This paper considers learning deep features from longtailed data. We observe that in the deep feature space, the head classes and the tail classes present different distribution patterns. The head classes have a relatively large spatial span, while the tail classes have significantly small spatial span, due to the lack of intra-class diversity. This uneven distribution between head and tail classes distorts the overall feature space, which compromises the discriminative ability of the learned features. Intuitively, we seek to expand the distribution of the tail classes by transferring from the head classes, so as to alleviate the distortion of the feature space. To this end, we propose to construct each feature into a " feature cloud". If a sample belongs to a tail class, the corresponding feature cloud will have relatively large distribution range, in compensation to its lack of diversity. It allows each tail sample to push the samples from other classes far away, recovering the intra-class diversity of tail classes. Extensive experimental evaluations on person re-identification and face recognition tasks confirm the effectiveness of our method.
In this work, we reported uniform layer-by-layer sublimation of black phosphorus under heating below 600 K. The uniformity and crystallinity of BP samples after thermal thinning were confirmed by Raman spectra and 2D Raman imaging. A uniform and crystalline bilayer black phosphorus flake with an area of 180 µm 2 was prepared with this method. No micron scale defects were observed. The sublimation rate of BP was around 0.18 nm / min at 500 K and 1.5 nm / min at 550 K. Both room and high temperature Raman peak intensity ratio Si A 2 g as functions of BP thickness were established for in-situ thickness determination.The sublimation thinning method was shown to be a controllable and scalable approach to prepare few-layer black phosphorus.
In this letter, we demonstrate ferroelectric memory devices with monolayer molybdenum disulfide (MoS2) as the channel material and aluminum (Al)-doped hafnium oxide (HfO2) as the ferroelectric gate dielectric. Metal-ferroelectric-metal capacitors with 16 nm thick Al-doped HfO2 are fabricated, and a remnant polarization of 3 μC/cm2 under a program/erase voltage of 5 V is observed. The capability of potential 10 years data retention was estimated using extrapolation of the experimental data. Ferroelectric transistors based on embedded ferroelectric HfO2 and MoS2 grown by chemical vapor deposition are fabricated. Clockwise hysteresis is observed at low program/erase voltages due to slow bulk traps located near the 2D/dielectric interface, while counterclockwise hysteresis is observed at high program/erase voltages due to ferroelectric polarization. In addition, the endurances of the devices are tested, and the effects associated with ferroelectric materials, such as the wake-up effect and polarization fatigue, are observed. Reliable writing/reading in MoS2/Al-doped HfO2 ferroelectric transistors over 2 × 104 cycles is achieved. This research can potentially lead to advances of two-dimensional (2D) materials in low-power logic and memory applications.
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