Point-Gap Bound States in Non-Hermitian Systems

In this paper, we systematically investigate the impurity-induced bound states in 1D non-Hermitian systems. By establishing an exact relationship between impurity potential and bound-state energy, we determine the minimum impurity potential required to generate bound states within each point energy gap. We demonstrate that the absence of Bloch saddle points necessitates a finite threshold of impurity potential; otherwise, infinitesimal impurity potential can create bound states. Furthermore, we show that the bound states residing in the point gaps with nonzero spectral winding exhibit sensitivity to boundary conditions and will be squeezed towards the edges when the boundaries are opened, indicating the bulk-boundary correspondence in terms of point-gap topology.Comment: 16 pages, 10 figure

Graphic characterization and clustering configuration descriptors of determinant space for molecules

Quantum Monte Carlo approaches based on the stochastic sampling of the determinant space have evolved to be powerful methods to compute the electronic states of molecules. These methods not only calculate the correlation energy at an unprecedented accuracy but also provides insightful information on the electronic structure of computed states, e.g. the population, connection, and clustering of determinants, which have not been fully explored. In this work, we devise a configuration graph for visualizing the determinant space, revealing the nature of the molecule's electronic structure. In addition, we propose two analytical descriptors to quantify the extent of configuration clustering of multi-determinant wave functions. The graph and descriptors provide us with a fresh perspective of the electronic structure of molecules and can assist the further development of configuration interaction based electronic structure methods

Tacchi: A Pluggable and Low Computational Cost Elastomer Deformation Simulator for Optical Tactile Sensors

Simulation is widely applied in robotics research to save time and resources. There have been several works to simulate optical tactile sensors that leverage either a smoothing method or Finite Element Method (FEM). However, elastomer deformation physics is not considered in the former method, whereas the latter requires a massive amount of computational resources like a computer cluster. In this work, we propose a pluggable and low computational cost simulator using the Taichi programming language for simulating optical tactile sensors, named as Tacchi . It reconstructs elastomer deformation using particles, which allows deformed elastomer surfaces to be rendered into tactile images and reveals contact information without suffering from high computational costs. Tacchi facilitates creating realistic tactile images in simulation, e.g., ones that capture wear-and-tear defects on object surfaces. In addition, the proposed Tacchi can be integrated with robotics simulators for a robot system simulation. Experiment results showed that Tacchi can produce images with better similarity to real images and achieved higher Sim2Real accuracy compared to the existing methods. Moreover, it can be connected with MuJoCo and Gazebo with only the requirement of 1G memory space in GPU compared to a computer cluster applied for FEM. With Tacchi, physical robot simulation with optical tactile sensors becomes possible. All the materials in this paper are available at https://github.com/zixichen007115/Tacchi .Comment: 8 pages, 6 figures, accepted by IEEE Robotics and Automation Letter

A Hybrid Adaptive Controller for Soft Robot Interchangeability

Soft robots have been leveraged in considerable areas like surgery, rehabilitation, and bionics due to their softness, flexibility, and safety. However, it is challenging to produce two same soft robots even with the same mold and manufacturing process owing to the complexity of soft materials. Meanwhile, widespread usage of a system requires the ability to fabricate replaceable components, which is interchangeability. Due to the necessity of this property, a hybrid adaptive controller is introduced to achieve interchangeability from the perspective of control approaches. This method utilizes an offline trained recurrent neural network controller to cope with the nonlinear and delayed response from soft robots. Furthermore, an online optimizing kinematics controller is applied to decrease the error caused by the above neural network controller. Soft pneumatic robots with different deformation properties but the same mold have been included for validation experiments. In the experiments, the systems with different actuation configurations and the different robots follow the desired trajectory with errors of 0.040 and 0.030 compared with the working space length, respectively. Such an adaptive controller also shows good performance on different control frequencies and desired velocities. This controller endows soft robots with the potential for wide application, and future work may include different offline and online controllers. A weight parameter adjusting strategy may also be proposed in the future.Comment: 8 pages, 9 figures, 4 table

Long-term high salt intake involves reduced SK Currents and Increased Excitability of PVN Neurons with projections to the rostral ventrolateral medulla in rats

Evidence indicates that high salt (HS) intake activates presympathetic paraventricular nucleus (PVN) neurons, which contributes to sympathoexcitation of salt-sensitive hypertension. The present study determined whether 5 weeks of HS (2% NaCl) intake alters the small conductance Ca2+-activated potassium channel (SK) current in presympathetic PVN neurons and whether this change affects the neuronal excitability. In whole-cell voltage-clamp recordings, HS-treated rats had significantly decreased SK currents compared to rats with normal salt (NS, 0.4% NaCl) intake in PVN neurons. The sensitivity of PVN neuronal excitability in response to current injections was greater in HS group compared to NS controls. The SK channel blocker apamin augmented the neuronal excitability in both groups but had less effect on the sensitivity of the neuronal excitability in HS group compared to NS controls. In the HS group, the interspike interval (ISI) was significantly shorter than that in NS controls. Apamin significantly shortened the ISI in NS controls but had less effect in the HS group. This data suggests that HS intake reduces SK currents, which contributes to increased PVN neuronal excitability at least in part through a decrease in spike frequency adaptation and may be a precursor to the development of salt-sensitive hypertension

