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Replacing the ferromagnet with ferrimagnet (FiM) in the magnetic tunnel junction (MTJ) allows faster magnetization switching in picoseconds. The operation of a memory cell that consists of the MTJ and a transistor requires reversable magnetization switching. When a constant voltage is applied, we find that the spin-transfer torque can only switch the FiM-MTJ from parallel to antiparallel state. This stems from the small switching window of FiM and the dynamic resistance variation during the magnetization switching. We find the resulting current variation can be suppressed by reducing the magnetoresistance ratio. Furthermore, we demonstrate that the switching window can be expanded by adjusting the amount of Gd in FiM. We predict that the polarity of both switching current (Jc,switch) and oscillation current (Jc,osc) reverses at the angular momentum compensation point but not the magnetization compensation point. This anomalous dynamic behavior is attributed to the different physical nature of magnetization switching and oscillation in FiM, which must be considered when designing FiM-based MRAM.

Optical tweezers can grab particles with their laser beam fingers, so these tiny point objects can be moved by using the laser light. However, manipulation of topological defects, especially higher-dimension ones becomes extremely difficult because they usually require non-local operation involving a great deal of operators, e.g. string operator. Here, using a large-scale quantum Monte Carlo method, we demonstrate the quantum string excitation can be grabbed in the Kagome Rydberg atom array with well-designed edges. After inserting an edge defect, the fractional charge at one end of the string is found to be pinned, allowing the quantum string to be manipulated. Since the quantum string is directed, its quantum fluctuations can be clearly observed due to the movement of the dangling fractional charge at the other end. Then, we can drag the two ends of the string in opposite directions, which is analogous to the separation of a quark-anti-quark pair connected by a gluon string. As the separation distance increases, the quantum fluctuations of the string are strongly suppressed. Eventually, a string-breaking phenomenon occurs. However, if both edge defects are located on the same side, the strings are always broken because both dangling fractional charges take the same gauge charge and can not annihilate each other into the vacuum. This work not only proposes an ideal experimental platform for analyzing quantum string excitations in the ground state but also provides a much simpler method for manipulating quantum string excitations in frustrated systems, such as spin ice., Comment: 5 pages, 4 figures, comments are welcome, and more information at http://cqutp.org/users/xfzhang/

The opening of the gravitational wave window has significantly enhanced our capacity to explore the universe's most extreme and dynamic sector. In the mHz frequency range, a diverse range of compact objects, from the most massive black holes at the farthest reaches of the Universe to the lightest white dwarfs in our cosmic backyard, generate a complex and dynamic symphony of gravitational wave signals. Once recorded by gravitational wave detectors, these unique fingerprints have the potential to decipher the birth and growth of cosmic structures over a wide range of scales, from stellar binaries and stellar clusters to galaxies and large-scale structures. The TianQin space-borne gravitational wave mission is scheduled for launch in the 2030s, with an operational lifespan of five years. It will facilitate pivotal insights into the history of our universe. This document presents a concise overview of the detectable sources of TianQin, outlining their characteristics, the challenges they present, and the expected impact of the TianQin observatory on our understanding of them., Comment: TianQin Gravitational Wave Whitepaper, 72 pages, 30 figures

Noncollinear chiral antiferromagnets, such as Mn3X (X = Sn, Ge), have garnered significant interest in spintronics due to their topologically protected Weyl nodes and large momentum-space Berry curvatures. In this study, we report rapid chirality domain-wall (CDW) motion in Mn3Sn, driven by spin-orbit torque at over 545.3 m.s^-1 a remarkably low current density of 9 10^10 A.m^-2. The results demonstrate that the chirality of the domain wall and the direction of the current collectively determine the displacement direction of the CDW. Theoretically, we provide ananalysis of the effective field experienced by the octupole moment, uncovering the underlying motion mechanism based on the unique profile of the chiral spin structure. Notably, CDWs with opposite chirality can form within the same Dzyaloshinskii-Moriya interaction sample, and the Neel-like CDW type is dictated by the orientation of the kagome plane rather than the negligible magnetostatic energy associated with the small magnetization (approximately 3.957 10^-3). Additionally, the CDW, with a considerable width of 770 nm, is segmented into three 60 portions due to the six-fold anisotropy in Mn3Sn. These emphasize that CDW motion in Mn3Sn cannot be quantitatively studied using ferromagnetic frameworks. We also demonstrate that a small external field can effectively regulate CDW velocity. Our comprehensive results and theoretical analysis provide crucial guidelines for integrating antiferromagnet CDWs into functional spintronic devices.

