Learning One-Shot Exemplar SVM from the Web for Face Verification

Abstract. We investigate the problem of learning from a single instance consisting of a pair of images, often encountered in unconstrained face verification where the pair of images to be verified contain large varia-tions and are captured from never seen subjects. Instead of constructing a separate discriminative model for each image in the couple and perform-ing cross-checking, we learn a single Exemplar-SVM model for the pair by augmenting it with a negative couple set, and then predict whether the pair are from the same subject or not by asking an oracle whether this Exemplar-SVM is for a client or imposter in nature. The oracle by itself is learnt from the behaviors of a large number of Exemplar-SVMs based on the labeled background set. For face representation we use a number of unlabeled face sets collected from the Web to train a series of decision stumps that jointly map a given face to a discriminative and distributional representation. Experiments on the challenging Labeled Faces in the Wild (LFW) verify the effectiveness and feasibility of the proposed method.

AutoAssign+: Automatic Shared Embedding Assignment in Streaming Recommendation

In the domain of streaming recommender systems, conventional methods for addressing new user IDs or item IDs typically involve assigning initial ID embeddings randomly. However, this practice results in two practical challenges: (i) Items or users with limited interactive data may yield suboptimal prediction performance. (ii) Embedding new IDs or low-frequency IDs necessitates consistently expanding the embedding table, leading to unnecessary memory consumption. In light of these concerns, we introduce a reinforcement learning-driven framework, namely AutoAssign+, that facilitates Automatic Shared Embedding Assignment Plus. To be specific, AutoAssign+ utilizes an Identity Agent as an actor network, which plays a dual role: (i) Representing low-frequency IDs field-wise with a small set of shared embeddings to enhance the embedding initialization, and (ii) Dynamically determining which ID features should be retained or eliminated in the embedding table. The policy of the agent is optimized with the guidance of a critic network. To evaluate the effectiveness of our approach, we perform extensive experiments on three commonly used benchmark datasets. Our experiment results demonstrate that AutoAssign+ is capable of significantly enhancing recommendation performance by mitigating the cold-start problem. Furthermore, our framework yields a reduction in memory usage of approximately 20-30%, verifying its practical effectiveness and efficiency for streaming recommender systems

Observation of giant nonreciprocal charge transport from quantum Hall edge states of single surface in topological insulator

Symmetry breaking in quantum materials is of great importance and leads to novel nonreciprocal charge transport. The topological insulator system provides a unique platform to study nonreciprocal charge transport due to the exotic surface state. But it is typically small in magnitude because the contributions from the top and bottom surface of topological insulator are usually opposite. Here, we report the observation of giant nonreciprocal charge transport mediated by the quantum Hall state in intrinsic topological insulator Sn-Bi1.1Sb0.9Te2S devices, which is attributed to the coexistence of quantum Hall states and Dirac surface states. A giant nonreciprocal coefficient of up to 2.26*10^5 A^-1 is found, because only a single surface of topological insulator contributes to the nonreciprocal charge transport. Our work not only reveals the intrinsic properties of nonreciprocal charge transport in topological insulators, but also paves the way for future electronic devices

Real-time mapping of photo-sono therapy induced cavitation using Doppler optical coherence tomography.

Photo-sono therapy (PST) is an innovative anti-vascular approach based on cavitation-induced spallation. Currently, passive cavitation detection (PCD) is the prevalent technique for cavitation monitoring during treatment. However, the limitations of PCD are the lack of spatial information of bubbles and the difficulty of integration with the PST system. To address this, we proposed a new, to the best of our knowledge, cavitation mapping method that integrates Doppler optical coherence tomography (OCT) with PST to visualize bubble dynamics in real time. The feasibility of the proposed system has been confirmed through experiments on vascular-mimicking phantoms and in vivo rabbit ear vessels, and the results are compared to high-speed camera observations and PCD data. The findings demonstrate that Doppler OCT effectively maps cavitation in real time and holds promise for guiding PST treatments and other cavitation-related clinical applications

Large Exchange Bias Effect and Coverage-Dependent Interfacial Coupling in CrI3/MnBi2Te4 van der Waals Heterostructures

Igniting interface magnetic ordering of magnetic topological insulators by building a van der Waals heterostructure can help to reveal novel quantum states and design functional devices. Here, we observe an interesting exchange bias effect, indicating successful interfacial magnetic coupling, in CrI3/MnBi2Te4 ferromagnetic insulator/antiferromagnetic topological insulator (FMI/AFM-TI) heterostructure devices. The devices originally exhibit a negative exchange bias field, which decays with increasing temperature and is unaffected by the back-gate voltage. When we change the device configuration to be half-covered by CrI3, the exchange bias becomes positive with a very large exchange bias field exceeding 300 mT. Such sensitive manipulation is explained by the competition between the FM and AFM coupling at the interface of CrI3 and MnBi2Te4, pointing to coverage-dependent interfacial magnetic interactions. Our work will facilitate the development of topological and antiferromagnetic devices

