Consumer behavior changes across income levels : meat market analysis

Title from PDF of title page (University of Missouri--Columbia, viewed on October 22, 2012).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Dissertation advisor: Dr. Scott BrownVita.Ph.D. University of Missouri-Columbia, 2011."May 2011"This study intends to test how consumer behavior changes across income levels using time series data of the meat markets in Korea and the U.S. An income growth is one of the most significant and powerful trends in the modern economy and expected to continue its trend in the future. So finding relationships between income growth and consumer behavior can offer a higher level of understanding of both the current and future demand. Three relationships are found in the Korean and American meat market: (1) Since meat products are more desirable, even though meat is more expensive than other food, substitution of meat for grain appears to occur at a low income level. As a consequence, they have a property of being luxury goods when income is low and eventually become a necessity as income rises. (2) As income growth occurs, it makes the relative price and the expenditure share for meat to become smaller. These diminished prices and shares are likely to make consumers feel less sensitive to changes in price or income. As a result, price and income elasticities become smaller in absolute value as income rises. (3) Consumption is expanded as income rises and this expansion follows the order of urgency. A good on the expansion frontier faces severe competition in terms of the marginal utility. As a result, a good for which demand increases with incomes has a high possibility to maintain its elasticities even though income rises.Includes bibliographical reference

A Design of MAC Model Based on the Separation of Duties and Data Coloring: DSDC-MAC

Among the access control methods for database security, there is Mandatory Access Control (MAC) model in which the security level is set to both the subject and the object to enhance the security control. Legacy MAC models have focused only on one thing, either confidentiality or integrity. Thus, it can cause collisions between security policies in supporting confidentiality and integrity simultaneously. In addition, they do not provide a granular security class policy of subjects and objects in terms of subjects\u27 roles or tasks. In this paper, we present the security policy of Bell_LaPadula Model (BLP) model and Biba model as one complemented policy. In addition, Duties Separation and Data Coloring (DSDC)-MAC model applying new data coloring security method is proposed to enable granular access control from the viewpoint of Segregation of Duty (SoD). The case study demonstrated that the proposed modeling work maintains the practicality through the design of Human Resources management System. The proposed model in this study is suitable for organizations like military forces or intelligence agencies where confidential information should be carefully handled. Furthermore, this model is expected to protect systems against malicious insiders and improve the confidentiality and integrity of data

Knowledge Distillation-aided End-to-End Learning for Linear Precoding in Multiuser MIMO Downlink Systems with Finite-Rate Feedback

We propose a deep learning-based channel estimation, quantization, feedback, and precoding method for downlink multiuser multiple-input and multiple-output systems. In the proposed system, channel estimation and quantization for limited feedback are handled by a receiver deep neural network (DNN). Precoder selection is handled by a transmitter DNN. To emulate the traditional channel quantization, a binarization layer is adopted at each receiver DNN, and the binarization layer is also used to enable end-to-end learning. However, this can lead to inaccurate gradients, which can trap the receiver DNNs at a poor local minimum during training. To address this, we consider knowledge distillation, in which the existing DNNs are jointly trained with an auxiliary transmitter DNN. The use of an auxiliary DNN as a teacher network allows the receiver DNNs to additionally exploit lossless gradients, which is useful in avoiding a poor local minimum. For the same number of feedback bits, our DNN-based precoding scheme can achieve a higher downlink rate compared to conventional linear precoding with codebook-based limited feedback.Comment: 6 pages, 4 figures, submitted to IEEE Transactions on Vehicular Technolog

