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1. On Landau equation with harmonic potential: nonlinear stability of time-periodic Maxwell-Boltzmann distributions

Author

Cao, Chuqi, He, Ling-Bing, and Ji, Jie

Subjects
Mathematics - Analysis of PDEs, 35Q20, 82B40
Abstract

We provide the first and rigorous confirmations of the hypotheses by Ludwig Boltzmann in his seminal paper \cite{Boltzmann} within the context of the Landau equation in the presence of a harmonic potential. We prove that (i) Each {\it entropy-invariant solution} can be identified as a {\it time-periodic Maxwell-Boltzmann distribution}. Moreover, these distributions can be characterized by thirteen conservation laws, which sheds light on the global dynamics. (ii) Each {\it time-periodic Maxwell-Boltzmann distribution} is nonlinearly stable, including neutral asymptotic stability and Lyapunov stability. Furthermore, the convergence rate is entirely reliant on the thirteen conservation laws and is optimal when compared to the linear scenario., Comment: 66 pages,0 figures

Published
2024

2. Data Overfitting for On-Device Super-Resolution with Dynamic Algorithm and Compiler Co-Design

Author

Li, Gen, Shu, Zhihao, Ji, Jie, Qin, Minghai, Afghah, Fatemeh, Niu, Wei, and Ma, Xiaolong

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Deep neural networks (DNNs) are frequently employed in a variety of computer vision applications. Nowadays, an emerging trend in the current video distribution system is to take advantage of DNN's overfitting properties to perform video resolution upscaling. By splitting videos into chunks and applying a super-resolution (SR) model to overfit each chunk, this scheme of SR models plus video chunks is able to replace traditional video transmission to enhance video quality and transmission efficiency. However, many models and chunks are needed to guarantee high performance, which leads to tremendous overhead on model switching and memory footprints at the user end. To resolve such problems, we propose a Dynamic Deep neural network assisted by a Content-Aware data processing pipeline to reduce the model number down to one (Dy-DCA), which helps promote performance while conserving computational resources. Additionally, to achieve real acceleration on the user end, we designed a framework that optimizes dynamic features (e.g., dynamic shapes, sizes, and control flow) in Dy-DCA to enable a series of compilation optimizations, including fused code generation, static execution planning, etc. By employing such techniques, our method achieves better PSNR and real-time performance (33 FPS) on an off-the-shelf mobile phone. Meanwhile, assisted by our compilation optimization, we achieve a 1.7$\times$ speedup while saving up to 1.61$\times$ memory consumption. Code available in https://github.com/coulsonlee/Dy-DCA-ECCV2024., Comment: ECCV2024

Published
2024

3. Source Localization for Cross Network Information Diffusion

Author

Ling, Chen, Chowdhury, Tanmoy, Ji, Jie, Li, Sirui, Züfle, Andreas, and Zhao, Liang

Subjects
Computer Science - Social and Information Networks
Abstract

Source localization aims to locate information diffusion sources only given the diffusion observation, which has attracted extensive attention in the past few years. Existing methods are mostly tailored for single networks and may not be generalized to handle more complex networks like cross-networks. Cross-network is defined as two interconnected networks, where one network's functionality depends on the other. Source localization on cross-networks entails locating diffusion sources on the source network by only giving the diffused observation in the target network. The task is challenging due to challenges including: 1) diffusion sources distribution modeling; 2) jointly considering both static and dynamic node features; and 3) heterogeneous diffusion patterns learning. In this work, we propose a novel method, namely CNSL, to handle the three primary challenges. Specifically, we propose to learn the distribution of diffusion sources through Bayesian inference and leverage disentangled encoders to separately learn static and dynamic node features. The learning objective is coupled with the cross-network information propagation estimation model to make the inference of diffusion sources considering the overall diffusion process. Additionally, we also provide two novel cross-network datasets collected by ourselves. Extensive experiments are conducted on both datasets to demonstrate the effectiveness of \textit{CNSL} in handling the source localization on cross-networks., Comment: Code and data are available at: https://github.com/tanmoysr/CNSL/

Published
2024

4. Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ): Rationale and Study Design of the Largest Global Prospective Cohort Study of Clinical High Risk for Psychosis.

