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Coherent wave control is of key importance across a broad range of fields such as electromagnetics, photonics, and acoustics. It enables us to amplify or suppress the outgoing waves via engineering amplitudes and phases of multiple incidences. However, within a purely spatially (temporally) engineered medium, coherent wave control requires the frequency of the associated incidences to be identical (opposite). In this work, we break this conventional constraint by generalizing coherent wave control into a spatiotemporally engineered medium, i.e., the system featuring a dynamic interface. Owing to the broken translational symmetry in space and time, both the subluminal and superluminal interfaces allow interference between scattered waves regardless of their different frequencies and wavevectors. Hence, one can flexibly eliminate the backward- or forward-propagating waves scattered from the dynamic interfaces by controlling the incident amplitudes and phases. Our work not only presents a generalized way for reshaping arbitrary waveforms but also provides a promising paradigm to generate ultrafast pulses using low-frequency signals. We have also implemented suppression of forward-propagating waves in microstrip transmission lines with fast photodiode switches.

Large Language Models (LLMs) are currently pre-trained and fine-tuned on large cloud servers. The next frontier is LLM personalization, where a foundation model can be fine-tuned with user/task-specific data. Given the sensitive nature of such private data, it is desirable to fine-tune these models on edge devices to improve user trust. However, fine-tuning on resource-constrained edge devices presents significant challenges due to substantial memory and computational demands, as well as limited infrastructure support. We observe that inference engines (e.g., ExecuTorch) can be repurposed for fine-tuning by leveraging zeroth-order (ZO) optimization, which uses multiple forward passes to approximate gradients. However, directly applying ZO methods on edge devices is impractical due to the high computational cost of multiple model perturbations required to achieve accuracy improvements. Based on these observations, we propose a memory- and computation-efficient LLM fine-tuning method for edge devices. Our approach has three key innovations: (1) We introduce a parallelized randomized gradient estimation (P-RGE) technique that achieves high parallel efficiency by leveraging outer-loop and inner-loop parallelization. This enables multiple function queries and forward passes to be executed in parallel, reducing training time. (2) We integrate P-RGE with parameter-efficient fine-tuning methods (e.g. LoRA) to further reduce computational and memory overhead. (3) We implement a P-RGE LoRA-FA module that fully supports fine-tuning with ExecuTorch. Our approach requires no modifications to ExecuTorch's runtime code, as it can be implemented with server-side code changes only. Experiments demonstrate that P-RGE achieves substantial runtime speedups and memory savings while improving fine-tuning accuracy, paving the way for practical deployment of LLMs in real-time, on-device applications., Comment: Accepted at NeurIPS 2024 ENLSP-IV workshop

To coordinate resources among multi-level stakeholders and enhance the integration of electric vehicles (EVs) into multi-microgrids, this study proposes an optimal dispatch strategy within a multi-microgrid cooperative alliance using a nuanced two-stage pricing mechanism. Initially, the strategy assesses electric energy interactions between microgrids and distribution networks to establish a foundation for collaborative scheduling. The two-stage pricing mechanism initiates with a leader-follower game, wherein the microgrid operator acts as the leader and users as followers. Subsequently, it adjusts EV tariffs based on the game's equilibrium, taking into account factors such as battery degradation and travel needs to optimize EVs' electricity consumption. Furthermore, a bi-level optimization model refines power interactions and pricing strategies across the network, significantly enhancing demand response capabilities and economic outcomes. Simulation results demonstrate that this strategy not only increases renewable energy consumption but also reduces energy costs, thereby improving the overall efficiency and sustainability of the system., Comment: Accepted by IEEE Transactions on Sustainable Energy, Paper no. TSTE-00122-2024

