212 research outputs found
Tactical Trajectory Planning for Stealth Unmanned Aerial Vehicle to Win the Radar Game
In this paper, problem of planning tactical trajectory for stealth unmanned aerial vehicle (UAV) to win the radar game is studied. Three principles of how to win the radar game are presented, and their utilizations for stealth UAV to evade radar tracking are analysed. The problem is formulated by integrating the model of stealth UAV, the constraints of radar detecting and the multi-objectives of the game. The pseudospectral multi-phase optimal control based trajectory planning algorithm is developed to solve the formulated problem. Pseudospectral method is employed to seek the optimal solution with satisfying convergence speed. The results of experiments show that the proposed method is feasible and effective. By following the planned trajectory with several times of switches between exposure and stealth, stealth UAV could win the radar game triumphantly.Defence Science Journal, 2012, 62(6), pp.375-381, DOI:http://dx.doi.org/10.14429/dsj.62.268
Plastic Responses in Growth, Morphology, and Biomass Allocation of Five Subtropical Tree Species to Different Degrees of Shading
We investigated how different degrees of shading affected growth, morphology, and biomass allocation in seedlings from two coniferous and three broadleaved species. The experiment was conducted in a shade house over a 1-year period. Our results showed that under increasing shade, seedlings from most species exhibited lower total biomass, net assimilation rates, relative growth rates, root mass ratios, and root/shoot ratios. In contrast, the slenderness quotients, leaf area ratios, and specific leaf areas increased with increasing shade. For coniferous species, growth traits were relatively more plastic (responsive to shade) than morphology or biomass allocation traits, whereas for broadleaved species, growth and biomass allocation were the most shade-sensitive traits. When comparing coniferous versus broadleaved species, the former had a higher growth plasticity index and lower allocation plasticity than the latter. Root biomass and stem mass ratio were the most and least plastic traits in response to shading. Our results indicate that shade differentially affects coniferous and broadleaved species in terms of their growth, morphology, and biomass allocation. These findings have important implications for the establishment and maintenance of mixed-species stands
Open-World Multi-Task Control Through Goal-Aware Representation Learning and Adaptive Horizon Prediction
We study the problem of learning goal-conditioned policies in Minecraft, a
popular, widely accessible yet challenging open-ended environment for
developing human-level multi-task agents. We first identify two main challenges
of learning such policies: 1) the indistinguishability of tasks from the state
distribution, due to the vast scene diversity, and 2) the non-stationary nature
of environment dynamics caused by partial observability. To tackle the first
challenge, we propose Goal-Sensitive Backbone (GSB) for the policy to encourage
the emergence of goal-relevant visual state representations. To tackle the
second challenge, the policy is further fueled by an adaptive horizon
prediction module that helps alleviate the learning uncertainty brought by the
non-stationary dynamics. Experiments on 20 Minecraft tasks show that our method
significantly outperforms the best baseline so far; in many of them, we double
the performance. Our ablation and exploratory studies then explain how our
approach beat the counterparts and also unveil the surprising bonus of
zero-shot generalization to new scenes (biomes). We hope our agent could help
shed some light on learning goal-conditioned, multi-task agents in challenging,
open-ended environments like Minecraft.Comment: This paper is accepted by CVPR202
Multi-task deep neural network acoustic models with model adaptation using discriminative speaker identity for whisper recognition
This paper presents a study on large vocabulary continuous whisper automatic recognition (wLVCSR). wLVCSR provides the ability to use ASR equipment in public places without concern for disturbing others or leaking private information. However the task of wLVCSR is much more challenging than normal LVCSR due to the absence of pitch which not only causes the signal to noise ratio (SNR) of whispers to be much lower than normal speech but also leads to flatness and formant shifts in whisper spectra. Furthermore, the amount of whisper data available for training is much less than for normal speech. In this paper, multi-task deep neural network (DNN) acoustic models are deployed to solve these problems. Moreover, model adaptation is performed on the multi-task DNN to normalize speaker and environmental variability in whispers based on discriminative speaker identity information. On a Mandarin whisper dictation task, with 55 hours of whisper data, the proposed SI multi-task DNN model can achieve 56.7% character error rate (CER) improvement over a baseline Gaussian Mixture Model (GMM), discriminatively trained only using the whisper data. Besides, the CER of the proposed model for normal speech can reach 15.2%, which is close to the performance of a state-of-the-art DNN trained with one thousand hours of speech data. From this baseline, the model-adapted DNN gains a further 10.9% CER reduction over the generic model
Phenotypic Plasticity of Cunninghamia lanceolata (Lamb.) Hook. Seedlings in Response to Varied Light Quality Treatments
Effects of light quality on phenotypic plasticity in Cunninghamialanceolata (Lamb.) Hook. seedlings during growth and development, and the underlying mechanisms, were investigated. The seedlings showed distinct morphological adjustments when exposed to an equal photosynthetic photon flux density (400 mu mol.m(-2).s(-1)) of different light qualities: monochromatic blue (BL), monochromatic red (RL), monochromatic far-red (FrL), mixed RL and FrL at 1:1 (RFr1:1L), mixed RL and FrL at 1:2 (RFr1:2L), and multi-wavelength white (WL, control). Compared with WL, FrL and BL significantly promoted height increment. However, BL was unfavorable for root growth. The seedling biomass was lower and the root-to-shoot ratio was smaller under BL. RL promoted leaf area enlargement, root growth, axillary bud number, and increased the root-to-shoot ratio, but inhibited stem elongation. Low R/Fr ratios or increased FrL proportion increased seedling stem elongation. The seedling growth under RFr1:1L treatment was poorer than that under other treatments; however, the number of axillary buds was the highest. The plasticity of leaf morphology traits was lower in different treatments, and that of axillary bud traits was crucial in the adaptation of C. lanceolata to light quality. Precise management of light quality and wavelength in controlled environments may maximize the economic efficiency of forest production and enhance its quality
