ALEX: Towards Effective Graph Transfer Learning with Noisy Labels

Graph Neural Networks (GNNs) have garnered considerable interest due to their exceptional performance in a wide range of graph machine learning tasks. Nevertheless, the majority of GNN-based approaches have been examined using well-annotated benchmark datasets, leading to suboptimal performance in real-world graph learning scenarios. To bridge this gap, the present paper investigates the problem of graph transfer learning in the presence of label noise, which transfers knowledge from a noisy source graph to an unlabeled target graph. We introduce a novel technique termed Balance Alignment and Information-aware Examination (ALEX) to address this challenge. ALEX first employs singular value decomposition to generate different views with crucial structural semantics, which help provide robust node representations using graph contrastive learning. To mitigate both label shift and domain shift, we estimate a prior distribution to build subgraphs with balanced label distributions. Building on this foundation, an adversarial domain discriminator is incorporated for the implicit domain alignment of complex multi-modal distributions. Furthermore, we project node representations into a different space, optimizing the mutual information between the projected features and labels. Subsequently, the inconsistency of similarity structures is evaluated to identify noisy samples with potential overfitting. Comprehensive experiments on various benchmark datasets substantiate the outstanding superiority of the proposed ALEX in different settings.Comment: Accepted by the ACM International Conference on Multimedia (MM) 202

3DSAM-adapter: Holistic Adaptation of SAM from 2D to 3D for Promptable Medical Image Segmentation

Despite that the segment anything model (SAM) achieved impressive results on general-purpose semantic segmentation with strong generalization ability on daily images, its demonstrated performance on medical image segmentation is less precise and not stable, especially when dealing with tumor segmentation tasks that involve objects of small sizes, irregular shapes, and low contrast. Notably, the original SAM architecture is designed for 2D natural images, therefore would not be able to extract the 3D spatial information from volumetric medical data effectively. In this paper, we propose a novel adaptation method for transferring SAM from 2D to 3D for promptable medical image segmentation. Through a holistically designed scheme for architecture modification, we transfer the SAM to support volumetric inputs while retaining the majority of its pre-trained parameters for reuse. The fine-tuning process is conducted in a parameter-efficient manner, wherein most of the pre-trained parameters remain frozen, and only a few lightweight spatial adapters are introduced and tuned. Regardless of the domain gap between natural and medical data and the disparity in the spatial arrangement between 2D and 3D, the transformer trained on natural images can effectively capture the spatial patterns present in volumetric medical images with only lightweight adaptations. We conduct experiments on four open-source tumor segmentation datasets, and with a single click prompt, our model can outperform domain state-of-the-art medical image segmentation models on 3 out of 4 tasks, specifically by 8.25%, 29.87%, and 10.11% for kidney tumor, pancreas tumor, colon cancer segmentation, and achieve similar performance for liver tumor segmentation. We also compare our adaptation method with existing popular adapters, and observed significant performance improvement on most datasets.Comment: 14 pages, 6 figures, 5 table

A Comprehensive Survey on Deep Graph Representation Learning

Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining. Classic graph embedding methods follow the basic idea that the embedding vectors of interconnected nodes in the graph can still maintain a relatively close distance, thereby preserving the structural information between the nodes in the graph. However, this is sub-optimal due to: (i) traditional methods have limited model capacity which limits the learning performance; (ii) existing techniques typically rely on unsupervised learning strategies and fail to couple with the latest learning paradigms; (iii) representation learning and downstream tasks are dependent on each other which should be jointly enhanced. With the remarkable success of deep learning, deep graph representation learning has shown great potential and advantages over shallow (traditional) methods, there exist a large number of deep graph representation learning techniques have been proposed in the past decade, especially graph neural networks. In this survey, we conduct a comprehensive survey on current deep graph representation learning algorithms by proposing a new taxonomy of existing state-of-the-art literature. Specifically, we systematically summarize the essential components of graph representation learning and categorize existing approaches by the ways of graph neural network architectures and the most recent advanced learning paradigms. Moreover, this survey also provides the practical and promising applications of deep graph representation learning. Last but not least, we state new perspectives and suggest challenging directions which deserve further investigations in the future

Examining the generalizability of research findings from archival data

This initiative examined systematically the extent to which a large set of archival research findings generalizes across contexts. We repeated the key analyses for 29 original strategic management effects in the same context (direct reproduction) as well as in 52 novel time periods and geographies; 45% of the reproductions returned results matching the original reports together with 55% of tests in different spans of years and 40% of tests in novel geographies. Some original findings were associated with multiple new tests. Reproducibility was the best predictor of generalizability—for the findings that proved directly reproducible, 84% emerged in other available time periods and 57% emerged in other geographies. Overall, only limited empirical evidence emerged for context sensitivity. In a forecasting survey, independent scientists were able to anticipate which effects would find support in tests in new samples

Can Environmental Regulation Reduce Urban Haze Concentration from the Perspective of China’s Five Urban Agglomerations?

