On the hopping pattern design for D2D discovery with invariant

In this paper, we focus on the hopping pattern design for device-to-device (D2D) discovery. The requirements of hopping pattern is discussed, where the impact of specific system constraints, e.g., frequency hopping, is also taken into consideration. Specifically speaking, we discover and utilize the novel feature of resource hopping, i.e., "hopping invariant" to design four new hopping patterns and analyze their performance. The hopping invariant can be used to deliver information for specific users without extra radio resources, and due to the connection between hopping invariant and resource location, receiver complexity can be significantly reduced. Furthermore, our schemes are designed to be independent of discovery frame number, which makes them more suitable to be implemented in practical systems

SLSSNN: High energy efficiency spike-train level spiking neural networks with spatio-temporal conversion

Brain-inspired spiking neuron networks (SNNs) have attracted widespread research interest due to their low power features, high biological plausibility, and strong spatiotemporal information processing capability. Although adopting a surrogate gradient (SG) makes the non-differentiability SNN trainable, achieving comparable accuracy for ANNs and keeping low-power features simultaneously is still tricky. In this paper, we proposed an energy-efficient spike-train level spiking neural network (SLSSNN) with low computational cost and high accuracy. In the SLSSNN, spatio-temporal conversion blocks (STCBs) are applied to replace the convolutional and ReLU layers to keep the low power features of SNNs and improve accuracy. However, SLSSNN cannot adopt backpropagation algorithms directly due to the non-differentiability nature of spike trains. We proposed a suitable learning rule for SLSSNNs by deducing the equivalent gradient of STCB. We evaluate the proposed SLSSNN on static and neuromorphic datasets, including Fashion-Mnist, Cifar10, Cifar100, TinyImageNet, and DVS-Cifar10. The experiment results show that our proposed SLSSNN outperforms the state-of-the-art accuracy on nearly all datasets, using fewer time steps and being highly energy-efficient

Comparative Study of IFN-Based Versus IFN-Free Regimens and Their Efficacy in Treatment of Chronic Hepatitis C Infections

The hepatitis C viral (HCV) infection is a global health burden, WHO estimates 130–150 million people chronically infected with hepatitis C virus worldwide. Additional 3–4 million people become newly infected annually and more than 350,000 people die each year of HCV-related liver diseases. HCV infection exhibits higher genetic diversity with regional variations in genotypic prevalence resulting big challenges on disease management. Introduction of DAAs revolutionised the new era of all oral therapy in treatment of chronic hepatitis C infection and is the regimens of choice in present days. However, IFN-based combination therapy with sofosbuvir has promising efficacy in genotypes 3, 4, 5 or 6 infections compared to genotypes 1 and 2 infections. So, these regimens could be an option in DAAs regimen failure cases. The poor availability of data on recent DAAs (IFN-free) regimens questioned on regular use and cost effectiveness is the another challenge with DAAs regimens. So phase III trials (sofosbuvir and velpatasvir) of recent DAAs with pangenotypic actions and better tolerability in HCV infected patients are the future advances in treatment of chronic hepatitis C. After all those recent combination therapies with better SVR, the combination of pegylated interferon with ribavirin is the only option available where unavailability of other regimens still exists

Mathematical modelling of bone remodelling cycles including the NFκB signalling pathway

RANKL can promote the differentiation of osteoclast precursors into mature osteoclasts by binding to RANK expressed on the surfaces of osteoclast progenitor cells during bone remodelling. The NF-κB signalling pathway is downstream of RANKL and transmits the RANKL signal to nuclear promoter-bound protein complexes from cell surface receptors, which then regulates target gene expression to facilitate osteoclastogenesis. However, this important role of the NF-κB signalling pathway is usually ignored in published mathematical models of bone remodelling. This paper describes the construction of a mathematical model of bone remodelling in a normal bone microenvironment with inclusion of the NF-κB signalling pathway. The model consisted of a set of ordinary differential equations and reconstructed variations in the bone cells, resultant bone volume, and biochemical factors involved in the NF-κB signalling pathway over time. The model was used to investigate how the NF-κB pathway is activated in osteoclast precursors to promote osteoclastogenesis during bone remodelling. Model simulations agreed well with published experimental data. It is hoped that this model can improve our understanding of bone remodelling. It has the obvious potential to examine the influence of NF-κB dysregulation on bone remodelling, and even propose potential therapeutic strategies to combat related bone diseases in future research

Molluscs of an intertidal soft-sediment area in China:Does overfishing explain a high density but low diversity community that benefits staging shorebirds?

