Few-Shot Learning with a Strong Teacher

Few-shot learning (FSL) aims to train a strong classifier using limited labeled examples. Many existing works take the meta-learning approach, sampling few-shot tasks in turn and optimizing the few-shot learner's performance on classifying the query examples. In this paper, we point out two potential weaknesses of this approach. First, the sampled query examples may not provide sufficient supervision for the few-shot learner. Second, the effectiveness of meta-learning diminishes sharply with increasing shots (i.e., the number of training examples per class). To resolve these issues, we propose a novel objective to directly train the few-shot learner to perform like a strong classifier. Concretely, we associate each sampled few-shot task with a strong classifier, which is learned with ample labeled examples. The strong classifier has a better generalization ability and we use it to supervise the few-shot learner. We present an efficient way to construct the strong classifier, making our proposed objective an easily plug-and-play term to existing meta-learning based FSL methods. We validate our approach in combinations with many representative meta-learning methods. On several benchmark datasets including miniImageNet and tiredImageNet, our approach leads to a notable improvement across a variety of tasks. More importantly, with our approach, meta-learning based FSL methods can consistently outperform non-meta-learning based ones, even in a many-shot setting, greatly strengthening their applicability

Entropic uncertainty relations for Markovian and non-Markovian processes under a structured bosonic reservoir

The uncertainty relation is a fundamental limit in quantum mechanics and is of great importance to quantum information processing as it relates to quantum precision measurement. Due to interactions with the surrounding environment, a quantum system will unavoidably suffer from decoherence. Here, we investigate the dynamic behaviors of the entropic uncertainty relation of an atom-cavity interacting system under a bosonic reservoir during the crossover between Markovian and non-Markovian regimes. Specifically, we explore the dynamic behavior of the entropic uncertainty relation for a pair of incompatible observables under the reservoir-induced atomic decay effect both with and without quantum memory. We find that the uncertainty dramatically depends on both the atom-cavity and the cavity-reservoir interactions, as well as the correlation time, $\tau$, of the structured reservoir. Furthermore, we verify that the uncertainty is anti-correlated with the purity of the state of the observed qubit-system. We also propose a remarkably simple and efficient way to reduce the uncertainty by utilizing quantum weak measurement reversal. Therefore our work offers a new insight into the uncertainty dynamics for multi-component measurements within an open system, and is thus important for quantum precision measurements.Comment: 17 pages, 9 figures, to appear in Scientific Report

Identifying the physical origin of gamma-ray bursts with supervised machine learning

The empirical classification of gamma-ray bursts (GRBs) into long and short GRBs based on their durations is already firmly established. This empirical classification is generally linked to the physical classification of GRBs originating from compact binary mergers and GRBs originating from massive star collapses, or Type I and II GRBs, with the majority of short GRBs belonging to Type I and the majority of long GRBs belonging to Type II. However, there is a significant overlap in the duration distributions of long and short GRBs. Furthermore, some intermingled GRBs, i.e., short-duration Type II and long-duration Type I GRBs, have been reported. A multi-parameter classification scheme of GRBs is evidently needed. In this paper, we seek to build such a classification scheme with supervised machine learning methods, chiefly XGBoost. We utilize the GRB Big Table and Greiner's GRB catalog and divide the input features into three subgroups: prompt emission, afterglow, and host galaxy. We find that the prompt emission subgroup performs the best in distinguishing between Type I and II GRBs. We also find the most important distinguishing feature in prompt emission to be T_{90}, hardness ratio, and fluence. After building the machine learning model, we apply it to the currently unclassified GRBs to predict their probabilities of being either GRB class, and we assign the most probable class of each GRB to be its possible physical class.Comment: 23 pages, 8 tables, 11 figures, accepted for publication by ApJ. Full version of Table 5 is available as ancillary materia

On the Convergence of Ritz Pairs and Refined Ritz Vectors for Quadratic Eigenvalue Problems

For a given subspace, the Rayleigh-Ritz method projects the large quadratic eigenvalue problem (QEP) onto it and produces a small sized dense QEP. Similar to the Rayleigh-Ritz method for the linear eigenvalue problem, the Rayleigh-Ritz method defines the Ritz values and the Ritz vectors of the QEP with respect to the projection subspace. We analyze the convergence of the method when the angle between the subspace and the desired eigenvector converges to zero. We prove that there is a Ritz value that converges to the desired eigenvalue unconditionally but the Ritz vector converges conditionally and may fail to converge. To remedy the drawback of possible non-convergence of the Ritz vector, we propose a refined Ritz vector that is mathematically different from the Ritz vector and is proved to converge unconditionally. We construct examples to illustrate our theory.Comment: 20 page

Deviation analysis of rotational inertia measurement based on torsion bar method

The test of moment of inertia has a wide range of applications in aerospace, vehicle engineering, precision machinery, motors and other fields, moment of inertia directly affects the reliability and performance of components or equipment, it is very essential to test the moment of inertia. By analyzing the principle of moment of inertia test, we could come to the conclusion that the theoretical value, the inertia of the disk, the period of the torsion swing of the standard body and the period of the empty disk of the moment of inertia and the moment of inertia of the standard body. By analyzing the measurement error, position error and damping during the test, we could reach the following conclusion that the test accuracy of the moment of inertia can reach 0.1 %

