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104. Do Risk Preferences Shape the Effect of Online Trading on Trading Frequency, Volume, and Portfolio Performance?

Author

Pan, Yang, Mithas, Sunil, Po-An Hsieh, J.J., and Liu, Che-Wei

Subjects
PORTFOLIO performance, INVESTORS, STOCKBROKERS, BEHAVIORAL economics, RATE of return
Abstract

How do investors' risk preferences influence the relationships between investors' online channel use intensity and both their trading behaviors and performance? This study answers this important question even as investors are increasingly rely on the Internet for their trading activities. We leverage rare and unique micro-level historical dataset from more than 7,000 investor accounts over a 44-month period between 2010 and 2013 at a large brokerage firm in China. The dataset and analyses enable us to provide new insights into how investors' online channel use intensity and risk preferences jointly influence their trading behaviors and performance, even though some other aspects of financial markets have changed considerably over the years. The findings reveal that although online channel use intensity is associated with increased trading volume, trading frequency, and investment returns, these effects differ across investors with different risk preferences. We find that while online channel use intensity has strong positive effects on transaction frequency for both risk-seeking and risk-averse investors, it has a much lower effect on trading volume for risk-averse investors than for risk-seeking investors. We further find that risk-averse investors with higher online channel use intensity outperform investors with other risk preferences in terms of investment performance. This paper contributes to the emerging literature at the intersection of information systems and behavioral finance by revealing the moderating role of risk preferences in the relationships between investors' online trading channel use intensity and both their trading behaviors and outcomes. We discuss the implications for research and practice. [ABSTRACT FROM AUTHOR]

Published
2023
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110. On the Prior Sensitivity of Thompson Sampling

Author

Liu, Che-Yu and Li, Lihong

Subjects
Computer Science - Learning, Computer Science - Artificial Intelligence, Statistics - Machine Learning
Abstract

The empirically successful Thompson Sampling algorithm for stochastic bandits has drawn much interest in understanding its theoretical properties. One important benefit of the algorithm is that it allows domain knowledge to be conveniently encoded as a prior distribution to balance exploration and exploitation more effectively. While it is generally believed that the algorithm's regret is low (high) when the prior is good (bad), little is known about the exact dependence. In this paper, we fully characterize the algorithm's worst-case dependence of regret on the choice of prior, focusing on a special yet representative case. These results also provide insights into the general sensitivity of the algorithm to the choice of priors. In particular, with $p$ being the prior probability mass of the true reward-generating model, we prove $O(\sqrt{T/p})$ and $O(\sqrt{(1-p)T})$ regret upper bounds for the bad- and good-prior cases, respectively, as well as \emph{matching} lower bounds. Our proofs rely on the discovery of a fundamental property of Thompson Sampling and make heavy use of martingale theory, both of which appear novel in the literature, to the best of our knowledge., Comment: Appears in the 27th International Conference on Algorithmic Learning Theory (ALT), 2016

Published
2015

112. A Passive Wireless Smart Washer for Locking Force Monitoring on the Orthopedic Pedicle Screw.

Author

Liu, Che-Fu, Wong, Tze-Hong, Wang, Hsin-Chuan, Sun, Asher, and Hsu, Wensyang

Subjects
SCREWS, SMART locks, SPINAL surgery, BONE density
Abstract

A pedicle screw is a component for fixation in spine fusion surgery, often for patients with osteoporosis. Spine fusion condition highly depends on whether the pedicle screw is tightly fixed on spine, if not, spine fusion will not work properly. After the surgery, the first 3 months is the most crucial period, and bone healing situation cannot be shown through X-ray before the first radiologic images taken around the sixth week. Therefore, it is helpful to have a nonradiative method to monitor the locking force of the pedicle screw after surgery, especially during the early stage. Here a passive wireless force sensor is developed for monitoring the locking force of the pedicle screw, as we call it a smart washer. By integrating a capacitive ring-shape force sensor with an inductor, a passive LC sensor can be built by measuring the resonant frequency wirelessly. The smart washer is designed and calibrated to establish the relation between the locking force and resonant frequency, and then it is fixed with a screw in a porcine femur, with and without medium between the reader and inductor. When the locking force decreases from 8.3 to 0.9 N, the error is less than 0.5 N, and the maximum wireless sensing distance is 72 mm. However, the medium between the reader and the sensor inductor will affect the resonant frequency, but not the sensitivity. Therefore, the locking force variation can still be calculated by the resonant frequency shift accurately. Furthermore, by designing another five LC sensors with different operating resonant frequency ranges, it is possible to identify locking forces at different locations for more pedicle screws. To our knowledge, no LC force sensor was proposed to monitor the locking force of pedicle screws after surgery in the past. [ABSTRACT FROM AUTHOR]

