T-SaS: Toward Shift-aware Dynamic Adaptation for Streaming Data

In many real-world scenarios, distribution shifts exist in the streaming data across time steps. Many complex sequential data can be effectively divided into distinct regimes that exhibit persistent dynamics. Discovering the shifted behaviors and the evolving patterns underlying the streaming data are important to understand the dynamic system. Existing methods typically train one robust model to work for the evolving data of distinct distributions or sequentially adapt the model utilizing explicitly given regime boundaries. However, there are two challenges: (1) shifts in data streams could happen drastically and abruptly without precursors. Boundaries of distribution shifts are usually unavailable, and (2) training a shared model for all domains could fail to capture varying patterns. This paper aims to solve the problem of sequential data modeling in the presence of sudden distribution shifts that occur without any precursors. Specifically, we design a Bayesian framework, dubbed as T-SaS, with a discrete distribution-modeling variable to capture abrupt shifts of data. Then, we design a model that enable adaptation with dynamic network selection conditioned on that discrete variable. The proposed method learns specific model parameters for each distribution by learning which neurons should be activated in the full network. A dynamic masking strategy is adopted here to support inter-distribution transfer through the overlapping of a set of sparse networks. Extensive experiments show that our proposed method is superior in both accurately detecting shift boundaries to get segments of varying distributions and effectively adapting to downstream forecast or classification tasks.Comment: CIKM 202

Faithful and Consistent Graph Neural Network Explanations with Rationale Alignment

Uncovering rationales behind predictions of graph neural networks (GNNs) has received increasing attention over recent years. Instance-level GNN explanation aims to discover critical input elements, like nodes or edges, that the target GNN relies upon for making predictions. %These identified sub-structures can provide interpretations of GNN's behavior. Though various algorithms are proposed, most of them formalize this task by searching the minimal subgraph which can preserve original predictions. However, an inductive bias is deep-rooted in this framework: several subgraphs can result in the same or similar outputs as the original graphs. Consequently, they have the danger of providing spurious explanations and failing to provide consistent explanations. Applying them to explain weakly-performed GNNs would further amplify these issues. To address this problem, we theoretically examine the predictions of GNNs from the causality perspective. Two typical reasons for spurious explanations are identified: confounding effect of latent variables like distribution shift, and causal factors distinct from the original input. Observing that both confounding effects and diverse causal rationales are encoded in internal representations, \tianxiang{we propose a new explanation framework with an auxiliary alignment loss, which is theoretically proven to be optimizing a more faithful explanation objective intrinsically. Concretely for this alignment loss, a set of different perspectives are explored: anchor-based alignment, distributional alignment based on Gaussian mixture models, mutual-information-based alignment, etc. A comprehensive study is conducted both on the effectiveness of this new framework in terms of explanation faithfulness/consistency and on the advantages of these variants.Comment: TIST2023. arXiv admin note: substantial text overlap with arXiv:2205.1373

Free-style and Fast 3D Portrait Synthesis

Efficiently generating a free-style 3D portrait with high quality and consistency is a promising yet challenging task. The portrait styles generated by most existing methods are usually restricted by their 3D generators, which are learned in specific facial datasets, such as FFHQ. To get a free-style 3D portrait, one can build a large-scale multi-style database to retrain the 3D generator, or use a off-the-shelf tool to do the style translation. However, the former is time-consuming due to data collection and training process, the latter may destroy the multi-view consistency. To tackle this problem, we propose a fast 3D portrait synthesis framework in this paper, which enable one to use text prompts to specify styles. Specifically, for a given portrait style, we first leverage two generative priors, a 3D-aware GAN generator (EG3D) and a text-guided image editor (Ip2p), to quickly construct a few-shot training set, where the inference process of Ip2p is optimized to make editing more stable. Then we replace original triplane generator of EG3D with a Image-to-Triplane (I2T) module for two purposes: 1) getting rid of the style constraints of pre-trained EG3D by fine-tuning I2T on the few-shot dataset; 2) improving training efficiency by fixing all parts of EG3D except I2T. Furthermore, we construct a multi-style and multi-identity 3D portrait database to demonstrate the scalability and generalization of our method. Experimental results show that our method is capable of synthesizing high-quality 3D portraits with specified styles in a few minutes, outperforming the state-of-the-art.Comment: project website: https://tianxiangma.github.io/FF3

