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MetaPoL: Immersive VR based Indoor Patterns of Life (PoL) and Anomalies Data Generation for Insider Threat Modeling in Nuclear Security
Insider threats are perhaps the most serious challenges that nuclear and radiological security systems face. Insiders pose such a great threat due to their access, authority, and knowledge, granting them opportunities to bypass dedicated nuclear and radiological security elements. For example, in one of the latest major insider threat incidents to nuclear security, the Doel-4 nuclear powerplant in Belgium suffered a shutdown, the threat of nuclear materials diversion, and long-term loss of tens of millions of dollars. Seven years of investigation concluded that it was an inside job and attempted sabotage. In this regard, there is an immediate need for R&D and technology integration in the domain of modeling indoor Patterns-of-Life (PoL) and anomaly detection. This can be achieved by using datasets of facility users’ mobility and activity, which can support the design of algorithms for insider threat modeling and detection. However, due to classification, privacy, sensitivity, and safety protocols, such datasets from real physical nuclear reactor facilities are not only hard to share, but also not always feasible to deploy and collect. Aiming to find an alternate solution, our proposed demonstration work - MetaPoL, is the first-ever (for the application space) immersive VR (virtual reality) environment of a real-world secure facility and allows users to move-and-stay through the designed indoor physical layout and also encounter NPCs (non-player characters) that emulate other facility users. In the MetaPoL an interactive user performs realistic spatio-temporal movement, dwelling and activities using a Meta Quest Pro VR headset, and that generates high-frequency (in time) high-resolution (in space) indoor spatial-temporal datasets that are valuable for PoL modeling and anomaly detection research specifically for insider threat modeling and detection mission. Such generated realistic, rich in context, and mission specific datasets can boost AI/Machine Learning based research for modeling and detecting insider threats in nuclear security and nonproliferation.
Surf-Deformer: Mitigating Dynamic Defects on Surface Code via Adaptive Deformation
In this paper, we introduce Surf-Deformer, a code deformation framework that seamlessly integrates adaptive defect mitigation functionality into the current surface code workflow. It crafts several basic deformation instructions based on fundamental gauge transformations, which can be combined to explore a larger design space than previous methods. This enables more optimized deformation processes tailored to specific defect situations, restoring the QEC capability of deformed codes more efficiently with minimal qubit resources. Additionally, we design an adaptive code layout that accommodates our defect mitigation strategy while ensuring efficient execution of logical operations. Our evaluation shows that Surf-Deformer outperforms previous methods by significantly reducing the end-to-end failure rate of various quantum programs by 35× to 70×, while requiring only about 50% of the qubit resources compared to the previous method to achieve the same level of failure rate. Ablation studies show that Surf-Deformer surpasses previous defect removal methods in preserving QEC capability and facilitates surface code communication by achieving nearly optimal throughput.
Cycle-by-cycle Delay Estimation at Signalized Intersections by using Machine Learning and Simulated Video Detection Data
Accurate estimation of delay is crucial for efficient traffic signal operations. Estimation of delay in the real-time manner using traditional loop detectors requires advanced detectors (in addition to stop-bar detection). In cases when this detection layout is not in place, delay estimates are approximated with a lower accuracy. Video detection is one of the most frequently deployed detection systems at signalized intersections in recent years. In most cases video detection operates in the same way as traditional inductive loops. However, when coupled with computer vision algorithms, video detection systems could be used to retrieve additional information (e.g., vehicular arrivals and departures) that cannot be taken out from the conventional systems (e.g., long stop-bar loop detectors). Although present for several decades, video detection data were not frequently examined for delay estimation purposes. In this study, we proposed a novel delay estimation model which can be developed with only data from stop-bar video detectors. Relevant data were collected from a simulation model of 11 signalized intersections at downtown Chattanooga, TN and processed to create needed inputs for model development. With the use of multigene genetic programming the authors developed a delay model that outperforms accuracy of multi regression model. Furthermore, authors evaluated the developed model by comparison with the other benchmark delay models, such as HCM and approach delay model. It was found that the developed MGGP delay model outperforms benchmark models for a wide range of traffic and signal operation conditions.
