Long-Tailed Recognition by Mutual Information Maximization between Latent Features and Ground-Truth Labels

Although contrastive learning methods have shown prevailing performance on a variety of representation learning tasks, they encounter difficulty when the training dataset is long-tailed. Many researchers have combined contrastive learning and a logit adjustment technique to address this problem, but the combinations are done ad-hoc and a theoretical background has not yet been provided. The goal of this paper is to provide the background and further improve the performance. First, we show that the fundamental reason contrastive learning methods struggle with long-tailed tasks is that they try to maximize the mutual information maximization between latent features and input data. As ground-truth labels are not considered in the maximization, they are not able to address imbalances between class labels. Rather, we interpret the long-tailed recognition task as a mutual information maximization between latent features and ground-truth labels. This approach integrates contrastive learning and logit adjustment seamlessly to derive a loss function that shows state-of-the-art performance on long-tailed recognition benchmarks. It also demonstrates its efficacy in image segmentation tasks, verifying its versatility beyond image classification.Comment: ICML 2023 camera-read

Self-Supervised Curriculum Generation for Autonomous Reinforcement Learning without Task-Specific Knowledge

A significant bottleneck in applying current reinforcement learning algorithms to real-world scenarios is the need to reset the environment between every episode. This reset process demands substantial human intervention, making it difficult for the agent to learn continuously and autonomously. Several recent works have introduced autonomous reinforcement learning (ARL) algorithms that generate curricula for jointly training reset and forward policies. While their curricula can reduce the number of required manual resets by taking into account the agent's learning progress, they rely on task-specific knowledge, such as predefined initial states or reset reward functions. In this paper, we propose a novel ARL algorithm that can generate a curriculum adaptive to the agent's learning progress without task-specific knowledge. Our curriculum empowers the agent to autonomously reset to diverse and informative initial states. To achieve this, we introduce a success discriminator that estimates the success probability from each initial state when the agent follows the forward policy. The success discriminator is trained with relabeled transitions in a self-supervised manner. Our experimental results demonstrate that our ARL algorithm can generate an adaptive curriculum and enable the agent to efficiently bootstrap to solve sparse-reward maze navigation tasks, outperforming baselines with significantly fewer manual resets.Comment: 8 pages, 5 figure

Learning Multi-Task Transferable Rewards via Variational Inverse Reinforcement Learning

Many robotic tasks are composed of a lot of temporally correlated sub-tasks in a highly complex environment. It is important to discover situational intentions and proper actions by deliberating on temporal abstractions to solve problems effectively. To understand the intention separated from changing task dynamics, we extend an empowerment-based regularization technique to situations with multiple tasks based on the framework of a generative adversarial network. Under the multitask environments with unknown dynamics, we focus on learning a reward and policy from the unlabeled expert examples. In this study, we define situational empowerment as the maximum of mutual information representing how an action conditioned on both a certain state and sub-task affects the future. Our proposed method derives the variational lower bound of the situational mutual information to optimize it. We simultaneously learn the transferable multi-task reward function and policy by adding an induced term to the objective function. By doing so, the multi-task reward function helps to learn a robust policy for environmental change. We validate the advantages of our approach on multi-task learning and multi-task transfer learning. We demonstrate our proposed method has the robustness of both randomness and changing task dynamics. Finally, we prove that our method has significantly better performance and data efficiency than existing imitation learning methods on various benchmarks.Comment: Accepted in ICRA 202

Evidence for a preformed Cooper pair model in the pseudogap spectra of a Ca10(Pt4As8)(Fe2As2)5 single crystal with a nodal superconducting gap

