Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes

There is a previously identified equivalence between wide fully connected neural networks (FCNs) and Gaussian processes (GPs). This equivalence enables, for instance, test set predictions that would have resulted from a fully Bayesian, infinitely wide trained FCN to be computed without ever instantiating the FCN, but by instead evaluating the corresponding GP. In this work, we derive an analogous equivalence for multi-layer convolutional neural networks (CNNs) both with and without pooling layers, and achieve state of the art results on CIFAR10 for GPs without trainable kernels. We also introduce a Monte Carlo method to estimate the GP corresponding to a given neural network architecture, even in cases where the analytic form has too many terms to be computationally feasible. Surprisingly, in the absence of pooling layers, the GPs corresponding to CNNs with and without weight sharing are identical. As a consequence, translation equivariance, beneficial in finite channel CNNs trained with stochastic gradient descent (SGD), is guaranteed to play no role in the Bayesian treatment of the infinite channel limit - a qualitative difference between the two regimes that is not present in the FCN case. We confirm experimentally, that while in some scenarios the performance of SGD-trained finite CNNs approaches that of the corresponding GPs as the channel count increases, with careful tuning SGD-trained CNNs can significantly outperform their corresponding GPs, suggesting advantages from SGD training compared to fully Bayesian parameter estimation.
Anti-Drift in Electronic Nose via Dimensionality Reduction: A Discriminative Subspace Projection Approach

Sensor drift is a well-known issue in the field of sensors and measurement. It has plagued the sensor community for many years. In this paper, we propose a sensor drift correction method to deal with the sensor drift problem. In contrast to domain regularized component analysis, the proposed method makes use of the class label of data in the source domain. Specifically, we propose a discriminative subspace projection approach for sensor drift reduction in electronic noses. The proposed method has multiple properties. (1) It inherits the merits of the subspace projection approach called domain regularized component analysis via introducing a regularization parameter to tackle the sample size imbalance problem. (2) The proposed method takes the source data label information into consideration, which minimizes the within-class variance of the projected source samples and at the same time maximizes the between-class variance. The label information is exploited to avoid overlapping of samples with different labels in the subspace. An efficient method based on generalized eigenvalue decomposition is employed to solve the optimization problem. Experiments on two sensor drift datasets have shown the effectiveness of the proposed approach.
Optimal Multi-Quality Multicast for 360 Virtual Reality Video

A 360 virtual reality (VR) video, recording a scene of interest in every direction, provides VR users with immersive viewing experience. However, transmission of a 360 VR video which is of a much larger size than a traditional video to mobile users brings a heavy burden to a wireless network. In this paper, we consider multi-quality multicast of a 360 VR video from a single server to multiple users using time division multiple access (TDMA). To improve transmission efficiency, tiling is adopted, and each tile is pre-encoded into multiple representations with different qualities. We optimize the quality level selection, transmission time allocation and transmission power allocation to maximize the total utility of all users under the transmission time and power allocation constraints as well as the quality smoothness constraints for mixed-quality tiles. The problem is a challenging mixed discrete-continuous optimization problem. We propose two low-complexity algorithms to obtain two suboptimal solutions, using continuous relaxation and DC programming, respectively. Finally, numerical results demonstrate the advantage of the proposed solutions.
GILT: Generating Images from Long Text

Creating an image reflecting the content of a long text is a complex process that requires a sense of creativity. For example, creating a book cover or a movie poster based on their summary or a food image based on its recipe. In this paper we present the new task of generating images from long text that does not describe the visual content of the image directly. For this, we build a system for generating high-resolution 256 $\times$ 256 images of food conditioned on their recipes. The relation between the recipe text (without its title) to the visual content of the image is vague, and the textual structure of recipes is complex, consisting of two sections (ingredients and instructions) both containing multiple sentences. We used the recipe1M dataset to train and evaluate our model that is based on a the StackGAN-v2 architecture.
Explaining AlphaGo: Interpreting Contextual Effects in Neural Networks

In this paper, we propose to disentangle and interpret contextual effects that are encoded in a pre-trained deep neural network. We use our method to explain the gaming strategy of the alphaGo Zero model. Unlike previous studies that visualized image appearances corresponding to the network output or a neural activation only from a global perspective, our research aims to clarify how a certain input unit (dimension) collaborates with other units (dimensions) to constitute inference patterns of the neural network and thus contribute to the network output. The analysis of local contextual effects w.r. t. certain input units is of special values in real applications. Explaining the logic of the alphaGo Zero model is a typical application. In experiments, our method successfully disentangled the rationale of each move during the Go game.
Semi-parametric dynamic contextual pricing

