Weili Xiong - RankMe

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Conditional Invertible Neural Network-Based Fault Detection. IEEE Trans. Control. Syst. Technol. Wenxin Sun, Xudong Shi, Sudao He, Weili Xiong, Hongtian Chen, Biao Huang
2026	J	`jnl`	A conditional invertible neural network-based particle filter. Autom. Wenxin Sun, Weili Xiong, Biao Huang, Hongtian Chen
2026	J	`jnl`	Discriminative and statistical transfer learning-based soft sensors under multiple working conditions. Trans. Inst. Meas. Control Jikun Zhu, Xiao Wang, Weili Xiong
2026	J	`jnl`	GraphSiamese: Novel spatial-temporal slow feature fusion network for industrial process quality prediction. Appl. Soft Comput. Jiayu Wang, Weili Xiong, Le Yao, Xiaoyu Jiang, Xiaohui Cui, Wei Yu, Brent R. Young
2026	J	`jnl`	Self-Constrained Weighted Garrote Ordered Memory Network With Adaptive Distribution Learning for Industrial Soft Sensor. IEEE Trans Autom. Sci. Eng. Lin Sui, Xudong Shi, William Holderbaum, Weili Xiong
2026	J	`jnl`	Speed-Adaptive Gated Recurrent Unit With Weight Dual-Garrote Regularization for Industrial Soft Sensing. IEEE Trans. Ind. Informatics Lin Sui, Xudong Shi, Kaiji Liao, Weili Xiong
2025	J	`jnl`	A Spatiotemporal Masked Pretraining Framework for Nonlinear Dynamic Soft Sensor Development. IEEE Trans. Instrum. Meas. Xudong Shi, Kangping Du, Wenxin Sun, Weili Xiong
2025	J	`jnl`	A Temporal Convolutional-Based Kolmogorov-Arnold Network for Industrial Soft Sensor Modeling. IEEE Trans. Instrum. Meas. Huanqi Sun, Wentao Liu, Fengrong Zeng, Lin Sui, Le Yao, Weili Xiong
2025	J	`jnl`	A deep patch network with spatiotemporal meta-parameter learning for soft sensor modeling of industrial processes. Eng. Appl. Artif. Intell. Xudong Shi, Kangping Du, Weili Xiong, Humberto Morales, Adriana Amicarelli
2025	J	`jnl`	Deep Spatial-Temporal Slow Feature Transfer Network for Multimode Chemical Process Soft Sensing on Imbalanced Data. IEEE Trans. Ind. Informatics Jiayu Wang, Le Yao, Weili Xiong, Xiaohui Cui, Wei Yu, Brent R. Young
2025	J	`jnl`	Maximum Likelihood-Based Neural Network for Monitoring Wastewater Treatment Processes. IEEE Trans. Instrum. Meas. Wentao Liu, Huanqi Sun, Wenxin Sun, Weili Xiong
2025	J	`jnl`	OSF-JS: Orthonormal Slow Feature-Based Quality-Related Data Decomposition Network With Jensen-Shannon Divergence for Chemical Processes Incipient Fault Detection. IEEE Trans. Instrum. Meas. Jiayu Wang, Le Yao, Weili Xiong, Xiaohui Cui, Wei Yu, Brent R. Young
2025	J	`jnl`	Online identification for output-error models with random time delays based on auxiliary model and recursive expectation maximization algorithm. Digit. Signal Process. Ronghuan Li, Junxia Ma, Yujie Ma, Weili Xiong
2025	J	`jnl`	Parameter estimation method for separable fractional-order Hammerstein nonlinear systems based on the on-line measurements. Appl. Math. Comput. Junwei Wang, Weili Xiong, Feng Ding, Yihong Zhou, Erfu Yang
2025	J	`jnl`	Semi-Supervised Probabilistic Learning Network for Soft Sensor Modeling With Partially Labeled Data. IEEE Trans Autom. Sci. Eng. Xudong Shi, Ronghuan Li, Humberto Morales, Adriana Amicarelli, Wangya Huang, Weili Xiong
2025	J	`jnl`	Time-Aware Rotary Transformer for Soft Sensing of Irregularly Sampled Industrial Time Sequences. IEEE Internet Things J. Xudong Shi, Kangping Du, Weili Xiong, Humberto Morales
2024	J	`jnl`	A Novel CVAE-Based Sequential Monte Carlo Framework for Dynamic Soft Sensor Applications. IEEE Trans. Ind. Informatics Wenxin Sun, Weili Xiong, Hongtian Chen, Ranjith Chiplunkar, Biao Huang
2024	J	`jnl`	An improved transfer learning approach based on geodesic flow kernel for multiphase batch process soft sensor modeling. Trans. Inst. Meas. Control Jikun Zhu, Weili Xiong