Based on the perspective of urban agglomerations, this paper explores the impact mechanism of environmental regulation on haze, and tries to find the most suitable environmental regulation intensity for haze control in urban agglomerations. This paper uses the fixed-effect model and panel threshold model to verify the effect of environmental regulations on haze concentration in 206 cities in China. A grouping test is also conducted to verify whether a regional heterogeneity arises due to different regional development levels for five urban agglomerations and non-five urban agglomerations, respectively. The results show that: (1) In the linear model, strengthening environmental regulation can reduce the haze concentration, but this effect is not significant. The effect of environmental regulation on haze control in the five major urban agglomerations is better than that in the non-five major urban agglomerations; (2) In the nonlinear model, the impact of environmental regulation on haze shows a “U” trend in the five major urban agglomerations and an inverted “U” trend in the non-five major urban agglomerations. Although the results are not significant, we can still conclude that the impact of environmental regulation on haze varies depending on the level of regional economic development. Therefore, the environmental regulation should be formulated according to local conditions; (3) In the threshold model, the impact of environmental regulation on the haze concentration in five major urban agglomerations has a threshold effect. In the five major urban agglomerations, although environmental regulation can effectively reduce haze concentration, the governance effect will weaken as the environmental regulation increases. This study plays a positive role in guiding local governments to adjust environmental regulation intensity according to local conditions and helping local environmental improvement

Generation of Large Polynuclear Rare Earth Metal-Containing Organic–Inorganic Polytungstoarsenate Aggregates

Eight members of a new family of polyoxometalate (POM)-ligated organic–inorganic rare earth metal compounds K₁₁LiH₂₁­[RE₃(H₂O)₇­{RE₂(H₂O)₄­As₂W₁₉O₆₈­(WO₂)₂­(C₆O₇H₄)₂}₃]·<i>n</i>H₂O (RE = Y (<b>1</b>), Tb (<b>2</b>), Dy (<b>3</b>), Ho (<b>4</b>), Er (<b>5</b>), Tm (<b>6</b>), Yb (<b>7</b>), Lu (<b>8</b>); for compounds <b>1</b>–<b>6</b> and <b>8</b>, <i>n</i> = 46; for compound <b>7</b>, <i>n</i> = 57; C₆H₈O₇ = citric acid) have been synthesized through conventional aqueous solution by introducing organic ligand citric acid into the arsenotungstates system, which were further characterized by elemental analyses, IR and UV spectroscopy, thermogravimetric analyses, and single-crystal X-ray diffraction. The polyoxoanions [RE₃(H₂O)₇­{RE₂(H₂O)₄­As₂W₁₉O₆₈­(WO₂)₂(C₆O₇H₄)₂}₃]^33– in compounds <b>1</b>–<b>8</b> are composed of three {RE₂(H₂O)₄­As₂W₁₉O₆₈­{WO₂(C₆O₇H₄)}₂} subunits linked by another three rare earth ions. What’s more, the fluorescence properties of <b>2</b> and <b>3</b> have also been investigated. Electron paramagnetic resonance (EPR) experiments further demonstrated the result of the interesting photochromic property

Effect of Moxibustion on the Serum Levels of MMP-1, MMP-3, and VEGF in Patients with Rheumatoid Arthritis

