Kirenol attenuates experimental autoimmune encephalomyelitis by inhibiting differentiation of Th1 and th17 cells and inducing apoptosis of effector T cells.

Experimental autoimmune encephalomyelitis (EAE), a model of multiple sclerosis (MS), is characterized by CNS demyelination mediated by autoreactive T cells. Kirenol, a biologically active substance isolated from Herba Siegesbeckiae, has potent anti-inflammatory activities. Here we investigated effects of kirenol on EAE. Kirenol treatment markedly delayed onset of disease and reduced clinical scores in EAE mice. Kirenol treatment reduced expression of IFN-γ and IL-17A in the serum and proportion of Th1 and Th17 cells in draining lymph nodes. Priming of lymphocytes was reduced and apoptosis of MOG-activated CD4+ T cells was increased in kirenol treated EAE mice. Kirenol treatment of healthy animals did not affect the lymphocytes in these non-immunized mice. Further in vitro studies showed that kirenol inhibited viability of MOG-specific lymphocytes and induced apoptosis of MOG-specific CD4+ T cells in a dose- and time-dependent manner. Kirenol treatment upregulated Bax,downregulated Bcl-2,and increased activation of caspase-3 and release of cytochrome c, indicating that a mitochondrial pathway was involved in kirenol induced apoptosis. Moreover, pretreatment with either a pan-caspase inhibitor z-VAD-fmk or a more specific caspase 3 inhibitor Ac-DEVD-CHO in lymphocytes reduced kirenol induced apoptosis. Our findings implicate kirenol as a useful agent for the treatment of MS

Bubble budgeting: throughput optimization for dynamic workloads by exploiting dark cores in many core systems

All the cores of a many-core chip cannot be active at the same time, due to reasons like low CPU utilization in server systems and limited power budget in dark silicon era. These free cores (referred to as bubbles) can be placed near active cores for heat dissipation so that the active cores can run at a higher frequency level, boosting the performance of applications that run on active cores. Budgeting inactive cores (bubbles) to applications to boost performance has the following three challenges. First, the number of bubbles varies due to open workloads. Second, communication distance increases when a bubble is inserted between two communicating tasks (a task is a thread or process of a parallel application), leading to performance degradation. Third, budgeting too many bubbles as coolers to running applications leads to insufficient cores for future applications. In order to address these challenges, in this paper, a bubble budgeting scheme is proposed to budget free cores to each application so as to optimize the throughput of the whole system. Throughput of the system depends on the execution time of each application and the waiting time incurred for newly arrived applications. Essentially, the proposed algorithm determines the number and locations of bubbles to optimize the performance and waiting time of each application, followed by tasks of each application being mapped to a core region. A Rollout algorithm is used to budget power to the cores as the last step. Experiments show that our approach achieves 50 percent higher throughput when compared to state-of-the-art thermal-aware runtime task mapping approaches. The runtime overhead of the proposed algorithm is in the order of 1M cycles, making it an efficient runtime task management method for large-scale many-core systems

Regret-Minimizing Double Oracle for Extensive-Form Games

By incorporating regret minimization, double oracle methods have demonstrated rapid convergence to Nash Equilibrium (NE) in normal-form games and extensive-form games, through algorithms such as online double oracle (ODO) and extensive-form double oracle (XDO), respectively. In this study, we further examine the theoretical convergence rate and sample complexity of such regret minimization-based double oracle methods, utilizing a unified framework called Regret-Minimizing Double Oracle. Based on this framework, we extend ODO to extensive-form games and determine its sample complexity. Moreover, we demonstrate that the sample complexity of XDO can be exponential in the number of information sets $|S|$, owing to the exponentially decaying stopping threshold of restricted games. To solve this problem, we propose the Periodic Double Oracle (PDO) method, which has the lowest sample complexity among regret minimization-based double oracle methods, being only polynomial in $|S|$. Empirical evaluations on multiple poker and board games show that PDO achieves significantly faster convergence than previous double oracle algorithms and reaches a competitive level with state-of-the-art regret minimization methods.Comment: Accepted at ICML, 202

Inscuteable and Staufen Mediate Asymmetric Localization and Segregation of prosperoRNA during Drosophila Neuroblast Cell Divisions

AbstractWhen neuroblasts divide, inscuteable acts to coordinate protein localization and mitotic spindle orientation, ensuring that asymmetrically localized determinants like Prospero partition into one progeny. staufen encodes a dsRNA-binding protein implicated in mRNA transport in oocytes. We demonstrate that prospero RNA is also asymmetrically localized and partitioned during neuroblast cell divisions, a process requiring both inscuteable and staufen. Inscuteable and Staufen interact and colocalize with prospero RNA on the apical cortex of interphase neuroblasts. Staufen binds prospero RNA in its 3′UTR. Our findings suggest that Inscuteable nucleates an apical complex and is required for protein localization, spindle orientation, and RNA localization. Stau, as one component of this complex, is required only for RNA localization. Hence staufen also acts zygotically, downstream of inscuteable, to effect aspects of neuroblast asymmetry

A convolutional neural network based Chinese text detection algorithm via text structure modeling

Text detection in natural scene environment plays an important role in many computer vision applications. While existing text detection methods are focused on English characters, there is strong application demands on text detection in other languages, such as Chinese. As Chinese characters are much more complex than English characters, innovative and more efficient text detection techniques are required for Chinese texts. In this paper, we present a novel text detection algorithm for Chinese characters based on a specific designed convolutional neural network (CNN). The CNN model contains a text structure component detector layer, a spatial pyramid layer and a multi-input-layer deep belief network (DBN). The CNN is pretrained via a convolutional sparse auto-encoder (CSAE) in an unsupervised way, which is specifically designed for extracting complex features from Chinese characters. In particular, the text structure component detectors enhance the accuracy and uniqueness of feature descriptors by extracting multiple text structure components in various ways. The spatial pyramid layer is then introduced to enhance the scale invariability of the CNN model for detecting texts in multiple scales. Finally, the multi-input-layer DBN is used as the fully connected layers in the CNN model to ensure that features from multiple scales are comparable. A multilingual text detection dataset, in which texts in Chinese, English and digits are labeled separately, is set up to evaluate the proposed text detection algorithm. The proposed algorithm shows a significant 10% performance improvement over the baseline CNN algorithms. In addition the proposed algorithm is evaluated over a public multilingual image benchmark and achieves state-of-the-art results for text detection under multiple languages. Furthermore a simplified version of the proposed algorithm with only general components is compared to existing general text detection algorithms on the ICDAR 2011 and 2013 datasets, showing comparable detection performance to the existing algorithms

ImmersiveNeRF: Hybrid Radiance Fields for Unbounded Immersive Light Field Reconstruction

This paper proposes a hybrid radiance field representation for unbounded immersive light field reconstruction which supports high-quality rendering and aggressive view extrapolation. The key idea is to first formally separate the foreground and the background and then adaptively balance learning of them during the training process. To fulfill this goal, we represent the foreground and background as two separate radiance fields with two different spatial mapping strategies. We further propose an adaptive sampling strategy and a segmentation regularizer for more clear segmentation and robust convergence. Finally, we contribute a novel immersive light field dataset, named THUImmersive, with the potential to achieve much larger space 6DoF immersive rendering effects compared with existing datasets, by capturing multiple neighboring viewpoints for the same scene, to stimulate the research and AR/VR applications in the immersive light field domain. Extensive experiments demonstrate the strong performance of our method for unbounded immersive light field reconstruction