RecXplainer: Post-Hoc Attribute-Based Explanations for Recommender Systems

Recommender systems are ubiquitous in most of our interactions in the current digital world. Whether shopping for clothes, scrolling YouTube for exciting videos, or searching for restaurants in a new city, the recommender systems at the back-end power these services. Most large-scale recommender systems are huge models trained on extensive datasets and are black-boxes to both their developers and end-users. Prior research has shown that providing recommendations along with their reason enhances trust, scrutability, and persuasiveness of the recommender systems. Recent literature in explainability has been inundated with works proposing several algorithms to this end. Most of these works provide item-style explanations, i.e., `We recommend item A because you bought item B.' We propose a novel approach, RecXplainer, to generate more fine-grained explanations based on the user's preference over the attributes of the recommended items. We perform experiments using real-world datasets and demonstrate the efficacy of RecXplainer in capturing users' preferences and using them to explain recommendations. We also propose ten new evaluation metrics and compare RecXplainer to six baseline methods.Comment: Awarded the Best Student Paper at TEA Workshop at NeurIPS 2022. 13 page

A robust and efficient method for estimating enzyme complex abundance and metabolic flux from expression data

A major theme in constraint-based modeling is unifying experimental data, such as biochemical information about the reactions that can occur in a system or the composition and localization of enzyme complexes, with highthroughput data including expression data, metabolomics, or DNA sequencing. The desired result is to increase predictive capability resulting in improved understanding of metabolism. The approach typically employed when only gene (or protein) intensities are available is the creation of tissue-specific models, which reduces the available reactions in an organism model, and does not provide an objective function for the estimation of fluxes, which is an important limitation in many modeling applications. We develop a method, flux assignment with LAD (least absolute deviation) convex objectives and normalization (FALCON), that employs metabolic network reconstructions along with expression data to estimate fluxes. In order to use such a method, accurate measures of enzyme complex abundance are needed, so we first present a new algorithm that addresses quantification of complex abundance. Our extensions to prior techniques include the capability to work with large models and significantly improved run-time performance even for smaller models, an improved analysis of enzyme complex formation logic, the ability to handle very large enzyme complex rules that may incorporate multiple isoforms, and depending on the model constraints, either maintained or significantly improved correlation with experimentally measured fluxes. FALCON has been implemented in MATLAB and ATS, and can be downloaded from: https://github.com/bbarker/FALCON. ATS is not required to compile the software, as intermediate C source code is available, and binaries are provided for Linux x86-64 systems. FALCON requires use of the COBRA Toolbox, also implemented in MATLAB.Comment: 30 pages, 12 figures, 4 table

Cell non-autonomous interactions during non-immune stromal progression in the breast tumor microenvironment

Summary The breast tumor microenvironment of primary and metastatic sites is a complex milieu of differing cell populations, consisting of tumor cells and the surrounding stroma. Despite recent progress in delineating the immune component of the stroma, the genomic expression landscape of the non-immune stroma (NIS) population and their role in mediating cancer progression and informing effective therapies are not well understood. Here we obtained 52 cell-sorted NIS and epithelial tissue samples across 37 patients from i) normal breast, ii) normal breast adjacent to primary tumor, iii) primary tumor, and iv) metastatic tumor sites. Deep RNA-seq revealed diverging gene expression profiles as the NIS evolves from normal to metastatic tumor tissue, with intra-patient normal-primary variation comparable to inter-patient variation. Significant expression changes between normal and adjacent normal tissue support the notion of a cancer field effect, but extended out to the NIS. Most differentially expressed protein-coding genes and lncRNAs were found to be associated with pattern formation, embryogenesis, and the epithelial-mesenchymal transition. We validated the protein expression changes of a novel candidate gene, C2orf88, by immunohistochemistry staining of representative tissues. Significant mutual information between epithelial ligand and NIS receptor gene expression, across primary and metastatic tissue, suggests a unidirectional model of molecular signaling between the two tissues. Furthermore, survival analyses of 827 luminal breast tumor samples demonstrated the predictive power of the NIS gene expression to inform clinical outcomes. Together, these results highlight the evolution of NIS gene expression in breast tumors and suggest novel therapeutic strategies targeting the microenvironment

Severe Hemolytic Anemia due to Vitamin B12 Deficiency in Six Months

Gastric bypass is a common cause of vitamin B12 deficiency. It can lead to patients presenting with symptoms of anemia. The body has significant reserves of vitamin B12 and loses vitamin B12 slowly. The following case is of a patient who underwent a gastric bypass five years ago and whose hemoglobin (Hgb) dropped from 12.2 g/dL to 4.4 g/dL over six months due to questionable adherence to vitamin supplements. Further work-up showed hemolytic anemia and thrombocytopenia due to a very low vitamin B12 level of 47 pg/mL, with his blood counts improving with vitamin B12 supplementation. The case points to the importance of thinking about vitamin deficiency as a cause of hemolysis to avoid unnecessary procedures

DOI: 10.1007/s11036-006-5187-8 Decentralized Utility-based Sensor Network Design

Abstract. Wireless sensor networks consist of energy-constrained sensor nodes operating unattended in highly dynamic environments. In this paper, we advocate a systematic decentralized approach towards the design of such networks based on utility functions. A local utility function is defined for each sensor node in the network. While each sensor node “selfishly ” optimizes its own utility, the network as a “whole ” converges to a desired global objective. For the purpose of demonstrating our approach, we consider the following two separate case studies for data gathering in sensor networks: (a) construction of a load balanced tree and (b) construction of an energy balanced tree. Our work suggests a significant departure from the existing view of sensor networks as consisting of cooperative nodes, i.e. “selfish”sensor nodes is a useful paradigm for designing efficient distributed algorithms for these networks. 1

Maximizing Data Extraction in Energy-Limited Sensor Networks

We examine the problem of maximizing data collection from an energy-limited store-and-extract wireless sensor network, which is analogous to the maximum lifetime problem of interest in continuous data-gathering sensor networks. One significant difference is that this problem requires attention to "data-awareness" in addition to "energy-awareness." We formulate the maximum data extraction problem as a linear program and present a iterative approximation algorithm for it. As a practical distributed implementation we develop a faster greedy heuristic for this problem that uses an exponential metric based on the approximation algorithm. We then show through simulation results that the greedy heuristic incorporating this exponential metric performs nearoptimally (within 1 to 20% of optimal, with low overhead) and significantly better than other shortest-path routing approaches, particularly when nodes are heterogeneous in their energy and data availability

The Acquire Mechanism for Efficient Querying in Sensor Networks

We propose a novel and efficient mechanism for obtaining information in sensor networks which we refer to as acquire. In acquire an active query is forwarded through the network, and intermediate nodes use cached local information (within a look-ahead of d hops) in order to partially resolve the query. When the query is fully resolved, a completed response is sent directly back to the querying node. We take a mathematical modeling approach to calculate the energy costs associated with acquire. The models permit us to characterize analytically the impact of critical parameters, and compare the performance of acquire with respect to alternatives such as flooding-based querying (FBQ) and expanding ring search (ERS). We show that with optimal parameter settings, depending on the update frequency, acquire obtains order of magnitude reduction over FBQ and a potential reduction of 60% over ERS in consumed energy