arXiv subjects

Yanrong Li

Publications and source records attributed to Yanrong Li.

17 recordsLinked to original sources

arXiv2025

How to Promote Autonomous Driving with Evolving Technology: Business Strategy and Pricing Decision

Recently, autonomous driving system (ADS) has been widely adopted due to its potential to enhance travel convenience and alleviate traffic congestion, thereby improving the driving experience for consumers and creating lucrative opportunities for manufacturers. With the advancement of data sensing and control technologies, the reliability of ADS and the purchase intentions of consumers are continually evolving, presenting challenges for manufacturers in promotion and pricing decisions. To address this issue, we develop a two-stage game-theoretical model to characterize the decision-making processes of manufacturers and consumers before and after a technology upgrade. Considering the unique structural characteristics of ADS, which consists of driving software and its supporting hardware (SSH), we propose different business strategies for SSH (bundle or unbundle with the vehicle) and driving software (perpetual licensing or subscription) from the manufacturer's perspective. We find that, first, SSH strategies influence the optimal software strategies by changing the consumers' entry barriers to the ADS market. Specifically, for manufacturers with mature ADS technology, the bundle strategy provides consumers with a lower entry barrier by integrating SSH, making the flexible subscription model a dominant strategy; while perpetual licensing outperforms under the unbundle strategy. Second, the software strategies influence the optimal SSH strategy by altering consumers' exit barriers. Perpetual licensing imposes higher exit barriers; when combined with a bundle strategy that lowers entry barriers, it becomes a more advantageous choice for manufacturers with mature ADS technology. In contrast, the subscription strategy allows consumers to easily exit the market, making the bundle strategy advantageous only when a substantial proportion of consumers are compatible with ADS.

econ.GN↗

arXiv2025

Superconductivity Favored Anisotropic Phase Stiffness in Infinite-Layer Nickelates

In unconventional superconductors such as cuprates and iron pnictides and chalcogenides, phase stiffness - a measure of the energy cost associated with superconducting phase variations - is on the same order of magnitude as the strength of Cooper pairing, translating to superconductivity governed by phase fluctuations. However, due to a lack of a direct experimental probe, there remains a fundamental gap in establishing microscopic picture between unconventional superconductivity and phase fluctuations. Here we show a vector current technique that allows for in-situ angle-resolved transport measurements, providing exclusive evidence suggesting an anisotropic nature of phase stiffness in infinite-layer nickelate superconductors. Pronounced anisotropy of in-plane resistance manifests itself in both normal and superconducting transition states, indicating crystal symmetry breaking. Remarkably, the electric conductivity of Nd0.8Sr0.2NiO2 peaks at 125{\deg} between the direction of the current and crystal principal axis, but this angle evolves to 160{\deg} near zero-resistance temperature. Further measurements reveal that the superconductivity is favored along a direction with minimized phase fluctuations, an orientation strikingly deviating from the symmetric direction imposed by both electronic anisotropy and the underlying crystal lattice. Identical measurements conducted on a prototypical cuprate superconductor yield consistent results, suggesting that this previously unknown behavior could be ubiquitous. By shielding insight into the contrasting anisotropy between electron fluid and superfluid, our findings provide clues for a unified framework for understanding unconventional superconductors

cond-mat.supr-con↗

arXiv2025

Quantum fluctuations-driven Melting Transitions in Two-dimensional Superconductors

Quantum fluctuations are pivotal in driving quantum phase transitions, exemplified by the quantum melting of Wigner crystals into Fermi liquids in electron systems. However, their impact on superconducting systems near zero temperature, particularly in the superconductor-insulator/metal transition, remains poorly understood. In this study, through electric transport measurements on the two-dimensional (2D) superconductor (SnS)1.17NbS2, we demonstrate that quantum fluctuations induce vortex displacement from their mean position, leading to the quantum melting of vortex solid near zero temperature. Quantitative analysis reveals the magnetic field-induced anomalous metal originates from this quantum melting transition, with energy dissipation governed by quantum fluctuations-driven vortex displacements. Remarkably, further extending this analysis to various 2D superconductors yields the same results, and many properties of anomalous metal can be qualitatively understood within the framework of quantum melting. The connection between the quantum melting of vortex solids and dissipative anomalous metal opens a novel pathway towards understanding quantum phase transitions through vortex dynamics, providing new insights on both fields.

cond-mat.supr-con↗

arXiv2025

Joint Bundle Design and Pricing for Extended Warranty Providers Servicing Multi-Tier Products

