A Bayesian adaptive marker‐stratified design for molecularly targeted agents with customized hierarchical modeling

It is well known that the treatment effect of a molecularly targeted agent (MTA) may vary dramatically, depending on each patient's biomarker profile. Therefore, for a clinical trial evaluating MTA, it is more reasonable to evaluate its treatment effect within different marker subgroups rather than evaluating the average treatment effect for the overall population. The marker‐stratified design (MSD) provides a useful tool to evaluate the subgroup treatment effects of MTAs. Under the Bayesian framework, the beta‐binomial model is conventionally used under the MSD to estimate the response rate and test the hypothesis. However, this conventional model ignores the fact that the biomarker used in the MSD is, in general, predictive only for the MTA. The response rates for the standard treatment can be approximately consistent across different subgroups stratified by the biomarker. In this paper, we proposed a Bayesian hierarchical model incorporating this biomarker information into consideration. The proposed model uses a hierarchical prior to borrow strength across different subgroups of patients receiving the standard treatment and, therefore, improve the efficiency of the design. Prior informativeness is determined by solving a “customized” equation reflecting the physician's professional opinion. We developed a Bayesian adaptive design based on the proposed hierarchical model to guide the treatment allocation and test the subgroup treatment effect as well as the predictive marker effect. Simulation studies and a real trial application demonstrate that the proposed design yields desirable operating characteristics and outperforms the existing designs

Análisis del grupo consolidado Moët Hennessy Louis Vuitton 2012-2017

Treballs Finals del Màster Oficial de Comptabilitat i Fiscalitat, Facultat d'Economia i Empresa, Universitat de Barcelona, Curs: 2018-2019, Tutor: Antonio Somoza LópezEste documento tiene objetivo analizar los estados financieros consolidados del Grupo Moët Hennessy Louis Vuitton. A través de los análisis patrimonial, financiero y económico, entendemos que es un grupo muy rentable, con un alto margende ventas y que ha estado trabajando para mejorar la rotación de activos en los últimosaños. El grupo es muy solvente y no tiene dificultad en la rentabilidad operativa. Para desarrollarse, además de adquirir varias marcas, también comenzó a adquirir minoristas y proveedores para expandir su cadena industrial. Desde todos los aspectos, podemos concluir que la industria de bienes de lujo sigue siendo un mercado muy prometedor

Credit Risk Modeling without Sensitive Features: An Adversarial Deep Learning Model for Fairness and Profit

We propose an adversarial deep learning model for credit risk modeling. We make use of sophisticated machine learning model’s ability to triangulate (i.e., infer the sensitive group affiliation by using only permissible features), which is often deemed “troublesome” in fair machine learning research, in a positive way to increase both borrower welfare and lender profits while improving fairness. We train and test our model on a dataset from a real-world microloan company. Our model significantly outperforms regular deep neural networks without adversaries and the most popular credit risk model XGBoost, in terms of both improving borrowers’ welfare and lenders’ profits. Our empirical findings also suggest that the traditional AUC metric cannot reflect a model\u27s performance on the borrowers’ welfare and lenders’ profits. Our framework is ready to be customized for other microloan firms, and can be easily adapted to many other decision-making scenarios

Co-pyrolysis of Corn-cob and Waste Cooking-oil in a Fixed Bed Reactor with HY Upgrading Process

AbstractCorn cob and waste oil were co-pyrolysed in a fixed bed at the temperatures of 500°C, 550°C, 600°C, respectively, under nitrogen atmosphere. Co-pyrolysis products were investigated with focus on the physical and chemical properties of oily products characterized by means of GC–MS and elemental analyser. The results show 550°C seems to be the optimum temperature considering maximum bio oil yields and bio oil properties. Co-pyrolysis of corn cob and waste oil produced more amount of liquid and less amount of solid residue than that of pyrolysis of corn cob solely. While weight ratio of waste oil: corn cob increases from 0 to 0.87, bio-oil yield increases dramatically from 44.7wt% to 70.62wt% with increasing acids content and decreasing phenols, acohols, ketones content. The upgraded bio-oil has the potential to be an alternative fuel for engine after upgraded by HY zeolite

Study on Evolution of China’s Construction Industry Based on Input-Output Analysis and Complex Network

Exploring the evolution of China’s construction industry is conducive to the formulation of industrial policies. The construction industry is associated with many industries. Hence, the policies formed according to internal evolution of the construction industry are easy to direct the industry toward an unfavourable direction. This study aims to analyze the evolution of construction industry based on the relationships between the construction industry and other industries. The pull coefficients and push coefficients of China’s construction industry during 2001-2015 were calculated based on the input-output table. Complex network topologies of industries were constructed, and network topologies were used to analyze the network centrality and the cohesive subgroups. The evolutionary trend of China’s construction industry in interactions with other industries was explored. Results show that the pull and push effects of China’s construction industry experience a sharp reduction. The construction industry has the inclination to be transformed from a pillar industry to an industry driven by other industries. The control of the construction industry in the network is weakened. In conclusion, using input-out analysis and complex network to study the evolution of China’s construction industry can consider interaction of different industries, and provide certain theoretical references to formulate reasonable policies

BSDF Importance Baking: A Lightweight Neural Solution to Importance Sampling General Parametric BSDFs

