In this the first of an anticipated four paper series, fundamental results of quantitative genetics are presented from a first principles approach. While none of these results are in any sense new, they are presented in extended detail to precisely distinguish between definition and assumption, with a further emphasis on distinguishing quantities from their usual approximations. Terminology frequently encountered in the field of human genetic disease studies will be defined in terms of their quantitive genetics form. Methods for estimation of both quantitative genetics and the related human genetics quantities will be demonstrated. While practitioners in the field of human quantitative disease studies may find this work pedantic in detail, the principle target audience for this work is trainees reasonably familiar with population genetics theory, but with less experience in its application to human disease studies. We introduce much of this formalism because in later papers in this series, we demonstrate that common areas of confusion in human disease studies can be resolved be appealing directly to these formal definitions. The second paper in this series will discuss polygenic ri
"Train While You Fight" (TWYF) advocates for continuous learning that occurs during operations, not just before or after. This paper examines the technical requirements that advanced distributed learning (ADL) platforms must meet to support TWYF, and how existing software engineering patterns can fulfill these requirements. Using a Design Science Research approach, we (i) derive challenges from PfPC/NATO documentation and recent practice, (ii) define solution objectives, and (iii) conduct a systematic mapping from challenges to proven patterns. We identify seven technical challenges: interoperability, resilience, multilingual support, data security and privacy, scalability, platform independence, and modularity. We illustrate the patterns with a national use case from the German armed forces.
The advancement of semiconductor materials has played a crucial role in the development of electronic and optical devices. However, scaling down semiconductor devices to the nanoscale has imposed limitations on device properties due to quantum effects. Hence, the search for successor materials has become a central focus in the fields of materials science and physics. Transition-metal nitrides (TMNs) are extraordinary materials known for their outstanding stability, biocompatibility, and ability to integrate with semiconductors. Over the past few decades, TMNs have been extensively employed in various fields. However, the synthesis of single-crystal TMNs has long been challenging, hindering the advancement of their high-performance electronics and plasmonics. Fortunately, progress in film deposition techniques has enabled the successful epitaxial growth of high-quality TMN films. In comparison to reported reviews, there is a scarcity of reviews on epitaxial TMN films from the perspective of materials physics and condensed matter physics, particularly at the atomic level. Therefore, this review aims to provide a brief summary of recent progress in epitaxial growth at atomic precision
To accommodate new applications such as extended reality, fully autonomous vehicular networks and the metaverse, next generation wireless networks are going to be subject to much more stringent performance requirements than the fifth-generation (5G) in terms of data rates, reliability, latency, and connectivity. It is thus necessary to develop next generation advanced transceiver (NGAT) technologies for efficient signal transmission and reception. In this tutorial, we explore the evolution of NGAT from three different perspectives. Specifically, we first provide an overview of new-field NGAT technology, which shifts from conventional far-field channel models to new near-field channel models. Then, three new-form NGAT technologies and their design challenges are presented, including reconfigurable intelligent surfaces, flexible antennas, and holographic multi-input multi-output (MIMO) systems. Subsequently, we discuss recent advances in semantic-aware NGAT technologies, which can utilize new metrics for advanced transceiver designs. Finally, we point out other promising transceiver technologies for future research.
Since the start of 5G work in 3GPP in early 2016, tremendous progress has been made in both standardization and commercial deployments. 3GPP is now entering the second phase of 5G standardization, known as 5G-Advanced, built on the 5G baseline in 3GPP Releases 15, 16, and 17. 3GPP Release 18, the start of 5G-Advanced, includes a diverse set of features that cover both device and network evolutions, providing balanced mobile broadband evolution and further vertical domain expansion and accommodating both immediate and long-term commercial needs. 5G-Advanced will significantly expand 5G capabilities, address many new use cases, transform connectivity experiences, and serve as an essential step in developing mobile communications towards 6G. This paper provides a comprehensive overview of the 3GPP 5G-Advanced development, introducing the prominent state-of-the-art technologies investigated in 3GPP and identifying key evolution directions for future research and standardization.