Published
2024
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113. Design of Wireless Multiple Gases Complementary Architecture Sensors Based on SnSe2 and PtSe2 Layered Films and Oscillating Circuits.

Author

Hsu, Che‐Hao, Liao, Hung‐Yu, Wang, Kuangye, Kuo, Tzu‐Wen, Liu, Che‐Chuan, Shih, Jiaw‐Ren, Lin, Chrong‐Jung, Chen, Chieh‐Ting, Peng, Yu‐Ren, Yang, Tzu‐Yi, Kao, Man‐Yun, Huang, Hai‐Feng, Shen, Hsin‐Yi, King, Ya‐Chin, and Chueh, Yu‐Lun

Subjects
GAS detectors, HOT carriers, P-type semiconductors, THRESHOLD voltage, GASES, COMPLEMENTARY metal oxide semiconductors, WIRELESS sensor networks, N-type semiconductors, ON-chip charge pumps
Abstract

Owing to the increasing demand for monitoring harmful toxic gases using small‐sized low‐power‐consumption gas sensors, a room‐temperature wireless complimentary gas sensor incorporating layered materials is proposed and demonstrated. The compact design allows the sensing module to be installed under all conditions and facilitates multiple detections of toxic gas. Highly chemical‐resistance‐sensitive materials based on n‐type SnSe2 and p‐type PtSe2 layered materials prepared by the plasma‐assisted chemical vapor reaction (PACVR) method at low temperatures are integrated with an oscillation circuit and antenna fabricated by a 28‐nm CMOS high‐k metal gate logic process to achieve dense, low‐powered, and wireless characteristics. By pairing n‐ and p‐type layered semiconductors, the gas sensors feature less influence from the surroundings, such as temperature and humidity, and enhance sensitivity to the target gas simultaneously. The wireless sensing detection method enables a sensor to make signal reception more convenient. Furthermore, a wireless gas sensor with self‐converging calibration using the advantage of a floating‐gate (FG) transistor to control the charge quantity by shifting the threshold voltage (Vth) of each transistor via the channel hot electron injection effect, which helps to reduce the impact of process variations, minimizing readout errors, is proposed. [ABSTRACT FROM AUTHOR]

Published
2024
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114. New Rigid-Soft Coupling Structure and Its Stiffness Adjusting Device

Author

Liu, Che, Mu, Hengyang, Chen, Diansheng, Wang, Min, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yu, Haibin, editor, Liu, Jinguo, editor, Liu, Lianqing, editor, Ju, Zhaojie, editor, Liu, Yuwang, editor, and Zhou, Dalin, editor

Published
2019
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115. Key Success Factors Analysis of Integration of O2O into 7-Eleven Convenient Store

Author

Kao, Chung-Chi, Liu, Che-Hung, Lu, Yang-Ming, Hsieh, Chia-Fen, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Nah, Fiona Fui-Hoon, editor, and Siau, Keng, editor

Published
2019
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123. Most Correlated Arms Identification

Author

Liu, Che-Yu and Bubeck, Sébastien

Subjects
Statistics - Machine Learning, Computer Science - Learning
Abstract

We study the problem of finding the most mutually correlated arms among many arms. We show that adaptive arms sampling strategies can have significant advantages over the non-adaptive uniform sampling strategy. Our proposed algorithms rely on a novel correlation estimator. The use of this accurate estimator allows us to get improved results for a wide range of problem instances.

