Quantum Probability Estimation for Randomness with Quantum Side Information

We develop a quantum version of the probability estimation framework [arXiv:1709.06159] for randomness generation with quantum side information. We show that most of the properties of probability estimation hold for quantum probability estimation (QPE). This includes asymptotic optimality at constant error and randomness expansion with logarithmic input entropy. QPE is implemented by constructing model-dependent quantum estimation factors (QEFs), which yield statistical confidence upper bounds on data-conditional normalized R\'enyi powers. This leads to conditional min-entropy estimates for randomness generation. The bounds are valid for relevant models of sequences of experimental trials without requiring independent and identical or stationary behavior. QEFs may be adapted to changing conditions during the sequence and trials can be stopped any time, such as when the results so far are satisfactory. QEFs can be constructed from entropy estimators to improve the bounds for conditional min-entropy of classical-quantum states from the entropy accumulation framework [Dupuis, Fawzi and Renner, arXiv:1607.01796]. QEFs are applicable to a larger class of models, including models permitting measurement devices with super-quantum but non-signaling behaviors and semi-device dependent models. The improved bounds are relevant for finite data or error bounds of the form $e^{-\kappa s}$, where $s$ is the number of random bits produced. We give a general construction of entropy estimators based on maximum probability estimators, which exist for many configurations. For the class of $(k,2,2)$ Bell-test configurations we provide schemas for directly optimizing QEFs to overcome the limitations of entropy-estimator-based constructions. We obtain and apply QEFs for examples involving the $(2,2,2)$ Bell-test configuration to demonstrate substantial improvements in finite-data efficiency.Comment: v2: Clarified soundness discussion and other edits, see the explanation after the references. v3: Clarified discussion of examples and comparisons. Parts of this paper have been published as Physical Review Research, 2, 013016, 2020, https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.2.01301

Modulation of Virulence of Streptococcus Pneumoniae by an Operon in Conjugative Transposon Tn5252

The aim of this work was to study of the function of the 7.4 kb DNA in the central region of Tn5252. Through virulence assay, we would determine if this operon is involved in the pathogenesis of S. pneumoniae/. Furthermore, if this operon was involved in pathogenesis, we would carry out experiments to determine how this operon is involved in the pathogenesis of S. pneumoniae/. Findings and conclusions./ The streptococcal mobile element, Tn5252 (47 kb), carries an 8 kb operon containing four genes the largest of which is about 6 kb and highly homologous to eukaryotic SNF2-like DNA methyl transferase/helicases that are involved in gene regulation. The helicase operon was introduced into the chromosome of clinical pneumococcal strains by additive transformation. When introduced intraperitoneally into young female BALB/c mice, strains bearing the intact helicase operon were found to be significantly less pathogenic than the parental wild type strain or the one with the mutated operon. However, when introduced iDepartment of Biochemistry and Molecular Biolog

CETN: Contrast-enhanced Through Network for CTR Prediction

Click-through rate (CTR) Prediction is a crucial task in personalized information retrievals, such as industrial recommender systems, online advertising, and web search. Most existing CTR Prediction models utilize explicit feature interactions to overcome the performance bottleneck of implicit feature interactions. Hence, deep CTR models based on parallel structures (e.g., DCN, FinalMLP, xDeepFM) have been proposed to obtain joint information from different semantic spaces. However, these parallel subcomponents lack effective supervisory signals, making it challenging to efficiently capture valuable multi-views feature interaction information in different semantic spaces. To address this issue, we propose a simple yet effective novel CTR model: Contrast-enhanced Through Network for CTR (CETN), so as to ensure the diversity and homogeneity of feature interaction information. Specifically, CETN employs product-based feature interactions and the augmentation (perturbation) concept from contrastive learning to segment different semantic spaces, each with distinct activation functions. This improves diversity in the feature interaction information captured by the model. Additionally, we introduce self-supervised signals and through connection within each semantic space to ensure the homogeneity of the captured feature interaction information. The experiments and research conducted on four real datasets demonstrate that our model consistently outperforms twenty baseline models in terms of AUC and Logloss

Proximal bundle methods for hybrid weakly convex composite optimization problems

This paper establishes the iteration-complexity of proximal bundle methods for solving hybrid (i.e., a blend of smooth and nonsmooth) weakly convex composite optimization (HWC-CO) problems. This is done in a unified manner by considering a proximal bundle framework (PBF) based on a generic bundle update scheme which includes various well-known bundle update schemes. In contrast to other wellknown stationary conditions in the context of HWC-CO, PBF uses a new stationarity measure which is easily verifiable and, at the same time, implies any of the former ones.Comment: 24 page

Anticancer activity of a thymidine quinoxaline conjugate is modulated by cytosolic thymidine pathways

