Thermal effects on bipartite and multipartite correlations in fiber coupled cavity arrays

We investigate the thermal influence of fibers on the dynamics of bipartite and multipartite correlations in fiber coupled cavity arrays where each cavity is resonantly coupled to a two-level atom. The atom-cavity systems connected by fibers can be considered as polaritonic qubits. We first derive a master equation to describe the evolution of the atom-cavity systems. The bipartite (multipartite) correlations is measured by concurrence and discord (spin squeezing). Then, we solve the master equation numerically and study the thermal effects on the concurrence, discord, and spin squeezing of qubits. On the one hand, at zero temperature, there are steady-state bipartite and multipartite correlations. One the other hand, the thermal fluctuations of a fiber may blockade the generation of entanglement of two qubits connected directly by the fiber while the discord can be generated and stored for a long time. This thermal-induced blockade effects of bipartite correlations may be useful for quantum information processing. The bipartite correlations of a longer chain of qubits is more robust than a shorter one in the presence of thermal fluctuations

The Auditor’s Application of Professional Judgment: Evidence from M&A-related Critical Audit Matters

In this study, I examine the extent to which auditor attributes affect the auditor’s decision to communicate a Critical Audit Matter (CAM) in the expanded auditor’s report. I expect the CAM communication decision to be adversely affected by threats to independence and by auditor overconfidence. I focus on a sample of companies that completed material mergers and acquisitions because these are likely to be considered as potential CAMs by meeting the minimal requirements of a CAM (i.e., material accounts or disclosures that involve especially challenging, subjective, or complex auditor judgment). Contrary to expectations, I find that the auditor’s CAM communication decision is influenced by the complexity of the M&A transaction and operations – a rather objective reason for communicating a CAM. While I find variation in CAM communication frequencies by the audit firm, I find little evidence suggesting that auditor attributes at the office- and firm-level affect the auditor’s CAM communication decision

A Dynamic Network for Efficient Point Cloud Registration

For the point cloud registration task, a significant challenge arises from non-overlapping points that consume extensive computational resources while negatively affecting registration accuracy. In this paper, we introduce a dynamic approach, widely utilized to improve network efficiency in computer vision tasks, to the point cloud registration task. We employ an iterative registration process on point cloud data multiple times to identify regions where matching points cluster, ultimately enabling us to remove noisy points. Specifically, we begin with deep global sampling to perform coarse global registration. Subsequently, we employ the proposed refined node proposal module to further narrow down the registration region and perform local registration. Furthermore, we utilize a spatial consistency-based classifier to evaluate the results of each registration stage. The model terminates once it reaches sufficient confidence, avoiding unnecessary computations. Extended experiments demonstrate that our model significantly reduces time consumption compared to other methods with similar results, achieving a speed improvement of over 41% on indoor dataset (3DMatch) and 33% on outdoor datasets (KITTI) while maintaining competitive registration recall requirements

Genetically engineered pre-microRNA-34a prodrug suppresses orthotopic osteosarcoma xenograft tumor growth via the induction of apoptosis and cell cycle arrest.

Osteosarcoma (OS) is the most common primary malignant bone tumor in children, and microRNA-34a (miR-34a) replacement therapy represents a new treatment strategy. This study was to define the effectiveness and safety profiles of a novel bioengineered miR-34a prodrug in orthotopic OS xenograft tumor mouse model. Highly purified pre-miR-34a prodrug significantly inhibited the proliferation of human 143B and MG-63 cells in a dose dependent manner and to much greater degrees than controls, which was attributed to induction of apoptosis and G2 cell cycle arrest. Inhibition of OS cell growth and invasion were associated with release of high levels of mature miR-34a from pre-miR-34a prodrug and consequently reduction of protein levels of many miR-34a target genes including SIRT1, BCL2, c-MET, and CDK6. Furthermore, intravenous administration of in vivo-jetPEI formulated miR-34a prodrug significantly reduced OS tumor growth in orthotopic xenograft mouse models. In addition, mouse blood chemistry profiles indicated that therapeutic doses of bioengineered miR-34a prodrug were well tolerated in these animals. The results demonstrated that bioengineered miR-34a prodrug was effective to control OS tumor growth which involved the induction of apoptosis and cell cycle arrest, supporting the development of bioengineered RNAs as a novel class of large molecule therapeutic agents