Stochastic Nested Compositional Bi-level Optimization for Robust Feature Learning

We develop and analyze stochastic approximation algorithms for solving nested compositional bi-level optimization problems. These problems involve a nested composition of $T$ potentially non-convex smooth functions in the upper-level, and a smooth and strongly convex function in the lower-level. Our proposed algorithm does not rely on matrix inversions or mini-batches and can achieve an $\epsilon$-stationary solution with an oracle complexity of approximately $\tilde{O}_T(1/\epsilon^{2})$, assuming the availability of stochastic first-order oracles for the individual functions in the composition and the lower-level, which are unbiased and have bounded moments. Here, $\tilde{O}_T$ hides polylog factors and constants that depend on $T$. The key challenge we address in establishing this result relates to handling three distinct sources of bias in the stochastic gradients. The first source arises from the compositional nature of the upper-level, the second stems from the bi-level structure, and the third emerges due to the utilization of Neumann series approximations to avoid matrix inversion. To demonstrate the effectiveness of our approach, we apply it to the problem of robust feature learning for deep neural networks under covariate shift, showcasing the benefits and advantages of our methodology in that context

Decentralized Stochastic Bilevel Optimization with Improved per-Iteration Complexity

Bilevel optimization recently has received tremendous attention due to its great success in solving important machine learning problems like meta learning, reinforcement learning, and hyperparameter optimization. Extending single-agent training on bilevel problems to the decentralized setting is a natural generalization, and there has been a flurry of work studying decentralized bilevel optimization algorithms. However, it remains unknown how to design the distributed algorithm with sample complexity and convergence rate comparable to SGD for stochastic optimization, and at the same time without directly computing the exact Hessian or Jacobian matrices. In this paper we propose such an algorithm. More specifically, we propose a novel decentralized stochastic bilevel optimization (DSBO) algorithm that only requires first order stochastic oracle, Hessian-vector product and Jacobian-vector product oracle. The sample complexity of our algorithm matches the currently best known results for DSBO, and the advantage of our algorithm is that it does not require estimating the full Hessian and Jacobian matrices, thereby having improved per-iteration complexity.Comment: ICML 202

A One-Sample Decentralized Proximal Algorithm for Non-Convex Stochastic Composite Optimization

We focus on decentralized stochastic non-convex optimization, where $n$ agents work together to optimize a composite objective function which is a sum of a smooth term and a non-smooth convex term. To solve this problem, we propose two single-time scale algorithms: Prox-DASA and Prox-DASA-GT. These algorithms can find $\epsilon$-stationary points in $\mathcal{O}(n^{-1}\epsilon^{-2})$ iterations using constant batch sizes (i.e., $\mathcal{O}(1)$). Unlike prior work, our algorithms achieve comparable complexity without requiring large batch sizes, more complex per-iteration operations (such as double loops), or stronger assumptions. Our theoretical findings are supported by extensive numerical experiments, which demonstrate the superiority of our algorithms over previous approaches. Our code is available at https://github.com/xuxingc/ProxDASA.Comment: UAI 202

Photocatalytic oxidation of methane over silver decorated zinc oxide nanocatalysts

The search for active catalysts that efficiently oxidize methane under ambient conditions remains a challenging task for both C1 utilization and atmospheric cleansing. Here, we show that when the particle size of zinc oxide is reduced down to the nanoscale, it exhibits high activity for methane oxidation under simulated sunlight illumination, and nano silver decoration further enhances the photo-activity via the surface plasmon resonance. The high quantum yield of 8% at wavelengths \u3c 400 nm and over 0.1% at wavelengths ¿ 470 nm achieved on the silver decorated zinc oxide nanostructures shows great promise for atmospheric methane oxidation. Moreover, the nano-particulate composites can efficiently photo-oxidize other small molecular hydrocarbons such as ethane, propane and ethylene, and in particular, can dehydrogenize methane to generate ethane, ethylene and so on. On the basis of the experimental results, a two-step photocatalytic reaction process is suggested to account for the methane photo-oxidation

Design Synthesis of Nitrogen-Doped TiO2@Carbon Nanosheets toward Selective Nitroaromatics Reduction under Mild Conditions

The development of a facile, low-cost, and ecofriendly approach to the synthesis of aromatic amines remains a great scientific challenge. TiO2, as a low-cost and earth abundant metal oxide, is usually not active for thermo-catalyzed nitro reduction. Herein, we report a composite nanosheet catalyst, composed of nitrogen-doped TiO2 and carbon (N-TiO2@C), which exhibits highly efficient, thermo-catalytic performance for selective nitroaromatic reduction at room temperature. The NTiO2@C nanosheet catalyst is synthesized via a facile approach where C3N4 nanosheets are utilized not only as a structuredirecting agent to control the shape, size, and crystal phase of TiO2 but also as a source of nitrogen for doping into both TiO2 and carbon nanosheets. Furthermore, the origin of the superior performance of the N-TiO2@C nanosheet composite catalyst, along with a possible nitroaromatic reduction mechanism, has also been explored.This work was financially supported by the National Key Project on Basic Research (Grant No. 2013CB933203), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB20000000), the Natural Science Foundation of China (Grants No. 21607153, 21373224 and 21577143), the Natural Science Foundation of Fujian Province (Grant No. 2015J05044), and the Frontier Science Key Project of the Chinese Academy of Sciences (QYZDB-SSW-JSC027). The work at ORNL was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Science and Engineering Division (STEM-EELS), and through a user project supported by ORNL’s Center for Nanophase Materials Sciences, which is sponsored by the Scientific User Facilities Division of U.S. DOE

