MiniMax Entropy Network: Learning Category-Invariant Features for Domain Adaptation

How to effectively learn from unlabeled data from the target domain is crucial for domain adaptation, as it helps reduce the large performance gap due to domain shift or distribution change. In this paper, we propose an easy-to-implement method dubbed MiniMax Entropy Networks (MMEN) based on adversarial learning. Unlike most existing approaches which employ a generator to deal with domain difference, MMEN focuses on learning the categorical information from unlabeled target samples with the help of labeled source samples. Specifically, we set an unfair multi-class classifier named categorical discriminator, which classifies source samples accurately but be confused about the categories of target samples. The generator learns a common subspace that aligns the unlabeled samples based on the target pseudo-labels. For MMEN, we also provide theoretical explanations to show that the learning of feature alignment reduces domain mismatch at the category level. Experimental results on various benchmark datasets demonstrate the effectiveness of our method over existing state-of-the-art baselines.Comment: 8 pages, 6 figure

Frequency Regularization for Improving Adversarial Robustness

Deep neural networks are incredibly vulnerable to crafted, human-imperceptible adversarial perturbations. Although adversarial training (AT) has proven to be an effective defense approach, we find that the AT-trained models heavily rely on the input low-frequency content for judgment, accounting for the low standard accuracy. To close the large gap between the standard and robust accuracies during AT, we investigate the frequency difference between clean and adversarial inputs, and propose a frequency regularization (FR) to align the output difference in the spectral domain. Besides, we find Stochastic Weight Averaging (SWA), by smoothing the kernels over epochs, further improves the robustness. Among various defense schemes, our method achieves the strongest robustness against attacks by PGD-20, C\&W and Autoattack, on a WideResNet trained on CIFAR-10 without any extra data.Comment: accepted by AAAI 2023 worksho

Follow-up study of neuropsychological scores of infant patients with cobalamin C defects and influencing factors of cerebral magnetic resonance imaging characteristics

PurposeThe purpose of this study was to investigate whether baseline cerebral magnetic resonance imaging (MRI) characteristics could predict therapeutic responsiveness in patients with cobalamin C (cblC) defects.Materials and methodsThe cerebral MRI results of 40 patients with cblC defects were evaluated by a neuroradiologist. Neuropsychological scores and imaging data were collected. Neuropsychological tests were performed before and after standardized treatment.ResultsThirty-eight patients initially underwent neuropsychological testing [developmental quotient (DQ)]. CblC defects with cerebellar atrophy, corpus callosum thinning and ventricular dilation had significantly lower DQs than those without (P < 0.05). Through a multivariate linear stepwise regression equation after univariate analysis, ventricular dilation was the most valuable predictor of lower DQs. Thirty-six patients (94.7%) underwent follow-up neuropsychological testing. The pre- and post-treatment DQ values were not significantly different (Z = −1.611, P = 0.107). The post-treatment DQ classification (normal, moderately low, or extremely low) showed nearly no change compared to the pretreatment DQ classification (k = 0.790, P < 0.001).ConclusionVentricular dilation, cerebral atrophy and corpus callosum thinning are the main MRI abnormalities of cblC defects, and these manifestations are significantly correlated with delayed development in children. MRI findings can be considered an important tool for determining the severity of cblC defects

Case report of retroperitoneal ectopic pancreas with adrenal adenoma

BackgroundEctopic pancreas is a congenital anomaly in which pancreatic tissue is anatomically separated from the main gland and without vascular or ductal continuity. A case of retroperitoneal ectopic pancreas with adrenal adenoma has never yet been reported.Case PresentationA 54-year-old man presented three masses in the left retroperitoneum, and two of them were resected. The pathologic findings were a retroperitoneal ectopic pancreas with adrenal adenoma.ConclusionWe report an extremely rare case of a retroperitoneal ectopic pancreas and its characterization with dynamic gadolinium-enhanced magnetic resonance imaging (MRI)