Streaming CTR Prediction: Rethinking Recommendation Task for Real-World Streaming Data

The Click-Through Rate (CTR) prediction task is critical in industrial recommender systems, where models are usually deployed on dynamic streaming data in practical applications. Such streaming data in real-world recommender systems face many challenges, such as distribution shift, temporal non-stationarity, and systematic biases, which bring difficulties to the training and utilizing of recommendation models. However, most existing studies approach the CTR prediction as a classification task on static datasets, assuming that the train and test sets are independent and identically distributed (a.k.a, i.i.d. assumption). To bridge this gap, we formulate the CTR prediction problem in streaming scenarios as a Streaming CTR Prediction task. Accordingly, we propose dedicated benchmark settings and metrics to evaluate and analyze the performance of the models in streaming data. To better understand the differences compared to traditional CTR prediction tasks, we delve into the factors that may affect the model performance, such as parameter scale, normalization, regularization, etc. The results reveal the existence of the ''streaming learning dilemma'', whereby the same factor may have different effects on model performance in the static and streaming scenarios. Based on the findings, we propose two simple but inspiring methods (i.e., tuning key parameters and exemplar replay) that significantly improve the effectiveness of the CTR models in the new streaming scenario. We hope our work will inspire further research on streaming CTR prediction and help improve the robustness and adaptability of recommender systems

Verbascoside Inhibits Glioblastoma Cell Proliferation, Migration and Invasion While Promoting Apoptosis Through Upregulation of Protein Tyrosine Phosphatase SHP-1 and Inhibition of STAT3 Phosphorylation

Background/Aims: As a natural antioxidant, verbascoside (VB) is proved to be a promising method for the treatment of oxidative-stress-related neurodegenerative diseases. Thus, this study aimed to investigate the effects of VB on glioblastoma cell proliferation, apoptosis, migration, and invasion as well as the mechanism involving signal transducer and activator of transcription 3 (STAT3) and Src homology 2 domain-containing protein tyrosine phosphatase 1 (SHP-1). Methods: U87 cells were assigned to different treatments. The MTT assay was used to test cell proliferation, flow cytometry was used to detect cell apoptosis, and a Transwell assay was used for cell migration and invasion. We analyzed the glioblastoma tumor growth in a xenograft mouse model. Western blot analysis was employed to determine the protein expression of related genes. Results: Glioblastoma cells exhibited decreased cell proliferation, migration, invasion, and increased apoptosis when treated with VB or TMZ. Western blot analysis revealed elevated SHP-1 expression and reduced phosphorylated (p)-STAT3 expression in glioblastoma cells treated with VB compared with controls. Correspondingly, in a xenograft mouse model treated with VB, glioblastoma tumor volume and growth were decreased. Glioblastoma xenograft tumors treated with VB showed elevated SHP-1, Bax, cleaved caspase-3, and cleaved PARP expression and reduced p-STAT3, Bcl-2, survivin, MMP-2, and MMP-9 expression. siRNA-SHP-1 inhibited the VB effects on glioblastoma. Conclusion: This study demonstrates that VB inhibits glioblastoma cell proliferation, migration, and invasion while promoting apoptosis via SHP-1 activation and inhibition of STAT3 phosphorylation

Seroprevalence of Toxoplasma gondii infection in household and stray cats in Lanzhou, northwest China

<p>Abstract</p> <p>Background</p> <p><it>Toxoplasma gondii </it>is an important protozoan parasite infecting humans and almost all warm-blooded animals. As the only definitive host, cats play a crucial role in the transmission of <it>T. gondii </it>infection by shedding parasite oocysts in their feces. However, little information on <it>T. gondii </it>infection in cats was available in Lanzhou, northwest China. This study was performed to determine the seroprevalence of <it>T. gondii </it>infection in household and stray cats in Lanzhou, northwest China.</p> <p>Results</p> <p>A total of 221 (179 households and 42 strays) blood samples were collected from clinically healthy cats admitted to several pet hospitals located in Lanzhou City, between November 2010 and July 2011 for the serological detection of <it>T. gondii </it>infection. The majority (207) of these cats represented Chinese Lihua cats. 47 of 221 (21.3%) examined cats were seropositive for <it>T. gondii </it>infection using the modified agglutination test (MAT) at the cut-off of 1:25. The seroprevalence in household and stray cats was assessed to be 15.6% and 45.2%, respectively, and the difference was statistically significant (<it>P <</it>0.05). The seroprevalence ranged from 15.1% to 25.8% among different age groups, but the differences were not statistically significant (<it>P ></it>0.05). Studies showed that there was no relationship between seroprevalence and the gender (<it>P ></it>0.05).</p> <p>Conclusions</p> <p>The present survey indicated the high seroprevalence of <it>T. gondii </it>in cats in Lanzhou, northwest China, which poses a threat to animal and human health. Therefore, measures should be taken to control and prevent toxoplasmosis of cats in this area.</p