2024	J	`jnl`	Fault Detection in Wastewater Treatment Process Using Broad Slow Feature Neural Network With Incremental Learning Ability. IEEE Trans. Ind. Informatics Peng Chang, Ying Xu, FanChao Meng, Weili Xiong
2024	J	`jnl`	Novel Semi-Supervised Deep Probabilistic Slow Feature Extraction for Online Chemical Process Soft Sensing Application. IEEE Trans. Instrum. Meas. Jiayu Wang, Le Yao, Weili Xiong
2024	J	`jnl`	Novel Two-Stream Deep Slow and Nonstationary Fast Feature Extraction for Chemical Process Soft Sensing Application. IEEE Trans. Ind. Informatics Jiayu Wang, Le Yao, Lin Sui, Weili Xiong
2023	J	`jnl`	Digital twins-based process monitoring for wastewater treatment processes. Reliab. Eng. Syst. Saf. Wentao Liu, Sudao He, Jianpeng Mou, Ting Xue, Hongtian Chen, Weili Xiong
2023	J	`jnl`	Incremental learning for Lagrangian ε-twin support vector regression. Soft Comput. Binjie Gu, Jie Cao, Feng Pan, Weili Xiong
2023	J	`jnl`	Input Variable Selection and Structure Optimization for LSTM-Based Soft Sensor With a Dual Nonnegative Garrote Approach. IEEE Trans. Instrum. Meas. Lin Sui, Kai Sun, Junxia Ma, Jiayu Wang, Weili Xiong
2021	J	`jnl`	Dynamic Soft Sensor Modeling for Time Sequence Data Based on Convolution Filters. IEEE Trans. Control. Syst. Technol. Weili Xiong, Wenxin Sun
2020	J	`jnl`	Adaptive ensemble learning strategy for semi-supervised soft sensing. J. Frankl. Inst. Xudong Shi, Weili Xiong
2020	J	`jnl`	Multi-Block Fault Detection for Plant-Wide Dynamic Processes Based on Fault Sensitive Slow Features and Support Vector Data Description. IEEE Access Chao Zhai, Xiaochen Sheng, Weili Xiong
2020	J	`jnl`	Smart Soft Sensor Design with Hierarchical Sampling Strategy of Ensemble Gaussian Process Regression for Fermentation Processes. Sensors Xiaochen Sheng, Junxia Ma, Weili Xiong
2019	J	`jnl`	Multi-mode process monitoring based on multi-block information extraction PCA method with local neighbourhood standardisation. Int. J. Model. Identif. Control. Bingbin Gu, Weili Xiong
2019	J	`jnl`	Online soft sensing method based on improved weighted Gaussian model. Int. J. Model. Identif. Control. Xiaoqing Che, Weili Xiong
2019	J	`jnl`	Robust identification of nonlinear time-delay system in state-space form. J. Frankl. Inst. Xin Liu, Xianqiang Yang, Pengbo Zhu, Weili Xiong
2019	J	`jnl`	Sliding Window Iterative Identification of Systems With Asymmetric Preload Nonlinearity Based on the Key Term Separation. IEEE Access Junxia Ma, Qiulin Fei, Weili Xiong
2018	J	`jnl`	A robust global approach for LPV FIR model identification with time-varying time delays. J. Frankl. Inst. Xin Liu, Xianqiang Yang, Weili Xiong
2018	J	`jnl`	Expectation maximization estimation algorithm for Hammerstein models with non-Gaussian noise and random time delay from dual-rate sampled-data. Digit. Signal Process. Junxia Ma, Jing Chen, Weili Xiong, Feng Ding
2018	J	`jnl`	Parameter identification of nonlinear multirate time-delay system with uncertain output delays. Trans. Inst. Meas. Control Xianqiang Yang, Weili Xiong, Zeyuan Wang, Xin Liu
2018	J	`jnl`	Soft sensor modeling with a selective updating strategy for Gaussian process regression based on probabilistic principle component analysis. J. Frankl. Inst. Weili Xiong, Xudong Shi
2017	J	`jnl`	A Computationally Efficient Received Signal Strength Based Localization Algorithm in Closed-Form for Wireless Sensor Network. Neural Process. Lett. Xinrong Zhang, Weili Xiong, Baoguo Xu
2017	J	`jnl`	Robust identification of Wiener time-delay system with expectation-maximization algorithm. J. Frankl. Inst. Xianqiang Yang, Weili Xiong, Junxia Ma, Zeyuan Wang
2016	J	`jnl`	Data filtering based forgetting factor stochastic gradient algorithm for Hammerstein systems with saturation and preload nonlinearities. J. Frankl. Inst. Junxia Ma, Weili Xiong, Feng Ding, Ahmed Alsaedi, Tasawar Hayat
