150813 research outputs found
Sort by
A parallel algorithm for fast reconstruction of primary vertices on heterogeneous architectures
No full textThe physics programme of the LHCb experiment at the Large Hadron Collider requires an efficient and precise reconstruction of the particle collision vertices. The LHCb Upgrade detector relies on a fully software-based trigger with an online reconstruction rate of 30 MHz , necessitating fast vertex finding algorithms. This paper describes a new approach to vertex reconstruction developed for this purpose. The algorithm is based on cluster finding within a histogram of the particle trajectory projections along the beamline and on an adaptive vertex fit. Its implementations and optimisations on x86 and GPU architectures and its performance on simulated samples are also discussed
Hyperspectral Remote Sensing for UXO Detection and Damage Assessment on Airfield Pavements
No full textIf an airfield being operated by the U.S. Air Force is attacked, the current method for assessing its condition is a slow visual and manual inspection process, exposing personnel to dangerous conditions and delaying repair operations. Developing a fully autonomous remote assessment solution would improve the speed and safety of this critical task, but remains an unsolved problem despite continued advances in drone technology, deep learning, and computer vision. This research explores using near-surface hyperspectral sensors as an alternative to red, green, blue (RGB) digital cameras, in hopes of improving detection precision and accuracy for airfield assessment. However, even with modern hyperspectral sensors the benefit of increasing spectral image resolution comes at a cost, creating addition complexity, uncertainty, and sensitivity in the acquisition, data correction, and downstream detection processes.
This work presents a series of tests, each designed to better understand and refine a full hyperspectral image detection sequence, starting with sensor selection and raw data acquisition, proceeding to radiometric correction, and culminating in image recognition by means of supervised deep learning (DL). Regarding sensor selection and data acquisition, these findings indicate that for many applications of computer vision, using a hyperspectral camera with high spectral resolution is unnecessary. It is more beneficial to select a camera with snapshot imaging that instead maximizes spectral range or spatial resolution. Radiometric correction is then explored, and experiments demonstrate that correction makes machine learning classification models less sensitive to changes in scene illumination, thus improving overall image recognition performance. Finally, deep learning models for image recognition are tested and a new method for generating synthetic hyperspectral data is developed and shown to be useful for estimating hyperspectral model performance on larger datasets, when real data are limited. Overall, the findings presented in this thesis suggest that by refining the methods used for data acquisition, correction, and detection, hyperspectral imaging improves image recognition when compared to traditional RGB cameras. This applies not only for airfield damage assessment but extends to other real-world applications requiring computer vision and scene understanding.Ph.D
Investigation of critical heat flux enhancement on nanoengineered surfaces in pressurized subcooled flow boiling using infrared thermometry
No full textEnhancing the flow boiling critical heat flux (CHF) is beneficial to the economics and safety margins of many industrial applications cooled by boiling heat transfer. While many studies have shown that surfaces with hydrophilic nanoscale and micro-scale features can enhance CHF in pool boiling, it is still not clear how these engineered surfaces affect the CHF in subcooled flow boiling at ambient pressure, let alone high-pressure conditions. Here, two nano-engineered surfaces, i.e., a surface coated with a porous layer of hydrophilic silica nanoparticles and a surface coated with zinc oxide nanowires, were tested. Flow boiling tests with a 10 K subcooling and a mass flux of 1000 kg/(m2·s) were conducted at 1 bar and 4 bars using infrared thermometry diagnostics. At 1 bar, the CHF enhancement is around 15% for both coatings. At 4 bars, the CHF enhancement is around 17% for the nanowire surface, and around 25% for the nano-porous surface. Infrared thermometry measurements reveal that the CHF enhancement comes from an increase of both two-phase heat transfer and single-phase heat transfer mechanisms, which is due to a change of bubble dynamics on the nanoengineered surfaces. It is also shown that the boiling crisis can be predicted using a percolation model based on Monte Carlo (MC) simulations
Why Schonland Failed in His Search for Runaway Electrons From Thunderstorms
No full textB.F.J. Schonland, advised and encouraged by C.T.R. Wilson, made two unsuccessful searches forrunaway electrons from thunderstorms in the 1930s. These findings stand in marked contrast with researchresults over the last decade and ironically set this field of research back many decades. Schonland's lack ofsuccess is traced to gamma ray attenuation in the atmosphere above Johannesburg (1,780 m MSL) and to hisrestriction to nine thunderstorms