Author

Wannan, Cassandra, Nelson, Barnaby, Addington, Jean, Allott, Kelly, Anticevic, Alan, Arango, Celso, Baker, Justin, McGorry, Patrick, Mittal, Vijay, Nordentoft, Merete, Nunez, Angela, Pasternak, Ofer, Pearlson, Godfrey, Perez, Jesus, Perkins, Diana, Powers, Albert, Roalf, David, Sabb, Fred, Schiffman, Jason, Shah, Jai, Smesny, Stefan, Spark, Jessica, Stone, William, Strauss, Gregory, Tamayo, Zailyn, Torous, John, Upthegrove, Rachel, Vangel, Mark, Verma, Swapna, Wang, Jijun, Rossum, Inge, Wolf, Daniel, Wolff, Phillip, Wood, Stephen, Yung, Alison, Agurto, Carla, Alvarez-Jimenez, Mario, Amminger, Paul, Armando, Marco, Asgari-Targhi, Ameneh, Cahill, John, Carrión, Ricardo, Castro, Eduardo, Cetin-Karayumak, Suheyla, Mallar Chakravarty, M, Cho, Youngsun, Cotter, David, DAlfonso, Simon, Ennis, Michaela, Fadnavis, Shreyas, Fonteneau, Clara, Gao, Caroline, Gupta, Tina, Gur, Raquel, Gur, Ruben, Hamilton, Holly, Hoftman, Gil, Jacobs, Grace, Jarcho, Johanna, Ji, Jie, Kohler, Christian, Lalousis, Paris, Lavoie, Suzie, Lepage, Martin, Liebenthal, Einat, Mervis, Josh, Murty, Vishnu, Nicholas, Spero, Ning, Lipeng, Penzel, Nora, Poldrack, Russell, Polosecki, Pablo, Pratt, Danielle, Rabin, Rachel, Rahimi Eichi, Habiballah, Rathi, Yogesh, Reichenberg, Avraham, Reinen, Jenna, Rogers, Jack, Ruiz-Yu, Bernalyn, Scott, Isabelle, Seitz-Holland, Johanna, Srihari, Vinod, Srivastava, Agrima, Thompson, Andrew, Turetsky, Bruce, Walsh, Barbara, Whitford, Thomas, Wigman, Johanna, Yao, Beier, Yuen, Hok, Ahmed, Uzair, Byun, Andrew, Chung, Yoonho, Do, Kim, Hendricks, Larry, Huynh, Kevin, Jeffries, Clark, Lane, Erlend, and Langholm, Carsten

Subjects
clinical high risk, consortium, early detection, prediction, prevention, psychosis, Humans, Psychotic Disorders, Schizophrenia, Prospective Studies, Adult, Prodromal Symptoms, Young Adult, International Cooperation, Adolescent, Research Design, Male, Female
Abstract

This article describes the rationale, aims, and methodology of the Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ). This is the largest international collaboration to date that will develop algorithms to predict trajectories and outcomes of individuals at clinical high risk (CHR) for psychosis and to advance the development and use of novel pharmacological interventions for CHR individuals. We present a description of the participating research networks and the data processing analysis and coordination center, their processes for data harmonization across 43 sites from 13 participating countries (recruitment across North America, Australia, Europe, Asia, and South America), data flow and quality assessment processes, data analyses, and the transfer of data to the National Institute of Mental Health (NIMH) Data Archive (NDA) for use by the research community. In an expected sample of approximately 2000 CHR individuals and 640 matched healthy controls, AMP SCZ will collect clinical, environmental, and cognitive data along with multimodal biomarkers, including neuroimaging, electrophysiology, fluid biospecimens, speech and facial expression samples, novel measures derived from digital health technologies including smartphone-based daily surveys, and passive sensing as well as actigraphy. The study will investigate a range of clinical outcomes over a 2-year period, including transition to psychosis, remission or persistence of CHR status, attenuated positive symptoms, persistent negative symptoms, mood and anxiety symptoms, and psychosocial functioning. The global reach of AMP SCZ and its harmonized innovative methods promise to catalyze the development of new treatments to address critical unmet clinical and public health needs in CHR individuals.

