177,283 results on '"Atkinson, A."'
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2. Career Education: Learning with a Purpose. Secondary Guide-Vol. 5. Mathematics and Career Clusters, Mathematics Related Activity Suggestions, Field Trip Sites and Guest Speakers.
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State Fair Community Coll., Sedalia, MO. and Atkinson, Marilyn
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The guide offers a compilation of teacher-developed career education materials which may be integrated with secondary level curriculum in mathematics. Suggested activities and ideas present the following units based on career clusters as they relate to mathematics: construction, communications and media, hospitality and recreation, public service, marine science, health, manufacturing, transportation, and agri-business and natural resources. Activity suggestions for other math-related units are also given including several "silent lectures" emphasizing logical problem solving and units on consumer economics, metrics, computer science, statistics, and other mathematical applications. Objectives, teaching procedure, and related resources and materials are presented for each unit. A 12-page list of suggested local field trip sites and guest speakers is included. (EC)
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- 2024
3. Initial Findings on Student Progress and Satisfaction in a New Model of Hyperflexible Online Delivery for University Students
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Colin Beer, Kate Ames, Noal Atkinson, Damien Clark, and Peter Hosie
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University degrees are usually delivered in defined sessions--by term, semester, or in week-based blocks--whereby students are required to complete their studies by the due date. Term or session-based schedules that require students to complete the study within set timeframes are, however, potentially restrictive. Temporal challenges associated with work and life can impede progress and add to the specific problem of student attrition in online learning. As universities seek to deliver innovative options for their students, increased attention is being paid to alternate models of delivery. This paper reports on the development of a hyperflexible online Master of Business Administration (MBA) course by a regional university in Australia, which has grown to more than 1,000 students since its launch in 2017. Delivered entirely online, the degree was specifically designed to address an inequity; MBA programs are traditionally expensive, and in Australia, the requirement for students to travel to attend residential schools and examinations adds significant cost to already expensive tuition fees. This paper analyzed enrollment data, course analytics over a two-year period, and student surveys conducted at the end of the second year of delivery (n = 98) to evaluate the development and implementation of the course as a hyperflexible course whereby students have almost complete control over their study at the postgraduate tertiary level. Results highlight the potential for the model to enable student success through flexibility.
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- 2024
4. Aboriginal and Torres Strait Islander peoples, gender and disaster: A commentary
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Rosenhek, Ruth and Atkinson, Caroline Carlie
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- 2024
5. Inference Plans for Hybrid Particle Filtering
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Cheng, Ellie Y., Atkinson, Eric, Baudart, Guillaume, Mandel, Louis, and Carbin, Michael
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Computer Science - Programming Languages ,Computer Science - Artificial Intelligence - Abstract
Advanced probabilistic programming languages (PPLs) use hybrid inference systems to combine symbolic exact inference and Monte Carlo methods to improve inference performance. These systems use heuristics to partition random variables within the program into variables that are encoded symbolically and variables that are encoded with sampled values, and the heuristics are not necessarily aligned with the performance evaluation metrics used by the developer. In this work, we present inference plans, a programming interface that enables developers to control the partitioning of random variables during hybrid particle filtering. We further present Siren, a new PPL that enables developers to use annotations to specify inference plans the inference system must implement. To assist developers with statically reasoning about whether an inference plan can be implemented, we present an abstract-interpretation-based static analysis for Siren for determining inference plan satisfiability. We prove the analysis is sound with respect to Siren's semantics. Our evaluation applies inference plans to three different hybrid particle filtering algorithms on a suite of benchmarks and shows that the control provided by inference plans enables speed ups of 1.76x on average and up to 206x to reach target accuracy, compared to the inference plans implemented by default heuristics; the results also show that inference plans improve accuracy by 1.83x on average and up to 595x with less or equal runtime, compared to the default inference plans. We further show that the static analysis is precise in practice, identifying all satisfiable inference plans in 27 out of the 33 benchmark-algorithm combinations.
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- 2024
6. Near-inertial echoes of ageostrophic instability in submesoscale filaments
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Atkinson, Erin, McWilliams, James, and Grisouard, Nicolas
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Physics - Fluid Dynamics ,Physics - Atmospheric and Oceanic Physics - Abstract
Ocean submesoscales, flows with characteristic size around 10m--10km, are transitional between the larger, rotationally-constrained mesoscale and three-dimensional turbulence. In this paper we present simulations of a submesoscale ocean filament. The strong vertical transport associated with submesoscale fronts and filaments ventilates the deeper water and provides a route for the observed forward cascade of energy in the ocean. In our case, the filament is strongly sheared in both vertical and cross-filament directions and is unstable. Instability indeed dominates the early behaviour with a fast extraction of kinetic energy from the vertically sheared thermal wind. The filament is expected to be subject to instabilities, including symmetric and Kelvin-Helmholtz instabilities. An instability emerges with a circulation that does not match either, though symmetric modes become apparent later. The action of the instability is sufficiently rapid that the filament does not respond in a geostrophically balanced sense. Instead, it later exhibits vertically sheared near-inertial oscillations with higher amplitude as the initial minimum Richardson number decreases. Horizontal gradients strengthen only briefly as the fronts restratify. These unstable filaments can be generated by strong mixing events at pre-existing stable structures. The oscillations must also be considered when designing initial conditions for numerical experiments of submesoscale flows, where the initial state is intended to be balanced., Comment: Submitted to Journal of Fluid Mechanics. 11 pages, 6 figures, 1 table. Supplementary data consists of one movie "Movie 1" available at https://youtu.be/0FUPSQWpfe4
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- 2024
7. Unsettled Law: Time to Generate New Approaches?
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Atkinson, David and Morrison, Jacob
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Computer Science - Computers and Society - Abstract
We identify several important and unsettled legal questions with profound ethical and societal implications arising from generative artificial intelligence (GenAI), focusing on its distinguishable characteristics from traditional software and earlier AI models. Our key contribution is formally identifying the issues that are unique to GenAI so scholars, practitioners, and others can conduct more useful investigations and discussions. While established legal frameworks, many originating from the pre-digital era, are currently employed in GenAI litigation, we question their adequacy. We argue that GenAI's unique attributes, including its general-purpose nature, reliance on massive datasets, and potential for both pervasive societal benefits and harms, necessitate a re-evaluation of existing legal paradigms. We explore potential areas for legal and regulatory adaptation, highlighting key issues around copyright, privacy, torts, contract law, criminal law, property law, and the First Amendment. Through an exploration of these multifaceted legal challenges, we aim to stimulate discourse and policy considerations surrounding GenAI, emphasizing a proactive approach to legal and ethical frameworks. While we refrain from advocating specific legal changes, we underscore the need for policymakers to carefully consider the issues raised. We conclude by summarizing key questions across these areas of law in a helpful table for easy reference., Comment: 14 pages
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- 2024
8. Token Erasure as a Footprint of Implicit Vocabulary Items in LLMs
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Feucht, Sheridan, Atkinson, David, Wallace, Byron, and Bau, David
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Computer Science - Computation and Language ,Computer Science - Machine Learning ,I.2.7 - Abstract
LLMs process text as sequences of tokens that roughly correspond to words, where less common words are represented by multiple tokens. However, individual tokens are often semantically unrelated to the meanings of the words/concepts they comprise. For example, Llama-2-7b's tokenizer splits the word "northeastern" into the tokens ['_n', 'ort', 'he', 'astern'], none of which correspond to semantically meaningful units like "north" or "east." Similarly, the overall meanings of named entities like "Neil Young" and multi-word expressions like "break a leg" cannot be directly inferred from their constituent tokens. Mechanistically, how do LLMs convert such arbitrary groups of tokens into useful higher-level representations? In this work, we find that last token representations of named entities and multi-token words exhibit a pronounced "erasure" effect, where information about previous and current tokens is rapidly forgotten in early layers. Using this observation, we propose a method to "read out" the implicit vocabulary of an autoregressive LLM by examining differences in token representations across layers, and present results of this method for Llama-2-7b and Llama-3-8B. To our knowledge, this is the first attempt to probe the implicit vocabulary of an LLM., Comment: 13 pages, 14 figures. Code and data at https://footprints.baulab.info/