Published
2014

127. Prior-free and prior-dependent regret bounds for Thompson Sampling

Author

Bubeck, Sébastien and Liu, Che-Yu

Subjects
Statistics - Machine Learning, Computer Science - Learning
Abstract

We consider the stochastic multi-armed bandit problem with a prior distribution on the reward distributions. We are interested in studying prior-free and prior-dependent regret bounds, very much in the same spirit as the usual distribution-free and distribution-dependent bounds for the non-Bayesian stochastic bandit. Building on the techniques of Audibert and Bubeck [2009] and Russo and Roy [2013] we first show that Thompson Sampling attains an optimal prior-free bound in the sense that for any prior distribution its Bayesian regret is bounded from above by $14 \sqrt{n K}$. This result is unimprovable in the sense that there exists a prior distribution such that any algorithm has a Bayesian regret bounded from below by $\frac{1}{20} \sqrt{n K}$. We also study the case of priors for the setting of Bubeck et al. [2013] (where the optimal mean is known as well as a lower bound on the smallest gap) and we show that in this case the regret of Thompson Sampling is in fact uniformly bounded over time, thus showing that Thompson Sampling can greatly take advantage of the nice properties of these priors., Comment: A previous version appeared under the title 'A note on the Bayesian regret of Thompson Sampling with an arbitrary prior'

Published
2013

128. The Interplay between Social Connections and Digital Technologies: Three Essays Examining Healthy Behaviors and Income Mobility

Author

Liu, Che-Wei

Abstract

In the past few decades, digital technologies have profoundly altered virtually every aspect of human life. While the direct impact of digital technologies on individuals' economic welfare or personal behaviors has attracted considerable attention, the interplay of digital technologies with social connections remains underexplored. Indeed, regardless of whether formed offline or online, social connections in the form of personal ties and affiliations that have long been the bedrock of human society continue to shape human behaviors and outcomes. To the extent that digitization will only continue to grow in scale and scope, an understanding of such effects is important for scholars, practitioners, and policymakers. I address two overarching research questions in my dissertation: (1) Whether, and to what extent digital technologies affect individuals' economic welfare and habituated behavior, and (2) How social connections such as personal ties and affiliations condition the impact of the digital technologies. My studies are conducted in two distinct contexts: mobile interventions for health, and computer ownership for social and economic welfare. Drawing on diverse bodies of literature and using various econometric methods, I seek to answer questions related to how interventions orchestrated on mobile platforms help individuals form healthy behaviors, and how computer ownership affects long-term income mobility. In the first essay, I show that a social norms intervention on a mobile platform is effective in increasing individuals' physical activity. In the second study, I investigate how the motivational incentive of reciprocity can be leveraged to promote healthy behavior. Finally, in my third essay, I show that computer ownership generates both private and social returns (IT spillovers) on individuals' income mobility. All three papers then consider how individuals' social connections condition the direct effects of digital technologies. The first two studies explore how online social ties and social relationships moderate the impact of mobile interventions, and the third study examines how caste groups affect the positive spillover effects of computer ownership. Collectively, the three studies advance our understanding of the heterogeneous effects of digital technologies on individuals and provide implications for researchers and practitioners. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

Published
2018

134. WIN55212-2 inhibits glycolysis and attenuates acute lung injury in septic mice by regulating mTOR/HIF-1α/PFKFB3 signaling pathway.

Author

DUAN Qianwen, DONG Xupeng, MA Yuan, LIU Che, ZHANG Ming, and MA Yuqing

Subjects
LACTATE dehydrogenase, LUNG injuries, CELLULAR signal transduction, GLYCOLYSIS, INTRAPERITONEAL injections
Abstract