2016	J	`jnl`	JITL based MWGPR soft sensor for multi-mode process with dual-updating strategy. Comput. Chem. Eng. Weili Xiong, Wei Zhang, Baoguo Xu, Biao Huang
2015	—	`conf`	Adaptive soft sensor for online prediction based on enhanced moving window GPR. ICCAIS Wei Zhang, Yanjun Li, Weili Xiong, Baoguo Xu
2015	—	`conf`	Design and simulation for sensor network performance evaluation system based on Matlab GUI. ICCAIS Shucai Zhu, Ningning Qin, Weili Xiong, Xin Zheng
2013	J	`jnl`	An iterative numerical algorithm for modeling a class of Wiener nonlinear systems. Appl. Math. Lett. Weili Xiong, Junxia Ma, Ruifeng Ding
2012	J	`jnl`	D-S Theory Based on an Improved PSO for Data Fusion. J. Networks Peiyi Zhu, Weili Xiong, Ningning Qin, Baoguo Xu
2012	J	`jnl`	Least-Squares Parameter Estimation Algorithm for a Class of Input Nonlinear Systems. J. Appl. Math. Weili Xiong, Wei Fan, Rui Ding
2010	—	`conf`	Image Fusion Using Self-constraint Pulse-coupled Neural Network. LSMS/ICSEE Zhuqing Jiao, Weili Xiong, Baoguo Xu

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0x11\n    \n    if (rsi_3 u<= 8)\n        jump(internal/abi.(*Type).ExportedMethods.jump7[rsi_3])\n    \n    rsi_4 = arg1 + 0x30\n\nif (rsi_4 == 0)\n    return 0\n\nuint64_t rdx = zx.q(*(rsi_4 + 6))\n\nif (rdx.w == 0)\n    return nullptr\n\nif (rdx u<= 0x10000)\n    return rsi_4 + zx.q(*(rsi_4 + 8))\n\nruntime.panicSlice3Alen()\nbreakpoint', 'decompiled_function_name': 'internal/abi.(*Type).ExportedMethods', 'decompiled_function_prototype': 'void* internal/abi.(*Type).ExportedMethods(void* arg1 @ rax)', 'decompiled_function_address': 4198880, 'function_type': 'USER', 'functions_caller': [], 'functions_call': ['jump(internal/abi.(*Type).ExportedMethods.jump7[rsi_3])', 'runtime.panicSlice3Alen()'], 'flattened_score': 0.0, 'mba_score': 0.125, 'disassembled_function_hash': 'e2558c9cf3ebac3a28dff29dcc0b8918965ca5091e4d5e0361af301ebc9d7fba'}, {'decompiled_function_hash': '407d3d24d10ffe65a17c09aa88e6d7283d00fcdd9df10741af968c91841cc9b2', 'decompiled_function': 'while (&__return_addr u<= *(arg3 + 0x10))\n    runtime.morestack_noctxt.abi0()\n\nuint64_t rcx_1 = zx.q(*(arg1 + 0x17)) & 0x1f\n\nif (rcx_1 == 0x14)\n    return *(arg1 + 0x40)\n\ninternal/abi.(*Type).ExportedMethods(arg1, rcx_1)\nreturn arg2', 'decompiled_function_name': 'internal/abi.(*Type).NumMethod', 'decompiled_function_prototype': 'int64_t internal/abi.(*Type).NumMethod(void* arg1 @ rax, int64_t arg2 @ rbx, void* arg3 @ r14)', 'decompiled_function_address': 4199072, 'function_type': 'USER', 'functions_caller': [], 'functions_call': ['internal/abi.(*Type).ExportedMethods(arg1, rcx_1)', 'runtime.morestack_noctxt.abi0()'], 'flattened_score': 1.0, 'mba_score': 0.14285714285714285, 'disassembled_function_hash': '270037f0c8addf478f25bc075eb0dc05fc54dff87fba6dff63ab8d0555e4929a'}, {'decompiled_function_hash': '9f2a44ad4622a0e2d0e1b439b1d46754301fd5f7060b91e3766482b1383494da', 'decompiled_function': 'if (arg1 == 0)\n    return 0\n\nint64_t rcx = 0\nint64_t rdx = 0\n\nwhile (true)\n    int64_t rbx_2 = rcx * 7\n    *(arg1 + rcx + 1)\n    \n    if (rbx_2 s< 0)\n        runtime.panicshift()\n        breakpoint\n    \n    uint32_t rdi_3 = zx.d(*(arg1 + rcx + 1))\n    rdx += (zx.q(rdi_3) & 0x7f) << rbx_2.b & sbb.q(rbx_2, rbx_2, rbx_2 u< 0x40)\n    \n    if ((rdi_3.b & 0x80) == 0)\n        break\n    \n    rcx += 1\n\nint64_t rcx_1\n\nif (rdx != 1)\n    rcx_1 = 0\nelse\n    rcx_1.b = *(arg1 + 2) == 0x5f\n\nreturn zx.q(rcx_1.d)', 'decompiled_function_name': 'internal/abi.Name.IsBlank', 'decompiled_function_prototype': 'uint64_t internal/abi.Name.IsBlank(void* arg1 @ rax)', 'decompiled_function_address': 