Quantum magnetism with multicomponent polar molecules in an optical lattice

We consider bosonic dipolar molecules in an optical lattice prepared in a mixture of different rotational states. The 1/r^3 interaction between molecules for this system is produced by exchanging a quantum of angular momentum between two molecules. We show that the Mott states of such systems have a large variety of non-trivial spin orderings including a state with ordering wave vector that can be changed by tilting the lattice. As the Mott insulating phase is melted, we also describe several exotic superfluid phases that will occur

Shallow Triple Stream Three-dimensional CNN (STSTNet) for Micro-expression Recognition

In the recent year, state-of-the-art for facial micro-expression recognition have been significantly advanced by deep neural networks. The robustness of deep learning has yielded promising performance beyond that of traditional handcrafted approaches. Most works in literature emphasized on increasing the depth of networks and employing highly complex objective functions to learn more features. In this paper, we design a Shallow Triple Stream Three-dimensional CNN (STSTNet) that is computationally light whilst capable of extracting discriminative high level features and details of micro-expressions. The network learns from three optical flow features (i.e., optical strain, horizontal and vertical optical flow fields) computed based on the onset and apex frames of each video. Our experimental results demonstrate the effectiveness of the proposed STSTNet, which obtained an unweighted average recall rate of 0.7605 and unweighted F1-score of 0.7353 on the composite database consisting of 442 samples from the SMIC, CASME II and SAMM databases.Comment: 5 pages, 1 figure, Accepted and published in IEEE FG 201

Correction: Design and characterization of a plasmonic Doppler grating for azimuthal angle-resolved surface plasmon resonances

The authors regret that Fig. 1e of the original paper contained an error in the curves displayed for the silver, aluminium and palladium gratings. Specifically, a different value of the ‘index of the environment’ (1.65) was used in the calculation of these curves compared to that used for calculating the optical response of the gold grating (1.33). The correct Fig. 1 below, displays the curves calculated with the same value of the index of the environment (1.33). No amendments are made to the caption of Fig. 1 or the other sub-figures presented in the figure. This error does not affect any of the results or conclusions reported in the paper; only the display of the figure. (Figure Presented) The Royal Society of Chemistry apologises for these errors and any consequent inconvenience to authors and readers

Towards Balanced Active Learning for Multimodal Classification

Training multimodal networks requires a vast amount of data due to their larger parameter space compared to unimodal networks. Active learning is a widely used technique for reducing data annotation costs by selecting only those samples that could contribute to improving model performance. However, current active learning strategies are mostly designed for unimodal tasks, and when applied to multimodal data, they often result in biased sample selection from the dominant modality. This unfairness hinders balanced multimodal learning, which is crucial for achieving optimal performance. To address this issue, we propose three guidelines for designing a more balanced multimodal active learning strategy. Following these guidelines, a novel approach is proposed to achieve more fair data selection by modulating the gradient embedding with the dominance degree among modalities. Our studies demonstrate that the proposed method achieves more balanced multimodal learning by avoiding greedy sample selection from the dominant modality. Our approach outperforms existing active learning strategies on a variety of multimodal classification tasks. Overall, our work highlights the importance of balancing sample selection in multimodal active learning and provides a practical solution for achieving more balanced active learning for multimodal classification.Comment: 12 pages, accepted by ACMMM 202

Compact Orthogonal Wideband Printed MIMO Antenna for WiFi/WLAN/LTE Applications

YesThis study presents a wideband multiple-input-multiple-output (MIMO) antenna for Wifi/WLAN/LTE applications. The antenna consists of two triangular patches as the radiating elements placed orthogonally to each other. Two T-slots and a rectangular slot were etched on the ground plane to improve return loss and isolation. The total dimension of the proposed antenna is 30 x 30 mm2. The antenna yields impedance bandwidth of 101.7% between 2.28 GHz up to 7 GHz with a reflection coefficient of < -10 dB, and mutual coupling of < -14 dB. The results including S-Parameters, MIMO characteristics with analysis of envelope correlation coefficient (ECC), total active reflection coefficient (TARC), capacity loss, channel capacity, VSWR, antenna gain and radiation patterns are evaluated. These characteristics indicate that the proposed antenna is suitable for MIMO wireless applications

Hamiltonian Formalism of the de-Sitter Invariant Special Relativity

Lagrangian of the Einstein's special relativity with universal parameter $c$ ($\mathcal{SR}_c$) is invariant under Poincar\'e transformation which preserves Lorentz metric $\eta_{\mu\nu}$. The $\mathcal{SR}_c$ has been extended to be one which is invariant under de Sitter transformation that preserves so called Beltrami metric $B_{\mu\nu}$. There are two universal parameters $c$ and $R$ in this Special Relativity (denote it as $\mathcal{SR}_{cR}$). The Lagrangian-Hamiltonian formulism of $\mathcal{SR}_{cR}$ is formulated in this paper. The canonic energy, canonic momenta, and 10 Noether charges corresponding to the space-time's de Sitter symmetry are derived. The canonical quantization of the mechanics for $\mathcal{SR}_{cR}$-free particle is performed. The physics related to it is discussed.Comment: 24 pages, no figur