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- 2024
9. Towards Supporting Legal Argumentation with NLP: Is More Data Really All You Need?
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Santosh, T. Y. S. S, Ashley, Kevin D., Atkinson, Katie, and Grabmair, Matthias
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence - Abstract
Modeling legal reasoning and argumentation justifying decisions in cases has always been central to AI & Law, yet contemporary developments in legal NLP have increasingly focused on statistically classifying legal conclusions from text. While conceptually simpler, these approaches often fall short in providing usable justifications connecting to appropriate legal concepts. This paper reviews both traditional symbolic works in AI & Law and recent advances in legal NLP, and distills possibilities of integrating expert-informed knowledge to strike a balance between scalability and explanation in symbolic vs. data-driven approaches. We identify open challenges and discuss the potential of modern NLP models and methods that integrate
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- 2024
10. Morescient GAI for Software Engineering
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Kessel, Marcus and Atkinson, Colin
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Computer Science - Software Engineering ,Computer Science - Artificial Intelligence ,D.2.1 ,D.2.4 ,I.2.2 ,I.2.7 - Abstract
The ability of Generative AI (GAI) technology to automatically check, synthesize and modify software engineering artifacts promises to revolutionize all aspects of software engineering. Using GAI for software engineering tasks is consequently one of the most rapidly expanding fields of software engineering research, with dozens of LLM-based code models having been published since 2021. However, the overwhelming majority of existing code models share a major weakness - they are exclusively trained on the syntactic facet of software, significantly lowering their trustworthiness in tasks dependent on software semantics. To address this problem, a new class of "Morescient" GAI is needed that is "aware" of (i.e., trained on) both the semantic and static facets of software. This, in turn, will require a new generation of software observation platforms capable of generating ultra-large quantities of execution observations in a structured and readily analyzable way. In this paper, we present a vision for how such "Morescient" GAI models can be engineered, evolved and disseminated according to the principles of open science.
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- 2024
11. A field-level emulator for modified gravity
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Saadeh, Daniela, Koyama, Kazuya, and Morice-Atkinson, Xan
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Astrophysics - Cosmology and Nongalactic Astrophysics ,General Relativity and Quantum Cosmology - Abstract
Stage IV dark energy surveys such as the Vera C. Rubin Observatory and Euclid present a unique opportunity to shed light on the nature of dark energy. However, the full constraining power of the new data cannot be unlocked unless accurate predictions are available at all observable scales. Currently, only the linear regime is well understood in models beyond $\Lambda$CDM: on the nonlinear scales, expensive numerical simulations become necessary, making their direct use impractical in the analyses of large datasets. Recently, machine learning techniques have shown potential to break this impasse: by training emulators, we can reproduce complex data fields in a fraction of the time it takes to produce them. In this work, we present a field-level emulator capable of turning a $\Lambda$CDM N-body simulation into one evolved under $f(R)$ gravity. To achieve this, we build on the map2map neural network, using the strength of modified gravity $|f_{R_0}|$ as style parameter. We find that our emulator correctly estimates the changes it needs to apply to the positions and velocities of the input N-body particles to produce the target simulation. We test the performance of our network against several summary statistics, finding $1\%$ agreement in the power spectrum up to $k \sim 1$ $h/$Mpc, and $1.5\%$ agreement against the independent boost emulator eMantis. Although the algorithm is trained on fixed cosmological parameters, we find it can extrapolate to models it was not trained on. Coupled with available field-level emulators and simulations suites for $\Lambda$CDM, our algorithm can be used to constrain modified gravity in the large-scale structure using full information available at the field-level., Comment: 13 pages, 9 figures; to be submitted to MNRAS
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- 2024
12. Seeds for Reclaiming Art in Education
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Dennis Atkinson
- Abstract
The concern of this paper is to provide a number of 'seeds' for a reclaiming of art in education by placing emphasis upon art's pedagogy or art's education. The notion of reclaiming does not infer a return to a utopian past or to a halcyon future, but it invokes a reaffirmation of the adventure of events of art practice that can take us beyond ourselves towards new creative assemblages and possibilities for becoming-with. Such reclaiming requires a culture of trust, care and response-ability. In relation to art's pedagogy the paper calls for opening up what is formally recognised as 'practice' in art education to a sensing towards what might be obscured by such recognition and in doing so reshape our ideas and modes of practice.
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- 2024
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13. Recommendations on best available science for the United States National Seismic Hazard Model
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Anderson, John G, Atkinson, Gail M, Baker, Jack W, Campbell, Kenneth W, DeShon, Heather R, Jordan, Thomas H, Kelson, Keith I, Shome, Nilesh, and Stewart, Jonathan P
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The 50 state update to the 2023 United States National Seismic Hazard Model (NSHM) is the latest in a sequence published by the U. S. Geological Survey (USGS). The 2023 NSHM is intended for use in building codes and similar applications at return periods of 475 years (corresponding to exceedance probabilities of 10% in 50 years) or longer. In reviewing the model, the NSHM Program Steering Committee, consisting of the authors of this paper, considered the characteristics of “best available science” that are applicable to the NSHM. Best available science must perform better than the previous NSHM, and there should be no available alternatives that could improve the models. The following are suggested characteristics of “best available science”:A) Clear objectivesB) Rigorous conceptual modelC) Timely, relevant and inclusiveD) Verified and reproducibleE) Validated intermediate and final modelsF) Replicable within uncertaintiesG) Peer reviewedH) Permanent documentationThis article focuses on the justification for, and intent of, the above criteria for best available science.
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- 2024
14. Responsible AI for Earth Observation
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Ghamisi, Pedram, Yu, Weikang, Marinoni, Andrea, Gevaert, Caroline M., Persello, Claudio, Selvakumaran, Sivasakthy, Girotto, Manuela, Horton, Benjamin P., Rufin, Philippe, Hostert, Patrick, Pacifici, Fabio, and Atkinson, Peter M.
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Computers and Society - Abstract
The convergence of artificial intelligence (AI) and Earth observation (EO) technologies has brought geoscience and remote sensing into an era of unparalleled capabilities. AI's transformative impact on data analysis, particularly derived from EO platforms, holds great promise in addressing global challenges such as environmental monitoring, disaster response and climate change analysis. However, the rapid integration of AI necessitates a careful examination of the responsible dimensions inherent in its application within these domains. In this paper, we represent a pioneering effort to systematically define the intersection of AI and EO, with a central focus on responsible AI practices. Specifically, we identify several critical components guiding this exploration from both academia and industry perspectives within the EO field: AI and EO for social good, mitigating unfair biases, AI security in EO, geo-privacy and privacy-preserving measures, as well as maintaining scientific excellence, open data, and guiding AI usage based on ethical principles. Furthermore, the paper explores potential opportunities and emerging trends, providing valuable insights for future research endeavors.
