2,432 research outputs found
Role Playing Learning for Socially Concomitant Mobile Robot Navigation
In this paper, we present the Role Playing Learning (RPL) scheme for a mobile
robot to navigate socially with its human companion in populated environments.
Neural networks (NN) are constructed to parameterize a stochastic policy that
directly maps sensory data collected by the robot to its velocity outputs,
while respecting a set of social norms. An efficient simulative learning
environment is built with maps and pedestrians trajectories collected from a
number of real-world crowd data sets. In each learning iteration, a robot
equipped with the NN policy is created virtually in the learning environment to
play itself as a companied pedestrian and navigate towards a goal in a socially
concomitant manner. Thus, we call this process Role Playing Learning, which is
formulated under a reinforcement learning (RL) framework. The NN policy is
optimized end-to-end using Trust Region Policy Optimization (TRPO), with
consideration of the imperfectness of robot's sensor measurements. Simulative
and experimental results are provided to demonstrate the efficacy and
superiority of our method
Channel Covariance Matrix Estimation via Dimension Reduction for Hybrid MIMO MmWave Communication Systems
Hybrid massive MIMO structures with lower hardware complexity and power
consumption have been considered as a potential candidate for millimeter wave
(mmWave) communications. Channel covariance information can be used for
designing transmitter precoders, receiver combiners, channel estimators, etc.
However, hybrid structures allow only a lower-dimensional signal to be
observed, which adds difficulties for channel covariance matrix estimation. In
this paper, we formulate the channel covariance estimation as a structured
low-rank matrix sensing problem via Kronecker product expansion and use a
low-complexity algorithm to solve this problem. Numerical results with uniform
linear arrays (ULA) and uniform squared planar arrays (USPA) are provided to
demonstrate the effectiveness of our proposed method
Matrix Completion-Based Channel Estimation for MmWave Communication Systems With Array-Inherent Impairments
Hybrid massive MIMO structures with reduced hardware complexity and power
consumption have been widely studied as a potential candidate for millimeter
wave (mmWave) communications. Channel estimators that require knowledge of the
array response, such as those using compressive sensing (CS) methods, may
suffer from performance degradation when array-inherent impairments bring
unknown phase errors and gain errors to the antenna elements. In this paper, we
design matrix completion (MC)-based channel estimation schemes which are robust
against the array-inherent impairments. We first design an open-loop training
scheme that can sample entries from the effective channel matrix randomly and
is compatible with the phase shifter-based hybrid system. Leveraging the
low-rank property of the effective channel matrix, we then design a channel
estimator based on the generalized conditional gradient (GCG) framework and the
alternating minimization (AltMin) approach. The resulting estimator is immune
to array-inherent impairments and can be implemented to systems with any array
shapes for its independence of the array response. In addition, we extend our
design to sample a transformed channel matrix following the concept of
inductive matrix completion (IMC), which can be solved efficiently using our
proposed estimator and achieve similar performance with a lower requirement of
the dynamic range of the transmission power per antenna. Numerical results
demonstrate the advantages of our proposed MC-based channel estimators in terms
of estimation performance, computational complexity and robustness against
array-inherent impairments over the orthogonal matching pursuit (OMP)-based CS
channel estimator.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
Quantum calculation of axion-photon transition in electromagnetodynamics for cavity haloscope
The Witten effect implies the presence of electric charge of magnetic monople
and possible relationship between axion and dyon. The axion-dyon dynamics can
be reliably built based on the quantum electromagnetodynamics (QEMD) which was
developed by Schwinger and Zwanziger in 1960's. A generic low-energy
axion-photon effective field theory can also be realized in the language of
``generalized symmetries'' with higher-form symmetries and background gauge
fields. In this work, we implement the quantum calculation of axion-single
photon transition rate inside a homogeneous electromagnetic field in terms of
the new axion interaction Hamiltonian in QEMD. This quantum calculation can
clearly imply the enhancement of conversion rate through resonant cavity in
axion haloscope experiments. We also show the promising potentials on the
cavity search of new axion-photon couplings in QEMD.Comment: 15 pages, 2 figure
Axion-like particle from primordial black hole evaporation and its detection in neutrino experiments
The primordial black holes (PBHs) play as a novel source to radiate light
elementary particles of energies in the region of a few hundred MeV. We explore
the possibility that the axion-like particles (ALPs) with mass less than 1 MeV
are produced from PBH evaporation. The absorption of light ALPs in the
underground detector targets then induces energetic photoelectron signatures in
current and future neutrino experiments. Utilizing the PBH ALP event rate, we
place general exclusion limits on the axion couplings at Super-K and Hyper-K.
We also translate these limits into the upper bound on the fraction of DM
composed of PBHs .Comment: 16 pages, 5 figure
Boosting Commit Classification with Contrastive Learning
Commit Classification (CC) is an important task in software maintenance,
which helps software developers classify code changes into different types
according to their nature and purpose. It allows developers to understand
better how their development efforts are progressing, identify areas where they
need improvement, and make informed decisions about when and how to release new
software versions. However, existing models need lots of manually labeled data
for fine-tuning processes, and ignore sentence-level semantic information,
which is often essential for discovering the difference between diverse
commits. Therefore, it is still challenging to solve CC in fewshot scenario.
To solve the above problems, we propose a contrastive learning-based commit
classification framework. Firstly, we generate sentences and pseudo-labels
according to the labels of the dataset, which aims to enhance the dataset.
Secondly, we randomly group the augmented data times to compare their
similarity with the positive and negative samples. We
utilize individual pretrained sentence transformers (ST)s to efficiently obtain
the sentence-level embeddings from different features respectively. Finally, we
adopt the cosine similarity function to limit the distribution of vectors,
similar vectors are more adjacent. The light fine-tuned model is then applied
to the label prediction of incoming commits.
Extensive experiments on two open available datasets demonstrate that our
framework can solve the CC problem simply but effectively in fewshot scenarios,
while achieving state-of-the-art(SOTA) performance and improving the
adaptability of the model without requiring a large number of training samples
for fine-tuning. The code, data, and trained models are available at
https://github.com/AppleMax1992/CommitFit
Incorprating Prompt tuning for Commit classification with prior Knowledge
Commit Classification(CC) is an important task in software maintenance since
it helps software developers classify code changes into different types
according to their nature and purpose. This allows them to better understand
how their development efforts are progressing, identify areas where they need
improvement. However, existing methods are all discriminative models, usually
with complex architectures that require additional output layers to produce
class label probabilities. Moreover, they require a large amount of labeled
data for fine-tuning, and it is difficult to learn effective classification
boundaries in the case of limited labeled data. To solve above problems, we
propose a generative framework that Incorporating prompt-tuning for commit
classification with prior knowledge (IPCK)
https://github.com/AppleMax1992/IPCK, which simplifies the model structure and
learns features across different tasks. It can still reach the SOTA performance
with only limited samples. Firstly, we proposed a generative framework based on
T5. This encoder-decoder construction method unifies different CC task into a
text2text problem, which simplifies the structure of the model by not requiring
an extra output layer. Second, instead of fine-tuning, we design an
prompt-tuning solution which can be adopted in few-shot scenarios with only
limit samples. Furthermore, we incorporate prior knowledge via an external
knowledge graph to map the probabilities of words into the final labels in the
speech machine step to improve performance in few-shot scenarios. Extensive
experiments on two open available datasets show that our framework can solve
the CC problem simply but effectively in few-shot and zeroshot scenarios, while
improving the adaptability of the model without requiring a large amount of
training samples for fine-tuning
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