4199168, 'function_type': 'USER', 'functions_caller': [], 'functions_call': ['runtime.panicshift()'], 'flattened_score': 0.7692307692307693, 'mba_score': 0.05, 'disassembled_function_hash': '7a693574e5a54de381301402832a96b61c8f4d2a2671d8150e93fb29f8692e0f'}, {'decompiled_function_hash': 'aaadbfaff6f26ed203060269065749f25f44d295aadde2386ab73f8c4a5dd767', 'decompiled_function': 'while (true)\n    int64_t* rsp\n    \n    if (rsp u> *(arg3 + 0x10))\n        *(rsp - 8) = arg2\n        rsp -= 8\n        arg2 = rsp\n        \n        if (arg1 == 0)\n            *rsp\n            return 0\n        \n        void* rcx_1 = nullptr\n        int64_t rdx_1 = 0\n        \n        while (true)\n            void* rsi_2 = rcx_1 * 7\n            *(rcx_1 + arg1 + 1)\n            \n            if (rsi_2 s< 0)\n                break\n            \n            uint32_t r8_3 = zx.d(*(rcx_1 + arg1 + 1))\n            rdx_1 += (zx.q(r8_3) & 0x7f) << rsi_2.b & sbb.q(rsi_2, rsi_2, rsi_2 u< 0x40)\n            \n            if ((r8_3.b & 0x80) == 0)\n                if (rdx_1 s>= 0)\n                    int64_t rcx_4 = neg.q(rcx_1 + arg1 + 2)\n                    \n                    if (rcx_4 u>= rdx_1)\n                        *rsp\n                        return rcx_1 + arg1 + 2\n                    \n                    if (rcx_1 + arg1 != -2)\n                        runtime.panicunsafestringlen(rcx_4)\n                    \n                    runtime.panicunsafestringnilptr()\n                \n                runtime.panicunsafestringlen()\n                break\n            \n            rcx_1 += 1\n        \n        arg1 = runtime.panicshift()\n    \n    rsp[1] = arg1\n    runtime.morestack_noctxt.abi0()\n    arg1 = rsp[1]', 'decompiled_function_name': 'internal/abi.Name.Name', 'decompiled_function_prototype': 'void* internal/abi.Name.Name(int64_t arg1 @ rax, int64_t* arg2 @ rbp, void* arg3 @ r14)', 'decompiled_function_address': 4199296, 'function_type': 'USER', 'functions_caller': [], 'functions_call': ['runtime.panicunsafestringlen(rcx_4)', 'runtime.panicunsafestringnilptr()', 'runtime.panicunsafestringlen()', 'arg1 = runtime.panicshift()', 'runtime.morestack_noctxt.abi0()'], 'flattened_score': 1.0, 'mba_score': 0.029411764705882353, 'disassembled_function_hash': '21ad7fe65b5907f2e2bd82711c3d5cda50e2cda11dd6cb13e472a1879a0f5ee0'}, {'decompiled_function_hash': '6bc616921202fbee6edcbe36548ca6c0123ae51ecb86c0eea217f3889c581d25', 'decompiled_function': 'while (true)\n    int64_t* rsp\n    \n    if (rsp u> *(arg3 + 0x10))\n        *(rsp - 8) = arg2\n        rsp -= 8\n        arg2 = rsp\n        \n        if ((*arg1 & 2) == 0)\n            *rsp\n            return 0\n        \n        void* rcx_1 = nullptr\n        void* rdx_2 = nullptr\n        \n        while (true)\n            void* rsi_2 = rcx_1 * 7\n            *(rcx_1 + arg1 + 1)\n            \n            if (rsi_2 s< 0)\n                break\n            \n            uint32_t r8_3 = zx.d(*(rcx_1 + arg1 + 1))\n            rdx_2 += (zx.q(r8_3) & 0x7f) << rsi_2.b & sbb.q(rsi_2, rsi_2, rsi_2 u< 0x40)\n            \n            if ((r8_3.b & 0x80) == 0)\n                int64_t rcx_3 = 0\n                int64_t rbx_3 = 0\n                \n                while (true)\n                    void* rsi_4 = rdx_2 + rcx_1 + rcx_3\n                    int64_t rdi_3 = rcx_3 * 7\n                    *(arg1 + rsi_4 + 2)\n                    \n                    if (rdi_3 s< 0)\n                        break\n                    \n                    uint32_t r8_9 = zx.d(*(arg1 + rsi_4 + 2))\n                    rbx_3 += (zx.q(r8_9) & 0x7f) << rdi_3.b & sbb.q(rdi_3, rdi_3, rdi_3 u< 0x40)\n                    \n                    if ((r8_9.b & 0x80) == 