Published
2024

5. Uncertainty Quantification for In-Context Learning of Large Language Models

Author

Ling, Chen, Zhao, Xujiang, Zhang, Xuchao, Cheng, Wei, Liu, Yanchi, Sun, Yiyou, Oishi, Mika, Osaki, Takao, Matsuda, Katsushi, Ji, Jie, Bai, Guangji, Zhao, Liang, and Chen, Haifeng

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

In-context learning has emerged as a groundbreaking ability of Large Language Models (LLMs) and revolutionized various fields by providing a few task-relevant demonstrations in the prompt. However, trustworthy issues with LLM's response, such as hallucination, have also been actively discussed. Existing works have been devoted to quantifying the uncertainty in LLM's response, but they often overlook the complex nature of LLMs and the uniqueness of in-context learning. In this work, we delve into the predictive uncertainty of LLMs associated with in-context learning, highlighting that such uncertainties may stem from both the provided demonstrations (aleatoric uncertainty) and ambiguities tied to the model's configurations (epistemic uncertainty). We propose a novel formulation and corresponding estimation method to quantify both types of uncertainties. The proposed method offers an unsupervised way to understand the prediction of in-context learning in a plug-and-play fashion. Extensive experiments are conducted to demonstrate the effectiveness of the decomposition. The code and data are available at: https://github.com/lingchen0331/UQ_ICL., Comment: Accepted to the main conference of NAACL 2024

Published
2024

6. Multi-Loss Convolutional Network with Time-Frequency Attention for Speech Enhancement

Author

Wan, Liang, Liu, Hongqing, Zhou, Yi, and Ji, Jie

Subjects
Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, Statistics - Machine Learning
Abstract

The Dual-Path Convolution Recurrent Network (DPCRN) was proposed to effectively exploit time-frequency domain information. By combining the DPRNN module with Convolution Recurrent Network (CRN), the DPCRN obtained a promising performance in speech separation with a limited model size. In this paper, we explore self-attention in the DPCRN module and design a model called Multi-Loss Convolutional Network with Time-Frequency Attention(MNTFA) for speech enhancement. We use self-attention modules to exploit the long-time information, where the intra-chunk self-attentions are used to model the spectrum pattern and the inter-chunk self-attention are used to model the dependence between consecutive frames. Compared to DPRNN, axial self-attention greatly reduces the need for memory and computation, which is more suitable for long sequences of speech signals. In addition, we propose a joint training method of a multi-resolution STFT loss and a WavLM loss using a pre-trained WavLM network. Experiments show that with only 0.23M parameters, the proposed model achieves a better performance than DPCRN.

Published
2023

7. Regularity estimates for the non-cutoff soft potential Boltzmann equation with typical rough and slowly decaying data

Author

He, Ling-Bing and Ji, Jie

Subjects
Mathematics - Analysis of PDEs
Abstract

In the present work, we investigate estimates of regularity for weak solutions to the non-cutoff Boltzmann equation with soft potentials. We restrict our focus to the so-called "typically rough and slowly decaying data", which is constructed to satisfy typical properties: low regularity and having exact polynomial decay in high velocity regimes. By exploring the degenerate and non-local properties of the collision operator, we demonstrate that (i) such data induce only finite smoothing effects for weak solutions in Sobolev spaces; (ii) this finite smoothing property implies that the Leibniz rule does not hold for high derivatives of the collision operator (even in the weak sense). Moreover, we can also prove that the average of the solution or the average of the collision operator on a special domain in $\mathbb{R}^3_v$ will induce discontinuity in the $x$ variable. These facts present major obstacles to proving the conjecture that solutions to the equation will instantly become infinitely smooth for both spatial and velocity variables at any positive time if the initial data has only polynomial decay in high velocity regimes., Comment: 53 pages

Published
2023

13. Towards High-Quality and Efficient Video Super-Resolution via Spatial-Temporal Data Overfitting

Author

Li, Gen, Ji, Jie, Qin, Minghai, Niu, Wei, Ren, Bin, Afghah, Fatemeh, Guo, Linke, and Ma, Xiaolong

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

As deep convolutional neural networks (DNNs) are widely used in various fields of computer vision, leveraging the overfitting ability of the DNN to achieve video resolution upscaling has become a new trend in the modern video delivery system. By dividing videos into chunks and overfitting each chunk with a super-resolution model, the server encodes videos before transmitting them to the clients, thus achieving better video quality and transmission efficiency. However, a large number of chunks are expected to ensure good overfitting quality, which substantially increases the storage and consumes more bandwidth resources for data transmission. On the other hand, decreasing the number of chunks through training optimization techniques usually requires high model capacity, which significantly slows down execution speed. To reconcile such, we propose a novel method for high-quality and efficient video resolution upscaling tasks, which leverages the spatial-temporal information to accurately divide video into chunks, thus keeping the number of chunks as well as the model size to minimum. Additionally, we advance our method into a single overfitting model by a data-aware joint training technique, which further reduces the storage requirement with negligible quality drop. We deploy our models on an off-the-shelf mobile phone, and experimental results show that our method achieves real-time video super-resolution with high video quality. Compared with the state-of-the-art, our method achieves 28 fps streaming speed with 41.6 PSNR, which is 14$\times$ faster and 2.29 dB better in the live video resolution upscaling tasks. Code available in https://github.com/coulsonlee/STDO-CVPR2023.git, Comment: CVPR 2023 Highlight Paper