Extended warranties (EWs) constitute a significant source of revenue for capital-intensive products. Such products comprise multiple subsystems, enabling flexible EW design. For example, providers can bundle tailored sets of subsystems within different EW contracts, facilitating the creation of a service menu with differentiated warranty options. From the perspective of a third-party EW provider servicing multi-tier products, we develop a novel model to jointly optimize bundle design and pricing for EW options in order to maximize the expected total profit. Specifically, the problem involves determining which contracts-each containing a differentiated bundle of subsystems-to recommend for the multi-tier products and identifying the appropriate price for each contract. As the complexity of the joint optimization problem increases exponentially with the number of subsystems, we devise two solution approaches. The first approach leverages a mixed-integer second-order cone programming reformulation, which guarantees optimality but is applicable only for a small number of subsystems. The second approach utilizes an iterative two-step process, offering enhanced computational efficiency for scenarios involving a large number of subsystems. Numerical experiments validate the effectiveness of our model, particularly in scenarios characterized by high failure probabilities and a large number of subsystems.

math.OC↗

arXiv2024

AUGlasses: Continuous Action Unit based Facial Reconstruction with Low-power IMUs on Smart Glasses

Recent advancements in augmented reality (AR) have enabled the use of various sensors on smart glasses for applications like facial reconstruction, which is vital to improve AR experiences for virtual social activities. However, the size and power constraints of smart glasses demand a miniature and low-power sensing solution. AUGlasses achieves unobtrusive low-power facial reconstruction by placing inertial measurement units (IMU) against the temporal area on the face to capture the skin deformations, which are caused by facial muscle movements. These IMU signals, along with historical data on facial action units (AUs), are processed by a transformer-based deep learning model to estimate AU intensities in real-time, which are then used for facial reconstruction. Our results show that AUGlasses accurately predicts the strength (0-5 scale) of 14 key AUs with a cross-user mean absolute error (MAE) of 0.187 (STD = 0.025) and achieves facial reconstruction with a cross-user MAE of 1.93 mm (STD = 0.353). We also integrated various preprocessing and training techniques to ensure robust performance for continuous sensing. Micro-benchmark tests indicate that our system consistently performs accurate continuous facial reconstruction with a fine-tuned cross-user model, achieving an AU MAE of 0.35.

cs.HC↗

arXiv2024

CogniDot: Vasoactivity-based Cognitive Load Monitoring with a Miniature On-skin Sensor

Vascular activities offer valuable signatures for psychological monitoring applications. We present CogniDot, an affordable, miniature skin sensor placed on the temporal area on the head that senses cognitive loads with a single-pixel color sensor. With its energy-efficient design, bio-compatible adhesive, and compact size (22mm diameter, 8.5mm thickness), it is ideal for long-term monitoring of mind status. We showed in detail the hardware design of our sensor. The user study results with 12 participants show that CogniDot can accurately differentiate between three levels of cognitive loads with a within-user accuracy of 97%. We also discuss its potential for broader applications.

cs.HC↗

arXiv2024

Tensor-based process control and monitoring for semiconductor manufacturing with unstable disturbances

With the development and popularity of sensors installed in manufacturing systems, complex data are collected during manufacturing processes, which brings challenges for traditional process control methods. This paper proposes a novel process control and monitoring method for the complex structure of high-dimensional image-based overlay errors (modeled in tensor form), which are collected in semiconductor manufacturing processes. The proposed method aims to reduce overlay errors using limited control recipes. We first build a high-dimensional process model and propose different tensor-on-vector regression algorithms to estimate parameters in the model to alleviate the curse of dimensionality. Then, based on the estimate of tensor parameters, the exponentially weighted moving average (EWMA) controller for tensor data is designed whose stability is theoretically guaranteed. Considering the fact that low-dimensional control recipes cannot compensate for all high-dimensional disturbances on the image, control residuals are monitored to prevent significant drifts of uncontrollable high-dimensional disturbances. Through extensive simulations and real case studies, the performances of parameter estimation algorithms and the EWMA controller in tensor space are evaluated. Compared with existing image-based feedback controllers, the superiority of our method is verified especially when disturbances are not stable.

stat.ML↗

arXiv2023

MFRL-BI: Design of a Model-free Reinforcement Learning Process Control Scheme by Using Bayesian Inference

Design of process control scheme is critical for quality assurance to reduce variations in manufacturing systems. Taking semiconductor manufacturing as an example, extensive literature focuses on control optimization based on certain process models (usually linear models), which are obtained by experiments before a manufacturing process starts. However, in real applications, pre-defined models may not be accurate, especially for a complex manufacturing system. To tackle model inaccuracy, we propose a model-free reinforcement learning (MFRL) approach to conduct experiments and optimize control simultaneously according to real-time data. Specifically, we design a novel MFRL control scheme by updating the distribution of disturbances using Bayesian inference to reduce their large variations during manufacturing processes. As a result, the proposed MFRL controller is demonstrated to perform well in a nonlinear chemical mechanical planarization (CMP) process when the process model is unknown. Theoretical properties are also guaranteed when disturbances are additive. The numerical studies also demonstrate the effectiveness and efficiency of our methodology.