A Survey of Singular Value Decomposition Methods for Distributed Tall/Skinny Data
The Singular Value Decomposition (SVD) is one of the most important matrix factorizations, enjoying a wide variety of applications across numerous application domains. In statistics and data analysis, the common applications of SVD inclue Principal Components Analysis (PCA) and regression. Usually these applications arise on data that has far more rows than columns, so-called "tall/skinny" matrices. In the big data analytics context, this may take the form of hundreds of millions to billions of rows with only a few hundred columns. There is a need, therefore, for fast, accurate, and scalable tall/skinny SVD implementations which can fully utilize modern computing resources. To that end, we present a survey of three different algorithms for computing the SVD for these kinds of tall/skinny data layouts using MPI for communication. We contextualize these with common big data analytics techniques. Finally, we present both CPU and GPU timing results from the Summit supercomputer, and discuss possible alternative approaches.
iFair: Achieving Fairness in the Allocation of Scarce Resources for Senior Health Care
Efficient resource allocation is crucial in many domains, particularly in senior care, where assigning resources to older adults must consider uncertainties associated with vulnerable populations. In collaboration with Senior Health Facilities (SHFs) and domain experts, this paper presents iFair, a novel framework designed to assist decision-makers in equitably allocating scarce resources to older adults. iFair was prototyped in the context of ongoing work on a data exchange platform, CAREDEX, used for enhancing older adults' resilience during disasters. A key novelty of iFair focuses on aligning resident preferences with resources in urgent situations, expediting care, and enhancing task efficiency. We integrate static and dynamic environmental data, including facility layouts and sensor data, with detailed resident profiles to cater to the individual needs and preferences of residents. While our framework primarily focuses on allocation within facilities, it also extends to a regional scale to support the planning and transfer of seniors to mutual aid facilities. Our experiments adapt data from a real SHF to emulate resource allocation in an emergency fire evacuation setting and highlight the delicate balance that decision-makers can achieve between efficiency and fairness.
Towards 20 T Hybrid Accelerator Dipole Magnets
We report the most effective way to achieve very high collision energies in a circular particle accelerator is to maximize the field strength of the main bending dipoles. In dipole magnets using Nb-Ti superconductor the practical field limit is considered to be 8-9 T. When Nb 3 Sn superconductor material is utilized, a field level of 15-16 T can be achieved. To further push the magnetic field beyond the Nb 3 Sn limits, High Temperature Superconductors (HTS) need to be considered in the magnet design. The most promising HTS materials for particle accelerator magnets are Bi2212 and REBCO. However, their outstanding performance comes with a significantly higher cost. Therefore, an economically viable option towards 20 T dipole magnets could consist in an “hybrid” solution, where both HTS and Nb3Sn materials are used. We discuss in this paper preliminary conceptual designs of various 20 T hybrid magnet concepts. After the definition of the overall design criteria, the coil dimensions and parameters are investigated with finite element models based on simple sector coils. Preliminary 2D cross-section computation results are then presented and three main layouts compared: cos-theta, block, and common-coil. Both traditional designs and more advanced stress-management options are considered.
