Unleashing the Potential of Spiking Neural Networks for Sequential Modeling with Contextual Embedding

The human brain exhibits remarkable abilities in integrating temporally distant sensory inputs for decision-making. However, existing brain-inspired spiking neural networks (SNNs) have struggled to match their biological counterpart in modeling long-term temporal relationships. To address this problem, this paper presents a novel Contextual Embedding Leaky Integrate-and-Fire (CE-LIF) spiking neuron model. Specifically, the CE-LIF model incorporates a meticulously designed contextual embedding component into the adaptive neuronal firing threshold, thereby enhancing the memory storage of spiking neurons and facilitating effective sequential modeling. Additionally, theoretical analysis is provided to elucidate how the CE-LIF model enables long-term temporal credit assignment. Remarkably, when compared to state-of-the-art recurrent SNNs, feedforward SNNs comprising the proposed CE-LIF neurons demonstrate superior performance across extensive sequential modeling tasks in terms of classification accuracy, network convergence speed, and memory capacity

Manipulating Electromagnetic Waves with Zero Index Materials

Zero-index material is a typical metamaterial with an effective zero refractive index, possessing a variety of exotic electromagnetic properties and particular functionalities. We have considered two kinds of zero-index materials with the first one a nearly matched zero index made of magnetic metamaterial and the second one a radially anisotropic zero index. The magnetic metamaterial-based systems are shown to be significant in wavefront engineering and flexibly tunable by an external magnetic field and a temperature field. The radially anisotropic zero-index-based systems can remarkably enhance the omnidirectional isotropic radiation by enclosing a line source and a dielectric particle within a shell configuration. The physical origin lies in that the dielectric particle effectively rescatters the trapped anisotropic higher order modes and converts them into the isotropic 0th order mode radiated outside the system. The case for the system with the loss is then examined and the energy compensation with a gain particle is also demonstrated

Molecular and morphological evidence support a new species of Rosaceae Prunus subg. Cerasus from Wuyishan National Park, southeast China

Prunus tongmuensis, a new species of cherry blossom, is described and illustrated from Wuyishan National Park, southeast China. This species is characterized by its tubular to nearly bottle-shaped receptacles and dark purple drupes. It can be distinguished from other wild cherry trees by its flowers and leaves, reddish brown young leaves, presence of 1–2 glands at the base of leaves, petioles densely covered with yellowish brown villi, longer pedicels (0.6–2.5 cm), villous pistil, and dark purple drupes. In the present study, we conducted a comprehensive morphological study based on specimens of the new species and its morphologically close species, field observations, and examination of pollen morphology. In addition, our phylogenetic analysis based on the complete plastid genome sequences further confirms the status of the new species and indicates that it is closely related to Prunus clarofolia, however, it notably differs in leaf shape, size, petiole villus color, gland location, timing of flower and leaf openings, and reflexed or spread sepals, as well as drupe color

Bio-inspired categorization using event-driven feature extraction and spike-based learning

This paper presents a fully event-driven feedforward architecture that accounts for rapid categorization. The proposed algorithm processes the address event data generated either from an image or from Address-Event-Representation (AER) temporal contrast vision sensor. Bio-inspired, cortex-like, spikebased features are obtained through event-driven convolution and neural competition. The extracted spike feature patterns are then classified by a network of leaky integrate-and-fire (LIF) spiking neurons, in which the weights are trained using tempotron learning rule. One appealing characteristic of our system is the fully event-driven processing. The input, the features, and the classification are all based on address events (spikes). Experimental results on three datasets have proved the efficacy of the proposed algorithm.Accepted versio

Modeling Climate Change Impacts on Water Balance of a Mediterranean Watershed Using SWAT+

The consequences of climate change on food security in arid and semi-arid regions can be serious. Understanding climate change impacts on water balance is critical to assess future crop performance and develop sustainable adaptation strategies. This paper presents a climate change impact study on the water balance components of an agricultural watershed in the Mediterranean region. The restructured version of the Soil and Water Assessment Tool (SWAT+) model was used to simulate the hydrological components in the Sulcis watershed (Sardinia, Italy) for the baseline period and compared to future climate projections at the end of the 21st century. The model was forced using data from two Regional Climate Models under the representative concentration pathways RCP4.5 and RCP8.5 scenarios developed at a high resolution over the European domain. River discharge data were used to calibrate and validate the SWAT+ model for the baseline period, while the future hydrological response was evaluated for the mid-century (2006–2050) and late-century (2051–2098). The model simulations indicated a future increase in temperature, decrease in precipitation, and consequently increase in potential evapotranspiration in both RCP scenarios. Results show that these changes will significantly decrease water yield, surface runoff, groundwater recharge, and baseflow. These results highlight how hydrological components alteration by climate change can benefit from modelling high-resolution future scenarios that are useful for planning mitigation measures in agricultural semi-arid Mediterranean regions

Magnetically manipulable perfect unidirectional absorber based on nonreciprocal magnetic surface plasmon

We demonstrate a design of a perfect unidirectional absorber by constructing a magnetic metamaterial (MM) made of an array of ferrite rods. Near the magnetic surface plasmon (MSP) resonance an incident transverse magnetic (TM) Gaussian beam can be absorbed completely at a particular direction, while at the symmetrically opposite direction an obvious reflected beam is observed. This unidirectionality originates from the time reversal symmetry (TRS)-breaking nature of the MM, which can also be shown numerically by comparing the scattering amplitude of the partial waves in two opposite directions. In addition, the unidirectionality can be reversed by reversing the magnetization of the ferrite rods. The working frequency can be controlled as well by tuning the external magnetic field (EMF). Another merit of this absorber is the robustness against position and size disorders of the ferrite rods