The quantum transverse Ising model and its extensions play a critical role in various fields, such as statistical physics, quantum magnetism, quantum simulations, and mathematical physics. Although it does not suffer from the sign problem in most cases, the corresponding quantum Monte Carlo algorithm performs inefficiently, especially at a large longitudinal field. The main hindrance is the lack of loop update method which can strongly decrease the auto-correlation between Monte Carlo steps. Here, we successfully develop a loop algorithm with a novel merge-unmerge process. It demonstrates a great advantage over the state-of-the-art algorithm when implementing it to simulate the Rydberg atom chain and Kagome qubit ice. This advanced algorithm suits many systems such as Rydberg atom arrays, trapped ions, quantum materials, and quantum annealers., Comment: 5 pages, 4 figures, comments are welcome and more information at http://cqutp.org/users/xfzhang/

In the single-field model, the preheating process occurs through self-resonance of inflaton field. We study the nonlinear structures generated during preheating in the $\alpha$-attractor models and monodromy models. The potentials have a power law form $\propto\left|\phi\right|^{2n}$ near the origin and a flat region away from bottom, which are consistent with current cosmological observations. The Floquet analysis shows that potential parameters in monodromy model have a significant influence on the region of resonance bands. The analytical oscillons solution for the $\alpha$-attractor T model with parameter $n=1$ is derived using the small amplitude analysis method. Besides we investigate the formation of nonlinear structures, the equation of state and the energy transfer through the (3+1) dimensional lattice simulation. We find that the symmetric T potential and the asymmetric E potential in the $\alpha$-attractor models have similar nonlinear dynamics. And the potential parameter $n$ in monodromy model significantly influences the lifetime of transients, whereas the parameter $q$ exerts minimal impact on the nonlinear dynamics., Comment: 14 pages, 12 figures, 2 tables

Recently significant attention has been paid to magnetic-field-dependent photoluminescence (PL) features of the negatively charged nitrogen-vacancy (NV) centers in diamond. These features are used for microwave-free sensing and are indicative of the spin-bath properties in the diamond sample. Examinating the temperature dependence of the PL features allows to identify both temperature dependent and independent features, and to utilize them in diamond-based quantum sensing and dynamic nuclear polarization applications. Here, we study the thermal variability of many different features visible in a wide range of magnetic fields. To this end, we first discuss the origin of the features and tentatively assign the previously unidentified features to cross relaxation of NV center containing multi-spin systems. The experimental results are compared with theoretically predicted temperature shifts deduced from a combination of thermal expansion and electron-phonon interactions. A deeper insight into the thermal behavior of a wide array of the features may come with important consequences for various applications in high-precision NV thermometry, gyroscopes, solid-state clocks, and biomagnetic measurements., Comment: 12 pages, 16 figures