Published
2023

14. Adaptive Time Horizon MPC Path Tracking Control Method for Mowing Robot

Author

HE Qing, JI Jie, FENG Wei, ZHAO Lijun, and ZHANG Bohan

Subjects
mowing robot, model predictive control, path tracking, fuzzy control, event-triggered mechanism, Agriculture (General), S1-972, Technology (General), T1-995
Abstract

ObjectiveThe traditional predictive control approach usually employs a fixed time horizon and often overlooks the impact of changes in curvature and road bends. This oversight leads to subpar tracking performance and inadequate adaptability of robots for navigating curves and paths. Although extending the time horizon of the standard fixed time horizon model predictive control (MPC) can improve curve path tracking accuracy, it comes with high computational costs, making it impractical in situations with restricted computing resources. Consequently, an adaptive time horizon MPC controller was developed to meet the requirements of complex tasks such as autonomous mowing.MethodsInitially, it was crucial to establish a kinematic model for the mowing robot, which required employing Taylor linearization and Euler method discretization techniques to ensure accurate path tracking. The prediction equation for the error model was derived after conducting a comprehensive analysis of the robot's kinematics model employed in mowing. Second, the size of the previewing area was determined by utilizing the speed data and reference path information gathered from the mowing robot. The region located a certain distance ahead of the robot's current position, was identified to as the preview region, enabling a more accurate prediction of the robot's future traveling conditions. Calculations for both the curve factor and curve change factor were carried out within this preview region. The curvature factor represented the initial curvature of the path, while the curvature change factor indicated the extent of curvature variation in this region. These two variables were then fed into a fuzzy controller, which adjusted the prediction time horizon of the MPC. The integration enabled the mowing robot to promptly adjust to changes in the path's curvature, thereby improving its accuracy in tracking the desired trajectory. Additionally, a novel technique for triggering MPC execution was developed to reduce computational load and improve real-time performance. This approach ensured that MPC activation occurred only when needed, rather than at every time step, resulting in reduced computational expenses especially during periods of smooth robot motion where unnecessary computation overhead could be minimized. By meeting kinematic and dynamic constraints, the optimization algorithm successfully identified an optimal control sequence, ultimately enhancing stability and reliability of the control system. Consequently, these set of control algorithms facilitated precise path tracking while considering both kinematic and dynamic limitations in complex environments.Results and DiscussionThe adaptive time-horizon MPC controller effectively limited the maximum absolute heading error and maximum absolute lateral error to within 0.13 rad and 11 cm, respectively, surpassing the performance of the MPC controller in the control group. Moreover, compared to both the first and fourth groups, the adaptive time-horizon MPC controller achieved a remarkable reduction of 75.39% and 57.83% in mean values for lateral error and heading error, respectively (38.38% and 31.84%, respectively). Additionally, it demonstrated superior tracking accuracy as evidenced by its significantly smaller absolute standard deviation of lateral error (0.025 6 m) and course error (0.025 5 rad), outperforming all four fixed time-horizon MPC controllers tested in the study. Furthermore, this adaptive approach ensured precise tracking and control capabilities for the mowing robot while maintaining a remarkably low average solution time of only 0.004 9 s, notably faster than that observed with other control data sets-reducing computational load by approximately 10.9 ms compared to maximum time-horizon MPC.ConclusionsThe experimental results demonstrated that the adaptive time-horizon MPC tracking approach effectively addressed the trade-off between control accuracy and computational complexity encountered in fixed time-horizon MPC. By dynamically adjusting the time horizon length the and performing MPC calculations based on individual events, this approach can more effectively handle scenarios with restricted computational resources, ensuring superior control precision and stability. Furthermore, it achieves a balance between control precision and real-time performance in curve route tracking for mowing robots, offering a more practical and reliable solution for their practical application.

