Cucurbit[n]urils (Q[n]s or CB[n]s), as a classical of artificial organic macrocyclic hosts, were found to have excellent advantages in the fabricating of tunable and smart organic luminescent materials in aqueous media and the solid state with high emitting efficiency under the rigid pumpkin-shaped structure-derived macrocyclic-confinement effect in recent years. This review aims to give a systematically up-to-date overview of the Q[n]based supramolecular organic luminescent emissions from the confined spaces triggered host−guest complexes, including the assembly fashions and the mechanisms of the macrocycle-based luminescent complexes, as well as their applications. Finally, challenges and outlook are provided. Since this class of Q[n]-based supramolecular organic luminescent emissions, which have essentially derived from the cavity-dependent confinement effect and the resulting assembly fashions, emerged only a few years ago, we hope this review will provide valuable information for the further development of macrocycle-based light-emitting materials and other related research fields.

During a long period of time we are combating overfitting in the CNN training process with model regularization, including weight decay, model averaging, data augmentation, etc. In this paper, we present DisturbLabel, an extremely simple algorithm which randomly replaces a part of labels as incorrect values in each iteration. Although it seems weird to intentionally generate incorrect training labels, we show that DisturbLabel prevents the network training from over-fitting by implicitly averaging over exponentially many networks which are trained with different label sets. To the best of our knowledge, DisturbLabel serves as the first work which adds noises on the loss layer. Meanwhile, DisturbLabel cooperates well with Dropout to provide complementary regularization functions. Experiments demonstrate competitive recognition results on several popular image recognition datasets. * This work was done when Lingxi Xie and Zhen Wei were interns at MSR.

An electrical current can transfer spin angular momentum to a ferromagnet 1-3 . This novel physical phenomenon, called spin transfer, offers unprecedented spatial and temporal control over the magnetic state of a ferromagnet and has tremendous potential in a broad range of technologies, including magnetic memory and recording. Recently, it has been predicted 4 that spin transfer is not limited to ferromagnets, but can also occur in antiferromagnetic materials and even be stronger under some conditions. In this paper we demonstrate transfer of spin angular momentum across an interface between ferromagnetic and antiferromagnetic metals. The spin transfer is mediated by an electrical current of high density (~10 12 A/m 2 ) and revealed by variation in the exchange bias at the ferromagnet/antiferromagnet interface. We find that, depending on the polarity of the electrical current flowing across the interface, the strength of the exchange bias can either increase or decrease. This finding is explained by the theoretical prediction that a spin polarized current generates a torque on magnetic moments in the antiferromagnet. Current-mediated variation of exchange bias can be used to control the magnetic state of spin-valve devices, e.g., in magnetic memory applications.Spin valves 5 are now used in magnetic field sensors, in read heads for hard drives, in galvanic isolators, and in non-volatile random access memory devices. The simplest type of spin valve consists of two ferromagnetic layers separated by a thin nonmagnetic spacer. The spin-valve resistance is smallest when the magnetizations of the two ferromagnetic layers are parallel and largest when the magnetizations are antiparallel. The antiparallel alignment is achieved by making the two layers respond differently to an external magnetic field; an antiferromagnet in contact with one of the layers is used to effectively 'pin' the magnetization in this layer through an effect called 'exchange bias' [6][7][8] . The exceptional responsiveness of spin valves to magnetic fields has enabled very high areal packing densities in hard drives. In our experiments we study how exchange bias behaves when extremely high current densities are driven across these spin valve structures using point contacts.Point contacts were instrumental both for the original observation of spin transfer in ferromagnetic materials 3 and in probing the high-frequency manifestation of this phenomenon 9-11 . The extremely small size, less than a trillionth of a square cm, qualifies point contact as the smallest probe of spin transfer phenomena today and enables current densities up to 10 13 A/m 2 . Our point contacts were made with a standard system 3, 12 , using a sharpened Cu wire and a differential screw mechanism to move the Cu tip toward a FeMn/CoFe/Cu/CoFe spin valve structure. The spin-valve structures were sputtered onto Si substrates with individual layer thicknesses from 3-10 nm using techniques already

The advertising industry depends on an effective assessment of the impact of advertising as a key performance metric for their products. However, current assessment methods have relied on either indirect inference from observing changes in consumer behavior after the launch of an advertising campaign, which has long cycle times and requires an ad campaign to have already have been launched (often meaning costs having been sunk). Or through surveys or focus groups, which have a potential for experimental biases, peer pressure, and other psychological and sociological phenomena that can reduce the effectiveness of the study. In this paper, we investigate a new approach to assess the impact of advertisement by utilizing low-cost EEG headbands to record and assess the measurable impact of advertising on the brain. Our evaluation shows the desired performance of our method based on user experiment with 30 recruited subjects after watching 220 different advertisements. We believe the proposed SVM method can be further developed to a general and scalable methodology that can enable advertising agencies to assess impact rapidly, quantitatively, and without bias.

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