We parameterize the equation of state of late-time dark energy as $w_{\mathrm{bin}}(z)$, with three redshift bins, characterized by a constant equation of state in each bin. Then, we constrain the parameters of the $w_{\mathrm{bin}}$CDM model using datasets from DESI BAO data, Planck CMB power spectrum, ACT DR6 lensing power spectrum, and type Ia supernova distance-redshift data of Pantheon Plus/DES Y5/Union3. The significances for $w_1>-1$ is $1.9\sigma$, $2.6\sigma$ and $3.3\sigma$, and $w_2$ is consistent with $-1$ within $1\sigma$ level, while $1.6\sigma$, $1.5\sigma$ and $1.5\sigma$ for $w_3<-1$ in these three data combinations with different choices of type Ia supernova datasets, respectively. Additionally, to alleviate $H_0$ tension, we incorporate the early dark energy (EDE) model in the early-time universe and add the SH0ES absolute magnitude $M_b$ prior (or $H_0$ prior) to further constrain the $w_{\mathrm{bin}}$EDE model. In the $w_{\mathrm{bin}}$EDE model, we find a $w_{\mathrm{bin}}(z)$ pattern similar to that in the $w_{\mathrm{bin}}$CDM model. The results of the three data combinations exhibit $w_1>-1$ at $1.9\sigma$, $1.7\sigma$ and $2.9\sigma$ level, meanwhile $w_3<-1$ at $1.3\sigma$, $1.3\sigma$ and $1.3\sigma$ level, respectively. In all, our results indicate that the transition of dark energy from phantom at high redshifts to quintessence at low redshifts is not conclusive., Comment: 10 pages, 4 figures, 2 tables

Absorption estimation, the base of spectroscopy, is crucial for probing the composition and dynamics of matter. Conventional methods of estimation rely on coherent laser sources, and in turn suffer from inherent limitations in estimating weak absorption. Here we propose a new measurement strategy with correlated photons to determine the weak absorption by distinguishing the output with and without photons, dubbed as the on-off measurement. Our implementation within the strategy allows the estimation precision to reach the ultimate quantum limit. We demonstrate that absorption spectroscopy that incorporates quantum correlations is capable of estimating weak absorption down to a single-photon level, even in noisy environments, achieving a precision comparable to that obtained through several hundred photons in conventional absorption spectroscopy. By introducing the quantum correlations, our work avoids the occurrence of light-induced damage while breaking the classical inherent limitations in spectroscopy., Comment: 7 pages, 3 figures

The scaling of magnetic memory into nanometer size calls for a theoretical model to accurately predict the switching current. Previous models show large discrepancy with experiments in studying the spin-orbit torque switching of perpendicular magnet. In this work, we find that the trajectory of magnetization shows a smooth transition during the switching. This contradicts the key assumption in previous models that magnetization needs to align to the spin polarization (\sigma) before the switching occurs. We demonstrate that aligning magnetization to \sigma requires a very large current, resulting in the unsatisfactory fitting between the previous models and experiments. In contrast, the smooth transition permits a lower switching current that is comparable to experiments. Guided by this refined physical picture, we pinpoint the reversal of precession chirality as a pivotal factor for achieving deterministic switching. This insight leads to the formulation of a new analytical model that demonstrates remarkable agreement with experimental data. Our work resolves an important issue confronting the experiment and theory. We provide a clear physical picture of the current-induced magnetization switching, which is invaluable in the development of spin-orbit torque magnetic random-access memory.

We report on the first measurement of the elemental fragmentation cross sections (EFCSs) of $^{29-33}\mathrm{Si}$ on a carbon target at $\sim$230~MeV/nucleon. The experimental data covering charge changes of $\Delta Z$ = 1-4 are reproduced well by the isospin-dependent quantum molecular dynamics (IQMD) coupled with the evaporation GEMINI (IQMD+GEMINI) model. We further explore the mechanisms underlying the single-proton removal reaction in this model framework. We conclude that the cross sections from direct proton knockout exhibit a overall weak dependence on the mass number of $\mathrm{Si}$ projectiles. The proton evaporation induced after the projectile excitation significantly affects the cross sections for neutron-deficient $\mathrm{Si}$ isotopes, while neutron evaporation plays a crucial role in the reactions of neutron-rich $\mathrm{Si}$ isotopes. It is presented that the relative magnitude of one-proton and one-neutron separation energies is an essential factor that influences evaporation processes., Comment: 7 pages, 4 figures