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- 2024
15. Recipes for forming a carbon-rich giant planet
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Mousis, Olivier, Cavalié, Thibault, Lunine, Jonathan I., Mandt, Kathleen E., Hueso, Ricardo, Aguichine, Artyom, Schneeberger, Antoine, Couzinou, Tom Benest, Atkinson, David H., Hue, Vincent, Hofstadter, Mark, and Srisuchinwong, Udomlerd
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Astrophysics - Earth and Planetary Astrophysics - Abstract
The exploration of carbon-to-oxygen ratios has yielded intriguing insights into the composition of close-in giant exoplanets, giving rise to a distinct classification: carbon-rich planets, characterized by a carbon-to-oxygen ratio $\ge$ 1 in their atmospheres, as opposed to giant planets exhibiting carbon-to-oxygen ratios close to the protosolar value. In contrast, despite numerous space missions dispatched to the outer solar system and the proximity of Jupiter, Saturn, Uranus, and Neptune, our understanding of the carbon-to-oxygen ratio in these giants remains notably deficient. Determining this ratio is crucial as it serves as a marker linking a planet's volatile composition directly to its formation region within the disk. This article provides an overview of the current understanding of the carbon-to-oxygen ratio in the four gas giants of our solar system and explores why there is yet no definitive dismissal of the possibility that Jupiter, Saturn, Uranus, or Neptune could be considered carbon-rich planets. Additionally, we delve into the three primary formation scenarios proposed in existing literature to account for a bulk carbon-to-oxygen ratio $\ge$ 1 in a giant planet. A significant challenge lies in accurately inferring the bulk carbon-to-oxygen ratio of our solar system's gas giants. Retrieval methods involve integrating in situ measurements from entry probes equipped with mass spectrometers and remote sensing observations conducted at microwave wavelengths by orbiters. However, these methods fall short of fully discerning the deep carbon-to-oxygen abundance in the gas giants due to their limited probing depth, typically within the 10-100 bar range., Comment: Space Science Reviews, in press
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- 2024
16. The global landscape of academic guidelines for generative AI and Large Language Models
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Jiao, Junfeng, Afroogh, Saleh, Chen, Kevin, Atkinson, David, and Dhurandhar, Amit
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Computer Science - Computers and Society ,Computer Science - Artificial Intelligence ,Computer Science - Computation and Language - Abstract
The integration of Generative Artificial Intelligence (GAI) and Large Language Models (LLMs) in academia has spurred a global discourse on their potential pedagogical benefits and ethical considerations. Positive reactions highlight some potential, such as collaborative creativity, increased access to education, and empowerment of trainers and trainees. However, negative reactions raise concerns about ethical complexities, balancing innovation and academic integrity, unequal access, and misinformation risks. Through a systematic survey and text-mining-based analysis of global and national directives, insights from independent research, and eighty university-level guidelines, this study provides a nuanced understanding of the opportunities and challenges posed by GAI and LLMs in education. It emphasizes the importance of balanced approaches that harness the benefits of these technologies while addressing ethical considerations and ensuring equitable access and educational outcomes. The paper concludes with recommendations for fostering responsible innovation and ethical practices to guide the integration of GAI and LLMs in academia.
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- 2024
17. Learning the Language of Protein Structure
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Gaujac, Benoit, Donà, Jérémie, Copoiu, Liviu, Atkinson, Timothy, Pierrot, Thomas, and Barrett, Thomas D.
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Quantitative Biology - Quantitative Methods ,Computer Science - Machine Learning - Abstract
Representation learning and \emph{de novo} generation of proteins are pivotal computational biology tasks. Whilst natural language processing (NLP) techniques have proven highly effective for protein sequence modelling, structure modelling presents a complex challenge, primarily due to its continuous and three-dimensional nature. Motivated by this discrepancy, we introduce an approach using a vector-quantized autoencoder that effectively tokenizes protein structures into discrete representations. This method transforms the continuous, complex space of protein structures into a manageable, discrete format with a codebook ranging from 4096 to 64000 tokens, achieving high-fidelity reconstructions with backbone root mean square deviations (RMSD) of approximately 1-5 \AA. To demonstrate the efficacy of our learned representations, we show that a simple GPT model trained on our codebooks can generate novel, diverse, and designable protein structures. Our approach not only provides representations of protein structure, but also mitigates the challenges of disparate modal representations and sets a foundation for seamless, multi-modal integration, enhancing the capabilities of computational methods in protein design.
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- 2024
18. Non-invasive Diver Respiration Rate Monitoring in Hyperbaric Lifeboat Environments using Short-Range Radar
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Czerkawski, Mikolaj, Stewart, Fraser, Ilioudis, Christos, Michie, Craig, Andonovic, Ivan, Atkinson, Robert, Coull, Maurice, Sandilands, Donald, Kerr, Gareth, Clemente, Carmine, and Tachtatzis, Christos
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Electrical Engineering and Systems Science - Signal Processing - Abstract
The monitoring of diver health during emergency events is crucial to ensuring the safety of personnel. A non-invasive system continuously providing a measure of the respiration rate of individual divers is exceedingly beneficial in this context. The paper reports on the application of short-range radar to record the respiration rate of divers within hyperbaric lifeboat environments. Results demonstrate that the respiratory motion can be extracted from the radar return signal applying routine signal processing. Further, evidence is provided that the radar-based approach yields a more accurate measure of respiration rate than an audio signal from a headset microphone. The system promotes an improvement in evacuation protocols under critical operational scenarios., Comment: Presented at IEEE OCEANS 2021 San Diego, Porto
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- 2024
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19. A Legal Risk Taxonomy for Generative Artificial Intelligence
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Atkinson, David and Morrison, Jacob
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Computer Science - Computers and Society - Abstract
For the first time, this paper presents a taxonomy of legal risks associated with generative AI (GenAI) by breaking down complex legal concepts to provide a common understanding of potential legal challenges for developing and deploying GenAI models. The methodology is based on (1) examining the legal claims that have been filed in existing lawsuits and (2) evaluating the reasonably foreseeable legal claims that may be filed in future lawsuits. First, we identified 29 lawsuits against prominent GenAI entities and tallied the claims of each lawsuit. From there, we identified seven claims that are cited at least four times across these lawsuits as the most likely claims for future GenAI lawsuits. For each of these seven claims, we describe the elements of the claim (what the plaintiff must prove to prevail) and provide an example of how it may apply to GenAI. Next, we identified 30 other potential claims that we consider to be more speculative, because they have been included in fewer than four lawsuits or have yet to be filed. We further separated those 30 claims into 19 that are most likely to be made in relation to pre-deployment of GenAI models and 11 that are more likely to be made in connection with post-deployment of GenAI models since the legal risks will vary between entities that create versus deploy them. For each of these claims, we describe the elements of the claim and the potential remedies that plaintiffs may seek to help entities determine their legal risks in developing or deploying GenAI. Lastly, we close the paper by noting the novelty of GenAI technology and propose some applications for the paper's taxonomy in driving further research., Comment: 29 pages, 2 tables, preprint
