arXiv subjects

Yuqi Li

Publications and source records attributed to Yuqi Li.

At least 109 records · Page 6Linked to original sources

arXiv2024

BatteryML:An Open-source platform for Machine Learning on Battery Degradation

Battery degradation remains a pivotal concern in the energy storage domain, with machine learning emerging as a potent tool to drive forward insights and solutions. However, this intersection of electrochemical science and machine learning poses complex challenges. Machine learning experts often grapple with the intricacies of battery science, while battery researchers face hurdles in adapting intricate models tailored to specific datasets. Beyond this, a cohesive standard for battery degradation modeling, inclusive of data formats and evaluative benchmarks, is conspicuously absent. Recognizing these impediments, we present BatteryML - a one-step, all-encompass, and open-source platform designed to unify data preprocessing, feature extraction, and the implementation of both traditional and state-of-the-art models. This streamlined approach promises to enhance the practicality and efficiency of research applications. BatteryML seeks to fill this void, fostering an environment where experts from diverse specializations can collaboratively contribute, thus elevating the collective understanding and advancement of battery research.The code for our project is publicly available on GitHub at https://github.com/microsoft/BatteryML.

cs.LG↗

arXiv2023

Accurate battery lifetime prediction across diverse aging conditions with deep learning

Accurately predicting the lifetime of battery cells in early cycles holds tremendous value for battery research and development as well as numerous downstream applications. This task is rather challenging because diverse conditions, such as electrode materials, operating conditions, and working environments, collectively determine complex capacity-degradation behaviors. However, current prediction methods are developed and validated under limited aging conditions, resulting in questionable adaptability to varied aging conditions and an inability to fully benefit from historical data collected under different conditions. Here we introduce a universal deep learning approach that is capable of accommodating various aging conditions and facilitating effective learning under low-resource conditions by leveraging data from rich conditions. Our key finding is that incorporating inter-cell feature differences, rather than solely considering single-cell characteristics, significantly increases the accuracy of battery lifetime prediction and its cross-condition robustness. Accordingly, we develop a holistic learning framework accommodating both single-cell and inter-cell modeling. A comprehensive benchmark is built for evaluation, encompassing 401 battery cells utilizing 5 prevalent electrode materials across 168 cycling conditions. We demonstrate remarkable capabilities in learning across diverse aging conditions, exclusively achieving 10% prediction error using the first 100 cycles, and in facilitating low-resource learning, almost halving the error of single-cell modeling in many cases. More broadly, by breaking the learning boundaries among different aging conditions, our approach could significantly accelerate the development and optimization of lithium-ion batteries.

eess.SP↗

arXiv2023

Infrared limit of left-handed string at genus one

We extend the left-handed string formalism at one-loop level to focus on only the infrared limit, where the Green's function for the left-handed string is expanded around the cusp of the modular parameter. This expansion leads to the separating degeneration limit of a Riemann surface corresponding to a sphere and a torus connected by a long tube. The well-behaved short-distance behavior of the Green's function requires all marked points to be inserted on the sphere. Analogous to the tree-level calculations, we obtain Dirac $δ$-functions by integrating out the anti-holomorphic variables. The constraints embedded in these $δ$-functions, associated with the marked points on the sphere part of the Riemann surface, are the same Scattering Equations at the tree-level. After the integration over the modular parameter, we observe the expected pattern of the infrared divergence, consistent with the one-loop results from the box diagram calculations in field theory.

hep-th↗

arXiv2023

Left-handed string and CHY amplitude at one loop

We propose a generalized left-handed (chiral) gauge choice for the genus one Riemann surface, realized through a singular gauge transformation of worldsheet coordinates. The transformation predominantly affects the logarithmic non-zero modes of the Green's function, leaving non-holomorphic and non-logarithmic modes unchanged. This procedure yields $δ$-functions for chiral coordinates and box-diagram-like integrals in terms of modular parameters. The resulting $δ$-functions formulate one-loop level Scattering Equations that simplify to satisfy the tree-level solutions, constraining the locations of the marked points. Subsequent integrals agree with the field-theoretic box diagram for the four-point amplitude, in accordance with the divergent $ε$ expansions derived from dimensional regularization in the infrared limit. We conclude by highlighting potential avenues for future research, including the exploration of methodologies that preclude the need for worldsheet coordinates reparametrization and their implications for accurately capturing infrared behavior from modular parameter integrals.