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Department of Energy EngineeringWith the emergence of deformable electronics, there is growing interest in wearable devices such as implantable medical devices, healthcare devices, artificial skins, and robotics with arbitrarily shaped surfaces. While there has been tremendous progress in the development of wearable devices, the research on deformable energy-storage devices that are able to maintain a large physical strain without sacrificing battery performance is still in an infant state. Thus, to fulfill the demand for reliable wearable devices, a key challenge is the development in shape of deformable energy-storage devices, which can foldable and crumply and even stretchable for supplying power to them. A formidable hurdle to the development of deformable energy devices is how to develop a deformable electrode owing to a trade-off relationship between mechanical deformability and electrical conductivity under a physical deformation. In this thesis, we propose the design of deformable electrode architecture and demonstrate their mechanical robustness and electrical properties at large deformation. In addition, we examine in-situ small-angle X-ray scattering measurement to substantiate the percolation behaviors of conductive fillers in an elastomer. Using the proposed deformable electrode, we fabricate various deformable energy devices such as foldable lithium-ion batteries (LIBs), stretchable aqueous rechargeable lithium-ion batteries (ARLBs), and stretchable rechargeable zinc-silver batteries. In chapter ???, we briefly introduce research background of stretchable electronics and wearable devices. Furthermore, we discuss various fabrication methods of the deformable electrode and its application to stretchable energy devices. In chapter ???, we propose the fabrication of crumply and super-flexible electrode based on the nanowire-wound micro-fiber substrate which consists of conductive nanowires as a binder and conducting agents, porous nonwoven substrate, and active materials. The proposed electrode design can be effectively utilized for superior mechanical durability (1000 folding cycles) with a high areal energy density through stacking electrode. Based on the electrodes, we demonstrate a highly flexible LIBs with a high energy density which can be repeatedly crumbled, folded, and even hammered without structural failure and performance loss. In chapter ???, we present a bio-inspired Jabuticaba-like multidimensional conductive fillers/elastomer composite as a stretchable electrode for stretchable batteries. In-situ small-angle X-ray scattering is a powerful tool for monitoring the structural evolution of nanofillers in a polymer matrix. As a result, multidimensional carbon fillers in polymer matrix retain the excellent percolation network under high strain, based on the analysis of Herman???s orientation parameter and 2D Porod length calculation. Using the polymer composite, we fabricate, for the first time, stretchable ARLBs as a stretchable power source for wearable devices. Our stretchable batteries show outstanding rate performance and exceptional cycle retention. Furthermore, the batteries can deliver constant power to a device at 100% strain. In chapter ???, we propose stretchable zinc-silver rechargeable batteries based on a Janus-faced electrode, comprising of a cathode and an anode on one electrode, are presented. In the Janus-faced electrode based on Ag/poly(styrene-b-butadiene-b-styrene) (SBS) polymer nanocomposite, a metallic zinc serves anode materials while silver nanoparticles exhibit bifunctional roles as cathode materials and conducting fillers as a current collector. Furthermore, the proposed stretchable energy device can tolerate a large strain and can deliver a stable electrochemical performance even under 200% strain while keeping its functional performance. In chapter ???, we report a gradient assembled polyurethane (GAP)-based stretchable conductor with fine controlled internal architecture assembled with gold nanoparticles (Au NPs) as a conductive filler in order to develop a universally applicable method for fabricating the geometrically designed nanocomposite conductor. In the present study, we demonstrate a novel assembly protocol, composite-by-composite (CbC) assembly, which integrates the advantages of both conventional vacuum-assisted filtration and layer-by-layer (LbL) assembly. Conversely, LbL assembly can manufacture with highly ordered architectures, allowing the fine nanoscale control over the thickness and composition of hybrid multi-components through the sequential assembly. Most uniquely, this GAP stretchable multilayer conductor demonstrates not only top-surface conductive structure with superior mechanical stretchability even above 300% strain. Using the GAP stretchable conductor, we demonstrate the highly stretchable energy storage device such as lithium-ion battery retaining stable electrochemical performance under strain.clos

Data-Reserved Periodic Diffusion LMS With Low Communication Cost Over Networks

In this paper, we analyze diffusion strategies in which all nodes attempt to estimate a common vector parameter for achieving distributed estimation in adaptive networks. Under diffusion strategies, each node essentially needs to share processed data with predefined neighbors. Although the use of internode communication has contributed significantly to improving convergence performance based on diffusion, such communications consume a huge quantity of power in data transmission. In developing low-power consumption diffusion strategies, it is very important to reduce the communication cost without significant degradation of convergence performance. For that purpose, we propose a data-reserved periodic diffusion least-mean-squares (LMS) algorithm in which each node updates and transmits an estimate periodically while reserving its measurement data even during non-update time. By applying these reserved data in an adaptation step at update time, the proposed algorithm mitigates the decline in convergence speed incurred by most conventional periodic schemes. For a period p, the total cost of communication is reduced to a factor of 1/p relative to the conventional adapt-then-combine (ATC) diffusion LMS algorithm. The loss of combination steps in this process leads naturally to a slight increase in the steady-state error as the period p increases, as is theoretically confirmed through mathematical analysis. We also prove an interesting property of the proposed algorithm, namely, that it suffers less degradation of the steady-state error than the conventional diffusion in a noisy communication environment. Experimental results show that the proposed algorithm outperforms related conventional algorithms and, in particular, outperforms ATC diffusion LMS over a network with noisy links.11Ysciescopu