0)\n                        if (rbx_3 s>= 0)\n                            int64_t rcx_6 = neg.q(arg1 + rsi_4 + 3)\n                            \n                            if (rcx_6 u>= rbx_3)\n                                *rsp\n                                return arg1 + rsi_4 + 3\n                            \n                            if (arg1 + rsi_4 != -3)\n                                runtime.panicunsafestringlen(rcx_6)\n                            \n                            runtime.panicunsafestringnilptr()\n                        \n                        runtime.panicunsafestringlen()\n                        break\n                    \n                    rcx_3 += 1\n                \n                runtime.panicshift()\n                break\n            \n            rcx_1 += 1\n        \n        arg1 = runtime.panicshift()\n    \n    rsp[1] = arg1\n    runtime.morestack_noctxt.abi0()\n    arg1 = rsp[1]', 'decompiled_function_name': 'internal/abi.Name.Tag', 'decompiled_function_prototype': 'void* internal/abi.Name.Tag(char* arg1 @ rax, int64_t* arg2 @ rbp, void* arg3 @ r14)', 'decompiled_function_address': 4199488, 'function_type': 'USER', 'functions_caller': [], 'functions_call': ['runtime.panicunsafestringlen(rcx_6)', 'runtime.panicunsafestringnilptr()', 'runtime.panicunsafestringlen()', 'runtime.panicshift()', 'arg1 = runtime.panicshift()', 'runtime.morestack_noctxt.abi0()'], 'flattened_score': 1.0, 'mba_score': 0.041666666666666664, 'disassembled_function_hash': '0621c0c17af04d429b48b7855fd82627f2e835ec864eaa49d0de3183fd3d9fbf'}, {'decompiled_function_hash': '917d57a1b59aad9ee7b1197909e67f4ee8d39a0d26f2390ceacbbc54bde37a43', 'decompiled_function': 'while (true)\n    void* rsp\n    \n    if (rsp - 0x30 u> *(arg9 + 0x10))\n        *(rsp - 8) = arg6\n        arg6 = rsp - 8\n        rsp -= 0xb0\n        *(rsp + 0xb8) = arg4\n        *(rsp + 0xc8) = arg1\n        \n        if (arg5 s< 0x20000000)\n            if (arg8 s< 0x20000000)\n                __builtin_memset(rsp + 0x84, 0, 0x14)\n                int64_t rdx = arg5\n                int64_t r9_1 = 0\n                \n                while (true)\n                    char r10_2 = arg5.b\n                    arg5 s>>= 7\n                    char r10_3 = r10_2 & 0x7f\n                    \n                    if (arg5 == 0)\n                        if (r9_1 u< 0xa)\n                            *(rsp + r9_1 + 0x8e) = r10_3\n                            int64_t rbx = arg8\n                            int64_t r10_4 = 0\n                            \n                            while (true)\n                                char r11_2 = arg8.b\n                                arg8 s>>= 7\n                                char r11_3 = r11_2 & 0x7f\n                                \n                                if (arg8 == 0)\n                                    if (r10_4 u< 0xa)\n                                        *(rsp + r10_4 + 0x84) = r11_3\n                                        int64_t r11_4 = rdx + r9_1\n                                        int64_t r12_3\n                                        \n                                        if (rbx == 0)\n                                            r12_3 = r9_1 + rdx + 2\n                                        else\n                                            r12_3 = rbx + r10_4 + r11_4 + 3\n                                            arg7 |= 2\n                                        \n                                        *(rsp + 0x78) = rdx\n                                        *(rsp + 0x48) = r9_1 + rdx + 2\n                                        *(rsp + 0x70) = rbx\n                                        *(rsp + 