AutoRec: An Automated Recommender System

Recommender systems are highly specialized to handle specific data and tasks. For example, Neural Collaborative Filtering [1] takes the implicit interaction between user and item IDs as the input data for the rating prediction task. Wide & Deep learning [2] ingests user and application attributes to predict app downloads for Google Play. And DeepFM [3] leverages both numerical and categorical data to estimate the click-through rate (CTR) for ad campaigns. However, a high degree of specialization comes at the expense of model adaptability and model tuning complexity. As shown in Figure 1, the originally apt model often either becomes obsolete or requires hyper-parameter tuning as the recommendation task at hand changes and additional types and amounts of data are collected over time. The efforts required to re-tune or re-build a model is often high. So far, several modular pipelines for building recommender systems, such as Open-Rec [4] and SMORe [5], have been proposed to address the adaptability issue. They contribute to the community by defining unified pipeline schema which divide recommendation models into a series of components (blocks) with specific functions and provide selectable modules for each. This design allows developers to quickly build and iterate recommendation models by assembling and swapping for the promising parts. Nevertheless, 1) determining which modules to use for each block and 2) hyper-parameter tuning for recommendation models remain challenging when models need to be adapted for continuously changing tasks and data

Enhanced Distillation Under Infrared Characteristic Radiation

This chapter introduces quasi-steady water vaporization under mid-infrared (IR) radiation and the IR absorption of characteristic radiation associated with the first-kind liquid-gaseous phase transition of water. When characteristic radiation in the mid-IR spectral range is applied to water surface, the strong volumetric absorption of radiation energy in the liquid-phase causes water to be nearly isothermal. In addition to volumetric absorption, surface absorption of characteristic radiation induces vaporization of water. The complete mechanism of liquid-gaseous phase-transition radiation involves the direct surface absorption/emission of infrared energy accompanied by evaporation/condensation of water. A direct consequence of excess characteristic radiation upon water surface is the induced supersaturation. This mechanism opens up a door for enhanced distillation under characteristic radiation. Blackbody-like materials such as black anodized aluminum surfaces and metal surfaces painted in black are recommended to be heated to ~250°C to serve as economical radiation sources. For isothermal water at room temperatures, ~20% supersaturation can be induced by hemispherical Blackbody radiation with temperature ~11°C higher than the water temperature. In this situation, energy extracted from the ambient for water vaporization can be as much as 80% of latent heat. With radiation-enhanced evaporation, the production cost for distilled water is significantly reduced as compared to distillation at the boiling point

Marginal Nodes Matter: Towards Structure Fairness in Graphs

In social network, a person located at the periphery region (marginal node) is likely to be treated unfairly when compared with the persons at the center. While existing fairness works on graphs mainly focus on protecting sensitive attributes (e.g., age and gender), the fairness incurred by the graph structure should also be given attention. On the other hand, the information aggregation mechanism of graph neural networks amplifies such structure unfairness, as marginal nodes are often far away from other nodes. In this paper, we focus on novel fairness incurred by the graph structure on graph neural networks, named \emph{structure fairness}. Specifically, we first analyzed multiple graphs and observed that marginal nodes in graphs have a worse performance of downstream tasks than others in graph neural networks. Motivated by the observation, we propose \textbf{S}tructural \textbf{Fair} \textbf{G}raph \textbf{N}eural \textbf{N}etwork (SFairGNN), which combines neighborhood expansion based structure debiasing with hop-aware attentive information aggregation to achieve structure fairness. Our experiments show \SFairGNN can significantly improve structure fairness while maintaining overall performance in the downstream tasks.Comment: SIGKDD Explorations (To Appear

Empiric antibiotic choices for community-acquired biliary tract infections

SummaryBackgroundThe study was conducted to reveal the most appropriate empiric antibiotics for the treatment of community-acquired biliary tract infections (CA-BTI) at a regional hospital in Taiwan.MethodsThe study was performed between October 1, 2010 and October 31, 2012. All positive bile culture results of presumptive community-acquired origins were collected. The associated etiologic microorganisms and their antimicrobial susceptibilities were analyzed. The appropriateness of empiric therapy (defined as the effectiveness of the antibiotics against the etiologic agents) and the subsequent treatment response were examined through the review of medical records.ResultsA total of 115 patients (cholecystitis, 83 cases, 72.2%; cholangitis, 32 cases, 27.8%) and 189 isolates (136 Gram-negative bacilli, 37 Gram-positive cocci, and 16 anaerobes) were analyzed. The most frequent pathogens were Escherichia coli (n = 69, 36.5%), Klebsiella spp. (n = 37, 19.6%), enterococci (n = 29, 15.3%), and Bacteroides spp. (n = 11, 5.8%). Penicillin resistance (5.4%) was low in Gram-positive cocci, whereas higher resistance (>20%) to cefazolin, cefuroxime, and ampicillin–sulbactam was found in Gram-negative bacilli. Anaerobes also demonstrated high resistance to clindamycin (37.5%) but less to metronidazole (12.5%). Appropriate empiric therapy was found in 92 (80%) cases, and among them, 83 (90.2%) were treated successfully. The treatment success rate (69.6%) was significantly lower among the remaining 23 cases with inappropriate empiric therapy (16 of 23 vs. 83 of 92, p < 0.05). A high treatment success rate (97.2%) was observed among cases empirically treated with ceftriaxone plus metronidazole.ConclusionThe combination of ceftriaxone plus metronidazole appears to be the most appropriate empiric antibiotics for the treatment of CA-BTI at this hospital. Because different hospitals may encounter microorganisms of different antimicrobial susceptibilities, similar approaches may be followed by other hospitals where appropriate empiric therapy has not yet been established for the treatment of CA-BTI

A quantitative analysis of monochromaticity in genetic interaction networks

<p>Abstract</p> <p>Background</p> <p>A genetic interaction refers to the deviation of phenotypes from the expected when perturbing two genes simultaneously. Studying genetic interactions help clarify relationships between genes, such as compensation and masking, and identify gene groups of functional modules. Recently, several genome-scale experiments for measuring quantitative (positive and negative) genetic interactions have been conducted. The results revealed that genes in the same module usually interact with each other in a consistent way (pure positive or negative); this phenomenon was designated as monochromaticity. Monochromaticity might be the underlying principle that can be utilized to unveil the modularity of cellular networks. However, no appropriate quantitative measurement for this phenomenon has been proposed.</p> <p>Results</p> <p>In this study, we propose the monochromatic index (MCI), which is able to quantitatively evaluate the monochromaticity of potential functional modules of genes, and the MCI was used to study genetic landscapes in different cellular subsystems. We demonstrated that MCI not only amend the deficiencies of MP-score but also properly incorporate the background effect. The results showed that not only within-complex but also between-complex connections present significant monochromatic tendency. Furthermore, we also found that significantly higher proportion of protein complexes are connected by negative genetic interactions in metabolic network, while transcription and translation system adopts relatively even number of positive and negative genetic interactions to link protein complexes.</p> <p>Conclusion</p> <p>In summary, we demonstrate that MCI improves deficiencies suffered by MP-score, and can be used to evaluate monochromaticity in a quantitative manner. In addition, it also helps to unveil features of genetic landscapes in different cellular subsystems. Moreover, MCI can be easily applied to data produced by different types of genetic interaction methodologies such as Synthetic Genetic Array (SGA), and epistatic miniarray profile (E-MAP).</p