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In the quest for novel quantum states driven by topology and correlation, kagome lattice materials have garnered significant interest due to their distinctive electronic band structures, featuring flat bands (FBs) arising from the quantum destructive interference of the electronic wave function. The tuning of the FBs to the chemical potential would lead to the possibility of liberating electronic instabilities that lead to emergent electronic orders. Despite extensive studies, direct evidence of FBs tuned to the chemical potential and their participation in emergent electronic orders have been lacking in bulk quantum materials. Here using a combination of Angle-Resolved Photoemission Spectroscopy (ARPES) and Density Functional Theory (DFT), we reveal that the low-energy electronic structure of the recently discovered Cr-based kagome metal superconductor CsCr3Sb5 is dominated by a pervasive FB in close proximity to, and below the Fermi level. A comparative analysis with orbital-projected DFT and polarization dependence measurement uncovers that an orbital-selective renormalization mechanism is needed to reconcile the discrepancy with the DFT calculations, which predict the FB to appear 200 meV above the Fermi level. Furthermore, we observe the FB to shift away from the Fermi level by 20 meV in the low-temperature density wave-ordered phase, highlighting the role of the FB in the emergent electronic order. Our results reveal CsCr3Sb5 to stand out as a promising platform for further exploration into the effects of FBs near the Fermi level on kagome lattices, and their role in emergent orders in bulk quantum materials.

Games are widely used as research environments for multi-agent reinforcement learning (MARL), but they pose three significant challenges: limited customization, high computational demands, and oversimplification. To address these issues, we introduce the first publicly available map editor for the popular mobile game Honor of Kings and design a lightweight environment, Mini Honor of Kings (Mini HoK), for researchers to conduct experiments. Mini HoK is highly efficient, allowing experiments to be run on personal PCs or laptops while still presenting sufficient challenges for existing MARL algorithms. We have tested our environment on common MARL algorithms and demonstrated that these algorithms have yet to find optimal solutions within this environment. This facilitates the dissemination and advancement of MARL methods within the research community. Additionally, we hope that more researchers will leverage the Honor of Kings map editor to develop innovative and scientifically valuable new maps. Our code and user manual are available at: https://github.com/tencent-ailab/mini-hok.

Van der Waals encapsulation of two-dimensional materials within hexagonal boron nitride (h-BN) stacks has proven to be a promising way to create ultrahigh-performance electronic devices. However, contemporary approaches for achieving van der Waals encapsulation, which involve artificial layer stacking using mechanical transfer techniques, are difficult to control, prone to contamination, and unscalable. Here, we report on the transfer-free direct growth of high-quality graphene nanoribbons (GNRs) within h-BN stacks. The as-grown embedded GNRs exhibit highly desirable features being ultralong (up to 0.25 mm), ultranarrow ( < 5 nm), and homochiral with zigzag edges. Our atomistic simulations reveal that the mechanism underlying the embedded growth involves ultralow GNR friction when sliding between AA'-stacked h-BN layers. Using the grown structures, we demonstrate the transfer-free fabrication of embedded GNR field-effect devices that exhibit excellent performance at room temperature with mobilities of up to 4,600 $cm^{2} V^{-1} s^{-1}$ and on-off ratios of up to $10^{6}$. This paves the way to the bottom-up fabrication of high-performance electronic devices based on embedded layered materials.

One objective of Speech Quality Assessment (SQA) is to estimate the ranks of synthetic speech systems. However, recent SQA models are typically trained using low-precision direct scores such as mean opinion scores (MOS) as the training objective, which is not straightforward to estimate ranking. Although it is effective for predicting quality scores of individual sentences, this approach does not account for speech and system preferences when ranking multiple systems. We propose a training framework of SQA models that can be trained with only preference scores derived from pairs of MOS to improve ranking prediction. Our experiment reveals conditions where our framework works the best in terms of pair generation, aggregation functions to derive system score from utterance preferences, and threshold functions to determine preference from a pair of MOS. Our results demonstrate that our proposed method significantly outperforms the baseline model in Spearman's Rank Correlation Coefficient., Comment: Accepted by Interspeech 2023, oral

Magnetism in two-dimensional (2D) materials has attracted considerable attention recently for both fundamental understanding of magnetism and their tunability towards device applications. The isostructural Fe$_3$GeTe$_2$ and Fe$_3$GaTe$_2$ are two members of the Fe-based van der Waals (vdW) ferromagnet family, but exhibit very different Curie temperatures (T$_C$) of 210 K and 360 K, respectively. Here, by using angle-resolved photoemission spectroscopy and density functional theory, we systematically compare the electronic structures of the two compounds. Qualitative similarities in the Fermi surface can be found between the two compounds, with expanded hole pockets in Fe$_3$GaTe$_2$ suggesting additional hole carriers compared to Fe$_3$GeTe$_2$. Interestingly, we observe no band shift in Fe$_3$GaTe$_2$ across its T$_C$ of 360 K, compared to a small shift in Fe$_3$GeTe$_2$ across its T$_C$ of 210 K. The weak temperature-dependent evolution strongly deviates from the expectations of an itinerant Stoner mechanism. Our results suggest that itinerant electrons have minimal contributions to the enhancement of T$_C$ in Fe$_3$GaTe$_2$ compared to Fe$_3$GeTe$_2$, and that the nature of ferromagnetism in these Fe-based vdW ferromagnets must be understood with considerations of the electron correlations.

Recently, unsupervised image-to-image translation methods based on contrastive learning have achieved state-of-the-art results in many tasks. However, in the previous works, the negatives are sampled from the input image itself, which inspires us to design a data augmentation method to improve the quality of the selected negatives. Moreover, the previous methods only preserve the content consistency via patch-wise contrastive learning in the embedding space, which ignores the domain consistency between the generated images and the real images of the target domain. In this paper, we propose a novel unsupervised image-to-image translation framework based on multi-cropping contrastive learning and domain consistency, called MCDUT. Specifically, we obtain the multi-cropping views via the center-cropping and the random-cropping with the aim of further generating the high-quality negative examples. To constrain the embeddings in the deep feature space, we formulate a new domain consistency loss, which encourages the generated images to be close to the real images in the embedding space of the same domain. Furthermore, we present a dual coordinate attention network by embedding positional information into the channel, which called DCA. We employ the DCA network in the design of generator, which makes the generator capture the horizontal and vertical global information of dependency. In many image-to-image translation tasks, our method achieves state-of-the-art results, and the advantages of our method have been proven through extensive comparison experiments and ablation research.

For deep learning-based speech enhancement (SE) systems, the training-test acoustic mismatch can cause notable performance degradation. To address the mismatch issue, numerous noise adaptation strategies have been derived. In this paper, we propose a novel method, called noise adaptive speech enhancement with target-conditional resampling (NASTAR), which reduces mismatches with only one sample (one-shot) of noisy speech in the target environment. NASTAR uses a feedback mechanism to simulate adaptive training data via a noise extractor and a retrieval model. The noise extractor estimates the target noise from the noisy speech, called pseudo-noise. The noise retrieval model retrieves relevant noise samples from a pool of noise signals according to the noisy speech, called relevant-cohort. The pseudo-noise and the relevant-cohort set are jointly sampled and mixed with the source speech corpus to prepare simulated training data for noise adaptation. Experimental results show that NASTAR can effectively use one noisy speech sample to adapt an SE model to a target condition. Moreover, both the noise extractor and the noise retrieval model contribute to model adaptation. To our best knowledge, NASTAR is the first work to perform one-shot noise adaptation through noise extraction and retrieval., Comment: Accepted to Interspeech 2022