Thickness measurement of dynamic thin liquid films generated by plug-annular flow in non-wetting microchannels

Surface tension forces are significant at millimeter length-scales, causing profoundly different flow morphologies in microchannels than in macroscale flows. The existence and morphology of thin liquid films is particularly relevant for predicting performance and operational stability of devices containing microscale two phase flows. Analytical, computational, and experimental methods previously employed in the study of thin liquid films are discussed. Thicknesses before and after a novel film morphology, referred to as a `shock,\u27 are measured with a novel film thickness measurement technique that uses confocal microscopy. Film thicknesses predicted by previous work are compared to experimental results. Methods for increasing the accuracy of the confocal film thickness measurement technique are discussed

Orthogonally Decoupled Variational Gaussian Processes

Gaussian processes (GPs) provide a powerful non-parametric framework for reasoning over functions. Despite appealing theory, its superlinear computational and memory complexities have presented a long-standing challenge. State-of-the-art sparse variational inference methods trade modeling accuracy against complexity. However, the complexities of these methods still scale superlinearly in the number of basis functions, implying that that sparse GP methods are able to learn from large datasets only when a small model is used. Recently, a decoupled approach was proposed that removes the unnecessary coupling between the complexities of modeling the mean and the covariance functions of a GP. It achieves a linear complexity in the number of mean parameters, so an expressive posterior mean function can be modeled. While promising, this approach suffers from optimization difficulties due to ill-conditioning and non-convexity. In this work, we propose an alternative decoupled parametrization. It adopts an orthogonal basis in the mean function to model the residues that cannot be learned by the standard coupled approach. Therefore, our method extends, rather than replaces, the coupled approach to achieve strictly better performance. This construction admits a straightforward natural gradient update rule, so the structure of the information manifold that is lost during decoupling can be leveraged to speed up learning. Empirically, our algorithm demonstrates significantly faster convergence in multiple experiments

Real-time association mining in large social networks.

There is a growing realisation that to combat the waning effectiveness of traditional marketing, social media platform owners need to find new ways to monetise their data. Social media data contains rich information describing how real world entities relate to each other. Understanding the allegiances, communities and structure of key entities is of vital importance for decision support in a swathe of industries that have hitherto relied on expensive, small scale survey data. In this paper, we present a real-time method to query and visualise regions of networks that are closely related to a set of input vertices. The input vertices can define an industry, political party, sport etc. The key idea is that in large digital social networks measuring similarity via direct connections between nodes is not robust, but that robust similarities between nodes can be attained through the similarity of their neighbourhood graphs. We are able to achieve real-time performance by compressing the neighbourhood graphs using minhash signatures and facilitate rapid queries through Locality Sensitive Hashing. These techniques reduce query times from hours using industrial desktop machines to milliseconds on standard laptops. Our method allows analysts to interactively explore strongly associated regions of large networks in real time. Our work has been deployed in software that is actively used by analysts to understand social network structure

Probabilistic movement modeling for intention inference in human-robot interaction.

Intention inference can be an essential step toward efficient humanrobot interaction. For this purpose, we propose the Intention-Driven Dynamics Model (IDDM) to probabilistically model the generative process of movements that are directed by the intention. The IDDM allows to infer the intention from observed movements using Bayes ’ theorem. The IDDM simultaneously finds a latent state representation of noisy and highdimensional observations, and models the intention-driven dynamics in the latent states. As most robotics applications are subject to real-time constraints, we develop an efficient online algorithm that allows for real-time intention inference. Two human-robot interaction scenarios, i.e., target prediction for robot table tennis and action recognition for interactive humanoid robots, are used to evaluate the performance of our inference algorithm. In both intention inference tasks, the proposed algorithm achieves substantial improvements over support vector machines and Gaussian processes.