For high-Tc superconductors, clarifying the role and origin of the pseudogap is essential for understanding the pairing mechanism. Among the various models describing the pseudogap, the preformed Cooper pair model is a potential candidate. Therefore, we present experimental evidence for the preformed Cooper pair model by studying the pseudogap spectrum observed in the optical conductivity of a Ca10(Pt4As8)(Fe2As2)5 (Tc = 34.6 K) single crystal. We observed a clear pseudogap structure in the optical conductivity and observed its temperature dependence. In the superconducting (SC) state, one SC gap with a gap size of {\Delta} = 26 cm-1, a scattering rate of 1/{\tau} = 360 cm-1 and a low-frequency extra Drude component were observed. Spectral weight analysis revealed that the SC gap and pseudogap are formed from the same Drude band. This means that the pseudogap is a gap structure observed as a result of a continuous temperature evolution of the SC gap observed below Tc. This provides clear experimental evidence for the preformed Cooper pair model.Comment: 15 pages, 4 figure

Atomistic Engineering of Phonons in Functional Oxide Heterostructures

Engineering of phonons, that is, collective lattice vibrations in crystals, is essential for manipulating physical properties of materials such as thermal transport, electron-phonon interaction, confinement of lattice vibration, and optical polarization. Most approaches to phonon-engineering have been largely limited to the high-quality heterostructures of III–V compound semiconductors. Yet, artificial engineering of phonons in a variety of materials with functional properties, such as complex oxides, will yield unprecedented applications of coherent tunable phonons in future quantum acoustic devices. In this study, artificial engineering of phonons in the atomic-scale SrRuO3/SrTiO3 superlattices is demonstrated, wherein tunable phonon modes are observed via confocal Raman spectroscopy. In particular, the coherent superlattices led to the backfolding of acoustic phonon dispersion, resulting in zone-folded acoustic phonons in the THz frequency domain. The frequencies can be largely tuned from 1 to 2 THz via atomic-scale precision thickness control. In addition, a polar optical phonon originating from the local inversion symmetry breaking in the artificial oxide superlattices is observed, exhibiting emergent functionality. The approach of atomic-scale heterostructuring of complex oxides will vastly expand material systems for quantum acoustic devices, especially with the viability of functionality integration

Low-temperature synthesis of CuO-interlaced nanodiscs for lithium ion battery electrodes

In this study, we report the high-yield synthesis of 2-dimensional cupric oxide (CuO) nanodiscs through dehydrogenation of 1-dimensional Cu(OH)2 nanowires at 60°C. Most of the nanodiscs had a diameter of approximately 500 nm and a thickness of approximately 50 nm. After further prolonged reaction times, secondary irregular nanodiscs gradually grew vertically into regular nanodiscs. These CuO nanostructures were characterized using X-ray diffraction, transmission electron microscopy, and Brunauer-Emmett-Teller measurements. The possible growth mechanism of the interlaced disc CuO nanostructures is systematically discussed. The electrochemical performances of the CuO nanodisc electrodes were evaluated in detail using cyclic voltammetry and galvanostatic cycling. Furthermore, we demonstrate that the incorporation of multiwalled carbon nanotubes enables the enhanced reversible capacities and capacity retention of CuO nanodisc electrodes on cycling by offering more efficient electron transport paths

Electrochemical performance of NixCo1-xMoO4 (0 ≤ x ≤ 1) nanowire anodes for lithium-ion batteries

NixCo1-xMoO4 (0 ≤ x ≤ 1) nanowire electrodes for lithium-ion rechargeable batteries have been synthesized via a hydrothermal method, followed by thermal post-annealing at 500°C for 2 h. The chemical composition of the nanowires was varied, and their morphological features and crystalline structures were characterized using field-emission scanning electron microscopy and X-ray powder diffraction. The reversible capacity of NiMoO4 and Ni0.75Co0.25MoO4 nanowire electrodes was larger (≈520 mA h/g after 20 cycles at a rate of 196 mA/g) than that of the other nanowires. This enhanced electrochemical performance of NixCo1-xMoO4 nanowires with high Ni content was ascribed to their larger surface area and efficient electron transport path facilitated by their one-dimensional nanostructure