Preparation, Characterization and Molecular Dynamics Simulation of Rutin–Cyclodextrin Inclusion Complexes

Rutin is a natural flavonoid that carries out a variety of biological activities, but its application in medicine and food is limited by its water solubility. One of the classical methods used to enhance drug solubility is encapsulation with cyclodextrins. In this paper, the encapsulation of different cyclodextrins with rutin was investigated using a combination of experimental and simulation methods. Three inclusions of rutin/beta-cyclodextrin (β-CD), rutin/2-hydroxypropyl beta-cyclodextrin (HP-β-CD) and rutin/2,6-dimethyl beta-cyclodextrin (DM-β-CD) were prepared by the freeze-drying method, and the inclusions were analyzed using Fourier infrared spectroscopy (FTIR), X-ray diffraction analysis (XRD), differential scanning calorimetry (DSC) and ultraviolet–visible spectroscopy (UV) to characterize and demonstrate the formation of the inclusion complexes. Phase solubility studies showed that rutin formed a 1:1 stoichiometric inclusion complex and significantly increased its solubility. β-CD, HP-β-CD, DM-β-CD, rutin and the three inclusion complexes were modeled by using MS2018 and AutoDock 4.0, and molecular dynamics simulations were performed to calculate the solubility parameters, binding energies, mean square displacement (MSD), hydrogen bonding and radial distribution functions (RDF) after the equilibration of the systems. The results of simulation and experiment showed that rutin/DM-β-CD had the best encapsulation effect among the three cyclodextrin inclusion complexes

Cooperative Navigation for Low-Cost UAV Swarm Based on Sigma Point Belief Propagation

As navigation is a key to task execution of micro unmanned aerial vehicle (UAV) swarm, the cooperative navigation (CN) method that integrates relative measurements between UAVs has attracted widespread attention due to its performance advantages. In view of the precision and efficiency of cooperative navigation for low-cost micro UAV swarm, this paper proposes a sigma point belief propagation-based (SPBP) CN method that can integrate self-measurement data and inter-UAV ranging in a distributed manner so as to improve the absolute positioning performance of UAV swarm. The method divides the sigma point filter into two steps: the first is to integrate local measurement data; the second is to approximate the belief of position based on the mean and covariance of the state, and pass message by augmentation, resampling and cooperative measurement update of the state to realize a low-complexity approximation to traditional message passing method. The simulation results and outdoor flight test results show that with similar performance, the proposed CN method has a calculation load more than 20 times less than traditional BP algorithms

Cooperative Navigation for Low-Cost UAV Swarm Based on Sigma Point Belief Propagation

As navigation is a key to task execution of micro unmanned aerial vehicle (UAV) swarm, the cooperative navigation (CN) method that integrates relative measurements between UAVs has attracted widespread attention due to its performance advantages. In view of the precision and efficiency of cooperative navigation for low-cost micro UAV swarm, this paper proposes a sigma point belief propagation-based (SPBP) CN method that can integrate self-measurement data and inter-UAV ranging in a distributed manner so as to improve the absolute positioning performance of UAV swarm. The method divides the sigma point filter into two steps: the first is to integrate local measurement data; the second is to approximate the belief of position based on the mean and covariance of the state, and pass message by augmentation, resampling and cooperative measurement update of the state to realize a low-complexity approximation to traditional message passing method. The simulation results and outdoor flight test results show that with similar performance, the proposed CN method has a calculation load more than 20 times less than traditional BP algorithms

Investigation of the Compatibility and Damping Performance of Graphene Oxide Grafted Antioxidant/Nitrile-Butadiene Rubber Composite: Insights from Experiment and Molecular Simulation

Rubber damping materials are widely used in electronics, electrical and other fields because of their unique viscoelasticity. How to prepare high-damping materials and prevent small molecule migration has attracted much attention. Antioxidant 4010NA was successfully grafted onto graphene oxide (GO) to prepare an anti-migration antioxidant (GO-4010NA). A combined molecular dynamics (MD) simulation and experimental study is presented to investigate the effects of small molecules 4010NA, GO, and GO-4010NA on the compatibility and damping properties of nitrile-butadiene rubber (NBR) composites. Differential scanning calorimetry (DSC) results showed that both 4010NA and GO-4010NA had good compatibility with the NBR matrix, and the Tg of GO-4010NA/NBR composite was improved. Dynamic mechanical analysis (DMA) data showed that the addition of GO-4010NA increased the damping performance of NBR than that of the addition of 4010NA. Molecular dynamics (MD) simulation results show GO-4010NA/NBR composites have the smallest free volume fraction (FFV) and the largest binding energy. GO-4010NA has a strong interaction with NBR due to the forming of hydrogen bonds (H-bonds). Grafting 4010NA onto GO not only inhibits the migration of 4010NA but also improves the damping property of NBR matrixes. This study provides new insights into GO grafted small molecules and the design of high-damping composites