Note on the emergence of cosmic space in modified gravities

Intrigued by the holographic principle, Padmanabhan recently proposed a novel idea, saying that our cosmic space is emergent as cosmic time progresses. In particular, the expansion rate of the Universe is related to the difference between the surface degrees of freedom on the holographic horizon and the bulk degrees of freedom inside. In this note, we generalize this interesting paradigm to brane world, scalar-tensor gravity, and f(R) theory, respectively, and find that in the cosmological setting the Friedmann equations can be successfully derived.Comment: 10pages, no figures, typos corrected, refs added, some refs updated, a footnote added, discussion improved, published in PR

Rethinking Item Importance in Session-based Recommendation

Session-based recommendation aims to predict users' based on anonymous sessions. Previous work mainly focuses on the transition relationship between items during an ongoing session. They generally fail to pay enough attention to the importance of the items in terms of their relevance to user's main intent. In this paper, we propose a Session-based Recommendation approach with an Importance Extraction Module, i.e., SR-IEM, that considers both a user's long-term and recent behavior in an ongoing session. We employ a modified self-attention mechanism to estimate item importance in a session, which is then used to predict user's long-term preference. Item recommendations are produced by combining the user's long-term preference and current interest as conveyed by the last interacted item. Experiments conducted on two benchmark datasets validate that SR-IEM outperforms the start-of-the-art in terms of Recall and MRR and has a reduced computational complexity

A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems

Bike sharing provides an environment-friendly way for traveling and is booming all over the world. Yet, due to the high similarity of user travel patterns, the bike imbalance problem constantly occurs, especially for dockless bike sharing systems, causing significant impact on service quality and company revenue. Thus, it has become a critical task for bike sharing systems to resolve such imbalance efficiently. In this paper, we propose a novel deep reinforcement learning framework for incentivizing users to rebalance such systems. We model the problem as a Markov decision process and take both spatial and temporal features into consideration. We develop a novel deep reinforcement learning algorithm called Hierarchical Reinforcement Pricing (HRP), which builds upon the Deep Deterministic Policy Gradient algorithm. Different from existing methods that often ignore spatial information and rely heavily on accurate prediction, HRP captures both spatial and temporal dependencies using a divide-and-conquer structure with an embedded localized module. We conduct extensive experiments to evaluate HRP, based on a dataset from Mobike, a major Chinese dockless bike sharing company. Results show that HRP performs close to the 24-timeslot look-ahead optimization, and outperforms state-of-the-art methods in both service level and bike distribution. It also transfers well when applied to unseen areas

DNA Damage and Its Links to Neurodegeneration

The integrity of our genetic material is under constant attack from numerous endogenous and exogenous agents. The consequences of a defective DNA damage response are well studied in proliferating cells, especially with regards to the development of cancer, yet its precise roles in the nervous system are relatively poorly understood. Here we attempt to provide a comprehensive overview of the consequences of genomic instability in the nervous system. We highlight the neuropathology of congenital syndromes that result from mutations in DNA repair factors and underscore the importance of the DNA damage response in neural development. In addition, we describe the findings of recent studies, which reveal that a robust DNA damage response is also intimately connected to aging and the manifestation of age-related neurodegenerative disorders such as Alzheimer's disease and amyotrophic lateral sclerosis. Video Abstract: In this Review, Madabhushi etal. summarize the current state of knowledge about how DNA damage and changes to the DNA damage response in neurons might underlie neurodegenerative diseases

Silicon nitride and silica quarter-wave stacks for low-thermal-noise mirror coatings

This study investigates a multilayer high reflector with new coating materials for next-generation laser interferometer gravitational wave detectors operated at cryogenic temperatures. We use the plasma-enhanced chemical vapor deposition method to deposit amorphous silicon nitride and silica quarter-wave high-reflector stacks and studied the properties pertinent to the coating thermal noise. Room- and cryogenic-temperature mechanical loss angles of the silicon nitride and silica quarter-wave bilayers are measured using the cantilever ring-down method. We show, for the first time, that the bulk and shear loss angles of the coatings can be obtained from the cantilever ring-down measurement, and we use the bulk and shear losses to calculate the coating thermal noise of silicon nitride and silica high-reflector coatings. The mechanical loss angle of the silicon nitride and silica bilayer is dispersive with a linear weakly positive frequency dependence, and, hence, the coating thermal noise of the high reflectors show a weakly positive frequency dependence in addition to the normal 1/ vf dependence. The coating thermal noise of the silicon nitride and silica high-reflector stack is compared to the lower limit of the coating thermal noise of the end test mirrors of ET-LF, KAGRA, LIGO Voyager, and the directly measured coating thermal noise of the current coatings of Advanced LIGO. The optical absorption of the silicon nitride and silica high reflector at 1550 nm is 45.9 ppm. Using a multimaterial system composed of seven pairs of ion-beam-sputter deposited Ti∶Ta2O5 and silica and nine pairs of silicon nitride and silica on a silicon substrate, the optical absorption can be reduced to 2 ppm, which meets the specification of LIGO Voyager