Imaging genetics is a growing field that employs structural or functional neuroimaging techniques to study individuals with genetic risk variants potentially linked to specific illnesses. This area presents considerable challenges to statisticians due to the heterogeneous information and different data forms it involves. In addition, both imaging and genetic data are typically high-dimensional, creating a "big data squared" problem. Moreover, brain imaging data contains extensive spatial information. Simply vectorizing tensor images and treating voxels as independent features can lead to computational issues and disregard spatial structure. This paper presents a novel statistical method for imaging genetics modeling while addressing all these challenges. We explore a Canonical Correlation Analysis based linear model for the joint modeling of brain imaging, genetic information, and clinical phenotype, enabling the simultaneous detection of significant brain regions and selection of important genetic variants associated with the phenotype outcome. Scalable algorithms are developed to tackle the "big data squared" issue. We apply the proposed method to explore the reaction speed, an i
Developing complex, reliable advanced accelerators requires a coordinated, extensible, and comprehensive approach in modeling, from source to the end of beam lifetime. We present highlights in Exascale Computing to scale accelerator modeling software to the requirements set for contemporary science drivers. In particular, we present the first laser-plasma modeling on an exaflop supercomputer using the US DOE Exascale Computing Project WarpX. Leveraging developments for Exascale, the new DOE SCIDAC-5 Consortium for Advanced Modeling of Particle Accelerators (CAMPA) will advance numerical algorithms and accelerate community modeling codes in a cohesive manner: from beam source, over energy boost, transport, injection, storage, to application or interaction. Such start-to-end modeling will enable the exploration of hybrid accelerators, with conventional and advanced elements, as the next step for advanced accelerator modeling. Following open community standards, we seed an open ecosystem of codes that can be readily combined with each other and machine learning frameworks. These will cover ultrafast to ultraprecise modeling for future hybrid accelerator design, even enabling virtual t
Integrating multiple (sub-)systems is essential to create advanced Information Systems (ISs). Difficulties mainly arise when integrating dynamic environments across the IS lifecycle. A traditional approach is a registry that provides the API documentation of the systems' endpoints. Large Language Models (LLMs) have shown to be capable of automatically creating system integrations (e.g., as service composition) based on this documentation but require concise input due to input token limitations, especially regarding comprehensive API descriptions. Currently, it is unknown how best to preprocess these API descriptions. Within this work, we (i) analyze the usage of Retrieval Augmented Generation (RAG) for endpoint discovery and the chunking, i.e., preprocessing, of OpenAPIs to reduce the input token length while preserving the most relevant information. To further reduce the input token length for the composition prompt and improve endpoint retrieval, we propose (ii) a Discovery Agent that only receives a summary of the most relevant endpoints and retrieves details on demand. We evaluate RAG for endpoint discovery using the RestBench benchmark, first, for the different chunking possib
This recounting of the history of the last three-and-a-half decades of advanced accelerator concepts is offered from a decidedly parochial point of view -- that of the career of the author, Prof. James Rosenzweig of the UCLA Dept. of Physics and Astronomy. This short voyage through a by-now long career will illustrate the very beginning of the compelling field of advanced accelerators, proceed through their maturation into one of the fastest growing areas of beam-based science, and give a look into their emerging importance in applications. An important aspect of advanced accelerators is their relationship to other burgeoning fields, particularly free-electron lasers. The framework of this retelling lends itself particularly well to illustrating this relationship. Likewise, this quick summary serves to demonstrate the essential team nature of our field, and the contributions of participants from all levels, ranging from students to those scientists whose careers may have developed in previous eras of positive ferment in accelerator science.
This is the interim publication of the first International Scientific Report on the Safety of Advanced AI. The report synthesises the scientific understanding of general-purpose AI -- AI that can perform a wide variety of tasks -- with a focus on understanding and managing its risks. A diverse group of 75 AI experts contributed to this report, including an international Expert Advisory Panel nominated by 30 countries, the EU, and the UN. Led by the Chair, these independent experts collectively had full discretion over the report's content. The final report is available at arXiv:2501.17805
The problem of inferring unknown graph edges from numerical data at a graph's nodes appears in many forms across machine learning. We study a version of this problem that arises in the field of \emph{landscape genetics}, where genetic similarity between organisms living in a heterogeneous landscape is explained by a weighted graph that encodes the ease of dispersal through that landscape. Our main contribution is an efficient algorithm for \emph{inverse landscape genetics}, which is the task of inferring this graph from measurements of genetic similarity at different locations (graph nodes). Inverse landscape genetics is important in discovering impediments to species dispersal that threaten biodiversity and long-term species survival. In particular, it is widely used to study the effects of climate change and human development. Drawing on influential work that models organism dispersal using graph \emph{effective resistances} (McRae 2006), we reduce the inverse landscape genetics problem to that of inferring graph edges from noisy measurements of these resistances, which can be obtained from genetic similarity data. Building on the NeurIPS 2018 work of Hoskins et al. 2018 on learn
The Advanced Accelerator Concepts (AAC) Seminar Series 2020 (https://aacseminarseries.lbl.gov/), organized and hosted by the Lawrence Berkeley National Laboratory, consisted of nine weekly sessions, each one dedicated to a particular topic of interest within the framework of advanced accelerator concepts research. The Seminar Series was a fully-remote event that provided a forum for the advanced accelerator community. The AAC Seminar Series was held in lieu of the AAC 2020 Workshop (https://aac2020.lbl.gov/), originally planned for June 2020 and canceled due to the COVID-19 pandemic. Since its inception in 1982, the biennial AAC Workshop has become the principal US and international meeting for advanced particle accelerator research and development.
Chronic wounds fail to proceed through an orderly and timely self healing process, resulting in cutaneous damage with full thickness in depth and leading to a major healthcare and economic burden worldwide. In the UK alone, 200,000 patients suffer from a chronic wound, whilst the global advanced wound care market is expected to reach nearly $11 million in 2022. Despite extensive research efforts so far, clinically-approved chronic wound therapies are still time-consuming, economically unaffordable and present restricted customisation. In this chapter, the role of collagen in the extracellular matrix of biological tissues and wound healing will be discussed, together with its use as building block for the manufacture of advanced wound dressings. Commercially-available collagen dressings and respective clinical performance will be presented, followed by an overview on the latest research advances in the context of multifunctional collagen systems for advanced wound care.