hep-th↗

arXiv2023

Post-main sequence thermal evolution of planetesimals

White dwarfs that have accreted planetary materials provide a powerful tool to probe the interiors and formation of exoplanets. In particular, the high Fe/Si ratio of some white dwarf pollutants suggests that they are fragments of bodies that were heated enough to undergo large-scale melting and iron core formation. In the solar system, this phenomenon is associated with bodies that formed early and so had short-lived radionuclides to power their melting, and/or grew large. However, if the planetary bodies accreted by white dwarfs formed during the (pre)-main sequence lifetime of the host star, they will have potentially been exposed to a second era of heating during the star's giant branches. This work aims to quantify the effect of stellar irradiation during the giant branches on planetary bodies by coupling stellar evolution to thermal and orbital evolution of planetesimals. We find that large-scale melting, sufficient to form an iron core, can be induced by stellar irradiation, but only in close-in small bodies: planetesimals with radii $\lesssim$ 30 km originally within $\sim$ 2 AU orbiting a 1$-$3$\,M_{\odot}$ host star with solar metallicity. Most of the observed white dwarf pollutants are too massive to be explained by the accretion of these small planetesimals that are melted during the giant branches. Therefore, we conclude that those white dwarfs that have accreted large masses of materials with enhanced or reduced Fe/Si remain an indicator of planetesimal's differentiation shortly after formation, potentially linked to radiogenic heating.

astro-ph.EP↗

arXiv2023

Fast and reconfigurable sort-in-memory system enabled by memristors

Sorting is fundamental and ubiquitous in modern computing systems. Hardware sorting systems are built based on comparison operations with Von Neumann architecture, but their performance are limited by the bandwidth between memory and comparison units and the performance of complementary metal-oxide-semiconductor (CMOS) based circuitry. Sort-in-memory (SIM) based on emerging memristors is desired but not yet available due to comparison operations that are challenging to be implemented within memristive memory. Here we report fast and reconfigurable SIM system enabled by digit read (DR) on 1-transistor-1-resistor (1T1R) memristor arrays. We develop DR tree node skipping (TNS) that support variable data quantity and data types, and extend TNS with multi-bank, bit-slice and multi-level strategies to enable cross-array TNS (CA-TNS) for practical adoptions. Experimented on benchmark sorting datasets, our memristor-enabled SIM system presents up to 3.32x~7.70x speedup, 6.23x~183.5x energy efficiency improvement and 2.23x~7.43x area reduction compared with state-of-the-art sorting systems. We apply such SIM system for shortest path search with Dijkstra's algorithm and neural network inference with in-situ pruning, demonstrating the capability in solving practical sorting tasks and the compatibility in integrating with other compute-in-memory (CIM) schemes. The comparison-free TNS/CA-TNS SIM enabled by memristors pushes sorting into a new paradigm of sort-in-memory for next-generation sorting systems.

cs.AR↗

arXiv2023

Spec-NeRF: Multi-spectral Neural Radiance Fields

We propose Multi-spectral Neural Radiance Fields(Spec-NeRF) for jointly reconstructing a multispectral radiance field and spectral sensitivity functions(SSFs) of the camera from a set of color images filtered by different filters. The proposed method focuses on modeling the physical imaging process, and applies the estimated SSFs and radiance field to synthesize novel views of multispectral scenes. In this method, the data acquisition requires only a low-cost trichromatic camera and several off-the-shelf color filters, making it more practical than using specialized 3D scanning and spectral imaging equipment. Our experiments on both synthetic and real scenario datasets demonstrate that utilizing filtered RGB images with learnable NeRF and SSFs can achieve high fidelity and promising spectral reconstruction while retaining the inherent capability of NeRF to comprehend geometric structures. Code is available at https://github.com/CPREgroup/SpecNeRF-v2.

