Improving Artificial-Immune-System-based computing by exploiting intrinsic features of computer architectures

Biological systems have become highly significant for traditional computer architectures as examples of highly complex self-organizing systems that perform tasks in parallel with no centralized control. However, few researchers have compared the suitability of different computing approaches for the unique features of Artificial Immune Systems (AIS) when trying to introduce novel computing architectures, and few consider the practicality of their solutions for real world machine learning problems. We propose that the efficacy of AIS-based computing for tackling real world datasets can be improved by the exploitation of intrinsic features of computer architectures. This paper reviews and evaluates current existing implementation solutions for AIS on different computing paradigms and introduces the idea of “C Principles” and “A Principles”. Three Artificial Immune Systems implemented on different architectures are compared using these principles to examine the possibility of improving AIS through taking advantage of intrinsic hardware features

Blur-Robust Face Recognition via Transformation Learning

Abstract. This paper introduces a new method for recognizing faces degraded by blur using transformation learning on the image feature. The basic idea is to transform both the sharp images and blurred im-ages to a same feature subspace by the method of multidimensional s-caling. Different from the method of finding blur-invariant descriptors, our method learns the transformation which both preserves the mani-fold structure of the original shape images and, at the same time, en-hances the class separability, resulting in a wide applications to various descriptors. Furthermore, we combine our method with subspace-based point spread function (PSF) estimation method to handle cases of un-known blur degree, by applying the feature transformation correspond-ing to the best matched PSF, where the transformation for each PSF is learned in the training stage. Experimental results on the FERET database show the proposed method achieve comparable performance a-gainst the state-of-the-art blur-invariant face recognition methods, such as LPQ and FADEIN.

Internalisation Theory and outward direct investment by emerging market multinationals

The rise of multinational enterprises from emerging countries (EMNEs) poses an important test for theories of the multinational enterprise such as internalisation theory. It has been contended that new phenomena need new theory. This paper proposes that internalisation theory is appropriate to analyse EMNEs. This paper examines four approaches to EMNEs—international investment strategies, domestic market imperfections, international corporate networks and domestic institutions—and three case studies—Chinese outward FDI, Indian foreign acquisitions and investment in tax havens—to show the enduring relevance and predictive power of internalisation theory. This analysis encompasses many other approaches as special cases of internalisation theory. The use of internalisation theory to analyse EMNEs is to be commended, not only because of its theoretical inclusivity, but also because it has the ability to connect and to explain seemingly desperate phenomena

Network Pollution Games

The problem of pollution control has been mainly studied in the environmental economics literature where the methodology of game theory is applied for the pollution control. To the best of our knowledge this is the first time this problem is studied from the computational point of view. We introduce a new network model for pollution control and present two applications of this model. On a high level, our model comprises a graph whose nodes represent the agents, which can be thought of as the sources of pollution in the network. The edges between agents represent the effect of spread of pollution. The government who is the regulator, is responsible for the maximization of the social welfare and sets bounds on the levels of emitted pollution in both local areas as well as globally in the whole network. We first prove that the above optimization problem is NP-hard even on some special cases of graphs such as trees. We then turn our attention on the classes of trees and planar graphs which model realistic scenarios of the emitted pollution in water and air, respectively. We derive approximation algorithms for these two kinds of networks and provide deterministic truthful and truthful in expectation mechanisms. In some settings of the problem that we study, we achieve the best possible approximation results under standard complexity theoretic assumptions. Our approximation algorithm on planar graphs is obtained by a novel decomposition technique to deal with constraints on vertices. We note that no known planar decomposition techniques can be used here and our technique can be of independent interest. For trees we design a two level dynamic programming approach to obtain an FPTAS. This approach is crucial to deal with the global pollution quota constraint. It uses a special multiple choice, multi-dimensional knapsack problem where coefficients of all constraints except one are bounded by a polynomial of the input size. We furthermore derive truthful in expectation mechanisms on general networks with bounded degree

