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Are editorial decisions biased? A recent discussion in Learned Publishing has focused on one aspect of potential bias in editorial decisions, namely seasonal (e.g., monthly) variations in acceptance rates of research journals. In this letter, we contribute to the discussion by analyzing data from Physical Review Letters (PRL), a journal published by the American Physical Society. We studied the 190,106 papers submitted to PRL from January 1990 until September 2012. No statistically significant variations were found in the monthly acceptance rates. We conclude that the time of year that the authors of a paper submit their work to PRL has no effect on the fate of the paper through the review process.
This is a comment on the paper : Quantum Interference between Light Sources Separated by 150 Million Kilometers by Deng et al, PHYSICAL REVIEW LETTERS 123, 080401 (2019)
When analyzing the content of Physics Letter B (PLB) web pages, one goal was to separate articles from particle physics(HEP) and nuclear physics(NP). PLB contains information about the subject area of an article: Astrophysics and Cosmology, Experiments, Phenomenology or Theory. Those subject areas have been used since 2004. To extend those areas to earlier dates and try to separate HEP and NP publications, the idea was to use the information on the arXiv to accomplish this, by correlating a publication in PLB with a publication in one of the different arXiv repositories for particle, nuclear or astrophysics. The arXiv articles go back to at least 1995. When building the correlation between an arXiv article and each PLB article, it was found that many PLB articles do not have an existing reference in an arXiv article. Given that, the same analysis was performed using particle, nuclear and astro physics publications in Physical Review Letters (PRL). A similar result was obtained, but for a much shorter period of time. Finally, a title match was performed between the two journals and the arXiv, to see whether the preprint version of the journal article existed on the arXiv at all. The
LLM-generated reviews for scientific papers are gaining considerable traction and are even being officially piloted by major conferences. We have to assume that not only reviewers are using LLM-assistance, but also that authors use LLMs to revise their papers before submitting. In this work, we perform empirical experiments on papers from the 2025 ACL Rolling Review (ARR) to evaluate LLM reviews from both the author and the reviewer perspective. First, we identify a limited alignment of LLM reviews with human ones. In the best-case scenario, the alignment is reasonable. However, we also find that LLM-human alignment varies substantially across prompts and models. Finally, we investigate the scenario in which the author uses an iterative draft-revise workflow to improve the submission according to the LLM review. We find that this "gaming" of LLM reviews can be effective in specific scenarios, leading to a statistically significant increase of overall scores for up to 35\% of papers. We publish our code: https://github.com/uhh-hcds/reviewarcade.
In recent years, advances in computational power and spatial data analysis (GIS, remote sensing, etc) have led to an increase in attempts to model the spread and behaviour of wildland fires across the landscape. This series of review papers endeavours to critically and comprehensively review all types of surface fire spread models developed since 1990. This paper reviews models of a physical or quasi-physical nature. These models are based on the fundamental chemistry and/or physics of combustion and fire spread. Other papers in the series review models of an empirical or quasi-empirical nature, and mathematical analogues and simulation models. Many models are extensions or refinements of models developed before 1990. Where this is the case, these models are also discussed but much less comprehensively.
This study examines whether there is any evidence of bias in two areas of common critique of open, non-anonymous peer review - and used in the post-publication, peer review system operated by the open-access scholarly publishing platform F1000Research. First, is there evidence of bias where a reviewer based in a specific country assesses the work of an author also based in the same country? Second, are reviewers influenced by being able to see the comments and know the origins of previous reviewer? Methods: Scrutinising the open peer review comments published on F1000Research, we assess the extent of two frequently cited potential influences on reviewers that may be the result of the transparency offered by a fully attributable, open peer review publishing model: the national affiliations of authors and reviewers, and the ability of reviewers to view previously-published reviewer reports before submitting their own. The effects of these potential influences were investigated for all first versions of articles published by 8 July 2019 to F1000Research. In 16 out of the 20 countries with the most articles, there was a tendency for reviewers based in the same country to give a more po
Owing to rapid recent progress, ultracold atomic lattice systems for simulating topological phases are now at a pivotal stage, evolving from established paradigms into increasingly versatile and programmable quantum simulators. In this review, we survey recent experimental advances across four major classes of platforms: optical lattices, including optical lattices with laser-assisted tunneling and optical Raman lattices; synthetic lattices in momentum or internal-state space; Floquet-engineered lattices; and optical tweezer arrays, all of which offer distinct capabilities for realizing and probing topological matter. For each class, we highlight representative experimental breakthroughs, the topological models that have been realized, and the advanced detection and characterization techniques employed, emphasizing how these complementary approaches collectively expand the frontier of quantum simulation. We also discuss emerging directions in strongly correlated and nonequilibrium topological phases, and conclude with an outlook on future prospects.