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- 2024
20. Locating and Editing Factual Associations in Mamba
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Sharma, Arnab Sen, Atkinson, David, and Bau, David
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Computer Science - Computation and Language - Abstract
We investigate the mechanisms of factual recall in the Mamba state space model. Our work is inspired by previous findings in autoregressive transformer language models suggesting that their knowledge recall is localized to particular modules at specific token locations; we therefore ask whether factual recall in Mamba can be similarly localized. To investigate this, we conduct four lines of experiments on Mamba. First, we apply causal tracing or interchange interventions to localize key components inside Mamba that are responsible for recalling facts, revealing that specific components within middle layers show strong causal effects at the last token of the subject, while the causal effect of intervening on later layers is most pronounced at the last token of the prompt, matching previous findings on autoregressive transformers. Second, we show that rank-one model editing methods can successfully insert facts at specific locations, again resembling findings on transformer LMs. Third, we examine the linearity of Mamba's representations of factual relations. Finally we adapt attention-knockout techniques to Mamba in order to dissect information flow during factual recall. We compare Mamba directly to a similar-sized autoregressive transformer LM and conclude that despite significant differences in architectural approach, when it comes to factual recall, the two architectures share many similarities., Comment: 18 pages, COLM-2024
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- 2024
21. A new approach for deducing rms proton radii from charge-changing reactions of neutron-rich nuclei and the reaction-target dependence
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Zhang, J. -C., Sun, B. -H., Tanihata, I., Kanungo, R., Scheidenberger, C., Terashima, S., Wang, Feng, Ameil, F., Atkinson, J., Ayyad, Y., Bagchi, S., Cortina-Gil, D., Dillmann, I., Estrade, A., Evdokimov, A., Farinon, F., Geissel, H., Guastalla, G., Janik, R., Kaur, S., Knobel, R., Kurcewicz, J., Litvinov, Yu. A., Marta, M., Mostazo, M., Mukha, I., Nociforo, C., Ong, H. J., Pietri, S., Prochazka, A., Sitar, B., Strmen, P., Takechi, M., Tanaka, J., Vargas, J., Weick, H., Winfield, J. S., and Collaboration, the Super-FRS Experiment
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Nuclear Experiment ,Nuclear Theory - Abstract
We report the charge-changing cross sections ($\sigma_{\text{cc}}$) of 24 $p$-shell nuclides on both hydrogen and carbon at about 900$A$ MeV, of which $^{8,9}$Li, $^{10\textendash12}$Be, $^{10,14,15}$B, $^{14,15,17\textendash22}$N and $^{16}$O on hydrogen and $^{8,9}$Li on carbon are for the first time. Benefiting from the data set,we found a new and robust relationship between the scaling factor of the Glauber model calculations and the separation energies of the nuclei of interest on both targets.This allows us to deduce proton radii ($R_p$) for the first time from the cross sections on hydrogen. Nearly identical $R_p$ values are deduced from both target data for the neutron-rich carbon isotopes, however, the $R_p$ from the hydrogen target is systematically smaller in the neutron-rich nitrogen isotopes.This calls for further experimental and theoretical investigations., Comment: 5 pages, 3 figures
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- 2024
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22. First Measurement of the $\nu_e$ and $\nu_\mu$ Interaction Cross Sections at the LHC with FASER's Emulsion Detector
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FASER Collaboration, Abraham, Roshan Mammen, Anders, John, Antel, Claire, Ariga, Akitaka, Ariga, Tomoko, Atkinson, Jeremy, Bernlochner, Florian U., Boeckh, Tobias, Boyd, Jamie, Brenner, Lydia, Burger, Angela, Cadoux, Franck, Cardella, Roberto, Casper, David W., Cavanagh, Charlotte, Chen, Xin, Coccaro, Andrea, Debieux, Stephane, D'Onofrio, Monica, Desai, Ansh, Dmitrievsky, Sergey, Eley, Sinead, Favre, Yannick, Fellers, Deion, Feng, Jonathan L., Fenoglio, Carlo Alberto, Ferrere, Didier, Fieg, Max, Filali, Wissal, Fujimori, Haruhi, Garabaglu, Ali, Gibson, Stephen, Gonzalez-Sevilla, Sergio, Gornushkin, Yuri, Gwilliam, Carl, Hayakawa, Daiki, Hsu, Shih-Chieh, Hu, Zhen, Iacobucci, Giuseppe, Inada, Tomohiro, Iodice, Luca, Jakobsen, Sune, Joos, Hans, Kajomovitz, Enrique, Kanai, Takumi, Kawahara, Hiroaki, Keyken, Alex, Kling, Felix, Kock, Daniela, Kontaxakis, Pantelis, Kose, Umut, Kotitsa, Rafaella, Kuehn, Susanne, Kugathasan, Thanushan, Lefebvre, Helena, Levinson, Lorne, Li, Ke, Liu, Jinfeng, Lutz, Margaret S., MacDonald, Jack, Magliocca, Chiara, Martinelli, Fulvio, McCoy, Lawson, McFayden, Josh, Medina, Andrea Pizarro, Milanesio, Matteo, Moretti, Theo, Munker, Magdalena, Nakamura, Mitsuhiro, Nakano, Toshiyuki, Neuhaus, Friedemann, Nevay, Laurie, Nonaka, Motoya, Okui, Kazuaki, Ohashi, Ken, Otono, Hidetoshi, Pang, Hao, Paolozzi, Lorenzo, Petersen, Brian, Prim, Markus, Queitsch-Maitland, Michaela, Rokujo, Hiroki, Ruiz-Choliz, Elisa, Rubbia, André, Sabater-Iglesias, Jorge, Sato, Osamu, Scampoli, Paola, Schmieden, Kristof, Schott, Matthias, Sfyrla, Anna, Shamim, Mansoora, Shively, Savannah, Takubo, Yosuke, Tarannum, Noshin, Theiner, Ondrej, Torrence, Eric, Vasina, Svetlana, Vormwald, Benedikt, Wang, Di, Wang, Yuxiao, Welch, Eli, Zahorec, Samuel, Zambito, Stefano, and Zhang, Shunliang
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High Energy Physics - Experiment ,High Energy Physics - Phenomenology ,Physics - Instrumentation and Detectors - Abstract
This paper presents the first results of the study of high-energy electron and muon neutrino charged-current interactions in the FASER$\nu$ emulsion/tungsten detector of the FASER experiment at the LHC. A subset of the FASER$\nu$ volume, which corresponds to a target mass of 128.6~kg, was exposed to neutrinos from the LHC $pp$ collisions with a centre-of-mass energy of 13.6~TeV and an integrated luminosity of 9.5 fb$^{-1}$. Applying stringent selections requiring electrons with reconstructed energy above 200~GeV, four electron neutrino interaction candidate events are observed with an expected background of $0.025^{+0.015}_{-0.010}$, leading to a statistical significance of 5.2$\sigma$. This is the first direct observation of electron neutrino interactions at a particle collider. Eight muon neutrino interaction candidate events are also detected, with an expected background of $0.22^{+0.09}_{-0.07}$, leading to a statistical significance of 5.7$\sigma$. The signal events include neutrinos with energies in the TeV range, the highest-energy electron and muon neutrinos ever detected from an artificial source. The energy-independent part of the interaction cross section per nucleon is measured over an energy range of 560--1740 GeV (520--1760 GeV) for $\nu_e$ ($\nu_{\mu}$) to be $(1.2_{-0.7}^{+0.8}) \times 10^{-38}~\mathrm{cm}^{2}\,\mathrm{GeV}^{-1}$ ($(0.5\pm0.2) \times 10^{-38}~\mathrm{cm}^{2}\,\mathrm{GeV}^{-1}$), consistent with Standard Model predictions. These are the first measurements of neutrino interaction cross sections in those energy ranges.