Thin and Deep Gaussian Processes

Gaussian processes (GPs) can provide a principled approach to uncertainty quantification with easy-to-interpret kernel hyperparameters, such as the lengthscale, which controls the correlation distance of function values. However, selecting an appropriate kernel can be challenging. Deep GPs avoid manual kernel engineering by successively parameterizing kernels with GP layers, allowing them to learn low-dimensional embeddings of the inputs that explain the output data. Following the architecture of deep neural networks, the most common deep GPs warp the input space layer-by-layer but lose all the interpretability of shallow GPs. An alternative construction is to successively parameterize the lengthscale of a kernel, improving the interpretability but ultimately giving away the notion of learning lower-dimensional embeddings. Unfortunately, both methods are susceptible to particular pathologies which may hinder fitting and limit their interpretability. This work proposes a novel synthesis of both previous approaches: Thin and Deep GP (TDGP). Each TDGP layer defines locally linear transformations of the original input data maintaining the concept of latent embeddings while also retaining the interpretation of lengthscales of a kernel. Moreover, unlike the prior solutions, TDGP induces non-pathological manifolds that admit learning lower-dimensional representations. We show with theoretical and experimental results that i) TDGP is, unlike previous models, tailored to specifically discover lower-dimensional manifolds in the input data, ii) TDGP behaves well when increasing the number of layers, and iii) TDGP performs well in standard benchmark datasets.Comment: Accepted at the Conference on Neural Information Processing Systems (NeurIPS) 202

The effect of the surface properties of poly(methyl methacrylate) on the attachment, adhesion and retention of fungal conidia

Poly(methyl methacrylate) (PMMA) surfaces, (commercial PMMA (PMMAc), spin coated PMMA (PMMAsc) and a 90% methylmethacrylate/10% 3-methacryloxypropyltrimethoxysilane random copolymer (P(MMA-co-gMPS)) were used to determine the effect of surface properties on conidia biofouling. The contact angles of the substrates demonstrated that the PMMAsc and the P(MMA-co-gMPS) polymer (62.8°) were more wettable than the PMMAc surface (71.0°). The PMMAsc had the greatest roughness value (32.0 nm) followed by the PMMAc (3.0 nm), then P(MMA-co-gMPS) (1 nm). Aspergillus niger 1957 conidia were spherical, smooth and hydrophobic (12.1%). Aspergillus niger 1988 conidia were spherical with spikes and hydrophobic (17.1%). Aureobasidium pullulans was elliptical with longitudinal ridges and hydrophilic (79.9%). Following attachment assays, cPMMA attached the greatest numbers of conidia. Following the adhesion and retention assays (washing step included in the protocol), A. niger 1957 and A. niger 1988 were least adhered to the P(MMA-co-gMPS) surface, whilst A. pulluans was least adhered to the PMMAsc surface. This work demonstrated that in the absence of a washing step, only the surface properties influenced the conidia attachment, whilst in the presence of a washing step, both the properties of the surfaces and the conidia affected conidia adhesion and retention. Hence, the methodology used (with or without a washing step) should reflect the environment in which the surface is to be applied

Hierarchical Reinforcement Learning of Multiple Grasping Strategies with Human Instructions

Grasping is an essential component for robotic manipulation and has been investigated for decades. Prior work on grasping often assumes that a sufficient amount of training data is available for learning and planning robotic grasps. However, since constructing such an exhaustive training dataset is very challenging in practice, it is desirable that a robotic system can autonomously learn and improves its grasping strategy. In this paper, we address this problem using reinforcement learning. Although recent work has presented autonomous data collection through trial and error, such methods are often limited to a single grasp type, e.g., vertical pinch grasp. We present a hierarchical policy search approach for learning multiple grasping strategies. Our framework autonomously constructs a database of grasping motions and point clouds of objects to learn multiple grasping types autonomously. We formulate the problem of selecting the grasp location and grasp policy as a bandit problem, which can be interpreted as a variant of active learning. We applied our reinforcement learning to grasping both rigid and deformable objects. The experimental results show that our framework autonomously learns and improves its performance through trial and error and can grasp previously unseen objects with a high accuracy