AIM: To investigate the effects of cannabinoid receptor agonist WIN55212-2 (WIN) on acute lung injury (ALI) in septic mice, and to explore its potential mechanisms through glycolysis. METHODS: A mouse model of septic ALI was established by intraperitoneal injections of lipopolysaccharide (LPS). Male C57BL/6J mice were randomly divided into 4 groups (n=6) : (1) control group; (2) LPS group, receiving intraperitoneal injections of LPS at 10 mg/kg; (3) LPS+WIN group, receiving 1 mg/kg WIN intraperitoneally 30 min prior to LPS injection; (4) LPS+WIN+MHY1485 [mammalian target of rapamycin (mTOR) activator] group, receiving 10 mg/kg MHY1485 intraperitoneally 1 d before LPS injection and 1 mg/kg WIN plus 10 mg/kg MHY1485 30 min before LPS injection. Tissues were collected 24 h after modeling for analysis. Lung indexes were calculated, and histopathological changes of lung tissues were observed via hematoxylin-eosin (HE) staining. Inflammatory cytokines interleukin-1β (IL-1β) and IL-10 in lung tissues, and lactic acid and lactate dehydrogenase A (LDHA) in serum were quantified using ELISA. The levels of mTOR/hypoxia-inducible factor-1α (HIF-1α)/6-phosphofructo-2-kinase/fructose-2, 6-biphosphatase 3 (PFKFB3) signaling pathway-related proteins were assessed by Western blot. RESULTS: Compared with control group, the LPS group exhibited an increased lung index, significant lung tissue damage, an increase in IL-1β levels (P<0. 05), a decrease in IL-10 levels (P<0. 05), and elevated expressions of lactate and LDHA (P<0. 05), along with increased levels of phosphorylated mTOR (p-mTOR), HIF-1α and PFKFB3 proteins (P<0. 05). The LPS+WIN group showed improvements with a reduced lung index (P< 0. 05), lessened lung injury, decreased IL-1β levels (P<0. 05), increased IL-10 levels (P<0. 05), and lower levels of lactic acid, LDHA, p-mTOR, HIF-1α, and PFKFB3 (P<0. 05). Conversely, the LPS+WIN+MHY1485 group displayed increased lung indexes and lung tissue damage, elevated IL-1β levels (P<0. 05), reduced IL-10 levels (P<0. 05), and higher expressions of lactic acid, LDHA, p-mTOR, HIF-1α and PFKFB3 (P<0. 05) compared to the LPS+WIN group. CONCLUSION: WIN55212-2 mitigates sepsis-induced ALI, potentially by modulating the mTOR/HIF-1α/PFKFB3 signaling pathway, thereby inhibiting glycolysis and alleviating inflammatory responses. [ABSTRACT FROM AUTHOR]

Published
2024
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135. Environment‐Adaptive Meta‐Channel

Author

He, Pei Hang, primary, Zhang, Hao Chi, additional, Fan, Yi, additional, Niu, Ling Yun, additional, Liu, Che, additional, Bao, Jianghan, additional, Zhang, Le Peng, additional, Li, Baiyu, additional, Lu, Zukun, additional, Liu, Shuo, additional, Tang, Wenxuan, additional, and Cui, Tie Jun, additional

Published
2023
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137. Leveraging Social Norms and Implementation Intentions for Better Health

Author

Liu, Che-Wei, Wang, Weiguang, Gao, Guodong (Gordon), Agarwal, Ritu, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Chen, Hsinchun, editor, Zeng, Daniel Dajun, editor, Karahanna, Elena, editor, and Bardhan, Indranil, editor

Published
2017
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139. Beauty Is in the Eye of the Beholder: Toward a Contextual Understanding of Compensation of Information Technology Professionals Within and Across Geographies

Author

Whitaker, Jonathan, Mithas, Sunil, and Liu, Che-Wei

Subjects
United States. Bureau of Labor Statistics -- International economic relations, Information technology workers -- Compensation and benefits, Emerging markets, Master of business administration degree, International business enterprises, Computers, Library and information science
Abstract

Abstract. How do multinational corporations (MNCs) and domestic Arms compensate technical and managerial skills of knowledge workers within and across geographies? This paper answers this question by examining how developed [...]