Background High levels of thymidine kinase 1 (TK1) and thymidine phosphorylase (TYMP) are key molecular targets by thymidine therapeutics in cancer treatment. The dual roles of TYMP as a tumor growth factor and a key activation enzyme of anticancer metabolites resulted in a mixed outcome in cancer patients. In this study, we investigated the roles of TK1 and TYMP on a thymidine quinoxaline conjugate to evaluate an alternative to circumvent the contradictive role of TYMP. Methods TK1 and TYMP levels in multiple liver cell lines were assessed along with the cytotoxicity of the thymidine conjugate. Cellular accumulation of the thymidine conjugate was determined with organelle-specific dyes. The impacts of TK1 and TYMP were evaluated with siRNA/shRNA suppression and pseudoviral overexpression. Immunohistochemical analysis was performed on both normal and tumor tissues. In vivo study was carried out with a subcutaneous liver tumor model. Results We found that the thymidine conjugate had varied activities in liver cancer cells with different levels of TK1 and TYMP. The conjugate mainly accumulated at endothelial reticulum and was consistent with cytosolic pathways. TK1 was responsible for the cytotoxicity yet high levels of TYMP counteracted such activities. Levels of TYMP and TK1 in the liver tumor tissues were significantly higher than those of normal liver tissues. Induced TK1 overexpression decreased the selectivity of dT-QX due to the concurring cytotoxicity in normal cells. In contrast, shRNA suppression of TYMP significantly enhanced the selective of the conjugate in vitro and reduced the tumor growth in vivo. Conclusions TK1 was responsible for anticancer activity of dT-QX while levels of TYMP counteracted such an activity. The counteraction by TYMP could be overcome with RNA silencing to significantly enhance the dT-QX selectivity in cancer cells

STUDY ON EARTHQUAKE DESTRUCTION MODE OF THE LARGEST CANAL CROSSING HIGHWAY BRIDGE BASED ON IEM BOUNDARY IN SOUTH-TO-NORTH WATER DIVERSION

  To study the dynamic failure mechanism and damage development law of highway bridge structure under the boundary effect in the process of seismic dynamic duration, the Wenchang Highway Bridge with the largest canal crossing in the South-to-North Water Diversion is taken as an example for seismic design analysis. Based on the finite element and infinite element coupling theory, the infinite element method boundary is introduced, the concrete damage plasticity is introduced, and the half-space free field model is established to study the energy dispersion phenomenon of waves in the boundary and the absorption effect of the infinite element method boundary on wave energy is verified. Under different peak acceleration intensities, the seismic response analysis of the bridge structure was carried out. The results show that: Under the action of selected artificial waves, the damage location of the bridge mainly concentrated in the junction of the box girder supported by the pier, the bottom of the pier and the junction of the pier and beam. The damage tends to develop downward near the bottom of the box girder. The damage at both ends of the beam extends from both ends to the middle. And the bottom and top of the pier have penetrating damage. These are weak points in seismic design. At a horizontal peak acceleration of 0.6g, in addition to damage to the pier column, damage also occurred to the bottom of the box girder. Therefore, when the horizontal peak acceleration of the seismic wave is greater than 0.6g, the failure of the bottom of the box girder is paid attention to. Moreover, the IEM boundary has a good control effect on the far-field energy dissipation of the wave, which is simpler and more efficient than the viscous–spring boundary

BMP Signaling Mediated by BMPR1A in Osteoclasts Negatively Regulates Osteoblast Mineralization Through Suppression of Cx43

Osteoblasts and osteoclasts are well orchestrated through different mechanisms of communication during bone remodeling. Previously, we found that osteoclast‐specific disruption of one of the BMP receptors, Bmpr1a, results in increased osteoblastic bone formation in mice. We hypothesized that BMPR1A signaling in osteoclasts regulates production of either membrane bound proteins or secreted molecules that regulated osteoblast differentiation. In our current study, we co‐cultured wild‐type osteoblasts with either control osteoclasts or osteoclasts lacking BMPR1A signaling activity. We found that loss of Bmpr1a in osteoclasts promoted osteoblast mineralization in vitro. Further, we found that the expression of Cx43/Gja1 in the mutant osteoclasts was increased, which encoded for one of the gap junction proteins connexin 43/gap junction alpha 1. Knockdown of Gja1 in the mutant osteoclasts for Bmpr1a reduced osteoblastic mineralization when co‐cultured. Our findings suggest that GJA1 may be one of the downstream targets of BMPR1A signaling in osteoclasts that mediates osteoclast–osteoblast communication during bone remodeling. J. Cell. Biochem. 118: 605–614, 2017. © 2016 Wiley Periodicals, Inc.Disruption of Bmpr1a in osteoclasts promoted osteoblast mineralization when co‐cultured. Up‐regulation of gap junction Cx43/Gja1 in mutant osteoclasts is responsible for the enhanced osteoblast function.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135668/1/jcb25746_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135668/2/jcb25746.pd

Making Language Models Better Tool Learners with Execution Feedback

Tools serve as pivotal interfaces that enable humans to understand and reshape the environment. With the advent of foundation models, AI systems can utilize tools to expand their capabilities and interact with the real world. Existing tool learning methodologies, encompassing supervised fine-tuning and prompt engineering approaches, often induce large language models to utilize tools indiscriminately, as complex tasks often exceed their own competencies. However, introducing tools for simple tasks, which the models themselves can readily resolve, can inadvertently propagate errors rather than enhance performance. This leads to the research question: can we teach language models when and how to use tools? To meet this need, we propose Tool leaRning wIth exeCution fEedback (TRICE), a two-stage end-to-end framework that enables the model to continually learn through feedback derived from tool execution, thereby learning when and how to use tools effectively. Experimental results, backed by further analysis, show that TRICE can make the large language model selectively use tools by improving the accuracy of tool usage while enhancing insufficient tool learning and mitigating excessive reliance on tools. Code is available at https://github.com/zjunlp/TRICE.Comment: NAACL 202