Tracking the time evolution of a quantum state allows one to verify the thermalization rate or the propagation speed of correlations in generic quantum systems. Inspired by the energy-time uncertainty principle, bounds have been demonstrated on the maximal speed at which a quantum state can change, resulting in immediate and practical tasks. Based on a programmable superconducting quantum processor, we test the dynamics of various emulated quantum mechanical systems encompassing single- and many-body states. We show that one can test the known quantum speed limits and that modifying a single Hamiltonian parameter allows the observation of the crossover of the different bounds on the dynamics. We also unveil the observation of minimal quantum speed limits in addition to more common maximal ones, i.e., the lowest rate of change of a unitarily evolved quantum state. Our results establish a comprehensive experimental characterization of quantum speed limits and pave the way for their subsequent study in engineered non-unitary conditions., Comment: 9 pages,4 figures + supplementary information

The advancement of industrialization has spurred the development of innovative swarm intelligence algorithms, with Lion Swarm Optimization (LSO) notable for its robustness, parallelism, simplicity, and efficiency. While LSO excels in single-objective optimization, its multi-objective variants face challenges such as poor initialization, local optima entrapment, and so on. This study proposes Dynamic Multi-Objective Lion Swarm Optimization with Multi-strategy Fusion (MF-DMOLSO) to address these limitations. MF-DMOLSO comprises three key components: initialization, swarm position update, and external archive update. The initialization unit employs chaotic mapping for uniform population distribution. The position update unit enhances behavior patterns and step size formulas for cub lions, incorporating crowding degree sorting, Pareto non-dominated sorting, and Levy flight to improve convergence speed and global search capabilities. Reference points guide convergence in higher-dimensional spaces, maintaining population diversity. An adaptive cold-hot start strategy generates a population responsive to environmental changes. The external archive update unit re-evaluates solutions based on non-domination and diversity to form the new population. Evaluations on benchmark functions showed MF-DMOLSO surpassed multi-objective particle swarm optimization, non-dominated sorting genetic algorithm II, and multi-objective lion swarm optimization, exceeding 90% accuracy for two-objective and 97% for three-objective problems. Compared to non-dominated sorting genetic algorithm III, MF-DMOLSO showed a 60% improvement. Applied to 6R robot trajectory planning, MF-DMOLSO optimized running time and maximum acceleration to 8.3s and 0.3pi rad/s^2, achieving a set coverage rate of 70.97% compared to 2% by multi-objective particle swarm optimization, thus improving efficiency and reducing mechanical dither.

Second-order topological insulators can be characterized by their bulk polarization, which is believed to be intrinsically connected to the center of the Wannier function. In this study, we demonstrate the existence of second-order topological insulators that feature a pair of partially degenerate photonic bands. These arise from the nonsymmorphic glide symmetry in an all-dielectric photonic crystal. The center of the maximally localized Wannier function (MLWF) is consistently located at the origin but is not equivalent with respect to the sum of constituent polarizations. As a result, topological corner modes can be identified by the distinctly hybridized MLWFs that truncate at the sample boundary. Through full-wave numerical simulations paired with microwave experiments, the second-order topology is clearly confirmed and characterized. These topological corner states exhibit notably unique modal symmetries, which are made possible by the inversion of the Wannier bands. Our results provide an alternative approach to explore higher-order topological physics with significant potential for applications in integrated and quantum photonics.

Microcavity-based microlasers are the kernel light sources for integrating photonics and optoelectronics. The traditional pump light frequency locking mainly utilizes a complex system with optoelectronic feedback, which requires a high-cost narrow-linewidth pump laser and limits the application of microlasers in integrated optoelectronic systems. We propose to utilize Rayleigh scattering of microcavities to lock the frequency of the pump laser to the resonant frequency of the laser microcavity with an all-optical method. While compressing the linewidth of the pump laser, it can greatly improve the long-term stability of the optically pumped microcavity laser. In the experiment, the linewidth of the semiconductor pump laser is compressed from the MHz level to the kHz level. The microcavity Brillouin laser achieves an ultra-narrow intrinsic linewidth of 100 Hz, with an ultra-low frequency noise of 35 Hz2/Hz. The constructed microlaser obtains a locking time up to 1 hour, which does not require any temperature control or vibration isolation of the laser system. This work is the first demonstration to achieve an optically pump-locked microcavity Brillouin laser, which provides a stable and reliable low-cost experimental platform for ultra-narrow linewidth lasers, precision laser sensors, microwave-photonic signal synthesizer, and optomechanical systems., Comment: 12 pages, 5 figures

