From coupled elementary units to the complexity of the glass transition

Supercooled liquids display fascinating properties upon cooling such as the emergence of dynamic length scales. Different models strongly vary with respect to the choice of the elementary subsystems (CRR) as well as their mutual coupling. Here we show via computer simulations of a glass former that both ingredients can be identified via analysis of finite-size effects within the continuous-time random walk framework. The CRR already contain complete information about thermodynamics and diffusivity whereas the coupling determines structural relaxation and the emergence of dynamic length scales

Soccer: is scoring goals a predictable Poissonian process?

The non-scientific event of a soccer match is analysed on a strictly scientific level. The analysis is based on the recently introduced concept of a team fitness (Eur. Phys. J. B 67, 445, 2009) and requires the use of finite-size scaling. A uniquely defined function is derived which quantitatively predicts the expected average outcome of a soccer match in terms of the fitness of both teams. It is checked whether temporary fitness fluctuations of a team hamper the predictability of a soccer match. To a very good approximation scoring goals during a match can be characterized as independent Poissonian processes with pre-determined expectation values. Minor correlations give rise to an increase of the number of draws. The non-Poissonian overall goal distribution is just a consequence of the fitness distribution among different teams. The limits of predictability of soccer matches are quantified. Our model-free classification of the underlying ingredients determining the outcome of soccer matches can be generalized to different types of sports events

Semi-supervised Learning based on Distributionally Robust Optimization

We propose a novel method for semi-supervised learning (SSL) based on data-driven distributionally robust optimization (DRO) using optimal transport metrics. Our proposed method enhances generalization error by using the unlabeled data to restrict the support of the worst case distribution in our DRO formulation. We enable the implementation of our DRO formulation by proposing a stochastic gradient descent algorithm which allows to easily implement the training procedure. We demonstrate that our Semi-supervised DRO method is able to improve the generalization error over natural supervised procedures and state-of-the-art SSL estimators. Finally, we include a discussion on the large sample behavior of the optimal uncertainty region in the DRO formulation. Our discussion exposes important aspects such as the role of dimension reduction in SSL

Learning Free-Form Deformations for 3D Object Reconstruction

Representing 3D shape in deep learning frameworks in an accurate, efficient and compact manner still remains an open challenge. Most existing work addresses this issue by employing voxel-based representations. While these approaches benefit greatly from advances in computer vision by generalizing 2D convolutions to the 3D setting, they also have several considerable drawbacks. The computational complexity of voxel-encodings grows cubically with the resolution thus limiting such representations to low-resolution 3D reconstruction. In an attempt to solve this problem, point cloud representations have been proposed. Although point clouds are more efficient than voxel representations as they only cover surfaces rather than volumes, they do not encode detailed geometric information about relationships between points. In this paper we propose a method to learn free-form deformations (FFD) for the task of 3D reconstruction from a single image. By learning to deform points sampled from a high-quality mesh, our trained model can be used to produce arbitrarily dense point clouds or meshes with fine-grained geometry. We evaluate our proposed framework on both synthetic and real-world data and achieve state-of-the-art results on point-cloud and volumetric metrics. Additionally, we qualitatively demonstrate its applicability to label transferring for 3D semantic segmentation.Comment: 16 pages, 7 figures, 3 table

Data-driven image color theme enhancement

Proceedings of the 3rd ACM SIGGRAPH Asia 2010, Seoul, South Korea, 15-18 December 2010It is often important for designers and photographers to convey or enhance desired color themes in their work. A color theme is typically defined as a template of colors and an associated verbal description. This paper presents a data-driven method for enhancing a desired color theme in an image. We formulate our goal as a unified optimization that simultaneously considers a desired color theme, texture-color relationships as well as automatic or user-specified color constraints. Quantifying the difference between an image and a color theme is made possible by color mood spaces and a generalization of an additivity relationship for two-color combinations. We incorporate prior knowledge, such as texture-color relationships, extracted from a database of photographs to maintain a natural look of the edited images. Experiments and a user study have confirmed the effectiveness of our method. © 2010 ACM.postprin

