Fast and Efficient Information Transmission with Burst Spikes in Deep Spiking Neural Networks

The spiking neural networks (SNNs) are considered as one of the most promising artificial neural networks due to their energy efficient computing capability. Recently, conversion of a trained deep neural network to an SNN has improved the accuracy of deep SNNs. However, most of the previous studies have not achieved satisfactory results in terms of inference speed and energy efficiency. In this paper, we propose a fast and energy-efficient information transmission method with burst spikes and hybrid neural coding scheme in deep SNNs. Our experimental results showed the proposed methods can improve inference energy efficiency and shorten the latency.Comment: Accepted to DAC 201

Stable and High-Power Calcium-Ion Batteries Enabled by Calcium Intercalation into Graphite

Calcium-ion batteries (CIBs) are considered to be promising next-generation energy storage systems because of the natural abundance of calcium and the multivalent calcium ions with low redox potential close to that of lithium. However, the practical realization of high-energy and high-power CIBs is elusive owing to the lack of suitable electrodes and the sluggish diffusion of calcium ions in most intercalation hosts. Herein, it is demonstrated that calcium-ion intercalation can be remarkably fast and reversible in natural graphite, constituting the first step toward the realization of high-power calcium electrodes. It is shown that a graphite electrode exhibits an exceptionally high rate capability up to 2 A g(-1), delivering approximate to 75% of the specific capacity at 50 mA g(-1) with full calcium intercalation in graphite corresponding to approximate to 97 mAh g(-1). Moreover, the capacity stably maintains over 200 cycles without notable cycle degradation. It is found that the calcium ions are intercalated into graphite galleries with a staging process. The intercalation mechanisms of the "calciated" graphite are elucidated using a suite of techniques including synchrotron in situ X-ray diffraction, nuclear magnetic resonance, and first-principles calculations. The versatile intercalation chemistry of graphite observed here is expected to spur the development of high-power CIBs.

Plant growth promotion and Penicillium citrinum

<p>Abstract</p> <p>Background</p> <p>Endophytic fungi are known plant symbionts. They produce a variety of beneficial metabolites for plant growth and survival, as well as defend their hosts from attack of certain pathogens. Coastal dunes are nutrient deficient and offer harsh, saline environment for the existing flora and fauna. Endophytic fungi may play an important role in plant survival by enhancing nutrient uptake and producing growth-promoting metabolites such as gibberellins and auxins. We screened roots of <it>Ixeris repenes </it>(L.) A. Gray, a common dune plant, for the isolation of gibberellin secreting endophytic fungi.</p> <p>Results</p> <p>We isolated 15 endophytic fungi from the roots of <it>Ixeris repenes </it>and screened them for growth promoting secondary metabolites. The fungal isolate IR-3-3 gave maximum plant growth when applied to waito-c rice and <it>Atriplex gemelinii </it>seedlings. Analysis of the culture filtrate of IR-3-3 showed the presence of physiologically active gibberellins, GA₁, GA₃, GA₄and GA₇(1.95 ng/ml, 3.83 ng/ml, 6.03 ng/ml and 2.35 ng/ml, respectively) along with other physiologically inactive GA₅, GA₉, GA₁₂, GA₁₅, GA₁₉, GA₂₀and, GA₂₄. The plant growth promotion and gibberellin producing capacity of IR-3-3 was much higher than the wild type <it>Gibberella fujikuroi</it>, which was taken as control during present study. GA₅, a precursor of bioactive GA₃was reported for the first time in fungi. The fungal isolate IR-3-3 was identified as a new strain of <it>Penicillium citrinum </it>(named as <it>P. citrinum </it>KACC43900) through phylogenetic analysis of 18S rDNA sequence.</p> <p>Conclusion</p> <p>Isolation of new strain of <it>Penicillium citrinum </it>from the sand dune flora is interesting as information on the presence of <it>Pencillium </it>species in coastal sand dunes is limited. The plant growth promoting ability of this fungal strain may help in conservation and revegetation of the rapidly eroding sand dune flora. <it>Penicillium citrinum </it>is already known for producing mycotoxin citrinin and cellulose digesting enzymes like cellulase and endoglucanase, as well as xylulase. Gibberellins producing ability of this fungus and the discovery about the presence of GA₅will open new aspects of research and investigations.</p

Pixel-Wise Warping for Deep Image Stitching

Existing image stitching approaches based on global or local homography estimation are not free from the parallax problem and suffer from undesired artifacts. In this paper, instead of relying on the homography-based warp, we propose a novel deep image stitching framework exploiting the pixel-wise warp field to handle the large-parallax problem. The proposed deep image stitching framework consists of a Pixel-wise Warping Module (PWM) and a Stitched Image Generating Module (SIGMo). For PWM, we obtain pixel-wise warp in a similar manner as estimating an optical flow (OF). In the stitching scenario, the input images usually include non-overlap (NOV) regions of which warp cannot be directly estimated, unlike the overlap (OV) regions. To help the PWM predict a reasonable warp on the NOV region, we impose two geometrical constraints: an epipolar loss and a line-preservation loss. With the obtained warp field, we relocate the pixels of the target image using forward warping. Finally, the SIGMo is trained by the proposed multi-branch training framework to generate a stitched image from a reference image and a warped target image. For training and evaluating the proposed framework, we build and publish a novel dataset including image pairs with corresponding pixel-wise ground truth warp and stitched result images. We show that the results of the proposed framework are quantitatively and qualitatively superior to those of the conventional methods

