18 research outputs found
Winning the 3rd Japan Automotive AI Challenge -- Autonomous Racing with the Autoware.Auto Open Source Software Stack
The 3rd Japan Automotive AI Challenge was an international online autonomous
racing challenge where 164 teams competed in December 2021. This paper outlines
the winning strategy to this competition, and the advantages and challenges of
using the Autoware.Auto open source autonomous driving platform for multi-agent
racing. Our winning approach includes a lane-switching opponent overtaking
strategy, a global raceline optimization, and the integration of various tools
from Autoware.Auto including a Model-Predictive Controller. We describe the use
of perception, planning and control modules for high-speed racing applications
and provide experience-based insights on working with Autoware.Auto. While our
approach is a rule-based strategy that is suitable for non-interactive
opponents, it provides a good reference and benchmark for learning-enabled
approaches.Comment: Accepted at Autoware Workshop at IV 202
Ensemble Gaussian Processes for Adaptive Autonomous Driving on Multi-friction Surfaces
Driving under varying road conditions is challenging, especially for
autonomous vehicles that must adapt in real-time to changes in the environment,
e.g., rain, snow, etc. It is difficult to apply offline learning-based methods
in these time-varying settings, as the controller should be trained on datasets
representing all conditions it might encounter in the future. While online
learning may adapt a model from real-time data, its convergence is often too
slow for fast varying road conditions. We study this problem in autonomous
racing, where driving at the limits of handling under varying road conditions
is required for winning races. We propose a computationally-efficient approach
that leverages an ensemble of Gaussian processes (GPs) to generalize and adapt
pre-trained GPs to unseen conditions. Each GP is trained on driving data with a
different road surface friction. A time-varying convex combination of these GPs
is used within a model predictive control (MPC) framework, where the model
weights are adapted online to the current road condition based on real-time
data. The predictive variance of the ensemble Gaussian process (EGP) model
allows the controller to account for prediction uncertainty and enables safe
autonomous driving. Extensive simulations of a full scale autonomous car
demonstrated the effectiveness of our proposed EGP-MPC method for providing
good tracking performance in varying road conditions and the ability to
generalize to unknown maps.Comment: 8 pages, 12 figures, accepted for publication in IFAC World Congress
202
Fiber Organization has Little Effect on Electrical Activation Patterns during Focal Arrhythmias in the Left Atrium
Over the past two decades there has been a steady trend towards the
development of realistic models of cardiac conduction with increasing levels of
detail. However, making models more realistic complicates their personalization
and use in clinical practice due to limited availability of tissue and cellular
scale data. One such limitation is obtaining information about myocardial fiber
organization in the clinical setting. In this study, we investigated a chimeric
model of the left atrium utilizing clinically derived patient-specific atrial
geometry and a realistic, yet foreign for a given patient fiber organization.
We discovered that even significant variability of fiber organization had a
relatively small effect on the spatio-temporal activation pattern during
regular pacing. For a given pacing site, the activation maps were very similar
across all fiber organizations tested
Teaching Autonomous Systems Hands-On: Leveraging Modular Small-Scale Hardware in the Robotics Classroom
Although robotics courses are well established in higher education, the
courses often focus on theory and sometimes lack the systematic coverage of the
techniques involved in developing, deploying, and applying software to real
hardware. Additionally, most hardware platforms for robotics teaching are
low-level toys aimed at younger students at middle-school levels. To address
this gap, an autonomous vehicle hardware platform, called F1TENTH, is developed
for teaching autonomous systems hands-on. This article describes the teaching
modules and software stack for teaching at various educational levels with the
theme of "racing" and competitions that replace exams. The F1TENTH vehicles
offer a modular hardware platform and its related software for teaching the
fundamentals of autonomous driving algorithms. From basic reactive methods to
advanced planning algorithms, the teaching modules enhance students'
computational thinking through autonomous driving with the F1TENTH vehicle. The
F1TENTH car fills the gap between research platforms and low-end toy cars and
offers hands-on experience in learning the topics in autonomous systems. Four
universities have adopted the teaching modules for their semester-long
undergraduate and graduate courses for multiple years. Student feedback is used
to analyze the effectiveness of the F1TENTH platform. More than 80% of the
students strongly agree that the hardware platform and modules greatly motivate
their learning, and more than 70% of the students strongly agree that the
hardware-enhanced their understanding of the subjects. The survey results show
that more than 80% of the students strongly agree that the competitions
motivate them for the course.Comment: 15 pages, 12 figures, 3 table
Are intrinsic neural timescales related to sensory processing? Evidence from abnormal behavioral states
The brain exhibits a complex temporal structure which translates into a hierarchy of distinct neural timescales. An open question is how these intrinsic timescales are related to sensory or motor information processing and whether these dynamics have common patterns in different behavioral states. We address these questions by investigating the brain\u27s intrinsic timescales in healthy controls, motor (amyotrophic lateral sclerosis, locked-in syndrome), sensory (anesthesia, unresponsive wakefulness syndrome), and progressive reduction of sensory processing (from awake states over N1, N2, N3). We employed a combination of measures from EEG resting-state data: auto-correlation window (ACW), power spectral density (PSD), and power-law exponent (PLE). Prolonged neural timescales accompanied by a shift towards slower frequencies were observed in the conditions with sensory deficits, but not in conditions with motor deficits. Our results establish that the spontaneous activity\u27s intrinsic neural timescale is related to the neural capacity that specifically supports sensory rather than motor information processing in the healthy brain
