Transport Exponents of Sturmian Hamiltonians

We consider discrete Schr\"odinger operators with Sturmian potentials and study the transport exponents associated with them. Under suitable assumptions on the frequency, we establish upper and lower bounds for the upper transport exponents. As an application of these bounds, we identify the large coupling asymptotics of the upper transport exponents for frequencies of constant type. We also bound the large coupling asymptotics uniformly from above for Lebesgue-typical frequency. A particular consequence of these results is that for most frequencies of constant type, transport is faster than for Lebesgue almost every frequency. We also show quasi-ballistic transport for all coupling constants, generic frequencies, and suitable phases.Comment: 30 page

Information interchange system and apparatus

To overcome the drawback of difficulties when interchanging a patient's health record among different health information management systems and yet keep the patient's privacy, this invention proposes a method comprising the steps of: extracting, from a certificate, a signature of a first service provider and a first identifier; generating a second identifier corresponding to the first identifier; sending a request to any one of a second identifier manager and the first service provider so as to request a record associated with the first identifier; receiving the requested record from any one of the second identifier manager and the first service provider; and associating the requested record with the second identifier. Use of the proposed method provides the advantage that there is no need to unify all health information management systems adopting the same pseudonymization service, and makes it easy to share health information among different health information management systems without disclosing the patient's privacy

NF-κB/p65 antagonizes Nrf2-ARE pathway by depriving CBP from Nrf2 and facilitating recruitment of HDAC3 to MafK

AbstractConstitutively activated NF-κB occurs in many inflammatory and tumor tissues. Does it interfere with anti-inflammatory or anti-tumor signaling pathway? Here, we report that NF-κB p65 subunit repressed the Nrf2-antioxidant response element (ARE) pathway at transcriptional level. In the cells where NF-κB and Nrf2 were simultaneously activated, p65 unidirectionally antagonized the transcriptional activity of Nrf2. In the p65-overexpressing cells, the ARE-dependent expression of heme oxygenase-1 was strongly suppressed. However, p65 inhibited the ARE-driven gene transcription in a way that was independent of its own transcriptional activity. Two mechanisms were found to coordinate the p65-mediated repression of ARE: (1) p65 selectively deprives CREB binding protein (CBP) from Nrf2 by competitive interaction with the CH1-KIX domain of CBP, which results in inactivation of Nrf2. The inactivation depends on PKA catalytic subunit-mediated phosphorylation of p65 at S276. (2) p65 promotes recruitment of histone deacetylase 3 (HDAC3), the corepressor, to ARE by facilitating the interaction of HDAC3 with either CBP or MafK, leading to local histone hypoacetylation. This investigation revealed the participation of NF-κB p65 in the negative regulation of Nrf2-ARE signaling, and might provide a new insight into a possible role of NF-κB in suppressing the expression of anti-inflammatory or anti-tumor genes

A Semiparametric Approach for Analyzing Nonignorable Missing Data

In missing data analysis, there is often a need to assess the sensitivity of key inferences to departures from untestable assumptions regarding the missing data process. Such sensitivity analysis often requires specifying a missing data model which commonly assumes parametric functional forms for the predictors of missingness. In this paper, we relax the parametric assumption and investigate the use of a generalized additive missing data model. We also consider the possibility of a non-linear relationship between missingness and the potentially missing outcome, whereas the existing literature commonly assumes a more restricted linear relationship. To avoid the computational complexity, we adopt an index approach for local sensitivity. We derive explicit formulas for the resulting semiparametric sensitivity index. The computation of the index is simple and completely avoids the need to repeatedly fit the semiparametric nonignorable model. Only estimates from the standard software analysis are required with a moderate amount of additional computation. Thus, the semiparametric index provides a fast and robust method to adjust the standard estimates for nonignorable missingness. An extensive simulation study is conducted to evaluate the effects of misspecifying the missing data model and to compare the performance of the proposed approach with the commonly used parametric approaches. The simulation study shows that the proposed method helps reduce bias that might arise from the misspecification of the functional forms of predictors in the missing data model. We illustrate the method in a Wage Offer dataset.

LMC: Large Model Collaboration with Cross-assessment for Training-Free Open-Set Object Recognition

Open-set object recognition aims to identify if an object is from a class that has been encountered during training or not. To perform open-set object recognition accurately, a key challenge is how to reduce the reliance on spurious-discriminative features. In this paper, motivated by that different large models pre-trained through different paradigms can possess very rich while distinct implicit knowledge, we propose a novel framework named Large Model Collaboration (LMC) to tackle the above challenge via collaborating different off-the-shelf large models in a training-free manner. Moreover, we also incorporate the proposed framework with several novel designs to effectively extract implicit knowledge from large models. Extensive experiments demonstrate the efficacy of our proposed framework. Code is available \href{https://github.com/Harryqu123/LMC}{here}.Comment: NeurIPS 202