110 research outputs found
Cross-relation Cross-bag Attention for Distantly-supervised Relation Extraction
Distant supervision leverages knowledge bases to automatically label
instances, thus allowing us to train relation extractor without human
annotations. However, the generated training data typically contain massive
noise, and may result in poor performances with the vanilla supervised
learning. In this paper, we propose to conduct multi-instance learning with a
novel Cross-relation Cross-bag Selective Attention (CSA), which leads to
noise-robust training for distant supervised relation extractor. Specifically,
we employ the sentence-level selective attention to reduce the effect of noisy
or mismatched sentences, while the correlation among relations were captured to
improve the quality of attention weights. Moreover, instead of treating all
entity-pairs equally, we try to pay more attention to entity-pairs with a
higher quality. Similarly, we adopt the selective attention mechanism to
achieve this goal. Experiments with two types of relation extractor demonstrate
the superiority of the proposed approach over the state-of-the-art, while
further ablation studies verify our intuitions and demonstrate the
effectiveness of our proposed two techniques.Comment: AAAI 201
DEIR: Efficient and Robust Exploration through Discriminative-Model-Based Episodic Intrinsic Rewards
Exploration is a fundamental aspect of reinforcement learning (RL), and its
effectiveness crucially decides the performance of RL algorithms, especially
when facing sparse extrinsic rewards. Recent studies showed the effectiveness
of encouraging exploration with intrinsic rewards estimated from novelty in
observations. However, there is a gap between the novelty of an observation and
an exploration in general, because the stochasticity in the environment as well
as the behavior of an agent may affect the observation. To estimate exploratory
behaviors accurately, we propose DEIR, a novel method where we theoretically
derive an intrinsic reward from a conditional mutual information term that
principally scales with the novelty contributed by agent explorations, and
materialize the reward with a discriminative forward model. We conduct
extensive experiments in both standard and hardened exploration games in
MiniGrid to show that DEIR quickly learns a better policy than baselines. Our
evaluations in ProcGen demonstrate both generalization capabilities and the
general applicability of our intrinsic reward.Comment: Accepted as a conference paper to the 32nd International Joint
Conference on Artificial Intelligence (IJCAI-23
Evolving modular soft robots without explicit inter-module communication using local self-attention
Modularity in robotics holds great potential. In principle, modular robots can be disassembled and reassembled in different robots, and possibly perform new tasks. Nevertheless, actually exploiting modularity is yet an unsolved problem: controllers usually rely on inter-module communication, a practical requirement that makes modules not perfectly interchangeable and thus limits their flexibility. Here, we focus on Voxel-based Soft Robots (VSRs), aggregations of mechanically identical elastic blocks. We use the same neural controller inside each voxel, but without any inter-voxel communication, hence enabling ideal conditions for modularity: modules are all equal and interchangeable. We optimize the parameters of the neural controller—shared among the voxels—by evolutionary computation. Crucially, we use a local self-attention mechanism inside the controller to overcome the absence of inter-module communication channels, thus enabling our robots to truly be driven by the collective intelligence of their modules. We show experimentally that the evolved robots are effective in the task of locomotion: thanks to self-attention, instances of the same controller embodied in the same robot can focus on different inputs. We also find that the evolved controllers generalize to unseen morphologies, after a short fine-tuning, suggesting that an inductive bias related to the task arises from true modularity
SayTap: Language to Quadrupedal Locomotion
Large language models (LLMs) have demonstrated the potential to perform
high-level planning. Yet, it remains a challenge for LLMs to comprehend
low-level commands, such as joint angle targets or motor torques. This paper
proposes an approach to use foot contact patterns as an interface that bridges
human commands in natural language and a locomotion controller that outputs
these low-level commands. This results in an interactive system for quadrupedal
robots that allows the users to craft diverse locomotion behaviors flexibly. We
contribute an LLM prompt design, a reward function, and a method to expose the
controller to the feasible distribution of contact patterns. The results are a
controller capable of achieving diverse locomotion patterns that can be
transferred to real robot hardware. Compared with other design choices, the
proposed approach enjoys more than 50% success rate in predicting the correct
contact patterns and can solve 10 more tasks out of a total of 30 tasks. Our
project site is: https://saytap.github.io
Collective Intelligence for Object Manipulation with Mobile Robots
While natural systems often present collective intelligence that allows them
to self-organize and adapt to changes, the equivalent is missing in most
artificial systems. We explore the possibility of such a system in the context
of cooperative object manipulation using mobile robots. Although conventional
works demonstrate potential solutions for the problem in restricted settings,
they have computational and learning difficulties. More importantly, these
systems do not possess the ability to adapt when facing environmental changes.
