Ultrafast Optical Spectroscopy Evidence of Pseudogap and Electron-Phonon Coupling in an Iron-Based Superconductor KCa$_2$Fe$_4$As$_4$F$_2$

We use ultrafast optical spectroscopy to study the nonequilibrium quasiparticle relaxation dynamics of the iron-based superconductor KCa$_2$Fe$_4$As$_4$F$_2$ with $T_c=33.5$ K. Our results reveal an evident pseudogap ($Delta_{PG}$ = 2.4 $pm$ 0.1 meV) below $T^*approx 50$ K but prior to the opening of a superconducting gap ($Delta_{SC}$(0) $approx$ 4.3 $pm$ 0.1 meV). Measurements under high pump fluence real two distinct coherent phonon oscillations with frequencies of 1.95 and 5.51 THz, respectively. The high-frequency mode corresponds to the $c-$axis polarized vibrations of As atoms ($A_{1g}$ mode) with a nominal electron-phonon coupling constant $lambda_{A_{1g}}$ = 0.194 $pm$ 0.02. Below $T_c$, the temperature dependence of both frequency and damping rate of $A_{1g}$ mode clearly deviate from the description of optical phonon anharmonic effects. These results suggest that the pseudogap is very likely a precursor of superconductivity, and the electron-phonon coupling may play an essential role in the superconducting pairing in KCa$_2$Fe$_4$As$_4$F$_2$.

Sequence Level Contrastive Learning for Text Summarization

Contrastive learning models have achieved great success in unsupervised visual representation learning, which maximize the similarities between feature representations of different views of the same image, while minimize the similarities between feature representations of views of different images. In text summarization, the output summary is a shorter form of the input document and they have similar meanings. In this paper, we propose a contrastive learning model for supervised abstractive text summarization, where we view a document, its gold summary and its model generated summaries as different views of the same mean representation and maximize the similarities between them during training. We improve over a strong sequence-to-sequence text generation model (i.e., BART) on three different summarization datasets. Human evaluation also shows that our model achieves better faithfulness ratings compared to its counterpart without contrastive objectives.

Sparse to Dense Motion Transfer for Face Image Animation

Face image animation from a single image has achieved remarkable progress. However, it remains challenging when only sparse landmarks are available as the driving signal. Given a source face image and a sequence of sparse face landmarks, our goal is to generate a video of the face imitating the motion of landmarks. We develop an efficient and effective method for motion transfer from sparse landmarks to the face image. We then combine global and local motion estimation in a unified model to faithfully transfer the motion. The model can learn to segment the moving foreground from the background and generate not only global motion, such as rotation and translation of the face, but also subtle local motion such as the gaze change. We further improve face landmark detection on videos. With temporally better aligned landmark sequences for training, our method can generate temporally coherent videos with higher visual quality. Experiments suggest we achieve results comparable to the state-of-the-art image driven method on the same identity testing and better results on cross identity testing.

Temporal Induced Self-Play for Stochastic Bayesian Games

One practical requirement in solving dynamic games is to ensure that the players play well from any decision point onward. To satisfy this requirement, existing efforts focus on equilibrium refinement, but the scalability and applicability of existing techniques are limited. In this paper, we propose Temporal-Induced Self-Play (TISP), a novel reinforcement learning-based framework to find strategies with decent performances from any decision point onward. TISP uses belief-space representation, backward induction, policy learning, and non-parametric approximation. Building upon TISP, we design a policy-gradient-based algorithm TISP-PG. We prove that TISP-based algorithms can find approximate Perfect Bayesian Equilibrium in zero-sum one-sided stochastic Bayesian games with finite horizon. We test TISP-based algorithms in various games, including finitely repeated security games and a grid-world game. The results show that TISP-PG is more scalable than existing mathematical programming-based methods and significantly outperforms other learning-based methods.

Instance Similarity Learning for Unsupervised Feature Representation

In this paper, we propose an instance similarity learning (ISL) method for unsupervised feature representation. Conventional methods assign close instance pairs in the feature space with high similarity, which usually leads to wrong pairwise relationship for large neighborhoods because the Euclidean distance fails to depict the true semantic similarity on the feature manifold. On the contrary, our method mines the feature manifold in an unsupervised manner, through which the semantic similarity among instances is learned in order to obtain discriminative representations. Specifically, we employ the Generative Adversarial Networks (GAN) to mine the underlying feature manifold, where the generated features are applied as the proxies to progressively explore the feature manifold so that the semantic similarity among instances is acquired as reliable pseudo supervision. Extensive experiments on image classification demonstrate the superiority of our method compared with the state-of-the-art methods. The code is available at https://github.com/ZiweiWangTHU/ISL.git.

