On network coding and routing in dynamic wireless multicast networks

We compare multicast network coding and routing for a time-varying wireless network model with interference- determined link capacities. We use dynamic back pressure algorithms that are optimal for intra-session network coding and routing respectively. Our results suggest that under such conditions, the gap in multicast capacity between network coding and routing can decrease relative to a collision-based wireless model with fixed link capacities

Interplay between Quantum Size Effect and Strain Effect on Growth of Nanoscale Metal Thin Film

We develop a theoretical framework to investigate the interplay between quantum size effect (QSE) and strain effect on the stability of metal nanofilms. The QSE and strain effect are shown to be coupled through the concept of "quantum electronic stress. First-principles calculations reveal large quantum oscillations in the surface stress of metal nanofilms as a function of film thickness. This adds extrinsically additional strain-coupled quantum oscillations to surface energy of strained metal nanofilms. Our theory enables a quantitative estimation of the amount of strain in experimental samples, and suggests strain be an important factor contributing to the discrepancies between the existing theories and experiments

A description of the transverse momentum distributions of charged particles produced in heavy ion collisions at RHIC and LHC energies

By assuming the existing of memory effects and long-range interactions in the hot and dense matter produced in high energy heavy ion collisions, the nonextensive statistics together with the relativistic hydrodynamics including phase transition is used to discuss the transverse momentum distributions of charged particles produced in heavy ion collisions. It is shown that the combined contributions from nonextensive statistics and hydrodynamics can give a good description to the experimental data in Au+Au collisions at sqrt(s_NN )= 200 GeV and in Pb+Pb collisions at sqrt(s_NN) )= 2.76 TeV for pi^(+ -) , K^(+ -) in the whole measured transverse momentum region, and for p(p-bar) in the region of p_T<= 2.0 GeV/c. This is different from our previous work, where, by using the conventional statistics plus hydrodynamics, the describable region is only limited in p_T<= 1.1 GeV/c.Comment: 14 pages, 3 figures, 2 table

The chloride channel cystic fibrosis transmembrane conductance regulator (CFTR) controls cellular quiescence by hyperpolarizing the cell membrane during diapause in the crustacean Artemia

Cellular quiescence, a reversible state in which growth, proliferation, and other cellular activities are arrested, is important for self-renewal, differentiation, development, regeneration, and stress resistance. However, the physiological mechanisms underlying cellular quiescence remain largely unknown. In the present study, we used embryos of the crustacean Artemia in the diapause stage, in which these embryos remain quiescent for prolonged periods, as a model to explore the relationship between cell-membrane potential (V-mem) and quiescence. We found that V-mem is hyperpolarized and that the intracellular chloride concentration is high in diapause embryos, whereas V-mem is depolarized and intracellular chloride concentration is reduced in postdiapause embryos and during further embryonic development. We identified and characterized the chloride ion channel protein cystic fibrosis transmembrane conductance regulator (CFTR) of Artemia (Ar-CFTR) and found that its expression is silenced in quiescent cells of Artemia diapause embryos but remains constant in all other embryonic stages. Ar-CFTR knockdown and GlyH-101-mediated chemical inhibition of Ar-CFTR produced diapause embryos having a high V-mem and intracellular chloride concentration, whereas control Artemia embryos released free-swimming nauplius larvae. Transcriptome analysis of embryos at different developmental stages revealed that proliferation, differentiation, and metabolism are suppressed in diapause embryos and restored in postdiapause embryos. Combined with RNA sequencing (RNA-Seq) of GlyH-101-treated MCF-7 breast cancer cells, these analyses revealed that CFTR inhibition down-regulates the Wnt and Aurora Kinase A (AURKA) signaling pathways and up-regulates the p53 signaling pathway. Our findings provide insight into CFTR-mediated regulation of cellular quiescence and V-mem in the Artemia model

Comparison of Network Coding and Non-Network Coding Schemes for Multi-hop Wireless Networks

Network coding has been shown to be useful for throughput and reliability in various network topologies, under a fixed-rate, point-to-multipoint wireless network model. We study the effect of introducing a wireless network model where link capacity depends on the network geometry and the signal to interference and noise ratio. In particular, we compare strategies with and without network coding on a multicast network with and without fading, and on single-user multiple path networks with fading. For the multicast network without fading, we find that the network geometry affects which scheme attains higher throughput. For the case with fading, we compare the throughput-outage probability curves achieved by network coding and repetition schemes. For the multiple path networks, we further consider the case where multiple simultaneous transmissions of identical information signals can be combined at a receiver. We find that the relative performance of the schemes we consider depends on the network geometry, the ratio of signal to noise power, whether multiple simultaneous transmissions can be combined, and the operating point on the throughput-outage probability curve

Deep Group Interest Modeling of Full Lifelong User Behaviors for CTR Prediction

Extracting users' interests from their lifelong behavior sequence is crucial for predicting Click-Through Rate (CTR). Most current methods employ a two-stage process for efficiency: they first select historical behaviors related to the candidate item and then deduce the user's interest from this narrowed-down behavior sub-sequence. This two-stage paradigm, though effective, leads to information loss. Solely using users' lifelong click behaviors doesn't provide a complete picture of their interests, leading to suboptimal performance. In our research, we introduce the Deep Group Interest Network (DGIN), an end-to-end method to model the user's entire behavior history. This includes all post-registration actions, such as clicks, cart additions, purchases, and more, providing a nuanced user understanding. We start by grouping the full range of behaviors using a relevant key (like item_id) to enhance efficiency. This process reduces the behavior length significantly, from O(10^4) to O(10^2). To mitigate the potential loss of information due to grouping, we incorporate two categories of group attributes. Within each group, we calculate statistical information on various heterogeneous behaviors (like behavior counts) and employ self-attention mechanisms to highlight unique behavior characteristics (like behavior type). Based on this reorganized behavior data, the user's interests are derived using the Transformer technique. Additionally, we identify a subset of behaviors that share the same item_id with the candidate item from the lifelong behavior sequence. The insights from this subset reveal the user's decision-making process related to the candidate item, improving prediction accuracy. Our comprehensive evaluation, both on industrial and public datasets, validates DGIN's efficacy and efficiency