214 research outputs found
Robotic Interestingness via Human-Informed Few-Shot Object Detection
Interestingness recognition is crucial for decision making in autonomous
exploration for mobile robots. Previous methods proposed an unsupervised online
learning approach that can adapt to environments and detect interesting scenes
quickly, but lack the ability to adapt to human-informed interesting objects.
To solve this problem, we introduce a human-interactive framework,
AirInteraction, that can detect human-informed objects via few-shot online
learning. To reduce the communication bandwidth, we first apply an online
unsupervised learning algorithm on the unmanned vehicle for interestingness
recognition and then only send the potential interesting scenes to a
base-station for human inspection. The human operator is able to draw and
provide bounding box annotations for particular interesting objects, which are
sent back to the robot to detect similar objects via few-shot learning. Only
using few human-labeled examples, the robot can learn novel interesting object
categories during the mission and detect interesting scenes that contain the
objects. We evaluate our method on various interesting scene recognition
datasets. To the best of our knowledge, it is the first human-informed few-shot
object detection framework for autonomous exploration
Comparison of Gene Regulatory Networks via Steady-State Trajectories
The modeling of genetic regulatory networks is becoming increasingly widespread in the study of biological systems. In the abstract, one would prefer quantitatively comprehensive models, such as a differential-equation model, to coarse models; however, in practice, detailed models require more accurate measurements for inference and more computational power to analyze than coarse-scale models. It is crucial to address the issue of model complexity in the framework of a basic scientific paradigm: the model should be of minimal complexity to provide the necessary predictive power. Addressing this issue requires a metric by which to compare networks. This paper proposes the use of a classical measure of difference between amplitude distributions for periodic signals to compare two networks according to the differences of their trajectories in the steady state. The metric is applicable to networks with both continuous and discrete values for both time and state, and it possesses the critical property that it allows the comparison of networks of different natures. We demonstrate application of the metric by comparing a continuous-valued reference network against simplified versions obtained via quantization
Context-specific gene regulatory networks subdivide intrinsic subtypes of breast cancer
<p>Abstract</p> <p>Background</p> <p>Breast cancer is a highly heterogeneous disease with respect to molecular alterations and cellular composition making therapeutic and clinical outcome unpredictable. This diversity creates a significant challenge in developing tumor classifications that are clinically reliable with respect to prognosis prediction.</p> <p>Results</p> <p>This paper describes an unsupervised context analysis to infer context-specific gene regulatory networks from 1,614 samples obtained from publicly available gene expression data, an extension of a previously published methodology. We use the context-specific gene regulatory networks to classify the tumors into clinically relevant subgroups, and provide candidates for a finer sub-grouping of the previously known intrinsic tumors with a focus on Basal-like tumors. Our analysis of pathway enrichment in the key contexts provides an insight into the biological mechanism underlying the identified subtypes of breast cancer.</p> <p>Conclusions</p> <p>The use of context-specific gene regulatory networks to identify biological contexts from heterogenous breast cancer data set was able to identify genomic drivers for subgroups within the previously reported intrinsic subtypes. These subgroups (contexts) uphold the clinical relevant features for the intrinsic subtypes and were associated with increased survival differences compared to the intrinsic subtypes. We believe our computational approach led to the generation of novel rationalized hypotheses to explain mechanisms of disease progression within sub-contexts of breast cancer that could be therapeutically exploited once validated.</p
AirDet: Few-Shot Detection without Fine-tuning for Autonomous Exploration
Few-shot object detection has attracted increasing attention and rapidly
progressed in recent years. However, the requirement of an exhaustive offline
fine-tuning stage in existing methods is time-consuming and significantly
hinders their usage in online applications such as autonomous exploration of
low-power robots. We find that their major limitation is that the little but
valuable information from a few support images is not fully exploited. To solve
this problem, we propose a brand new architecture, AirDet, and surprisingly
find that, by learning class-agnostic relation with the support images in all
modules, including cross-scale object proposal network, shots aggregation
module, and localization network, AirDet without fine-tuning achieves
comparable or even better results than many fine-tuned methods, reaching up to
30-40% improvements. We also present solid results of onboard tests on
real-world exploration data from the DARPA Subterranean Challenge, which
strongly validate the feasibility of AirDet in robotics. To the best of our
knowledge, AirDet is the first feasible few-shot detection method for
autonomous exploration of low-power robots. The code and pre-trained models are
released at https://github.com/Jaraxxus-Me/AirDet.Comment: 23 pages, 9 figure
Multi-Robot Multi-Room Exploration with Geometric Cue Extraction and Spherical Decomposition
This work proposes an autonomous multi-robot exploration pipeline that
coordinates the behaviors of robots in an indoor environment composed of
multiple rooms. Contrary to simple frontier-based exploration approaches, we
aim to enable robots to methodically explore and observe an unknown set of
rooms in a structured building, keeping track of which rooms are already
explored and sharing this information among robots to coordinate their
behaviors in a distributed manner. To this end, we propose (1) a geometric cue
extraction method that processes 3D map point cloud data and detects the
locations of potential cues such as doors and rooms, (2) a spherical
decomposition for open spaces used for target assignment. Using these two
components, our pipeline effectively assigns tasks among robots, and enables a
methodical exploration of rooms. We evaluate the performance of our pipeline
using a team of up to 3 aerial robots, and show that our method outperforms the
baseline by 36.6% in simulation and 26.4% in real-world experiments
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