eess.IV↗

arXiv2023

Tunable optical topological transitions of plasmon polaritons in WTe2 van der Waals films

Naturally existing in-plane hyperbolic polaritons and the associated optical topological transitions, which avoid the nano-structuring to achieve hyperbolicity, can outperform their counterparts in artificial metasurfaces. Such plasmon polaritons are rare, but experimentally revealed recently in WTe2 van der Waals thin films. Different from phonon polaritons, hyperbolic plasmon polaritons originate from the interplay of free carrier Drude response and interband transitions, which promise good intrinsic tunability. However, tunable in-plane hyperbolic plasmon polariton and its optical topological transition of the isofrequency contours to the elliptic topology in a natural material have not been realized. Here we demonstrate the tuning of the optical topological transition through Mo-doping and temperature. The optical topological transition energy is tuned over a wide range, with frequencies ranging from 429 cm-1 (23.3 microns) for pure WTe2 to 270 cm-1 (37.0 microns) at the 50% Mo-doping level at 10 K. Moreover, the temperature-induced blueshift of the optical topological transition energy is also revealed, enabling active and reversible tuning. Surprisingly, the localized surface plasmon resonance in skew ribbons shows unusual polarization dependence, accurately manifesting its topology, which renders a reliable means to track the topology with far-field techniques. Our results open an avenue for reconfigurable photonic devices capable of plasmon polariton steering, such as canaling, focusing and routing, and pave a way for low-symmetry plasmonic nanophotonics based on anisotropic natural materials.

physics.optics↗

arXiv2023

Towards $α'$-finiteness: $q$-deformed open string amplitude

Revisiting the Coon amplitude, a deformation of the Veneziano amplitude with a logarithmic generalization of linear Regge trajectories, we scrutinize its potential origins in a worldsheet theory by proposing a definition of its $q$-deformation through the integral representation of the $q$-beta function. By utilizing $q$-deformed commutation relations and vertex operators, we derive the Coon amplitude within the framework of the dual resonance model. We extend this to the open-string context by $q$-deforming the Lie algebra $\mathfrak{su}(1,1)$, resulting in a well-defined $q$-deformed open superstring amplitude. We further demonstrate that the $q$-prefactor in the Coon amplitude arises naturally from the property of the $q$-integral. Furthermore, we find that two different types of $q$-prefactors, corresponding to different representations of the same scattering amplitude, are essentially the same by leveraging the properties of $q$-numbers. Our findings indicate that the $q$-deformed string amplitude defines a continuous family of amplitudes, illustrating how string amplitudes with a finite $α^\prime$ uniquely flow to the amplitudes of scalar scattering in field theory at energy scale $Λ$ as $q$ changes from $1$ to $0$. This happens without the requirement of an $α^\prime$ expansion, presenting a fresh perspective on the connection between string and field theories.

hep-th↗

arXiv2023

Catch Me If You Can: A New Low-Rate DDoS Attack Strategy Disguised by Feint

While collaborative systems provide convenience to our lives, they also face many security threats. One of them is the Low-rate Distributed Denial-of-Service (LDDoS) attack, which is a worthy concern. Unlike volumetric DDoS attacks that continuously send large volumes of traffic, LDDoS attacks are more stealthy and difficult to be detected owing to their low-volume feature. Due to its stealthiness and harmfulness, LDDoS has become one of the most destructive attacks in cloud computing. Although a few LDDoS attack detection and defense methods have been proposed, we observe that sophisticated LDDoS attacks (being more stealthy) can bypass some of the existing LDDoS defense methods. To verify our security observation, we proposed a new Feint-based LDDoS (F-LDDoS) attack strategy. In this strategy, we divide a Pulse Interval into a Feinting Interval and an Attack Interval. Unlike the previous LDDoS attacks, the bots also send traffic randomly in the Feinting Interval, thus disguise themselves as benign users during the F-LDDoS attack. In this way, although the victim detects that it is under an LDDoS attack, it is difficult to locate the attack sources and apply mitigation solutions. Experimental results show that F-LDDoS attack can degrade TCP bandwidth 6.7%-14% more than the baseline LDDoS attack. Besides, F-LDDoS also reduces the similarities between bot traffic and aggregated attack traffic, and increases the uncertainty of packet arrival. These results mean that the proposed F-LDDoS is more effective and more stealthy than normal LDDoS attacks. Finally, we discuss the countermeasures of F-LDDoS to draw the attention of defenders and improve the defense methods.

