Stop memorizing: A data-dependent regularization framework for intrinsic pattern learning

Deep neural networks (DNNs) typically have enough capacity to fit random data by brute force even when conventional data-dependent regularizations focusing on the geometry of the features are imposed. We find out that the reason for this is the inconsistency between the enforced geometry and the standard softmax cross entropy loss. To resolve this, we propose a new framework for data-dependent DNN regularization, the Geometrically-Regularized-Self-Validating neural Networks (GRSVNet). During training, the geometry enforced on one batch of features is simultaneously validated on a separate batch using a validation loss consistent with the geometry. We study a particular case of GRSVNet, the Orthogonal-Low-rank Embedding (OLE)-GRSVNet, which is capable of producing highly discriminative features residing in orthogonal low-rank subspaces. Numerical experiments show that OLE-GRSVNet outperforms DNNs with conventional regularization when trained on real data. More importantly, unlike conventional DNNs, OLE-GRSVNet refuses to memorize random data or random labels, suggesting it only learns intrinsic patterns by reducing the memorizing capacity of the baseline DNN

3-Fluoro-4-(4-hy­droxy­phen­oxy)benzonitrile

The title compound, C13H8FNO2, was synthesized from 3,4-difluoro­benzonitrile and hydro­quinone. The dihedral angle between the two aromatic rings is 70.9 (2)°. In the crystal structure, mol­ecules are linked by O—H⋯N hydrogen bonds, forming zigzag chains

4-(4-Cyano-2-fluoro­phen­oxy)phenyl 4-methyl­benzene­sulfonate

The title compound, C20H14FNO4S, was synthesized from hydro­quinone, p-toluene­sulfonyl chloride and 3,4-difluoro­benzonitrile. A folded conformation is adopted by the crystal structure. Inter­molecular C—H⋯N hydrogen bonds form dimers arranged around inversion centers

Exploring pleiotropy using principal components

A standard multivariate principal components (PCs) method was utilized to identify clusters of variables that may be controlled by a common gene or genes (pleiotropy). Heritability estimates were obtained and linkage analyses performed on six individual traits (total cholesterol (Chol), high and low density lipoproteins, triglycerides (TG), body mass index (BMI), and systolic blood pressure (SBP)) and on each PC to compare our ability to identify major gene effects. Using the simulated data from Genetic Analysis Workshop 13 (Cohort 1 and 2 data for year 11), the quantitative traits were first adjusted for age, sex, and smoking (cigarettes per day). Adjusted variables were standardized and PCs calculated followed by orthogonal transformation (varimax rotation). Rotated PCs were then subjected to heritability and quantitative multipoint linkage analysis. The first three PCs explained 73% of the total phenotypic variance. Heritability estimates were above 0.60 for all three PCs. We performed linkage analyses on the PCs as well as the individual traits. The majority of pleiotropic and trait-specific genes were not identified. Standard PCs analysis methods did not facilitate the identification of pleiotropic genes affecting the six traits examined in the simulated data set. In addition, genes contributing 20% of the variance in traits with over 0.60 heritability estimates could not be identified in this simulated data set using traditional quantitative trait linkage analyses. Lack of identification of pleiotropic and trait-specific genes in some cases may reflect their low contribution to the traits/PCs examined or more importantly, characteristics of the sample group analyzed, and not simply a failure of the PC approach itself

Orca: A Few-shot Benchmark for Chinese Conversational Machine Reading Comprehension

