18,036 research outputs found
Survival strategies: a New Zealand hospitality habitual entrepreneur
The purpose of this paper is to report the findings of case study research conducted in Auckland, New Zealand in 2010. This research examined the motivators prompting a habitual entrepreneur to engage within new venture start ups within the vibrant hospitality industry. This paper focuses on explaining how and why an entrepreneur within the highly competitive hospitality industry seeks business expansion via habitual entrepreneurship during economic recession. The research involves in-depth interviews of a successful hospitality habitual entrepreneur. This paper reports on the New Zealand entrepreneur’s personal and business profile, motivations that prompt entrepreneurship, business management strategies and the wider factors affecting his success. This study provides insights into the characteristics and behaviours of habitual entrepreneurs within a New Zealand hospitality context. Therefore, this paper contributes to entrepreneurship literature by demystifying New Zealand’s unique and innovative mindset the ‘number eight wire’ mentality
Hydrostatic pressure induced Dirac semimetal in black phosphorus
Motivated by recent experimental observation of an hydrostatic pressure
induced transition from semiconductor to semimetal in black phosphorus [Chen et
al. in arXiv:1504.00125], we present the first principles calculation on the
pressure effect of the electronic structures of black phosphorus. It is found
that the band crossover and reversal at the Z point occur around the critical
pressure Pc1=1.23 Gpa, and the band inversion evolves into 4 twofold-degenerate
Dirac cones around the Z point, suggesting a 3D Dirac semimetal. With further
increasing pressure the Dirac cones in the Gamma-Z line move toward the Gamma
point and evolve into two hole-type Fermi pockets, and those in the Z-M lines
move toward the M point and evolve into 2 hole-type Fermi pockets up to P=4.0
Gpa. It demonstrates clearly that the Lifshitz transition occurs at
from semiconductor to 3D Dirac semimetal protected by the nonsymmorphic space
symmetry of bulk. This suggests the bright perspective of black phosphorus for
optoelectronic and electronic devices due to its easy modulation by pressure.Comment: 7 pages, 9 figures, and 2 table
A memory saving vector fast multipole algorithm for solving the augmented EFIE
An augmented EFIE (A-EFIE)[9], [10] has been proposed to separate the contributions of the vector potential and the scalar potential for avoiding the imbalance at low frequencies. The corresponding low frequency fast multipole algorithm (LFFMA) [11] was also developed for solving the A-EFIE. Instead of the factorization of the scalar Green's function by using scalar addition theorem in the LF-FMA, we adopt the vector addition theorem for the factorization of the dyadic Green's function to realize memory savings. We are to develop a vector fast multipole algorithm for solving the A-EFIE. © 2010 IEEE.published_or_final_versionThe URSI International Symposium on Electromagnetic Theory (EMTS 2010), Berlin, Germany, 16-19 August 2010. In Proceedings of the URSI International Symposium on Electromagnetic Theory, 2010, p. 134-13
Quantum computing by optical control of electron spins
We review the progress and main challenges in implementing large-scale quantum computing by optical control of electron spins in quantum dots (QDs). Relevant systems include self-assembled QDs of III-V or II-VI compound semiconductors (such as InGaAs and CdSe), monolayer fluctuation QDs in compound semiconductor quantum wells, and impurity centres in solids, such as P-donors in silicon and nitrogen-vacancy centres in diamond. The decoherence of the electron spin qubits is discussed and various schemes for countering the decoherence problem are reviewed. We put forward designs of local nodes consisting of a few qubits which can be individually addressed and controlled. Remotely separated local nodes are connected by photonic structures (microcavities and waveguides) to form a large-scale distributed quantum system or a quantum network. The operation of the quantum network consists of optical control of a single electron spin, coupling of two spins in a local nodes, optically controlled quantum interfacing between stationary spin qubits in QDs and flying photon qubits in waveguides, rapid initialization of spin qubits and qubit-specific single-shot non-demolition quantum measurement. The rapid qubit initialization may be realized by selectively enhancing certain entropy dumping channels via phonon or photon baths. The single-shot quantum measurement may be in situ implemented through the integrated photonic network. The relevance of quantum non-demolition measurement to large-scale quantum computation is discussed. To illustrate the feasibility and demand, the resources are estimated for the benchmark problem of factorizing 15 with Shor's algorithm. © 2010 Taylor & Francis.postprin
Zero-Annotation Object Detection with Web Knowledge Transfer
Object detection is one of the major problems in computer vision, and has
been extensively studied. Most of the existing detection works rely on
labor-intensive supervision, such as ground truth bounding boxes of objects or
at least image-level annotations. On the contrary, we propose an object
detection method that does not require any form of human annotation on target
tasks, by exploiting freely available web images. In order to facilitate
effective knowledge transfer from web images, we introduce a multi-instance
multi-label domain adaption learning framework with two key innovations. First
of all, we propose an instance-level adversarial domain adaptation network with
attention on foreground objects to transfer the object appearances from web
domain to target domain. Second, to preserve the class-specific semantic
structure of transferred object features, we propose a simultaneous transfer
mechanism to transfer the supervision across domains through pseudo strong
label generation. With our end-to-end framework that simultaneously learns a
weakly supervised detector and transfers knowledge across domains, we achieved
significant improvements over baseline methods on the benchmark datasets.Comment: Accepted in ECCV 201
Co-benefits of greenhouse gas mitigation: A review and classification by type, mitigation sector, and geography
The perceived inability of climate change mitigation goals alone to mobilize sufficient climate change mitigation efforts has, among other factors, led to growing research on the co-benefits of reducing greenhouse gas (GHG) emissions. This study conducts a systematic review (SR) of the literature on the co-benefits of mitigating GHG emissions resulting in 1554 papers. We analyze these papers using
bibliometric analysis, including a keyword co-occurrence analysis. We then iteratively develop and present a typology of co-benefits, mitigation sectors, geographic scope, and methods based on the manual double coding of the papers resulting from the SR. We find that the co-benefits from GHG mitigation that have received the largest attention of researchers are impacts on ecosystems, economic activity, health, air pollution, and resource efficiency. The co-benefits that have received the least attention include the impacts on conflict and disaster resilience, poverty alleviation (or exacerbation), energy security, technological spillovers and innovation, and food security. Most research has
investigated co-benefits from GHG mitigation in the agriculture, forestry and other land use (AFOLU), electricity, transport, and residential sectors, with the industrial sector being the subject of significantly less research. The largest number of co-benefits publications provide analysis at a global level, with relatively few studies providing local (city) level analysis or studying co-benefits in Oceanian or African contexts. Finally, science and engineering methods, in contrast to economic or social science methods, are the methods most commonly employed in co-benefits papers. We conclude that given the potential mobilizing power of understudied co-benefits (e.g. poverty alleviation) and local impacts, the magnitude of GHG emissions from the industrial sector, and the fact that Africa and South America are likely to be severely affected by climate change, there is an opportunity for the research community to fill these gaps
Predicting Local Congestion at Fine-grain Levels in Networks-on-Chip Using Spiking Neural Networks
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