GROOT: Learning to Follow Instructions by Watching Gameplay Videos
We study the problem of building a controller that can follow open-ended
instructions in open-world environments. We propose to follow reference videos
as instructions, which offer expressive goal specifications while eliminating
the need for expensive text-gameplay annotations. A new learning framework is
derived to allow learning such instruction-following controllers from gameplay
videos while producing a video instruction encoder that induces a structured
goal space. We implement our agent GROOT in a simple yet effective
encoder-decoder architecture based on causal transformers. We evaluate GROOT
against open-world counterparts and human players on a proposed Minecraft
SkillForge benchmark. The Elo ratings clearly show that GROOT is closing the
human-machine gap as well as exhibiting a 70% winning rate over the best
generalist agent baseline. Qualitative analysis of the induced goal space
further demonstrates some interesting emergent properties, including the goal
composition and complex gameplay behavior synthesis. The project page is
available at https://craftjarvis-groot.github.io
Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents
We investigate the challenge of task planning for multi-task embodied agents
in open-world environments. Two main difficulties are identified: 1) executing
plans in an open-world environment (e.g., Minecraft) necessitates accurate and
multi-step reasoning due to the long-term nature of tasks, and 2) as vanilla
planners do not consider how easy the current agent can achieve a given
sub-task when ordering parallel sub-goals within a complicated plan, the
resulting plan could be inefficient or even infeasible. To this end, we propose
"escribe, xplain, lan and
elect" (), an interactive planning approach based
on Large Language Models (LLMs). DEPS facilitates better error correction on
initial LLM-generated by integrating of
the plan execution process and providing self- of
feedback when encountering failures during the extended planning phases.
Furthermore, it includes a goal , which is a trainable
module that ranks parallel candidate sub-goals based on the estimated steps of
completion, consequently refining the initial plan. Our experiments mark the
milestone of the first zero-shot multi-task agent that can robustly accomplish
70+ Minecraft tasks and nearly double the overall performances. Further testing
reveals our method's general effectiveness in popularly adopted non-open-ended
domains as well (i.e., ALFWorld and tabletop manipulation). The ablation and
exploratory studies detail how our design beats the counterparts and provide a
promising update on the grand challenge with our
approach. The code is released at https://github.com/CraftJarvis/MC-Planner.Comment: NeurIPS 202
Referring Image Segmentation via Cross-Modal Progressive Comprehension
Referring image segmentation aims at segmenting the foreground masks of the
entities that can well match the description given in the natural language
expression. Previous approaches tackle this problem using implicit feature
interaction and fusion between visual and linguistic modalities, but usually
fail to explore informative words of the expression to well align features from
the two modalities for accurately identifying the referred entity. In this
paper, we propose a Cross-Modal Progressive Comprehension (CMPC) module and a
Text-Guided Feature Exchange (TGFE) module to effectively address the
challenging task. Concretely, the CMPC module first employs entity and
attribute words to perceive all the related entities that might be considered
by the expression. Then, the relational words are adopted to highlight the
correct entity as well as suppress other irrelevant ones by multimodal graph
reasoning. In addition to the CMPC module, we further leverage a simple yet
effective TGFE module to integrate the reasoned multimodal features from
different levels with the guidance of textual information. In this way,
features from multi-levels could communicate with each other and be refined
based on the textual context. We conduct extensive experiments on four popular
referring segmentation benchmarks and achieve new state-of-the-art
performances.Comment: Accepted by CVPR 2020. Code is available at
https://github.com/spyflying/CMPC-Refse
Adonis: Practical and Efficient Control Flow Recovery through OS-Level Traces
Control flow recovery is critical to promise the software quality, especially for large-scale software in production environment.
However, the efficiency of most current control flow recovery techniques is compromised due to their runtime overheads along with
deployment and development costs. To tackle this problem, we propose a novel solution, Adonis, which harnesses OS-level traces,
such as dynamic library calls and system call traces, to efficiently and safely recover control flows in practice. Adonis operates in
two steps: it first identifies the call-sites of trace entries, then it executes a pair-wise symbolic execution to recover valid execution
paths. This technique has several advantages. First, Adonis does not require the insertion of any probes into existing applications,
thereby minimizing runtime cost. Second, given that OS-level traces are hardware-independent, Adonis can be implemented across
various hardware configurations without the need for hardware-specific engineering efforts, thus reducing deployment cost. Third, as
Adonis is fully automated and does not depend on manually created logs, it circumvents additional development cost. We conducted an
evaluation of Adonis on representative desktop applications and real-world IoT applications. Adonis can faithfully recover the control
flow with 86.8% recall and 81.7% precision. Compared to the state-of-the-art log-based approach, Adonis can not only cover all the
execution paths recovered, but also recover 74.9% of statements that cannot be covered. In addition, the runtime cost of Adonis is
18.3× lower than the instrument-based approach; the analysis time and storage cost (indicative of the deployment cost) of Adonis is
50× smaller and 443× smaller than the hardware-based approach, respectively. To facilitate future replication and extension of this
work, we have made the code and data publicly available
- …