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The programmable metasurface is regarded as one of the most promising transformative technologies for next-generation wireless system applications. Due to the lack of effective perception ability of the external electromagnetic environment, there are numerous challenges in the intelligent regulation of wireless channels, and it still relies on external sensors to reshape electromagnetic environment as desired. To address that problem, we propose an adaptive metasurface (AMS) which integrates the capabilities of acquiring wireless environment information and manipulating reflected electromagnetic (EM) waves in a programmable manner. The proposed design endows the metasurfaces with excellent capabilities to sense the complex electromagnetic field distributions around them and then dynamically manipulate the waves and signals in real time under the guidance of the sensed information, eliminating the need for prior knowledge or external inputs about the wireless environment. For verification, a prototype of the proposed AMS is constructed, and its dual capabilities of sensing and manipulation are experimentally validated. Additionally, different integrated sensing and communication (ISAC) scenarios with and without the aid of the AMS are established. The effectiveness of the AMS in enhancing communication quality is well demonstrated in complex electromagnetic environments, highlighting its beneficial application potential in future wireless systems.

With the versatile manipulation capability, programmable metasurfaces are rapidly advancing in their intelligence, integration, and commercialization levels. However, as the programmable metasurfaces scale up, their control configuration becomes increasingly complicated, posing significant challenges and limitations. Here, we propose a multiple-partition cross-modulation (MPCM) programmable metasurface to enhance the wireless communication coverage with low hardware complexity. We firstly propose an innovative encoding scheme to multiply the control voltage vectors of row-column crossing, achieving high beamforming precision in free space while maintaining low control hardware complexity and reducing memory requirements for coding sequences. We then design and fabricate an MPCM programmable metasurface to confirm the effectiveness of the proposed encoding scheme. The simulated and experimental results show good agreements with the theoretically calculated outcomes in beam scanning across the E and H planes and in free-space beam pointing. The MPCM programmable metasurface offers strong flexibility and low complexity by allowing various numbers and combinations of partition items in modulation methods, catering to diverse precision demands in various scenarios. We demonstrate the performance of MPCM programmable metasurface in a realistic indoor setting, where the transmissions of videos to specific receiver positions are successfully achieved, surpassing the capabilities of traditional programmable metasurfaces. We believe that the proposed programmable metasurface has great potentials in significantly empowering the wireless communications while addressing the challenges associated with the programmable metasurface's design and implementation.

Quantum computing presents a promising alternative for the direct simulation of quantum systems with the potential to explore chemical problems beyond the capabilities of classical methods. However, current quantum algorithms are constrained by hardware limitations and the increased number of measurements required to achieve chemical accuracy. To address the measurement challenge, techniques for grouping commuting and anti-commuting terms, driven by heuristics, have been developed to reduce the number of measurements needed in quantum algorithms on near-term quantum devices. In this work, we propose a probabilistic framework using GFlowNets to group fully (FC) or qubit-wise commuting (QWC) terms within a given Hamiltonian. The significance of this approach is demonstrated by the reduced number of measurements for the found groupings; 51% and 67% reduction factors respectively for FC and QWC partitionings with respect to greedy coloring algorithms, highlighting the potential of GFlowNets for future applications in the measurement problem. Furthermore, the flexibility of our algorithm extends its applicability to other resource optimization problems in Hamiltonian simulation, such as circuit design., Comment: 8 pages, 2 figures. Accepted for Machine Learning and the Physical Sciences Workshop, NeurIPS 2024. Submission Number: 167

Modern radar typically employs a chain architecture that consists of radio-frequency (RF) and intermediate frequency (IF) units, baseband digital signal processor, and information display. However, this architecture often results in high costs, significant hardware demands, and integration challenges. Here we propose a simplified radar architecture based on space-time-coding (STC) information metasurfaces. With their powerful capabilities to generate multiple harmonic frequencies and customize their phases, the STC metasurfaces play a key role in chirp signal generation, transmission, and echo reception. Remarkably, the receiving STC metasurface can implement dechirp processing directly on the RF level and realize the digital information outputs, which are beneficial to lower the hardware requirement at the receiving end while potentially shortening the time needed for conventional digital processing. As a proof of concept, the proposed metasurface radar is tested in a series of experiments for target detection and range/speed measurement, yielding results comparable to those obtained by conventional methods. This study provides valuable inspiration for a new radar system paradigm to combine the RF front ends and signal processors on the information metasurface platform that offers essential functionalities while significantly reducing the system complexity and cost., Comment: 25 pages, 10 figures