Scaling Carbon-Cement Supercapacitors for Energy Storage Use-Cases
No full textThe urgent global transition to renewable energy is constrained by the intermittent nature of solar and wind sources, highlighting the critical need for scalable energy storage solutions. This thesis presents a comprehensive investigation into the development of structurally integrated supercapacitors based on carbon-doped cement composites, known as EC3 cells. These multifunctional materials combine structural performance with electrochemical energy storage capabilities, enabling integration directly into civil infrastructure. The research focuses on three essential challenges for real-world deployment: (1) replacing laboratory acrylic casings with hydrophobic sealants compatible with cementitious systems, (2) quantifying and mitigating shrinkage and swelling in nanocarbon cement matrices under electrolyte exposure, and (3) identifying corrosion-resistant current collectors that maintain conductivity and mechanical durability under harsh conditions. Bitumen-based coatings were found to be promising sealants for moisture containment. Shrinkage studies [ are underway, I will complete this part shortly]. Meanwhile, corrosion testing of various collector materials revealed that graphene sheets and stainless steel–reinforced graphillic papers offered optimal trade-offs between conductivity, corrosion resistance, and mechanical performance. The thesis concludes with two field-implementation design proposals—a vertical column and a vaulted arch—both of which leverage compression to improve electrochemical contact and stability. Altogether, this work establishes a foundational framework for embedding energy storage directly into the built environment.M.Eng
Forage: Understanding RAG-based Sensemaking for Community Conversations
No full textCHI EA ’25, Yokohama, JapanWe introduce Forage, a RAG-based and LLM-augmented search engine, which we apply to the problem of sensemaking for community conversation data. We report on formative user studies introducing Forage to two distinct user groups: NPR journalists and municipal staff in the city of Durham, North Carolina. We taxonomize the query types users make with the tool, use cases that include synthesizing insights across conversations and finding content about a particular subject. We find that users tend to gravitate towards using the system for synthesis more than for pure search. We report on challenges and opportunities surfaced by performing sensemaking with an open-ended interface like Forage, such as the benefits of finding content quickly, but also the challenges users face interacting with a system in natural language. Insights from this formative study confirm the usefulness of Forage for sensemaking, but also make follow-up work, such as systematically evaluating system performance and developing appropriate design, urgent
CH−π Interactions Are Required for Human Galectin-3 Function
No full textGlycan-binding proteins, or lectins, recognize distinct structural elements of polysaccharides, to mediate myriad biological functions. Targeting glycan-binding proteins involved in human disease has been challenging due to an incomplete understanding of the molecular mechanisms that govern protein-glycan interactions. Bioinformatics and structural studies of glycan-binding proteins indicate that aromatic residues with the potential for CH-π interactions are prevalent in glycan-binding sites. However, the contributions of these CH-π interactions to glycan binding and their relevance in downstream function remain unclear. An emblematic lectin, human galectin-3, recognizes lactose and N-acetyllactosamine-containing glycans by positioning the electropositive face of a galactose residue over the tryptophan 181 (W181) indole forming a CH-π interaction. We generated a suite of galectin-3 W181 variants to assess the importance of these CH-π interactions to glycan binding and function. As determined experimentally and further validated with computational modeling, variants with smaller or less electron-rich aromatic side chains (W181Y, W181F, W181H) or sterically similar but nonaromatic residues (W181M, W181R) showed poor or undetectable binding to lactose and attenuated ability to bind mucins or agglutinate red blood cells. The latter functions depend on multivalent binding, highlighting that weakened CH-π interactions cannot be overcome by avidity. Two galectin-3 variants with disrupted hydrogen bonding interactions (H158A and E184A) showed similarly impaired lactose binding. Molecular simulations demonstrate that all variants have decreased binding orientation stability relative to native galectin-3. Thus, W181 collaborates with the endogenous hydrogen bonding network to enhance binding affinity for lactose, and abrogation of these CH-π interactions is as deleterious as eliminating key hydrogen bonding interactions. These findings underscore the critical roles of CH-π interactions in carbohydrate binding and lectin function and will aid the development of novel lectin inhibitors
A long exact sequence in symmetry breaking: order parameter constraints, defect anomaly-matching, and higher Berry phases