Language models (LMs) have greatly propelled the research on natural language processing. However, LMs also raise concerns regarding the generation of biased or toxic content and the potential disclosure of private information from the training dataset. In this work, we present a new efficient approach, Ethos, that rectifies LMs to mitigate toxicity and bias in outputs and avoid privacy leakage. Ethos is built on task arithmetic. However, unlike current task arithmetic algorithms, Ethos distinguishes general beneficial and undesired knowledge when reconstructing task vectors. Specifically, Ethos first obtains a set of principal components from the pre-trained models using singular value decomposition. Then, by projecting the task vector onto principal components, Ethos identifies the principal components that encode general or undesired knowledge. Ethos performs negating using the task vector with undesired knowledge only, thereby minimizing collateral damage on general model utility. We demonstrate the efficacy of our approach on three different tasks: debiasing, detoxification, and memorization unlearning. Evaluations show Ethos is more effective in removing undesired knowledge and maintaining the overall model performance compared to current task arithmetic methods.

Optical-fiber-based polarization scramblers can reduce the impact of polarization sensitive performance of various optical fiber systems. Here, we propose a simple and efficient polarization scrambler based on an all optical Mach-Zehnder structure by combining polarization beam splitter and amplified fiber ring. To totally decoherence one polarization splitted beam, a fiber ring together with an amplifier are incorporated. The ratio of two orthogonal beams can be controlled by varying the amplification factor, and we observe different evolution trajectories of the output state of polarizations on Poincare sphere. When the amplification factor exceeds a certain threshold, the scrambler system exhibits chaotical behavior. A commercial single wavelength laser with linewidth of 3 MHz is utilized to characterize the scrambling performance. We found that when the sampling rate is 1.6 MSa/s, a scrambling speed up to 2000 krad/s can be obtained for the average degree of polarization being less than 0.1. We also exploit these chaotic polarization fluctuations to generate random binary number, indicating that the proposed technique is a good candidate for random bit generator.

Device-to-device (D2D) technology is one of the key research areas in 5G/6G networks, and full-duplex (FD) D2D will further enhance its spectral efficiency (SE). In recent years, deep learning approaches have shown remarkable performance in D2D resource allocation tasks. However, most schemes only model the channel state information (CSI) as an independent feature, neglecting the spatial relationships among multiple channels and users within the scenario. In this paper, we first design an objective function for FD D2D communication resource allocation, which aims to maximize the SE of D2D users while ensuring the minimal required SE of cellular users. Then, considering the complex CSI constituted by all the users in different channels as a three-dimensional vector, a centralized resource allocation model based on multi-dimensional spatial convolutional networks and attention mechanisms (SP-Conv-Att) is proposed. To alleviate the burden of base station, we develop two distributed models, Dist-Att and Dist-Att-Conv, to facilitate users to perform channel and power allocation locally, based on attention and multi-user convolutional networks respectively. Numerical results demonstrate that our models outperform traditional schemes and recent deep neural network models, significantly approximating the optimal solution computed by exhaustive algorithm with extremely low latency., Comment: 7 pages, 5 figures, 5 tables

In the present work, magnetic properties of single crystal (Fe$_{1-x}$Mn$_x$)$_2$Mo$_3$O$_8$ ($00.3$. Meanwhile, sizeable electric polarization of spin origin is commonly observed in all samples, no matter what the magnetic state is. For the samples hosting a ferrimagnetic state, square-like magnetic hysteresis loops are revealed, while the remnant magnetization and coercive field can be tuned drastically by simply varying the Mn-content or temperature. Possible coexistence of the antiferromagnetic and ferrimagnetic phases is proposed to be responsible for the remarkable modulation of magnetic properties in the samples.