Published
2019
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143. Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review

Author

Cheng, Sibo, primary, Quilodrán-Casas, César, additional, Ouala, Said, additional, Farchi, Alban, additional, Liu, Che, additional, Tandeo, Pierre, additional, Fablet, Ronan, additional, Lucor, Didier, additional, Iooss, Bertrand, additional, Brajard, Julien, additional, Xiao, Dunhui, additional, Janjic, Tijana, additional, Ding, Weiping, additional, Guo, Yike, additional, Carrassi, Alberto, additional, Bocquet, Marc, additional, and Arcucci, Rossella, additional

Published
2023
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147. On the Prior Sensitivity of Thompson Sampling

Author

Liu, Che-Yu, Li, Lihong, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Ortner, Ronald, editor, Simon, Hans Ulrich, editor, and Zilles, Sandra, editor

Published
2016
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148. In-Memory Acceleration of Hyperdimensional Genome Matching on Unreliable Emerging Technologies

Author

Barkam, Hamza E., Yun, Sanggeon, Genssler, Paul R., Liu, Che-Kai, Zou, Zhuowen, Amrouch, Hussam, and Imani, Mohsen

Abstract

Novel computer architectures like Compute-in-Memory (CiM) merge the memory and processing units, mimicking the human brain. Simultaneously, Hyperdimensional Computing (HDC) is emerging as a brain-inspired machine learning (ML) approach. Both developments hold promise for the realm of AI and computing, especially for genome-matching tasks, where large data movements overwhelm traditional von Neumann architectures. FeFET is one of the up-and-coming emerging technologies that promises to enable ultra-efficient and compact CiM architectures. However, the adoption of FeFETs is hindered by their 10 nm-thick Ferroelectric (FE) layer and process variation. Thus, calculations with FeFETs have errors (noise) that traditional ML genome-matching models cannot tolerate. To overcome these challenges, this work is the first one to i) present a reliable HDC framework (HDGIM) for highly-scaled (down to merely 3nm), multi-bit FeFET technology, ii) introduce temperature-thickness modeled noise from FeFET to the HDC system, and iii) extensively define the memorization capacity of HDC hyperparameters in order to evaluate the performance before deployment theoretically. Our novel HDC learning framework iteratively uses two models: a full-precision 32-bit HDC model, an ideal model for training, and a reduced bit-precision by a novel quantization method for validation and inference. Our results demonstrate that highly-scaled FeFET, realizing 3-bit and even 4-bit, can withstand any modeled noise given high dimensionality during inference. Considering the noise during model adjustment improves the inherent robustness by almost 9% on the 4-bit case.

Published
2024
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149. Towards Efficient Hyperdimensional Computing Using Photonics

Author

Fayza, Farbin, Demirkiran, Cansu, Chen, Hanning, Liu, Che-Kai, Mohan, Avi, Errahmouni, Hamza, Yun, Sanggeon, Imani, Mohsen, Zhang, David, Bunandar, Darius, Joshi, Ajay, Fayza, Farbin, Demirkiran, Cansu, Chen, Hanning, Liu, Che-Kai, Mohan, Avi, Errahmouni, Hamza, Yun, Sanggeon, Imani, Mohsen, Zhang, David, Bunandar, Darius, and Joshi, Ajay

Abstract

Over the past few years, silicon photonics-based computing has emerged as a promising alternative to CMOS-based computing for Deep Neural Networks (DNN). Unfortunately, the non-linear operations and the high-precision requirements of DNNs make it extremely challenging to design efficient silicon photonics-based systems for DNN inference and training. Hyperdimensional Computing (HDC) is an emerging, brain-inspired machine learning technique that enjoys several advantages over existing DNNs, including being lightweight, requiring low-precision operands, and being robust to noise introduced by the nonidealities in the hardware. For HDC, computing in-memory (CiM) approaches have been widely used, as CiM reduces the data transfer cost if the operands can fit into the memory. However, inefficient multi-bit operations, high write latency, and low endurance make CiM ill-suited for HDC. On the other hand, the existing electro-photonic DNN accelerators are inefficient for HDC because they are specifically optimized for matrix multiplication in DNNs and consume a lot of power with high-precision data converters. In this paper, we argue that photonic computing and HDC complement each other better than photonic computing and DNNs, or CiM and HDC. We propose PhotoHDC, the first-ever electro-photonic accelerator for HDC training and inference, supporting the basic, record-based, and graph encoding schemes. Evaluating with popular datasets, we show that our accelerator can achieve two to five orders of magnitude lower EDP than the state-of-the-art electro-photonic DNN accelerators for implementing HDC training and inference. PhotoHDC also achieves four orders of magnitude lower energy-delay product than CiM-based accelerators for both HDC training and inference.

Published
2023

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