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- 2024
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23. Spatial Latent Gaussian Modelling with Change of Support
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Chacón-Montalván, Erick A., Atkinson, Peter M., Nemeth, Christopher, Taylor, Benjamin M., and Moraga, Paula
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Statistics - Methodology - Abstract
Spatial data are often derived from multiple sources (e.g. satellites, in-situ sensors, survey samples) with different supports, but associated with the same properties of a spatial phenomenon of interest. It is common for predictors to also be measured on different spatial supports than the response variables. Although there is no standard way to work with spatial data with different supports, a prevalent approach used by practitioners has been to use downscaling or interpolation to project all the variables of analysis towards a common support, and then using standard spatial models. The main disadvantage with this approach is that simple interpolation can introduce biases and, more importantly, the uncertainty associated with the change of support is not taken into account in parameter estimation. In this article, we propose a Bayesian spatial latent Gaussian model that can handle data with different rectilinear supports in both the response variable and predictors. Our approach allows to handle changes of support more naturally according to the properties of the spatial stochastic process being used, and to take into account the uncertainty from the change of support in parameter estimation and prediction. We use spatial stochastic processes as linear combinations of basis functions where Gaussian Markov random fields define the weights. Our hierarchical modelling approach can be described by the following steps: (i) define a latent model where response variables and predictors are considered as latent stochastic processes with continuous support, (ii) link the continuous-index set stochastic processes with its projection to the support of the observed data, (iii) link the projected process with the observed data. We show the applicability of our approach by simulation studies and modelling land suitability for improved grassland in Rhondda Cynon Taf, a county borough in Wales., Comment: 45 pages, 16 figures
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- 2024
24. Algorithmic progress in language models
- Author
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Ho, Anson, Besiroglu, Tamay, Erdil, Ege, Owen, David, Rahman, Robi, Guo, Zifan Carl, Atkinson, David, Thompson, Neil, and Sevilla, Jaime
- Subjects
Computer Science - Computation and Language ,Computer Science - Artificial Intelligence - Abstract
We investigate the rate at which algorithms for pre-training language models have improved since the advent of deep learning. Using a dataset of over 200 language model evaluations on Wikitext and Penn Treebank spanning 2012-2023, we find that the compute required to reach a set performance threshold has halved approximately every 8 months, with a 95% confidence interval of around 5 to 14 months, substantially faster than hardware gains per Moore's Law. We estimate augmented scaling laws, which enable us to quantify algorithmic progress and determine the relative contributions of scaling models versus innovations in training algorithms. Despite the rapid pace of algorithmic progress and the development of new architectures such as the transformer, our analysis reveals that the increase in compute made an even larger contribution to overall performance improvements over this time period. Though limited by noisy benchmark data, our analysis quantifies the rapid progress in language modeling, shedding light on the relative contributions from compute and algorithms.
- Published
- 2024
25. President's letter
- Author
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Atkinson, Laurie
- Published
- 2024
26. Revealing Hidden Proficiencies: An Intersectional Journey into Learners' Realities with Micro-Credentials
- Author
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Rita Fennelly-Atkinson and Deblina Pakhira
- Abstract
Intersectionality and positionality can be used to examine how various aspects of identity are analyzed in the context of learners' lived experiences. When examining how learners are recognized for their skills and competencies, there are several ways in which education and credentials can be leveraged. Autoethnographies were used to examine the lived experience of how the authors used micro-credentials and digital badges to verify skills that were unrecognized in traditional contexts. Lessons from these lived experiences will be derived from a co-reflection process. Further, these lessons will be used to provide suggestions for how learning ecologies can be designed to support historically and systematically excluded learners with ways to have their skills recognized and verified.
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- 2024
- Full Text
- View/download PDF
27. Vaccination for Patients Receiving Dialysis.
- Author
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Sam, Ramin, Rankin, Laura, Ulasi, Ifeoma, Frantzen, Luc, Nitsch, Dorothea, Henner, David, Molony, Donald, Wagner, John, Chen, Jing, Agarwal, Sanjay, Howard, Andrew, Atkinson, Ralph, Landry, Daniel, Pastan, Stephen, and Kalantar-Zadeh, Kamyar
- Subjects
Dialysis ,SARS-CoV2 ,influenza ,pneumococcus ,vaccination - Abstract
Vaccinating patients receiving dialysis may prevent morbidity and mortality in this vulnerable population. The National Forum of End-Stage Renal Disease Networks (the Forum) published a revised vaccination toolkit in 2021 to update evidence and recommendations on vaccination for patients receiving dialysis. Significant changes in the last 10 years include more data supporting the use of a high-dose influenza vaccine, the introduction of the Heplisav-B vaccine for hepatitis B, and changes in pneumococcal vaccines, including the approval of the PCV15 and PCV20 to replace the PCV13 and PPSV23 vaccines. Additional key items include the introduction of vaccines against severe acute respiratory syndrome coronavirus 2, the virus that causes coronavirus disease 2019 (COVID-19), and a new vaccine to prevent respiratory syncytial virus disease. Historically, influenza and pneumococcal vaccinations were routinely administered by dialysis facilities, and because of possible risks of hematogenous spread of hepatitis B, dialysis providers often have detailed hepatitis B vaccine protocols. In March 2021, COVID-19 vaccines were made available for dialysis facilities to administer, although with the end of the public health emergency, vaccine policies by dialysis facilities against COVID-19 remains uncertain. The respiratory syncytial virus vaccine was authorized in 2023, and how dialysis facilities will approach this vaccine also remains uncertain. This review summarizes the Forums vaccination toolkit and discusses the role of the dialysis facility in vaccinating patients to reduce the risk of severe infections.
