Two Faces: Effects of Business Groups on Innovation in Emerging Economies

This paper argues that business groups in emerging economies exert dual effects on innovation. While groups encourage innovation by providing institutional infrastructures, groups also discourage innovation by creating entry barriers for small and non-group firms and inhibiting the proliferation of new ideas. Using OLS and panel data estimation techniques, followed by nonparametric analysis and semiparametric kernel regression, we find evidence of an inverted-U relation between group market share and innovation in industrial sectors of both Korea and Taiwan, during the 1981-1995 period. Institutional differences between Korea and Taiwan in terms of market structure and industrial policies provide useful conceptual implications from the empirical comparison.

Where Can Capabilities Come From? How the Content of Network Ties Affects Capability Acquisition

While strategy researchers have devoted considerable attention to the role of firm-specific capabilities in the pursuit of competitive advantage, less attention has been directed at how firms obtain these capabilities from outside a firm's boundaries. This study analyzes how firms' network ties represent one important source of capability acquisition. Theoretically, we go beyond the traditional focus on network structure and offer a novel contingency model that specifies how differences in the content of network ties (e.g., buyer-supplier, equity, and director ties) will differentially affect the process of R&D capability acquisition. Empirically, we also seek to provide an original contribution to the capabilities literature by utilizing a stochastic frontier estimation to rigorously measure firm capabilities, and we demonstrate the value of this approach using longitudinal data on business groups in emerging economies. The supportive results of our analysis show that the effect of network ties on the acquisition of new affiliate capabilities is clearly and predictably contingent on the content of the ties.

Iterative Compression of End-to-End ASR Model using AutoML

Increasing demand for on-device Automatic Speech Recognition (ASR) systems has resulted in renewed interests in developing automatic model compression techniques. Past research have shown that AutoML-based Low Rank Factorization (LRF) technique, when applied to an end-to-end Encoder-Attention-Decoder style ASR model, can achieve a speedup of up to 3.7x, outperforming laborious manual rank-selection approaches. However, we show that current AutoML-based search techniques only work up to a certain compression level, beyond which they fail to produce compressed models with acceptable word error rates (WER). In this work, we propose an iterative AutoML-based LRF approach that achieves over 5x compression without degrading the WER, thereby advancing the state-of-the-art in ASR compression

Protein tyrosine phosphatase 1B inhibitors isolated from Artemisia roxburghiana

Artemisia roxburghiana is used in traditional medicine for treating various diseases including diabetes. The present study was designed to evaluate the antidiabetic potential of active constituents by using protein tyrosine phosphatase 1B (PTP1B) as a validated target for management of diabetes. Various compounds were isolated as active principles from the crude methanolic extract of aerial parts of A. roxburghiana. All compounds were screened for PTP1B inhibitory activity. Molecular docking simulations were performed to investigate the mechanism behind PTP1B inhibition of the isolated compound and positive control, ursolic acid. Betulinic acid, betulin and taraxeryl acetate were the active PTP1B principles with IC50 values 3.49 ± 0.02, 4.17 ± 0.03 and 87.52 ± 0.03 µM, respectively. Molecular docking studies showed significant molecular interactions of the triterpene inhibitors with Gly220, Cys215, Gly218 and Asp48 inside the active site of PTP1B. The antidiabetic activity of A. roxburghiana could be attributed due to PTP1B inhibition by its triterpene constituents, betulin, betulinic acid and taraxeryl acetate. Computational insights of this study revealed that the C-3 and C-17 positions of the compounds needs extensive optimization for the development of new lead compounds

Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving

In Autonomous Vehicles (AVs), one fundamental pillar is perception, which leverages sensors like cameras and LiDARs (Light Detection and Ranging) to understand the driving environment. Due to its direct impact on road safety, multiple prior efforts have been made to study its the security of perception systems. In contrast to prior work that concentrates on camera-based perception, in this work we perform the first security study of LiDAR-based perception in AV settings, which is highly important but unexplored. We consider LiDAR spoofing attacks as the threat model and set the attack goal as spoofing obstacles close to the front of a victim AV. We find that blindly applying LiDAR spoofing is insufficient to achieve this goal due to the machine learning-based object detection process. Thus, we then explore the possibility of strategically controlling the spoofed attack to fool the machine learning model. We formulate this task as an optimization problem and design modeling methods for the input perturbation function and the objective function. We also identify the inherent limitations of directly solving the problem using optimization and design an algorithm that combines optimization and global sampling, which improves the attack success rates to around 75%. As a case study to understand the attack impact at the AV driving decision level, we construct and evaluate two attack scenarios that may damage road safety and mobility. We also discuss defense directions at the AV system, sensor, and machine learning model levels.Comment: Accepted at the ACM Conference on Computer and Communications Security (CCS), 201

Model Independent Analysis of the Forward-Backward Asymmetry for the B→K1μ+μ−B\to K_{1}\mu^{+}\mu^{-} Decay

The sensitivity of the zero position of the forward backward asymmetry $\mathcal{A}_{FB}$ for the exclusive $B\rightarrow K_{1}(1270)\mu^{+}\mu^{-}$ decay is examined by using most general non-standard 4-fermion interactions. Our analysis shows that the zero position of the forward backward asymmetry is very sensitive to the sign and size of the Wilson coefficients corresponding to the new vector type interactions, which are the counter partners of the usual Standard Model operators but have opposite chirality. In addition to these, the other significant effect comes from the interference of Scalar-Pseudoscalar and Tensor type operators. These results will not only enhance our theoretical understanding about the axial vector mesons but will also serve as a good tool to look for physics beyond the SM.Comment: 14 pages, 8 figures, Published version that appears in EPJ

Lactate Dehydrogenase-B Is Silenced by Promoter Methylation in a High Frequency of Human Breast Cancers

Objective: Under normoxia, non-malignant cells rely on oxidative phosphorylation for their ATP production, whereas cancer cells rely on Glycolysis; a phenomenon known as the Warburg effect. We aimed to elucidate the mechanisms contributing to the Warburg effect in human breast cancer. Experimental design: Lactate Dehydrogenase (LDH) isoenzymes were profiled using zymography. LDH-B subunit expression was assessed by reverse transcription PCR in cells, and by Immunohistochemistry in breast tissues. LDH-B promoter methylation was assessed by sequencing bisulfite modified DNA. Results: Absent or decreased expression of LDH isoenzymes 1-4, were seen in T-47D and MCF7 cells. Absence of LDH-B mRNA was seen in T-47D cells, and its expression was restored following treatment with the demethylating agent 5'Azacytadine. LDH-B promoter methylation was identified in T-47D and MCF7 cells, and in 25/ 25 cases of breast cancer tissues, but not in 5/ 5 cases of normal breast tissues. Absent immuno-expression of LDH-B protein (<10% cells stained), was seen in 23/ 26 (88%) breast cancer cases, and in 4/8 cases of adjacent ductal carcinoma in situ lesions. Exposure of breast cancer cells to hypoxia (1% O2), for 48 hours resulted in significant increases in lactate levels in both MCF7 (14.0 fold, p = 0.002), and T-47D cells (2.9 fold, p = 0.009), but not in MDA-MB-436 (-0.9 fold, p = 0.229), or MCF10AT (1.2 fold, p = 0.09) cells. Conclusions: Loss of LDH-B expression is an early and frequent event in human breast cancer occurring due to promoter methylation, and is likely to contribute to an enhanced glycolysis of cancer cells under hypoxia