Diffusion Augmentation for Sequential Recommendation

Sequential recommendation (SRS) has become the technical foundation in many applications recently, which aims to recommend the next item based on the user's historical interactions. However, sequential recommendation often faces the problem of data sparsity, which widely exists in recommender systems. Besides, most users only interact with a few items, but existing SRS models often underperform these users. Such a problem, named the long-tail user problem, is still to be resolved. Data augmentation is a distinct way to alleviate these two problems, but they often need fabricated training strategies or are hindered by poor-quality generated interactions. To address these problems, we propose a Diffusion Augmentation for Sequential Recommendation (DiffuASR) for a higher quality generation. The augmented dataset by DiffuASR can be used to train the sequential recommendation models directly, free from complex training procedures. To make the best of the generation ability of the diffusion model, we first propose a diffusion-based pseudo sequence generation framework to fill the gap between image and sequence generation. Then, a sequential U-Net is designed to adapt the diffusion noise prediction model U-Net to the discrete sequence generation task. At last, we develop two guide strategies to assimilate the preference between generated and origin sequences. To validate the proposed DiffuASR, we conduct extensive experiments on three real-world datasets with three sequential recommendation models. The experimental results illustrate the effectiveness of DiffuASR. As far as we know, DiffuASR is one pioneer that introduce the diffusion model to the recommendation

1,2-Bis[5-(4-cyano­phen­yl)-2-methyl-3-thien­yl]-3,3,4,4,5,5-hexa­fluoro­cyclo­pent-1-ene: a photochromic diaryl­ethene compound

The mol­ecules of the title compound, C29H16F6N2S2, a photochromic dithienylethene with 4-cyano­phenyl substituents, adopt an anti­parallel arrangement that is reponsible for photoactivity. The mol­ecule lies on a twofold rotation axis. The dihedral angle between the nearly planar cyclo­pentenyl and heteroaryl rings is 142.5 (3)°, and that between the heteroaryl and benzene rings is 22.4 (3)°. The distance between the heteroaryl rings of adjacent mol­ecules is 3.601 (2) Å, indicating a π–π interaction

3,3,4,4,5,5-Hexafluoro-1,2-bis­(5-formyl-2-methylsulfanyl-3-thienyl)cyclo­pent-1-ene

In the crystal structure of the title diaryl­ethyl­ene compound, C17H10F6O2S4, the two 3-thienyl substituents are aligned at 44.9 (1) and 40.2 (1)° with respect to the –C—C=C—C– fragment of the central cyclo­pentenyl ring. The five-membered cyclo­pentenyl ring adopts an envelope conformation. The flap atom of this ring and the two F atoms bonded to it are disordered over two positions with occupancies 0.810 (5)/0.190 (5)

A global monthly field of seawater pH over 3 decades: a machine learning approach

The continuous uptake of anthropogenic CO2 by the ocean leads to ocean acidification, which is an ongoing threat to the marine ecosystem. The ocean acidification rate was globally documented in the surface ocean but limited below the surface. Here, we present a monthly four-dimensional 1°×1° gridded product of global seawater pH, derived from a machine learning algorithm trained on pH observations at total scale and in-situ temperature from the Global Ocean Data Analysis Project (GLODAP). The constructed pH product covers the years 1992–2020 and depths from the surface to 2 km on 41 levels. Three types of machine learning algorithms were used in the pH product construction, including self-organizing map neural networks for region dividing, a stepwise algorithm for predictor selection, and feed-forward neural networks (FFNN) for non-linear relationship regression. The performance of the machine learning algorithm was validated using real observations by a cross validation method, where four repeating iterations were carried out with 25 % varied observations for each evaluation and 75 % for training. The constructed pH product is evaluated through comparisons to time series observations and the GLODAP pH climatology. The overall root mean square error between the FFNN constructed pH and the GLODAP measurements is 0.028, ranging from 0.044 in the surface to 0.013 at 2000 m. The pH product is distributed through the data repository of the Marine Science Data Center of the Chinese Academy of Sciences at http://dx.doi.org/10.12157/IOCAS.20230720.001 (Zhong et al., 2023)

Research Progress on the Gut-Brain Axis Effects of Sugars and Sweeteners and Their Evaluation Methods