- Published
- 2024
28. Achievement of Target Gain Larger than Unity in an Inertial Fusion Experiment
- Author
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Abu-Shawareb, H, Acree, R, Adams, P, Adams, J, Addis, B, Aden, R, Adrian, P, Afeyan, BB, Aggleton, M, Aghaian, L, Aguirre, A, Aikens, D, Akre, J, Albert, F, Albrecht, M, Albright, BJ, Albritton, J, Alcala, J, Alday, C, Alessi, DA, Alexander, N, Alfonso, J, Alfonso, N, Alger, E, Ali, SJ, Ali, ZA, Allen, A, Alley, WE, Amala, P, Amendt, PA, Amick, P, Ammula, S, Amorin, C, Ampleford, DJ, Anderson, RW, Anklam, T, Antipa, N, Appelbe, B, Aracne-Ruddle, C, Araya, E, Archuleta, TN, Arend, M, Arnold, P, Arnold, T, Arsenlis, A, Asay, J, Atherton, LJ, Atkinson, D, Atkinson, R, Auerbach, JM, Austin, B, Auyang, L, Awwal, AAS, Aybar, N, Ayers, J, Ayers, S, Ayers, T, Azevedo, S, Bachmann, B, Back, CA, Bae, J, Bailey, DS, Bailey, J, Baisden, T, Baker, KL, Baldis, H, Barber, D, Barberis, M, Barker, D, Barnes, A, Barnes, CW, Barrios, MA, Barty, C, Bass, I, Batha, SH, Baxamusa, SH, Bazan, G, Beagle, JK, Beale, R, Beck, BR, Beck, JB, Bedzyk, M, Beeler, RG, Behrendt, W, Belk, L, Bell, P, Belyaev, M, Benage, JF, Bennett, G, Benedetti, LR, Benedict, LX, Berger, RL, Bernat, T, Bernstein, LA, Berry, B, Bertolini, L, Besenbruch, G, Betcher, J, and Bettenhausen, R
- Subjects
Nuclear and Plasma Physics ,Physical Sciences ,Indirect Drive ICF Collaboration ,Mathematical Sciences ,Engineering ,General Physics ,Mathematical sciences ,Physical sciences - Abstract
On December 5, 2022, an indirect drive fusion implosion on the National Ignition Facility (NIF) achieved a target gain G_{target} of 1.5. This is the first laboratory demonstration of exceeding "scientific breakeven" (or G_{target}>1) where 2.05 MJ of 351 nm laser light produced 3.1 MJ of total fusion yield, a result which significantly exceeds the Lawson criterion for fusion ignition as reported in a previous NIF implosion [H. Abu-Shawareb et al. (Indirect Drive ICF Collaboration), Phys. Rev. Lett. 129, 075001 (2022)PRLTAO0031-900710.1103/PhysRevLett.129.075001]. This achievement is the culmination of more than five decades of research and gives proof that laboratory fusion, based on fundamental physics principles, is possible. This Letter reports on the target, laser, design, and experimental advancements that led to this result.
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- 2024
29. Neutrino Rate Predictions for FASER
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FASER Collaboration, Abraham, Roshan Mammen, Anders, John, Antel, Claire, Ariga, Akitaka, Ariga, Tomoko, Atkinson, Jeremy, Bernlochner, Florian U., Boeckh, Tobias, Boyd, Jamie, Brenner, Lydia, Burger, Angela, Cadoux, Franck, Cardella, Roberto, Casper, David W., Cavanagh, Charlotte, Chen, Xin, Coccaro, Andrea, Débieux, Stephane, D'Onofrio, Monica, Desai, Ansh, Dmitrievsky, Sergey, Eley, Sinead, Favre, Yannick, Fellers, Deion, Feng, Jonathan L., Fenoglio, Carlo Alberto, Ferrere, Didier, Fieg, Max, Filali, Wissal, Gibson, Stephen, Gonzalez-Sevilla, Sergio, Gornushkin, Yuri, Gwilliam, Carl, Hayakawa, Daiki, Hsu, Shih-Chieh, Hu, Zhen, Iacobucci, Giuseppe, Inada, Tomohiro, Iodice, Luca, Jakobsen, Sune, Joos, Hans, Kajomovitz, Enrique, Kawahara, Hiroaki, Keyken, Alex, Kling, Felix, Köck, Daniela, Kontaxakis, Pantelis, Kose, Umut, Kotitsa, Rafaella, Kuehn, Susanne, Kugathasan, Thanushan, Lefebvre, Helena, Levinson, Lorne, Li, Ke, Liu, Jinfeng, Lutz, Margaret S., MacDonald, Jack, Magliocca, Chiara, Martinelli, Fulvio, McCoy, Lawson, McFayden, Josh, Medina, Andrea Pizarro, Milanesio, Matteo, Moretti, Théo, Munker, Magdalena, Nakamura, Mitsuhiro, Nakano, Toshiyuki, Neuhaus, Friedemann, Nevay, Laurie, Ohashi, Ken, Otono, Hidetoshi, Pang, Hao, Paolozzi, Lorenzo, Petersen, Brian, Prim, Markus, Queitsch-Maitland, Michaela, Rokujo, Hiroki, Ruiz-Choliz, Elisa, Rubbia, André, Sabater-Iglesias, Jorge, Sato, Osamu, Scampoli, Paola, Schmieden, Kristof, Schott, Matthias, Sfyrla, Anna, Shamim, Mansoora, Shively, Savannah, Takubo, Yosuke, Tarannum, Noshin, Theiner, Ondrej, Torrence, Eric, Vasina, Svetlana, Vormwald, Benedikt, Wang, Di, Wang, Yuxiao, Welch, Eli, Zahorec, Samuel, Zambito, Stefano, and Zhang, Shunliang
- Subjects
High Energy Physics - Experiment ,High Energy Physics - Phenomenology - Abstract
The Forward Search Experiment (FASER) at CERN's Large Hadron Collider (LHC) has recently directly detected the first collider neutrinos. Neutrinos play an important role in all FASER analyses, either as signal or background, and it is therefore essential to understand the neutrino event rates. In this study, we update previous simulations and present prescriptions for theoretical predictions of neutrino fluxes and cross sections, together with their associated uncertainties. With these results, we discuss the potential for possible measurements that could be carried out in the coming years with the FASER neutrino data to be collected in LHC Run 3 and Run 4., Comment: 19 pages, 10 figures
- Published
- 2024
30. Disorder controlled sound speed and thermal conductivity of hybrid metalcone films
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Hoque, Md Shafkat Bin, Nye, Rachel A., Zare, Saman, Atkinson, Stephanie, Wang, Siyao, Jones, Andrew H., Gaskins, John T., Parsons, Gregory, and Hopkins, Patrick E.
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Physics - Applied Physics - Abstract
The multifaceted applications of polymers are often limited by their thermal conductivity. Therefore, understanding the mechanisms of thermal transport in polymers is of vital interest. Here, we leverage molecular layer deposition to grow three types of hybrid metalcone (i.e., alucone, zincone, and tincone) films and study their thermal and acoustic properties. The thermal conductivity of the hybrid polymer films ranges from 0.43 to 1.14 W/mK. Using kinetic theory, we trace the origin of thermal conductivity difference to sound speed change, which is dictated by the structural disorder within the films. Changing the disorder has negligible impacts on volumetric heat capacity and vibrational lifetimes. Our findings provide means to improve the thermal conductivity of organic, hybrid, and inorganic polymer films.