cs.CR↗

arXiv2023

Team AcieLee: Technical Report for EPIC-SOUNDS Audio-Based Interaction Recognition Challenge 2023

In this report, we describe the technical details of our submission to the EPIC-SOUNDS Audio-Based Interaction Recognition Challenge 2023, by Team "AcieLee" (username: Yuqi\_Li). The task is to classify the audio caused by interactions between objects, or from events of the camera wearer. We conducted exhaustive experiments and found learning rate step decay, backbone frozen, label smoothing and focal loss contribute most to the performance improvement. After training, we combined multiple models from different stages and integrated them into a single model by assigning fusion weights. This proposed method allowed us to achieve 3rd place in the CVPR 2023 workshop of EPIC-SOUNDS Audio-Based Interaction Recognition Challenge.

cs.SD↗

arXiv2023

Stochastic Partial Differential Equation SEIRS Epidemic Models: Well-posedness and Longtime Behavior

The study of epidemic models plays an important role in mathematical epidemiology. There are many researches on epidemic models using ordinary differential equations, partial differential equations or stochastic differential equations. In contrast to these researches, our work analyzes the SEIRS (Susceptible-Exposed-Infected-Recovered-Susceptible) model using stochastic partial differential equations. Specifically, we consider the effects of spatial variables and space-time white noise on the epidemic model. In the means of mild solution, we construct a contraction mapping and prove the well-posedness for the stochastic partial differential equation using the fixed point argument and the factorization method. We also find sufficient conditions for permanence and extinction to determine the longtime behavior of the solution. We hope that this work can provide some new ideas for the study of increasingly complex epidemic models and related numerical simulations.

math.PR↗

arXiv2022

Achieving a Given Financial Goal with Optimal Deferred Term Insurance Purchasing Policy

This paper researches the problem of purchasing deferred term insurance in the context of financial planning to maximize the probability of achieving a personal financial goal. Specifically, our study starts from the perspective of hedging death risk and longevity risk, and considers the purchase of deferred term life insurance and deferred term pure endowment to achieve a given financial goal for the first time in both deterministic and stochastic framework. In particular, we consider income, consumption and risky investment in the stochastic framework, extending previous results in \cite{Bayraktar2016}. The time cutoff m and n make the work more difficult. However, by establishing new controls,``\emph{quasi-ideal value}" and``\emph{ideal value}", we solve the corresponding ordinary differential equations or stochastic differential equations, and give the specific expressions for the maximum probability. Then we provide the optimal life insurance purchasing strategies and the optimal risk investment strategies. In general, when m \geqslant 0, n>0, deferred term insurance or term life insurance is a better choice for those who want to achieve their financial or bequest goals but are not financially sound. In particular, if m >0, n \rightarrow \infty, our viewpoint also sheds light on reaching a bequest goal by purchasing deferred whole life insurance. It is worth noting that when m=0, n \rightarrow \infty, our problem is equivalent to achieving the just mentioned bequest goal by purchasing whole life insurance, at which point the maximum probability and the life insurance purchasing strategies we provide are consistent with those in \cite{Bayraktar2014, Bayraktar2016}.

q-fin.PM↗

arXiv2022

Yield Evaluation of Citrus Fruits based on the YoloV5 compressed by Knowledge Distillation