Buckminsterfullerene C60 has received extensive research interest ever since its discovery. In addition to its interesting intrinsic properties of exceptional stability and electron-accepting ability, the broad chemical tunability by decoration or substitution on the C60-fullerene surface makes it a fascinating molecule. However, to date there is uncertainty about the binding location of such decorations on the C60 surface, even for a single adsorbed metal atom. In this work, we report the gas-phase synthesis of the C60V+ complex and its in-situ characterization by mass spectrometry and in-frared spectroscopy with the help of quantum chemical calculations and molecular dynamics simulations. We identify the most probable binding position of a vanadium cation on C60 above a pentagon center in eta5-fashion, demonstrate a high thermal stability for this complex, and explore the bonding nature between C60 and the vanadium cation, reveal-ing that large orbital and electrostatic interactions lie at the origin of the stability of the eta5-C60V+ complex., Comment: 29 pages (11 pages for main text and 17 pages for the supporting information)

Recent reports of a possible room-temperature superconductor called LK-99 have generated a lot of attention worldwide. In just a few days, a large amount of experimental works attempted to reproduce this sample and verify its properties. At the same time a large amount of theoretical works have also been reported. However, many experiments have drawn different conclusions, and many theoretical results are not consistent with experimental results. For one of the structures of LK-99 with the chemical formula as Pb9Cu(PO4)6O, many first-principles calculations did not consider spin-orbit coupling and concluded that it is a flat band metal. However, spin-orbit coupling is often not negligible in systems with heavy elements, and LK-99 contains a large amount of heavy element Pb. We performed calculations of electronic structure of Pb9Cu(PO4)6O with spin-orbit coupling, and the results show that it's not a metal but a semiconductor. This is consistent with many experimental results. In the ferromagnetic state Pb9Cu(PO4)6O is an indirect-bandgap semiconductor with a bandgap of 292 meV. Moreover, its conduction band is a flat band. At an electron doping level of 0.5 e/unit cell, Pb9Cu(PO4)6O becomes metallic and has a flat band with a width of only 25 meV at the Fermi level in the ferromagnetic state. While in the antiferromagnetic-A state, Pb9Cu(PO4)6O is a direct-bandgap semiconductor with a bandgap of 300 meV. As a magnetic narrowband semiconductor, Pb9Cu(PO4)6O may have potential application value in the field of optoelectronic device, photocatalytic, photodetector and spintronics device., Comment: 24 pages, 11 figures. Supporting information is included

Fullerenes, including C60, C70, and C60+, are widespread in space through their characteristic infrared vibrational features (C60+ also reveals its presence in the interstellar medium through its electronic transitions) and offer great insights into the carbon chemistry and stellar evolution. The potential existence of fullerene-related species in space has long been speculated and recently put forward by a set of laboratory experiments of C60+, C60H+, C60O+, C60OH+, C70H+, and [C60-Metal]+ complexes. The advent of the James Webb Space Telescope (JWST) provides a unique opportunity to search for these fullerene-related species in space. To facilitate JWST search, analysis, and interpretation, an accurate knowledge of their vibrational properties is essential. Here, we compile a VibFullerene database and conduct a systematic theoretical study on those species. We derive a set of range-specific scaling factors for vibrational frequencies, to account for the deficiency of density functional theory calculations in predicting the accurate frequencies. Scaling factors with low root-mean-square and median errors for the frequencies are obtained, and their performance is evaluated, from which the best-performing methods are recommended for calculating the infrared spectra of fullerene derivatives which balance the accuracy and computational cost. Finally, the recommended vibrational frequencies and intensities of fullerene derivatives are presented for future JWST detection., Comment: 19 pages, 8 figures, 5 tables. Accepted for publication in MNRAS

Whether SiC$_2$ is a parent species, that is formed in the photosphere or as a by-product of high-temperature dust formation, or a daughter species, formed in a chemistry driven by the photodestruction of parent species in the outer envelope, has been debated for a long time. Here, we analyze the ALMA observations of four SiC$_2$ transitions in the CSEs of three C-rich AGB stars (AI Vol, II Lup, and RAFGL 4211), and found that SiC$_2$ exhibits an annular, shell-like distribution in these targets, suggesting that SiC$_2$ can be a daughter species in the CSEs of carbon-rich AGB stars. The results can provide important references for future chemical models., Comment: Accepted in Frontiers in Astronomy and Space Sciences