In this work, we show that by distilling a planner derived from a
gradient-based soft-body physics simulator into an attention-based neural
network, our multi-robot manipulation system can achieve better performance
than baselines. In addition, our system also generalizes to unseen
configurations during training and is able to adapt toward task completions
when external turbulence and environmental changes are applied
Exploring Effective Distillation of Self-Supervised Speech Models for Automatic Speech Recognition
Recent years have witnessed great strides in self-supervised learning (SSL)
on the speech processing. The SSL model is normally pre-trained on a great
variety of unlabelled data and a large model size is preferred to increase the
modeling capacity. However, this might limit its potential applications due to
the expensive computation and memory costs introduced by the oversize model.
Miniaturization for SSL models has become an important research direction of
practical value. To this end, we explore the effective distillation of
HuBERT-based SSL models for automatic speech recognition (ASR). First, in order
to establish a strong baseline, a comprehensive study on different student
model structures is conducted. On top of this, as a supplement to the
regression loss widely adopted in previous works, a discriminative loss is
introduced for HuBERT to enhance the distillation performance, especially in
low-resource scenarios. In addition, we design a simple and effective algorithm
to distill the front-end input from waveform to Fbank feature, resulting in 17%
parameter reduction and doubling inference speed, at marginal performance
degradation.Comment: Submitted to ICASSP 202
Crystallization-Driven Self-Assembly of Metallo-Polyelectrolyte Block Copolymers with a Polycaprolactone Core-Forming Segment
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Auxin response factor 6A regulates photosynthesis, sugar accumulation, and fruit development in tomato.
Auxin response factors (ARFs) are involved in auxin-mediated transcriptional regulation in plants. In this study, we performed functional characterization of SlARF6A in tomato. SlARF6A is located in the nucleus and exhibits transcriptional activator activity. Overexpression of SlARF6A increased chlorophyll contents in the fruits and leaves of tomato plants, whereas downregulation of SlARF6A decreased chlorophyll contents compared with those of wild-type (WT) plants. Analysis of chloroplasts using transmission electron microscopy indicated increased sizes of chloroplasts in SlARF6A-overexpressing plants and decreased numbers of chloroplasts in SlARF6A-downregulated plants. Overexpression of SlARF6A increased the photosynthesis rate and accumulation of starch and soluble sugars, whereas knockdown of SlARF6A resulted in opposite phenotypes in tomato leaves and fruits. RNA-sequence analysis showed that regulation of SlARF6A expression altered the expression of genes involved in chlorophyll metabolism, photosynthesis and sugar metabolism. SlARF6A directly bound to the promoters of SlGLK1, CAB, and RbcS genes and positively regulated the expression of these genes. Overexpression of SlARF6A also inhibited fruit ripening and ethylene production, whereas downregulation of SlARF6A increased fruit ripening and ethylene production. SlARF6A directly bound to the SAMS1 promoter and negatively regulated SAMS1 expression. Taken together, these results expand our understanding of ARFs with regard to photosynthesis, sugar accumulation and fruit development and provide a potential target for genetic engineering to improve fruit nutrition in horticulture crops
ROMPI-CDSA: Ring-Opening Metathesis Polymerization-Induced Crystallization-Driven Self-Assembly of Metallo-Block Copolymers
Polymerization-induced self-assembly (PISA) and crystallization-driven self-assembly (CDSA) are among the most prevailing methods for block copolymer self-assembly. Taking the merits of scalability of PISA and dimension control of CDSA, we report one-pot synchronous PISA and CDSA ring-opening metathesis polymerization (ROMP) to prepare nano-objects based on a crystalline poly(ruthenocene) motif. We denote this self-assembly methodology as ROMPI-CDSA to enable a simple, yet robust approach for the preparation of functional nanomaterials
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