Association between methionine sulfoxide and risk of moyamoya disease

ObjectiveMethionine sulfoxide (MetO) has been identified as a risk factor for vascular diseases and was considered as an important indicator of oxidative stress. However, the effects of MetO and its association with moyamoya disease (MMD) remained unclear. Therefore, we performed this study to evaluate the association between serum MetO levels and the risk of MMD and its subtypes.MethodsWe eventually included consecutive 353 MMD patients and 88 healthy controls (HCs) with complete data from September 2020 to December 2021 in our analyzes. Serum levels of MetO were quantified using liquid chromatography-mass spectrometry (LC–MS) analysis. We evaluated the role of MetO in MMD using logistic regression models and confirmed by receiver-operating characteristic (ROC) curves and area under curve (AUC) values.ResultsWe found that the levels of MetO were significantly higher in MMD and its subtypes than in HCs (p < 0.001 for all). After adjusting for traditional risk factors, serum MetO levels were significantly associated with the risk of MMD and its subtypes (p < 0.001 for all). We further divided the MetO levels into low and high groups, and the high MetO level was significantly associated with the risk of MMD and its subtypes (p < 0.05 for all). When MetO levels were assessed as quartiles, we found that the third (Q3) and fourth (Q4) MetO quartiles had a significantly increased risk of MMD compared with the lowest quartile (Q3, OR: 2.323, 95%CI: 1.088–4.959, p = 0.029; Q4, OR: 5.559, 95%CI: 2.088–14.805, p = 0.001).ConclusionIn this study, we found that a high level of serum MetO was associated with an increased risk of MMD and its subtypes. Our study raised a novel perspective on the pathogenesis of MMD and suggested potential therapeutic targets

MR-NET: exploiting mutual relation for visual relationship detection

Inferring the interactions between objects, a.k.a visual relationship detection, is a crucial point for vision understanding, which captures more definite concepts than object detection. Most previous work that treats the interaction between a pair of objects as a one way fail to exploit the mutual relation between objects, which is essential to modern visual application. In this work, we propose a mutual relation net, dubbed MR-Net, to explore the mutual relation between paired objects for visual relationship detection. Specifically, we construct a mutual relation space to model the mutual interaction of paired objects, and employ linear constraint to optimize the mutual interaction, which is called mutual relation learning. Our mutual relation learning does not introduce any parameters, and can adapt to improve the performance of other methods. In addition, we devise a semantic ranking loss to discriminatively penalize predicates with semantic similarity, which is ignored by traditional loss function (e.g., cross entropy with softmax). Then, our MR-Net optimizes the mutual relation learning together with semantic ranking loss with a siamese network. The experimental results on two commonly used datasets (VG and VRD) demonstrate the superior performance of the proposed approach

Data for: The influence of sedimentary heterogeneity on the diffusion of radionuclides in the sandy facies of Opalinus Clay at the field scale

Radionuclide migration in clay-rich formations is typically dominated by diffusion considering the low permeability of these formations. An accurate estimation of radionuclide migration in host rocks using numerical tools plays a key role in the safety assessment of disposal concepts for nuclear waste. In the sandy facies of the Opalinus Clay (SF-OPA), the spatial variability of the pore space network and compositional heterogeneity at the pore scale (nm to µm) cause heterogeneous diffusion at the core scale (cm to dm). Such heterogeneous diffusion patterns affect the migration of radionuclides in various sedimentary layers even above the core scale (m). In this work, we study the heterogeneous diffusion of cations based on a two-dimensional (2D) structural model at the m-scale. As key parameters for the diffusive transport calculation, the effective diffusion coefficients in different sedimentary layers are quantified based on our previous developed up-scaling workflow from pore- to core-scale simulation combined with the multi-scale digital rock models. The heterogeneous effective diffusivities are then implemented into the large-scale structural model for diffusive transport simulation using the FEM-based OpenGeoSys-6 simulator. The sensitivity analysis focuses on the effects of the SF-OPA bedding angle and the effect of different layer-succession layout with different canister emplacement on the spatio-temporal evolution of radionuclide diffusion front line. Results show that the moving distance of the diffusion front is farther away from the canister center, along the direction with the neighboring layer having lower diffusion coefficient within the total simulation time of 2000 years. When the bedding angle increases, the diffusion front moves farther in in vertical upward direction direction, which has less retardation effect for the radionuclide from the ground surface point. For different layer-succession layout with different canister emplacement, the smallest migration distance of the diffusion front line is 1.65 m. Within 2000 years, for the conceptual model 2B that the canister is emplaced in the layer with the highest diffusivity coefficient, the diffusion front can migrate 0.19 m farther along vertical downward direction due to the influence of the neighboring layer. The numerical results provide insight into the effects of rocks heterogeneity on diffusion of radionuclides, contributing to enhanced long-term predictability of radionuclide migration in SF-OPA as potential host rock for a deep geological repository