Published
2024
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15. Localization Method for Agricultural Robots Based on Fusion of LiDAR and IMU

Author

LIU Yang, JI Jie, PAN Deng, ZHAO Lijun, and LI Mingsheng

Subjects
agricultural robots, lidar localization, point cloud matching, extended kalman filter, sensors fusion, Agriculture (General), S1-972, Technology (General), T1-995
Abstract

ObjectiveHigh-precision localization technology serves as the crucial foundation in enabling the autonomous navigation operations of intelligent agricultural robots. However, the traditional global navigation satellite system (GNSS) localization method faces numerous limitations, such as tree shadow, electromagnetic interference, and other factors in the agricultural environment brings challenges to the accuracy and reliability of localization technology. To address the deficiencies and achieve precise localization of agricultural robots independent of GNSS, a localization method was proposed based on the fusion of three-dimensional light detection and ranging (LiDAR) data and inertial measurement unit (IMU) information to enhance localization accuracy and reliability.MethodsLiDAR was used to obtain point cloud data in the agricultural environment and realize self-localization via point cloud matching. By integrating real-time motion parameter measurements from the IMU with LiDAR data, a high-precision localization solution for agricultural robots was achieved through a specific fusion algorithm. Firstly, the LiDAR-obtained point cloud data was preprocessed and the depth map was used to save the data. This approach could reduce the dimensionality of the original LiDAR point cloud, and eliminate the disorder of the original LiDAR point cloud arrangement, facilitating traversal and clustering through graph search. Given the presence of numerous distinct crops like trees in the agricultural environment, an angle-based clustering method was adopted. Specific angle-based clustering criteria were set to group the point cloud data, leading to the segmentation of different clusters of points, and obvious crops in the agricultural environment was effectively perceived. Furthermore, to improve the accuracy and stability of positioning, an improved three-dimensional normal distribution transform (3D-NDT) localization algorithm was proposed. This algorithm operated by matching the LiDAR-scanned point cloud data in real time with the pre-existing down sampled point cloud map to achieve real-time localization. Considering that direct down sampling of LiDAR point clouds in the agricultural environment could result in the loss of crucial environmental data, a point cloud clustering operation was used in place of down sampling operation, thereby improving matching accuracy and positioning precision. Secondly, to address potential constraints and shortcomings of using a single sensor for robot localization, a multi-sensor information fusion strategy was deployed to improve the localization accuracy. Specifically, the extended Kalman filter algorithm (EKF) was chosen to fuse the localization data from LiDAR point cloud and the IMU odometer information. The IMU provided essential motion parameters such as acceleration and angular velocity of the agricultural robot, and by combining with the LiDAR-derived localization information, the localization of the agricultural robot could be more accurately estimated. This fusion approach maximized the advantages of different sensors, compensated for their individual limitations, and improved the overall localization accuracy of the agricultural robot.Results and DiscussionsA series of experimental results in the Gazebo simulation environment of the robot operating system (ROS) and real operation scenarios showed that the fusion localization method proposed had significant advantages. In the simulation environment, the average localization errors of the proposed multi-sensor data fusion localization method were 1.7 and 1.8 cm, respectively, while in the experimental scenario, these errors were 3.3 and 3.3 cm, respectively, which were significantly better than the traditional 3D-NDT localization algorithm. These findings showed that the localization method proposed in this study could achieve high-precision localization in the complex agricultural environment, and provide reliable localization assistance for the autonomous functioning of agricultural robots.ConclusionsThe proposed localization method based on the fusion of LiDAR data and IMU information provided a novel localization solution for the autonomous operation of agricultural robots in areas with limited GNSS reception. Through the comprehensive utilization of multi-sensor information and adopting advanced data processing and fusion algorithms, the localization accuracy of agricultural robots could be significantly improved, which could provide a new reference for the intelligence and automation of agricultural production.

