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This document aligns with the Department of Energy Strategic Plan for 2014-2018 and provides a framework for integrating our missions and direction for pursuing DOE’s strategic goals. The vision is a guide to advancing world-class science and engineering, supporting our people, modernizing our infrastructure, and developing a management culture that operates a safe and secure enterprise in an efficient manner.
The reconfiguration of the ARM facility, formally initiated in early 2014, is geared toward implementing the Next Generation of the ARM Facility, which will more tightly link ARM measurements and atmospheric models. The strategy is outlined in the ARM Climate Research Facility Decadal Vision (DOE 2014a). The strategy includes the implementation of a high-resolution model, initially at the Southern Great Plains (SGP) site, and enhancements at the SGP and North Slope of Alaska (NSA) sites to provide additional observations to support modeling and process studies. Enhancements at the SGP site focus on ground-based instruments while enhancements at the NSA make use of Unmanned Aerial Systems (UAS) and Tethered Balloon Systems (TBS). It is also recognized that new data tools and data products will need to be developed to take full advantage of these improvements. This document provides an update on the status of these ARM facility enhancements, beginning with the measurement enhancements at the SGP and NSA, followed by a discussion of the modeling project including associated data-processing activities.
Embedded intelligent systems ranging from tiny im- plantable biomedical devices to large swarms of autonomous un- manned aerial systems are becoming pervasive in our daily lives. While we depend on the flawless functioning of such intelligent systems, and often take their behavioral correctness and safety for granted, it is notoriously difficult to generate test cases that expose subtle errors in the implementations of machine learning algorithms. Hence, the validation of intelligent systems is usually achieved by studying their behavior on representative data sets, using methods such as cross-validation and bootstrapping.In this paper, we present a new testing methodology for studying the correctness of intelligent systems. Our approach uses symbolic decision procedures coupled with statistical hypothesis testing to. We also use our algorithm to analyze the robustness of a human detection algorithm built using the OpenCV open-source computer vision library. We show that the human detection implementation can fail to detect humans in perturbed video frames even when the perturbations are so small that the corresponding frames look identical to the naked eye.
This white paper summarizes the workshop "U.S. Cosmic Visions: New Ideas in Dark Matter" held at University of Maryland on March 23-25, 2017.
The National Nuclear Security Administration’s enduring mission is to protect our nation by maintaining a safe, secure, and effective nuclear weapons stockpile, reducing global nuclear threats, and providing our submarines and aircraft carriers with militarily effective nuclear propulsion. Although we have numerous strategic partners that enable, contribute to, and benefit from our efforts, no other organization, government or civilian, can accomplish our unique mission on behalf of the American people. In this area, we stand alone. This strategic vision pursues urgent change at a significant scale to develop creative approaches, make sustained investments, and be disciplined in our approach to develop a nuclear capability for our time and for the foreseeable future. It is incumbent on us to anticipate our future security challenges and ensure our country is ready to meet them. Not doing so would have far-reaching consequences. Failure to meet our vital mission objectives will erode confidence in the security that the United States strategic deterrent umbrella provides. In assessing the current mission environment in NNSA, we can count on one thing—change: The geopolitical environment for our deterrent is ever-changing Threats from rogue states and terrorists are evolving Our nuclear weapons and the infrastructure that supports them are aging Science and technology continue to move forward at an ever-increasing pace In this dynamic environment, it is not sufficient to remedy today’s situation and meet our current mission requirements. We must lean forward in the development, planning, and execution of programs that address our current challenges while developing the workforce, infrastructure, tools, and governance systems that will enable us to meet our deterrence, non-proliferation, and naval propulsion mission requirements for the next 50 years. We must embrace a next-generation enterprise that is flexible enough to respond to future challenges. With decades of stagnation following the end of the Cold War, we find ourselves in the unenviable position of implementing near-term fixes while using those lessons learned to better prepare for the future. Without sustained and predictable investments to restore and modernize our infrastructure, we will rapidly lose our ability to deliver on our promise of a safe and reliable nuclear weapons stockpile, which will degrade our deterrent capability and render legacy weapon systems irrelevant to the defense of our nation. We must not and will not allow the next generation of our nuclear security enterprise to simply maintain the status quo. We have a storied past that guides us. For 75 years our enterprise has overcome every challenge, led the country in incredible scientific and engineering endeavors and discoveries, and successfully ensured constant vigilance in maintaining an effective deterrent, reducing global nuclear threats, and powering our nuclear navy. Now is the time for us to take decisive action in forging the enterprise of the future. As I have traveled to all of our labs, plants, and sites and met with people across our complex, I have been left with one overwhelming impression: the incredible workforce that makes up our nuclear security enterprise is up to all of the challenges we have before us. Together we can and will ensure that those in the future workforce have the resources and the responsive, agile infrastructure they need to maintain the systems of today and to design and field the systems of tomorrow.