This paper reviews the current progress in applying machine learning (ML) tools to solve NP-hard combinatorial optimization problems, with a focus on routing problems such as the traveling salesman problem (TSP) and the vehicle routing problem (VRP). Due to the inherent complexity of these problems, exact algorithms often require excessive computational time to find optimal solutions, while heuristics can only provide approximate solutions without guaranteeing optimality. With the recent success of machine learning models, there is a growing trend in proposing and implementing diverse ML techniques to enhance the resolution of these challenging routing problems. We propose a taxonomy categorizing ML-based routing methods into construction-based and improvement-based approaches, highlighting their applicability to various problem characteristics. This review aims to integrate traditional OR methods with state-of-the-art ML techniques, providing a structured framework to guide future research and address emerging VRP variants.
Context is an important factor in computer vision as it offers valuable information to clarify and analyze visual data. Utilizing the contextual information inherent in an image or a video can improve the precision and effectiveness of object detectors. For example, where recognizing an isolated object might be challenging, context information can improve comprehension of the scene. This study explores the impact of various context-based approaches to object detection. Initially, we investigate the role of context in object detection and survey it from several perspectives. We then review and discuss the most recent context-based object detection approaches and compare them. Finally, we conclude by addressing research questions and identifying gaps for further studies. More than 265 publications are included in this survey, covering different aspects of context in different categories of object detection, including general object detection, video object detection, small object detection, camouflaged object detection, zero-shot, one-shot, and few-shot object detection. This literature review presents a comprehensive overview of the latest advancements in context-based object detecti
This review systematically examines the progression of the You Only Look Once (YOLO) object detection algorithms from YOLOv1 to the recently unveiled YOLOv12. Employing a reverse chronological analysis, this study examines the advancements introduced by YOLO algorithms, beginning with YOLOv12 and progressing through YOLO11 (or YOLOv11), YOLOv10, YOLOv9, YOLOv8, and subsequent versions to explore each version's contributions to enhancing speed, detection accuracy, and computational efficiency in real-time object detection. Additionally, this study reviews the alternative versions derived from YOLO architectural advancements of YOLO-NAS, YOLO-X, YOLO-R, DAMO-YOLO, and Gold-YOLO. Moreover, the study highlights the transformative impact of YOLO models across five critical application areas: autonomous vehicles and traffic safety, healthcare and medical imaging, industrial manufacturing, surveillance and security, and agriculture. By detailing the incremental technological advancements in subsequent YOLO versions, this review chronicles the evolution of YOLO, and discusses the challenges and limitations in each of the earlier versions. The evolution signifies a path towards integrating
This note corrects some errors in a previously published paper: Size-dependent piezoelectricity and elasticity in nanostructures due to the flexoelectric effect[PHYSICAL REVIEW B 77, 125424 (2008)]. The comlete published paper is also included in this note.
Hypernetworks, or hypernets for short, are neural networks that generate weights for another neural network, known as the target network. They have emerged as a powerful deep learning technique that allows for greater flexibility, adaptability, dynamism, faster training, information sharing, and model compression. Hypernets have shown promising results in a variety of deep learning problems, including continual learning, causal inference, transfer learning, weight pruning, uncertainty quantification, zero-shot learning, natural language processing, and reinforcement learning. Despite their success across different problem settings, there is currently no comprehensive review available to inform researchers about the latest developments and to assist in utilizing hypernets. To fill this gap, we review the progress in hypernets. We present an illustrative example of training deep neural networks using hypernets and propose categorizing hypernets based on five design criteria: inputs, outputs, variability of inputs and outputs, and the architecture of hypernets. We also review applications of hypernets across different deep learning problem settings, followed by a discussion of general
Recent progresses in the description of glassy relaxation and ageing are reviewed for the wide class of network-forming materials such as $GeO_2$, Ge$_x$Se$_{1-x}$, silicates (SiO$_2$-Na$_2$O) or borates (B$_2$O$_3$-Li$_2$O), all of them having an important usefulness in domestic, geological or optoelectronic applications. A brief introduction of the glass transition phenomenology is given, together with the salient features that are revealed both from theory and experiments. Standard experimental methods used for the characterization of the slowing down of the dynamics are reviewed. We then discuss the important role played by aspect of network topology and rigidity for the understanding of the relaxation of the glass transition, while also permitting analytical predictions of glass properties from simple and insightful models based on the network structure. We also emphasize the great utility of computer simulations which probe the dynamics at the molecular level, and permit to calculate various structure-related functions in connection with glassy relaxation and the physics of ageing which reveals the off-equilibrium nature of glasses. We discuss the notion of spatial variations
Entanglement detection is an important problem in quantum information theory because quantum entanglement is a key resource in quantum information processing. Realignment criteria is a powerful tool for detection of entangled states in bipartite and multipartite quantum system. It is an important criteria for entanglement detection because it works well; not only for negative partial transpose entangled states (NPTES) but also for positive partial transpose entangled states (PPTES). Since the matrix corresponding to realignment map is indefinite so the experimental implementation of the map is an obscure task. In this work, firstly, we have approximated the realignment map to a positive map using the method of structural physical approximation (SPA) and then we have shown that the structural physical approximation of realignment map (SPA-R) is completely positive. Positivity of the constructed map is characterized using moments which can be physically measured. Next, we develop a separability criterion based on our SPA-R map in the form of an inequality and have shown that the developed criterion not only detect NPTES but also PPTES. We have provided some examples to support the re
This is the introduction to the section on Quantum Mechanics in the centennial collection of noteworthy articles appearing in The Physical Review and Physical Review Letters through 1983, since it all began in 1893. The selections for this section are "Can Quantum-Mechanical Description of Physical Reality Be Considered Complete?" by Einstein, Podolsky and Rosen; Bohr's response, with the same title; Bohm's first hidden variables paper: "A Suggested Interpretation of the Quantum Theory in Terms of `Hidden' Variables. I"; Aharonov and Bohm's "Significance of Electromagnetic Potentials in the Quantum Theory"; and "Experimental Test of Bell's Inequalities Using Time-Varying Analyzers" by Aspect, Dalibard and Roger.