No full textWe study defects in symmetry breaking phases, such as domain walls, vortices, and hedgehogs. In particular, we focus on the localized gapless excitations which sometimes occur at the cores of these objects. These are topologically protected by an ’t Hooft anomaly. We classify different symmetry breaking phases in terms of the anomalies of these defects, and relate them to the anomaly of the broken symmetry by an anomaly-matching formula. We also derive the obstruction to the existence of a symmetry breaking phase with a local defect. We obtain these results using a long exact sequence of groups of invertible field theories, which we call the “symmetry breaking long exact sequence” (SBLES). The mathematical backbone of the SBLES is studied in a companion paper [1]. Our work further develops the theory of higher Berry phase and its bulk-boundary correspondence, and serves as a new computational tool for classifying symmetry protected topological phases
Ab initio modeling of superconducting nanowire single-photon detectors
No full textSingle-photon detectors are widely used in modern communication, sensing, and computing technology. Among these detectors, superconducting nanowire single-photon detectors (SNSPDs) possess the highest detection efficiencies, the shortest timing jitter, and the lowest dark count rates. However, for several applications, including those in the biological, astronomical, and quantum computation fields, there remains a desire to push the capabilities of modern detectors even further. To realize these improvements, it is necessary to develop an understanding of the physical mechanisms underpinning single-photon detection in these devices. However, current models are phenomenological, requiring experimental data for input, or can only recover qualitative agreement, severely limiting their predictive ability. In this thesis, we begin by describing the existing theoretical frameworks used to model superconducting materials and devices, both in equilibrium and nonequilibrium. We then illustrate an example of a phenomenological approach to modeling superconducting devices by developing an electrothermal model for the superconducting nanowire cryotron and demonstrating its efficacy in predicting the DC behavior and power dissipation of the device. Finally, we expand upon the current state-of-the-art SNSPD theory by utilizing recent advances in density functional theory to develop an ab initio model for the photon detection mechanism of SNSPDs. We then validate the predictions of our model with experimental data from the literature. The resulting model requires no experimental input, provides quantitative predictions of SNSPD performance, and can be extended to describe other superconducting devices, thus enabling the possibility of conducting a systematic search of materials for enhanced device performance.S.M
Accelerating Embedded HOWFSC Algorithms
No full textThe quest to directly image planets of other solar systems demands not only state-of-the- art coronagraphs, but also places extreme performance demands on space-based processors. Direct imaging requires precise wavefront control to acquire the 1010 contrast necessary to reveal a dim, Earth-like exoplanet. This precise level of control is only possible if high-order wavefront sensing and control (HOWFSC) algorithms are executed with enough speed to offset wavefront error accumulation. Of the many aspects that make high-contrast imaging difficult, a central bottleneck is the speed at which we can run these algorithms. At the center of this work, we aim to accelerate the execution of two foundational HOWFSC algorithms: optical modeling and Electric Field Conjugation (EFC). Optical modeling underpins both Jacobian-based EFC, and a relatively new variant of EFC, called adjoint-based EFC.
The two main contributions of this thesis are to port bottleneck HOWFSC algorithms to the relevant computing environments, and quantify speedups attained by both algorithm choice and implementation optimization. This work explores the acceleration of optical modeling for a vector vortex coronagraph through the use of the FFTW library, and the acceleration of EFC by implementing adjoint-based EFC in an embedded context. We utilize functional analogs to radiation-hardened processors, using the NXP T1040 in place of the BAE RAD5545, and the NXP LS1046 in place of the LS1046-Space. We find that the FFTW library enabled a factor of six speedup for 4096 × 4096 fast Fourier transforms (FFTs), and a factor of five for 2048 × 2048 FFTs. With these significant speedups, the bottleneck within the vortex operations of the optical model shifts from the FFT to matrix multiplication. We additionally time the execution of the underlying routines of Jacobian-based EFC and AD-EFC to estimate that AD-EFC is 46 times faster than Jacobian-based EFC. Despite these speedups, AD-EFC is still a factor of 124 away from 100-second latency for our specific optical model. These results demonstrate that one to two orders of magnitude of speedup must be attained by either further optimizing algorithm implementations, or exploring other parallelization strategies, computing architectures, and mission paradigms to achieve a latency on the order of 100 seconds.S.M