Approximations and Bounds for (n, k) Fork-Join Queues: A Linear Transformation Approach

Compared to basic fork-join queues, a job in (n, k) fork-join queues only needs its k out of all n sub-tasks to be finished. Since (n, k) fork-join queues are prevalent in popular distributed systems, erasure coding based cloud storages, and modern network protocols like multipath routing, estimating the sojourn time of such queues is thus critical for the performance measurement and resource plan of computer clusters. However, the estimating keeps to be a well-known open challenge for years, and only rough bounds for a limited range of load factors have been given. In this paper, we developed a closed-form linear transformation technique for jointly-identical random variables: An order statistic can be represented by a linear combination of maxima. This brand-new technique is then used to transform the sojourn time of non-purging (n, k) fork-join queues into a linear combination of the sojourn times of basic (k, k), (k+1, k+1), ..., (n, n) fork-join queues. Consequently, existing approximations for basic fork-join queues can be bridged to the approximations for non-purging (n, k) fork-join queues. The uncovered approximations are then used to improve the upper bounds for purging (n, k) fork-join queues. Simulation experiments show that this linear transformation approach is practiced well for moderate n and relatively large k.Comment: 10 page

ESL-SNNs: An Evolutionary Structure Learning Strategy for Spiking Neural Networks

Spiking neural networks (SNNs) have manifested remarkable advantages in power consumption and event-driven property during the inference process. To take full advantage of low power consumption and improve the efficiency of these models further, the pruning methods have been explored to find sparse SNNs without redundancy connections after training. However, parameter redundancy still hinders the efficiency of SNNs during training. In the human brain, the rewiring process of neural networks is highly dynamic, while synaptic connections maintain relatively sparse during brain development. Inspired by this, here we propose an efficient evolutionary structure learning (ESL) framework for SNNs, named ESL-SNNs, to implement the sparse SNN training from scratch. The pruning and regeneration of synaptic connections in SNNs evolve dynamically during learning, yet keep the structural sparsity at a certain level. As a result, the ESL-SNNs can search for optimal sparse connectivity by exploring all possible parameters across time. Our experiments show that the proposed ESL-SNNs framework is able to learn SNNs with sparse structures effectively while reducing the limited accuracy. The ESL-SNNs achieve merely 0.28% accuracy loss with 10% connection density on the DVS-Cifar10 dataset. Our work presents a brand-new approach for sparse training of SNNs from scratch with biologically plausible evolutionary mechanisms, closing the gap in the expressibility between sparse training and dense training. Hence, it has great potential for SNN lightweight training and inference with low power consumption and small memory usage

Neuromorphic Auditory Perception by Neural Spiketrum

Neuromorphic computing holds the promise to achieve the energy efficiency and robust learning performance of biological neural systems. To realize the promised brain-like intelligence, it needs to solve the challenges of the neuromorphic hardware architecture design of biological neural substrate and the hardware amicable algorithms with spike-based encoding and learning. Here we introduce a neural spike coding model termed spiketrum, to characterize and transform the time-varying analog signals, typically auditory signals, into computationally efficient spatiotemporal spike patterns. It minimizes the information loss occurring at the analog-to-spike transformation and possesses informational robustness to neural fluctuations and spike losses. The model provides a sparse and efficient coding scheme with precisely controllable spike rate that facilitates training of spiking neural networks in various auditory perception tasks. We further investigate the algorithm-hardware co-designs through a neuromorphic cochlear prototype which demonstrates that our approach can provide a systematic solution for spike-based artificial intelligence by fully exploiting its advantages with spike-based computation.Comment: This work has been submitted to the IEEE for possible publicatio

Mass spectrometric and first principles study of Aln_nC−^- clusters

We study the carbon-dope aluminum clusters by using time-of-flight mass spectrum experiments and {\em ab initio} calculations. Mass abundance distributions are obtained for anionic aluminum and aluminum-carbon mixed clusters. Besides the well-known magic aluminum clusters such as Al$_{13}^-$ and Al$_{23}^-$, Al$_7$C$^-$ cluster is found to be particularly stable among those Al$_n$C$^-$ clusters. Density functional calculations are performed to determine the ground state structures of Al$_n$C$^-$ clusters. Our results show that the Al$_7$C$^-$ is a magic cluster with extremely high stability, which might serve as building block of the cluster-assembled materials.Comment: 4 pages, 6 figure

Immobilization of Interfacial Activated Candida rugosa Lipase Onto Magnetic Chitosan Using Dialdehyde Cellulose as Cross-Linking Agent

Candidarugosa lipase (CRL) was activated with surfactants (sodium dodecyl sulfate [SDS]) and covalently immobilized onto a nanocomposite (Fe3O4-CS-DAC) fabricated by combining magnetic nanoparticles Fe3O4 with chitosan (CS) using polysaccharide macromolecule dialdehyde cellulose (DAC) as the cross-linking agent. Fourier transform infrared spectroscopy, transmission electron microscope, thermogravimetric analysis, and X-ray diffraction characterizations confirmed that the organic–inorganic nanocomposite support modified by DAC was successfully prepared. Enzymology experiments confirmed that high enzyme loading (60.9 mg/g) and 1.7 times specific enzyme activity could be obtained under the optimal immobilization conditions. The stability and reusability of immobilized CRL (Fe3O4-CS-DAC-SDS-CRL) were significantly improved simultaneously. Circular dichroism analysis revealed that the active conformation of immobilized CRL was maintained well. Results demonstrated that the inorganic–organic nanocomposite modified by carbohydrate polymer derivatives could be used as an ideal support for enzyme immobilization

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

Triacylglycerol Synthesis Enzymes Mediate Lipid Droplet Growth by Relocalizing from the ER to Lipid Droplets

Lipid droplets (LDs) store metabolic energy and membrane lipid precursors. With excess metabolic energy, cells synthesize triacylglycerol (TG) and form LDs that grow dramatically. It is unclear how TG synthesis relates to LD formation and growth. Here, we identify two LD subpopulations: smaller LDs of relatively constant size, and LDs that grow larger. The latter population contains isoenzymes for each step of TG synthesis. Glycerol-3-phosphate acyltransferase 4 (GPAT4), which catalyzes the first and rate-limiting step, relocalizes from the endoplasmic reticulum (ER) to a subset of forming LDs, where it becomes stably associated. ER-to-LD targeting of GPAT4 and other LD-localized TG synthesis isozymes is required for LD growth. Key features of GPAT4 ER-to-LD targeting and function in LD growth are conserved between Drosophila and mammalian cells. Our results explain how TG synthesis is coupled with LD growth and identify two distinct LD subpopulations based on their capacity for localized TG synthesis

Seipin is required for converting nascent to mature lipid droplets

How proteins control the biogenesis of cellular lipid droplets (LDs) is poorly understood. Using Drosophila and human cells, we show here that seipin, an ER protein implicated in LD biology, mediates a discrete step in LD formation—the conversion of small, nascent LDs to larger, mature LDs. Seipin forms discrete and dynamic foci in the ER that interact with nascent LDs to enable their growth. In the absence of seipin, numerous small, nascent LDs accumulate near the ER and most often fail to grow. Those that do grow prematurely acquire lipid synthesis enzymes and undergo expansion, eventually leading to the giant LDs characteristic of seipin deficiency. Our studies identify a discrete step of LD formation, namely the conversion of nascent LDs to mature LDs, and define a molecular role for seipin in this process, most likely by acting at ER-LD contact sites to enable lipid transfer to nascent LDs. DOI: http://dx.doi.org/10.7554/eLife.16582.00