Nonlocality-controlled interaction of spatial solitons in nematic liquid crystals

We demonstrate experimentally that the interactions between a pair of nonlocal spatial optical solitons in a nematic liquid crystal (NLC) can be controlled by the degree of nonlocality. For a given beam width, the degree of nonlocality can be modulated by varying the pretilt angle of NLC molecules via the change of the bias. When the pretilt angle is smaller than pi/4, the nonlocality is strong enough to guarantee the independence of the interactions on the phase difference of the solitons. As the pretilt angle increases, the degree of nonlocality decreases. When the degree is below its critical value, the two solitons behavior in the way like their local counterpart: the two in-phase solitons attract and the two out-of-phase solitons repulse.Comment: 3 pages, 4 figure

Learning to Purification for Unsupervised Person Re-identification

Unsupervised person re-identification is a challenging and promising task in computer vision. Nowadays unsupervised person re-identification methods have achieved great progress by training with pseudo labels. However, how to purify feature and label noise is less explicitly studied in the unsupervised manner. To purify the feature, we take into account two types of additional features from different local views to enrich the feature representation. The proposed multi-view features are carefully integrated into our cluster contrast learning to leverage more discriminative cues that the global feature easily ignored and biased. To purify the label noise, we propose to take advantage of the knowledge of teacher model in an offline scheme. Specifically, we first train a teacher model from noisy pseudo labels, and then use the teacher model to guide the learning of our student model. In our setting, the student model could converge fast with the supervision of the teacher model thus reduce the interference of noisy labels as the teacher model greatly suffered. After carefully handling the noise and bias in the feature learning, our purification modules are proven to be very effective for unsupervised person re-identification. Extensive experiments on three popular person re-identification datasets demonstrate the superiority of our method. Especially, our approach achieves a state-of-the-art accuracy 85.8\% @mAP and 94.5\% @Rank-1 on the challenging Market-1501 benchmark with ResNet-50 under the fully unsupervised setting. The code will be released

Optical Fiber Harsh Environment Sensors

Various optical fiber harsh environment sensors were reported, including the miniaturized inline Fabry-Perot interferometer sensor by femtosecond laser micromachining, the long period fiber grating sensor and the inline core-cladding mode interferometer by CO2 laser irradiations

Fermionology in the Kondo-Heisenberg model: the case of CeCoIn5_{5}

Fermi surface of heavy electron systems plays a fundamental role in understanding their variety of puzzling phenomena, for example, quantum criticality, strange metal behavior, unconventional superconductivity and even enigmatic phases with yet unknown order parameters. The spectroscopy measurement of typical heavy fermion superconductor CeCoIn$_{5}$ has demonstrated multi-Fermi surface structure, which has not been in detail studied theoretically in a model system like the Kondo-Heisenberg model. In this work, we make a step toward such an issue with revisiting the Kondo-Heisenberg model. It is surprising to find that the usual self-consistent calculation cannot reproduced the fermionology of the experimental observation of the system due to the unfounded sign binding between the hopping of the conduction electrons and the mean-field valence-bond order. To overcome such inconsistency, we assume that the sign binding should be relaxed and the mean-field valence-bond order can be considered as a free/fit parameter so as to meet with real-life experiments. Given the fermionology, the calculated effective mass enhancement, entropy, superfluid density and Knight shift are all in qualitative agreement with the experimental results of CeCoIn$_{5}$, which confirms our assumption. Our result supports a $d_{x^{2}-y^{2}}$-wave pairing structure in heavy fermion material CeCoIn$_{5}$. In addition, we have also provided the scanning tunneling microscopy (STM) spectra of the system, which is able to be tested by the present STM experiments.Comment: 9 pages, 11 figures, heavily revise

DexCatch: Learning to Catch Arbitrary Objects with Dexterous Hands

Achieving human-like dexterous manipulation remains a crucial area of research in robotics. Current research focuses on improving the success rate of pick-and-place tasks. Compared with pick-and-place, throw-catching behavior has the potential to increase picking speed without transporting objects to their destination. However, dynamic dexterous manipulation poses a major challenge for stable control due to a large number of dynamic contacts. In this paper, we propose a Stability-Constrained Reinforcement Learning (SCRL) algorithm to learn to catch diverse objects with dexterous hands. The SCRL algorithm outperforms baselines by a large margin, and the learned policies show strong zero-shot transfer performance on unseen objects. Remarkably, even though the object in a hand facing sideward is extremely unstable due to the lack of support from the palm, our method can still achieve a high level of success in the most challenging task. Video demonstrations of learned behaviors and the code can be found on the supplementary website