54 research outputs found
Multi-Architecture Multi-Expert Diffusion Models
Diffusion models have achieved impressive results in generating diverse and
realistic data by employing multi-step denoising processes. However, the need
for accommodating significant variations in input noise at each time-step has
led to diffusion models requiring a large number of parameters for their
denoisers. We have observed that diffusion models effectively act as filters
for different frequency ranges at each time-step noise. While some previous
works have introduced multi-expert strategies, assigning denoisers to different
noise intervals, they overlook the importance of specialized operations for
high and low frequencies. For instance, self-attention operations are effective
at handling low-frequency components (low-pass filters), while convolutions
excel at capturing high-frequency features (high-pass filters). In other words,
existing diffusion models employ denoisers with the same architecture, without
considering the optimal operations for each time-step noise. To address this
limitation, we propose a novel approach called Multi-architecturE Multi-Expert
(MEME), which consists of multiple experts with specialized architectures
tailored to the operations required at each time-step interval. Through
extensive experiments, we demonstrate that MEME outperforms large competitors
in terms of both generation performance and computational efficiency
Steel Module-to-Concrete Core Connection Methods in High Rise Modular Buildings: A Critical Review
Modularization in a high-rise building is different from a small building, as it is exposed to more lateral forces like wind and earthquakes. The integrity, robustness, and overall stability of the modules and their performance is based on the joining techniques and strong structural systems. High lateral stiff construction structures like concrete shear walls and frames, braced steel frames, and steel moment frames are used for the stability of high-rise modular buildings. Similarly, high-rise stick-built buildings have concrete cores and perimeter frames for lateral load strength and stiffness. Methods for general steel-concrete connections are available in many works of literature. However, there are few modular-related papers describing this connection system in modular buildings. This paper aims to review the various research and practice adopted for steel-to-concrete connections in construction and compare the methods between stick-built buildings and modular buildings. The literature review shows that the practice of steel module-to-concrete core connection in high-rise modular buildings is like outrigger beams-to-concrete core connection in stick-built framed buildings. This paper concludes that further studies are needed in developing proper guidelines for a steel module-to-concrete core connection system in high-rise modular buildings
Lessons Learned during the Early Phases of a Modular Project: A Case Study of UNLV\u27s Solar Decathlon 2020 Project
The U.S. Department of Energy conducts the Solar Decathlon competition as a student-based achievement that encourages sustainable design with energy efficiency and solar energy technologies. In the 2020 competition, the University of Nevada, Las Vegas (UNLV) team designed, fabricated, and constructed a net-zero modular house that applies innovative and highly efficient building technologies. This paper focused on the lessons learned during the early phases of this ongoing modular project. The research methodology included obtaining feedback from key project participants using a well-structured questionnaire. The results showed that the major items/challenges in the project’s planning phase included selecting the modular size, planning the construction system, planning the materials and procurement, estimating costs and duration, selecting a fabricator, collaboration and communication, safety, and planning module transportation. These findings will help modular practitioners and future Solar Decathlon competition participants better understand how and what factors they should consider most during the early phases through the lessons learned
Cutting-edge Technologies to Achieve a Higher Level of Modular Construction – Literature Review
Cost overruns, schedule delays, and a shortage of skilled labor are common problems the construction industry is currently experiencing. Modularization and standardization strategies have the potential to resolve the various problems mentioned above and have been applied for various construction applications for a long time. However, the level of modularization remains low, and modular construction projects have not been getting the full benefits. Thus, this review investigated the cutting-edge technologies currently being utilized to develop the modular construction field. For this paper, qualified research papers were identified using predetermined keywords from previous related research papers. Identified literature was then filtered and analyzed. According to the included reviews, several technologies are being developed for modular construction. For example, automated design and monitoring systems for modularization were developed. In addition, research labs are utilizing robotic arms for modular construction to achieve a high level of completion in the construction industry, as is seen in the manufacturing industry. Despite these efforts, more research and development are necessary because some automation technologies still require manual activities. Thus, there is great potential for further development of modularization techniques, and further research is recommended to achieve high levels of modularization
The Current State and Future Directions of Industrial Robotic Arms in Modular Construction
Industrial robotic arms are widely adopted in numerous industries for manufacturing automation under factory settings, which eliminates the limitations of manual labor and provides significant productivity and quality benefits. The U.S. modular construction industry, despite having similar controlled factory environments, still heavily relies on manual labor. Thus, this study investigates the U.S., Canada, and Europe-based leading modular construction companies and research labs implementing industrial robotic arms for manufacturing automation. The investigation mainly considered the current research scope, industry state, and constraints, as well as identifying the types and specifications of the robotic arms in use. First, the study investigated well-recognized modular building associations, the Modular Building Institute (MBI), and renowned architecture design magazine, Dezeen to gather industry updates. The authors discovered one university lab and a few companies that adopted Switzerland-based robotic arms, ABB. Researching ABB robotics led to the discovery of ABB’s competitor, Germany-based KUKA robotic arms. Consequently, research extended to the companies and labs adopting KUKA models. In total, this study has identified seven modular companies and four research labs. All companies employed robotic arms and gantry robot combinations in a production-line-like system for partial automation, and some adopted design standardization for optimization. The common goal among the labs was to achieve greater flexibility and full automation with robotic arms. This study will help companies better implement robotic arm automation by providing recommendations from investigating its current industry status
Addressing Negative Transfer in Diffusion Models
Diffusion-based generative models have achieved remarkable success in various
domains. It trains a model on denoising tasks that encompass different noise
levels simultaneously, representing a form of multi-task learning (MTL).