Knowledge distillation (KD) is known as a promising solution to compress large language models (LLMs) via transferring their knowledge to smaller models. During this process, white-box KD methods usually minimize the distance between the output distributions of the two models so that more knowledge can be transferred. However, in the current white-box KD framework, the output distributions are from the respective output spaces of the two models, using their own prediction heads. We argue that the space discrepancy will lead to low similarity between the teacher model and the student model on both representation and distribution levels. Furthermore, this discrepancy also hinders the KD process between models with different vocabularies, which is common for current LLMs. To address these issues, we propose a dual-space knowledge distillation (DSKD) framework that unifies the output spaces of the two models for KD. On the basis of DSKD, we further develop a cross-model attention mechanism, which can automatically align the representations of the two models with different vocabularies. Thus, our framework is not only compatible with various distance functions for KD (e.g., KL divergence) like the current framework, but also supports KD between any two LLMs regardless of their vocabularies. Experiments on task-agnostic instruction-following benchmarks show that DSKD significantly outperforms the current white-box KD framework with various distance functions, and also surpasses existing KD methods for LLMs with different vocabularies., Comment: The camera-ready version for EMNLP 2024 main conference. 17 pages, 11 figures, code available at: https://github.com/songmzhang/DSKD

Multilingual knowledge editing (MKE) aims to simultaneously revise factual knowledge across multilingual languages within large language models (LLMs). However, most existing MKE methods just adapt existing monolingual editing methods to multilingual scenarios, overlooking the deep semantic connections of the same factual knowledge between different languages, thereby limiting edit performance. To address this issue, we first investigate how LLMs represent multilingual factual knowledge and discover that the same factual knowledge in different languages generally activates a shared set of neurons, which we call language-agnostic factual neurons. These neurons represent the semantic connections between multilingual knowledge and are mainly located in certain layers. Inspired by this finding, we propose a new MKE method by locating and modifying Language-Agnostic Factual Neurons (LAFN) to simultaneously edit multilingual knowledge. Specifically, we first generate a set of paraphrases for each multilingual knowledge to be edited to precisely locate the corresponding language-agnostic factual neurons. Then we optimize the update values for modifying these located neurons to achieve simultaneous modification of the same factual knowledge in multiple languages. Experimental results on Bi-ZsRE and MzsRE benchmarks demonstrate that our method outperforms existing MKE methods and achieves remarkable edit performance, indicating the importance of considering the semantic connections among multilingual knowledge., Comment: 12 pages, 4 figures, 7 tables

The early inspiral from stellar-mass binary black holes can emit milli-Hertz gravitational wave signals, making them detectable sources for space-borne gravitational wave missions like TianQin. However, the traditional matched filtering technique poses a significant challenge for analyzing this kind of signals, as it requires an impractically high number of templates ranging from $10^{31}$ to $10^{40}$. We propose a search strategy that involves two main parts: initially, we reduce the dimensionality of the simulated signals using incremental principal component analysis (IPCA). Subsequently we train the convolutional neural networks (CNNs) based on the compressed TianQin data obtained from IPCA, aiming to develop both a detection model and a point parameter estimation model. The compression efficiency for the trained IPCA model achieves a cumulative variance ratio of 95.6% when applied to $10^6$ simulated signals. To evaluate the performance of CNN we generate the receiver operating characteristic curve for the detection model which is applied to the test data with varying signal-to-noise ratios. At a false alarm probability of 5% the corresponding true alarm probability for signals with a signal-to-noise ratio of 50 is 86.5%. Subsequently, we introduce the point estimation model to evaluate the value of the chirp mass of corresponding sBBH signals with an error. For signals with a signal-to-noise ratio of 50, the trained point estimation CNN model can estimate the chirp mass of most test events, with a standard deviation error of 2.48 $M_{\odot}$ and a relative error precision of 0.12., Comment: 12 pages, 10 figures

