Tensor-network-assisted variational quantum algorithm

Near-term quantum devices generally suffer from shallow circuit depth and hence limited expressivity due to noise and decoherence. To address this, we propose tensor-network-assisted parametrized quantum circuits, which concatenate a classical tensor-network operator with a quantum circuit to effectively increase the circuit's expressivity without requiring a physically deeper circuit. We present a framework for tensor-network-assisted variational quantum algorithms that can solve quantum many-body problems using shallower quantum circuits. We demonstrate the efficiency of this approach by considering two examples of unitary matrix-product operators and unitary tree tensor networks, showing that they can both be implemented efficiently. Through numerical simulations, we show that the expressivity of these circuits is greatly enhanced with the assistance of tensor networks. We apply our method to two-dimensional Ising models and one-dimensional time-crystal Hamiltonian models with up to 16 qubits and demonstrate that our approach consistently outperforms conventional methods using shallow quantum circuits.Comment: 12 pages, 8 figures, 37 reference

Calibration of the Radical Installation Limit Error of the Accelerometer in the Gravity Gradient Instrument

Gravity gradient instrument (GGI) is the core of the gravity gradiometer, so the structural error of the sensor has a great impact on the measurement results. In order not to affect the aimed measurement accuracy, limit error is required in the installation of the accelerometer. In this paper, based on the established measuring principle model, the radial installation limit error is calibrated, which is taken as an example to provide a method to calculate the other limit error of the installation under the premise of ensuring the accuracy of the measurement result. This method provides the idea for deriving the limit error of the geometry structure of the sensor, laying the foundation for the mechanical precision design and physical design

Interleukin-17 Inhibits Adult Hippocampal Neurogenesis

Interleukin 17(A) (IL-17) is a potent pro-inflammatory cytokine that acts as a central regulator of inflammatory response within the brain, but its physiological roles under non-inflammatory conditions remain elusive. Here we report that endogenous IL-17 ablates neurogenesis in the adult dentate gyrus (DG) of hippocampus. Genetic deletion of IL-17 increased the number of adult-born neurons in the DG. Further, we found that IL-17 deletion altered cytokine network, facilitated basal excitatory synaptic transmission, enhanced intrinsic neuronal excitability, and increased expression of proneuronal genes in neuronal progenitor cells (NPCs). Our findings suggest a profound role of IL-17 in the negative regulation of adult hippocampal neurogenesis under physiology conditions

Weight-based Channel-model Matrix Framework provides a reasonable solution for EEG-based cross-dataset emotion recognition

Cross-dataset emotion recognition as an extremely challenging task in the field of EEG-based affective computing is influenced by many factors, which makes the universal models yield unsatisfactory results. Facing the situation that lacks EEG information decoding research, we first analyzed the impact of different EEG information(individual, session, emotion and trial) for emotion recognition by sample space visualization, sample aggregation phenomena quantification, and energy pattern analysis on five public datasets. Based on these phenomena and patterns, we provided the processing methods and interpretable work of various EEG differences. Through the analysis of emotional feature distribution patterns, the Individual Emotional Feature Distribution Difference(IEFDD) was found, which was also considered as the main factor of the stability for emotion recognition. After analyzing the limitations of traditional modeling approach suffering from IEFDD, the Weight-based Channel-model Matrix Framework(WCMF) was proposed. To reasonably characterize emotional feature distribution patterns, four weight extraction methods were designed, and the optimal was the correction T-test(CT) weight extraction method. Finally, the performance of WCMF was validated on cross-dataset tasks in two kinds of experiments that simulated different practical scenarios, and the results showed that WCMF had more stable and better emotion recognition ability.Comment: 18 pages, 12 figures, 8 table

Mathematical Modeling of the Working Principle of Gravity Gradient Instrument

Gravity field is of great significance in geoscience, national economy and national security, and gravitational gradient measurement has been extensively studied due to its higher accuracy than gravity measurement. Gravity gradient sensor, being one of core devices of the gravity gradient instrument, plays a key role in measuring accuracy. Therefore, this paper starts from analyzing the working principle of the gravity gradient sensor by Newton's law, and then considers the relative motion between inertial and non-inertial systems to build a relatively adequate mathematical model, laying a foundation for the measurement error calibration, measurement accuracy improvement

