Mean-Variance Serum Sodium Associations with Diabetes Patients

Serum sodium (SNa) is a critically significant component of hyponatremia and bones, has firmly been established as a risk factor correlated with many diseases such as diabetes, heart, anaemia, etc and the incidence of fragility fractures. Note that the fragility fractures are a general complication in type 2 diabetes (T2D) patients, contributing to high rates of mortality and morbidity together with mounting public health costs. SNa is a fundamental component for normal physiological processes, and T2D patients may experience osmotic diuresis as a consequence of disease-related hyperglycemia, contributing to the excess excretion of sodium in the urine and resulting in hyponatremia. Dysnatremias [hyponatremia (<136 mmol/L) and hypernatremia (>145mmol/L)] can severely affect several physiologic organ systems and functions. Diabetes is correlated with many important electrolyte disorders, predominantly affecting magnesium, SNa, and potassium. However, the correlation/ association of SNa with diabetes patients is not clear. This can be confirmed based on the proper probabilistic model of SNa with diabetes status along with the other explanatory factors of the disease. On the other hand, this type of association can be obtained based on the models of fasting glucose/post glucose/random glucose/HbA1c level  with SNa and other explanatory factors of the diabetes disease

Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation

The rapid proliferation of new users and items on the social web has aggravated the gray-sheep user/long-tail item challenge in recommender systems. Historically, cross-domain co-clustering methods have successfully leveraged shared users and items across dense and sparse domains to improve inference quality. However, they rely on shared rating data and cannot scale to multiple sparse target domains (i.e., the one-to-many transfer setting). This, combined with the increasing adoption of neural recommender architectures, motivates us to develop scalable neural layer-transfer approaches for cross-domain learning. Our key intuition is to guide neural collaborative filtering with domain-invariant components shared across the dense and sparse domains, improving the user and item representations learned in the sparse domains. We leverage contextual invariances across domains to develop these shared modules, and demonstrate that with user-item interaction context, we can learn-to-learn informative representation spaces even with sparse interaction data. We show the effectiveness and scalability of our approach on two public datasets and a massive transaction dataset from Visa, a global payments technology company (19% Item Recall, 3x faster vs. training separate models for each domain). Our approach is applicable to both implicit and explicit feedback settings.Comment: SIGIR 202

MapRat: Meaningful Explanation, Interactive Exploration and Geo-Visualization of Collaborative Ratings

ISSN: 2150-8097 - www.vldb2012.org - Demonstration session: Information Retrieval, Web, and Mobility at VLDB 2012 (Very Large Data Bases Conference, Istanbul, Turkey, 2012)International audienceCollaborative rating sites such as IMDB and Yelp have become rich resources that users consult to form judgments about and choose from among competing items. Most of these sites either provide a plethora of information for users to interpret all by themselves or a simple overall aggregate information. Such aggregates (e.g., average rating over all users who have rated an item, aggregates along pre-defined dimensions, etc.) can not help a user quickly decide the desirability of an item. In this paper, we build a system MapRat that allows a user to explore multiple carefully chosen aggregate analytic details over a set of user demographics that meaningfully explain the ratings associated with item(s) of interest. MapRat allows a user to systematically explore, visualize and understand user rating patterns of input item(s) so as to make an informed decision quickly. In the demo, participants are invited to explore collaborative movie ratings for popular movies

Tackling Diverse Minorities in Imbalanced Classification

Imbalanced datasets are commonly observed in various real-world applications, presenting significant challenges in training classifiers. When working with large datasets, the imbalanced issue can be further exacerbated, making it exceptionally difficult to train classifiers effectively. To address the problem, over-sampling techniques have been developed to linearly interpolating data instances between minorities and their neighbors. However, in many real-world scenarios such as anomaly detection, minority instances are often dispersed diversely in the feature space rather than clustered together. Inspired by domain-agnostic data mix-up, we propose generating synthetic samples iteratively by mixing data samples from both minority and majority classes. It is non-trivial to develop such a framework, the challenges include source sample selection, mix-up strategy selection, and the coordination between the underlying model and mix-up strategies. To tackle these challenges, we formulate the problem of iterative data mix-up as a Markov decision process (MDP) that maps data attributes onto an augmentation strategy. To solve the MDP, we employ an actor-critic framework to adapt the discrete-continuous decision space. This framework is utilized to train a data augmentation policy and design a reward signal that explores classifier uncertainty and encourages performance improvement, irrespective of the classifier's convergence. We demonstrate the effectiveness of our proposed framework through extensive experiments conducted on seven publicly available benchmark datasets using three different types of classifiers. The results of these experiments showcase the potential and promise of our framework in addressing imbalanced datasets with diverse minorities

Sharpness-Aware Graph Collaborative Filtering

Graph Neural Networks (GNNs) have achieved impressive performance in collaborative filtering. However, GNNs tend to yield inferior performance when the distributions of training and test data are not aligned well. Also, training GNNs requires optimizing non-convex neural networks with an abundance of local and global minima, which may differ widely in their performance at test time. Thus, it is essential to choose the minima carefully. Here we propose an effective training schema, called {gSAM}, under the principle that the \textit{flatter} minima has a better generalization ability than the \textit{sharper} ones. To achieve this goal, gSAM regularizes the flatness of the weight loss landscape by forming a bi-level optimization: the outer problem conducts the standard model training while the inner problem helps the model jump out of the sharp minima. Experimental results show the superiority of our gSAM

Who tags what? an analysis framework

The rise of Web 2.0 is signaled by sites such as Flickr, del.icio.us, and YouTube, and social tagging is essential to their success. A typical tagging action involves three components, user, item (e.g., photos in Flickr), and tags (i.e., words or phrases). Analyzing how tags are assigned by certain users to certain items has important implications in helping users search for desired information. In this paper, we explore common analysis tasks and propose a dual mining framework for social tagging behavior mining. This framework is centered around two opposing measures, similarity and diversity, being applied to one or more tagging components, and therefore enables a wide range of analysis scenarios such as characterizing similar users tagging diverse items with similar tags, or diverse users tagging similar items with diverse tags, etc. By adopting different concrete measures for similarity and diversity in the framework, we show that a wide range of concrete analysis problems can be defined and they are NP-Complete in general. We design efficient algorithms for solving many of those problems and demonstrate, through comprehensive experiments over real data, that our algorithms significantly out-perform the exact brute-force approach without compromising analysis result quality. 1