Chinese-style modernization involves the modernization of a large population, requiring top-level design in terms of scale and structure. The population perspective in Xi Jinping's Thought on Socialism with Chinese Characteristics for a New Era serves as the fundamental guide for population policies. The three-child policy and delayed retirement will affect the supply of labor in China and challenge the previous assessments of China's Lewis Turning Point. This study examines the rural surplus labor transfer from 2013 to 2022 based on urban and rural data. The results indicate that China's overall wage levels have continuously increased, the urban-rural income gap has narrowed, and the transfer of surplus rural labor has slowed. China has passed the first turning point and entered a transitional phase. Factors such as the level of agricultural mechanization, urbanization rate, and urban-rural income gap are more significant in influencing the transfer of surplus labor than the normal working-age population ratio. The delayed retirement policy has a more immediate impact on the supply and transfer of rural surplus labor than the three-child policy. Additionally, delayed retirement can offset the negative impact of the reduced relative surplus labor supply caused by the three-child policy, although the three-child policy could increase the future absolute surplus labor supply., Comment: Due to unresolved issues among co-authors, we have decided to withdraw the manuscript

In this article, we consider the complete independence test of high-dimensional data. Based on Chatterjee coefficient, we pioneer the development of quadratic test and extreme value test which possess good testing performance for oscillatory data, and establish the corresponding large sample properties under both null hypotheses and alternative hypotheses. In order to overcome the shortcomings of quadratic statistic and extreme value statistic, we propose a testing method termed as power enhancement test by adding a screening statistic to the quadratic statistic. The proposed method do not reduce the testing power under dense alternative hypotheses, but can enhance the power significantly under sparse alternative hypotheses. Three synthetic data examples and two real data examples are further used to illustrate the performance of our proposed methods.

Fully Homomorphic Encryption (FHE) enables computations on encrypted data, preserving confidentiality without the need for decryption. However, FHE is often hindered by significant performance overhead, particularly for high-precision and complex data like images. Due to serious efficiency issues, traditional FHE methods often encrypt images by monolithic data blocks (such as pixel rows), instead of pixels. However, this strategy compromises the advantages of homomorphic operations and disables pixel-level image processing. In this study, we address these challenges by proposing and implementing a pixel-level homomorphic encryption approach, iCHEETAH, based on the CKKS scheme. To enhance computational efficiency, we introduce three novel caching mechanisms to pre-encrypt radix values or frequently occurring pixel values, substantially reducing redundant encryption operations. Extensive experiments demonstrate that our approach achieves up to a 19-fold improvement in encryption speed compared to the original CKKS, while maintaining high image quality. Additionally, real-world image applications such as mean filtering, brightness enhancement, image matching and watermarking are tested based on FHE, showcasing up to a 91.53% speed improvement. We also proved that our method is IND-CPA (Indistinguishability under Chosen Plaintext Attack) secure, providing strong encryption security. These results underscore the practicality and efficiency of iCHEETAH, marking a significant advancement in privacy-preserving image processing at scale., Comment: 16 pages, 10 figures

Quantum computing algorithms have been shown to produce performant quantum kernels for machine-learning classification problems. Here, we examine the performance of quantum kernels for regression problems of practical interest. For an unbiased benchmarking of quantum kernels, it is necessary to construct the most optimal functional form of the classical kernels and the most optimal quantum kernels for each given data set. We develop an algorithm that uses an analog of the Bayesian information criterion to optimize the sequence of quantum gates used to estimate quantum kernels for Gaussian process models. The algorithm increases the complexity of the quantum circuits incrementally, while improving the performance of the resulting kernels, and is shown to yield much higher model accuracy with fewer quantum gates than a fixed quantum circuit ansatz. We demonstrate that quantum kernels thus obtained can be used to build accurate models of global potential energy surfaces (PES) for polyatomic molecules. The average interpolation error of the six-dimensional PES obtained with a random distribution of 2000 energy points is 16 cm$^{-1}$ for H$_3$O$^+$, 15 cm$^{-1}$ for H$_2$CO and 88 cm$^{-1}$ for HNO$_2$. We show that a compositional optimization of classical kernels for Gaussian process regression converges to the same errors. This indicates that quantum kernels can achieve the same, though not better, expressivity as classical kernels for regression problems.