Motivated by the application of real-time pricing in e-commerce platforms, we consider the problem of revenue-maximization in a setting where the seller can leverage contextual information describing the customer's history and the product's type to predict her valuation of the product. However, her true valuation is unobservable to the seller, only binary outcome in the form of success-failure of a transaction is observed. Unlike in usual contextual bandit settings, the optimal price/arm given a covariate in our setting is sensitive to the detailed characteristics of the residual uncertainty distribution. We develop a semi-parametric model in which the residual distribution is non-parametric and provide the first algorithm which learns both regression parameters and residual distribution with $\tilde O(\sqrt{n})$ regret. We empirically test a scalable implementation of our algorithm and observe good performance.
A mathematical model of contact inhibition of locomotion: coupling contractility and focal adhesions

Cell migration is often accompanied by collisions with other cells, which can lead to cessation of movement, repolarization, and migration away from the contact site - a process termed contact inhibition of locomotion (CIL). During CIL, the coupling between actomyosin contractilityand cell-substrate adhesions is modified. However, mathematical models describing stochastic cell migration and collision outcomes as a result of the coupling remain elusive. Here, we extend our previously developed stochastic model of single cell migration to include CIL. Our simulation results explain, in terms of the modified contractility and adhesion dynamics, several experimentally observed findings regarding CIL. These include response modulation in the presence of an external cue and alterations of group migration in the absence of CIL. Together with our previous findings, our work is able to explain a wide range of observations about single and collective cell migration.
Robust and High Performance Face Detector

In recent years, face detection has experienced significant performance improvement with the boost of deep convolutional neural networks. In this report, we reimplement the state-of-the-art detector SRN and apply some tricks proposed in the recent literatures to obtain an extremely strong face detector, named VIM-FD. In specific, we exploit more powerful backbone network like DenseNet-121, revisit the data augmentation based on data-anchor-sampling proposed in PyramidBox, and use the max-in-out label and anchor matching strategy in SFD. In addition, we also introduce the attention mechanism to provide additional supervision. Over the most popular and challenging face detection benchmark, i.e., WIDER FACE, the proposed VIM-FD achieves state-of-the-art performance.
Learning with Fenchel-Young Losses

Over the past decades, numerous loss functions have been been proposed for a variety of supervised learning tasks, including regression, classification, ranking, and more generally structured prediction. Understanding the core principles and theoretical properties underpinning these losses is key to choose the right loss for the right problem, as well as to create new losses which combine their strengths. In this paper, we introduce Fenchel-Young losses, a generic way to construct a convex loss function for a regularized prediction function. We provide an in-depth study of their properties in a very broad setting, covering all the aforementioned supervised learning tasks, and revealing new connections between sparsity, generalized entropies, and separation margins. We show that Fenchel-Young losses unify many well-known loss functions and allow to create useful new ones easily. Finally, we derive efficient predictive and training algorithms, making Fenchel-Young losses appealing both in theory and practice.
Effects of Social Media Photography on Memory

As social media sites such as Instagram, Facebook, Twitter, and Snapchat become more pervasive, we see more people sharing photos of experiences and updating friends on life events through digital platforms. Despite this increased presence of social media in our lives, little research has been published on the effects that social media photography can have on people. A particular area of concern is how this photography affects our minds. With this project, I explore how social media photography, posting, and reviewing of photos affect memory. I synthesize the results of many peer-reviewed articles published since 2005 that study the effects of social media photography on memory, and I suggest some best practices for people to use when engaging with social media in order to avoid hampering memory. Based on this literature review, I propose future work in the field that may help us better understand the complex and wide-ranging effects of social media—and images in particular—on our memories. Results have shown that specific processes seen in social media photography have interesting effects on memory. One of these processes is cognitive offloading, which occurs when a person does not have to store information in their own memory because it is reliably stored somewhere else, such as in a photograph. Distraction also plays a large role in affecting memory, as one’s emotions, circumstances, and method of photography can all distract from the present experience. New findings show that point-of-view changes memory as we are adopting a third-person perspective in memories due to social media photography instead of the traditional first-person view because of our obsession with self-presentation. The complicated interactions between these processes and their effects reveal that some social media practices can improve memory of events, while many others are detrimental to memory formation.
A Quantitative Descriptive Cross Sectional Study About Knowledge Levels of Drug Resistant Tuberculosis Among the Residents of Port Elizabeth, Eastern Cape Province, South Africa

