262 research outputs found
生物付着担体充填塔を用いたバイオガスからの硫化水素とシロキサンの同時除去に関する研究
京都大学新制・課程博士博士(工学)甲第23181号工博第4825号新制||工||1754(附属図書館)京都大学大学院工学研究科都市環境工学専攻(主査)教授 高岡 昌輝, 教授 橋本 訓, 准教授 大下 和徹学位規則第4条第1項該当Doctor of Philosophy (Engineering)Kyoto UniversityDFA
Self-Prompting Large Vision Models for Few-Shot Medical Image Segmentation
Recent advancements in large foundation models have shown promising potential
in the medical industry due to their flexible prompting capability. One such
model, the Segment Anything Model (SAM), a prompt-driven segmentation model,
has shown remarkable performance improvements, surpassing state-of-the-art
approaches in medical image segmentation. However, existing methods primarily
rely on tuning strategies that require extensive data or prior prompts tailored
to the specific task, making it particularly challenging when only a limited
number of data samples are available. In this paper, we propose a novel
perspective on self-prompting in medical vision applications. Specifically, we
harness the embedding space of SAM to prompt itself through a simple yet
effective linear pixel-wise classifier. By preserving the encoding capabilities
of the large model, the contextual information from its decoder, and leveraging
its interactive promptability, we achieve competitive results on multiple
datasets (i.e. improvement of more than 15% compared to fine-tuning the mask
decoder using a few images).Comment: 8.5 pages + 2 pages of supplementary materials + 2 pages of
references, 3 figures, submitted to 5th MICCAI Workshop on Domain Adaptation
and Representation Transfer (DART
Large-scale Continuous Gesture Recognition Using Convolutional Neural Networks
This paper addresses the problem of continuous gesture recognition from
sequences of depth maps using convolutional neutral networks (ConvNets). The
proposed method first segments individual gestures from a depth sequence based
on quantity of movement (QOM). For each segmented gesture, an Improved Depth
Motion Map (IDMM), which converts the depth sequence into one image, is
constructed and fed to a ConvNet for recognition. The IDMM effectively encodes
both spatial and temporal information and allows the fine-tuning with existing
ConvNet models for classification without introducing millions of parameters to
learn. The proposed method is evaluated on the Large-scale Continuous Gesture
Recognition of the ChaLearn Looking at People (LAP) challenge 2016. It achieved
the performance of 0.2655 (Mean Jaccard Index) and ranked place in
this challenge
Channel sensing for holographic MIMO surfaces based on interference principle
The Holographic Multiple-Input and Multiple-Output (HMIMO) provides a new
paradigm for building a more cost-effective wireless communication
architecture. In this paper, we derive the principles of holographic
interference theory for electromagnetic wave reception and transmission,
whereby the optical holography is extended to communication holography and a
channel sensing architecture for holographic MIMO surfaces is established.
Unlike the traditional pilot-based MIMO channel estimation approaches, the
proposed architecture circumvents the complicated processes like filtering,
analog to digital conversion (ADC), down conversion. Instead, it relies on
interfering the object waves with a pre-designed reference wave, and therefore
reduces the hardware complexity and requires less time-frequency resources for
channel estimation. To address the self-interference problem in the holographic
recording process, we propose a phase shifting-based interference suppression
(PSIS) method according to the structural characteristics of communication
hologram and interference composition. We then propose a Prony-based multi-user
channel segmentation (PMCS) algorithm to acquire the channel state information
(CSI). Our theoretical analysis shows that the estimation error of the PMCS
algorithm converges to zero when the number of HMIMO surface antennas is large
enough. Simulation results show that under the holographic architecture, our
proposed algorithm can accurately estimate the CSI in multi-user scenarios
Learning-based Single-step Quantitative Susceptibility Mapping Reconstruction Without Brain Extraction
Quantitative susceptibility mapping (QSM) estimates the underlying tissue
magnetic susceptibility from MRI gradient-echo phase signal and typically
requires several processing steps. These steps involve phase unwrapping, brain
volume extraction, background phase removal and solving an ill-posed inverse
problem. The resulting susceptibility map is known to suffer from inaccuracy
near the edges of the brain tissues, in part due to imperfect brain extraction,
edge erosion of the brain tissue and the lack of phase measurement outside the
brain. This inaccuracy has thus hindered the application of QSM for measuring
the susceptibility of tissues near the brain edges, e.g., quantifying cortical
layers and generating superficial venography. To address these challenges, we
propose a learning-based QSM reconstruction method that directly estimates the
magnetic susceptibility from total phase images without the need for brain
extraction and background phase removal, referred to as autoQSM. The neural
network has a modified U-net structure and is trained using QSM maps computed
by a two-step QSM method. 209 healthy subjects with ages ranging from 11 to 82
years were employed for patch-wise network training. The network was validated
on data dissimilar to the training data, e.g. in vivo mouse brain data and
brains with lesions, which suggests that the network has generalized and
learned the underlying mathematical relationship between magnetic field
perturbation and magnetic susceptibility. AutoQSM was able to recover magnetic
susceptibility of anatomical structures near the edges of the brain including
the veins covering the cortical surface, spinal cord and nerve tracts near the
mouse brain boundaries. The advantages of high-quality maps, no need for brain
volume extraction and high reconstruction speed demonstrate its potential for
future applications.Comment: 26 page
NeuRI: Diversifying DNN Generation via Inductive Rule Inference
Deep Learning (DL) is prevalently used in various industries to improve
decision-making and automate processes, driven by the ever-evolving DL
libraries and compilers. The correctness of DL systems is crucial for trust in
DL applications. As such, the recent wave of research has been studying the
automated synthesis of test-cases (i.e., DNN models and their inputs) for
fuzzing DL systems. However, existing model generators only subsume a limited