Transitioning from an onsite Maintenance & Diagnostics Center to cloud-based services offers many new opportunities with computing power and storage, but also new challenges in terms of networking and security. This report will cover everything required for that transition including data processing and uploading to cloud services, feature selection, model creation, and result visualization for decision making. Although there are several other cloud-based services (e.g. Amazon Web Services and Google Cloud), this report explores Microsoft Azure to simplify nomenclature and maintain a consistent focus. Many of the services offered by Microsoft Azure are also available in the other cloud-based services, and their differences have been recorded in other literature. The Azure services most important to a nuclear power plant including networking & security, storage & databases, and Artificial Intelligence (AI) are reviewed here. Networking covers all aspects related to communication to Azure resources including security, privacy, and redundancy. Storage & databases includes data storage, upgrading, patching, backups, and monitoring. The AI services allows the user access to the machine learning (ML) techniques developed with Azure including automated ML, anomaly detection, computer vision, and natural language processing. This report summaries the features, capabilities, and challenges when using cloud-based services in a user-friendly manner.
Cameco Corporation is a world leader in uranium production. Cameco is also a major supplier of uranium processing services required to produce fuel for the generation of clean energy. These operations are based in Blind River, Cobourg and Port Hope, Ontario and are collectively referred to as the Fuel Services Division. The Fuel Services Division produces uranium trioxide from uranium ore concentrate at the Blind River Refinery. Cameco produces uranium hexafluoride and uranium dioxide at the Port Hope Conversion Facility. Cameco operates a fuel manufacturing facility in Port Hope, Ontario and a metal fabrication facility located in Cobourg, Ontario. The company manufactures fuel bundles utilized in the Candu reactors. Cameco is undertaking a major site cleanup and renewal of its Port Hope Conversion Facility (PHCF) which is located in the Municipality of Port Hope (MPH). This undertaking is known as the Vision in Motion (VIM) project. This project is unique and timely opportunity that has been made possible because of the Port Hope Area Initiative (PHAI). The PHAI Port Hope project is being undertaken by Canadian Nuclear Laboratories (CNL) and includes the construction of a long-term waste management facility (LTWMF) in the MPH. VIM entails an extensive remediation and redevelopment of much of the PHCF and includes: - the cleanup and demolition of a number of old or underutilized buildings; - the removal of contaminated soils, building materials and stored wastes; and - modifications to existing buildings at the PHCF with associated landscaping and infrastructure. Overall, the VIM project will allow for environmental remediation, improved site operations, enhancement of site safety and security through site design and improved aesthetics. This paper will discuss the status of this project underway at the Port Hope Conversion Facility. After years of community consultation, planning, and a successful environmental assessment and licensing of the project, the VIM project at the PHCF is now underway. Physical transfer of waste materials began in June 2018. Cameco is committed to seeing the project through to completion and is working with CNL to ensure this happens within the operating time frame of the LTWMF. Cameco keeps the CNSC and MPH well informed of all project related activities. (authors)
Images are increasingly becoming the currency for documenting biodiversity on the planet, providing novel opportunities for accelerating scientific discoveries in the field of organismal biology, especially with the advent of large vision-language models (VLMs). We ask if pre-trained VLMs can aid scientists in answering a range of biologically relevant questions without any additional fine-tuning. In this paper, we evaluate the effectiveness of 12 state-of-the-art (SOTA) VLMs in the field of organismal biology using a novel dataset, VLM4Bio, consisting of 469K question8 answer pairs involving 30K images from three groups of organisms: fishes, birds, and butterflies, covering five biologically relevant tasks. We also explore the effects of applying prompting techniques and tests for reasoning hallucination on the performance of VLMs, shedding new light on the capabilities of current SOTA VLMs in answering biologically relevant questions using images
