Factorizing LambdaMART for cold start recommendations

Recommendation systems often rely on point-wise loss metrics such as the mean squared error. However, in real recommendation settings only few items are presented to a user. This observation has recently encouraged the use of rank-based metrics. LambdaMART is the state-of-the-art algorithm in learning to rank which relies on such a metric. Despite its success it does not have a principled regularization mechanism relying in empirical approaches to control model complexity leaving it thus prone to overfitting. Motivated by the fact that very often the users' and items' descriptions as well as the preference behavior can be well summarized by a small number of hidden factors, we propose a novel algorithm, LambdaMART Matrix Factorization (LambdaMART-MF), that learns a low rank latent representation of users and items using gradient boosted trees. The algorithm factorizes lambdaMART by defining relevance scores as the inner product of the learned representations of the users and items. The low rank is essentially a model complexity controller; on top of it we propose additional regularizers to constraint the learned latent representations that reflect the user and item manifolds as these are defined by their original feature based descriptors and the preference behavior. Finally we also propose to use a weighted variant of NDCG to reduce the penalty for similar items with large rating discrepancy. We experiment on two very different recommendation datasets, meta-mining and movies-users, and evaluate the performance of LambdaMART-MF, with and without regularization, in the cold start setting as well as in the simpler matrix completion setting. In both cases it outperforms in a significant manner current state of the art algorithms

Reflectance reconstruction for multispectral imaging by adaptive Wiener estimation

Author name used in this publication: Si-Jie ShaoAuthor name used in this publication: John H. Xin2007-2008 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Magnetic coupling at rare earth ferromagnet/transition metal ferromagnet interfaces: A comprehensive study of Gd/Ni.

Thin film magnetic heterostructures with competing interfacial coupling and Zeeman energy provide a fertile ground to study phase transition between different equilibrium states as a function of external magnetic field and temperature. A rare-earth (RE)/transition metal (TM) ferromagnetic multilayer is a classic example where the magnetic state is determined by a competition between the Zeeman energy and antiferromagnetic interfacial exchange coupling energy. Technologically, such structures offer the possibility to engineer the macroscopic magnetic response by tuning the microscopic interactions between the layers. We have performed an exhaustive study of nickel/gadolinium as a model system for understanding RE/TM multilayers using the element-specific measurement technique x-ray magnetic circular dichroism, and determined the full magnetic state diagrams as a function of temperature and magnetic layer thickness. We compare our results to a modified Stoner-Wohlfarth-based model and provide evidence of a thickness-dependent transition to a magnetic fan state which is critical in understanding magnetoresistance effects in RE/TM systems. The results provide important insight for spintronics and superconducting spintronics where engineering tunable magnetic inhomogeneity is key for certain applications.T.D.C.H. and J.W.A.R. acknowledge funding from the EPSRC [EP/I038047/1], the EPSRC Programme grant “Superconducting Spintronics” [EP/N017242/1] and the Leverhulme Trust [IN-2013-033]. S.B. acknowledges support from the Knut and Alice Wallenberg Foundation. X.L.W. and J.H.Z. acknowledge support from the MOST of China [2015CB921500]. Research at SLAC and Stanford was supported through the Stanford Institute for Materials and Energy Sciences (SIMES) which like the SSRL user facility and the scanning SQUID microscopy is funded by the Office of Basic Energy Sciences of the U.S. Department of Energy.This is the final version of the article. It first appeared from Nature Publishing Group at http://dx.doi.org/10.1038/srep30092

Intensive expression of Bmi-1 is a new independent predictor of poor outcome in patients with ovarian carcinoma

Background: It has been suggested that the B-cell specific moloney leukemia virus insertion site 1 (Bmi-1) gene plays an oncogenic role in several types of human cancer, but the status of Bmi-1 amplification and expression in ovarian cancer and its clinical/prognostic significance are unclear.Methods: The methods of immunohistochemistry and fluorescence in situ hybridization were utilized to examine protein expression and amplification of Bmi-1 in 30 normal ovaries, 30 ovarian cystadenomas, 40 borderline ovarian tumors and 179 ovarian carcinomas.Results: Intensive expression of Bmi-1 was detected in none of the normal ovaries, 3% cystadenomas, 10% borderline tumors, and 37% ovarian carcinomas, respectively. Amplification of Bmi-1 was detected in 8% of ovarian carcinomas. In ovarian carcinomas, significant positive associations were found between intensive expression of Bmi-1 and the tumors ascending histological grade, later pT/pN/pM and FIGO stages (P < 0.05). In univariate survival analysis of the ovarian carcinoma cohorts, a significant association of intensive expression of Bmi-1 with shortened patient survival (mean 49.3 months versus 100.3 months, p < 0.001) was demonstrated. Importantly, Bmi-1 expression provided significant independent prognostic parameters in multivariate analysis (p = 0.005).Conclusions: These findings provide evidence that intensive expression of Bmi-1 might be important in the acquisition of an invasive and/or aggressive phenotype of ovarian carcinoma, and serve as a independent biomarker for shortened survival time of patients. © 2010 Yang et al; licensee BioMed Central Ltd.published_or_final_versio

Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database

Radiologists in their daily work routinely find and annotate significant abnormalities on a large number of radiology images. Such abnormalities, or lesions, have collected over years and stored in hospitals' picture archiving and communication systems. However, they are basically unsorted and lack semantic annotations like type and location. In this paper, we aim to organize and explore them by learning a deep feature representation for each lesion. A large-scale and comprehensive dataset, DeepLesion, is introduced for this task. DeepLesion contains bounding boxes and size measurements of over 32K lesions. To model their similarity relationship, we leverage multiple supervision information including types, self-supervised location coordinates and sizes. They require little manual annotation effort but describe useful attributes of the lesions. Then, a triplet network is utilized to learn lesion embeddings with a sequential sampling strategy to depict their hierarchical similarity structure. Experiments show promising qualitative and quantitative results on lesion retrieval, clustering, and classification. The learned embeddings can be further employed to build a lesion graph for various clinically useful applications. We propose algorithms for intra-patient lesion matching and missing annotation mining. Experimental results validate their effectiveness.Comment: Accepted by CVPR2018. DeepLesion url adde

Change Actions: Models of Generalised Differentiation

Cai et al. have recently proposed change structures as a semantic framework for incremental computation. We generalise change structures to arbitrary cartesian categories and propose the notion of change action model as a categorical model for (higher-order) generalised differentiation. Change action models naturally arise from many geometric and computational settings, such as (generalised) cartesian differential categories, group models of discrete calculus, and Kleene algebra of regular expressions. We show how to build canonical change action models on arbitrary cartesian categories, reminiscent of the F\`aa di Bruno construction

Annexin-A5 assembled into two-dimensional arrays promotes cell membrane repair

Eukaryotic cells possess a universal repair machinery that ensures rapid resealing of plasma membrane disruptions. Before resealing, the torn membrane is submitted to considerable tension, which functions to expand the disruption. Here we show that annexin-A5 (AnxA5), a protein that self-assembles into two-dimensional (2D) arrays on membranes upon Ca2+ activation, promotes membrane repair. Compared with wild-type mouse perivascular cells, AnxA5-null cells exhibit a severe membrane repair defect. Membrane repair in AnxA5-null cells is rescued by addition of AnxA5, which binds exclusively to disrupted membrane areas. In contrast, an AnxA5 mutant that lacks the ability of forming 2D arrays is unable to promote membrane repair. We propose that AnxA5 participates in a previously unrecognized step of the membrane repair process: triggered by the local influx of Ca2+, AnxA5 proteins bind to torn membrane edges and form a 2D array, which prevents wound expansion and promotes membrane resealing

Strain distribution in epitaxial SrTiO₃thin films

2006-2007 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Understanding the dynamics of Toll-like Receptor 5 response to flagellin and its regulation by estradiol

© 2017 The Author(s). Toll-like receptors (TLRs) are major players of the innate immune system. Once activated, they trigger a signalling cascade that leads to NF-ΰ B translocation from the cytoplasm to the nucleus. Single cell analysis shows that NF-ΰ B signalling dynamics are a critical determinant of transcriptional regulation. Moreover, the outcome of innate immune response is also affected by the cross-talk between TLRs and estrogen signalling. Here, we characterized the dynamics of TLR5 signalling, responsible for the recognition of flagellated bacteria, and those changes induced by estradiol in its signalling at the single cell level. TLR5 activation in MCF7 cells induced a single and sustained NF-k B translocation into the nucleus that resulted in high NF-k B transcription activity. The overall magnitude of NF-k B transcription activity was not influenced by the duration of the stimulus. No significant changes are observed in the dynamics of NF-k B translocation to the nucleus when MCF7 cells are incubated with estradiol. However, estradiol significantly decreased NF-k B transcriptional activity while increasing TLR5-mediated AP-1 transcription. The effect of estradiol on transcriptional activity was dependent on the estrogen receptor activated. This fine tuning seems to occur mainly in the nucleus at the transcription level rather than affecting the translocation of the NF-k B transcription factor

Semi-sparse PCA

It is well-known that the classical exploratory factor analysis (EFA) of data with more observations than variables has several types of indeterminacy. We study the factor indeterminacy and show some new aspects of this problem by considering EFA as a specific data matrix decomposition. We adopt a new approach to the EFA estimation and achieve a new characterization of the factor indeterminacy problem. A new alternative model is proposed, which gives determinate factors and can be seen as a semi-sparse principal component analysis (PCA). An alternating algorithm is developed, where in each step a Procrustes problem is solved. It is demonstrated that the new model/algorithm can act as a specific sparse PCA and as a low-rank-plus-sparse matrix decomposition. Numerical examples with several large data sets illustrate the versatility of the new model, and the performance and behaviour of its algorithmic implementation