FlatMatch: Bridging Labeled Data and Unlabeled Data with Cross-Sharpness for Semi-Supervised Learning

Semi-Supervised Learning (SSL) has been an effective way to leverage abundant unlabeled data with extremely scarce labeled data. However, most SSL methods are commonly based on instance-wise consistency between different data transformations. Therefore, the label guidance on labeled data is hard to be propagated to unlabeled data. Consequently, the learning process on labeled data is much faster than on unlabeled data which is likely to fall into a local minima that does not favor unlabeled data, leading to sub-optimal generalization performance. In this paper, we propose FlatMatch which minimizes a cross-sharpness measure to ensure consistent learning performance between the two datasets. Specifically, we increase the empirical risk on labeled data to obtain a worst-case model which is a failure case that needs to be enhanced. Then, by leveraging the richness of unlabeled data, we penalize the prediction difference (i.e., cross-sharpness) between the worst-case model and the original model so that the learning direction is beneficial to generalization on unlabeled data. Therefore, we can calibrate the learning process without being limited to insufficient label information. As a result, the mismatched learning performance can be mitigated, further enabling the effective exploitation of unlabeled data and improving SSL performance. Through comprehensive validation, we show FlatMatch achieves state-of-the-art results in many SSL settings.Comment: NeurIPS 202

Regularly Truncated M-estimators for Learning with Noisy Labels

The sample selection approach is very popular in learning with noisy labels. As deep networks learn pattern first, prior methods built on sample selection share a similar training procedure: the small-loss examples can be regarded as clean examples and used for helping generalization, while the large-loss examples are treated as mislabeled ones and excluded from network parameter updates. However, such a procedure is arguably debatable from two folds: (a) it does not consider the bad influence of noisy labels in selected small-loss examples; (b) it does not make good use of the discarded large-loss examples, which may be clean or have meaningful information for generalization. In this paper, we propose regularly truncated M-estimators (RTME) to address the above two issues simultaneously. Specifically, RTME can alternately switch modes between truncated M-estimators and original M-estimators. The former can adaptively select small-losses examples without knowing the noise rate and reduce the side-effects of noisy labels in them. The latter makes the possibly clean examples but with large losses involved to help generalization. Theoretically, we demonstrate that our strategies are label-noise-tolerant. Empirically, comprehensive experimental results show that our method can outperform multiple baselines and is robust to broad noise types and levels.Comment: 16 pages, 11 tables, 9 figure

Holographic Storage of Biphoton Entanglement

Coherent and reversible storage of multi-photon entanglement with a multimode quantum memory is essential for scalable all-optical quantum information processing. Although single photon has been successfully stored in different quantum systems, storage of multi-photon entanglement remains challenging because of the critical requirement for coherent control of photonic entanglement source, multimode quantum memory, and quantum interface between them. Here we demonstrate a coherent and reversible storage of biphoton Bell-type entanglement with a holographic multimode atomic-ensemble-based quantum memory. The retrieved biphoton entanglement violates Bell's inequality for 1 microsecond storage time and a memory-process fidelity of 98% is demonstrated by quantum state tomography.Comment: 5 pages, 4 figures, accepted by Phys. Rev. Let

Bis{2-eth­oxy-6-[2-(methyl­ammonio)ethyl­imino­meth­yl]phenolato}thio­cyanato­zinc(II) nitrate

In the title compound, [Zn(NCS)(C12H18N2O2)2]NO3, the ZnII ion is chelated by the phenolate O and imine N atoms from two zwitterionic Schiff base ligands and is also coordinated by the N atom of a thio­cyanate ligand, giving a distorted trigonal-bipyramidal geometry. Intra­molecular N—H⋯O hydrogen bonds are observed in the complex cation. The nitrate anions are linked to the complex cations through N—H⋯O hydrogen bonds

Transcranial Direct Current Stimulation of motor cortex enhances running performance.

Transcranial direct current stimulation (tDCS) is a technique used to modulate neuronal excitability through non-invasive brain stimulation that can enhance exercise performance. We hypothesize that tDCS would improve submaximal running time to exhaustion (TTE) and delay the increase in the rating of perceived exertion (RPE) over time. We also hypothesize that tDCS would not lead to difference in cardiorespiratory responses. We employed a randomized, single-blinded, and counterbalanced design in which 10 trained men participated. After receiving either 20 min of 1.98 mA anodal tDCS applied over the primary motor cortex (M1) or sham-operated control on separate days, participants completed a constant-load test involving running at a speed equivalent to 80% of their own maximum oxygen consumption (VO2max). During this constant-load test, RPE, heart rate (HR), VO2, pulmonary ventilation (VE), respiratory exchange ratio (RER), and ventilatory threshold (VT) were continuously monitored. TTE was recorded at the end of the test. TTEs were significantly longer in the tDCS than in the sham conditions (21.18 ± 7.13 min; 18.44 ± 6.32 min; p = 0.011). For TTE, no significant differences were found in RPE between conditions at isotime. In addition, no significant differences in HR, VO2, VE, RER, and VT were found during TTE between the two stimulation conditions at any time point. These results indicate that the application of tDCS does not induce a change of the exercise performance-related index; however, it can affect the increase of the exercise duration due to the stimuli in the M1 area