Are intrinsic neural timescales related to sensory processing? Evidence from abnormal behavioral states
The brain exhibits a complex temporal structure which translates into a hierarchy of distinct neural timescales. An open question is how these intrinsic timescales are related to sensory or motor information processing and whether these dynamics have common patterns in different behavioral states. We address these questions by investigating the brain\u27s intrinsic timescales in healthy controls, motor (amyotrophic lateral sclerosis, locked-in syndrome), sensory (anesthesia, unresponsive wakefulness syndrome), and progressive reduction of sensory processing (from awake states over N1, N2, N3). We employed a combination of measures from EEG resting-state data: auto-correlation window (ACW), power spectral density (PSD), and power-law exponent (PLE). Prolonged neural timescales accompanied by a shift towards slower frequencies were observed in the conditions with sensory deficits, but not in conditions with motor deficits. Our results establish that the spontaneous activity\u27s intrinsic neural timescale is related to the neural capacity that specifically supports sensory rather than motor information processing in the healthy brain
Are intrinsic neural timescales related to sensory processing? Evidence from abnormal behavioral states
The brain exhibits a complex temporal structure which translates into a hierarchy of distinct neural timescales. An open question is how these intrinsic timescales are related to sensory or motor information processing and whether these dynamics have common patterns in different behavioral states. We address these questions by investigating the brain\u27s intrinsic timescales in healthy controls, motor (amyotrophic lateral sclerosis, locked-in syndrome), sensory (anesthesia, unresponsive wakefulness syndrome), and progressive reduction of sensory processing (from awake states over N1, N2, N3). We employed a combination of measures from EEG resting-state data: auto-correlation window (ACW), power spectral density (PSD), and power-law exponent (PLE). Prolonged neural timescales accompanied by a shift towards slower frequencies were observed in the conditions with sensory deficits, but not in conditions with motor deficits. Our results establish that the spontaneous activity\u27s intrinsic neural timescale is related to the neural capacity that specifically supports sensory rather than motor information processing in the healthy brain
Exploring the role of pyroptosis in shaping the tumor microenvironment of colorectal cancer by bulk and single-cell RNA sequencing
Abstract Background Emerging studies have shown that pyroptosis plays a non-negligible role in the development and treatment of tumors. However, the mechanism of pyroptosis in colorectal cancer (CRC) remains still unclear. Therefore, this study investigated the role of pyroptosis in CRC. Methods A pyroptosis-related risk model was developed using univariate Cox regression and LASSO Cox regression analyses. Based on this model, pyroptosis-related risk scores (PRS) of CRC samples with OS time > 0 from Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database were calculated. The abundance of immune cells in CRC tumor microenvironment (TME) was predicted by single-sample gene-set enrichment analysis (ssGSEA). Then, the responses to chemotherapy and immunotherapy were predicted by pRRophetic algorithm, the tumor immune dysfunction and exclusion (TIDE) and SubMap algorithms, respectively. Moreover, the Cancer Therapeutics Response Portal (CTRP) and PRISM Repurposing dataset (PRISM) were used to explore novel drug treatment strategies of CRC. Finally, we investigated pyroptosis-related genes in the level of single-cell and validated the expression levels of these genes between normal and CRC cell lines by RT-qPCR. Results Survival analysis showed that CRC samples with low PRS had better overall survival (OS) and progression-free survival (PFS). CRC samples with low PRS had higher immune-related gene expression and immune cell infiltration than those with high PRS. Besides, CRC samples with low PRS were more likely to benefit from 5-fluorouracil based chemotherapy and anti-PD-1 immunotherapy. In novel drug prediction, some compounds such as C6-ceramide and noretynodrel, were inferred as potential drugs for CRC with different PRS. Single-cell analysis revealed pyroptosis-related genes were highly expressed in tumor cells. RT-qPCR also demonstrated different expression levels of these genes between normal and CRC cell lines. Conclusions Taken together, this study provides a comprehensive investigation of the role of pyroptosis in CRC at the bulk RNA sequencing (RNA-seq) and single-cell RNA sequencing (scRNA-seq) levels, advances our understanding of CRC characteristics, and guides more effective treatment regimens. Graphical Abstrac
Table_1_Prolonged neoadjuvant chemotherapy without radiation versus total neoadjuvant therapy for locally advanced rectal cancer: A propensity score matched study.docx
BackgroundAlthough neoadjvuant chemoradiotherapy (CRT) improves the local control rate of locally advanced rectal cancer (LARC), it fails to significantly improve disease-free survival (DFS) and overall survival (OS). We explored the efficacy of prolonged neoadjuvant chemotherapy (pNCT) without radiation and compared this schema with total neoadjuvant therapy (TNT).Material and methodsPatients diagnosed with LARC and received TNT (4 cycles of induction CapeOX/FOLFOX followed with CRT) or pNCT (6~8 cycles of CapeOX/FOLFOX) between June 2016 and October 2021 were retrospective analyzed. All patients underwent total mesorectal excision (TME). A 1:1 propensity score match was performed to adjust baseline potential confounders. The tumor response, toxicity, recurrence-free survival (RFS) and OS were observed.ResultsA total of 184 patients with 92 patients in each group were finally enrolled. The median follow-up time was 35 months. TNT showed better pathological complete response (pCR) rate (25.0% vs 16.3%) and objective regression rate (73.9% vs 59.8%) than pNCT. TNT and pNCT produce similar 3-year RFS and OS rates in patients with mid-to-upper rectal cancer. TNT was associated with improved tumor responsiveness in all patients and improved 3-year RFS rates in those with low rectal cancer.ConclusionpNCT is an option for patients with mid-to-upper rectal cancer, but radiation is still necessary for low rectal cancer. To determine optimal schema for neoadjuvant therapy and patient selection, additional randomized controlled studies are needed.</p