Background: A high level of knowledge about Multi-Drug Resistant Tuberculosis is very important for TB prevention and control among community members. Objective: The aim of this study was to assess knowledge levels of Multi-Drug Resistant Tuberculosis among the residents of Port Elizabeth. Material and methods: A quantitative descriptive cross sectional study was conducted. The research sample consisted of 400 residents (190 males and 210 females) who were purposefully and conveniently selected in Port Elizabeth. Four point likert scale close-ended questionnaires were used to collect data. Results: The majority (56%) of the residents of Port Elizabeth had poor knowledgeable levels about Multi-Drug Resistant Tuberculosis. Secondly, more females and adults showed poor knowledge levels of Multi-Drug Resistant Tuberculosis than males and youth. Conclusion: Participants had poor knowledge and misconceptions about the disease. More adults and females were more vulnerable and at risk of infection than youth and males. There is a need for increased coverage and develop educational material in different languages that are appropriate for the various levels of literacy. The aims of the MDR-TB de-centralisation was to improve access and acceptability. Acceptability can be achieved if people have proper knowledge about the disease and hence a need to scale up education interventions, the involvement of the infected and traditional practitioners in the delivery of such interventions.
School nurses' perceptions of undergraduate children's nursing safeguarding education

Safeguarding children is a substantial practice area for children's nurses and school nurses, remaining a challenging environment to work within. The literature suggests that professionals involved in safeguarding have been under-prepared by their training to work in this area. This small qualitative study aimed to explore school nurse perceptions of their undergraduate children's nursing education to ascertain if they were adequately prepared to practice in this area. This study used interpretative phenomenological analysis as a methodology, and three practicing school nurses, who are qualified children's nurses were interviewed. Themes highlight that undergraduate education did not prepare them to practice in safeguarding through a combination of factors. These included lack of exposure to practice in this area as a student and as an inexperienced staff nurse; lack of adequate succession planning; and lack of information sharing, leading to a feeling of practice isolation.
Effects of online multitasking on reading comprehension of expository text

Much of college students’ computer use, including for academic reading, occurs under conditions of multitasking. In three experiments, we investigated their technology use and habitual multitasking and the learning effects of multitasking with online communication while reading expository text. In Experiment 1 (n = 35), participants engaged in a primary content learning task and a secondary communication task either sequentially or concurrently. Experiment 2 (n = 90 ) used a modified primary learning task involving reading comprehension and recall with a within-subjects design, wherein task difficulty (easy, difficult) and condition (sequential, concurrent) were within-subjects factors. Experiment 3 (n=40) used a moderately difficult task with condition (sequential, concurrent) as a within-subjects factor and a filler task for participants in the concurrent condition. Our results suggested that our college student participants were comfortable with technology and reported that on average they multitasked with four other activities while reading. Across the three experiments, we found no evidence that multitasking while reading disrupted content learning, reading comprehension, and recall. On the contrary, we found a beneficial effect of multitasking for the easy task (Experiment 2) and a trend toward a beneficial effect for the moderately difficult task (Experiment 3). We discuss possible explanations for why multitasking might enhance performance at lower levels of cognitive load and identify future directions for research.
On the Assessment of an Unstructured Finite-Volume DES/LES Solver for Turbomachinery Applications

Improvements in mean flow and performances simulation in turbomachinery has brought research to focus more demanding topics like turbulence effects on turbines. Although overall performances are well predicted by Unsteady-RANS, other phenomena such as aerodynamic noise or transition need more accurate prediction of turbulent flow features. Thus different kinds of equation modeling other than URANS are needed to cope with this issue. The success of Detached-Eddy Simulation and Large-Eddy Simulation applications in reproducing physical behavior of flow turbulence is well documented in literature. Despite that, LES simulations are still computationally very expensive and their use for investigating industrial configurations requires a careful assessment of both numerical and closure modeling techniques. Moreover LES solvers are usually developed on a structured mesh topology for sake of simplicity of high-order schemes implementation. Application to complex geometries like those of turbomachinery is therefore difficult. The present work addresses this issue considering the feasibility of converting an operative in-house URANS solver, widely validated for applicative purposes, into higher resolution DES and LES, in order to face turbulence computation of turbomachinery technical cases. The solver presents a 3D unstructured finite-volume formulation, which is kept in LES approach in order to handle complex geometries and it is developed to perform unsteady simulations on turbine stages. Preliminary assessment of the solver has been performed to evaluate and improve the accuracy of the convective fluxes discretization on an inviscid bump test case. First a DES-based approach has been implemented, as it is less computationally challenging and numerically demanding than LES. A square cylinder test case has been assessed and compared with experiments. Then, a pure LES with a Smagorinsky sub-grid scale model has been evaluated on the test case of incompressible periodic channel flow in order to assess the capability of the solver to correctly sustain a time developing turbulent field. Key-Words: DES, Inflow Boundary, Numerical accuracy, Turbulent channel flow, Unstructured LES
Urodynamics: Hydrodynamics of the Ureter and Renal Pelvis

