to use illegal leaked copies of LLaMA to write their essays and do their homework.

This week, Stability AI released StableLM, a language model with a similar number of parameters to LLaMA, under a CreativeCommons license that allows free re-use even for commercial purposes.

Barriers towards widespread adoption of large language models are dropping fast!

Strong backscattering in valley Hall photonic crystal waveguides

Just published in Nature Photonics this week: Observation of strong backscattering in valley-Hall photonic topological interface modes

Researchers from the Technical University of Denmark report experiments with state-of-the-art photonic crystal waveguides, comparing the performance of valley Hall waveguides against conventional line defect waveguides. In the slow light regime, of most interest for enhancing light-matter interactions or nonlinear optical effects, the conventional line defect waveguides have substantially lower propagation losses (0.5 dB / cm) compared to the topological valley Hall waveguides (15-200 dB / cm). 

Interestingly, while earlier theoretical estimates (e.g. in Phys. Rev. Research 2, 043109 (2020) and discussed in Section 5 of our review article) focused on potential out-of-plane scattering losses, which might in principle be mitigated by embedding the 2D photonic crystal in a suitable cladding material, the present experiments establish in-plane disorder-induced backscattering leading to Anderson localization as the dominant loss mechanism. There is no obvious way to inhibit this kind of backscattering except for improving fabrication precision.

The stronger losses be intuitively understood intuitively by comparing scanning electron microscope images of the conventional and valley Hall waveguides:

Scattering arises because the boundaries between the air holes and silicon are not perfectly smooth, but have nanometer-scale roughness. The conventional waveguide design minimizes potential roughness-induced by scattering by localizing the guided mode to the hole-free region. In contrast, the valley Hall waveguide has additional triangular air holes leading to a larger area of rough sidewalls.

Experts working on topological photonics will probably not be that surprised by this result; several pessimistic theoretical estimates of valley Hall waveguide performance have been published in the past few years. Unfortunately, high profile journals generally prefer positivity and bold claims over more subdued but realistic assessments. It is great to see important negative results such as this published in a high profile journal. Hopefully this will stop numerous authors from repeating uncritically misleading claims that topological photonic crystals offer robustness against all forms of disorder.

Recommendation systems for papers

The volume of papers being published and preprints being submitted to the arXiv has grown enormously since the time when I started my PhD studies. Moreover, competing preprint platforms such as Optica Open have been launched. This deluge of papers is impossible to keep up with. 

Therefore, there is growing interest in developing advanced bibliometric tools in order to stay abreast of the most important developments in one's own research field. There are two distinct approaches to solving this issue. 

The first is crowdsourcing. Websites such as scirate (founded by and most widely used by quantum physicists) and PubPeer (seems to be popular in life sciences) and allow users to upvote and comment on preprints they think are interesting. Preprints that are upvoted more appear higher on the page and therefore receive more attention and more views. The idea then is that the most important works will be upvoted more and will be seen by more people.

Other approaches are based on machine learning or bibliometric analysis methods where a new paper is analyzed by model that takes as its input various attributes of the paper, such as the authors, the topic, keywords, and its reference list. Models can be trained to pick out papers which are likely to relevant or more important and show them to the end user. 

One example of this now supported by arXiv is Litmaps, which constructs a graph that places an article in the context of previous and subsequent works. The visualisation can be customised to highlight different features, such as the publication date and total number of citations in the plot below. It seems at least that for the example below the "Seed" map seems biased towards highlighting review articles (missing hot recent results such as those on quantized Thouless pumping of solitons). The other visualisations offered are "Discover" (for finding overlooked papers) and "Map" (for telling a "research story"), but they require an account, and presumably a subscription for serious use.

Litmap of "Edge solitons in nonlinear photonic topological insulators"

Each approach has its own pros and cons. One problem with the crowdsourcing approach is that it can be sensitive to initial perturbations and tends to amplify existing well-known authors while inhibiting the promotion of less well-known authors. For example, people may upvote a paper just because it is written by familiar names and the title and abstract look interesting, leading to a winner-takes-all effect. 

This I think is a particularly important problem in research. At least my style is that I prefer not to work on ideas that are already very popular. I think to really make a deep breakthrough in research we need to see something that nobody else has noticed before. This often will involve getting insights from papers which have been forgotten or overlooked by the wider community. In this context crowdsourcing has the danger of leading to a groupthink in which the popularity of certain topics may exceed their promise, due to people working on them simply because many other people are also working on them. Thus, the choices of a few early adopters or "academic influencers" will end up getting amplified more and more.

The machine learning approach has the potential to give a more thorough and systematic coverage of the literature by being able to analyze all new papers and find the most interesting ones without being subject to this reliance on or sensitivity to initial fluctuations and early upwards. While there is promise of course, the big question is how are you supposed to develop a model to rank individual papers without studying deeply the science they contain? And how much can you trust a proprietary, closed-source model whose inner workings and potential biases are unknown?

For example, a crude first approximation might involve analyzing the references of a new paper to see what is cited. If a new paper cites important previous results (which can be estimated by how often they have been cited) then hopefully the paper will be worth reading. However simply counting the raw citations or references will be prone to bias. Different fields have different standards as to what is and should be cited. In some fields now you will see paper introductions which cite dozens or even hundreds of papers. In this case the value of an individual citation is relatively low and so looking at the citations alone we won't give much information as to what the paper is roughly about or whether it is worth reading.

It seems therefore that more sophisticated approaches are necessary. The context of a citation is important. Papers cited in bulk are not that valuable. For example, in an introductory paragraph "x has received a lot of interest lately [1-103]" only reveals that x is a hot topic. On the other hand, a sentence like "We apply the method of Ref. [16]..." tell us that there is probably a very close connection to whatever Ref. [16] is about. Thus, the integration of large language models such as BioGPT with paper recommendation systems is likely to improve their performance, thereby greatly improving the productivity of researchers who use them.

What do you think? Are there any other tricks for keeping up with the literature in your field?