Database Management Systems (DBMSs) are vital components in modern data-driven systems. Their complexity often leads to logic bugs, which are implementation errors within the DBMSs that can lead to incorrect query results, data exposure, unauthorized access, etc., without necessarily causing visible system failures. Existing detection employs two strategies: rule-based bug detection and coverage-guided fuzzing. In general, rule specification itself is challenging; as a result, rule-based detection is limited to specific and simple rules. Coverage-guided fuzzing blindly explores code paths or blocks, many of which are unlikely to contain logic bugs; therefore, this strategy is cost-ineffective. In this paper, we design SQLaser, a SQL-clause-guided fuzzer for detecting logic bugs in DBMSs. Through a comprehensive examination of most existing logic bugs across four distinct DBMSs, excluding those causing system crashes, we have identified 35 logic bug patterns. These patterns manifest as certain SQL clause combinations that commonly result in logic bugs, and behind these clause combinations are a sequence of functions. We therefore model logic bug patterns as error-prone function chains (ie, sequences of functions). We further develop a directed fuzzer with a new path-to-path distance-calculation mechanism for effectively testing these chains and discovering additional logic bugs. This mechanism enables SQLaser to swiftly navigate to target sites and uncover potential bugs emerging from these paths. Our evaluation, conducted on SQLite, MySQL, PostgreSQL, and TiDB, demonstrates that SQLaser significantly accelerates bug discovery compared to other fuzzing approaches, reducing detection time by approximately 60%.

Testing a program's capability to effectively handling errors is a significant challenge, given that program errors are relatively uncommon. To solve this, Software Fault Injection (SFI)-based fuzzing integrates SFI and traditional fuzzing, injecting and triggering errors for testing (error handling) code. However, we observe that current SFI-based fuzzing approaches have overlooked the correlation between paths housing error points. In fact, the execution paths of error points often share common paths. Nonetheless, Fuzzers usually generate test cases repeatedly to test error points on commonly traversed paths. This practice can compromise the efficiency of the fuzzer(s). Thus, this paper introduces HuntFUZZ, a novel SFI-based fuzzing framework that addresses the issue of redundant testing of error points with correlated paths. Specifically, HuntFUZZ clusters these correlated error points and utilizes concolic execution to compute constraints only for common paths within each cluster. By doing so, we provide the fuzzer with efficient test cases to explore related error points with minimal redundancy. We evaluate HuntFUZZ on a diverse set of 42 applications, and HuntFUZZ successfully reveals 162 known bugs, with 62 of them being related to error handling. Additionally, due to its efficient error point detection method, HuntFUZZ discovers 7 unique zero-day bugs, which are all missed by existing fuzzers. Furthermore, we compare HuntFUZZ with 4 existing fuzzing approaches, including AFL, AFL++, AFLGo, and EH-FUZZ. Our evaluation confirms that HuntFUZZ can cover a broader range of error points, and it exhibits better performance in terms of bug finding speed.

Minifilaments are widespread small-scale structures in the solar atmosphere. To better understand their formation and eruption mechanisms, we investigate the entire life of a sigmoidal minifilament located below a large quiescent filament observed by BBSO/GST on 2015 August 3. The H{\alpha} structure initially appears as a group of arched threads, then transforms into two J-shaped arcades, and finally forms a sigmoidal shape. SDO/AIA observations in 171{\AA} show that two coronal jets occur around the southern footpoint of the minifilament before the minifilament eruption. The minifilament eruption starts from the southern footpoint, then interacts with the overlying filament and fails. The aforementioned observational changes correspond to three episodes of flux cancellations observed by SDO/HMI. Unlike previous studies, the flux cancellation occurs between the polarity where southern footpoint of the minifilament is rooted in and an external polarity. We construct two magnetic field models before the eruption using the flux rope insertion method, and find an hyperbolic flux tube (HFT) above the flux cancellation site. The observation and modeling results suggest that the eruption is triggered by the external magnetic reconnection between the core field of the minifilament and the external fields due to flux cancellations. This study reveals a new triggering mechanism for minifilament eruptions and a new relationship between minifilament eruptions and coronal jets.