Although this book would appear from the title to have limited appeal, it should have a very wide appeal to all those who work with the kidney and the urinary passageway. It has been generally known that there is a very intimate relationship between renal function and the transport of urine from the kidney to the bladder. Yet the intimate details of the way in which such transport takes place, normally and under pathological circumstances, and how it influences renal function has not been described in one overall volume as in this book. A tremendous amount of new material on the ureter, its embryology and its function, has been presented in this book which fundamentally summarizes a workshop on the urodynamics of the upper urinary tract. This workshop made it possible for representatives of some 30 active laboratories to meet and to carry out a continuum of discussion between the
Understanding Aromatase: A Mechanistic Basis for Drug Interactions and New Inhibitors

Indiana University-Purdue University Indianapolis (IUPUI)%%%%Aromatase is the cytochrome P450 enzyme that converts androgens to estrogens. Aromatase is the target of the aromatase inhibitor class of drugs widely used to treat estrogen-mediated conditions including breast cancer. Little is known about the role of this enzyme in drug metabolism or in drug interactions. Since this lack of knowledge has been an impediment to optimal therapy, it is important to understand these roles of aromatase. Therefore, a comprehensive series of studies was carried out to characterize its ability to metabolize drugs and its susceptibility to inhibition by xenobiotics. The overall objective of this work was to better understand the interactions of small molecules with aromatase and to use this new knowledge to predict aromatase-mediated drug interactions and anticipate novel molecular structures that interact with the enzyme. Aromatase was shown to be a drug metabolizing enzyme able to metabolize methadone both in vitro (Km of 314 μM) and in vivo (22% of methadone clearance). A number of novel aromatase inhibitors that employ diverse kinetic mechanisms were identified. These include a potent competitive inhibitor: norendoxifen (Ki of 35 nM), two non-competitive inhibitors: endoxifen (Ki of 4.0 μM) and N-desmethyl-tamoxifen (Ki of 15.9 μM), a mechanism-based inhibitor: methadone (KI of 40.6 ± 2.8 μM; kinact of 0.061 ± 0.001 min-1), and a stereoselective inhibitor: naringenin (IC50s of 2.8 μM for (R)-enatiomer and 1.4 μM for (S)-enatiomer). Through investigation of the structure-potency relationships so discovered, a series of new biochemical structures to be exploited as aromatase inhibitors were identified. These studies have identified new roles for aromatase as a catalyst for methadone metabolism and as a mediator of the effects of tamoxifen by demonstrating that a number of its metabolites can act as aromatase inhibitors. This work also provides a new mechanistic framework for the design of novel aromatase inhibitors that can be used in breast cancer. Overall, the data suggest ways to more consistently treat breast cancer with current medications, to better anticipate drug interactions, and therefore to improve the quality of life of patients in ways that minimize side effects, while optimizing therapeutic benefits, in each person treated.
Exploring Professional Culture in the Context of Family Health Team Interprofessional Collaboration

BACKGROUND While family health teams (FHTs) seek to deliver collaborative patient-centered care, the barriers that can arise due to a practitioner’s professional culture pose a challenge to attaining interprofessional collaboration. The effect of professional culture in relation to FHT collaboration has not yet been examined, and a heightened awareness and appreciation of how this concept influences team dynamics holds promise to improve interprofessional collaboration on these and other evolving health care teams. METHODS Qualitative secondary data analysis was conducted on data collected from in-depth semi-structured focus groups (n=5). A non-random convenience sample consisted of 42 participants from medicine, nursing, and allied health professions at the Family Health Centre and Diabetes Education Centre in a large academic teaching hospital in urban Canada. Discussions were audio-taped and transcribed verbatim. Transcripts were analyzed for themes using a modified directed content analysis approach. FINDINGS Three main themes emerged: professional culture; FHT culture; and resources. Professional culture cannot be neatly separated from one’s personal, social or professional history, which ties in with opinions of accountability, power and hierarchy. Structure and processes of the FHT that encourage collaborative processes; clearly articulated scopes of practice, skills, authority; clarifications of roles and responsibilities; and opportunities to develop team relationships are necessary to diffuse the tension that exists between professional and FHT cultures. CONCLUSIONS FHTs are multidisciplinary groups co-located but with a lack of meaningful structures and processes to support collaboration. There is heavy physician dominance and physicians seem to adhere to old hierarchical structures and beliefs, consistent with their professional culture. In general, the health care providers need to build collaborative competencies (e.g. role clarity, effective communication) in order to move a group of interdisciplinary health care providers toward being a highly performing interprofessional team.