cs.LG↗

arXiv2021

Reinforcement Learning for Process Control with Application in Semiconductor Manufacturing

Process control is widely discussed in the manufacturing process, especially for semiconductor manufacturing. Due to unavoidable disturbances in manufacturing, different process controllers are proposed to realize variation reduction. Since reinforcement learning (RL) has shown great advantages in learning actions from interactions with a dynamic system, we introduce RL methods for process control and propose a new controller called RL-based controller. Considering the fact that most existing process controllers mainly rely on a linear model assumption for the process input-output relationship, we first propose theoretical properties of RL-based controllers according to the linear model assumption. Then the performance of RL-based controllers and traditional process controllers (e.g., exponentially weighted moving average (EWMA), general harmonic rule (GHR) controllers) are compared for linear processes. Furthermore, we find that the RL-based controllers have potential advantages to deal with other complicated nonlinear processes that are with and without assumed explicit model formulations. The intensive numerical studies validate the advantages of the proposed RL-based controllers.

eess.SY↗

arXiv2021

A Two-stage Pricing Strategy Considering Learning Effects and Word-of-Mouth

This paper proposes a two-stage pricing strategy for nondurable (such as typical electronics) products, where retail price is cut down at certain time points of the product lifecycle. We consider learning effect of electronic products that, with the accumulation of production, average production cost decreases over time as manufacturers get familiar with the production process. Moreover, word-of-mouth (WOM) of existing customers is used to analyze future demand, which is sensitive to the difference between the actual reliability and the perceived reliability of products. We theoretically prove the existence and uniqueness of the optimal switch time between the two stages and the optimal price in each stage. In addition, warranty as another important factor of electronic products is also considered, whose interaction with word-of-mouth as well as the corresponding influences on total profit are analyzed. Interestingly, our findings indicate that (1) the main reason for manufacturers to cut down prices for electronic products pertains to the learning effects; (2) even through both internal factors (e.g., the learning effects of manufacturers) and external factors (e.g., the price elasticity of customers) have impacts on product price, their influence on manufacturer's profit is widely divergent; (3) generally warranty weakens the influence of external advertising on the reliability estimate, because warranty price only partially reflects the actual reliability information of products; (4) and the optimal warranty price can increase the profits for the manufacturer by approximately 10%.

econ.GN↗

arXiv2021

Sum frequency generation spectroscopy of the attachment disc of a spider

The pyriform silk of the attachment disc of a spider was studied using infrared-visible vibrational sum frequency generation (SFG) spectroscopy. The spider can attach dragline and radial lines to many kinds of substrates in nature (concrete, alloy, metal, glass, plant branches, leaves, etc.) with the attachment disc. The adhesion can bear the spider's own weight, and resist the wind on its orb web. From our SFG spectroscopy study, the NH group of arginine side chain and/or NH$_{2}$ group of arginine and glutamine side chain in the amino acid sequence of the attachment silk proteins are suggested to be oriented in the disc. It was inferred from the observed doublet SFG peaks at around 3300 cm$^{-1}$ that the oriented peptide contains two kinds of structures.

physics.app-ph↗

arXiv2021

A strange metal in a bosonic system

Fermi liquid theory forms the basis for our understanding of the majority of metals, which is manifested in the description of transport properties that the electrical resistivity goes as temperature squared in the limit of zero temperature. However, the observations of strange metal states in various quantum materials, notably high-temperature superconductors, bring this spectacularly successful theoretical framework into crisis. When electron scattering rate 1/τ hits its limit, kBT/{\hbar} where {\hbar} is the reduced Planck's constant, T represents absolute temperature and kB denotes Boltzmann's constant, Planckian dissipation occurs and lends strange metals a surprising link to black holes, gravity, and quantum information theory. Here, we show the characteristic signature of strange metallicity arising unprecedentedly in a bosonic system. Our nanopatterned YBa2Cu3O7-δ(YBCO) film arrays reveal T-linear resistance as well as B-linear magnetoresistance over an extended temperature and magnetic field range in a quantum critical region in the phase diagram. Moreover, the slope of the T-linear resistance α_cp appears bounded by α_cp {\approx} h/2e^2 [1/T]_c^onset where T_c^onset is the temperature at which Cooper pairs form, intimating a common scale-invariant transport mechanism corresponding to Planckian dissipation.In contrast to fermionic systems where the temperature and magnetic field dependent scattering rates combine in quadrature of {\hbar}/τ {\approx} {\sqrt} (((k_B T)^2+(μ_B B)^2)), both terms linearly combine in the present bosonic system, i.e. {\hbar}/τ {\approx} (k_B T+[γμ]_B B), where γ is a constant. By extending the reach of strange metal phenomenology to a bosonic system, our results suggest that there is a fundamental principle governing their transport which transcends particle statistics.