The stripe phase, an intertwined order observed in high-temperature superconductors, is regarded as playing a key role in elucidating the underlying mechanism of superconductivity, especially in cuprates. Following Jan Zaanen's early scenario, the full-filled charge stripe can be taken as the interactive elastic quantum strings of holes, stabilized by $\pi$-phase shifts between neighboring magnetic domains. However, this scenario is challenging to explain, particularly in terms of electron pairing, which necessitates hole pairs. In this work, we propose a new effective model for describing the stripe phase in the hole-doped $t$-$J_z$ model. With respect to the antiferromagnetic background, the model comprises three types of color-labeled point-defects coupling to {an effective} spin field. Comparing with numerical results from large-scale density matrix renormalization group (DMRG) simulations, we find semi-quantitative agreement in local hole density, magnetic moment, and the newly proposed spectrum features of the {effective} spin field. By systematically analyzing the hole-density distribution and the scaling of groundstate energy at different system sizes, we determine the effective core radius and the effective hopping amplitude of the quantum string. Furthermore, the local pinning field can be finely adjusted to drag the quantum string, offering a potential method for detecting it in optical lattices., Comment: 10 pages, 10 figures, comments are welcome, and more information at http://cqutp.org/users/xfzhang/

GWnext 2024 was a meeting held in the Kavli Institute for Astronomy and Astrophysics at Peking University in March $4^\text{th} - 8^\text{th}$, 2024. In the meeting researchers at different career stages -- with a particular focus on early career scientists -- working on the different aspects of gravitational wave (GW) astronomy gathered to discuss the current status as well as prospects of the field. The meeting was divided into three core sessions: Astrophysics, GW Theory, and Detection. Each session consisted of introductory talks and extended discussion sessions. Moreover, there was a poster session where students could present their results. In this paper, we summarize the results presented during the meeting and present the most important outcomes.

Most recent multispectral object detectors employ a two-branch structure to extract features from RGB and thermal images. While the two-branch structure achieves better performance than a single-branch structure, it overlooks inference efficiency. This conflict is increasingly aggressive, as recent works solely pursue higher performance rather than both performance and efficiency. In this paper, we address this issue by improving the performance of efficient single-branch structures. We revisit the reasons causing the performance gap between these structures. For the first time, we reveal the information interference problem in the naive early-fusion strategy adopted by previous single-branch structures. Besides, we find that the domain gap between multispectral images, and weak feature representation of the single-branch structure are also key obstacles for performance. Focusing on these three problems, we propose corresponding solutions, including a novel shape-priority early-fusion strategy, a weakly supervised learning method, and a core knowledge distillation technique. Experiments demonstrate that single-branch networks equipped with these three contributions achieve significant performance enhancements while retaining high efficiency. Our code will be available at \url{https://github.com/XueZ-phd/Efficient-RGB-T-Early-Fusion-Detection}., Comment: This paper has been accepted by IEEE T-IV journal. Please jump to External DOI to view the official version

Modeling complex systems using standard neural ordinary differential equations (NODEs) often faces some essential challenges, including high computational costs and susceptibility to local optima. To address these challenges, we propose a simulation-free framework, called Fourier NODEs (FNODEs), that effectively trains NODEs by directly matching the target vector field based on Fourier analysis. Specifically, we employ the Fourier analysis to estimate temporal and potential high-order spatial gradients from noisy observational data. We then incorporate the estimated spatial gradients as additional inputs to a neural network. Furthermore, we utilize the estimated temporal gradient as the optimization objective for the output of the neural network. Later, the trained neural network generates more data points through an ODE solver without participating in the computational graph, facilitating more accurate estimations of gradients based on Fourier analysis. These two steps form a positive feedback loop, enabling accurate dynamics modeling in our framework. Consequently, our approach outperforms state-of-the-art methods in terms of training time, dynamics prediction, and robustness. Finally, we demonstrate the superior performance of our framework using a number of representative complex systems.

Chirality in solid-state materials has sparked significant interest due to potential applications of topologically-protected chiral states in next-generation information technology. The electrical magneto-chiral effect (eMChE), arising from relativistic spin-orbit interactions, shows great promise for developing chiral materials and devices for electronic integration. Here we demonstrate an angle-resolved eMChE in an A-B-C-C type atomic-layer superlattice lacking time and space inversion symmetry. We observe non-superimposable enantiomers of left-handed and right-handed tilted uniaxial magnetic anisotropy as the sample rotates under static fields, with the tilting angle reaching a striking 45 degree. Magnetic force microscopy and atomistic simulations correlate the tilt to the emergence and evolution of chiral spin textures. The Dzyaloshinskii-Moriya interaction lock effect in competition with Zeeman effect is demonstrated to be responsible for the angle-resolved eMChE. Our findings open up a new horizon for engineering angle-resolved magneto-chiral anisotropy, shedding light on the development of novel angle-resolved sensing or writing techniques in chiral spintronics.