Conceptual Design of 20 T Hybrid Accelerator Dipole Magnets
Hybrid magnets are currently under consideration as an economically viable option towards 20 T dipole magnets for next generation of particle accelerators. In these magnets, High Temperature Superconducting (HTS) materials are used in the high field part of the coil with so-called insert coils, and Low Temperature Superconductors (LTS) like Nb 3 Sn and Nb-Ti superconductors are used in the lower field region with so-called outsert coils. The attractiveness of the hybrid option lays on the fact that, on the one hand, the 20 T field level is beyond the Nb 3 Sn practical limits of 15-16 T for accelerator magnets and can be achieved only via HTS materials; on the other hand, the high cost of HTS superconductors compared to LTS superconductors makes it advantageous exploring a hybrid approach, where the HTS portion of the coil is minimized. Here we present in this paper an overview of different design options aimed at generating 20 T field in a 50 mm clear aperture. The coil layouts investigated include the Cos-theta design (CT), with its variations to reduce the conductor peak stress, namely the Canted Cos-theta design (CCT) and the Stress Management Cos-theta design (SMCT), and, in addition, the Block-type design (BL) including a form of stress management and the Common-Coil design (CC). Results from a magnetic and mechanical analysis are discussed, with particular focus on the comparison between the different options regarding quantity of superconducting material, field quality, conductor peak stress, and quench protection.
A 2K Design for the Low Beta Quadrupoles Q1ApF/Q1BpF for the Interaction Region of the Electron-Ion Collider (EIC)
The forward hadron magnets of the planned electron-ion collider (EIC) at Brookhaven National Laboratory (BNL) present a set of unique challenges. In addition to the typical magnet requirements in terms of aperture, gradient, and field quality, the field leakage from the hadron magnets to the electron apertures must be negligible. Due to the close proximity of the two apertures the shielding solution for the electron beam can affect the field quality of the hadron magnets. Here, in this paper, a design for the Q1ApF/Q1BpF low beta quadrupoles based on NbTi Rutherford cable operating at 2K is presented. In this design the electron beam shielding and the required field quality in the hadron magnets are achieved using a set of cutouts in the iron yoke solely without resorting to active shielding. The chosen layout along with the key parameters are presented and their effect on hadron magnets in term of field quality and operational margins are also discussed.
The Quench Protection of Direct Wind Magnets
The direct wind magnets have several unique features. The conductor layout is characterized by thin single conductors wound over long lengths and separated from the other turns with thick layer of insulation. Moreover, there are several layers of winding. The strong non-uniformity in the magnetic field distribution results in different current and thermal margins for quenching in different regions. The quench protection modeling involves multi-physics coupling between electrical, thermal and magnetic transient. The material properties vary over time and location. This coupled with the complexity in the geometry adds to long computation times. The objective of this study is to understand the quench propagation in an EIC high inductance direct wind magnet which enables one to design appropriate quench protection strategies to protect the magnet. An advanced modeling program has been developed to simulate direct wind magnet quench. In conclusion, we validate theoretical simulations with experimental data.
Magnetic Field Mapping of a 2.5 T Fixed-Field HTS Gantry Magnet for Proton Therapy
We present results from testing a high-temperature superconducting (HTS) magnet prototype for proton therapy. This magnet is specifically designed for a novel rotating gantry capable of delivering the entire proton beam energy range (70225 MeV) while maintaining a fixed magnetic field in the superconducting magnets. The gantry layout simplifies the magnet design, enabling the use of straight, flat racetrack Bi-2223 (DI-BSCCO) coil technology and operation at higher temperatures (1015 K). The magnet has a non-linear field distribution for bending and focusing the proton beams. To validate this feature, we developed a system for measuring the magnetic field distribution in the magnet aperture. We present the design of this hall probe array and experimental results from two different magnet tests at 4.2 K in a liquid helium bath. These results are compared with the simulated field distribution and discussed in the context of the required field quality for the application.