In the field of planting fruit trees, pre-harvest estimation of fruit yield is important for fruit storage and price evaluation. However, considering the cost, the yield of each tree cannot be assessed by directly picking the immature fruit. Therefore, the problem is a very difficult task. In this paper, a fruit counting and yield assessment method based on computer vision is proposed for citrus fruit trees as an example. Firstly, images of single fruit trees from different angles are acquired and the number of fruits is detected using a deep Convolutional Neural Network model YOLOv5, and the model is compressed using a knowledge distillation method. Then, a linear regression method is used to model yield-related features and evaluate yield. Experiments show that the proposed method can accurately count fruits and approximate the yield.

cs.CV↗

arXiv2022

Remarks on the critical dimension of left-handed string and its quasiconformal nature

The chiral string without a singular gauge limit is argued to have the same critical dimension as its corresponding conventional closed string. Thus, its central charge would be the same as its conventional counterpart in the conformal gauge. Here, we would re-examine the critical dimension of the chiral string in the singular Hohm-Siegel-Zwiebach limit. A straight forward calculation of the operator product expansion (OPE) of the corresponding would-be stress tensor shows that the central charge term is not the same as its conventional counterpart when taking the singular limit. Instead of having a conformal transformation on the worldsheet, the coordinate reparametrization provides a set of quasiconformal mappings.

hep-th↗

arXiv2021

Complex excitations for the derivative nonlinear Schrödinger equation

The Darboux transformation (DT) formulae for the derivative nonlinear Schrödinger (DNLS) equation are expressed in concise forms, from which the multi-solitons, n-periodic solutions, higher-order hybrid-pattern solitons and some mixed solutions are obtained. These complex excitations can be constructed thanks to more general semi-degenerate DTs. Even the non-degenerate N-fold DT with a zero seed can generate complicated n-periodic solutions. It is proved that the solution q[N] at the origin depends only on the summation of the spectral parameters. We find the maximum amplitudes of several classes of the wave solutions are determined by the summation. Many interesting phenomena are discovered from these new solutions. For instance, the interactions between n-periodic waves produce peaks with different amplitudes and sizes; A soliton on a single periodic wave background shares a similar feature as a breather due to the interference of the periodic background. In addition, the results are extended to the reverse-space-time DNLS equation.

nlin.SI↗

arXiv2021

ISP-Agnostic Image Reconstruction for Under-Display Cameras

Under-display cameras have been proposed in recent years as a way to reduce the form factor of mobile devices while maximizing the screen area. Unfortunately, placing the camera behind the screen results in significant image distortions, including loss of contrast, blur, noise, color shift, scattering artifacts, and reduced light sensitivity. In this paper, we propose an image-restoration pipeline that is ISP-agnostic, i.e. it can be combined with any legacy ISP to produce a final image that matches the appearance of regular cameras using the same ISP. This is achieved with a deep learning approach that performs a RAW-to-RAW image restoration. To obtain large quantities of real under-display camera training data with sufficient contrast and scene diversity, we furthermore develop a data capture method utilizing an HDR monitor, as well as a data augmentation method to generate suitable HDR content. The monitor data is supplemented with real-world data that has less scene diversity but allows us to achieve fine detail recovery without being limited by the monitor resolution. Together, this approach successfully restores color and contrast as well as image detail.

eess.IV↗

arXiv2021

Asymptotics of bordered Toeplitz determinants and next-to-diagonal Ising correlations

We prove the analogue of the strong Szeg{\H o} limit theorem for a large class of bordered Toeplitz determinants. In particular, by applying our results to the formula of Au-Yang and Perk \cite{YP} for the next-to-diagonal correlations $\langle σ_{0,0}σ_{N-1,N} \rangle$ in the anisotropic square lattice Ising model, we rigorously justify that the next-to-diagonal long-range order is the same as the diagonal and horizontal ones in the low temperature regime. The anisotropy-dependence of the subleading term in the asymptotics of the next-to-diagonal correlations is also established. We use Riemann-Hilbert and operator theory techniques, independently and in parallel, to prove these results.

math-ph↗