AGENT-BASED MODELING TO INFORM ECOLOGICAL COEXISTENCE OF ONLINE COMMUNITIES: IMPACT OF COMMUNITY SIZE, PARTICIPATION COST, TOPIC BREADTH AND POPULATION INTEREST DISTRIBUTION

Master'sMASTER OF SCIENC

Optimal Time of Arrival Estimation for MIMO Backscatter Channels

In this paper, we propose a novel time of arrival (TOA) estimator for multiple-input-multiple-output (MIMO) backscatter channels in closed form. The proposed estimator refines the estimation precision from the topological structure of the MIMO backscatter channels, and can considerably enhance the estimation accuracy. Particularly, we show that for the general $M \times N$ bistatic topology, the mean square error (MSE) is $\frac{M+N-1}{MN}\sigma^2_0$, and for the general $M \times M$ monostatic topology, it is $\frac{2M-1}{M^2}\sigma^2_0$ for the diagonal subchannels, and $\frac{M-1}{M^2}\sigma^2_0$ for the off-diagonal subchannels, where $\sigma^2_0$ is the MSE of the conventional least square estimator. In addition, we derive the Cramer-Rao lower bound (CRLB) for MIMO backscatter TOA estimation which indicates that the proposed estimator is optimal. Simulation results verify that the proposed TOA estimator can considerably improve both estimation and positioning accuracy, especially when the MIMO scale is large

EXPECTATION SHORTFALL IN THE HIGHLY SPECIALIZED B2B IT INNOVATION

Expectation shortfall is a common occurrence in outsourcing. Prior literature suggests that strategies such as strict contract terms and proper evaluation of the vendor capabilities are adopted to avoid expectation shortfall. However, in the case of highly specialised technical products custom made to vendor requirements (i.e., B2B IT innovation), traditional strategies in managing outsourcing projects may not work as expected. This is mainly due to the complexity of the product requirements and the inability to assess the scope of the project in depth at the beginning. In this research, we adopt the vendor’s perspective to better understand how organizations in the highly specialized B2B IT innovation handle outsourced projects to avoid expectation shortfall. We uncover a dynamic innovation process which the client and the vendor go through. In addition, we suggest strategies to achieve B2B IT innovation in a win-win scenario while elucidating reasons of failure

Machine Extraction of Tax Laws from Legislative Texts

peer reviewedUsing a corpus of compiled codes from U.S. states containing labeled tax law sections, we train text classifiers to automatically tag tax-law documents and, further, to identify the associated revenue source (e.g. income, property, or sales). After evaluating classifier performance in held-out test data, we apply them to an historical corpus of U.S. state legislation to extract the flow of relevant laws over the years 1910 through 2010. We document that the classifiers are effective in the historical corpus, for example by automatically detecting establishments of state personal income taxes. The trained models with replication code are published at https://github.com/luyang521/tax-classification

PSNet : fast data structuring for hierarchical deep learning on point cloud

In order to retain more feature information of local areas on a point cloud, local grouping and subsampling are the necessary data structuring steps in most hierarchical deep learning models. Due to the disorder nature of the points in a point cloud, the significant time cost may be consumed when grouping and subsampling the points, which consequently results in poor scalability. This paper proposes a fast data structuring method called PSNet (Point Structuring Net). PSNet transforms the spatial features of the points and matches them to the features of local areas in a point cloud. PSNet achieves grouping and sampling at the same time while the existing methods process sampling and grouping in two separate steps (such as using FPS plus kNN). PSNet performs feature transformation pointwise while the existing methods uses the spatial relationship among the points as the reference for grouping. Thanks to these features, PSNet has two important advantages: 1) the grouping and sampling results obtained by PSNet is stable and permutation invariant; and 2) PSNet can be easily parallelized. PSNet can replace the data structuring methods in the mainstream point cloud deep learning models in a plug-and-play manner. We have conducted extensive experiments. The results show that PSNet can improve the training and reasoning speed significantly while maintaining the model accuracy

Manipulating topological states by imprinting non-collinear spin textures

Topological magnetic states, such as chiral skyrmions, are of great scientific interest and show huge potential for novel spintronics applications, provided their topological charges can be fully controlled. So far skyrmionic textures have been observed in noncentrosymmetric crystalline materials with low symmetry and at low temperatures. We propose theoretically and demonstrate experimentally the design of spin textures with topological charge densities that can be tailored at ambient temperatures. Tuning the interlayer coupling in vertically stacked nanopatterned magnetic heterostructures, such as a model system of a Co/Pd multilayer coupled to Permalloy, the in-plane non-collinear spin texture of one layer can be imprinted into the out-of-plane magnetised material. We observe distinct spin textures, e.g. vortices, magnetic swirls with tunable opening angle, donut states and skyrmion core configurations. We show that applying a small magnetic field, a reliable switching between topologically distinct textures can be achieved at remanence