Published
2024
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16. Bond behaviour between carbon fibre strand and mortar under marine environment

Author

Ji, Jie

Subjects
carbon fibre strand, sea sand and seawater mortar, marine environments, long-term durability, textile reinforced mortar, carbon fibre strand reinforced mortar (CFSRM) specimens
Abstract

Owing to the steel corrosion in traditional steel reinforced concrete structures exposed to marine environment, the present study explores carbon fibre textile reinforced mortar (TRM) as the alternative construction material. Moreover, sea sand and seawater can be used in casting mortar in this study, because carbon fibre is non-corrosive. Focusing on the long-term bond degradation at the interface between the carbon fibre strand (CFS) and mortar in carbon fibre TRM under marine environments, experimental and numerical investigations have been carried out by using single CFS-reinforced mortar (CFSRM) specimens. Interfacial debonding is one of the key failure modes in TRM, due to the poor bonding between CFS and mortar compared with traditional steel rebar to mortar. Since the pull-out performance is selected to assess the interfacial durability of TRM, this thesis derived an analytical solution of interfacial bond-slip behaviour during the pull-out process. The moisture-induced degradation has also been discussed in this research, with a particular focus on the effects of hygroscopic expansion and interfacial ageing. Considering that there exists a scientific challenge of understanding CFSRM durability before it can be applied to practice extensively, this study focuses on the real working condition in the offshore area and thus investigates its durability problems by coupling two different degradation mechanisms altogether, namely hygroscopic expansion and interfacial ageing. Although each of them has been individually studied in literature so far, this study innovatively takes them into account simultaneously to investigate the coupling effect. At last, a long-term prediction model based on multiphysics is proposed to estimate its interfacial degradation behaviour under marine environment. The present study includes both experimental work and numerical testing. A total of 125 samples are tested in experiments. Mortar samples are used for strength, flowability and diffusion tests, containing 24 cubes (100 mm), 24 cylinders (200×100 mm height/diameter), three prisms (40×40×160 mm), fresh mortar and eight small cylinders (50×20 mm height/diameter). 28 CFS samples are used for mechanical and physical tests. 32 CFSRM cylinders (50×20 mm height/diameter) are used for pull-out tests. All above-mentioned tests are carried out in University of Plymouth. According to material parameters acquired from experiments, COMSOL Multiphysics software is used to build the multi-field numerical model which simulates the coupling effects of water diffusion, hygroscopic expansion, and immersion temperature on the bond degradation of CFSRM. As the present thesis proposes a three-parameter durability (TPD) model based on Arrhenius Law to predict the interfacial ageing over time, TPD model is further used in the numerical testing and then validated by the experimental records. Finally, the present thesis proposes a hand calculation method of pull-out force retention under long-term conditioning, based on above-mentioned experimental and numerical analyses altogether.

Published
2023

17. Accelerated cortical thinning precedes and predicts conversion to psychosis: The NAPLS3 longitudinal study of youth at clinical high-risk

Author

Collins, Meghan A, Ji, Jie Lisa, Chung, Yoonho, Lympus, Cole A, Afriyie-Agyemang, Yvette, Addington, Jean M, Goodyear, Bradley G, Bearden, Carrie E, Cadenhead, Kristin S, Mirzakhanian, Heline, Tsuang, Ming T, Cornblatt, Barbara A, Carrión, Ricardo E, Keshavan, Matcheri, Stone, Wiliam S, Mathalon, Daniel H, Perkins, Diana O, Walker, Elaine F, Woods, Scott W, Powers, Albert R, Anticevic, Alan, and Cannon, Tyrone D

Subjects
Behavioral and Social Science, Prevention, Serious Mental Illness, Pediatric, Mental Health, Neurosciences, Brain Disorders, Clinical Research, Humans, Female, Adolescent, Male, Longitudinal Studies, Cerebral Cortical Thinning, Ethnicity, Minority Groups, Psychotic Disorders, Prodromal Symptoms, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

Progressive grey matter loss has been demonstrated among clinical high-risk (CHR) individuals who convert to psychosis, but it is unknown whether these changes occur prior to psychosis onset. Identifying illness-related neurobiological mechanisms that occur prior to conversion is essential for targeted early intervention. Among participants in the third wave of the North American Prodrome Longitudinal Study (NAPLS3), this report investigated if steeper cortical thinning was observable prior to psychosis onset among CHR individuals who ultimately converted (CHR-C) and assessed the shortest possible time interval in which rates of cortical thinning differ between CHR-C, CHR non-converters (CHR-NC), and health controls (HC). 338 CHR-NC, 42 CHR-C, and 62 HC participants (age 19.3±4.2, 44.8% female, 52.5% racial/ethnic minority) completed up to 5 MRI scans across 8 months. Accelerated thinning among CHR-C compared to CHR-NC and HC was observed in multiple prefrontal, temporal, and parietal cortical regions. CHR-NC also exhibited accelerated cortical thinning compared to HC in several of these areas. Greater percent decrease in cortical thickness was observed among CHR-C compared to other groups across 2.9±1.8 months, on average, in several cortical areas. ROC analyses discriminating CHR-C from CHR-NC by percent thickness change in a left hemisphere region of interest, scanner, age, age2, and sex had an AUC of 0.74, with model predictive power driven primarily by percent thickness change. Findings indicate that accelerated cortical thinning precedes psychosis onset and differentiates CHR-C from CHR-NC and HC across short time intervals. Mechanisms underlying cortical thinning may provide novel treatment targets prior to psychosis onset.