HLA-J, a Non-Pseudogene as a New Prognostic Marker for Therapy Response and Survival in Breast Cancer

The human leukocyte antigen (HLA) genes are cell-surface proteins, essential for immune cell interaction. HLA-G is known for their high immunosuppressive effect and its potential as predictive marker in breast cancer. However, nothing is known about the HLA-J and its immunosuppressive, prognostic and predictive features, as it is assumed to be a pseudogene by in silico sequence interpretation. HLA-J, ESR1, ERBB2, KRT5 and KRT20 mRNA expression were analysed in 29 fresh frozen breast cancer biopsies and their corresponding resectates obtained from patients treated with neoadjuvant chemotherapy (NACT). mRNA was analysed with gene specific TaqMan-based Primer/Probe sets and normalized to Calmodulin 2. All breast cancer samples did express HLA-J and frequently increased HLA-J mRNA levels after NACT. HLA-J mRNA was significantly associated with overexpression of the ESR1 mRNA status (Spearman ρ 0,5679; p = 0.0090) and KRT5 mRNA (Spearman ρ 0,6121; p = 0.0041) in breast cancer core biopsies and dominated in luminal B subtype. Kaplan Meier analysis revealed that an increase of HLA-J mRNA expression after NACT had worse progression free survival (p = 0,0096), indicating a counterreaction of tumor tissues presumably to prevent elimination by enhanced immune infiltration induced by NACT. This counterreaction is associated with worse prognosis. To our knowledge this is the first study identifying HLA-J as a new predictive marker in breast cancer being involved in immune evasion mechanisms.Humane Leukozyten-Antigene (HLA) sind Proteine auf der Zelloberfläche, die essenziell für die Immunzellinteraktion sind. HLA-G ist für seine hohe immunosuppressive Wirkung sowie als potenzieller prädikativer Marker für Brustkrebs bekannt. Dagegen ist kaum etwas über HLA-J und seine immunosuppressiven, prognostischen und prädiktiven Eigenschaften bekannt, da es basierend auf In-silico-Sequenzanalysen als „Pseudogen“ interpretiert wurde. Die Expression von HLA-J, ESR1, ERBB2, KRT5 und KRT20 mRNA wurde in 29 frisch gefrorenen Brustkrebsbiopsien analysiert und mit den klinisch-pathologischen Daten von Patientinnen, welche mit neoadjuvanter Chemotherapie behandelt wurden, verglichen. Die mRNA-Expression wurde mit genspezifischen TaqMan-basierten Primer/Probe-Sets analysiert und auf Calmodulin 2 normalisiert. Alle Gewebeproben von Patientinnen mit Brustkrebs exprimierten HLA-J, und der HLA-J-mRNA-Spiegel war nach NACT oft erhöht. In den Brustkrebsstanzbiopsien war die HLA-J-mRNA-Expression signifikant mit der Überexpression von ESR1-mRNA (Spearmans ρ 0,5679; p = 0,0090) und KRT5-mRNA (Spearmans ρ 0,6121; p = 0,0041) assoziiert und dominierte im Luminal-B-Subtyp. Die Kaplan-Meier-Analyse zeigte, dass ein Anstieg der HLA-J-mRNA-Expression nach NACT mit einem schlechteren progressionsfreien Überleben einhergeht (p = 0,0096), womöglich als Gegenreaktion des Tumorgewebes, um eine Eliminierung durch tumorinfiltrierende Lymphozyten, welche durch eine NACT induziert wurden, zu verhindern. Diese Gegenreaktion ist mit einer schlechteren Prognose assoziiert. Soweit uns bekannt, handelt es sich hierbei um die erste Studie, die HLA-J als neuen prädiktiven Marker im Brustkrebs identifiziert hat und möglicherweise zur Immunevasion beiträgt

Activated Polymorphonuclear Leukocytes Rapidly Synthesize Retinoic Acid Receptor-α: A Mechanism for Translational Control of Transcriptional Events