cond-mat.supr-con↗

arXiv2019

Quantum coherence across bosonic superconductor-anomalous metal-insulator transitions

After decades of explorations, suffering from low critical temperature and subtle nature, whether a metallic ground state exists in a two-dimensional system beyond Anderson localization is still a mystery. Supremely, phase coherence could be the key that unlocks its intriguing nature. This work reveals how quantum phase coherence evolves across bosonic superconductor-metal-insulator transitions via magneto-conductance quantum oscillations in high-Tc superconducting films. A robust intervening anomalous metallic state characterized by both resistance and oscillation amplitude saturations in the low temperature regime is detected. By contrast, with decreasing temperature the oscillation amplitude monotonically grows on the superconducting side, but decreases at low temperatures on the insulating side. It suggests that the saturation of phase coherence plays a prominent role in the formation of this anomalous metallic state.

cond-mat.supr-con↗

arXiv2018

Observation of Spider Silk by Femtosecond Pulse Laser Second Harmonic Generation Microscopy

An asymmetric \b{eta}-sheet structure of spider silk is said to induce optical second harmonic generation. In this paper, using an in-house non-scanning type femtosecond pulse laser second harmonic generation microscope, we characterized the behavior of the \b{eta}-sheet of spider silk under an applied external force. The orientation of the \b{eta}-sheets was more unidirectional when the silk was extended. One of the origins of the high mechanical strength of the dragline is suggested to be the physical arrangement of its \b{eta}-sheets.

cond-mat.soft↗

arXiv2018

Ungerminated Rice Grains Observed by Femtosecond Pulse Laser Second-Harmonic Generation Microscopy

As a demonstration that second-order nonlinear optical microscopy is a powerful tool for rice grain science, we observed second-harmonic generation (SHG) images of amylose-free glutinous rice and amylose-containing nonglutinous rice grains. The images obtained from SHG microscopy and photographs of the iodine-stained starch granules indicate that the distribution of starch types in the embryo-facing endosperm region (EFR) depends on the type of rice and suggests that glucose, maltose, or both are localized on the testa side of the embryo. In the testa side of the embryo, crystallized glucose or maltose are judged to be detected by SHG. These monosaccharides and disaccharides play an important role, as they trigger energy in the initial stage of germination. These results confirm SHG microscopy is a good method to monitor the distribution of such sugars and amylopectin in the embryo and its neighboring regions of rice grains.

physics.bio-ph↗

arXiv2018

Electric Field Effect on Optical Second-harmonic Generation of Amphoteric Megamolecule Aggregates

We have detected second-order nonlinear optical response from an amphoteric mega-saccharides named sacran under a needle/ring electrode stimulus by using an optical second harmonic generation (SHG) microscope. SHG was observed from sacran in the neighborhood of the negatively biased needle electrode below -4.5 V with respect to the outer ring electrode. From the incident light polarization dependence of SHG, this sacran was suggested to be oriented toward the electrode needle. SHG of the sacran polymer was judged to be induced by sacran aggregates with net positive charge.

physics.app-ph↗

arXiv2015

Screening for Two dimensional MX$_2$ semiconductors with possible high room temperature mobility

We calculated the electron mobility of 14 two dimensional semiconductors with composition of MX$_2$, where M (= Mo, W, Sn, Hf, Zr and Pt) is the transition metal, and X is S, Se and Te. We treated the scattering matrix by deformation potential approximation. Long wave longitudinal acoustical and optical phonon scatterings are included. Piezoelectric scattering in the compounds without inversion symmetry is also taken into account. We found that out of the 14 compounds, WSe$_2$, PtS$_2$ and PtSe$_2$, are promising regarding to the possible high electron mobility and finite band gap. The phonon limited mobility in PtSe$_2$ reaches about 3000 cm$^2$V$^{-1}$s$^{-1}$ at room temperature which is the highest among the compounds. The bandgap under the local density approximation is 1.25 eV. Our results can be a guide for experiments to search for better two-dimensional materials for future semiconductor devices.

cond-mat.mtrl-sci↗