Hetero-manifold Regularisation for Cross-modal Hashing

Recently, cross-modal search has attracted considerable attention but remains a very challenging task because of the integration complexity and heterogeneity of the multi-modal data. To address both challenges, in this paper, we propose a novel method termed hetero-manifold regularisation (HMR) to supervise the learning of hash functions for efficient cross-modal search. A hetero-manifold integrates multiple sub-manifolds defined by homogeneous data with the help of cross-modal supervision information. Taking advantages of the hetero-manifold, the similarity between each pair of heterogeneous data could be naturally measured by three order random walks on this hetero-manifold. Furthermore, a novel cumulative distance inequality defined on the hetero-manifold is introduced to avoid the computational difficulty induced by the discreteness of hash codes. By using the inequality, cross-modal hashing is transformed into a problem of hetero-manifold regularised support vector learning. Therefore, the performance of cross-modal search can be significantly improved by seamlessly combining the integrated information of the hetero-manifold and the strong generalisation of the support vector machine. Comprehensive experiments show that the proposed HMR achieve advantageous results over the state-of-the-art methods in several challenging cross-modal tasks

Evolution equation for quantum coherence

Quantum coherence plays an important role in quantum resource theory, which is strongly related with entanglement. Similar to the entanglement evolution equation, we find the coherence evolution equation of quantum states through fully and strictly incoherent operation (FSIO) channels. In order to quantify the full coherence of qudit states, we define G-coherence and convex roof of G-coherence, and prove that the G-coherence is a strong coherence monotone and the convex roof of G-coherence is a coherence measure under FSIO, respectively. Furthermore, we prove a coherence evolution equation for arbitrary $d$-dimensional quantum pure and mixed states under FSIO channels, which generalizes the entanglement evolution equation for bipartite pure states. Our results will play an important role in the simplification of dynamical coherence measure.Comment: 9 pages, 4 figure

Action Sensitivity Learning for Temporal Action Localization

Temporal action localization (TAL), which involves recognizing and locating action instances, is a challenging task in video understanding. Most existing approaches directly predict action classes and regress offsets to boundaries, while overlooking the discrepant importance of each frame. In this paper, we propose an Action Sensitivity Learning framework (ASL) to tackle this task, which aims to assess the value of each frame and then leverage the generated action sensitivity to recalibrate the training procedure. We first introduce a lightweight Action Sensitivity Evaluator to learn the action sensitivity at the class level and instance level, respectively. The outputs of the two branches are combined to reweight the gradient of the two sub-tasks. Moreover, based on the action sensitivity of each frame, we design an Action Sensitive Contrastive Loss to enhance features, where the action-aware frames are sampled as positive pairs to push away the action-irrelevant frames. The extensive studies on various action localization benchmarks (i.e., MultiThumos, Charades, Ego4D-Moment Queries v1.0, Epic-Kitchens 100, Thumos14 and ActivityNet1.3) show that ASL surpasses the state-of-the-art in terms of average-mAP under multiple types of scenarios, e.g., single-labeled, densely-labeled and egocentric.Comment: Accepted to ICCV 202

Machine learning study of the relationship between the geometric and entropy discord

As an important resource to realize quantum information, quantum correlation displays different behaviors, freezing phenomenon and non-localization, which are dissimilar to the entanglement and classical correlation, respectively. In our setup, the ordering of quantum correlation is represented for different quantization methods by considering an open quantum system scenario. The machine learning method (neural network method) is then adopted to train for the construction of a bridge between the R\`{e}nyi discord ($\alpha=2$) and the geometric discord (Bures distance) for $X$ form states. Our results clearly demonstrate that the machine learning method is useful for studying the differences and commonalities of different quantizing methods of quantum correlation

Nanotube ferroelectric tunnel junctions with giant tunneling electroresistance ratio

Low-dimensional ferroelectric tunnel junctions are appealing for the realization of nanoscale nonvolatile memory devices due to their inherent advantage of device miniaturization. Those based on current mechanisms still have restrictions including low tunneling electroresistance (TER) effects and complex heterostructures. Here, we introduce an entirely new TER mechanism to construct the nanotube ferroelectric tunnel junction with ferroelectric nanotubes as the tunneling region. When rolling a ferroelectric monolayer into a nanotube, due to the coexistence of its intrinsic ferroelectric polarization with the flexoelectric polarization induced by bending, there occurs metal-insulator transition depending on radiative polarization states. For the pristine monolayer, its out-of-plane polarization is tunable by an in-plane electric field, the conducting states of the ferroelectric nanotube can thus be tuned between metallic and insulating via axial electric means. Using {\alpha}-In2Se3 as an example, our first-principles density functional theory calculations and nonequilibrium Green's function formalism confirm the feasibility of the TER mechanism and indicate an ultrahigh TER ratio exceeding 9.9*10^10% of the proposed nanotube ferroelectric tunnel junctions. Our findings provide a promising approach based on simple homogeneous structures for high density ferroelectric microelectronic devices with excellent ON/OFF performance.Comment: 15 pages, 5 figure

Bis(2,2′-bipyridine)(2-hy­droxy-2,2-diphenyl­acetato)­copper(II) nitrate dihydrate

In the title complex, [Cu(C14H11O3)(C10H8N2)2]NO3·2H2O, the CuII atom is coordinated by four N atoms from two 2,2′-bipyridine ligands and two O atoms from one benzilate ligand in a distorted octa­hedral geometry. A supra­molecular network is formed via inter­molecular O—H⋯O and C—H⋯O hydrogen-bonding inter­actions. π–π stacking inter­actions between neighboring pyridine rings are also present, the centroid—centroid distance being 3.808 (2) Å