Real-Time Anomalous Branch Behavior Inference with a GPU-inspired Engine for Machine Learning Models

Attacks on embedded devices are likely to occur any time in unexpected manners. Thus, the defense systems based on fixed sets of rules will easily be subverted by such unexpected, unknown attacks. Learning-based anomaly detection may potentially prevent new unknown zero-day attacks by leveraging the capability of machine learning (ML) to learn the intricate true nature of software hidden within raw information. This paper introduces our work to develop an MPSoC, called RTAD, which can efficiently support in hardware various ML models that run to detect anomalous behaviors on embedded devices in a real-time fashion, and thus enable the devices to counteract the anomalies in the field. In the IoT era, the importance of security for embedded devices cannot be exaggerated because they will become an enticing target for adversaries as they are being integrated into everyday life to provide users with various services. The above-mentioned potential of learning-based detection is believed to benefit those deployed devices under attacks occurring any time during their field operations in unexpected manners. We hereby assume that ML models are trained with runtime branch information as their data features since a sequence of branches serves as a record of control flow transfers during program execution. In fact, there have been numerous ML studies that examine various types of branches in order to infer (or detect) anomaly in branch behaviors that may be induced by diverse attacks that can cause deviant control flow in software. Our goal of real-time anomalous branch behavior inference poses two challenges to our development of RTAD. Firstly, RTAD must collect and transfer in a timely fashion a sequence of branches as the input to the ML model. Secondly, RTAD must be able to promptly process the delivered branch data with the ML model. To tackle these challenges, we have implemented in RTAD two core components: an input generation module and a GPU-inspired ML processing engine. According to our experiments, RTAD enables various ML models to infer anomaly instantly after the victim program behaves aberrantly as the result of attacks being injected into the system.N

AutoSNN: Towards Energy-Efficient Spiking Neural Networks

Spiking neural networks (SNNs) that mimic information transmission in the brain can energy-efficiently process spatio-temporal information through discrete and sparse spikes, thereby receiving considerable attention. To improve accuracy and energy efficiency of SNNs, most previous studies have focused solely on training methods, and the effect of architecture has rarely been studied. We investigate the design choices used in the previous studies in terms of the accuracy and number of spikes and figure out that they are not best-suited for SNNs. To further improve the accuracy and reduce the spikes generated by SNNs, we propose a spike-aware neural architecture search framework called AutoSNN. We define a search space consisting of architectures without undesirable design choices. To enable the spike-aware architecture search, we introduce a fitness that considers both the accuracy and number of spikes. AutoSNN successfully searches for SNN architectures that outperform hand-crafted SNNs in accuracy and energy efficiency. We thoroughly demonstrate the effectiveness of AutoSNN on various datasets including neuromorphic datasets.Comment: Accepted in ICML2

Simple and Effective Gas-Phase Doping for Lithium Metal Protection in Lithium Metal Batteries

Increasing demands for advanced lithium batteries with higher energy density have resurrected the use of lithium metal as an anode, whose practical implementation has still been restricted, because of its intrinsic problems originating from the high reactivity of elemental lithium metal. Herein, we explore a facile strategy of doping gas phase into electrolyte to stabilize lithium metal and suppress the selective lithium growth through the formation of stable and homogeneous solid electrolyte interphase (SEI) layer. We find that the sulfur dioxide gas additive doped in electrolyte significantly improves both chemical and electrochemical stability of lithium metal electrodes. It is demonstrated that the cycle stability of the lithium cells can be remarkably prolonged, because of the compact and homogeneous SEI layers consisting of Li S-O reduction products formed on the lithium metal surface. Simulations on the lithium metal growth process suggested the homogeneity of the protective layer induced by the gas-phase doping is attributable for the effective prevention of the selective growth of lithium metal. This study introduces a new simple approach to stabilize the lithium metal electrode with gas-phase doping, where the SEI layer can be rationally tunable by the composition of gas phase.

Anionic Redox Activity Regulated by Transition Metal in Lithium-Rich Layered Oxides

The anionic redox activity in lithium-rich layered oxides has the potential to boost the energy density of lithium-ion batteries. Although it is widely accepted that the anionic redox activity stems from the orphaned oxygen energy level, its regulation and structural stabilization, which are essential for practical employment, remain still elusive, requiring an improved fundamental understanding. Herein, the oxygen redox activity for a wide range of 3dtransition-metal-based Li(2)TMO(3)compounds is investigated and the intrinsic competition between the cationic and anionic redox reaction is unveiled. It is demonstrated that the energy level of the orphaned oxygen state (and, correspondingly, the activity) is delicately governed by the type and number of neighboring transition metals owing to the pi-type interactions between Li-O-Li and Mt(2g)states. Based on these findings, a simple model that can be used to estimate the anionic redox activity of various lithium-rich layered oxides is proposed. The model explains the recently reported significantly different oxygen redox voltages or inactivity in lithium-rich materials despite the commonly observed Li-O-Li states with presumably unhybridized character. The discovery of hidden factors that rule the anionic redox in lithium-rich cathode materials will aid in enabling controlled cumulative cationic and anionic redox reactions.