Published
2023

18. Propagation of moments and sharp convergence rate for inhomogeneous non-cutoff Boltzmann equation with soft potentials

Author

Cao, Chuqi, He, Ling-Bing, and Ji, Jie

Subjects
Mathematics - Analysis of PDEs
Abstract

We prove the well-posedness for the non-cutoff Boltzmann equation with soft potentials when the initial datum is close to the {\it global Maxwellian} and has only polynomial decay at the large velocities in $L^2$ space. As a result, we get the {\it propagation of the exponential moments} and the {\it sharp rates} of the convergence to the {\it global Maxwellian} which seems the first results for the original equation with soft potentials. The new ingredients of the proof lie in localized techniques, the semigroup method as well as the propagation of the polynomial and exponential moments in $L^2$ space., Comment: 71

Published
2022

19. TRPV4 affects visual signals in photoreceptors and rod bipolar cells

Author

Ye Long, Maxim Kozhemyakin, Samuel M. Wu, and Ji-Jie Pang

Subjects
TRPV4, rod bipolar cell, photoreceptor, light response, ERG, patch-clamp, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

IntroductionMechanical sensitive channels expressed in mammalian retinas are effectors of elevated pressure stresses, but it is unclear how their activation affects visual function in pressure-related retinal disorders.MethodsThis study investigated the role of the transient potential channel vanilloid TRPV4 in photoreceptors and rod bipolar cells (RBCs) with immunohistochemistry, confocal microscopy, electroretinography (ERG), and patch-clamp techniques.ResultsTRPV4 immunoreactivity (IR) was found in the outer segments of photoreceptors, dendrites and somas of PKCα-positive RBCs and other BCs, plexiform layers, and retinal ganglion cells (RGCs) in wild-type mice. TRPV4-IR was largely diminished in the retinas of homozygous TRPV4 transgenic mice. Genetically suppressing TRPV4 expression moderately but significantly enhanced the amplitude of ERG a- and b-waves evoked by scotopic and mesopic lights (0.55 to 200 Rh*rod−1 s−1) and photopic lights (105–106 Rh*rod−1 s−1) compared to wild-type mice in fully dark-adapted conditions. The implicit time evoked by dim lights (0.55 to 200 Rh*rod−1 s−1) was significantly decreased for b-waves and elongated for a-waves in the transgenic mice. ERG b-wave evoked by dim lights is primarily mediated by RBCs, and under voltage-clamp conditions, the latency of the light-evoked cation current in RBCs of the transgenic mice was significantly shorter compared to wild-type mice. About 10% of the transgenic mice had one eye undeveloped, and the percentage was significantly higher than in wild-type mice.ConclusionsThe data indicates that TRPV4 involves ocular development and is expressed and active in outer retinal neurons, and interventions of TRPV4 can variably affect visual signals in rods, cones, RBCs, and cone ON BCs.

Published
2024
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20. Realization of a photonic topological insulator in Kagome crystals at terahertz wavelengths

Author

Shen, Yun, Ji, Jie, Zhang, Le, Jepsen, Peter Uhd, Yu, Xin, Yan, Shubin, Li, Huichang, Shen, Qian, Madhi, Daena, Zhou, Binbin, and Deng, Xiaohua