Estimation of Dry Matter and N Nutrient Status of Choy Sum by Analyzing Canopy Images and Plant Height Information

The estimation accuracy of plant dry matter by spectra- or remote sensing-based methods tends to decline when canopy coverage approaches closure; this is known as the saturation problem. This study aimed to enhance the estimation accuracy of plant dry matter and subsequently use the critical nitrogen dilution curve (CNDC) to diagnose N in Choy Sum by analyzing the combined information of canopy imaging and plant height. A three-year experiment with different N levels (0, 25, 50, 100, 150, and 200 kg center dot ha(-1)) was conducted on Choy Sum. Variables of canopy coverage (CC) and plant height were used to build the dry matter and N estimation model. The results showed that the yields of N-0 and N-25 were significantly lower than those of high-N treatments (N-50, N-100, N-150, and N-200) for all three years. The variables of CC x Height had a significant linear relationship with dry matter, with R-2 values above 0.87. The good performance of the CC x Height-based model implied that the saturation problem of dry matter prediction was well-addressed. By contrast, the relationship between dry matter and CC was best fitted by an exponential function. CNDC models built based on CC x Height information could satisfactorily differentiate groups of N deficiency and N abundance treatments, implying their feasibility in diagnosing N status. N application rates of 50-100 kgN/ha are recommended as optimal for a good yield of Choy Sum production in the study region

Using crop intercepted solar radiation and vegetation index to estimate dry matter yield of Choy Sum

An accurate assessment of vegetable yield is essential for agricultural production and management. One approach to estimate yield with remote sensing is via vegetation indices, which are selected in a statistical and empirical approach, rather than a mechanistic way. This study aimed to estimate the dry matter of Choy Sum by both a causality-guided intercepted radiation-based model and a spectral reflectance-based model and compare their performance. Moreover, the effect of nitrogen (N) rates on the radiation use efficiency (RUE) of Choy Sum was also evaluated. A 2-year field experiment was conducted with different N rate treatments (0 kg/ha, 25 kg/ha, 50 kg/ha, 100 kg/ha, 150 kg/ha, and 200 kg/ha). At different growth stages, canopy spectra, photosynthetic active radiation, and canopy coverage were measured by RapidScan CS-45, light quantum sensor, and camera, respectively. The results reveal that exponential models best match the connection between dry matter and vegetation indices, with coefficients of determination (R2) all below 0.80 for normalized difference red edge (NDRE), normalized difference vegetation index (NDVI), red edge ratio vegetation index (RERVI), and ratio vegetation index (RVI). In contrast, accumulated intercepted photosynthetic active radiation (Aipar) showed a significant linear correlation with the dry matter of Choy Sum, with root mean square error (RMSE) of 9.4 and R2 values of 0.82, implying that the Aipar-based estimation model performed better than that of spectral-based ones. Moreover, the RUE of Choy Sum was significantly affected by the N rate, with 100 kg N/ha, 150 kg N/ha, and 200 kg N/ha having the highest RUE values. The study demonstrated the potential of Aipar-based models for precisely estimating the dry matter yield of vegetable crops and understanding the effect of N application on dry matter accumulation of Choy Sum

Clinical Value of Spectral Imaging Combined with MAR for CTA after Embolization of Intracranial Aneurysms

