Design Management Capability in Entrepreneurship: A Case Study of Xiaomi

In recent years, entrepreneurship has become a popular topic and attracted many young people to start their own companies. In entrepreneurship, design was generally viewed as essential to innovation, replacing the conventional role of the engineer. Unlike traditional businesses, which generally take a longer time to become established in the more stable economic context of mass-production, current start-ups have to face fierce competition and have the tendency to expand rapidly and accommodate the dynamic business environment. Consequently, design management is considered to be crucial to business growth, since it contributes to both competitive advantages and strategic flexibility. However, start-up companies are well-known for their high failure ratio. This triggered our initial research question: what is the role of design in a start-up to support it in achieving success? Through a case study of Xiaomi, a well-known successful entrepreneurship in China, the new capabilities of entrepreneurial design management were reported. It was further classified into three key topics in line with the three stages of entrepreneurial business development. Difference with design management capabilities reported in previous studies, the new capabilities show the dynamic nature of entrepreneurial design management

Dilaton - fixed scalar correlators and AdS_5 x S^5 - SYM correspondence

We address the question of AdS/CFT correspondence in the case of the 3-point function . O_4 and O_8 are particular primary states represented by F^2 + ... and F^4 + ... operators in \N=4 SYM theory and dilaton \phi and massive `fixed' scalar \nu in D=5 supergravity. While the value of <O_4 O_4 O_8> computed in large N weakly coupled SYM theory is non-vanishing, the D=5 action of type IIB supergravity compactified on S^5 does not contain \phi\phi\nu coupling and thus the corresponding correlator seems to vanish on the AdS_5 side. This is in obvious contradiction with arguments suggesting non-renormalization of 2- and 3-point functions of states from short multiplets and implying agreement between the supergravity and SYM expressions for them. We propose a natural resolution of this paradox which emphasizes the 10-dimensional nature of the correspondence. The basic idea is to treat the constant mode of the dilaton as a part of the full S^5 Kaluza-Klein family of dilaton modes. This leads to a non-zero result for the correlator on the supergravity side.Comment: 16 pages, harvmac; references adde

TT-adic exponential sums of polynomials in one variable

The $T$-adic exponential sum of a polynomial in one variable is studied. An explicit arithmetic polygon in terms of the highest two exponents of the polynomial is proved to be a lower bound of the Newton polygon of the $C$-function of the T-adic exponential sum. This bound gives lower bounds for the Newton polygon of the $L$-function of exponential sums of $p$-power order

Logic Programming Applications: What Are the Abstractions and Implementations?

This article presents an overview of applications of logic programming, classifying them based on the abstractions and implementations of logic languages that support the applications. The three key abstractions are join, recursion, and constraint. Their essential implementations are for-loops, fixed points, and backtracking, respectively. The corresponding kinds of applications are database queries, inductive analysis, and combinatorial search, respectively. We also discuss language extensions and programming paradigms, summarize example application problems by application areas, and touch on example systems that support variants of the abstractions with different implementations

Constructive simulation and topological design of protocols

We give a topological simulation for tensor networks that we call the two-string model. In this approach we give a new way to design protocols, and we discover a new multipartite quantum communication protocol. We introduce the notion of topologically-compressed transformations. Our new protocol can implement multiple, non-local compressed transformations among multi-parties using one multipartite resource state.Comment: 16 page

Revisiting the hot matter in the center of gamma-ray bursts and supernova

Hot matter with nucleons can be produced in the inner region of the neutrino-dominated accretion flow in gamma-ray bursts or during the proto-neutron star birth in successful supernova. The composition and equation of state of the matter depend on the dynamic $\beta$ equilibrium under various neutrino opacities. The strong interaction between nucleons may also play an important role. We plan to extend the previous studies by incorporating these two aspects in our model. The modification of the $\beta$-equilibrium condition from neutrino optically thin to thick has been modeled by an equilibrium factor $\chi$ ranging between the neutrino-freely-escaping case and the neutrino-trapped case. We employ the microscopic Brueckner-Hartree-Fock approach extended to the finite temperature regime to study the interacting nucleons. We show that the composition and chemical potentials of the hot nuclear matter for different densities and temperatures at each stage of $\beta$ equilibrium. We also compare our realistic equation of states with those of the free gas model. We find the neutrino opacity and the strong interaction between nucleons are important for the description and should be taken into account in model calculations.Comment: accepted Astronomy & Astrophysics (2013

A Bayesian network approach to explaining time series with changing structure

Many examples exist of multivariate time series where dependencies between variables change over time. If these changing dependencies are not taken into account, any model that is learnt from the data will average over the different dependency structures. Paradigms that try to explain underlying processes and observed events in multivariate time series must explicitly model these changes in order to allow non-experts to analyse and understand such data. In this paper we have developed a method for generating explanations in multivariate time series that takes into account changing dependency structure. We make use of a dynamic Bayesian network model with hidden nodes. We introduce a representa- tion and search technique for learning such models from data and test it on synthetic time series and real-world data from an oil refinery, both of which contain changing underlying structure. We compare our method to an existing EM-based method for learning structure. Results are very promising for our method and we include sample explanations, generated from models learnt from the refinery dataset