Subjects
Physics - Optics
Abstract

Topological systems are inherently robust to disorder and continuous perturbations, resulting in dissipation-free edge transport of electrons in quantum solids, or reflectionless guiding of photons and phonons in classical wave systems characterized by topological invariants. Despite considerable efforts, direct experimental demonstration of theoretically predicted robust, lossless energy transport in topological insulators operating at terahertz frequencies is needed further investigations to shed affirmative light on the unique properties enabled by topological protection. Here, we introduce Kagome lattice that exhibits a new class of symmetry-protected topological phases with very low Berry curvature but nontrivial bulk polarization, and fabricate an optical topological insulator that provide the valley hall effect. Theoretical analysis show that four type edge states can be obtained. Measurements of THz-TDs with high time-resolution demonstrate that terahertz wave propagating along the straight topological edge and Z-shape edge with sharp turns have almost same high transmission in 0.440 THz to 0.457 THz domain range. Those results quantitatively illustrate the suppression of backscattering due to the non-trivial topology of the structure. The THz-TDs measurement yields amplitude and phase information, showing significant advantage compared to general broadband infrared, single wavelength continuous-wave THz measurements and visible spectroscopy. It allows further exploration of the effective refractive index, group velocity and dispersion relations of edge states. Our work offers possibilities for advanced control of the propagation and manipulation of THz waves, and facilitates the applications including sixth-generation (6G) wireless communication, terahertz integrated circuits, and interconnects for intrachip and interchip communication.

Published
2022

28. The effect of induced defecation technique with deep colonic precision localization in patients with complex constipation (结肠精准定位诱导排便技术在复杂性便秘患者中的应用研究)

Author

ZHAO Jihong (赵继红), JIANG Yunlu (姜蕴璐), and JI Jie (纪婕)

Subjects
colon, abdominal pain, complex constipation, precise positioning, induced defecation, bowel hydrotherapy, 结肠, 腹痛, 复杂性便秘, 精准定位, 诱导排便, 肠道水疗, Nursing, RT1-120
Abstract

Objective To investigate the effect of induced defecation technique with deep colonic precision localization in patients with complex constipation. Methods Fifty-three patients with complex constipation who were admitted to and consulted by the Department of Rehabilitation Medicine Center from November 2021 to May 2022 were recruited as research subjects. All patients received induced defecation with deep colonic precision positioning, and intervention effect was analyzed, Results The colonic deep-precision positioning induced defecation technique was reported to significantly relieve abdominal discomfort, bloating, constipation, diarrhea, and other intestinal disorders in 53 individuals with complex constipation. After the intervention, the patients had defecation in 49 (92. 45%)cases and no defecation in 4 (7. 55%)cases; 51 (96. 23%) cases and no defecation in 2 (3. 77%)case after the intervention; abdominal symptoms improved in a total of 53 (100. 00%)cases. The Wexner constipation score of the patients after the intervention was significantly lower than that before the intervention (P＜0. 05). Conclusion On patients with difficult constipation, the approach of induced defecation with deep colonic precision positioning provided considerable therapeutic benefits and significantly alleviated their clinical symptoms. (目的 探讨结肠精准定位的诱导排便技术在复杂性便秘患者中的应用效果。方法 对2021年11月—2022年5月江苏省人民医院康复医学中心收治及会诊的53例复杂性便秘患者进行结肠精准定位的诱导排便, 评估干预效果。结果 53例复杂性便秘患者在排除禁忌后接受结肠精准定位的诱导排便技术干预, 腹痛、腹胀、便秘、腹泻等肠道问题得到缓解。干预后患者排便49(92. 45%)例、未排便4(7. 55%)例; 干预后排气51(96. 23%)例、未排气2(3. 77%)例; 腹部症状改善共计53(100. 00%)例, 干预后患者Wexner便秘评分低于干预前, 差异有统计学意义(P＜0. 05)。结论 结肠精准定位的诱导排便技术对复杂性便秘患者有明显治疗效果, 能够有效减轻患者临床症状。)

Published
2023
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41. Uniqueness and global dynamics of spatially homogeneous non cutoff Boltzmann equation with moderate soft potentials

Author

He, Ling-Bing and Ji, Jie

Subjects
Mathematics - Analysis of PDEs
Abstract

Departing from the weak solution, we prove the uniqueness, smoothing estimates and the global dynamics for the non cutoff spatially homogeneous Boltzmann equation with moderate soft potentials. Our results show that the behavior of the solution(including the production of regularity and the longtime behavior) can be {\it characterized quantitatively} by the initial data at the large velocities, i.e.(i). initially polynomial decay at the large velocities in $L^1$ space will induce the finite smoothing estimates in weighted Sobolev spaces and the polynomial convergence rate (including the lower and upper bounds) to the equilibrium; (ii). initially the exponential decay at the large velocities in $L^1$ space will induce $C^\infty$ regularization effect and the stretched exponential convergence rate. The new ingredients of the proof lie in the development of the localized techniques in phase and frequency spaces and the propagation of the exponential momentum., Comment: The paper will be separated

Published
2021

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