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- 2024
31. Direct numerical simulation of a thermal turbulent boundary layer: an analogy to simulate bushfires and a testbed for artificial intelligence remote sensing of bushfire propagation
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Soria, Julio, Karami, Shahram, Atkinson, Callum, and Li, Minghang
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Physics - Fluid Dynamics - Abstract
Direct numerical simulation of a turbulent thermal boundary layer (TTBL) can perform the role of an analogy to simulate bushfires that can serve as a testbed for artificial intelligence (AI) enhanced remote sensing of bushfire propagation. By solving the Navier-Stokes equations for a turbulent flow, DNS predicts the flow field and allows for a detailed study of the interactions between the turbulent flow and thermal plumes. In addition to potentially providing insights into the complex bushfire behaviour, direct numerical simulation (DNS) can generate synthetic remote sensing data to train AI algorithms such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which can process large amounts of remotely sensed data associated with bushfire. Using the results of DNS as training data can improve the accuracy of AI remote sensing in predicting firefront propagation of bushfires. DNS can also test the accuracy of the AI remote sensing algorithms by generating synthetic remote sensing data that allows their performance assessment and uncertainty quantification in predicting the evolution of a bushfire. The combination of DNS and AI can improve our understanding of bushfire dynamics, develop more accurate prediction models, and aid in bushfire management and mitigation., Comment: 16 pages, 8 figures; aspects of this paper presented as a Plenary Lecture at: International Symposium on Unmanned Systems: AI, Design and Efficiency (ISUDEF23), June 7-9, 2023, KTH, Stockholm, Sweden
- Published
- 2024
32. OLMo: Accelerating the Science of Language Models
- Author
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Groeneveld, Dirk, Beltagy, Iz, Walsh, Pete, Bhagia, Akshita, Kinney, Rodney, Tafjord, Oyvind, Jha, Ananya Harsh, Ivison, Hamish, Magnusson, Ian, Wang, Yizhong, Arora, Shane, Atkinson, David, Authur, Russell, Chandu, Khyathi Raghavi, Cohan, Arman, Dumas, Jennifer, Elazar, Yanai, Gu, Yuling, Hessel, Jack, Khot, Tushar, Merrill, William, Morrison, Jacob, Muennighoff, Niklas, Naik, Aakanksha, Nam, Crystal, Peters, Matthew E., Pyatkin, Valentina, Ravichander, Abhilasha, Schwenk, Dustin, Shah, Saurabh, Smith, Will, Strubell, Emma, Subramani, Nishant, Wortsman, Mitchell, Dasigi, Pradeep, Lambert, Nathan, Richardson, Kyle, Zettlemoyer, Luke, Dodge, Jesse, Lo, Kyle, Soldaini, Luca, Smith, Noah A., and Hajishirzi, Hannaneh
- Subjects
Computer Science - Computation and Language - Abstract
Language models (LMs) have become ubiquitous in both NLP research and in commercial product offerings. As their commercial importance has surged, the most powerful models have become closed off, gated behind proprietary interfaces, with important details of their training data, architectures, and development undisclosed. Given the importance of these details in scientifically studying these models, including their biases and potential risks, we believe it is essential for the research community to have access to powerful, truly open LMs. To this end, we have built OLMo, a competitive, truly Open Language Model, to enable the scientific study of language models. Unlike most prior efforts that have only released model weights and inference code, we release OLMo alongside open training data and training and evaluation code. We hope this release will empower the open research community and inspire a new wave of innovation.
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- 2024
33. Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research
- Author
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Soldaini, Luca, Kinney, Rodney, Bhagia, Akshita, Schwenk, Dustin, Atkinson, David, Authur, Russell, Bogin, Ben, Chandu, Khyathi, Dumas, Jennifer, Elazar, Yanai, Hofmann, Valentin, Jha, Ananya Harsh, Kumar, Sachin, Lucy, Li, Lyu, Xinxi, Lambert, Nathan, Magnusson, Ian, Morrison, Jacob, Muennighoff, Niklas, Naik, Aakanksha, Nam, Crystal, Peters, Matthew E., Ravichander, Abhilasha, Richardson, Kyle, Shen, Zejiang, Strubell, Emma, Subramani, Nishant, Tafjord, Oyvind, Walsh, Pete, Zettlemoyer, Luke, Smith, Noah A., Hajishirzi, Hannaneh, Beltagy, Iz, Groeneveld, Dirk, Dodge, Jesse, and Lo, Kyle
- Subjects
Computer Science - Computation and Language - Abstract
Information about pretraining corpora used to train the current best-performing language models is seldom discussed: commercial models rarely detail their data, and even open models are often released without accompanying training data or recipes to reproduce them. As a result, it is challenging to conduct and advance scientific research on language modeling, such as understanding how training data impacts model capabilities and limitations. To facilitate scientific research on language model pretraining, we curate and release Dolma, a three-trillion-token English corpus, built from a diverse mixture of web content, scientific papers, code, public-domain books, social media, and encyclopedic materials. We extensively document Dolma, including its design principles, details about its construction, and a summary of its contents. We present analyses and experimental results on intermediate states of Dolma to share what we have learned about important data curation practices. Finally, we open-source our data curation toolkit to enable reproduction of our work as well as support further research in large-scale data curation., Comment: Accepted at ACL 2024; Dataset: https://hf.co/datasets/allenai/dolma; Code: https://github.com/allenai/dolma
- Published
- 2024
34. Charge order near the antiferromagnetic quantum critical point in the trilayer high $T_c$ cuprate HgBa$_2$Ca$_2$Cu$_3$O$_{8+ \delta}$
- Author
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Oliviero, V., Gilmutdinov, I., Vignolles, D., Benhabib, S., Bruyant, N., Forget, A., Colson, D., Atkinson, W. A., and Proust, C.
- Subjects
Condensed Matter - Superconductivity ,Condensed Matter - Strongly Correlated Electrons - Abstract
We study the transport properties of underdoped trilayer cuprate HgBa$_2$Ca$_2$Cu$_3$O$_{8+ \delta}$ with doping level $p$ = 0.1 - 0.12 in magnetic field up to 88 T. We report for the first time in a cuprate superconductor a dramatic change of the quantum oscillation spectrum versus temperature, which is accompanied by a sign change of the Hall effect below $T \approx$ 10 K. Based on numerical simulations, we infer a Fermi surface reconstruction in the inner plane from an antiferromagnetic state (hole pockets) to a biaxial charge density wave state (electron pockets). We show that both orders compete and share the same hotspots of the Fermi surface and we discuss our result in the context of spin-fermion models., Comment: Main + Supplemental Information
- Published
- 2024
35. BioDiffusion: A Versatile Diffusion Model for Biomedical Signal Synthesis
- Author
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Li, Xiaomin, Sakevych, Mykhailo, Atkinson, Gentry, and Metsis, Vangelis
- Subjects
Electrical Engineering and Systems Science - Signal Processing ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning - Abstract
Machine learning tasks involving biomedical signals frequently grapple with issues such as limited data availability, imbalanced datasets, labeling complexities, and the interference of measurement noise. These challenges often hinder the optimal training of machine learning algorithms. Addressing these concerns, we introduce BioDiffusion, a diffusion-based probabilistic model optimized for the synthesis of multivariate biomedical signals. BioDiffusion demonstrates excellence in producing high-fidelity, non-stationary, multivariate signals for a range of tasks including unconditional, label-conditional, and signal-conditional generation. Leveraging these synthesized signals offers a notable solution to the aforementioned challenges. Our research encompasses both qualitative and quantitative assessments of the synthesized data quality, underscoring its capacity to bolster accuracy in machine learning tasks tied to biomedical signals. Furthermore, when juxtaposed with current leading time-series generative models, empirical evidence suggests that BioDiffusion outperforms them in biomedical signal generation quality.
- Published
- 2024
36. Spinterface Mediated Magnetic Properties of Co20Fe60B20/Alq3 Heterostructures
- Author
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Mahanta, Swayang Priya, Sahoo, Antarjami, Nayak, Sagarika, Hase, T. P. A., Atkinson, Del, and Bedanta, Subhankar
- Subjects
Condensed Matter - Materials Science - Abstract
Organic semiconductors (OSCs) are suitable materials for spintronics applications as they form a spinterface when placed next to a ferromagnet, which in turn leads to novel functionalities. The evolution of spinterface can tune the global magnetic anisotropy, magnetization reversal, magnetization dynamics, etc. Planar tris-(8-hydroxyquinoline)aluminum (Alq3) OSC has shown tremendous potential for spintronics applications, thanks to its efficient spin-polarized current transport ability. Here, we establish the spinterface when the Alq3 molecules are deposited on amorphous ferromagnet Co20Fe60B20(CFB). The $\pi$-d hybridization in CFB/Alq3 enhances the coercive field and significantly modifies the shape and size of the magnetic domains. A $\sim$100% increase in uniaxial anisotropic energies and a reduction in magnetic damping are also evident owing to the strong interfacial hybridization.