Background. Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease, which will eventually lead to joints deformity and functional damage. The aim of this research is to evaluate the effect of moxibustion on the serum indicators related to bone and cartilage metabolism, matrix metalloproteinase 1 (MMP-1), matrix metalloproteinase 3 (MMP-3), and vascular endothelial growth factor (VEGF) in patients with RA and to explore the mechanism of moxibustion in the treatment of RA. Methods. We recruited 70 RA patients who met the inclusion criteria, and they were randomly divided into two groups, a treatment group and a control group in equal ratio. The control group took methotrexate, folate, or leflunomide orally, while the treatment group received methotrexate, folate, or leflunomide orally and moxibustion at ST36 (Zusanli), BL23 (Shen shu), and Ashi points. We compared the clinical symptoms, RA serological disease markers and serum contents of interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α), MMP-1, MMP-3, and VEGF of RA patients before and after treatment. Results. (1) The clinical symptoms and RA serological disease markers of the two groups improved after treatment (P  0.05, P > 0.05). Above all, the contents of IL-1β, TNF-α, MMP-1, MMP-3, and VEGF in the treatment group decreased more significantly than those in the control group (P < 0.05). Conclusion. The improvement effect of moxibustion on the clinical symptoms of RA patients may be related to influence on the contents of IL-1β, TNF-α, MMP-1, MMP-3, and VEGF, and moxibustion may play a potential role in bone protection

Wearable biosensors for human fatigue diagnosis: A review

Abstract Fatigue causes deleterious effects to physical and mental health of human being and may cause loss of lives. Therefore, the adverse effects of fatigue on individuals and the society are massive. With the ever‐increasing frequency of overtraining among modern military and sports personnel, timely, portable and accurate fatigue diagnosis is essential to avoid fatigue‐induced accidents. However, traditional detection methods require complex sample preparation and blood sampling processes, which cannot meet the timeliness and portability of fatigue diagnosis. With the development of flexible materials and biosensing technology, wearable biosensors have attracted increased attention to the researchers. Wearable biosensors collect biomarkers from noninvasive biofluids, such as sweat, saliva, and tears, followed by biosensing with the help of biosensing modules continuously and quantitatively. The detection signal can then be transmitted through wireless communication modules that constitute a method for real‐time understanding of abnormality. Recent developments of wearable biosensors are focused on miniaturized wearable electrochemistry and optical biosensors for metabolites detection, of which, few have exhibited satisfactory results in medical diagnosis. However, detection performance limits the wide‐range applicability of wearable fatigue diagnosis. In this article, the application of wearable biosensors in fatigue diagnosis has been discussed. In fact, exploration of the composition of different biofluids and their potential toward fatigue diagnosis have been discussed here for the very first time. Moreover, discussions regarding the current bottlenecks in wearable fatigue biosensors and the latest advancements in biochemical reaction and data communication modules have been incorporated herein. Finally, the main challenges and opportunities were discussed for wearable fatigue diagnosis in the future

Characterization of allodiploid and allotriploid fish derived from hybridization between Cyprinus carpio haematopterus (♀) and Gobiocypris rarus (♂)

The production of hybrid progeny through distant hybridization holds great significance in enriching germplasm resources for fish breeding. In this study, a hybridization experiment was conducted between female KOC (Cyprinus carpio haematopterus) and male GR (Gobiocypris rarus), resulting in the production of two distinct types of hybrid offspring. These progenies were classified as allodiploid and allotriploid based on their DNA content and chromosome numbers, hereafter referred to as CG and CCG. Subsequently, a comprehensive comparative analysis was performed between the CG and CCG hybrids and their parents, focusing on countable traits, measurable traits, erythrocyte morphology, as well as karyogene and mitochondrial gene composition. The majority of the examined countable and measurable traits in both CG and CCG exhibited similarities predominantly with GR, except for the ratios of body length (BL) to body height (BH) and head length (HL). Moreover, observing erythrocytes revealed the presence of dumbbell-shaped nuclei in CCG, a characteristic not observed in CG hybrids or the parents. Sequencing alignment revealed that the homeobox (Hox) genes and 5S RNA in CG and CCG were inherited from both KOC and GR, signifying their status as allodiploid and allotriploid organisms. The mitochondrial genes in CG and CCG showed substantial similarity to KOC, albeit with a few sites displaying paternal leakage inheritance from GR. In comparison to CG, the growth rate of CCG was found to be significantly faster, which could be attributed to the upregulation of growth hormone 1 (gh1) and the downregulation of myostatin b (mstn). This study successfully produced two hybrid offspring with distinct growth characteristics but similar genetic backgrounds, making them ideal subjects for future investigations into growth traits. The findings of this research established a fundamental basis for investigating the growth mechanism of fish and provided significant implications for the advancement of fish breeding through hybridization