Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective

This paper takes a problem-oriented perspective and presents a comprehensive review of transfer learning methods, both shallow and deep, for cross-dataset visual recognition. Specifically, it categorises the cross-dataset recognition into seventeen problems based on a set of carefully chosen data and label attributes. Such a problem-oriented taxonomy has allowed us to examine how different transfer learning approaches tackle each problem and how well each problem has been researched to date. The comprehensive problem-oriented review of the advances in transfer learning with respect to the problem has not only revealed the challenges in transfer learning for visual recognition, but also the problems (e.g. eight of the seventeen problems) that have been scarcely studied. This survey not only presents an up-to-date technical review for researchers, but also a systematic approach and a reference for a machine learning practitioner to categorise a real problem and to look up for a possible solution accordingly

Large-scale consensus with endo-confidence under probabilistic linguistic circumstance and its application

In real decision-making problems, decision makers (DMs) usually select the most potential project from several ones. However, they unconsciously show different confidence levels in decisionmaking process because they come from various backgrounds and have different experiences, etc., which affects the decision results. Moreover, the probabilistic linguistic term set, which not only includes the linguistic expressions used by DMs in their daily life but also contains the probability for each linguistic term, can well portray the real perceptions of DMs for the projects. Furthermore, large-scale consensus has gradually been a popular way to effectively solve complex decision-making problems. To sum up, in this paper, we are dedicated to constructing a largescale consensus model considering the confidence levels of DMs under probabilistic linguistic circumstance. Firstly, the endo-confidence is defined and measured by DM’s probabilistic linguistic information. Then, the DMs are clustered according to the similarities of both evaluation information and the endo-confidence levels. Both evaluation of the non-consensus cluster and evaluation integrated by the clusters with higher endo-confidence level than this non-consensus cluster are used as the reference to adjust its evaluation information. Then, a case study and the comparative analysis are carried out. Finally, some conclusions and future work are given

CO₂/N₂ triggered switchable Pickering emulsions stabilized by alumina nanoparticles in combination with a conventional anionic surfactant

Stable n-decane-in-water Pickering emulsions were prepared using positively charged alumina nanoparticles in combination with a trace amount of the anionic surfactant sodium dodecyl sulfate (SDS) as stabilizer. Particles were hydrophobized in situ by adsorption of surfactant enhancing their surface activity. Emulsions can be readily demulsified by addition of an equal amount of a switchable surfactant, N'-dodecyl-N,N-dimethylacetamidine (DDAA), which can be transformed between a surface-active amidinium/cationic form and a surface-inactive amidine/neutral form by bubbling CO₂ or N₂, respectively. Following addition of cationic DDAA which prefers to form ion pairs with SDS, desorption of SDS from particles surfaces occurs and alumina particles are rendered hydrophilic resulting in demulsification of the emulsion. However, by bubbling N₂ into the demulsified mixture, DDAA molecules are converted to the amidine/neutral form leading to collapse of the ion pairs and re-establishment of the in situ hydrophobization of particles. Stable Pickering emulsions can be prepared again following homogenization. This simple demulsification/re-stabilization cycle can be repeated several times. Experimental evidence including measurement of the adsorption isotherm, zeta potentials, extent of particle adsorption at droplets interfaces in emulsions and microscopy is given to support the postulated mechanisms

TCF1 and LEF1 Control Treg Competitive Survival and Tfr Development to Prevent Autoimmune Diseases.

CD4+ Foxp3+ T regulatory (Treg) cells are key players in preventing lethal autoimmunity. Tregs undertake differentiation processes and acquire diverse functional properties. However, how Treg's differentiation and functional specification are regulated remains incompletely understood. Here, we report that gradient expression of TCF1 and LEF1 distinguishes Tregs into three distinct subpopulations, particularly highlighting a subset of activated Treg (aTreg) cells. Treg-specific ablation of TCF1 and LEF1 renders the mice susceptible to systemic autoimmunity. TCF1 and LEF1 are dispensable for Treg's suppressive capacity but essential for maintaining a normal aTreg pool and promoting Treg's competitive survival. As a consequence, the development of T follicular regulatory (Tfr) cells, which are a subset of aTreg, is abolished in TCF1/LEF1-conditional knockout mice, leading to unrestrained T follicular helper (Tfh) and germinal center B cell responses. Thus, TCF1 and LEF1 act redundantly to control the maintenance and functional specification of Treg subsets to prevent autoimmunity