Objective: To evaluate the application value of combining spectral imaging and metal artifact reduction (MAR) in head and neck CTA after the embolization of intracranial aneurysms. Methods: We collected 37 patients who experienced embolization of intracranial aneurysms then received spectral imaging of head and neck CTA. Monochromatic images with energy ranging from 70～140 keV, 120 kVp-like mixed energic images, 70～140 keV MAR images, and 120 kVp-like MAR images were generated. The region of interest was placed on the area near the coil and with the most serious metal artifact. CT attenuation and standard deviation were measured, and artifact index (AI) and signal-noise ratio (SNR) were calculated. Two radiologists independently subjectively evaluated the metal artifact and the display of surrounding vessels using Likert 5 scales. The subjective scores and objective parameters between MAR and non-MAR images were compared. The Wilcoxon ranking test, paired sample t test, and independent sample t test were utilized to compare parameters between the groups. Results: MAR images had significantly lower AI than did non-MAR images for all eight monochromatic energies. When energies ranged from 80~110 keV, SNR was higher for MAR images than for non-MAR images, and the difference was statistically significant. With same energies, MAR images had higher artifact and vessel display scores than did non-MAR images. For non-MAR images, the different coil diameters did not make a statistical difference in AI and vessel display scores. For MAR images, a larger coil diameter (>8.79 mm) led to higher AI and lower vessel display scores than did normal diameters (≤8.79 mm). Conclusion: The combination of spectral imaging and MAR could effectively reduce the metal artifact of implants for the embolization of intracranial aneurysms and improve the surrounding vessel display. Moreover, the metal artifact reduction effect was more significant for the coils with smaller diameters

Constructional Meaning and Inheritance in Mandarin Chinese LVCs: The Case of the GIVE Group

This study focuses on aspectual properties of three light verb constructions in Mandarin Chinese involving the verbs jiyu, yuyi and jiayi, all meaning ‘give’. These verbs combine with an action nominal to form a complex predicate, but unlike jiyu and yuyi, the jiayi-construction is incompatible with any aspectual markers, as evidenced by my investigation into a million-word Mandarin corpus. Assuming that light verbs typically follow the grammaticalisation path from independent verbs to grammatical morphemes, I propose that the three GIVE verbs are at different stages of grammaticalisation. The verb jiayi is the most grammaticalised item. However, jiyu and yuyi have not fully grammaticalised as aspectual morphemes, so aspectuality has to be encoded by means of aspectual markers such as the perfective markers -le and -guo. Taking into account their different aspectual properties, this study gives a constructional presentation on the family of GIVE light verb constructions, which demonstrates both conventional meaning common to all GIVE light verb constructions and idiosyncratic features particular to each light verb construction

Using crop intercepted solar radiation and vegetation index to estimate dry matter yield of Choy Sum

An accurate assessment of vegetable yield is essential for agricultural production and management. One approach to estimate yield with remote sensing is via vegetation indices, which are selected in a statistical and empirical approach, rather than a mechanistic way. This study aimed to estimate the dry matter of Choy Sum by both a causality-guided intercepted radiation-based model and a spectral reflectance-based model and compare their performance. Moreover, the effect of nitrogen (N) rates on the radiation use efficiency (RUE) of Choy Sum was also evaluated. A 2-year field experiment was conducted with different N rate treatments (0 kg/ha, 25 kg/ha, 50 kg/ha, 100 kg/ha, 150 kg/ha, and 200 kg/ha). At different growth stages, canopy spectra, photosynthetic active radiation, and canopy coverage were measured by RapidScan CS-45, light quantum sensor, and camera, respectively. The results reveal that exponential models best match the connection between dry matter and vegetation indices, with coefficients of determination (R2) all below 0.80 for normalized difference red edge (NDRE), normalized difference vegetation index (NDVI), red edge ratio vegetation index (RERVI), and ratio vegetation index (RVI). In contrast, accumulated intercepted photosynthetic active radiation (Aipar) showed a significant linear correlation with the dry matter of Choy Sum, with root mean square error (RMSE) of 9.4 and R2 values of 0.82, implying that the Aipar-based estimation model performed better than that of spectral-based ones. Moreover, the RUE of Choy Sum was significantly affected by the N rate, with 100 kg N/ha, 150 kg N/ha, and 200 kg N/ha having the highest RUE values. The study demonstrated the potential of Aipar-based models for precisely estimating the dry matter yield of vegetable crops and understanding the effect of N application on dry matter accumulation of Choy Sum