Discrete Conditional Diffusion for Reranking in Recommendation

Reranking plays a crucial role in modern multi-stage recommender systems by rearranging the initial ranking list to model interplay between items. Considering the inherent challenges of reranking such as combinatorial searching space, some previous studies have adopted the evaluator-generator paradigm, with a generator producing feasible sequences and a evaluator selecting the best one based on estimated listwise utility. Inspired by the remarkable success of diffusion generative models, this paper explores the potential of diffusion models for generating high-quality sequences in reranking. However, we argue that it is nontrivial to take diffusion models as the generator in the context of recommendation. Firstly, diffusion models primarily operate in continuous data space, differing from the discrete data space of item permutations. Secondly, the recommendation task is different from conventional generation tasks as the purpose of recommender systems is to fulfill user interests. Lastly, real-life recommender systems require efficiency, posing challenges for the inference of diffusion models. To overcome these challenges, we propose a novel Discrete Conditional Diffusion Reranking (DCDR) framework for recommendation. DCDR extends traditional diffusion models by introducing a discrete forward process with tractable posteriors, which adds noise to item sequences through step-wise discrete operations (e.g., swapping). Additionally, DCDR incorporates a conditional reverse process that generates item sequences conditioned on expected user responses. Extensive offline experiments conducted on public datasets demonstrate that DCDR outperforms state-of-the-art reranking methods. Furthermore, DCDR has been deployed in a real-world video app with over 300 million daily active users, significantly enhancing online recommendation quality

Ion and Electron Acoustic Bursts during Anti-Parallel Magnetic Reconnection Driven by Lasers

Magnetic reconnection converts magnetic energy into thermal and kinetic energy in plasma. Among numerous candidate mechanisms, ion acoustic instabilities driven by the relative drift between ions and electrons, or equivalently electric current, have been suggested to play a critical role in dissipating magnetic energy in collisionless plasmas. However, their existence and effectiveness during reconnection have not been well understood due to ion Landau damping and difficulties in resolving the Debye length scale in the laboratory. Here we report a sudden onset of ion acoustic bursts measured by collective Thomson scattering in the exhaust of anti-parallel magnetically driven reconnection using high-power lasers. The ion acoustic bursts are followed by electron acoustic bursts with electron heating and bulk acceleration. We reproduce these observations with 1D and 2D particle-in-cell simulations in which electron outflow jet drives ion-acoustic instabilities, forming double layers. These layers induce electron two-stream instabilities that generate electron acoustic bursts and energize electrons. Our results demonstrate the importance of ion and electron acoustic dynamics during reconnection when ion Landau damping is ineffective, a condition applicable to a range of astrophysical plasmas including near-Earth space, stellar flares, and black hole accretion engines

Not performing an OGTT results in underdiagnosis, inadequate risk assessment and probable cost increases of (pre)diabetes in Han Chinese over 40 years: a population-based prospective cohort study

Objective: To explore the influence by not performing an oral glucose tolerance test (OGTT) in Han Chinese over 40 years. Design: Overall, 6682 participants were included in the prospective cohort study and were followed up for 3 years. Methods: Fasting plasma glucose (FPG), 2-h post-load plasma glucose (2h-PG), FPG and 2h-PG (OGTT), and HbA1c testing using World Health Organization (WHO) or American Diabetes Association (ADA) criteria were employed for strategy analysis. Results: The prevalence of diabetes is 12.4% (95% CI: 11.6–13.3), while the prevalence of prediabetes is 34.1% (95% CI: 32.9–35.3) and 56.5% (95% CI: 55.2–57.8) using WHO and ADA criteria, respectively. 2h-PG determined more diabetes individuals than FPG and HbA1c. The testing cost per true positive case of OGTT is close to FPG and less than 2h-PG or HbA1c. FPG, 2h-PG and HbA1c strategies would increase costs from complications for false-positive (FP) or false-negative (FN) results compared with OGTT. Moreover, the least individuals identified as normal by OGTT at baseline developed (pre)diabetes, and the most prediabetes individuals identified by HbA1c or FPG using ADA criteria developed diabetes. Conclusions: The prevalence of isolated impaired glucose tolerance and isolated 2-h post-load diabetes were high, and the majority of individuals with (pre)diabetes were undetected in Chinese Han population. Not performing an OGTT results in underdiagnosis, inadequate developing risk assessment and probable cost increases of (pre)diabetes in Han Chinese over 40 years and great consideration should be given to OGTT in detecting (pre)diabetes in this population. Further population-based prospective cohort study of longer-term effects is necessary to investigate the risk assessment and cost of (pre)diabetes