Data-Centric Financial Large Language Models

Large language models (LLMs) show promise for natural language tasks but struggle when applied directly to complex domains like finance. LLMs have difficulty reasoning about and integrating all relevant information. We propose a data-centric approach to enable LLMs to better handle financial tasks. Our key insight is that rather than overloading the LLM with everything at once, it is more effective to preprocess and pre-understand the data. We create a financial LLM (FLLM) using multitask prompt-based finetuning to achieve data pre-processing and pre-understanding. However, labeled data is scarce for each task. To overcome manual annotation costs, we employ abductive augmentation reasoning (AAR) to automatically generate training data by modifying the pseudo labels from FLLM's own outputs. Experiments show our data-centric FLLM with AAR substantially outperforms baseline financial LLMs designed for raw text, achieving state-of-the-art on financial analysis and interpretation tasks. We also open source a new benchmark for financial analysis and interpretation. Our methodology provides a promising path to unlock LLMs' potential for complex real-world domains

Effect of ultrasonic degradation on the physicochemical property and bioactivity of polysaccharide produced by Chaetomium globosum CGMCC 6882

Similar to the enzymatic process, there might also be an active fragment in polysaccharides, how to obtain is important for investigating the bioactivity and pharmacological mechanism of polysaccharides. Presently, a Gynostemma pentaphyllum endophytic fungus Chaetomium globosum CGMCC 6882 polysaccharide [Genistein Combined Polysaccharide (GCP)] was degraded by ultrasonic treatment, two polysaccharide fragments of GCP-F1 and GCP-F2 were obtained. Physicochemical results showed that GCP-F1 and GCP-F2 had the same monosaccharide composition of arabinose, galactose, glucose, xylose, mannose, and glucuronic acid as compared to GCP with slightly different molar ratios. However, weight-average molecular weights of GCP-F1 and GCP-F2 decreased from 8.093 × 104 Da (GCP) to 3.158 × 104 Da and 1.027 × 104 Da, respectively. In vitro scavenging assays illustrated that GCP-F1 and GCP-F2 had higher antioxidant activity against 2,2′-azinobis-(3-ethylbenzothiazoline-6-sulfonic acid (ABTS) radical, 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical, superoxide anions, and hydroxyl radical than GCP, the order was GCP < GCP-F1 < GCP-F2. Meanwhile, antibacterial tests showed that ultrasonic degradation increased the antibacterial activity of GCP-F1 as compared to GCP, but GCP-F2 almost lost its antibacterial activity with further ultrasound treatment. Changes in the antioxidant and antibacterial activities of GCP-F1 and GCP-F2 might be related to the variation of their molecular weights

Multi-ancestry study of blood lipid levels identifies four loci interacting with physical activity.

Many genetic loci affect circulating lipid levels, but it remains unknown whether lifestyle factors, such as physical activity, modify these genetic effects. To identify lipid loci interacting with physical activity, we performed genome-wide analyses of circulating HDL cholesterol, LDL cholesterol, and triglyceride levels in up to 120,979 individuals of European, African, Asian, Hispanic, and Brazilian ancestry, with follow-up of suggestive associations in an additional 131,012 individuals. We find four loci, in/near CLASP1, LHX1, SNTA1, and CNTNAP2, that are associated with circulating lipid levels through interaction with physical activity; higher levels of physical activity enhance the HDL cholesterol-increasing effects of the CLASP1, LHX1, and SNTA1 loci and attenuate the LDL cholesterol-increasing effect of the CNTNAP2 locus. The CLASP1, LHX1, and SNTA1 regions harbor genes linked to muscle function and lipid metabolism. Our results elucidate the role of physical activity interactions in the genetic contribution to blood lipid levels

Revisit to the yield ratio of triton and 3^3He as an indicator of neutron-rich neck emission

The neutron rich neck zone created in heavy ion reaction is experimentally probed by the production of the $A=3$ isobars. The energy spectra and angular distributions of triton and $^3$He are measured with the CSHINE detector in $^{86}$Kr +$^{208}$Pb reactions at 25 MeV/u. While the energy spectrum of $^{3}$He is harder than that of triton, known as "$^{3}$He-puzzle", the yield ratio $R({\rm t/^3He})$ presents a robust rising trend with the polar angle in laboratory. Using the fission fragments to reconstruct the fission plane, the enhancement of out-plane $R({\rm t/^3He})$ is confirmed in comparison to the in-plane ratios. Transport model simulations reproduce qualitatively the experimental trends, but the quantitative agreement is not achieved. The results demonstrate that a neutron rich neck zone is formed in the reactions. Further studies are called for to understand the clustering and the isospin dynamics related to neck formation