Search

Berry (geometric) phase has attracted a lot of interest and permeated into all aspects of physics including photonics, crystal dynamics, electromagnetism and heat transfer since it was discovered, leading to various unprecedented effects both in classical and quantum systems, such as Hannay angle, quantum Hall effect, orbital magnetism and Thouless pumping. Heat pumping is one of the most prominent and fantastic application of geometric phase in heat transport. Here we derive a general heat pumping theory based on classical diffusion equation and continuous modulation of system parameters in macroscopic thermal diffusion system and obtain a formula which is reminiscent of contact between Berry phase and the Berry curvature. Furthermore, we discuss two cases of non-trivial zero heat flux after one cycle which is fundamentally different from the trivial zero heat flux generated by static zero heat bias in physical nature. Then we analyze the dependence of the effect on the system thermal parameters, including some counterintuitive phenomenon. Finally, under the guidance of this theory, we conduct an experiment to demonstrate the accuracy and effectiveness of our theory and observe the heat pumping effect regardless of the presence and the absence of the thermal bias between two ports of system. In general, our work clearly derives the universal form of heat pumping theory under arbitrary form of the modulation in the macroscopic thermal diffusion system, this is of great significance for better heat energy transport, heat manipulation and so on. It also establishes the foundation of achieving other non-reciprocity devices or topological devices with the aid of spatiotemporal modulation., Comment: 17 pages, 2 figures

Phase modulation has scarcely been mentioned in diffusive systems since the diffusion process does not carry momentum like waves. Recently, the non-Hermitian physics provides a new perspective for understanding diffusion and shows prospects in the phase regulation of heat flow, for example, the discovery of anti-parity-time (APT) symmetry in diffusive systems. The precise control of thermal phase however remains elusive hitherto and can hardly be realized in APT-symmetric thermal systems due to the existence of phase oscillation. Here we construct the counterpart of APT-symmetric diffusive systems, i.e., PT-symmetric diffusive systems, which can achieve complete suppression of thermal phase oscillation. We find the real coupling of diffusive fields can be readily established through a strong convective background, where the decay-rate detuning is enabled by thermal metamaterial design. Moreover, we observe phase transition of PT symmetry breaking in diffusive systems with the symmetry-determined amplitude distribution and phase regulation of coupled temperature fields. Our work uncovers the existence of PT-symmetry in dissipative energy exchanges and provides a unique approach for harnessing the mass transfer of particles, the wave propagation in strongly scattering systems as well as thermal conduction.

The paradigm shift of the Hermitian systems into the non-Hermitian regime profoundly modifies the inherent topological property, leading to various unprecedented effects such as the non-Hermitian skin effect (NHSE). In the past decade, the NHSE effect has been demonstrated in quantum, optical and acoustic systems. Besides in those non-Hermitian wave systems, the NHSE in diffusive systems has not yet been explicitly demonstrated, despite recent abundant advances in the study of topological thermal diffusion. Here we first design a thermal diffusion lattice based on a modified Su-Schrieffer-Heeger model which enables the observation of diffusive NHSE. In the proposed model, the periodic heat exchange rate among adjacent unit cells and the asymmetric temperature field coupling inside unit cells can be judiciously realized by appropriate configurations of structural parameters of unit cells. The transient concentration feature of temperature field on the boundary regardless of initial excitation conditions can be clearly observed, indicating the occurrence of transient thermal skin effect. Nonetheless, we experimentally demonstrated the NHSE and verified the remarkable robustness against various defects. Our work provides a platform for exploration of non-Hermitian physics in the diffusive systems, which has important applications in efficient heat collection, highly sensitive thermal sensing and others., Comment: 23 pages, 5 figures

Higher-dimensional topological meta-materials have more flexible than one-dimensional topological materials, which are more convenient to apply and solve practical problems. However, in diffusion systems, higher-dimensional topological states have not been well studied. In this work, we experimentally realized the 2D topological structure based on a kagome lattice of thermal metamaterial. Due to the anti-Hermitian properties of the diffusion Hamiltonian, it has purely imaginary eigenvalues corresponding to the decay rate. By theoretical analysis and directly observing the decay rate of temperature through experiments, we present the various corner states in 2D topological diffusive system. Our work constitutes the first realization of multiple corner states with high decay rates in a pure diffusion system, which provides a new idea for the design of topological protected thermal metamaterial in the future.