This paper presents a novel approach to addressing the long-sequence problem in high-resolution medical images for Vision Transformers (ViTs). Using smaller patches as tokens can enhance ViT performance, but quadratically increases computation and memory requirements. Therefore, the common practice for applying ViTs to high-resolution images is either to: (a) employ complex sub-quadratic attention schemes or (b) use large to medium-sized patches and rely on additional mechanisms within the model to capture the spatial hierarchy of details. We propose Symmetrical Hierarchical Forest (SHF), a lightweight approach that adaptively patches the input image to increase token information density and encode hierarchical spatial structures into the input embedding. We then apply a reverse depatching scheme to the output embeddings of the transformer encoder, eliminating the need for convolution-based decoders. Unlike previous methods that modify attention mechanisms or use a complex hierarchy of interacting models, SHF can be retrofitted to any ViT model to allow it to learn the hierarchical structure of details in high-resolution images without requiring architectural changes. Experimental results demonstrate significant gains in computational efficiency and performance: on the PAIP WSI dataset, we achieved a 3∼32×speedup or a 2.95%∼7.03% increase in accuracy (measured by Dice score) at a 64K2 resolution with the same computational budget, compared to state-of-the-art production models. On the 3D medical datasets BTCV and KiTS, training was 6×faster, with accuracy gains of 6.93% and 5.9%, respectively, compared to models without SHF.
Mixture of Experts (MoE) models have enabled the scaling of Large Language Models (LLMs) and Vision Language Models (VLMs) by achieving massive parameter counts while maintaining computational efficiency. However, MoEs introduce several inference-time challenges, including load imbalance across experts and the additional routing computational overhead. To address these challenges and fully harness the benefits of MoE, a systematic evaluation of hardware acceleration techniques is essential. We present MoE-Inference-Bench, a comprehensive study to evaluate MoE performance across diverse scenarios. We analyze the impact of batch size, sequence length, and critical MoE hyperparameters such as FFN dimensions and number of experts on throughput. We evaluate several optimization techniques on Nvidia H100 GPUs, including pruning, Fused MoE operations, speculative decoding, quantization, and various parallelization strategies. Our evaluation includes MoEs from the Mixtral, DeepSeek, OLMoE and Qwen families. The results reveal performance differences across configurations and provide insights for the efficient deployment of MoEs.
Deploying geospatial foundation models on resource-constrained edge devices demands compact architectures that maintain high downstream performance. However, their large parameter counts and the accuracy loss often induced by compression limit practical adoption.In this work, we leverage manifold-constrained optimization framework DLRT to compress large vision transformer–based geospatial foundation models during transfer learning. By enforcing structured low-dimensional parameterizations aligned with downstream objectives, this approach achieves strong compression while preserving task-specific accuracy. We show that the method outperforms of-the-shelf low-rank methods as LoRA. Experiments on diverse geospatial benchmarks confirm substantial parameter reduction with minimal accuracy loss, enabling high-performing, on-device geospatial models.
Quantum computing calibration depends on interpreting experimental data, and calibration plots provide the most universal human-readable representation for this task, yet no systematic evaluation exists of how well vision-language models (VLMs) interpret them. We introduce QCalEval, the first VLM benchmark for quantum calibration plots: 243 samples across 87 scenario types from 22 experiment families, spanning superconducting qubits and neutral atoms, evaluated on six question types in both zero-shot and in-context learning settings. The best general-purpose zero-shot model reaches a mean score of 72.3, and many open-weight models degrade under multi-image in-context learning, whereas frontier closed models improve substantially. A supervised fine-tuning ablation at the 9-billion-parameter scale shows that SFT improves zero-shot performance but cannot close the multimodal in-context learning gap. As a reference case study, we release NVIDIA Ising Calibration 1, an open-weight model based on Qwen3.5-35B-A3B that reaches 74.7 zero-shot average score.
