The Battlefield of IoT: Competitive and Cooperative Relationship Among Smart Home Vendors

The integration between electronic devices and networking enable convenient, comfortable, efficient and secure life. So there are a variety of smart home hubs and accessories migrating to daily life with all kinds of possibilities and extending the scope and coverage of Internet of Things (IoT). For an example, Amazon’s Echo launched in June 2015 and offers amazing capabilities. Its artificial intelligence voice assistant Alexa is a breakthrough invention, and people can not only search information, play music, read daily news, and place order instantly but also control auxiliary devices as television, air conditions, curtains, lights by their voice. In addition, the membership of Amazon, a giant of e-commerce, has reached to 65 millions prime membership and promotes Echo with affordable price $179 and five millions sales record. To exploit the value of network of this emerging market, Amazon has welcomed and invited third-party vendors to develop unique voice skills together and to establish the ecosystem effectively. Although Google’s Home launched lately in October 2016, its artificial intelligence voice assistant -Google Assistant is embedded strengths of Google search engine and sells for a competitive price$ 139 only. Google adopts the similar strategy to expand the eco-systems of smart home actively enhance smart home network effect with their huge resources. On the contrary, smart home hub vendors, Amazon, Google, and Samsung, not only compete each other, but also cooperate to enlarge this booming market together. For examples of cooperation case, the products of Nest which owned by Google compatible with Amazon’s Echo. Relatively, smart home auxiliary vendors, Philips, Nest, and Smartthings, adopt several strategic alliance with smart home hub vendors to compete on the one side and cooperate on the other side. Therefore, the interaction between hub vendors and auxiliary device vendors is an amazing game and affects the development of IoT future. Our study will outline its competition status and answer the question “how do stakeholders of smart home market cooperate and compete each other in the emerging stage?” The research method adopts three steps analysis for thorough study of the question. The Resource-based model from industry organization to analyze those smart home hub vendors for the 1st step. Value net model, Porter’s diamond model, and Ansoff matrix are analysis methods to be applied in the 2nd step for discussing external environment. And then, we enrich interaction discussion through case study as the last step. In the resource-based model, we address internal resources, capabilities, and core competences for both hub vendors and auxiliary device vendors to analyze competitive advantages. External analysis uses value net model to figure out how to develop strategies of horizontal and vertical competition in smart home industry. Also, we evaluate the capabilities of potential revenues and profits to analyze the relationship of each smart home hub enterprises, customers, suppliers, competitors, and complementors (smart home auxiliary vendors). Porter’s diamond model mainly focuses on market demand and government policies whether affect enterprises decisions, and chooses Ansoff matrix to analyze the development of different smart home products in different markets development decisions. Based on the prior analysis results, then we can take overall analysis on market level about the co-opetition relationship of network effect on smart home

Improving Robustness for Joint Optimization of Camera Poses and Decomposed Low-Rank Tensorial Radiance Fields

In this paper, we propose an algorithm that allows joint refinement of camera pose and scene geometry represented by decomposed low-rank tensor, using only 2D images as supervision. First, we conduct a pilot study based on a 1D signal and relate our findings to 3D scenarios, where the naive joint pose optimization on voxel-based NeRFs can easily lead to sub-optimal solutions. Moreover, based on the analysis of the frequency spectrum, we propose to apply convolutional Gaussian filters on 2D and 3D radiance fields for a coarse-to-fine training schedule that enables joint camera pose optimization. Leveraging the decomposition property in decomposed low-rank tensor, our method achieves an equivalent effect to brute-force 3D convolution with only incurring little computational overhead. To further improve the robustness and stability of joint optimization, we also propose techniques of smoothed 2D supervision, randomly scaled kernel parameters, and edge-guided loss mask. Extensive quantitative and qualitative evaluations demonstrate that our proposed framework achieves superior performance in novel view synthesis as well as rapid convergence for optimization.Comment: AAAI 2024. Project page: https://alex04072000.github.io/Joint-TensoRF

Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation

While representation learning aims to derive interpretable features for describing visual data, representation disentanglement further results in such features so that particular image attributes can be identified and manipulated. However, one cannot easily address this task without observing ground truth annotation for the training data. To address this problem, we propose a novel deep learning model of Cross-Domain Representation Disentangler (CDRD). By observing fully annotated source-domain data and unlabeled target-domain data of interest, our model bridges the information across data domains and transfers the attribute information accordingly. Thus, cross-domain joint feature disentanglement and adaptation can be jointly performed. In the experiments, we provide qualitative results to verify our disentanglement capability. Moreover, we further confirm that our model can be applied for solving classification tasks of unsupervised domain adaptation, and performs favorably against state-of-the-art image disentanglement and translation methods.Comment: CVPR 2018 Spotligh

Comparison of Particle Swarm Optimization and Self-Adaptive Dynamic Differential Evolution for the Imaging of a Periodic Conductor