To meet the demands of densely deploying communication and sensing devices in the next generation of wireless networks, integrated sensing and communication (ISAC) technology is employed to alleviate spectrum scarcity, while stochastic geometry (SG) serves as a tool for low-complexity performance evaluation. To assess network-level performance, there is a natural interaction between ISAC technology and the SG method. From ISAC network perspective, we illustrate how to leverage SG analytical framework to evaluate ISAC network performance by introducing point process distributions and stochastic fading channel models. From SG framework perspective, we summarize the unique performance metrics and research objectives of ISAC networks, thereby extending the scope of SG research in the field of wireless communications. Additionally, considering the limited discussion in the existing SG-based ISAC works in terms of distribution and channel modeling, a case study is designed to exploit topology and channel fading awareness to provide relevant network insights.

In the present study, we consider the numerical method for Toeplitz-like linear systems arising from the $d$-dimensional Riesz space fractional diffusion equations (RSFDEs). We apply the Crank-Nicolson (CN) technique to discretize the temporal derivative and apply a quasi-compact finite difference method to discretize the Riesz space fractional derivatives. For the $d$-dimensional problem, the corresponding coefficient matrix is the sum of a product of a (block) tridiagonal matrix multiplying a diagonal matrix and a $d$-level Toeplitz matrix. We develop a sine transform based preconditioner to accelerate the convergence of the GMRES method. Theoretical analyses show that the upper bound of relative residual norm of the preconditioned GMRES method with the proposed preconditioner is mesh-independent, which leads to a linear convergence rate. Numerical results are presented to confirm the theoretical results regarding the preconditioned matrix and to illustrate the efficiency of the proposed preconditioner., Comment: 17 pages, 3 figures

We interact with the world with our hands and see it through our own (egocentric) perspective. A holistic 3Dunderstanding of such interactions from egocentric views is important for tasks in robotics, AR/VR, action recognition and motion generation. Accurately reconstructing such interactions in 3D is challenging due to heavy occlusion, viewpoint bias, camera distortion, and motion blur from the head movement. To this end, we designed the HANDS23 challenge based on the AssemblyHands and ARCTIC datasets with carefully designed training and testing splits. Based on the results of the top submitted methods and more recent baselines on the leaderboards, we perform a thorough analysis on 3D hand(-object) reconstruction tasks. Our analysis demonstrates the effectiveness of addressing distortion specific to egocentric cameras, adopting high-capacity transformers to learn complex hand-object interactions, and fusing predictions from different views. Our study further reveals challenging scenarios intractable with state-of-the-art methods, such as fast hand motion, object reconstruction from narrow egocentric views, and close contact between two hands and objects. Our efforts will enrich the community's knowledge foundation and facilitate future hand studies on egocentric hand-object interactions., Comment: Accepted to ECCV 2024

In the quantum Monte Carlo (QMC) method, the Pseudo-Random Number Generator (PRNG) plays a crucial role in determining the computation time. However, the hidden structure of the PRNG may lead to serious issues such as the breakdown of the Markov process. Here, we systematically analyze the performance of the different PRNGs on the widely used QMC method -- stochastic series expansion (SSE) algorithm. To quantitatively compare them, we introduce a quantity called QMC efficiency that can effectively reflect the efficiency of the algorithms. After testing several representative observables of the Heisenberg model in one and two dimensions, we recommend using LCG as the best choice of PRNGs. Our work can not only help improve the performance of the SSE method but also shed light on the other Markov-chain-based numerical algorithms., Comment: 5 pages, 1 figure, almost published version, comments are welcome and more information at http://cqutp.org/users/xfzhang/