The conversational machine reading comprehension (CMRC) task aims to answer questions in conversations, which has been a hot research topic in recent years because of its wide applications. However, existing CMRC benchmarks in which each conversation is assigned a static passage are inconsistent with real scenarios. Thus, model's comprehension ability towards real scenarios are hard to evaluate reasonably. To this end, we propose the first Chinese CMRC benchmark Orca and further provide zero-shot/few-shot settings to evaluate model's generalization ability towards diverse domains. We collect 831 hot-topic driven conversations with 4,742 turns in total. Each turn of a conversation is assigned with a response-related passage, aiming to evaluate model's comprehension ability more reasonably. The topics of conversations are collected from social media platform and cover 33 domains, trying to be consistent with real scenarios. Importantly, answers in Orca are all well-annotated natural responses rather than the specific spans or short phrase in previous datasets. Besides, we implement three strong baselines to tackle the challenge in Orca. The results indicate the great challenge of our CMRC benchmark. Our datatset and checkpoints are available at https://github.com/nuochenpku/Orca.Comment: 14 page

Association between hibernating myocardium and collateral circulation in patients with coronary chronic total occlusion

ObjectiveTo explore the association between the quantity of hibernating myocardium (HM) and collateral circulation in patients with coronary chronic total occlusion (CTO).Materials and methods88 CTO patients were retrospectively analyzed who underwent evaluation for HM using both 99mTc-sestamibi Single photon emission computed tomography (99mTc-MIBI SPECT) myocardial perfusion imaging (MPI) combined with 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) myocardial metabolism imaging (MMI). They were divided into two groups according Rentrop grading: the poorly/well-developed collateral circulation group (PD/WD group, Rentrop grades 0–1/2–3). After adjusting for the potential confounding factors and conducting a stratified analysis, we explored the association between the HM index within CTO region and the grading of collateral circulation.ResultsIn the WD group, the HM index was notably higher than PD group (46.2 ± 15.7% vs. 20.9 ± 16.7%, P < 0.001). When dividing the HM index into tertiles and after adjusting for potential confounders, we observed that the proportion of patients with WD rose as the HM index increased (OR: 1.322, 95% CI: 0.893–1.750, P < 0.001), the proportion of patients with WD was 17.4%, 63.3%, and 88.6% for Tertile 1 to Tertile 3.This increasing trend was statistically significant (OR: 1.369, 95% CI: 0.873–1.864, P < 0.001), especially between Tertile 3 vs. Tertile 1 (OR: 4.330, 95% CI: 1.459–12.850, P = 0.008). Curve fitting displaying an almost linear positive correlation between the two.ConclusionThe HM index within CTO region is an independent correlation factor for the grading of coronary collateral circulation. A greater HM index corresponded to an increased likelihood of WD

Circadian Clock Genes in the Metabolism of Non-alcoholic Fatty Liver Disease

Non-alcoholic fatty liver disease (NAFLD) is a common disease, which is characterized by the accumulation of triglycerides in the hepatocytes without excess alcohol intake. Circadian rhythms can participate in lipid, glucose, and cholesterol metabolism and are closely related to metabolism seen in this disease. Circadian clock genes can modulate liver lipid metabolism. Desynchrony of circadian rhythms and the influences imparted by external environmental stimuli can increase morbidity. By contrast, synchronizing circadian rhythms can help to alleviate the metabolic disturbance seen in NAFLD. In this review, we have discussed the current research connections that exist between the circadian clock and the metabolism of NAFLD, and we have specifically focused on the key circadian clock genes, Bmal1, Clock, Rev-Erbs, Rors, Pers, Crys, Nocturnin, and DECs

Bubble-Wall Plot: A New Tool for Data Visualization

This research aimed to design a new tool for data visualization with performed features - named Bubble-Wall Plot and assumed that it could be an effective tool for developing data visualization systems. This research reviewed seven data visualization approaches for identifying the outliers, including Line Charts, Parallel Coordinates Plot, Scatter Plots, TreeMap, Glyphs, Pixel-based techniques, and Redial visualizations. The challenges for current data visualization approaches were also summarized. Two principles were addressed to design the new tool- keep it simple strategy with the smallest strategy. As a result, the newly designed Bubble-Wall Plot has successfully been adopted to develop a warning system for identifying the outliers in a Case Study company, which was deployed for user acceptance testing in May 2021. The main contribution is that this newly designed tool with the simplest style was well-designed and proven to effectively develop a warning visualization system