The eigenvalue of a non-Hermitian Hamiltonian often forms a self-intersecting Riemann surface, leading to a unique mode conversion phenomenon when the Hamiltonian evolves along certain loop paths around an exceptional point (EP). However, two fundamental problems exist with the conventional scheme of EP encircling: the speed of mode conversion is restricted by the adiabatic requirement, and the chirality cannot be freely controlled. Here, we introduce a method which dynamically engineers the adiabaticity in the evolution of non-Hermitian Hamiltonians that allows for both chiral and non-chiral mode conversion on the same path. Our method is based on quantifying and controlling the instantaneous adiabaticity, allowing for non-uniform evolution throughout the entire path. We apply our method into the microwave waveguide system and by optimizing the distributed adiabaticity along the evolution loop, we achieve the same quality of mode conversion as conventional quasi-adiabatic evolution in only one-fourth of the time. Our approach provides a comprehensive and universal solution to address the speed and chirality challenges associated with EP encircling. It also facilitates the dynamic manipulation and regulation of non-adiabatic processes, thereby accelerating the operation and allowing for a selection among various mode conversion patterns.

Compared to periodic systems, quasicrystals without translational invariance exhibit unexpected localization properties. The extended-localized transition in quasicrystals has been observed in both quantum and classical wave systems. However, its manifestation in diffusion systems, which serve as novel platforms for exploring phases of matter in condensed matter physics, remains unexplored. Here, we present the implementation of the extended-localized transition in a diffusive quasicrystal based on the coupled ring chain structure. By modulating the thermal conductivities of rings, we obtain the diffusive one-dimensional Aubry-Andr\'e-Harper (AAH) model, which exhibits an extended-localized transition. Thanks to the ring-shaped chain, we clearly demonstrate the extended-localized transition under the uniform excitation through temperature field simulations. For the localized state, the temperature field clearly demonstrates a multiple localization centers phenomenon, which has no counterpart in wave systems. We also quantitatively investigate the temperature evolution and size effect of this transition. Furthermore, the local excitation has been adopted to demonstrate the temperature field for both the extended and localized states. Besides, we implement the non-Hermitian diffusive AAH model by rotating rings, whose temperature field shows a moving multiple localization centers phenomenon in the localized phase. Finally, we give the experimental suggestions for the diffusive AAH model and propose a potential application named as double-trace distributed generator. Our results can facilitate the design of flexible thermal devices and efficient heat management.

Compared with conventional topological insulator that carries topological state at its boundaries, the higher-order topological insulator exhibits lower-dimensional gapless boundary states at its corners and hinges. Leveraging the form similarity between Schrodinger equation and diffusion equation, researches on higher-order topological insulators have been extended from condensed matter physics to thermal diffusion. Unfortunately, all the corner states of thermal higher-order topological insulator reside within the band gap. Another kind of corner state, which is embedded in the bulk states, has not been realized in pure diffusion systems so far. Here, we construct higher-dimensional Su-Schrieffer-Heeger models based on sphere-rod structure to elucidate these corner states, which we term ``in-bulk corner states". Due to the anti-Hermitian properties of diffusive Hamiltonian, we investigate the thermal behaviour of these corner states through theoretical calculation, simulation, and experiment. Furthermore, we study the different thermal behaviours of in-bulk corner state and in-gap corner state. Our results would open a different gate for diffusive topological states and provide a distinct application for efficient heat dissipation.

Attention mechanisms have significantly boosted the performance of video classification neural networks thanks to the utilization of perspective contexts. However, the current research on video attention generally focuses on adopting a specific aspect of contexts (e.g., channel, spatial/temporal, or global context) to refine the features and neglects their underlying correlation when computing attentions. This leads to incomplete context utilization and hence bears the weakness of limited performance improvement. To tackle the problem, this paper proposes an efficient attention-in-attention (AIA) method for element-wise feature refinement, which investigates the feasibility of inserting the channel context into the spatio-temporal attention learning module, referred to as CinST, and also its reverse variant, referred to as STinC. Specifically, we instantiate the video feature contexts as dynamics aggregated along a specific axis with global average and max pooling operations. The workflow of an AIA module is that the first attention block uses one kind of context information to guide the gating weights calculation of the second attention that targets at the other context. Moreover, all the computational operations in attention units act on the pooled dimension, which results in quite few computational cost increase ($<$0.02\%). To verify our method, we densely integrate it into two classical video network backbones and conduct extensive experiments on several standard video classification benchmarks. The source code of our AIA is available at \url{https://github.com/haoyanbin918/Attention-in-Attention}., Comment: 13 pages

In this paper, we propose an extended mixed finite element method for elliptic interface problems. By adding some stabilization terms, we present a mixed approximation form based on Brezzi-Douglas-Marini element space and the piecewise constant function space, and show that the discrete inf-sup constant is independent of how the interface intersects the triangulation. Furthermore, we derive that the optimal convergence holds independent of the location of the interface relative to the mesh. Finally, some numerical examples are presented to verify our theoretical results.