[[abstract]]The application of two techniques to reconstruct the shape of a two-dimensional periodic perfect conductor from mimic the measurement data is presented. A periodic conducting cylinder of unknown periodic length and shape scatters the incident wave in half-space and the scattered field is recorded outside. After an integral formulation, the microwave imaging is recast as a nonlinear optimization problem; a cost functional is defined by the norm of a difference between the measured scattered electric fields and the calculated scattered fields for an estimated shape of a conductor. Thus, the shape of conductor can be obtained by minimizing the cost function. In order to solve this inverse scattering problem, transverse magnetic (TM) waves are incident upon the objects and two techniques are employed to solve these problems. The first is based on an particle swarm optimization (PSO) and the second is a self-adaptive dynamic differential evolution (SADDE). Both techniques have been tested in the case of simulated mimic the measurement data contaminated by additive white Gaussian noise. Numerical results indicate that the SADDE algorithm is better than the PSO in reconstructed accuracy and convergence speed.[[notice]]補正完畢[[incitationindex]]SC

Plasmonic Circular Nanostructure for Enhanced Light Absorption in Organic Solar Cells

This study attempts to enhance broadband absorption in advanced plasmonic circular nanostructures (PCN). Experimental results indicate that the concentric circular metallic gratings can enhance broadband optical absorption, due to the structure geometry and the excitation of surface plasmon mode. The interaction between plasmonic enhancement and the absorption characteristics of the organic materials (P3HT:PCBM and PEDOT:PSS) are also examined. According to those results, the organic material's overall optical absorption can be significantly enhanced by up to ~51% over that of a planar device. Additionally, organic materials are enhanced to a maximum of 65% for PCN grating pitch = 800 nm. As a result of the PCN's enhancement in optical absorption, incorporation of the PCN into P3HT:PCBM-based organic solar cells (OSCs) significantly improved the performance of the solar cells: short-circuit current increased from 10.125 to 12.249 and power conversion efficiency from 3.2% to 4.99%. Furthermore, optimizing the OSCs architectures further improves the performance of the absorption and PCE enhancement

The Adaptor Protein SH2B3 (Lnk) Negatively Regulates Neurite Outgrowth of PC12 Cells and Cortical Neurons

SH2B adaptor protein family members (SH2B1-3) regulate various physiological responses through affecting signaling, gene expression, and cell adhesion. SH2B1 and SH2B2 were reported to enhance nerve growth factor (NGF)-induced neuronal differentiation in PC12 cells, a well-established neuronal model system. In contrast, SH2B3 was reported to inhibit cell proliferation during the development of immune system. No study so far addresses the role of SH2B3 in the nervous system. In this study, we provide evidence suggesting that SH2B3 is expressed in the cortex of embryonic rat brain. Overexpression of SH2B3 not only inhibits NGF-induced differentiation of PC12 cells but also reduces neurite outgrowth of primary cortical neurons. SH2B3 does so by repressing NGF-induced activation of PLCγ, MEK-ERK1/2 and PI3K-AKT pathways and the expression of Egr-1. SH2B3 is capable of binding to phosphorylated NGF receptor, TrkA, as well as SH2B1β. Our data further demonstrate that overexpression of SH2B3 reduces the interaction between SH2B1β and TrkA. Consistent with this finding, overexpressing the SH2 domain of SH2B3 is sufficient to inhibit NGF-induced neurite outgrowth. Together, our data demonstrate that SH2B3, unlike the other two family members, inhibits neuronal differentiation of PC12 cells and primary cortical neurons. Its inhibitory mechanism is likely through the competition of TrkA binding with the positive-acting SH2B1 and SH2B2

Improved Noisy Student Training for Automatic Speech Recognition

Recently, a semi-supervised learning method known as "noisy student training" has been shown to improve image classification performance of deep networks significantly. Noisy student training is an iterative self-training method that leverages augmentation to improve network performance. In this work, we adapt and improve noisy student training for automatic speech recognition, employing (adaptive) SpecAugment as the augmentation method. We find effective methods to filter, balance and augment the data generated in between self-training iterations. By doing so, we are able to obtain word error rates (WERs) 4.2%/8.6% on the clean/noisy LibriSpeech test sets by only using the clean 100h subset of LibriSpeech as the supervised set and the rest (860h) as the unlabeled set. Furthermore, we are able to achieve WERs 1.7%/3.4% on the clean/noisy LibriSpeech test sets by using the unlab-60k subset of LibriLight as the unlabeled set for LibriSpeech 960h. We are thus able to improve upon the previous state-of-the-art clean/noisy test WERs achieved on LibriSpeech 100h (4.74%/12.20%) and LibriSpeech (1.9%/4.1%).Comment: 5 pages, 5 figures, 4 tables; v2: minor revisions, reference adde

Expanding KK-theoretic Schur QQ-functions

We derive several identities involving Ikeda and Naruse's $K$-theoretic Schur $P$- and $Q$-functions. Our main result is a formula conjectured by Lewis and the second author which expands each $K$-theoretic Schur $Q$-function in terms of $K$-theoretic Schur $P$-functions. This formula extends to some more general identities relating the skew and dual versions of both power series. We also prove a shifted version of Yeliussizov's skew Cauchy identity for symmetric Grothendieck polynomials. Finally, we discuss some conjectural formulas for the dual $K$-theoretic Schur $P$- and $Q$-functions of Nakagawa and Naruse. We show that one such formula would imply a basis property expected of the $K$-theoretic Schur $Q$-functions.Comment: 30 page