However, analyzing and improving diffusion models from an MTL perspective
remains under-explored. In particular, MTL can sometimes lead to the well-known
phenomenon of , which results in the performance
degradation of certain tasks due to conflicts between tasks. In this paper, we
aim to analyze diffusion training from an MTL standpoint, presenting two key
observations: the task affinity between denoising tasks
diminishes as the gap between noise levels widens, and negative
transfer can arise even in the context of diffusion training. Building upon
these observations, our objective is to enhance diffusion training by
mitigating negative transfer. To achieve this, we propose leveraging existing
MTL methods, but the presence of a huge number of denoising tasks makes this
computationally expensive to calculate the necessary per-task loss or gradient.
To address this challenge, we propose clustering the denoising tasks into small
task clusters and applying MTL methods to them. Specifically, based on
, we employ interval clustering to enforce temporal proximity
among denoising tasks within clusters. We show that interval clustering can be
solved with dynamic programming and utilize signal-to-noise ratio, timestep,
and task affinity for clustering objectives. Through this, our approach
addresses the issue of negative transfer in diffusion models by allowing for
efficient computation of MTL methods. We validate the proposed clustering and
its integration with MTL methods through various experiments, demonstrating
improved sample quality of diffusion models.Comment: 22 pages, 12 figures, under revie
Towards Practical Plug-and-Play Diffusion Models
Diffusion-based generative models have achieved remarkable success in image
generation. Their guidance formulation allows an external model to
plug-and-play control the generation process for various tasks without
fine-tuning the diffusion model. However, the direct use of publicly available
off-the-shelf models for guidance fails due to their poor performance on noisy
inputs. For that, the existing practice is to fine-tune the guidance models
with labeled data corrupted with noises. In this paper, we argue that this
practice has limitations in two aspects: (1) performing on inputs with
extremely various noises is too hard for a single model; (2) collecting labeled
datasets hinders scaling up for various tasks. To tackle the limitations, we
propose a novel strategy that leverages multiple experts where each expert is
specialized in a particular noise range and guides the reverse process at its
corresponding timesteps. However, as it is infeasible to manage multiple
networks and utilize labeled data, we present a practical guidance framework
termed Practical Plug-And-Play (PPAP), which leverages parameter-efficient
fine-tuning and data-free knowledge transfer. We exhaustively conduct ImageNet
class conditional generation experiments to show that our method can
successfully guide diffusion with small trainable parameters and no labeled
data. Finally, we show that image classifiers, depth estimators, and semantic
segmentation models can guide publicly available GLIDE through our framework in
a plug-and-play manner
A Novel Polymer-Encapsulated Multi-Imaging Modality Fiducial Marker with Positive Signal Contrast for Image-Guided Radiation Therapy
BACKGROUND: Current fiducial markers (FMs) in external-beam radiotherapy (EBRT) for prostate cancer (PCa) cannot be positively visualized on magnetic resonance imaging (MRI) and create dose perturbation and significant imaging artifacts on computed tomography (CT) and MRI. We report our initial experience with clinical imaging of a novel multimodality FM, NOVA.
METHODS: We tested Gold Anchor [G-FM], BiomarC [carbon, C-FM], and NOVA FMs in phantoms imaged with kilovoltage (kV) X-rays, transrectal ultrasound (TRUS), CT, and MRI. Artifacts of the FMs on CT were quantified by the relative streak artifacts level (rSAL) metric. Proton dose perturbations (PDPs) were measured with Gafchromic EBT3 film, with FMs oriented either perpendicular to or parallel with the beam axis. We also tested the performance of NOVA-FMs in a patient.
RESULTS: NOVA-FMs were positively visualized on all 4 imaging modalities tested. The rSAL on CT was 0.750 ± 0.335 for 2-mm reconstructed slices. In F-tests, PDP was associated with marker type and depth of measurement (
CONCLUSIONS: NOVA-FMs were positively visible and produced less PDP than G-FMs or C-FMs. NOVA-FMs facilitated MRI/CT fusion and identification of regions of interest
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Performance/outcomes data and physician process challenges for practical big data efforts in radiation oncology
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146290/1/mp13136.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146290/2/mp13136_am.pd
Optimizing Outpatient Radiation Oncology Consult Workflow by Using Time-Driven Activity-Based Costing: Efficiency and Financial Impacts
PURPOSE: Clinical efficiency is a key component of value-based health care. Our objective here was to identify workflow inefficiencies by using time-driven activity-based costing (TDABC) and evaluate the implementation of a new clinical workflow in high-volume outpatient radiation oncology clinics.
METHODS: Our quality improvement study was conducted with the Departments of GI, Genitourinary (GU), and Thoracic Radiation Oncology at a large academic cancer center and four community network sites. TDABC was used to create process maps and optimize workflow for outpatient consults. Patient encounter metrics were captured with a real-time status function in the electronic medical record. Time metrics were compared using Mann-Whitney U tests.
RESULTS: Individual patient encounter data for 1,328 consults before the intervention and 1,234 afterward across all sections were included. The median overall cycle time was reduced by 21% in GI (19 minutes), 18% in GU (16 minutes), and 12% at the community sites (9 minutes). The median financial savings per consult were 33 USD for GU, 42 USD for the community sites. Patient satisfaction surveys (from 127 of 228 patients) showed that 99% of patients reported that their providers spent adequate time with them and 91% reported being seen by a care provider in a timely manner.
CONCLUSION: TDABC can effectively identify opportunities to improve clinical efficiency. Implementing workflow changes on the basis of our findings led to substantial reductions in overall encounter cycle times across several departments, as well as high patient satisfaction and significant financial savings
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