number of operators, lacking the ability to pervasively model operator
constraints. To address this challenge, we propose NeuRI, a fully automated
approach for generating valid and diverse DL models composed of hundreds of
types of operators. NeuRI adopts a three-step process: (i) collecting valid and
invalid API traces from various sources; (ii) applying inductive program
synthesis over the traces to infer the constraints for constructing valid
models; and (iii) using hybrid model generation which incorporates both
symbolic and concrete operators. Our evaluation shows that NeuRI improves
branch coverage of TensorFlow and PyTorch by 24% and 15% over the
state-of-the-art model-level fuzzers. NeuRI finds 100 new bugs for PyTorch and
TensorFlow in four months, with 81 already fixed or confirmed. Of these, 9 bugs
are labelled as high priority or security vulnerability, constituting 10% of
all high-priority bugs of the period. Open-source developers regard
error-inducing tests reported by us as "high-quality" and "common in practice"
Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation
Program synthesis has been long studied with recent approaches focused on
directly using the power of Large Language Models (LLMs) to generate code
according to user intent written in natural language. Code evaluation datasets,
containing curated synthesis problems with input/output test-cases, are used to
measure the performance of various LLMs on code synthesis. However, test-cases
in these datasets can be limited in both quantity and quality for fully
assessing the functional correctness of the generated code. Such limitation in
the existing benchmarks begs the following question: In the era of LLMs, is the
code generated really correct? To answer this, we propose EvalPlus -- a code
synthesis benchmarking framework to rigorously evaluate the functional
correctness of LLM-synthesized code. In short, EvalPlus takes in the base
evaluation dataset and uses an automatic input generation step to produce and
diversify large amounts of new test inputs using both LLM-based and
mutation-based input generators to further validate the synthesized code. We
extend the popular HUMANEVAL benchmark and build HUMANEVAL+ with 81x
additionally generated tests. Our extensive evaluation across 14 popular LLMs
demonstrates that HUMANEVAL+ is able to catch significant amounts of previously
undetected wrong code synthesized by LLMs, reducing the pass@k by 15.1% on
average! Moreover, we even found several incorrect ground-truth implementations
in HUMANEVAL. Our work not only indicates that prior popular code synthesis
evaluation results do not accurately reflect the true performance of LLMs for
code synthesis but also opens up a new direction to improve programming
benchmarks through automated test input generation
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Dietary Glycemic Index, Glycemic Load, and Risk of Coronary Heart Disease, Stroke, and Stroke Mortality: A Systematic Review with Meta-Analysis
Background: The relationship between dietary glycemic index, glycemic load and risk of coronary heart disease (CHD), stroke, and stroke-related mortality is inconsistent. Methods: We systematically searched the MEDLINE, EMBASE, and Science Citation Index Expanded databases using glycemic index, glycemic load, and cardiovascular disease and reference lists of retrieved articles up to April 30, 2012. We included prospective studies with glycemic index and glycemic load as the exposure and incidence of fatal and nonfatal CHD, stroke, and stroke-related mortality as the outcome variable. Pooled relative risks (RR) and 95% confidence intervals (CI) were calculated using random-effects models. Results: Fifteen prospective studies with a total of 438,073 participants and 9,424 CHD cases, 2,123 stroke cases, and 342 deaths from stroke were included in the meta-analysis. Gender significantly modified the effects of glycemic index and glycemic load on CHD risk, and high glycemic load level was associated with higher risk of CHD in women (RR = 1.49, 95%CI 1.27−1.73), but not in men (RR = 1.08, 95%CI 0.91−1.27). Stratified meta-analysis by body mass index indicated that among overweight and obese subjects, dietary glycemic load level were associated with increased risk of CHD (RR = 1.49, 95%CI 1.27−1.76; P for interaction = 0.003). Higher dietary glycemic load, but not glycemic index, was positively associated with stroke (RR = 1.19, 95% CI 1.00−1.43). There is a linear dose-response relationship between dietary glycemic load and increased risk of CHD, with pooled RR of 1.05 (95%CI 1.02−1.08) per 50-unit increment in glycemic load level. Conclusion: High dietary glycemic load is associated with a higher risk of CHD and stroke, and there is a linear dose-response relationship between glycemic load and CHD risk. Dietary glycemic index is slightly associated with risk of CHD, but not with stroke and stroke-related death. Further studies are needed to verify the effects of gender and body weight on cardiovascular diseases
Joint Rigid Motion Correction and Sparse-View CT via Self-Calibrating Neural Field
Neural Radiance Field (NeRF) has widely received attention in Sparse-View
Computed Tomography (SVCT) reconstruction tasks as a self-supervised deep
learning framework. NeRF-based SVCT methods represent the desired CT image as a
continuous function of spatial coordinates and train a Multi-Layer Perceptron
(MLP) to learn the function by minimizing loss on the SV sinogram. Benefiting
from the continuous representation provided by NeRF, the high-quality CT image
can be reconstructed. However, existing NeRF-based SVCT methods strictly
suppose there is completely no relative motion during the CT acquisition
because they require \textit{accurate} projection poses to model the X-rays
that scan the SV sinogram. Therefore, these methods suffer from severe
performance drops for real SVCT imaging with motion. In this work, we propose a
self-calibrating neural field to recover the artifacts-free image from the
rigid motion-corrupted SV sinogram without using any external data.
Specifically, we parametrize the inaccurate projection poses caused by rigid
motion as trainable variables and then jointly optimize these pose variables
and the MLP. We conduct numerical experiments on a public CT image dataset. The
results indicate our model significantly outperforms two representative
NeRF-based methods for SVCT reconstruction tasks with four different levels of
rigid motion.Comment: 5 page
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