Disentangled Attention as Intrinsic Regularization for Bimanual Multi-Object Manipulation

We address the problem of solving complex bimanual robot manipulation tasks on multiple objects with sparse rewards. Such complex tasks can be decomposed into sub-tasks that are accomplishable by different robots concurrently or sequentially for better efficiency. While previous reinforcement learning approaches primarily focus on modeling the compositionality of sub-tasks, two fundamental issues are largely ignored particularly when learning cooperative strategies for two robots: (i) domination, i.e., one robot may try to solve a task by itself and leaves the other idle; (ii) conflict, i.e., one robot can easily interrupt anothers workspace when executing different sub-tasks simultaneously. To tackle these two issues, we propose a novel technique called disentangled attention, which provides an intrinsic regularization for two robots to focus on separate sub-tasks and objects. We evaluate our method on four bimanual manipulation tasks. Experimental results show that our proposed intrinsic regularization successfully avoids domination and reduces conflicts for the policies, which leads to significantly more effective cooperative strategies than all the baselines. Our project page with videos is at https://mehooz.github.io/bimanual-attention.

Fully Transformer Networks for Semantic Image Segmentation

Transformers have shown impressive performance in various natural language processing and computer vision tasks, due to the capability of modeling long-range dependencies. Recent progress has demonstrated to combine such transformers with CNN-based semantic image segmentation models is very promising. However, it is not well studied yet on how well a pure transformer based approach can achieve for image segmentation. In this work, we explore a novel framework for semantic image segmentation, which is encoder-decoder based Fully Transformer Networks (FTN). Specifically, we first propose a Pyramid Group Transformer (PGT) as the encoder for progressively learning hierarchical features, while reducing the computation complexity of the standard visual transformer(ViT). Then, we propose a Feature Pyramid Transformer (FPT) to fuse semantic-level and spatial-level information from multiple levels of the PGT encoder for semantic image segmentation. Surprisingly, this simple baseline can achieve new state-of-the-art results on multiple challenging semantic segmentation benchmarks, including PASCAL Context, ADE20K and COCO-Stuff. The source code will be released upon the publication of this work.

Hunting for states in the recent LHCb di-$J/psi$ invariant mass spectrum

Partial wave analysis is performed, with effective potentials as dynamical inputs, to scrutinize the recent LHCb data on the di-$J/psi$ invariant mass spectrum. Coupled-channel effects are incorporated in the production amplitude via final state interactions. The LHCb data can be well described. A dynamical generated pole structure, which can be identified as the $X(6900)$ state, is found. Our analysis also provides hints for the existence of three other possible states: a bound state $X(6200)$, a broad resonant state $X(6680)$ and a narrow resonant state $X(7200)$. The $J^{PC}$ quantum numbers of the $X(6680)$ and $X(6900)$ states should be $2^{++}$, while the $X(6200)$ and $X(7200)$ states prefer $0^{++}$. To determine the above states more precisely, more experimental data for the channels, such as $J/psipsi(2S)$, $J/psipsi(3770)$, di-$psi(2S)$, are required.

Anisotropic c-f hybridization in the ferromagnetic quantum critical metal CeRh$_6$Ge$_4$

Heavy fermion compounds exhibiting a ferromagnetic quantum critical point have attracted considerable interest. Common to two known cases, i.e., CeRh$_6$Ge$_4$ and YbNi$_4$P$_2$, is that the 4f moments reside along chains with a large inter-chain distance, exhibiting strong magnetic anisotropy that was proposed to be vital for the ferromagnetic quantum criticality. Here we report an angle-resolved photoemission study on CeRh6Ge4, where we observe sharp momentum-dependent 4f bands and clear bending of the conduction bands near the Fermi level, indicating considerable hybridization between conduction and 4f electrons. The extracted hybridization strength is anisotropic in momentum space and is obviously stronger along the Ce chain direction. The hybridized 4f bands persist up to high temperatures, and the evolution of their intensity shows clear band dependence. Our results provide spectroscopic evidence for anisotropic hybridization between conduction and 4f electrons in CeRh$_6$Ge$_4$, which could be important for understanding the electronic origin of the ferromagnetic quantum criticality.

Bandwidth-control orbital-selective delocalization of 4f electrons in epitaxial Ce films

The 4f-electron delocalization plays a key role in the low-temperature properties of rare-earth metals and intermetallics, including heavy fermions and mix-valent compounds, and is normally realized by the many-body Kondo coupling between 4f and conduction electrons. Due to the large onsite Coulomb repulsion of 4f electrons, the bandwidth-control Mott-type delocalization, commonly observed in d-electron systems, is difficult in 4f-electron systems and remains elusive in spectroscopic experiments. Here we demonstrate that the bandwidth-control orbital-selective delocalization of 4f electrons can be realized in epitaxial Ce films by thermal annealing, which results in a metastable surface phase with a reduced layer spacing. The resulting quasiparticle bands exhibit large dispersion with exclusive 4f character near E_F and extend reasonably far below the Fermi energy, which can be explained from the Mott physics. The experimental quasiparticle dispersion agrees surprisingly well with density-functional theory calculation and also exhibits unusual temperature dependence, which could be a direct consequence of the delicate interplay between the bandwidth-control Mott physics and the coexisting Kondo hybridization. Our work therefore opens up the opportunity to study the interaction between two well-known localization-delocalization mechanisms in correlation physics, i.e., Kondo vs Mott, which can be important for a fundamental understanding of 4f-electron systems.