The Acceptability and Influencing Factors of an Internet-Based Tinnitus Multivariate Integrated Sound Therapy for Patients With Tinnitus

Objective: To explore the acceptability and influencing factors of an Internet-based Tinnitus Multivariate Integrated Sound Therapy (iT-MIST). The individually tailored sound therapy used narrowband noise centered on the patient’s tinnitus frequency in combination with natural sounds and relaxing music. Design: Patients with tinnitus were given a 1-week trial of iT-MIST. Semistructured interviews were then carried out and a thematic analysis used to analyze, identify, organize, and report factors discovered in the data. Study Sample: Semistructured interviews were carried out with 11 participants, 2 women and 9 men, mean age 39.82 years. Results: The first theme identified from patient interview analysis was their motivation to undertake and expectations of iT-MIST. Nearly half of the participants indicated that advice from the physician was considered very important and professional. Benefits acknowledged by most participants from their iT-MIST experience were accessibility, convenience, time- and cost-effectiveness, and emotional benefit. However, a few participants with poor understanding of tinnitus and iT-MIST showed a negative acceptability with doubtful thoughts and complaints about technical issues such as being easily interrupted by messages and phone calls. Conclusion: Patients with tinnitus in this study were not universally accepting of the iT-MIST therapy. Concerns about their tinnitus and ability to comply with doctor’s recommendations were the main influencing factors. Attitude or willingness to explore new therapies facilitated its use. Emotional benefits, for example, relaxation and comfort, were seen to sustain motivation, while doubtful thoughts and technical problems negatively affected acceptability

Case Report: Giant abdominal hemangioma originating from the liver

BackgroundHepatic hemangioma is among the most common benign liver lesions. However, giant pedunculated hepatic hemangiomas are exceptionally rare and associated with additional risks, such as torsion.Case presentationWe present the case of a 63-year-old female patient who presented with abdominal distension and pain. Barium meal examination and gastroscopy revealed a large, smooth-surfaced submucosal bulge located at the fundus of the stomach. Subsequent MRI examination identified a mass measuring approximately 6.4 x 7 cm in the left upper abdomen. Surgical intervention was planned for mass removal. However, intraoperative exploration revealed the origin of the mass to be the liver, and subsequent histopathological examination confirmed it as a hemangioma.ConclusionWe systematically summarized the characteristics of our case along with 31 previously reported cases. Giant pedunculated hepatic hemangiomas typically occur in the left lobe of the liver. Due to their atypical presentation, a combination of imaging methods such as ultrasound, CT, and/or MRI is essential for accurate diagnosis. Furthermore, surgical intervention is recommended due to the potential risks of bleeding, rupture, and torsion

A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability

Graph Neural Networks (GNNs) have made rapid developments in the recent years. Due to their great ability in modeling graph-structured data, GNNs are vastly used in various applications, including high-stakes scenarios such as financial analysis, traffic predictions, and drug discovery. Despite their great potential in benefiting humans in the real world, recent study shows that GNNs can leak private information, are vulnerable to adversarial attacks, can inherit and magnify societal bias from training data and lack interpretability, which have risk of causing unintentional harm to the users and society. For example, existing works demonstrate that attackers can fool the GNNs to give the outcome they desire with unnoticeable perturbation on training graph. GNNs trained on social networks may embed the discrimination in their decision process, strengthening the undesirable societal bias. Consequently, trustworthy GNNs in various aspects are emerging to prevent the harm from GNN models and increase the users' trust in GNNs. In this paper, we give a comprehensive survey of GNNs in the computational aspects of privacy, robustness, fairness, and explainability. For each aspect, we give the taxonomy of the related methods and formulate the general frameworks for the multiple categories of trustworthy GNNs. We also discuss the future research directions of each aspect and connections between these aspects to help achieve trustworthiness

Methodological quality of radiomic-based prognostic studies in gastric cancer: a cross-sectional study