- Published
- 2023
37. The Effects of Signal-to-Noise Ratio on Generative Adversarial Networks Applied to Marine Bioacoustic Data
- Author
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Atkinson, Georgia, Wright, Nick, McGough, A. Stephen, and Berggren, Per
- Subjects
Computer Science - Sound ,Computer Science - Machine Learning ,Electrical Engineering and Systems Science - Audio and Speech Processing - Abstract
In recent years generative adversarial networks (GANs) have been used to supplement datasets within the field of marine bioacoustics. This is driven by factors such as the cost to collect data, data sparsity and aid preprocessing. One notable challenge with marine bioacoustic data is the low signal-to-noise ratio (SNR) posing difficulty when applying deep learning techniques such as GANs. This work investigates the effect SNR has on the audio-based GAN performance and examines three different evaluation methodologies for GAN performance, yielding interesting results on the effects of SNR on GANs, specifically WaveGAN., Comment: 6 pages, 6 figures
- Published
- 2023
38. Psychobabble: A Preliminary Study of the Novel Educational Game to Teach Psychiatric Interviewing Techniques
- Author
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Thiessen, Gabriella M. and Atkinson, Dean J.
- Published
- 2024
- Full Text
- View/download PDF
39. Rapid Synthesis and Evaluation of Resveratrol-Piperazine Cocrystals by Ultrasound and Microwave Methods
- Author
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Ioniţă, Simona, Pătrașcu, Mariana, Soare, Elena Mirabela, Lincu, Daniel, Atkinson, Irina, Rusu, Adriana, Pop, Mihaela Maria, Iordache, Coca, Ușurelu, Cătălina-Diana, Baltac, Andreea Simona, Mitran, Raul-Augustin, Pandele-Cuşu, Jeanina, and Fruth, Victor
- Published
- 2024
- Full Text
- View/download PDF
40. Mechanistic safety assessment via multi-omic characterisation of systemic pathway perturbations following in vivo MAT2A inhibition
- Author
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Fogal, Valentina, Michopoulos, Filippos, Jarnuczak, Andrew F., Hamza, Ghaith M., Harlfinger, Stephanie, Davey, Paul, Hulme, Heather, Atkinson, Stephen J., Gabrowski, Piotr, Cheung, Tony, Grondine, Michael, Hoover, Clare, Rose, Jonathan, Bray, Chandler, Foster, Alison J., Askin, Sean, Majumder, Muntasir Mamun, Fitzpatrick, Paul, Miele, Eric, Macdonald, Ruth, Keun, Hector C., and Coen, Muireann
- Published
- 2024
- Full Text
- View/download PDF
41. Bilateral renal agenesis: fetal intervention and outcomes
- Author
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Jones, Katherine, Keiser, Amaris M., Miller, Jena L., and Atkinson, Meredith A.
- Published
- 2024
- Full Text
- View/download PDF
42. Responding to the workforce crisis: consensus recommendations from the Second Workforce Summit of the American Society of Pediatric Nephrology
- Author
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Soranno, Danielle E., Amaral, Sandra, Ashoor, Isa, Atkinson, Meredith A., Barletta, Gina-Marie, Braun, Michael C., Carlson, Joann, Carter, Caitlin, Chua, Annabelle, Dharnidharka, Vikas R., Drake, Keri, Erkan, Elif, Feig, Dan, Goldstein, Stuart L., Hains, David, Harshman, Lyndsay A., Ingulli, Elizabeth, Kula, Alexander J., Leonard, Mary, Mannemuddhu, Sudha, Menon, Shina, Modi, Zubin J., Moxey-Mims, Marva, Nada, Arwa, Norwood, Victoria, Starr, Michelle C., Verghese, Priya S., Weidemann, Darcy, Weinstein, Adam, and Smith, Jodi
- Published
- 2024
- Full Text
- View/download PDF
43. Vitamin D and its associations with blood pressure in the Chronic Kidney Disease in Children (CKiD) cohort
- Author
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Kumar, Juhi, Roem, Jennifer, Furth, Susan L., Warady, Bradley A., Atkinson, Meredith A., and Flynn, Joseph T.
- Published
- 2024
- Full Text
- View/download PDF
44. Structural basis of Cfr-mediated antimicrobial resistance and mechanisms to evade it
- Author
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Aleksandrova, Elena V., Wu, Kelvin J. Y., Tresco, Ben I. C., Syroegin, Egor A., Killeavy, Erin E., Balasanyants, Samson M., Svetlov, Maxim S., Gregory, Steven T., Atkinson, Gemma C., Myers, Andrew G., and Polikanov, Yury S.
- Published
- 2024
- Full Text
- View/download PDF
45. Real world study of sacituzumab govitecan in metastatic triple-negative breast cancer in the United Kingdom
- Author
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Hanna, Daire, Merrick, Sophie, Ghose, Aruni, Devlin, Michael John, Yang, Dorothy D., Phillips, Edward, Okines, Alicia, Chopra, Neha, Papadimatraki, Elisavet, Ross, Kirsty, Macpherson, Iain, Boh, Zhuang Y., Michie, Caroline O., Swampillai, Angela, Gupta, Sunnia, Robinson, Tim, Germain, Lewis, Twelves, Chris, Atkinson, Charlotte, Konstantis, Apostolos, Riddle, Pippa, Cresti, Nicola, Naik, Jay D., Borley, Annabel, Guppy, Amy, Schmid, Peter, and Phillips, Melissa
- Published
- 2024
- Full Text
- View/download PDF
46. Cytokine polarized, alternatively activated bone marrow neutrophils drive axon regeneration
- Author
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Jerome, Andrew D., Sas, Andrew R., Wang, Yan, Hammond, Luke A., Wen, Jing, Atkinson, Jeffrey R., Webb, Amy, Liu, Tom, and Segal, Benjamin M.
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- 2024
- Full Text
- View/download PDF
47. Layered double hydroxides matrix as host for curcumin loading: structural and spectroscopic investigations
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Todan, Ligia, Voicescu, Mariana, Culita, Daniela C., Atkinson, Irina, Soare, Elena M., Ioniţă, Simona, and Radu, Dana
- Published
- 2024
- Full Text
- View/download PDF
48. Theme-Based Book Review: the Government Role in Development and Public Finance
- Author
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Atkinson, Christopher L.
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- 2024
- Full Text
- View/download PDF
49. CJEM debate series: #StillTheOne—while more challenging than ever, emergency medicine is still the best career path available for medical students
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Rosenberg, Hans, Syed, Shahbaz, and Atkinson, Paul
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- 2024
- Full Text
- View/download PDF
50. Construction and validation of an infant chest phantom for paediatric computed tomography
- Author
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Rodgers, Seonaid, Atkinson, Janette, Cryer, David, Storm, Cameron, Nezich, Rikki, Ebert, Martin A., and Rowshanfarzad, Pejman
- Published
- 2024
- Full Text
- View/download PDF
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