Metabolomics Reveals the Impact of Overexpression of Cytosolic Fructose-1,6-Bisphosphatase on Photosynthesis and Growth in <i>Nannochloropsis gaditana</i>

Nannochloropsis gaditana, a microalga known for its photosynthetic efficiency, serves as a cell factory, producing valuable biomolecules such as proteins, lipids, and pigments. These components make it an ideal candidate for biofuel production and pharmaceutical applications. In this study, we genetically engineered N. gaditana to overexpress the enzyme fructose-1,6-bisphosphatase (cyFBPase) using the Hsp promoter, aiming to enhance sugar metabolism and biomass accumulation. The modified algal strain, termed NgFBP, exhibited a 1.34-fold increase in cyFBPase activity under photoautotrophic conditions. This modification led to a doubling of biomass production and an increase in eicosapentaenoic acid (EPA) content in fatty acids to 20.78–23.08%. Additionally, the genetic alteration activated the pathways related to glycine, protoporphyrin, thioglucosides, pantothenic acid, CoA, and glycerophospholipids. This shift in carbon allocation towards chloroplast development significantly enhanced photosynthesis and growth. The outcomes of this study not only improve our understanding of photosynthesis and carbon allocation in N. gaditana but also suggest new biotechnological methods to optimize biomass yield and compound production in microalgae

Data used in paper "Deciphering driver regulators of cell fate decisions from single-cell transcriptomics data with CEFCON"

&lt;p&gt;This directory contains the data resources of the following paper:&lt;/p&gt; &lt;p&gt;"Deciphering driver regulators of cell fate decisions from single-cell transcriptomics data with CEFCON"&lt;/p&gt

Deciphering driver regulators of cell fate decisions from single-cell transcriptomics data with CEFCON

Abstract Single-cell technologies enable the dynamic analyses of cell fate mapping. However, capturing the gene regulatory relationships and identifying the driver factors that control cell fate decisions are still challenging. We present CEFCON, a network-based framework that first uses a graph neural network with attention mechanism to infer a cell-lineage-specific gene regulatory network (GRN) from single-cell RNA-sequencing data, and then models cell fate dynamics through network control theory to identify driver regulators and the associated gene modules, revealing their critical biological processes related to cell states. Extensive benchmarking tests consistently demonstrated the superiority of CEFCON in GRN construction, driver regulator identification, and gene module identification over baseline methods. When applied to the mouse hematopoietic stem cell differentiation data, CEFCON successfully identified driver regulators for three developmental lineages, which offered useful insights into their differentiation from a network control perspective. Overall, CEFCON provides a valuable tool for studying the underlying mechanisms of cell fate decisions from single-cell RNA-seq data

Direct measurement of non-thermal electron acceleration from magnetically driven reconnection in a laboratory plasma

Magnetic reconnection rapidly converts magnetic energy into some combination of plasma flow energy, thermal energy and non-thermal energetic particles. Various reconnection acceleration mechanisms have been theoretically proposed and numerically studied in different collisionless and low-β environments, where β refers to the plasma-to-magnetic pressure ratio. These mechanisms include Fermi acceleration, betatron acceleration, parallel electric field acceleration along magnetic fields and direct acceleration by the reconnection electric field. However, none of them have been experimentally confirmed, as the direct observation of non-thermal particle acceleration in laboratory experiments has been difficult due to short Debye lengths for in situ measurements and short mean free paths for ex situ measurements. Here we report the direct measurement of accelerated non-thermal electrons from magnetically driven reconnection at low β in experiments using a laser-powered capacitor coil platform. We use kilojoule lasers to drive parallel currents to reconnect megagauss-level magnetic fields in a quasi-axisymmetric geometry. The angular dependence of the measured electron energy spectrum and the resulting accelerated energies, supported by particle-in-cell simulations, indicate that the mechanism of direct electric field acceleration by the out-of-plane reconnection electric field is at work. Scaled energies using this mechanism show direct relevance to astrophysical observations