Effects of fast density dependent dispersal on pre-emptive competition dynamics

The aim of this work is to investigate the effect of density-dependent dispersal on the outcome of competition in a heterogeneous environment. We present a classical spatial interspecific competition model with two patches connected by fast dispersal. We assume local pre-emptive competition, i.e. on each isolated patch, the species having the largest initial density would exclude the other one. We assume that individuals of species 1 disperse at constant dispersal rates, while species 2 dispersal is density dependent. Species 2 individuals are more likely to disperse when there are many species 1 competitors in a patch. We investigate if a dispersal strategy that aims at avoiding its competitor can be beneficial. Dispersal between patches is assumed to be faster than local population dynamics on each patch. We take advantage of these time scales in order to reduce the complete model into an aggregated model governing total densities of both species at a slow time scale. The analysis of this global model shows that using a density dependent strategy for dispersal is beneficial for species 2 in the sense that it prevents its own extinction and even it allows species 2 to exclude species 1 under some conditions

Liquefaction of immersed granular media under isotropic compression

We report an observation of the spontaneous liquefaction of glass beads immersed in water and compacted by external isotropic stress. We show that during compression, loose granular samples exhibit a series of sudden rearrangements accompanied by a transient overpressure of interstitial fluid. Ultimately, spontaneous liquefaction with large deformation of the sample is observed. By contrast, denser samples do not show a liquefaction by maintaining its shape integrity. We then discuss the potential mechanisms which could explain this unexpected liquefaction

Taenia solium taeniosis/cysticercosis in Asia: epidemiology, impact and issues

Several reports of patients with cysticercosis from many countries in Asia such as India, China, Indonesia, Thailand, Korea, Taiwan and Nepal are a clear indicator of the wide prevalence of Taenia solium cysticercosis and taeniosis in these and other Asian countries. However, epidemiological data from community based studies are sparse and available only for a few countries in Asia. Cysticercosis is the cause of epilepsy in up to 50% of Indian patients presenting with partial seizures. It is also a major cause of epilepsy in Bali (Indonesia), Vietnam and possibly China and Nepal. Seroprevalence studies indicate high rates of exposure to the parasite in several countries (Vietnam, China, Korea and Bali (Indonesia)) with rates ranging from 0.02 to 12.6%. Rates of taeniosis, as determined by stool examination for ova, have also been reported to range between 0.1 and 6% in the community in India, Vietnam, China, and Bali (Indonesia). An astonishingly high rate of taeniosis of 50% was reported from an area in Nepal populated by pig rearing farmers. In addition to poor sanitation, unhealthy pig rearing practices, low hygienic standards, unusual customs such as consumption of raw pork is an additional factor contributing to the spread of the disease in some communities of Asia. Undoubtedly, cysticercosis is a major public health problem in several Asian countries effecting several million people by not only causing neurological morbidity but also imposing economic hardship on impoverished populations. However, there are wide variations in the prevalence rates in different regions and different socio-economic groups in the same country. It is important to press for the recognition of cysticercosis as one of the major public health problems in Asia that needs to be tackled vigorously by the governments and public health authorities of the region

An In-Situ Dynamic Quantization With 3D Stacking Synaptic Memory for Power-Aware Neuromorphic Architecture

Spiking Neural Networks (SNNs) show their potential for lightweight low-power inferences because they mimic the functionality of the biological brain. However, one of the major challenges of SNNs like other neural networks is memory-wall and power-wall when accessing data (synaptic weights) from memory. It limits the potential of spiking neural networks implemented on edge devices. In this paper, we present a novel spiking computing hardware architecture named NASH-3DM using 3D-IC-based stacking memory with power supply awareness to effectively decrease power consumption for AI-enabled edge devices. Instead of storing one or multiple weights in a single memory word, we split them into small subsets and allocate each subset into a separate memory in every stacking layer. With the natural separation of stack layers, our system can activate and deactivate each layer separately. Therefore, it can offer in-situ (online, post-manufacture, and without interruption) dynamic quantization with multiple operating modes. With the CMOS 45nm technology, our energy per synaptic operation for MNIST classification can reduce by 36.67% while having 0.93%-1.14% accuracy loss at 5-bit quantization. The energy per synaptic operation reduction for the CIFAR10 dataset is 36.68% when switching from the 16-bit active operation to the in-situ 10-bit one with an accuracy loss of 5.69%

Noncoherent joint transmission beamforming for dense small cell networks:global optimality, efficient solution and distributed implementation

Abstract We investigate the coordinated multi-point noncoherent joint transmission (JT) in dense small cell networks. The goal is to design beamforming vectors for macro cell and small cell base stations (BSs) such that the weighted sum rate of the system is maximized, subject to a total transmit power at individual BSs. The optimization problem is inherently nonconvex and intractable, making it difficult to explore the full potential performance of the scheme. To this end, we first propose an algorithm to find a globally optimal solution based on the generic monotonic branch reduce and bound optimization framework. Then, for a more computationally efficient method, we adopt the inner approximation (InAp) technique to efficiently derive a locally optimal solution, which is numerically shown to achieve near-optimal performance. In addition, for decentralized networks such as those comprising of multi-access edge computing servers, we develop an algorithm based on the alternating direction method of multipliers, which distributively implements the InAp-based solution. Our main conclusion is that the noncoherent JT is a promising transmission scheme for dense small cell networks, since it can exploit the densitification gain, outperforms the coordinated beamforming, and is amenable to distributed implementation