Neoplastic therapy : on violence and aesthetics

The goal of this thesis is to create a new theoretical framework to examine and understand the meaning of an art object and its relational social existence. This thesis serves as a critique on contemporary media culture and hierarchical social oppression. At the same time, it adopts a pseudoscientific way to introduce notions of art’s autonomy and its opposition to social functionality. By merging political theories on individualism, capitalism, and metaphysics with foundational structures of art creation, I am attempting to construct a new system of thinking that challenges traditional ways of understanding mediums, functions and the viewer’s relationship with art objects. My thinking links beauty with violence as a necessary attribute to the creation of art. This argument is central to my thesis and to my theoretical framework. This book is also a formula for my own art practice and for being a good human artist. It records and inspires my life long study of aesthetics and of beauty as a metaphysical object

Regularized estimation of linear functionals of precision matrices for high-dimensional time series

This paper studies a Dantzig-selector type regularized estimator for linear functionals of high-dimensional linear processes. Explicit rates of convergence of the proposed estimator are obtained and they cover the broad regime from i.i.d. samples to long-range dependent time series and from sub-Gaussian innovations to those with mild polynomial moments. It is shown that the convergence rates depend on the degree of temporal dependence and the moment conditions of the underlying linear processes. The Dantzig-selector estimator is applied to the sparse Markowitz portfolio allocation and the optimal linear prediction for time series, in which the ratio consistency when compared with an oracle estimator is established. The effect of dependence and innovation moment conditions is further illustrated in the simulation study. Finally, the regularized estimator is applied to classify the cognitive states on a real fMRI dataset and to portfolio optimization on a financial dataset.Comment: 44 pages, 4 figure

A Scheduling Strategy of Mobile Parcel Lockers for the Last Mile Delivery Problem

In the form of unattended Collection-and-Delivery Points (CDP), the fixed parcel lockers can save courier miles and improve the delivery efficiency. However, due to the fixed location and combination, the fixed parcel locker cannot accommodate the change of demands effectively. In this paper, an approach to supplementing fixed lockers by mobile parcel lockers to meet the demands of the last mile delivery has been proposed. With the goal of minimizing the operating cost, the location and route optimization problems of mobile parcel lockers are integrated into a non-linear integer programming model. An embedded GA has been developed to optimally determine the locations of distribution points, the number of mobile parcel lockers needed by each distribution point and the schedules and routes of mobile parcel lockers, simultaneously. Finally, a numerical example is given to compare the optimization results of the schemes with and without the aggregation problem. The results show that the scheme with the aggregation problem can greatly save the delivery time. However, for the scheme without the aggregation problem, time windows are more continuous, so it saves the number of vehicles

Estimation of dynamic networks for high-dimensional nonstationary time series

This paper is concerned with the estimation of time-varying networks for high-dimensional nonstationary time series. Two types of dynamic behaviors are considered: structural breaks (i.e., abrupt change points) and smooth changes. To simultaneously handle these two types of time-varying features, a two-step approach is proposed: multiple change point locations are first identified based on comparing the difference between the localized averages on sample covariance matrices, and then graph supports are recovered based on a kernelized time-varying constrained $L_1$-minimization for inverse matrix estimation (CLIME) estimator on each segment. We derive the rates of convergence for estimating the change points and precision matrices under mild moment and dependence conditions. In particular, we show that this two-step approach is consistent in estimating the change points and the piecewise smooth precision matrix function, under certain high-dimensional scaling limit. The method is applied to the analysis of network structure of the S\&P 500 index between 2003 and 2008

Simple and Efficient Partial Graph Adversarial Attack: A New Perspective

As the study of graph neural networks becomes more intensive and comprehensive, their robustness and security have received great research interest. The existing global attack methods treat all nodes in the graph as their attack targets. Although existing methods have achieved excellent results, there is still considerable space for improvement. The key problem is that the current approaches rigidly follow the definition of global attacks. They ignore an important issue, i.e., different nodes have different robustness and are not equally resilient to attacks. From a global attacker's view, we should arrange the attack budget wisely, rather than wasting them on highly robust nodes. To this end, we propose a totally new method named partial graph attack (PGA), which selects the vulnerable nodes as attack targets. First, to select the vulnerable items, we propose a hierarchical target selection policy, which allows attackers to only focus on easy-to-attack nodes. Then, we propose a cost-effective anchor-picking policy to pick the most promising anchors for adding or removing edges, and a more aggressive iterative greedy-based attack method to perform more efficient attacks. Extensive experimental results demonstrate that PGA can achieve significant improvements in both attack effect and attack efficiency compared to other existing graph global attack methods

Compact hollow waveguide mid-infrared gas sensor for simultaneous measurements of ambient CO2 and water vapor

A compact, sensitive and stable hollow waveguide (HWG) mid-infrared gas sensor, based on gas absorption lines using wavelength modulation spectroscopy with a second harmonic (WMS-2f) detection scheme, was developed for simultaneous measurements of ambient CO 2 and water vapor. Optimization of the laser modulation parameters and pressure parameter in the HWG are performed to improve the strength of the WMS-2f signal and hence the detection limit, where 14.5-time for CO 2 and 8.5-time for water vapor improvement in system detection limit is achieved compared to those working at 1 atm. The stability of the sensor has been improved significantly by optimizing environmental disturbances, incoupling alignment of the HWG and laser scanning frequency. An Allan variance analysis shows detection limit of the developed sensor of ~3 ppmv for CO 2 and 0.018% for water vapor, which correspond to an absorbance of 2.4 × 10 -5 and 2.7 × 10 -5 , with a stability time of 160 s, respectively. Ambient CO 2 and water vapor measurement have been performed in two days in winter and spring separately. The measurement precision is further improved by applying a Kalman adaptive filter. The HWG gas sensor demonstrates the ability in environmental monitoring and the potential to be used in other areas, such as industrial production and biomedical diagnosis

Soil characteristics and microbial responses in post-mine reclamation areas in a typical resource-based city, China

Mining activities worldwide have resulted in soil nutrient loss, which pose risks to crop and environmental health. We investigated the effects of post-mine reclamation activities on soil physicochemical properties and microbial communities based on 16S rRNA sequencing and the further statistical analysis in the coal base in Peixian city, China. The results revealed significant differences in soil microbial relative abundance between reclamation and reference soils. Proteobacteria was the most abundant phyla in all seven mine sites regardless of reclamation age while considerable differences were found in microbial community structure at other levels among different sites. Notebly, Gammaproteobacteria, member of the phylum Proteobacteria, had relatively high abundance in most sites. Furthermore, Kendall’s tau-b correlation heatmap revealed that potentially toxic elements and other physicochemical properties play vital roles in microbial community composition

Application of coherence analysis study on identification of vehicle noise sources

Structure-Air noise sources in different frequencies were identified based on analysis of frequency and testing of vibration and noise under idling condition, and a method for signal sources priority was developed under identifying the kinds of noise sources. The partial coherence equations of the six input and single output systems were derived based on the theory of coherence. Coefficient of partial coherence of the test data of vibration and noise in vehicle was calculated by using MATLAB. Coherence analysis results show that working engine incentive transferred to the driving cab in low frequency range caused structure noise, engine RH mounting is the main noise source; The noise in middle frequency range is caused by the coupling effects of vibration of engine left mounting and noise of the engine compartment to the driving cab, between which left hanging mount vibration affected more; Engine compartment noise in high frequency leaked through the air to the cab, engine noise is the main source of noise inside