The recent advancement of information and communication technology makes digitalisation of an entire manufacturing shop-floor possible where physical processes are tightly intertwined with their cyber counterparts. This led to an emergence of a concept of digital twin, which is a realistic virtual copy of a physical object. Digital twin will be the key technology in Cyber-Physical Production Systems (CPPS) and its market is expected to grow significantly in the coming years. Nevertheless, digital twin is still relatively a new concept that people have different perspectives on its requirements, capabilities, and limitations. To better understand an effect of digital twin's operations, mitigate complexity of capturing dynamics of physical phenomena, and improve analysis and predictability, it is important to have a development tool with a strong semantic foundation that can accurately model, simulate, and synthesise the digital twin. This paper reviews current state-of-art on tools and developments of digital twin in manufacturing and discusses potential design challenges.
We review particle physics model building in type IIB string theory and F-theory. This is a region in the landscape where in principle many of the key ingredients required for a realistic model of particle physics can be combined successfully. We begin by reviewing moduli stabilisation within this framework and its implications for supersymmetry breaking. We then review model building tools and developments in the weakly coupled type IIB limit, for both local D3-branes at singularities and global models of intersecting D7-branes. Much of recent model building work has been in the strongly coupled regime of F-theory due to the presence of exceptional symmetries which allow for the construction of phenomenologically appealing Grand Unified Theories. We review both local and global F-theory model building starting from the fundamental concepts and tools regarding how the gauge group, matter sector and operators arise, and ranging to detailed phenomenological properties explored in the literature.
Automated planning is a prominent area of Artificial Intelligence, and an important component for intelligent autonomous agents. A cornerstone of domain-independent planning is the separation between planning logic, i.e. the automated reasoning side, and the knowledge model, that encodes a formal representation of domain knowledge needed to reason upon a given problem to synthesise a solution plan. Such a separation enables the use of reformulation techniques, which transform how a model is represented in order to improve the efficiency of plan generation. Over the past decades, significant research effort has been devoted to the design of reformulation techniques. In this paper, we present a systematic review of the large body of work on reformulation techniques for classical planning, aiming to provide a holistic view of the field and to foster future research in the area. As a tangible outcome, we provide a qualitative comparison of the existing classes of techniques, that can help researchers gain an overview of their strengths and weaknesses.
We review lattice results relevant for pion and kaon physics with the aim of making them easily accessible to the particle physics community. Specifically, we review the determination of the light-quark masses, the form factor f_+(0), relevant for the semileptonic K -> pi transition at zero momentum transfer as well as the ratio f_K/f_pi of decay constants and discuss the consequences for the elements V_{us} and V_{ud} of the CKM matrix. Furthermore, we describe the results obtained on the lattice for some of the low-energy constants of SU(2)_LxSU(2)_R and SU(3)_LxSU(3)_R Chiral Perturbation Theory and review the determination of the B_K parameter of neutral kaon mixing. We introduce quality criteria and use these when forming averages. Although subjective and imperfect, these criteria may help the reader to judge different aspects of current lattice computations. Our main results are summarized in section 1.2, but we stress the importance of the detailed discussion that underlies these results and constitutes the bulk of the present review.
This short paper compiles the big ideas behind some philosophical views, definitions, and examples of causality. This collection spans the realms of the four commonly adopted approaches to causality: Humes regularity, counterfactual, manipulation, and mechanisms. This short review is motivated by presenting simplified views and definitions and then supplements them with examples from various fields, including economics, education, medicine, politics, physics, and engineering. It is the hope that this short review comes in handy for new and interested readers with little knowledge of causality and causal inference.