BackgroundMachine learning radiomics models are increasingly being used to predict gastric cancer prognoses. However, the methodological quality of these models has not been evaluated. Therefore, this study aimed to evaluate the methodological quality of radiomics studies in predicting the prognosis of gastric cancer, summarize their methodological characteristics and performance.MethodsThe PubMed and Embase databases were searched for radiomics studies used to predict the prognosis of gastric cancer published in last 5 years. The characteristics of the studies and the performance of the models were extracted from the eligible full texts. The methodological quality, reporting completeness and risk of bias of the included studies were evaluated using the RQS, TRIPOD and PROBAST. The discrimination ability scores of the models were also compared.ResultsOut of 283 identified records, 22 studies met the inclusion criteria. The study endpoints included survival time, treatment response, and recurrence, with reported discriminations ranging between 0.610 and 0.878 in the validation dataset. The mean overall RQS value was 15.32 ± 3.20 (range: 9 to 21). The mean adhered items of the 35 item of TRIPOD checklist was 20.45 ± 1.83. The PROBAST showed all included studies were at high risk of bias.ConclusionThe current methodological quality of gastric cancer radiomics studies is insufficient. Large and reasonable sample, prospective, multicenter and rigorously designed studies are required to improve the quality of radiomics models for gastric cancer prediction.Study registrationThis protocol was prospectively registered in the Open Science Framework Registry (https://osf.io/ja52b)

Fine Mapping of a Novel Heading Date Gene, TaHdm605, in Hexaploid Wheat

The heading date is critical in determining the adaptability of plants to specific natural environments. Molecular characterization of the wheat genes that regulate heading not only enhances our understanding of the mechanisms underlying wheat heading regulation but also benefits wheat breeding programs by improving heading phenotypes. In this study, we characterized a late heading date mutant, m605, obtained by ethyl methanesulfonate (EMS) mutation. Compared with its wild-type parent, YZ4110, m605 was at least 7 days late in heading when sown in autumn. This late heading trait was controlled by a single recessive gene named TaHdm605. Genetic mapping located the TaHdm605 locus between the molecular markers cfd152 and barc42 on chromosome 3DL using publicly available markers and then further mapped this locus to a 1.86 Mb physical genomic region containing 26 predicted genes. This fine genetic and physical mapping will be helpful for the future map-based cloning of TaHdm605 and for breeders seeking to engineer changes in the wheat heading date trait

Dietary sodium enhances the expression of SLC4 family transporters, IRBIT, L-IRBIT, and PP1 in rat kidney: Insights into the molecular mechanism for renal sodium handling

The kidney plays a central role in maintaining the fluid and electrolyte homeostasis in the body. Bicarbonate transporters NBCn1, NBCn2, and AE2 are expressed at the basolateral membrane of the medullary thick ascending limb (mTAL). In a previous study, NBCn1, NBCn2, and AE2 are proposed to play as a regulatory pathway to decrease NaCl reabsorption in the mTAL under high salt condition. When heterologously expressed, the activity of these transporters could be stimulated by the InsP3R binding protein released with inositol 1,4,5-trisphosphate (IRBIT), L-IRBIT (collectively the IRBITs), or protein phosphatase PP1. In the present study, we characterized by immunofluorescence the expression and localization of the IRBITs, and PP1 in rat kidney. Our data showed that the IRBITs were predominantly expressed from the mTAL through the distal renal tubules. PP1 was predominantly expressed in the TAL, but is also present in high abundance from the distal convoluted tubule through the medullary collecting duct. Western blotting analyses showed that the abundances of NBCn1, NBCn2, and AE2 as well as the IRBITs and PP1 were greatly upregulated in rat kidney by dietary sodium. Co-immunoprecipitation study provided the evidence for protein interaction between NBCn1 and L-IRBIT in rat kidney. Taken together, our data suggest that the IRBITs and PP1 play an important role in sodium handling in the kidney. We propose that the IRBITs and PP1 stimulates NBCn1, NBCn2, and AE2 in the basolateral mTAL to inhibit sodium reabsorption under high sodium condition. Our study provides important insights into understanding the molecular mechanism for the regulation of sodium homeostasis in the body