Interlayer magnetic interactions play a pivotal role in determining the magnetic arrangement within van der Waals (vdW) magnets, and the remarkable tunability of these interactions through applied pressure further enhances their significance. Here, we investigate NiI2 flakes, a representative vdW magnet, under hydrostatic pressures up to 11 GPa. We reveal a notable increase in magnetic transition temperatures for both helimagnetic and antiferromagnetic states, and find that a reversible transition from helimagnetic to antiferromagnetic (AFM) phases at approximately 7 GPa challenges established theoretical and experimental expectations. While the increase in transition temperature aligns with pressure-enhanced overall exchange interaction strengths, we identify the significant role of the second-nearest neighbor interlayer interaction, which competes with intra-layer frustration and favors the AFM state as demonstrated in the Monte Carlo simulations. Experimental and simulated results converge on the existence of an intermediate helimagnetic ordered state in NiI2 before transitioning to the AFM state. These findings underscore the pivotal role of interlayer interactions in shaping the magnetic ground state, providing fresh perspectives for innovative applications in nanoscale magnetic device design.

The exchange bias (EB) effect, pivotal in magnetic data storage and sensing devices, has been observed not only in interfacial regions but also in intrinsic ferromagnetic materials. Here, we've uncovered a robust and stable exchange bias effect within the layered van der Waals (vdW) ferromagnet VI3 employing magnetic circular dichroism microscopy. At 10 K, we observed a significant exchange field of approximately 0.1 T, accompanied by random shifts (positive or negative relative to zero magnetic field) after zero-field cooling. Notably, this effect is effectively controllable after field cooling, with shift direction opposing the applied magnetic field. The presence of strong magnetic anisotropic energy within VI3 results in larger coercivity-bound magnetic domains. These domains dictate the neighboring ferromagnetic alignment and induce shifts in the hysteresis loop. Our study not only contributes to comprehending fundamental nanoscale magnetic interactions but also sheds light on emergent phenomena within layered van der Waals magnets.

In this paper, we present the imaging and spectroscopic observations of the simultaneous horizontal and vertical large-amplitude oscillation of a quiescent filament triggered by an EUV wave on 2022 October 02. Particularly, the filament oscillation involved winking phenomenon in Ha images and horizontal motions in EUV images. Originally, a filament and its overlying loops across AR 13110 and 13113 erupted with a highly inclined direction, resulting in an X1.0 flare and a non-radial CME. The fast lateral expansion of loops excited an EUV wave and the corresponding Moreton wave propagating northward. Once the EUV wavefront arrived at the quiescent filament, the filament began to oscillate coherently along the horizontal direction and the winking filament appeared concurrently in Ha images. The horizontal oscillation involved an initial amplitude of 10.2 Mm and a velocity amplitude of 46.5 km/s, lasting for 3 cycles with a period of 18.2 minutes and a damping time of 31.1 minutes. The maximum Doppler velocities of the oscillating filament are 18 km/s (redshift) and 24 km/s (blueshift), which was derived from the spectroscopic data provided by CHASE/HIS. The three-dimensional velocity of the oscillation is determined to be 50 km/s at an angle of 50 to the local photosphere plane. Based on the wave-filament interaction, the minimum energy of the EUV wave is estimated to be 2.7 10 20 J. Furthermore, this event provides evidence that Moreton wavesshould be excited by the highly inclined eruptions.