Serpentine Magnet Designs for the Interaction Region of the Electron-Ion Collider (EIC)
The Electron-Ion Collider (EIC), hosted by Brookhaven National Laboratory, is designed to deliver a peak luminosity of 1 × 10 34 cm −2 sec −1 . The interaction region (IR) of the EIC imposes several constraints in terms of field quality, aperture, and spatial layout, which necessitates the development of several unique superconducting serpentine direct wind magnets. These magnets are constructed using either a single strand or a small-diameter 6-around-1 NbTi cable, presenting unique challenges for design and optimization. This paper introduces a new computational code specifically developed to streamline and integrate the design process for these magnets, enabling faster design iterations while addressing their complex requirements. Here, in this paper, we first introduce the code, which builds on established electromagnetic fundamentals. The code incorporates tools for optimizing winding patterns and for correcting magnetic multipoles; additionally, it interfaces with established magnet design software. We also present the design of several serpentine magnets for the EIC IR, demonstrating the code’s capability to deliver precise and efficient solutions. These designs highlight the code’s ability to accelerate the development cycle, ensuring the serpentine magnets meet the demanding specifications of the EIC project.
Evaluation of the Room Temperature Field Quality Measurements of HL-LHC MQXFA Magnet Assemblies
As a member of the multi-lab U.S. High Luminosity LHC Accelerator Upgrade Project (HL-LHC AUP), Lawrence Berkeley National Laboratory (LBNL) is assembling high-field Nb3Sn low-beta MQXFA quadrupole magnets for eventual installation at CERN as part of the HL-LHC upgrade. Each magnet undergoes room temperature magnetic measurements at two points during the assembly process: once after completion of the coil pack sub-assembly and once after the magnet is fully assembled and pre-loaded. Beyond its use to verify that each magnet assembly can meet operational requirements, the data from this two-stage measurement process allow for investigation into the impact of the pre-loading operation on field quality. Recent measurements on magnet rebuilds as well as exploratory coil pack reconfigurations also provide an opportunity to see in isolation the effects of changes to a magnet’s coil selection or coil layout. In this work, we present the magnetic measurement results of MQXFA magnets assembled or currently in process, focusing particularly on magnets with data corresponding to multiple builds, and discuss trends and insights which may be beneficial for the remaining MQXFA magnet production or for future Nb3Sn accelerator magnets.
ARETE: Accurate Error Assessment via Machine Learning-Guided Dynamic-Timing Analysis
Nanometer circuits are increasingly prone to timing errors, escalating the need for fault injection frameworks to accurately evaluate their impact on applications. Here in this paper, we propose ARETE, a novel cross-layer, fault-injection framework that combines dynamic-binary instrumentation with machine learning-guided dynamic-timing analysis. ARETE enables accurate fault-injection into any application by estimating the location of the injecting errors via dynamic-timing analysis. To accelerate fault-injection, we develop a novel, data-aware, machine learning-based mechanism that dynamically pre-selects the error-prone instructions and limits the application of the costly dynamic-timing analysis only to them. To evaluate ARETE's accuracy, our fully automated toolflow is configured to support fault-injection based on detailed post-layout gate-level simulations as well as via existing workload-agnostic error models. Our results for various workloads, including an autonomous-driving library, show that the location and time of injected errors performed by ARETE, is 89.9% consistent with fault-injection based on full gate-level simulation. On average, ARETE executes 84.6x faster than gate-level simulation and at a cost of 3.4% loss in the program output quality estimation. When compared to the existing statistical fault-injection tools that are based on workload-agnostic error models, ARETE improves the accuracy of fault-injection rate and output quality estimation by 143.9% and 40.4% on average, respectively.
Network-Wide Traffic Signal Control Using Bilinear System Modeling and Adaptive Optimization
This study proposes a new multi-input multi-output optimal bilinear signal control method in which a bilinear dynamic model approximation is used to capture the nonlinear dynamics of the urban traffic networks. With signal green time splits as the control input and traffic delay changes as the output for each intersections in the network, a bilinear system model was developed, which, on the basis of linear system modeling, takes interactions among traffic delays and signal timing splits into consideration. Based on the bilinear system modeling framework, we conducted two steps in each time interval to derive traffic control strategies: (1) we used the normalized least-squared algorithm to estimate system parameters; and (2) we solved an online optimization problem to obtain the updated traffic control inputs for the signal timing that minimizes future traffic delays. We evaluated the proposed method in a microscopic traffic simulation environment (VISSIM) with a 35-intersection network of Bellevue city in Washington. Two different traffic demand patterns: (1) normal traffic demands; and (2) time-varying traffic demands were simulated to compare the performance of different control strategies. Experimental results show that (1) the proposed bilinear system model can better describe traffic system dynamics than linear-model based methods, such as our previously developed linear-quadratic regulator control; and (2) the proposed method outperforms the state-of-the-art signal control strategies, namely the max-pressure and the self-organizing traffic light control methods. We have also shown that the proposed method is applicable to all other possible network layouts and signal controller phasing structures.