In addition to releasing preformed granular proteins, polymorphonuclear leukocytes (PMNs) synthesize chemokines and other factors under transcriptional control. Here we demonstrate that PMNs express an inducible transcriptional modulator by signal-dependent activation of specialized mechanisms that regulate messenger RNA (mRNA) translation. HL-60 myelocytic cells differentiated to surrogate PMNs respond to activation by platelet activating factor by initiating translation and with appearance of specific mRNA transcripts in polyribosomes. cDNA array analysis of the polyribosome fraction demonstrated that retinoic acid receptor (RAR)-α, a transcription factor that controls the expression of multiple genes, is one of the polyribosome-associated transcripts. Quiescent surrogate HL60 PMNs and primary human PMNs contain constitutive message for RAR-α but little or no protein. RAR-α protein is rapidly synthesized in response to platelet activating factor under the control of a specialized translational regulator, mammalian target of rapamycin, and is blocked by the therapeutic macrolide rapamycin, events consistent with features of the 5′ untranslated region of the transcript. Newly synthesized RAR-α modulates production of interleukin-8. Rapid expression of a transcription factor under translational control is a previously unrecognized mechanism in human PMNs that indicates unexpected diversity in gene regulation in this critical innate immune effector cell

QuickSel: Quick Selectivity Learning with Mixture Models

Estimating the selectivity of a query is a key step in almost any cost-based query optimizer. Most of today's databases rely on histograms or samples that are periodically refreshed by re-scanning the data as the underlying data changes. Since frequent scans are costly, these statistics are often stale and lead to poor selectivity estimates. As an alternative to scans, query-driven histograms have been proposed, which refine the histograms based on the actual selectivities of the observed queries. Unfortunately, these approaches are either too costly to use in practice---i.e., require an exponential number of buckets---or quickly lose their advantage as they observe more queries. In this paper, we propose a selectivity learning framework, called QuickSel, which falls into the query-driven paradigm but does not use histograms. Instead, it builds an internal model of the underlying data, which can be refined significantly faster (e.g., only 1.9 milliseconds for 300 queries). This fast refinement allows QuickSel to continuously learn from each query and yield increasingly more accurate selectivity estimates over time. Unlike query-driven histograms, QuickSel relies on a mixture model and a new optimization algorithm for training its model. Our extensive experiments on two real-world datasets confirm that, given the same target accuracy, QuickSel is 34.0x-179.4x faster than state-of-the-art query-driven histograms, including ISOMER and STHoles. Further, given the same space budget, QuickSel is 26.8%-91.8% more accurate than periodically-updated histograms and samples, respectively

A Game Theoretic Approach To Learning Shape Categories and Contextual Similarities

Abstract. The search of a model for representing and evaluating the similarities between shapes in a perceptually coherent way is still an open issue. One reason for this is that our perception of similarities is strongly influenced by the underlying category structure. In this paper we aim at jointly learning the categories from examples and the similar-ity measures related to them. There is a chicken and egg dilemma here: class knowledge is required to determine perceived similarities, while the similarities are needed to extract class knowledge in an unsuper-vised way. The problem is addressed through a game theoretic approach which allows us to compute 2D shape categories based on a skeletal rep-resentation. The approach provides us with both the cluster information needed to extract the categories, and the relevance information needed to compute the category model and, thus, the similarities. Experiments on a database of 1000 shapes showed that the approach outperform other clustering approaches that do not make use of the underlying contextual information and provides similarities comparable with a state-of-the-art label-propagation approach which, however, cannot extract categories.

Shape Retrieval of Non-rigid 3D Human Models

3D models of humans are commonly used within computer graphics and vision, and so the ability to distinguish between body shapes is an important shape retrieval problem. We extend our recent paper which provided a benchmark for testing non-rigid 3D shape retrieval algorithms on 3D human models. This benchmark provided a far stricter challenge than previous shape benchmarks. We have added 145 new models for use as a separate training set, in order to standardise the training data used and provide a fairer comparison. We have also included experiments with the FAUST dataset of human scans. All participants of the previous benchmark study have taken part in the new tests reported here, many providing updated results using the new data. In addition, further participants have also taken part, and we provide extra analysis of the retrieval results. A total of 25 different shape retrieval methods are compared