Rare germline variants in DNA repair genes and the angiogenesis pathway predispose prostate cancer patients to develop metastatic disease

Background Prostate cancer (PrCa) demonstrates a heterogeneous clinical presentation ranging from largely indolent to lethal. We sought to identify a signature of rare inherited variants that distinguishes between these two extreme phenotypes. Methods We sequenced germline whole exomes from 139 aggressive (metastatic, age of diagnosis < 60) and 141 non-aggressive (low clinical grade, age of diagnosis ≥60) PrCa cases. We conducted rare variant association analyses at gene and gene set levels using SKAT and Bayesian risk index techniques. GO term enrichment analysis was performed for genes with the highest differential burden of rare disruptive variants. Results Protein truncating variants (PTVs) in specific DNA repair genes were significantly overrepresented among patients with the aggressive phenotype, with BRCA2, ATM and NBN the most frequently mutated genes. Differential burden of rare variants was identified between metastatic and non-aggressive cases for several genes implicated in angiogenesis, conferring both deleterious and protective effects. Conclusions Inherited PTVs in several DNA repair genes distinguish aggressive from non-aggressive PrCa cases. Furthermore, inherited variants in genes with roles in angiogenesis may be potential predictors for risk of metastases. If validated in a larger dataset, these findings have potential for future clinical application

A Bayesian Nonparametric Approach to Modeling Motion Patterns

The most difficult—and often most essential— aspect of many interception and tracking tasks is constructing motion models of the targets to be found. Experts can often provide only partial information, and fitting parameters for complex motion patterns can require large amounts of training data. Specifying how to parameterize complex motion patterns is in itself a difficult task. In contrast, nonparametric models are very flexible and generalize well with relatively little training data. We propose modeling target motion patterns as a mixture of Gaussian processes (GP) with a Dirichlet process (DP) prior over mixture weights. The GP provides a flexible representation for each individual motion pattern, while the DP assigns observed trajectories to particular motion patterns. Both automatically adjust the complexity of the motion model based on the available data. Our approach outperforms several parametric models on a helicopter-based car-tracking task on data collected from the greater Boston area

Growth arrest-specific transcript 5 associated snoRNA levels are related to p53 expression and DNA damage in colorectal cancer

BACKGROUND The growth arrest-specific transcript 5 gene (GAS5) encodes a long noncoding RNA (lncRNA) and hosts a number of small nucleolar RNAs (snoRNAs) that have recently been implicated in multiple cellular processes and cancer. Here, we investigate the relationship between DNA damage, p53, and the GAS5 snoRNAs to gain further insight into the potential role of this locus in cell survival and oncogenesis both in vivo and in vitro. METHODS We used quantitative techniques to analyse the effect of DNA damage on GAS5 snoRNA expression and to assess the relationship between p53 and the GAS5 snoRNAs in cancer cell lines and in normal, pre-malignant, and malignant human colorectal tissue and used biological techniques to suggest potential roles for these snoRNAs in the DNA damage response. RESULTS GAS5-derived snoRNA expression was induced by DNA damage in a p53-dependent manner in colorectal cancer cell lines and their levels were not affected by DICER. Furthermore, p53 levels strongly correlated with GAS5-derived snoRNA expression in colorectal tissue. CONCLUSIONS In aggregate, these data suggest that the GAS5-derived snoRNAs are under control of p53 and that they have an important role in mediating the p53 response to DNA damage, which may not relate to their function in the ribosome. We suggest that these snoRNAs are not processed by DICER to form smaller snoRNA-derived RNAs with microRNA (miRNA)-like functions, but their precise role requires further evaluation. Furthermore, since GAS5 host snoRNAs are often used as endogenous controls in qPCR quantifications we show that their use as housekeeping genes in DNA damage experiments can lead to inaccurate results