Phononic topological insulators with tunable pseudospin physics

Efficient control of phonons is crucial to energy-information technology, but limited by the lacking of tunable degrees of freedom like charge or spin. Here we suggest to utilize crystalline symmetry-protected pseudospins as new quantum degrees of freedom to manipulate phonons. Remarkably, we reveal a duality between phonon pseudospins and electron spins by presenting Kramers-like degeneracy and pseudospin counterparts of spin-orbit coupling, which lays the foundation for "pseudospin phononics". Furthermore, we report two types of three-dimensional phononic topological insulators, which give topologically protected, gapless surface states with linear and quadratic band degeneracies, respectively. These topological surface states display unconventional phonon transport behaviors attributed to the unique pseudospin-momentum locking, which are useful for phononic circuits, transistors, antennas, etc. The emerging pseudospin physics offers new opportunities to develop future phononics

Reciprocal Recommendation System for Online Dating

Online dating sites have become popular platforms for people to look for potential romantic partners. Different from traditional user-item recommendations where the goal is to match items (e.g., books, videos, etc) with a user's interests, a recommendation system for online dating aims to match people who are mutually interested in and likely to communicate with each other. We introduce similarity measures that capture the unique features and characteristics of the online dating network, for example, the interest similarity between two users if they send messages to same users, and attractiveness similarity if they receive messages from same users. A reciprocal score that measures the compatibility between a user and each potential dating candidate is computed and the recommendation list is generated to include users with top scores. The performance of our proposed recommendation system is evaluated on a real-world dataset from a major online dating site in China. The results show that our recommendation algorithms significantly outperform previously proposed approaches, and the collaborative filtering-based algorithms achieve much better performance than content-based algorithms in both precision and recall. Our results also reveal interesting behavioral difference between male and female users when it comes to looking for potential dates. In particular, males tend to be focused on their own interest and oblivious towards their attractiveness to potential dates, while females are more conscientious to their own attractiveness to the other side of the line

Deep Short Text Classification with Knowledge Powered Attention

Short text classification is one of important tasks in Natural Language Processing (NLP). Unlike paragraphs or documents, short texts are more ambiguous since they have not enough contextual information, which poses a great challenge for classification. In this paper, we retrieve knowledge from external knowledge source to enhance the semantic representation of short texts. We take conceptual information as a kind of knowledge and incorporate it into deep neural networks. For the purpose of measuring the importance of knowledge, we introduce attention mechanisms and propose deep Short Text Classification with Knowledge powered Attention (STCKA). We utilize Concept towards Short Text (C- ST) attention and Concept towards Concept Set (C-CS) attention to acquire the weight of concepts from two aspects. And we classify a short text with the help of conceptual information. Unlike traditional approaches, our model acts like a human being who has intrinsic ability to make decisions based on observation (i.e., training data for machines) and pays more attention to important knowledge. We also conduct extensive experiments on four public datasets for different tasks. The experimental results and case studies show that our model outperforms the state-of-the-art methods, justifying the effectiveness of knowledge powered attention

Spin Josephson effects in Exchange coupled Anti-ferromagnets

The energy of exchange coupled antiferromagnetic insulators (AFMIs) is a periodic function of the relative in-plane orientation of the N\'{e}el vector fields. We show that this leads to oscillations in the relative magnetization of exchange coupled AFMIs separated by a thin metallic barrier. These oscillations pump a spin current ($I_{S}$) through the metallic spacer that is proportional to the rate of change of the relative in-plane orientation of the N\'{e}el vector fields. By considering spin-transfer torque induced by a spin chemical potential ($V_{S}$) at one of the interfaces, we predict non-Ohmic $I_{S}$-$V_{S}$ characteristics of AFMI exchange coupled hetero-structures, which leads to a non-local voltage across a spin-orbit coupled metallic spacer

Anomalous Circularly Polarized Light Emission induced by the Optical Berry Curvature Dipole

The ability to selectively excite light with fixed handedness is crucial for circularly polarized light emission. It is commonly believed that the luminescent material chirality determines the emitted light handedness, regardless of the light emitting direction. In this work, we propose an anomalous circular polarized light emission (ACPLE) whose handedness actually relies on the emission direction and current direction in electroluminescence. In a solid semiconductor, the ACPLE originates in the band structure topology characterized by the optical Berry curvature dipole. ACPLE exists in inversion-symmetry breaking materials including chiral materials. We exemplify the ACPLE by estimating the high circular polarization ratio in monolayer WS$_2$. In addition, the ACPLE can be further generalized to magnetic semiconductors in which the optical Berry curvature plays a leading role instead. Our finding reveals intriguing consequences of band topology in light emission and promises optoelectric applications.Comment: 7 pages, 4 figure

Three Mechanisms of Feature Learning in the Exact Solution of a Latent Variable Model

We identify and exactly solve the learning dynamics of a one-hidden-layer linear model at any finite width whose limits exhibit both the kernel phase and the feature learning phase. We analyze the phase diagram of this model in different limits of common hyperparameters including width, layer-wise learning rates, scale of output, and scale of initialization. Our solution identifies three novel prototype mechanisms of feature learning: (1) learning by alignment, (2) learning by disalignment, and (3) learning by rescaling. In sharp contrast, none of these mechanisms is present in the kernel regime of the model. We empirically demonstrate that these discoveries also appear in deep nonlinear networks in real tasks