Anti-HIV-1 Activity of a New Scorpion Venom Peptide Derivative Kn2-7

For over 30 years, HIV/AIDS has wreaked havoc in the world. In the absence of an effective vaccine for HIV, development of new anti-HIV agents is urgently needed. We previously identified the antiviral activities of the scorpion-venom-peptide-derived mucroporin-M1 for three RNA viruses (measles viruses, SARS-CoV, and H5N1). In this investigation, a panel of scorpion venom peptides and their derivatives were designed and chosen for assessment of their anti-HIV activities. A new scorpion venom peptide derivative Kn2-7 was identified as the most potent anti-HIV-1 peptide by screening assays with an EC50 value of 2.76 µg/ml (1.65 µM) and showed low cytotoxicity to host cells with a selective index (SI) of 13.93. Kn2-7 could inhibit all members of a standard reference panel of HIV-1 subtype B pseudotyped virus (PV) with CCR5-tropic and CXCR4-tropic NL4-3 PV strain. Furthermore, it also inhibited a CXCR4-tropic replication-competent strain of HIV-1 subtype B virus. Binding assay of Kn2-7 to HIV-1 PV by Octet Red system suggested the anti-HIV-1 activity was correlated with a direct interaction between Kn2-7 and HIV-1 envelope. These results demonstrated that peptide Kn2-7 could inhibit HIV-1 by direct interaction with viral particle and may become a promising candidate compound for further development of microbicide against HIV-1

FePt and CoPt nanoparticles prepared by micellar method: Effects of A1 -> L1 0 transition on oxidation resistance and magnetic properties

FePt and CoPt alloy nanoparticles with diameters of 2-12 nm and interparticle distances of 20-140 nm are prepared with reverse micelles. X-ray magnetic circular dichroism (XMCD) measurements on 5.8 nm FePt nanoparticles after hydrogen plasma reduction at 300°C reveals the magnetic moment per Fe atom and magnetic anisotropy energy matching chemically disordered FePt in A1 phase. Annealing at 650°C transform portion of FePt particles to chemically ordered L1 0 phase. The presence of nanoparticles in L1 0 phase is identified by high-resolution transmission electron microscopy (HRTEM), where it is also observed that large fraction of the particles contain defects such as twin boundaries. By increasing the annealing temperature or prolonging annealing time, ratio of transformed particles increases. The average magnetic anisotropy energy of the transformed particles is below 30% of the value of bulk FePt in L1 0 phase. Annealing at above 750°C , however, decreases the average magnetic anisotropy in the sample. Similar A1 to L1 0 transition is observed in FePt nanoparticles with different diameters as well as in CoPt nanoparticles. The spin moment of Fe in FePt nanoparticles decreases with smaller particle diameter, while the orbital moment stays almost constant. Magnetic moments at room temperature are significantly reduced compared to those at low temperature, suggesting the Curie temperatures in FePt and CoPt nanoparticles are significantly lower than in the bulk. The annealing also induces Pt segregation towards the surface in FePt nanoparticles, which is identified by the decreased apparent Fe content measured by X-ray photoelectron spectroscopy (XPS) and X-ray absorption spectroscopy (XAS). The segregation of Pt enhance the resistance against oxidation in nanoparticle larger than 5 nm by a factor of 100-1000, but has no influence on smaller particles. Such difference is explained by the thickness of Pt segregation, which is estimated by a core-shell modeling

Exponential Stability of Switched Neural Networks with Partial State Reset and Time-Varying Delays

This paper mainly investigates the exponential stability of switched neural networks (SNNs) with partial state reset and time-varying delays, in which partial state reset means that only a fraction of the states can be reset at each switching instant. Moreover, both stable and unstable subsystems are also taken into account and therefore, switched systems under consideration can take several switched systems as special cases. The comparison principle, the Halanay-like inequality, and the time-dependent switched Lyapunov function approach are used to obtain sufficient conditions to ensure that the considered SNNs with delays and partial state reset are exponentially stable. Numerical examples are provided to demonstrate the reliability of the developed results