Stationary Light Pulses in Cold Atomic Media

Stationary light pulses (SLPs), i.e., light pulses without motion, are formed via the retrieval of stored probe pulses with two counter-propagating coupling fields. We show that there exist non-negligible hybrid Raman excitations in media of cold atoms that prohibit the SLP formation. We experimentally demonstrate a method to suppress these Raman excitations and realize SLPs in laser-cooled atoms. Our work opens the way to SLP studies in cold as well as in stationary atoms and provides a new avenue to low-light-level nonlinear optics.Comment: 4 pages, 4 figure

Ludwigia octovalvis extract improves glycemic control and memory performance in diabetic mice

Ethnopharmacological relevance Ludwigia octovalvis (Jacq.) P.H. Raven (Onagraceae) extracts have historically been consumed as a healthful drink for treating various conditions, including edema, nephritis, hypotension and diabetes. Aim of the study We have previously shown that Ludwigia octovalvis extract (LOE) can significantly extend lifespan and improve age-related memory deficits in Drosophila melanogaster through activating AMP-activated protein kinase (AMPK). Since AMPK has become a critical target for treating diabetes, we herein investigate the anti-hyperglycemic potential of LOE. Materials and methods Differentiated C2C12 muscle cells, HepG2 hepatocellular cells, streptozotocin (STZ)-induced diabetic mice and high fat diet (HFD)-induced diabetic mice were used to investigate the anti-hyperglycemic potential of LOE. The open field test and novel object recognition test were used to evaluate spontaneous motor activity and memory performance of HFD-induced diabetic mice. Results In differentiated C2C12 muscle cells and HepG2 hepatocellular cells, treatments with LOE and its active component (β-sitosterol) induced significant AMPK phosphorylation. LOE also enhanced uptake of a fluorescent glucose derivative (2-NBDG) and inhibited glucose production in these cells. The beneficial effects of LOE were completely abolished when an AMPK inhibitor, dorsomorphin, was added to the culture system, suggesting that LOE requires AMPK activation for its action in vitro. In streptozotocin (STZ)-induced diabetic mice, we found that both LOE and β-sitosterol induced an anti-hyperglycemic effect comparable to that of metformin, a drug that is commonly prescribed to treat diabetes. Moreover, LOE also improved glycemic control and memory performance of mice fed a HFD. Conclusions These results indicate that LOE is a potent anti-diabetic intervention that may have potential for future clinical applications

Towards Zero Memory Footprint Spiking Neural Network Training

Biologically-inspired Spiking Neural Networks (SNNs), processing information using discrete-time events known as spikes rather than continuous values, have garnered significant attention due to their hardware-friendly and energy-efficient characteristics. However, the training of SNNs necessitates a considerably large memory footprint, given the additional storage requirements for spikes or events, leading to a complex structure and dynamic setup. In this paper, to address memory constraint in SNN training, we introduce an innovative framework, characterized by a remarkably low memory footprint. We \textbf{(i)} design a reversible SNN node that retains a high level of accuracy. Our design is able to achieve a $\mathbf{58.65\times}$ reduction in memory usage compared to the current SNN node. We \textbf{(ii)} propose a unique algorithm to streamline the backpropagation process of our reversible SNN node. This significantly trims the backward Floating Point Operations Per Second (FLOPs), thereby accelerating the training process in comparison to current reversible layer backpropagation method. By using our algorithm, the training time is able to be curtailed by $\mathbf{23.8\%}$ relative to existing reversible layer architectures

Reducing Side Effects of Hiding Sensitive Itemsets in Privacy Preserving Data Mining

Data mining is traditionally adopted to retrieve and analyze knowledge from large amounts of data. Private or confidential data may be sanitized or suppressed before it is shared or published in public. Privacy preserving data mining (PPDM) has thus become an important issue in recent years. The most general way of PPDM is to sanitize the database to hide the sensitive information. In this paper, a novel hiding-missing-artificial utility (HMAU) algorithm is proposed to hide sensitive itemsets through transaction deletion. The transaction with the maximal ratio of sensitive to nonsensitive one is thus selected to be entirely deleted. Three side effects of hiding failures, missing itemsets, and artificial itemsets are considered to evaluate whether the transactions are required to be deleted for hiding sensitive itemsets. Three weights are also assigned as the importance to three factors, which can be set according to the requirement of users. Experiments are then conducted to show the performance of the proposed algorithm in execution time, number of deleted transactions, and number of side effects

Prokaryotic assemblages and metagenomes in pelagic zones of the South China Sea

BACKGROUND: Prokaryotic microbes, the most abundant organisms in the ocean, are remarkably diverse. Despite numerous studies of marine prokaryotes, the zonation of their communities in pelagic zones has been poorly delineated. By exploiting the persistent stratification of the South China Sea (SCS), we performed a 2-year, large spatial scale (10, 100, 1000, and 3000 m) survey, which included a pilot study in 2006 and comprehensive sampling in 2007, to investigate the biological zonation of bacteria and archaea using 16S rRNA tag and shotgun metagenome sequencing. RESULTS: Alphaproteobacteria dominated the bacterial community in the surface SCS, where the abundance of Betaproteobacteria was seemingly associated with climatic activity. Gammaproteobacteria thrived in the deep SCS, where a noticeable amount of Cyanobacteria were also detected. Marine Groups II and III Euryarchaeota were predominant in the archaeal communities in the surface and deep SCS, respectively. Bacterial diversity was higher than archaeal diversity at all sampling depths in the SCS, and peaked at mid-depths, agreeing with the diversity pattern found in global water columns. Metagenomic analysis not only showed differential %GC values and genome sizes between the surface and deep SCS, but also demonstrated depth-dependent metabolic potentials, such as cobalamin biosynthesis at 10 m, osmoregulation at 100 m, signal transduction at 1000 m, and plasmid and phage replication at 3000 m. When compared with other oceans, urease at 10 m and both exonuclease and permease at 3000 m were more abundant in the SCS. Finally, enriched genes associated with nutrient assimilation in the sea surface and transposase in the deep-sea metagenomes exemplified the functional zonation in global oceans. CONCLUSIONS: Prokaryotic communities in the SCS stratified with depth, with maximal bacterial diversity at mid-depth, in accordance with global water columns. The SCS had functional zonation among depths and endemically enriched metabolic potentials at the study site, in contrast to other oceans. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-015-1434-3) contains supplementary material, which is available to authorized users

Boosting Logical Reasoning in Large Language Models through a New Framework: The Graph of Thought

Recent advancements in large-scale models, such as GPT-4, have showcased remarkable capabilities in addressing standard queries. However, when facing complex problems that require multi-step logical reasoning, their accuracy dramatically decreases. Current research has explored the realm of \textit{prompting engineering} to bolster the inferential capacities of these models. Our paper unveils a pioneering prompting technique, dubbed \textit{Graph of Thoughts (GoT)}. Through testing on a trio of escalating challenges: the 24-point game, resolution of high-degree polynomial equations, and derivation of formulas for recursive sequences, our method outperformed GPT-4, achieving accuracy improvements of $89.7\%$, $86\%$, and $56\%$ for each respective task. Moreover, when juxtaposed with the state-of-the-art (SOTA) prompting method, \textit{Tree of Thought (ToT)}, our approach registered an average accuracy boost of $23\%$, $24\%$, and $15\%$