Sweeteners cannot completely replace the satisfaction provided by sugars, even though they offer a similar flavor perception and much higher sweetness intensity than sugars. Clarifying the biological mechanism of this phenomenon is important to improve the functional evaluation system for sweeteners and promote sweetener innovations. Herein, we review the research progress on the difference in the behavioral preferences of animals, the activity of brain regions and the activation patterns of the gut-brain axis induced by sugars and sweeteners, and we uncover the underlying reason why the brain distinguishes sugars from sweeteners, causing differences in individual behavioral preferences. Moreover, we propose that animal behavior, neural activity in brain regions, and the capacity to activate key receptors can be used to evaluate the gut-brain axis effects of sweeteners, which will provide a reference for innovative developments in the field of sweeteners

The water lily genome and the early evolution of flowering plants

Water lilies belong to the angiosperm order Nymphaeales. Amborellales, Nymphaeales and Austrobaileyales together form the so-called ANA-grade of angiosperms, which are extant representatives of lineages that diverged the earliest from the lineage leading to the extant mesangiosperms1–3. Here we report the 409-megabase genome sequence of the blue-petal water lily (Nymphaea colorata). Our phylogenomic analyses support Amborellales and Nymphaeales as successive sister lineages to all other extant angiosperms. The N. colorata genome and 19 other water lily transcriptomes reveal a Nymphaealean whole-genome duplication event, which is shared by Nymphaeaceae and possibly Cabombaceae. Among the genes retained from this whole-genome duplication are homologues of genes that regulate flowering transition and flower development. The broad expression of homologues of floral ABCE genes in N. colorata might support a similarly broadly active ancestral ABCE model of floral organ determination in early angiosperms. Water lilies have evolved attractive floral scents and colours, which are features shared with mesangiosperms, and we identified their putative biosynthetic genes in N. colorata. The chemical compounds and biosynthetic genes behind floral scents suggest that they have evolved in parallel to those in mesangiosperms. Because of its unique phylogenetic position, the N. colorata genome sheds light on the early evolution of angiosperms.Supplementary Tables: This file contains Supplementary Tables 1-21.National Natural Science Foundation of China, the open funds of the State Key Laboratory of Crop Genetics and Germplasm Enhancement (ZW201909) and State Key Laboratory of Tree Genetics and Breeding, the Fujian provincial government in China, the European Union Seventh Framework Programme (FP7/2007-2013) under European Research Council Advanced Grant Agreement and the Special Research Fund of Ghent University.http://www.nature.com/naturecommunicationsam2021BiochemistryGeneticsMicrobiology and Plant Patholog

Experimental and Numerical Resistance Analysis for a Cruise Ship W/O Fin Stabilizers

Applying fin stabilizers is an effective solution for ship rolls on waves in a seaway. They generally consist of one or two pairs of retractable fins that are symmetrically mounted to both sides of the ship, effectively reducing the roll motion at low or moderate speeds. Fin stabilizers are commonly used by cruise ships for the comfort and safety of passengers. However, there is still little experimental and numerical analysis of the fins’ effect on hydrodynamic performance. In this study, the resistance performance of a cruise ship was investigated with/without fin stabilizers at different fin angles and ship velocities by model tests and numerical analysis. The CFD analysis provides a flow-detailed interpretation of the physical phenomenon, especially at an asymmetric maximum fin angle. The significant fin-induced resistance is newly discovered and averages up to 19% in calm water conditions, while the added resistance in waves is evaluated with a smaller increment up to 1.31%. By comparing the numerical and experimental results, this study provides insight into the resistance induced by overhanging fins, which provides an accurate prediction reference for cruise ship performance and benefits the fin stabilizers’ design and selection

Numerical and Testing Analysis of Fin Stabilizers of A Medium Sized Cruise Ship with Overset Grids

Fin stabilizers represent an effective solution to address the roll motion of ships and improve the comfort of passengers on cruise ships. These devices typically comprise of one or two pairs of retractable fins, symmetrically mounted on either side of the ship, which utilize hydrodynamic lift to dampen motion through a control algorithm. However, coupling analysis of fin stabilizers and ships at various speeds and angles of attack remains limited, particularly with regard to the impact of the hull flow field on fin resistance. This paper investigates the drag performance and towing motion of a cruise ship using model tests and numerical analysis methods, and compares the results of the numerical and model tests. It also examines the drag resulting from fin stabilizers and the coupling motion of the ship, offering insight for the design and selection of fin stabilizers, cruise ship design, and performance prediction