Vision-Language-Action (VLA) models enable test-time behavioral steering via neuron-level interventions, but existing methods use fixed strengths and operate in open loop. This static modulation fails under evolving task dynamics, leading to overcorrection, oscillations, and reduced task success—especially for temporal attributes like speed. We propose CTRL-STEER, a control-theoretic framework that casts activation steering as closed-loop feedback with adaptive, time-varying interventions. Instead of assuming neurons encode temporal concepts, we steer along motion-aligned residual directions and regulate intervention magnitude via feedback. We instantiate this with both PID and reinforcement learning controllers that jointly optimize concept adherence and task success. Experiments on fine-tuned OpenVLA policies across four LIBERO suites show improved stability and a better steering–success trade-off over fixed-coefficient baselines, without retraining the base model.
A comprehensive framework to automatically perform size and morphology recognition of nanoparticles in SEM images in a high-throughput manner.
Plant regeneration is an important dimension of plant propagation and a key step in the production of transgenic plants. However, regeneration capacity varies widely among genotypes and species, the molecular basis of which is largely unknown. Association mapping methods such as genome-wide association studies (GWAS) have long demonstrated abilities to help uncover the genetic basis of trait variation in plants; however, the performance of these methods depends on the accuracy and scale of phenotyping. To enable a large-scale GWAS of in planta callus and shoot regeneration in the model tree Populus, we developed a phenomics workflow involving semantic segmentation to quantify regenerating plant tissues over time. We found that the resulting statistics were of highly non-normal distributions, and thus employed transformations or permutations to avoid violating assumptions of linear models used in GWAS. We report over 200 statistically supported quantitative trait loci (QTLs), with genes encompassing or near to top QTLs including regulators of cell adhesion, stress signaling, and hormone signaling pathways, as well as other diverse functions. Our results encourage models of hormonal signaling during plant regeneration to consider keystone roles of stress-related signaling (e.g. involving jasmonates and salicylic acid), in addition to the auxin and cytokinin pathways commonly considered. The putative regulatory genes and biological processes we identified provide new insights into the biological complexity of plant regeneration, and may serve as new reagents for improving regeneration and transformation of recalcitrant genotypes and species.
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Scholars worldwide leverage science gateways/virtual research environments (VREs) for a wide variety of research and education endeavors spanning diverse scientific fields. Evaluating the value of a given science gateway/VRE to its constituent community is critical in obtaining the financial and human resources necessary to sustain operations and increase adoption in the user community. In this article, we feature a variety of exemplar science gateways/VREs and detail how they define impact in terms of, for example, their purpose, operation principles, and size of user base. Further, the exemplars recognize that their science gateways/VREs will continuously evolve with technological advancements and standards in cloud computing platforms, web service architectures, data management tools and cybersecurity. We also present a number of technology advances that could be incorporated in next-generation science gateways/VREs to enhance their scope and scale of their operations for greater success/impact. The exemplars are selected from owners of science gateways in the Science Gateways Community Institute (SGCI) clientele in the United States, and from the owners of VREs in the International Virtual Research Environment Interest Group (VRE-IG) of the Research Data Alliance. Thus, community-driven best practices and technology advances are compiled from diverse expert groups with an international perspective to envisage futuristic science gateway/VRE innovations.
Abstract A common and costly challenge in the nascent biorefinery industry is the consistent handling and conveyance of biomass feedstock materials, which can vary widely in their chemical, physical, and mechanical properties. Solutions to cope with varying feedstock qualities will be required, including advanced process controls to adjust equipment and reject feedstocks that do not meet a quality standard. In this work, we present and evaluate methods to autonomously assess corn stover feedstock quality in real time and provide data to process controls with low-cost camera hardware. We explore the use of neural networks to classify feedstocks based on actual processing behavior and pixel matrix feature parameterization to further assess particle attributes that may explain the variable processing behavior. We used the pretrained ResNet neural network coupled with a gated recurrent unit (GRU) time-series classifier trained on our image data, resulting in binary classification of feedstock anomalies with favorable performance. The textural aspects of the image data were statistically analyzed to determine if the textural features were predictive of operational disruptions. The significant textural features were angular second moment, prominence, mean height of surface profile, mean resultant vector, shade, skewness, variation of the polar facet orientation, and direction of azimuthal facets. Expansion of these models is recommended across a wider variety of labeled feedstock images of different qualities and species to develop a more robust tool that may be deployed using low-cost cameras within biorefineries.