Hot channels (HCs) are prevalent in the solar corona and play a critical role in driving flares and CMEs. In this paper, we estimate the energy contents of an X1.4 eruptive flare with a fast CME generated by a HC eruption on 2011 September 22. Originating from NOAA AR11302, the HC is the most dramatic feature in 131 and 94 {\AA} images observed by SDO/AIA. The flare is simultaneously observed by SDO/AIA, RHESSI, and STEREO-B/EUVI. The CME is simultaneously detected by the white-light coronagraphs of SOHO/LASCO and STEREO-B/COR1. Using multiwavelength and multiview observations of the eruption, various energy components of the HC, flare, and CME are calculated. The thermal and kinetic energies of the HC are (1.77$\pm$0.61)$\times10^{30}$ erg and (2.90$\pm$0.79)$\times10^{30}$ erg, respectively. The peak thermal energy of the flare and total radiative loss of SXR-emitting plasma are (1.63$\pm$0.04)$\times10^{31}$ erg and (1.03$-$1.31)$\times10^{31}$ erg, respectively. The ratio between the thermal energies of HC and flare is 0.11$\pm$0.03, suggesting that thermal energy of the HC is not negligible. The kinetic and potential energies of the CME are (3.43$\pm$0.94)$\times10^{31}$ erg and (2.66$\pm$0.49)$\times10^{30}$ erg, yielding a total energy of (3.69$\pm$0.98)$\times10^{31}$ erg for the CME. Continuous heating of the HC is required to balance the rapid cooling by heat conduction, which probably originate from intermittent magnetic reconnection at the flare current sheet. Our investigation may provide insight into the buildup, release, and conversion of energies in large-scale solar eruptions., Comment: 11 pages, 9 figures, accepted for publication by ApJ

In this paper, we carry out a detailed analysis of the M1.6 class eruptive flare occurring in NOAA active region 13078 on 2022 August 19. The flare is associated with a fast coronal mass ejection (CME) propagating in the southwest direction with an apparent speed of $\sim$926 km s$^{-1}$. Meanwhile, a shock wave is driven by the CME at the flank. The eruption of CME generates an extreme-ultraviolet (EUV) wave expanding outward from the flare site with an apparent speed of $\geq$200 km s$^{-1}$. As the EUV wave propagates eastward, it encounters and interacts with the low-lying adjacent coronal loops (ACLs), which are composed of two loops. The compression of EUV wave results in contraction, expansion, and transverse vertical oscillations of ACLs. The commencements of contraction are sequential from western to eastern footpoints and the contraction lasts for $\sim$15 minutes. The speeds of contraction lie in the range of 13$-$40 km s$^{-1}$ in 171 {\AA} and 8$-$54 km s$^{-1}$ in 193 {\AA}. A long, gradual expansion follows the contraction at lower speeds. Concurrent vertical oscillations are superposed on contraction and expansion of ACLs. The oscillations last for 2$-$9 cycles and the amplitudes are $\leq$4 Mm. The periods are between 3 to 12 minutes with an average value of 6.7 minutes. The results show rich dynamics of coronal loops., Comment: 10 pages, 10 figures, accepted for publication in ApJ

Kernel models of potential energy surfaces (PES) for polyatomic molecules are often restricted by a specific choice of the kernel function. This can be avoided by optimizing the complexity of the kernel function. For regression problems with very expensive data, the functional form of the model kernels can be optimized in the Gaussian process (GP) setting through compositional function search guided by the Bayesian information criterion. However, the compositional kernel search is computationally demanding and relies on greedy strategies, which may yield sub-optimal kernels. An alternative strategy of increasing complexity of GP kernels treats a GP as a Bayesian neural network (NN) with a variable number of hidden layers, which yields NNGP models. Here, we present a direct comparison of GP models with composite kernels and NNGP models for applications aiming at the construction of global PES for polyatomic molecules. We show that NNGP models of PES can be trained much more efficiently and yield better generalization accuracy without relying on any specific form of the kernel function. We illustrate that NNGP models trained by distributions of energy points at low energies produce accurate predictions of PES at high energies. We also illustrate that NNGP models can extrapolate in the input variable space by building the free energy surface of the Heisenberg model trained in the paramagnetic phase and validated in the ferromagnetic phase. By construction, composite kernels yield more accurate models than kernels with a fixed functional form. Therefore, by illustrating that NNGP models outperform GP models with composite kernels, our work suggests that NNGP models should be a preferred choice of kernel models for PES.