0xb8) = arg4\n                                        *(rsp + 0xc8) = arg1\n                                        *(rsp + 0x68) = r11_4\n                                        *(rsp + 0x50) = r10_4\n                                        *(rsp + 0x58) = r9_1\n                                        \n                                        if (arg3 != 0)\n                                            arg7 |= 8\n                                        \n                                        *(rsp + 0x3f) = arg7.b\n                                        *(rsp + 0x60) = r12_3\n                                        char* result = runtime.makeslice(r12_3)\n                                        void* rcx_1 = *(rsp + 0x60)\n                                        \n                                        if (rcx_1 u> 0)\n                                            *(rsp + 0xa0) = result\n                                            *result = *(rsp + 0x3f)\n                                            void* rsi = rcx_1 - 1\n                                            uint64_t rdx_6 = (zx.q((neg.q(rcx_1 - 1) s>> 0x3f).d) & 1) + result\n                                            void* rdi_2 = *(rsp + 0x58)\n                                            \n                                            if (rdi_2 + 1 s< rsi)\n                                                rsi = rdi_2 + 1\n                                            \n                                            if (rsp + 0x8e != rdx_6)\n                                                runtime.memmove(rsi, rdx_6, rsp + 0x8e)\n                                                result = *(rsp + 0xa0)\n                                                rcx_1 = *(rsp + 0x60)\n                                                rdi_2 = *(rsp + 0x58)\n                                            \n                                            if (rcx_1 u>= rdi_2 + 2)\n                                                void* rdi_3 = rcx_1 - rdi_2 - 2\n                                                void* rdx_9 = ((rdi_2 + 2) & neg.q(rcx_1 - rdi_2 - 2) s>> 0x3f) + result\n                                                void* rsi_6 = *(rsp + 0x78)\n                                                \n                                                if (rsi_6 s< rdi_3)\n                                                    rdi_3 = rsi_6\n                                                \n                                                int128_t* rbx_3 = *(rsp + 0xb8)\n                                                void* rdx_10\n                                                bool cond:0_1\n                                                \n                                                if (rbx_3 != rdx_9)\n                                                    runtime.memmove(rdi_3, rdx_9, rbx_3)\n                                                    rdx_10 = *(rsp + 0x70)\n                                                    cond:0_1 = rdx_10 == 0\n                                                    result = *(rsp + 0xa0)\n                                                    rcx_1 = *(rsp + 0x60)\n                                                else\n                                                    rdx_10 = *(rsp + 0x70)\n                                                    cond:0_1 = rdx_10 == 0\n                                                \n                                                if (cond:0_1)\n                                                    *(rsp + 0xa8)\n                                                    return result\n                                                \n                                                uint64_t rsi_7 = *(rsp + 0x48)\n