Diagnostic Approaches for Idiopathic Pulmonary Fibrosis

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, fibrosing interstitial pneumonia with a very poor prognosis. Accurate diagnosis of IPF is essential for good outcomes but remains a major medical challenge due to variability in clinical presentation and the shortcomings of existing diagnostic tests. Medical history collection is the first and most important step in the IPF diagnosis process; the clinical probability of IPF is high if the suspected patient is 60 years or older, male, and has a history of cigarette smoking. Systemic assessment for connective tissue disease is essential in the initial evaluation of patients with suspected IPF to identify potential causes of interstitial lung disease (ILD). Radiologic examination using high-resolution computed tomography plays a pivotal role in the evaluation of patients with ILD, and prone and expiratory computed tomography images can be considered. If additional tests such as surgical lung biopsy or transbronchial lung cryobiopsy are needed, transbronchial lung cryobiopsy should be considered as an alternative to surgical lung biopsy in medical centers with experience performing this procedure. Diagnosis through multidisciplinary discussion (MDD) is strongly recommended as MDD has become the cornerstone for diagnosis of IPF, and the scope of MDD has expanded to monitoring of disease progression and suggestion of appropriate treatment options

Robust Distributed Clustering Algorithm Over Multitask Networks

We propose a new adaptive clustering algorithm that is robust to various multitask environments. Positional relationships among optimal vectors and a reference signal are determined by using the mean-square deviation relation derived from a one-step least-mean-square update. Clustering is performed by combining determinations on the positional relationships at several iterations. From this geometrical basis, unlike the conventional clustering algorithms using simple thresholding method, the proposed algorithm can perform clustering accurately in various multitask environments. Simulation results show that the proposed algorithm has more accurate estimation accuracy than the conventional algorithms and is insensitive to parameter selection.11Ysciescopu

An Enhanced Double-layered P2P System for the Reliability in Dynamic Mobile Environments

The double-layered peer-to-peer (P2P) systems were introduced to reduce the network traffic in MANET. The peers in the systems are classified into super peers and sub-peers. Super peers manage their neighboring sub-peers. The network communications in the systems are done mostly among super peers. In case when a pair of neighboring super peers is too far to communicate, one or two of their sub-peers bridges the super peers. However, the double-layered systems need to improve the reliability that guarantees communications among peers. In this paper, we propose a new double-layered P2P system in which super peers are selected based on their mobility. We also propose two reliability improvement schemes, the avoidance scheme and the role changing scheme. They are applied to the proposed system to enhance the reliability of the system. The proposed system is implemented in the dynamic mobile P2P environment where peers may join and leave the network dynamically and the number of peers varies. The various experiments are done with the Network Simulator-2 v2.33. The experimental results show that the proposed system with the two schemes improved the reliability over other double-layered systems in terms of the failure rate by up to 25 %, while increasing the network traffic marginally

Query-Efficient Black-Box Red Teaming via Bayesian Optimization

The deployment of large-scale generative models is often restricted by their potential risk of causing harm to users in unpredictable ways. We focus on the problem of black-box red teaming, where a red team generates test cases and interacts with the victim model to discover a diverse set of failures with limited query access. Existing red teaming methods construct test cases based on human supervision or language model (LM) and query all test cases in a brute-force manner without incorporating any information from past evaluations, resulting in a prohibitively large number of queries. To this end, we propose Bayesian red teaming (BRT), novel query-efficient black-box red teaming methods based on Bayesian optimization, which iteratively identify diverse positive test cases leading to model failures by utilizing the pre-defined user input pool and the past evaluations. Experimental results on various user input pools demonstrate that our method consistently finds a significantly larger number of diverse positive test cases under the limited query budget than the baseline methods. The source code is available at https://github.com/snu-mllab/Bayesian-Red-Teaming.Comment: ACL 2023 Long Paper - Main Conferenc