Survival trends and prognostic factors for patients with extramedullary plasmacytoma: A population-based study

BackgroundExtramedullary plasmacytoma (EMP) is a localized plasma cell neoplasm that originates from tissues other than bone. The survival trends and prognostic factors of patients with EMP in recent years remain unreported.MethodsWe used the SEER databases to extract the data. Survival curves were calculated using the Kaplan-Meier method and a nomogram was created based on the Cox’s proportional hazards model.ResultsA total of 1676 cases of EMP were identified. Patients in period-2 (2008-2016) show similar survival (p=0.8624) to those in period-1(1975-2007). Age, gender, race, and sites were prognostic of patient outcomes. And the use of surgery was associated with improved survival. The patients were randomly assigned to the training cohort and the validation cohort in a ratio of 2:1. Four factors including age, gender, race, and sites were identified to be independently predictive of the overall survival of patients with EMP. A prognostic model (EMP prognostic index, EMP-PI) comprising these four factors was constructed. Within the training cohort, three risk groups displayed significantly different 10-year survival rates: low-risk (73.0%, [95%CI 66.9-78.2]), intermediate-risk (39.3%, [95%CI 34.3-44.3]), and high-risk (22.6%, [95%CI 15.3-30.9]) (p<0.0001). Three risk groups were confirmed in the internal validation cohort. We also constructed a 5-factor nomogram based on multivariate logistic analyses.ConclusionThe survival of patients with EMP did not improve in recent years. The EMP-PI will facilitate the risk stratification and guide the risk-adapted therapy in patients with EMP

Single-cell transcriptomic atlas throughout anti-BCMA CAR-T therapy in patients with multiple myeloma

IntroductionThe emergence of chimeric antigen receptor (CAR)-T therapy targeting B cell maturation antigen (BCMA) has improved the prognosis of patients with multiple myeloma (MM); however, the majority of patients eventually experience relapse.MethodsIn this study, employing the latest single-cell RNA sequencing technology, we examined 24 bone marrow or peripheral blood samples collected throughout the course of anti-BCMA CAR-T therapy, analyzing a total of 59,725 bone marrow cells and 72,479 peripheral blood cells.ResultsOur findings reveal that tumor cells in relapsed patient exhibit higher expression levels of HSP90B1 and HSPA5, and demonstrate significantly enriched pathways regarding endoplasmic reticulum stress and unfolded protein response. In the analysis of T cells, we observed that patient with impaired effector function and increased expression of immune checkpoints in endogenous T cell are more susceptible to relapse. Notably, T cells from both the bone marrow microenvironment and peripheral blood share highly similar biological characteristics.DiscussionOverall, this study provides a comprehensive atlas of endogenous immune cells, particularly in the relatively long term, after CAR-T therapy. It offers clinical evidence for a deeper understanding of the internal environment post CAR-T treatment and for identifying mechanisms underlying relapse

Long Non-Coding RNA MEG3 Functions as a Competing Endogenous RNA to Regulate HOXA11 Expression by Sponging miR-181a in Multiple Myeloma

Background/Aims: Long non-coding RNA maternally expressed gene 3 (MEG3) has been reported to play an essential role in cancer progression and metastasis. However, the overall biological role and regulatory mechanism of MEG3 in multiple myeloma (MM) development and progression remains largely ill-defined. Methods: MEG3 and miR-181a expression of MM patients were analyzed by publicly available MM data sets. Cell counting kit-8 and flow cytometry analysis were used to identify the function of MEG3 on MM in vitro. Additionally, we conducted tumor formation experiments in mice models to explain the role of MEG3 on MM in vivo. Then, several mechanism experiments, including dual-luciferase reporter assay and RNA immunoprecipitation were performed to evaluate the emulative relationship between MEG3 and miR-181a. Results: In this research, we found that MEG3 was downregulated in MM patients, which was linked with tumor progression. In addition, we demonstrated that miR-181a was overexpressed in MM patients in consistent with its cancer-promoting function. Importantly, several mechanism experiments revealed that MEG3, acting as an endogenous competitive RNA, could contend with miR-181a to inhibit tumor progression. Furthermore, as the target mRNA of miR-181a, homeobox gene A11(HOXA11) could be positively regulated by MEG3 through sponging miR-181a competitively in vitro. Conclusion: Our present work supplies the first discovery of a MEG3/miR-181a/HOXA11 regulatory network in MM and highlights that MEG3 may serve as a promising target for MM therapy in the future

Simultaneous Optical Pulse Multiplication and Shaping Based on the Amplitude-Assisted Phase-Only Filter Utilizing a Fiber Bragg Grating

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Ultrahigh-channel-count fiber Bragg grating based on the triple sampling method

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