A common sample descriptor in human genomics studies is that of 'genetic ancestry group', with terms such as 'European genetic ancestry' or 'East Asian genetic ancestry' frequently used in publications to describe the genetics of groups of individuals based on the analysis of their genotypes. In this Perspective, I argue that these terms are imprecise and potentially misleading and that, for most applications, simple statements of genetic similarity represent a more accurate description.
A key step in control of precision mechatronic systems is Frequency Response Function (FRF) identification. The aim of this paper is to illustrate relevant developments and solutions for FRF identification for advanced motion control. Specifically dealing with transient and/or closed-loop conditions that can normally lead to inaccurate estimation results. This yields essential insights for FRF identification for advanced motion control that are illustrated through a simulation study and validated on an experimental setup.
It is widely accepted that population genetics theory is the cornerstone of evolutionary analyses. Empirical tests of the theory, however, are challenging because of the complex relationships between space, dispersal, and evolution. Critically, we lack quantitative validation of the spatial models of population genetics. Here we combine analytics, on and off-lattice simulations, and experiments with bacteria to perform quantitative tests of the theory. We study two bacterial species, the gut microbe Escherichia coli and the opportunistic pathogen Pseudomonas aeruginosa, and show that spatio-genetic patterns in colony biofilms of both species are accurately described by an extension of the one-dimensional stepping-stone model. We use one empirical measure, genetic diversity at the colony periphery, to parameterize our models and show that we can then accurately predict another key variable: the degree of short-range cell migration along an edge. Moreover, the model allows us to estimate other key parameters including effective population size (density) at the expansion frontier. While our experimental system is a simplification of natural microbial community, we argue it is a proof
The Nernst effect, the generation of a transverse electric voltage in the presence of longitudinal thermal gradient, has garnered significant attention in the realm of magnetic topological materials due to its superior potential for thermoelectric applications. In this work, we investigate electronic and thermoelectric transport properties of a Kagome magnet ErMn$_6$Sn$_6$, a compound showing an incommensurate antiferromagnetic phase followed by a ferrimagnetic phase transition upon cooling. We show that in the antiferromagnetic phase ErMn$_6$Sn$_6$ exhibits both topological Nernst effect and anomalous Nernst effect, analogous to the electric Hall effects, with the Nernst coefficient reaching 1.71 uV/K at 300 K and 3 T. This value surpasses that of most of previously reported state-of-the-art canted antiferromagnetic materials and is comparable to recently reported other members of RMn$_6$Sn$_6$ (R = rare-earth, Y, Lu, Sc) compounds, which makes ErMn$_6$Sn$_6$ a promising candidate for advancing the development of Nernst effect-based thermoelectric devices.
Purpose: To evaluate nerve fiber layer (NFL) reflectance for glaucoma diagnosis using a large dataset. Methods: Participants were imaged with 4.9mm ONH scans using spectral-domain optical coherence tomography (OCT). The NFL reflectance map was reconstructed from 13 concentric rings of optic nerve head(ONH) scan, then processed by an azimuthal filter to reduce directional reflectance bias due to variation of beam incidence angle. The peripapillary thickness and reflectance maps were both divided into 96 superpixels. Low-reflectance and low-thickness superpixels were defined as values below the 5th percentile normative reference for that location. Focal reflectance loss was measured by summing loss, relative to the normal reference average, in low-reflectance superpixels. Focal thickness loss was calculated in a similar fashion. The area under receiving characteristic curve (AROC) was used to assess diagnostic accuracy. Results: Fifty-three normal, 196 pre-perimetric, 132 early perimetric, and 59 moderate and advanced perimetric glaucoma participants were included from the Advanced Imaging for Glaucoma Study. Sixty-seven percent of glaucomatous reflectance maps showed characteristic
This research contributes to the security design of an advanced smart drone swarm network based on a variant of the Blockchain Governance Game (BGG), which is the theoretical game model to predict the moments of security actions before attacks, and the Strategic Alliance for Blockchain Governance Game (SABGG), which is one of the BGG variants which has been adapted to construct the best strategies to take preliminary actions based on strategic alliance for protecting smart drones in a blockchain-based swarm network. Smart drones are artificial intelligence (AI)-enabled drones which are capable of being operated autonomously without having any command center. Analytically tractable solutions from the SABGG allow us to estimate the moments of taking preliminary actions by delivering the optimal accountability of drones for preventing attacks. This advanced secured swarm network within AI-enabled drones is designed by adapting the SABGG model. This research helps users to develop a new network-architecture-level security of a smart drone swarm which is based on a decentralized network.
Mathematical population genetics is only one of Kingman's many research interests. Nevertheless, his contribution to this field has been crucial, and moved it in several important new directions. Here we outline some aspects of his work which have had a major influence on population genetics theory.