Parametric Bidirectional Scattering Distribution Functions (BSDFs) are pervasively used because of their flexibility to represent a large variety of material appearances by simply tuning the parameters. While efficient evaluation of parametric BSDFs has been well-studied, high-quality importance sampling techniques for parametric BSDFs are still scarce. Existing sampling strategies either heavily rely on approximations, resulting in high variance, or solely perform sampling on a portion of the whole BSDF slice. Moreover, many of the sampling approaches are specifically paired with certain types of BSDFs. In this paper, we seek an efficient and general way for importance sampling parametric BSDFs. We notice that the nature of importance sampling is the mapping between a uniform distribution and the target distribution. Specifically, when BSDF parameters are given, the mapping that performs importance sampling on a BSDF slice can be simply recorded as a 2D image that we name as importance map. Following this observation, we accurately precompute the importance maps using a mathematical tool named optimal transport. Then we propose a lightweight neural network to efficiently compress the precomputed importance maps. In this way, we have brought parametric BSDF important sampling to the precomputation stage, avoiding heavy runtime computation. Since this process is similar to light baking where a set of images are precomputed, we name our method importance baking. Together with a BSDF evaluation network and a PDF (probability density function) query network, our method enables full multiple importance sampling (MIS) without any revision to the rendering pipeline. Our method essentially performs perfect importance sampling. Compared with previous methods, we demonstrate reduced noise levels on rendering results with a rich set of appearances

Contactless Haptic Display Through Magnetic Field Control

Haptic rendering enables people to touch, perceive, and manipulate virtual objects in a virtual environment. Using six cascaded identical hollow disk electromagnets and a small permanent magnet attached to an operator's finger, this paper proposes and develops an untethered haptic interface through magnetic field control. The concentric hole inside the six cascaded electromagnets provides the workspace, where the 3D position of the permanent magnet is tracked with a Microsoft Kinect sensor. The driving currents of six cascaded electromagnets are calculated in real-time for generating the desired magnetic force. Offline data from an FEA (finite element analysis) based simulation, determines the relationship between the magnetic force, the driving currents, and the position of the permanent magnet. A set of experiments including the virtual object recognition experiment, the virtual surface identification experiment, and the user perception evaluation experiment were conducted to demonstrate the proposed system, where Microsoft HoloLens holographic glasses are used for visual rendering. The proposed magnetic haptic display leads to an untethered and non-contact interface for natural haptic rendering applications, which overcomes the constraints of mechanical linkages in tool-based traditional haptic devices

Overview of Upgrading of Pyrolysis Oil of Biomass

AbstractPyrolysis oil, obtained from fast pyrolysis of biomass, is a promising renewable energy source which has received widespread interests for its characteristics as combustion fuels used in boiler, engines or gas turbines and resources in chemical industries. However, the pyrolysis oil as a fuel has many unfavourable properties due to its chemical composition, making it corrosive, viscose and thermally instability. Therefore, bio-oil must be properly upgraded to produce high quality biofuel for using as transportation fuels. In this review article, various types of upgrading processes have been discussed in detail including physical refining routes, chemical refining and total pyrolysis refined routes. Finally, a new upgrading route, Physical-Chemical Refining (PCR) is proposed, which will be a very promising refining route of bio-oil

Enhancing Visibility in Nighttime Haze Images Using Guided APSF and Gradient Adaptive Convolution

Visibility in hazy nighttime scenes is frequently reduced by multiple factors, including low light, intense glow, light scattering, and the presence of multicolored light sources. Existing nighttime dehazing methods often struggle with handling glow or low-light conditions, resulting in either excessively dark visuals or unsuppressed glow outputs. In this paper, we enhance the visibility from a single nighttime haze image by suppressing glow and enhancing low-light regions. To handle glow effects, our framework learns from the rendered glow pairs. Specifically, a light source aware network is proposed to detect light sources of night images, followed by the APSF (Angular Point Spread Function)-guided glow rendering. Our framework is then trained on the rendered images, resulting in glow suppression. Moreover, we utilize gradient-adaptive convolution, to capture edges and textures in hazy scenes. By leveraging extracted edges and textures, we enhance the contrast of the scene without losing important structural details. To boost low-light intensity, our network learns an attention map, then adjusted by gamma correction. This attention has high values on low-light regions and low values on haze and glow regions. Extensive evaluation on real nighttime haze images, demonstrates the effectiveness of our method. Our experiments demonstrate that our method achieves a PSNR of 30.38dB, outperforming state-of-the-art methods by 13$\%$ on GTA5 nighttime haze dataset. Our data and code is available at: \url{https://github.com/jinyeying/nighttime_dehaze}.Comment: Accepted to ACM'MM2023, https://github.com/jinyeying/nighttime_dehaz

NightHaze: Nighttime Image Dehazing via Self-Prior Learning

Masked autoencoder (MAE) shows that severe augmentation during training produces robust representations for high-level tasks. This paper brings the MAE-like framework to nighttime image enhancement, demonstrating that severe augmentation during training produces strong network priors that are resilient to real-world night haze degradations. We propose a novel nighttime image dehazing method with self-prior learning. Our main novelty lies in the design of severe augmentation, which allows our model to learn robust priors. Unlike MAE that uses masking, we leverage two key challenging factors of nighttime images as augmentation: light effects and noise. During training, we intentionally degrade clear images by blending them with light effects as well as by adding noise, and subsequently restore the clear images. This enables our model to learn clear background priors. By increasing the noise values to approach as high as the pixel intensity values of the glow and light effect blended images, our augmentation becomes severe, resulting in stronger priors. While our self-prior learning is considerably effective in suppressing glow and revealing details of background scenes, in some cases, there are still some undesired artifacts that remain, particularly in the forms of over-suppression. To address these artifacts, we propose a self-refinement module based on the semi-supervised teacher-student framework. Our NightHaze, especially our MAE-like self-prior learning, shows that models trained with severe augmentation effectively improve the visibility of input haze images, approaching the clarity of clear nighttime images. Extensive experiments demonstrate that our NightHaze achieves state-of-the-art performance, outperforming existing nighttime image dehazing methods by a substantial margin of 15.5% for MUSIQ and 23.5% for ClipIQA