The Yellow Sea is a key staging ground for shorebirds that migrate from Australasia to the Arctic each spring. A lot of attention has been paid to the impact of habitat loss due to land reclamation on shorebird survival, but any effects of overfishing of coastal resources are unclear. In this study, the abundance of molluscs in the intertidal mudflats of northern Bohai Bay on the Chinese Yellow Sea was investigated in 2008–2014 from the perspective of their importance as food for northward migrating shorebirds, especially Red Knots Calidris canutus. Numerically contributing 96% to the numbers of 17 species found in spring 2008, the bivalve Potamocorbula laevis (the staple food of Red Knots and other shorebirds) dominated the intertidal mollusc community. In the spring of 2008–2014, the densities of P. laevis were surprisingly high, varying between 3900 and 41,000 individuals/m2 at distinctly small sizes (average shell lengths of 1.1 to 4.8 mm), and thus reaching some of the highest densities of marine bivalves recorded worldwide and providing good food for shorebirds. The distribution of P. laevis was associated with relatively soft sediments in close proximity to the recently built seawalls. A monthly sampling programme showed steep seasonal changes in abundance and size. P. laevis were nearly absent in winter, each year settling on the intertidal mudflats anew. Peak densities were reached in spring, when 0-age P. laevis were 1–3 mm long. The findings point to a highly unusual demographic structure of the species, suggesting that some interfering factors are at play. We hypothesise that the current dominance of young P. laevis in Bohai Bay reflects the combined pressures of a nearly complete active removal of adult populations from mid-summer to autumn for shrimp farming (this clearing of adults may offer space for recruitment during the next spring) and low numbers of epibenthic predators of bivalves, such as shrimps and crabs, due to persistent overfishing in recent decades (allowing freshly settled juveniles to reach high densities). To the best of our knowledge, the idea that overfishing of competing marine mesopredators benefits staging shorebirds, at least in the short term, is novel; it now needs further experimental and comparative scrutiny. The long-term effects of overfishing on benthic communities of the mudflats need further investigation

Development of a resource-efficient FPGA-based neural network regression model for the ATLAS muon trigger upgrades

In this paper, a resource-efficient FPGA-based neural network regression model is developed for potential applications in the future hardware muon trigger system of the ATLAS experiment at the Large Hadron Collider (LHC). Effective real-time selection of muon candidates is the cornerstone of the ATLAS physics programme. With the planned upgrades, the entirely new FPGA-based hardware muon trigger system will be installed in 2025-2026 that will process full muon detector data within a 10 ${\mu}s$ latency window. The planned large FPGA devices should have sufficient spare resources to allow deployment of machine learning methods for improving identification of muon candidates and searching for new exotic particles. Our model promises to improve the rejection of the dominant source of background events in the central detector region, which are due to muon candidates with low transverse momenta. This neural network was implemented in the hardware description language using 65 digital signal processors and about 10,000 lookup tables. The simulated network latency and deadtime are 245 and 60 ns, respectively, when implemented in the FPGA device using a 400 MHz clock frequency. These results are well within the requirements of the future ATLAS muon trigger system, therefore opening a possibility for deploying machine learning methods for data taking by the ATLAS experiment at the High Luminosity LHC.Comment: 12 pages, 17 figure

Structure and function of pancreatic lipase-related protein 2 and its relationship with pathological states

Pancreatic lipase is critical for the digestion and absorption of dietary fats. The most abundant lipolytic enzymes secreted by the pancreas are pancreatic triglyceride lipase (PTL or PNLIP) and its family members, pancreatic lipase-related protein 1 (PNLIPRP1or PLRP1) and pancreatic lipase-related protein 2 (PNLIPRP2 or PLRP2). Unlike the family\u27s other members, PNLIPRP2 plays an elemental role in lipid digestion, especially for newborns. Therefore, if genetic factors cause gene mutation, or other factors lead to non-expression, it may have an effect on fat digestion and absorption, on the susceptibility to pancreas and intestinal pathogens. In this review, we will summarize what is known about the structure and function of PNLIPRP2 and the levels of PNLIPRP2 and associated various pathological states

A General Implicit Framework for Fast NeRF Composition and Rendering

A variety of Neural Radiance Fields (NeRF) methods have recently achieved remarkable success in high render speed. However, current accelerating methods are specialized and incompatible with various implicit methods, preventing real-time composition over various types of NeRF works. Because NeRF relies on sampling along rays, it is possible to provide general guidance for acceleration. To that end, we propose a general implicit pipeline for composing NeRF objects quickly. Our method enables the casting of dynamic shadows within or between objects using analytical light sources while allowing multiple NeRF objects to be seamlessly placed and rendered together with any arbitrary rigid transformations. Mainly, our work introduces a new surface representation known as Neural Depth Fields (NeDF) that quickly determines the spatial relationship between objects by allowing direct intersection computation between rays and implicit surfaces. It leverages an intersection neural network to query NeRF for acceleration instead of depending on an explicit spatial structure.Our proposed method is the first to enable both the progressive and interactive composition of NeRF objects. Additionally, it also serves as a previewing plugin for a range of existing NeRF works.Comment: 7 pages for main conten