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Strain-Induced Spin-Nematic State and Nematic Susceptibility Arising from 2×2 Fe Clusters in KFe_{0.8}Ag_{1.2}Te_{2}.
Spin nematics break spin-rotational symmetry while maintaining time-reversal symmetry, analogous to liquid crystal nematics that break spatial rotational symmetry while maintaining translational symmetry. Although several candidate spin nematics have been proposed, the identification and characterization of such a state remain challenging because the spin-nematic order parameter does not couple directly to experimental probes. KFe_{0.8}Ag_{1.2}Te_{2} (K_{5}Fe_{4}Ag_{6}Te_{10}, KFAT) is a local-moment magnet consisting of well-separated 2×2 Fe clusters, and in its ground state the clusters order magnetically, breaking both spin-rotational and time-reversal symmetries. Using uniform magnetic susceptibility and neutron scattering measurements, we find a small strain induces sizable spin anisotropy in the paramagnetic state of KFAT, manifestly breaking spin-rotational symmetry while retaining time-reversal symmetry, resulting in a strain-induced spin-nematic state in which the 2×2 clusters act as the spin analog of molecules in a liquid crystal nematic. The strain-induced spin anisotropy in KFAT allows us to probe its nematic susceptibility, revealing a divergentlike increase upon cooling, indicating the ordered ground state is driven by a spin-orbital entangled nematic order parameter
Numerical Simulation on Thermal Energy Storage Behavior of Cu/paraffin nanofluids PCMs
AbstractPCMs have foreseeable applications in residential buildings for effective use of solar energy. Paraffin is cheap and has moderate thermal energy storage density but low thermal conductivity. In this paper, we numerically investigate the melting processes of Cu/paraffin nanofluids PCMs. The results strongly suggested that the phase change heat transfer of paraffin was enhanced due to the addition of nanoparticles. For 1 wt% Cu/paraffin, the melting time can be saved 13.1%. The numerical results have a good agreement with the experimental results in describing the melting phenomena. These results show that adding nanoparticles is an efficient way to enhance the heat transfer in latent heat thermal energy storage system
A Hierarchical Regression Chain Framework for Affective Vocal Burst Recognition
As a common way of emotion signaling via non-linguistic vocalizations, vocal
burst (VB) plays an important role in daily social interaction. Understanding
and modeling human vocal bursts are indispensable for developing robust and
general artificial intelligence. Exploring computational approaches for
understanding vocal bursts is attracting increasing research attention. In this
work, we propose a hierarchical framework, based on chain regression models,
for affective recognition from VBs, that explicitly considers multiple
relationships: (i) between emotional states and diverse cultures; (ii) between
low-dimensional (arousal & valence) and high-dimensional (10 emotion classes)
emotion spaces; and (iii) between various emotion classes within the
high-dimensional space. To address the challenge of data sparsity, we also use
self-supervised learning (SSL) representations with layer-wise and temporal
aggregation modules. The proposed systems participated in the ACII Affective
Vocal Burst (A-VB) Challenge 2022 and ranked first in the "TWO'' and "CULTURE''
tasks. Experimental results based on the ACII Challenge 2022 dataset
demonstrate the superior performance of the proposed system and the
effectiveness of considering multiple relationships using hierarchical
regression chain models.Comment: 5 pages, 3 figures, 5 table
Adaptive Policy Learning for Offline-to-Online Reinforcement Learning
Conventional reinforcement learning (RL) needs an environment to collect
fresh data, which is impractical when online interactions are costly. Offline
RL provides an alternative solution by directly learning from the previously
collected dataset. However, it will yield unsatisfactory performance if the
quality of the offline datasets is poor. In this paper, we consider an
offline-to-online setting where the agent is first learned from the offline
dataset and then trained online, and propose a framework called Adaptive Policy
Learning for effectively taking advantage of offline and online data.
Specifically, we explicitly consider the difference between the online and
offline data and apply an adaptive update scheme accordingly, that is, a
pessimistic update strategy for the offline dataset and an optimistic/greedy
update scheme for the online dataset. Such a simple and effective method
provides a way to mix the offline and online RL and achieve the best of both
worlds. We further provide two detailed algorithms for implementing the
framework through embedding value or policy-based RL algorithms into it.
Finally, we conduct extensive experiments on popular continuous control tasks,
and results show that our algorithm can learn the expert policy with high
sample efficiency even when the quality of offline dataset is poor, e.g.,
random dataset.Comment: AAAI202
Learning To Teach Large Language Models Logical Reasoning
Large language models (LLMs) have gained enormous attention from both
academia and industry, due to their exceptional ability in language generation
and extremely powerful generalization. However, current LLMs still output
unreliable content in practical reasoning tasks due to their inherent issues
(e.g., hallucination). To better disentangle this problem, in this paper, we
conduct an in-depth investigation to systematically explore the capability of
LLMs in logical reasoning. More in detail, we first investigate the deficiency
of LLMs in logical reasoning on different tasks, including event relation
extraction and deductive reasoning. Our study demonstrates that LLMs are not
good reasoners in solving tasks with rigorous reasoning and will produce
counterfactual answers, which require us to iteratively refine. Therefore, we
comprehensively explore different strategies to endow LLMs with logical
reasoning ability, and thus enable them to generate more logically consistent
answers across different scenarios. Based on our approach, we also contribute a
synthesized dataset (LLM-LR) involving multi-hop reasoning for evaluation and
pre-training. Extensive quantitative and qualitative analyses on different
tasks also validate the effectiveness and necessity of teaching LLMs with logic
and provide insights for solving practical tasks with LLMs in future work
Direct imaging of a zero-field target skyrmion and its polarity switch in a chiral magnetic nanodisk
A target skyrmion is a flux-closed spin texture that has two-fold degeneracy
and is promising as a binary state in next generation universal memories.
Although its formation in nanopatterned chiral magnets has been predicted, its
observation has remained challenging. Here, we use off-axis electron holography
to record images of target skyrmions in a 160-nm-diameter nanodisk of the
chiral magnet FeGe. We compare experimental measurements with numerical
simulations, demonstrate switching between two stable degenerate target
skyrmion ground states that have opposite polarities and rotation senses and
discuss the observed switching mechanism.Comment: 18 pages, 4 figure
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