Electromagnetic and Engineering Design of a High-Current 15-MeV/u Cyclotron
The capability to produce high current, low loss, and extracted external ion beams presents an enabling technology for nuclear security, isotope production, and medical applications. We report on a novel, continuous-wave cyclotron for accelerating light ions with a charge-to-mass ratio of 1/2, which is designed for 1 mA maximum and 15 MeV/u energy. This article details the conceptual electromagnetic (EM) and engineering design including the 1.1 T sector magnets and 45 MHz, 200 kV radio frequency (RF) cavities. We discuss the cyclotron beam parameters with respect to potential commercial and industrial applications. The general concept and the accelerator’s layout are followed by a detailed EM design of magnets and RF cavities. Finally, practical engineering design considerations will be provided.
A Reconfigurable Neural Network ASIC for Detector Front-End Data Compression at the HL-LHC
Despite advances in the programmable logic capabilities of modern trigger systems, a significant bottleneck remains in the amount of data to be transported from the detector to off-detector logic where trigger decisions are made. We demonstrate that a neural network (NN) autoencoder model can be implemented in a radiation-tolerant application-specific integrated circuit (ASIC) to perform lossy data compression alleviating the data transmission problem while preserving critical information of the detector energy profile. For our application, we consider the high-granularity calorimeter from the Compact Muon Solenoid (CMS) experiment at the CERN Large Hadron Collider. The advantage of the machine learning approach is in the flexibility and configurability of the algorithm. By changing the NN weights, a unique data compression algorithm can be deployed for each sensor in different detector regions and changing detector or collider conditions. To meet area, performance, and power constraints, we perform quantization-aware training to create an optimized NN hardware implementation. The design is achieved through the use of high-level synthesis tools and the hls4ml framework and was processed through synthesis and physical layout flows based on a low-power (LP)-CMOS 65-nm technology node. The flow anticipates 200 Mrad of ionizing radiation to select gates and reports a total area of 3.6 mm 2 and consumes 95 mW of power. The simulated energy consumption per inference is 2.4 nJ. Furthermore, this is the first radiation-tolerant on-detector ASIC implementation of an NN that has been designed for particle physics applications.
Recent Developments in the Design of Vertical Borehole Ground Heat Exchangers for Cost Reduction and Thermal Energy Storage
Ground source (geothermal) heat pumps (GSHPs) can meet the thermal demands of buildings in an energy-efficient manner. The current high installation costs and long payback period limit the attractiveness of GSHP installation in the United States. Vertical borehole ground heat exchangers (VBGHEs), which are commonly used in GSHP systems, contribute most to the cost premium of GSHPs. Reducing the cost of VBGHEs could help increase market penetration of GSHP systems. This paper reviews recent developments for VBGHEs, including improvements in borehole heat transfer and borehole field layout, integration with thermal energy storage, and new design tools. Improvements in the borehole design and materials are more likely to be justified when the ground has high thermal conductivity. Integrating thermal energy storage can provide additional value to the GSHP system, especially when flexible electric demand at buildings becomes more valuable. Here, advanced design tools for VBGHEs that account for the thermal response of irregularly shaped borehole fields and that are more closely integrated with whole-building energy simulation programs may facilitate more innovations and optimization of GSHP system designs.