Video Action Recognition with Attentive Semantic Units

Visual-Language Models (VLMs) have significantly advanced action video recognition. Supervised by the semantics of action labels, recent works adapt the visual branch of VLMs to learn video representations. Despite the effectiveness proved by these works, we believe that the potential of VLMs has yet to be fully harnessed. In light of this, we exploit the semantic units (SU) hiding behind the action labels and leverage their correlations with fine-grained items in frames for more accurate action recognition. SUs are entities extracted from the language descriptions of the entire action set, including body parts, objects, scenes, and motions. To further enhance the alignments between visual contents and the SUs, we introduce a multi-region module (MRA) to the visual branch of the VLM. The MRA allows the perception of region-aware visual features beyond the original global feature. Our method adaptively attends to and selects relevant SUs with visual features of frames. With a cross-modal decoder, the selected SUs serve to decode spatiotemporal video representations. In summary, the SUs as the medium can boost discriminative ability and transferability. Specifically, in fully-supervised learning, our method achieved 87.8% top-1 accuracy on Kinetics-400. In K=2 few-shot experiments, our method surpassed the previous state-of-the-art by +7.1% and +15.0% on HMDB-51 and UCF-101, respectively.Comment: Accepted at ICCV 202

Structure basis for the unique specificity of medaka enteropeptidase light chain

Thermal stresses concern not renewed type of stresses, that is once having liberated, they cannot accumulate more. The estimation of purely thermoelastic contribution to a lithosphere stress state gives the additional information, allowing to predict the danger connected with such natural factors, as seismic and volcanic activity. Some theoretical thermoelastic problems for the geological environment of a difficult outline with non-uniform thermophysical characteristics are considered. The decision is received on the basis of a numerical finite elements method. Influence of the model fixation, the geometrical factor and boundary conditions on distribution of thermal stresses and dislocation is investigated. Computing experiments have shown, that the size of the maximum thermal stresses reaches 500 bar. The maximum values of vertical dislocation are reached by 90 m, and horizontal — 50 m. Neutral plane position are precisely defined. Термоупругие напряжения относятся к невозобновляемому типу напряжений, то есть, однажды высвободившись, напряжения не могут накапливаться вновь. Расчет термоупругого вклада в напряженное состояние литосферы дает дополнительную информацию, позволяющую оценить опасность, связанную с такими природными явлениями, как сейсмичность и вулканическая активность. Рассмотрено несколько теоретических моделей для геологической среды сложного очертания с неоднородными теплофизическими характеристиками. Решение получено на основе численного метода конечных элементов. Исследовано влияние «закрепления» модели, геометрического фактора, неоднородных граничных условий на распределение термоупругих напряжений и перемещений. Вычислительные эксперименты показали, что величина максимальных термоупругих напряжений достигает 500 б. Максимальные величины вертикальных перемещений не превышают 90 м, горизонтальных — 50 м. Положение нейтральной плоскости определяется точно. На основі методу скінченних елементів отримано детальний розподіл термопружних напружень і переміщень для неоднорідного геологічного середовища. Досліджено взаємний вплив геометрії середовища й неоднорідних граничних умов на розподіл термопружних напружень та переміщень

Long-distance propagation of high-velocity antiferromagnetic spin waves

We report on coherent propagation of antiferromagnetic (AFM) spin waves over a long distance ($\sim$10 $\mu$m) at room temperature in a canted AFM $\alpha$-Fe$_2$O$_3$ with the Dzyaloshinskii-Moriya interaction (DMI). Unprecedented high group velocities (up to 22.5 km/s) are characterized by microwave transmission using all-electrical spin wave spectroscopy. We derive analytically AFM spin-wave dispersion in the presence of the DMI which accounts for our experimental results. The AFM spin waves excited by nanometric coplanar waveguides with large wavevectors enter the exchange regime and follow a quasi-linear dispersion relation. Fitting of experimental data with our theoretical model yields an AFM exchange stiffness length of 1.7 angstrom. Our results provide key insights on AFM spin dynamics and demonstrate high-speed functionality for AFM magnonics

Deep Learning Enables Large Depth-of-Field Images for Sub-Diffraction-Limit Scanning Superlens Microscopy

Scanning electron microscopy (SEM) is indispensable in diverse applications ranging from microelectronics to food processing because it provides large depth-of-field images with a resolution beyond the optical diffraction limit. However, the technology requires coating conductive films on insulator samples and a vacuum environment. We use deep learning to obtain the mapping relationship between optical super-resolution (OSR) images and SEM domain images, which enables the transformation of OSR images into SEM-like large depth-of-field images. Our custom-built scanning superlens microscopy (SSUM) system, which requires neither coating samples by conductive films nor a vacuum environment, is used to acquire the OSR images with features down to ~80 nm. The peak signal-to-noise ratio (PSNR) and structural similarity index measure values indicate that the deep learning method performs excellently in image-to-image translation, with a PSNR improvement of about 0.74 dB over the optical super-resolution images. The proposed method provides a high level of detail in the reconstructed results, indicating that it has broad applicability to chip-level defect detection, biological sample analysis, forensics, and various other fields.Comment: 13 pages,7 figure