Various unusual behaviors of artificial materials are governed by their topological properties, among which the edge state at the boundary of a photonic or phononic lattice has been captivated as a popular notion. However, this remarkable bulk-boundary correspondence and the related phenomena are missing in thermal materials. One reason is that heat diffusion is described in a non-Hermitian framework because of its dissipative nature. The other is that the relevant temperature field is mostly composed of modes that extend over wide ranges, making it difficult to be rendered within the tight-binding theory as commonly employed in wave physics. Here, we overcome the above challenges and perform systematic studies on heat diffusion in thermal lattices. Based on a continuum model, we introduce a state vector to link the Zak phase with the existence of the edge state, and thereby analytically prove the thermal bulk-boundary correspondence. We experimentally demonstrate the predicted edge states with a topologically protected and localized heat dissipation capacity. Our finding sets up a solid foundation to explore the topology in novel heat transfer manipulations.

As more and more online search queries come from voice, automatic speech recognition becomes a key component to deliver relevant search results. Errors introduced by automatic speech recognition (ASR) lead to irrelevant search results returned to the user, thus causing user dissatisfaction. In this paper, we introduce an approach, Mondegreen, to correct voice queries in text space without depending on audio signals, which may not always be available due to system constraints or privacy or bandwidth (for example, some ASR systems run on-device) considerations. We focus on voice queries transcribed via several proprietary commercial ASR systems. These queries come from users making internet, or online service search queries. We first present an analysis showing how different the language distribution coming from user voice queries is from that in traditional text corpora used to train off-the-shelf ASR systems. We then demonstrate that Mondegreen can achieve significant improvements in increased user interaction by correcting user voice queries in one of the largest search systems in Google. Finally, we see Mondegreen as complementing existing highly-optimized production ASR systems, which may not be frequently retrained and thus lag behind due to vocabulary drifts., Comment: Accepted in KDD 2021

The reciprocity principle governs the symmetry in transmission of electromagnetic and acoustic waves, as well as the diffusion of heat between two points in space, with important consequences for thermal management and energy harvesting. There has been significant recent interest in materials with time-modulated properties, which have been shown to efficiently break reciprocity for light, sound, and even charge diffusion. Quite surprisingly, here we show that, from a practical point of view, time modulation cannot generally be used to break reciprocity for thermal diffusion. We establish a theoretical framework to accurately describe the behavior of diffusive processes under time modulation, and prove that thermal reciprocity in dynamic materials is generally preserved by the continuity equation, unless some external bias or special material is considered. We then experimentally demonstrate reciprocal heat transfer in a time-modulated device. Our findings correct previous misconceptions regarding reciprocity breaking for thermal diffusion, revealing the generality of symmetry constraints in heat transfer, and clarifying its differences from other transport processes in what concerns the principles of reciprocity and microscopic reversibility.

Many recent advances in neural information retrieval models, which predict top-K items given a query, learn directly from a large training set of (query, item) pairs. However, they are often insufficient when there are many previously unseen (query, item) combinations, often referred to as the cold start problem. Furthermore, the search system can be biased towards items that are frequently shown to a query previously, also known as the 'rich get richer' (a.k.a. feedback loop) problem. In light of these problems, we observed that most online content platforms have both a search and a recommender system that, while having heterogeneous input spaces, can be connected through their common output item space and a shared semantic representation. In this paper, we propose a new Zero-Shot Heterogeneous Transfer Learning framework that transfers learned knowledge from the recommender system component to improve the search component of a content platform. First, it learns representations of items and their natural-language features by predicting (item, item) correlation graphs derived from the recommender system as an auxiliary task. Then, the learned representations are transferred to solve the target search retrieval task, performing query-to-item prediction without having seen any (query, item) pairs in training. We conduct online and offline experiments on one of the world's largest search and recommender systems from Google, and present the results and lessons learned. We demonstrate that the proposed approach can achieve high performance on offline search retrieval tasks, and more importantly, achieved significant improvements on relevance and user interactions over the highly-optimized production system in online experiments., Comment: Accepted at CIKM 2020