Forecasting Player Behavioral Data and Simulating in-Game Events

Understanding player behavior is fundamental in game data science. Video games evolve as players interact with the game, so being able to foresee player experience would help to ensure a successful game development. In particular, game developers need to evaluate beforehand the impact of in-game events. Simulation optimization of these events is crucial to increase player engagement and maximize monetization. We present an experimental analysis of several methods to forecast game-related variables, with two main aims: to obtain accurate predictions of in-app purchases and playtime in an operational production environment, and to perform simulations of in-game events in order to maximize sales and playtime. Our ultimate purpose is to take a step towards the data-driven development of games. The results suggest that, even though the performance of traditional approaches such as ARIMA is still better, the outcomes of state-of-the-art techniques like deep learning are promising. Deep learning comes up as a well-suited general model that could be used to forecast a variety of time series with different dynamic behaviors

Vibrational microscopy and imaging of skin: from single cells to intact tissue

Vibrational microscopy and imaging offer several advantages for a variety of dermatological applications, ranging from studies of isolated single cells (corneocytes) to characterization of endogenous components in intact tissue. Two applications are described to illustrate the power of these techniques for skin research. First, the feasibility of tracking structural alterations in the components of individual corneocytes is demonstrated. Two solvents, DMSO and chloroform/methanol, commonly used in dermatological research, are shown to induce large reversible alterations (α-helix to β-sheet) in the secondary structure of keratin in isolated corneocytes. Second, factor analysis of image planes acquired with confocal Raman microscopy to a depth of 70 μm in intact pigskin, demonstrates the delineation of specific skin regions. Two particular components that are difficult to identify by other means were observed in the epidermis. One small region was formed from a conformationally ordered lipid phase containing cholesterol. In addition, the presence of nucleated cells in the tissue (most likely keratinocytes) was revealed by the spectral signatures of the phosphodiester and cytosine moieties of cellular DNA

Experimental and theoretical investigation of ligand effects on the synthesis of ZnO nanoparticles

ZnO nanoparticles with highly controllable particle sizes(less than 10 nm) were synthesized using organic capping ligands in Zn(Ac)2 ethanolic solution. The molecular structure of the ligands was found to have significant influence on the particle size. The multi-functional molecule tris(hydroxymethyl)-aminomethane (THMA) favoured smaller particle distributions compared with ligands possessing long hydrocarbon chains that are more frequently employed. The adsorption of capping ligands on ZnnOn crystal nuclei (where n = 4 or 18 molecular clusters of(0001) ZnO surfaces) was modelled by ab initio methods at the density functional theory (DFT) level. For the molecules examined, chemisorption proceeded via the formation of Zn...O, Zn...N, or Zn...S chemical bonds between the ligands and active Zn2+ sites on ZnO surfaces. The DFT results indicated that THMA binds more strongly to the ZnO surface than other ligands, suggesting that this molecule is very effective at stabilizing ZnO nanoparticle surfaces. This study, therefore, provides new insight into the correlation between the molecular structure of capping ligands and the morphology of metal oxide nanostructures formed in their presence

A retrospective and agenda for future research on Chinese outward foreign direct investment

Our original paper “The determinants of Chinese Outward Foreign Direct Investment” was the first theoretically based empirical analysis of the phenomenon. It utilised internalisation theory to show that Chinese state-owned firms reacted to home country market imperfections to surmount barriers to foreign entry arising from naivety and the lack of obvious ownership advantages, leveraging institutional factors including favourable policy stimuli. This special theory explained outward foreign direct investment (OFDI) but provided surprises. These included the apparent appetite for risk evinced by these early investors, causing us to conjecture that domestic market imperfections, particularly in the domestic capital market, might be responsible. The article stimulated a massive subsequent, largely successful, research effort on emerging country multinationals. In this Retrospective article we review some of the main strands of research that ensued, for the insight they offer for the theme of our commentary. Our theme is that theoretical development can only come through embracing yet more challenging, different, and new contexts, and we make suggestions for future research directions