Specifically, we propose a model to predict actions of future unseen frames without using frame level annotations during training. Using Fisher vector features, our supervised model outperforms the state-of-the-art action forecasting model by 0.83% and 7.09% on the Breakfast and the 50Salads datasets respectively. Our weakly supervised model is only 0.6% behind the most recent state-of-the-art supervised model and obtains comparable results to other published fully supervised methods, and sometimes even outperforms them on the Breakfast dataset. Most interestingly, our weakly supervised model outperforms prior models by 1.04% leveraging on proposed weakly supervised architecture, and effective use of attention mechanism and loss functions.In the existing works of person re-identification (ReID), batch hard triplet loss has achieved great success. However, it only cares about the hardest samples within the batch. For any probe, there are massive mismatched samples (crucial samples) outside the batch which are closer than the matched samples. To reduce the disruptive influence of crucial samples, we propose a novel isosceles contraint for triplet. Theoretically, we show that if a matched pair has equal distance to any one of mismatched sample, the matched pair should be infinitely close. Motivated by this, the isosceles constraint is designed for the two mismatched pairs of each triplet, to restrict some matched pairs with equal distance to different mismatched samples. Meanwhile, to ensure that the distance of mismatched pairs are larger than the matched pairs, margin constraints are necessary. Minimizing the isosceles and margin constraints with respect to the feature extraction network makes the matched pairs closer and the mismatched pairs farther away than the matched ones. By this way, crucial samples are effectively reduced and the performance on ReID is improved greatly. Likewise, our isosceles contraint can be applied to quadruplet as well. Comprehensive experimental evaluations on Market-1501, DukeMTMC-reID and CUHK03 datasets demonstrate the advantages of our isosceles constraint over the related state-of-the-art approaches.Zero-shot sketch-based image retrieval (ZS-SBIR) is a specific cross-modal retrieval task that involves searching natural images through the use of free-hand sketches under the zero-shot scenario. Most previous methods project the sketch and image features into a low-dimensional common space for efficient retrieval, and meantime align the projected features to their semantic features (e.g., category-level word vectors) in order to transfer knowledge from seen to unseen classes. However, the projection and alignment are always coupled; as a result, there is a lack of alignment that consequently leads to unsatisfactory zero-shot retrieval performance. To address this issue, we propose a novel progressive cross-modal semantic network. More specifically, it first explicitly aligns the sketch and image features to semantic features, then projects the aligned features to a common space for subsequent retrieval. We further employ cross-reconstruction loss to encourage the aligned features to capture complete knowledge about the two modalities, along with multi-modal Euclidean loss that guarantees similarity between the retrieval features from a sketch-image pair. Extensive experiments conducted on two popular large-scale datasets demonstrate that our proposed approach outperforms state-of-the-art competitors to a remarkable extent by more than 3% on the Sketchy dataset and about 6% on the TU-Berlin dataset in terms of retrieval accuracy.Relaxor-PbTiO3 (PT-based) ferroelectric single crystals are important for many piezoelectric applications because of their outstanding performances. In order to expand their usage and avoid the failure during application, frequency dependence of coercive fields EC for [001]- and [011]-poled rhombohedral Pb(In1/2Nb1/2)O3-Pb(Mg1/3Nb2/3)O3-PbTiO3 (PIN-PMN-PT) single crystals were investigated from the frequency of 1 Hz to 300 kHz by using hysteresis loop measurement (frequency from 1 Hz to 2 kHz) and a critical value method (frequency from 5 kHz to 300 kHz). The results showed that the coercive field EC, remnant polarization Pr, and saturation polarization Ps strongly depended on the crystal orientation and frequency. Compared with the [001]-poled crystals, the [011]-poled PIN-PMN-PT single crystals possessed higher EC, Pr, and Ps at the same testing frequencies. With increasing frequency from 1 Hz to 300 kHz, the coercive fields EC of the [001]- and [011]-poled PIN-PMN-PT single crystals increased from 4.0 and 4.8 kV/cm to 11.5 and 14.9 kV/cm, respectively. Moreover, the frequency dependence of the coercive fields was found to be consistent with the growth kinetics model, where the coercive fields EC was proportional to fβ . This work benefit to the design of piezoelectric devices based on PIN-PMN-PT single crystals operated at high frequencies and high driving fields.Plane wave imaging (PWI), a typical ultrafast medical ultrasound imaging mode, adopts single plane wave emission without focusing to achieve a high frame rate. However, the imaging quality is severely degraded in comparison with the commonly used focused line scan mode. Conventional adaptive beamformers can improve imaging quality at the cost of additional computation. In this paper, we propose to use a deep neural network (DNN) to enhance the performance of PWI while maintaining a high frame rate. In particular, the PWI response from a single point target is used as the network input, while the focused scan response from the same point serves as the desired output, which is the main contribution of this method. To evaluate the performance of the proposed method, simulations, phantom experiments and in vivo studies are conducted. The delay-and-sum (DAS), the coherence factor (CF), a previously proposed deep learning-based method and the DAS with focused scan are used for comparison. Numerical metrics, including the contrast ratio (CR), the contrast-to-noise ratio (CNR) and the speckle signal-to-noise ratio (sSNR), are used to quantify the performance. The results indicate that the proposed method can achieve superior resolution and contrast performance. Specifically, the proposed method performs better than the DAS in all metrics. Although the CF provides a higher CR, its CNR and sSNR are much lower than those of the proposed method. The overall performance is also better than that of the previous deep learning method and at the same level with focused scan performance. selleck chemical Additionally, in comparison with the DAS, the proposed method requires little additional computation, which ensures high temporal resolution. These results validate that the proposed method can achieve a high imaging quality while maintaining the high frame rate associated with PWI.selleck chemical