Deletion of TRPC6 attenuates NMDA receptor-mediated Ca\u3csup\u3e2+\u3c/sup\u3e Entry and Ca\u3csup\u3e2+\u3c/sup\u3e-induced neurotoxicity following cerebral ischemia and oxygen-glucose deprivation

Transient receptor potential canonical 6 (TRPC6) channels are permeable to Na+ and Ca2+ and are widely expressed in the brain. In this study, the role of TRPC6 was investigated following ischemia/reperfusion (I/R) and oxygen-glucose deprivation (OGD). We found that TRPC6 expression was increased in wild-type (WT) mice cortical neurons following I/R and in primary neurons with OGD, and that deletion of TRPC6 reduced the I/R-induced brain infarct in mice and the OGD- /neurotoxin-induced neuronal death. Using live-cell imaging to examine intracellular Ca2+ levels ([Ca2+]i), we found that OGD induced a significant higher increase in glutamate-evoked Ca2+ influx compared to untreated control and such an increase was reduced by TRPC6 deletion. Enhancement of TRPC6 expression using AdCMV-TRPC6-GFP infection in WT neurons increased [Ca2+]i in response to glutamate application compared to AdCMV-GFP control. Inhibition of N-methyl-d-aspartic acid receptor (NMDAR) with MK801 decreased TRPC6-dependent increase of [Ca2+]i in TRPC6 infected cells, indicating that such a Ca2+ influx was NMDAR dependent. Furthermore, TRPC6-dependent Ca2+ influx was blunted by blockade of Na+ entry in TRPC6 infected cells. Finally, OGD-enhanced Ca2+ influx was reduced, but not completely blocked, in the presence of voltage-dependent Na+ channel blocker tetrodotoxin (TTX) and dl-α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) blocker CNQX. Altogether, we concluded that I/R-induced brain damage was, in part, due to upregulation of TRPC6 in cortical neurons. We postulate that overexpression of TRPC6 following I/R may induce neuronal death partially through TRPC6-dependent Na+ entry which activated NMDAR, thus leading to a damaging Ca2+ overload. These findings may provide a potential target for future intervention in stroke-induced brain damage

Deletion of TRPC6 attenuates NMDA receptor-mediated Ca\u3csup\u3e2+\u3c/sup\u3e Entry and Ca\u3csup\u3e2+\u3c/sup\u3e-induced neurotoxicity following cerebral ischemia and oxygen-glucose deprivation

Transient receptor potential canonical 6 (TRPC6) channels are permeable to Na+ and Ca2+ and are widely expressed in the brain. In this study, the role of TRPC6 was investigated following ischemia/reperfusion (I/R) and oxygen-glucose deprivation (OGD). We found that TRPC6 expression was increased in wild-type (WT) mice cortical neurons following I/R and in primary neurons with OGD, and that deletion of TRPC6 reduced the I/R-induced brain infarct in mice and the OGD- /neurotoxin-induced neuronal death. Using live-cell imaging to examine intracellular Ca2+ levels ([Ca2+]i), we found that OGD induced a significant higher increase in glutamate-evoked Ca2+ influx compared to untreated control and such an increase was reduced by TRPC6 deletion. Enhancement of TRPC6 expression using AdCMV-TRPC6-GFP infection in WT neurons increased [Ca2+]i in response to glutamate application compared to AdCMV-GFP control. Inhibition of N-methyl-d-aspartic acid receptor (NMDAR) with MK801 decreased TRPC6-dependent increase of [Ca2+]i in TRPC6 infected cells, indicating that such a Ca2+ influx was NMDAR dependent. Furthermore, TRPC6-dependent Ca2+ influx was blunted by blockade of Na+ entry in TRPC6 infected cells. Finally, OGD-enhanced Ca2+ influx was reduced, but not completely blocked, in the presence of voltage-dependent Na+ channel blocker tetrodotoxin (TTX) and dl-α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) blocker CNQX. Altogether, we concluded that I/R-induced brain damage was, in part, due to upregulation of TRPC6 in cortical neurons. We postulate that overexpression of TRPC6 following I/R may induce neuronal death partially through TRPC6-dependent Na+ entry which activated NMDAR, thus leading to a damaging Ca2+ overload. These findings may provide a potential target for future intervention in stroke-induced brain damage

Responses of Nucleus Tractus Solitarius (NTS) early and late neurons to blood pressure changes in anesthetized F344 rats

Previously, many different types of NTS barosensitive neurons were identified. However, the time course of NTS barosensitive neuronal activity (NA) in response to arterial pressure (AP) changes, and the relationship of NA-AP changes, have not yet been fully quantified. In this study, we made extracellular recordings of single NTS neurons firing in response to AP elevation induced by occlusion of the descending aorta in anesthetized rats. Our findings were that: 1) Thirty-five neurons (from 46 neurons) increased firing, whereas others neurons either decreased firing upon AP elevation, or were biphasic: first decreased firing upon AP elevation and then increased firing during AP decrease. 2) Fourteen neurons with excitatory responses were activated and rapidly increased their firing during the early phase of AP increase (early neurons); whereas 21 neurons did not increase firing until the mean arterial pressure changes (ΔMAP) reached near/after the peak (late neurons). 3) The early neurons had a significantly higher firing rate than late neurons during AP elevation at a similar rate. 4) Early neuron NA-ΔMAP relationship could be well fitted and characterized by the sigmoid logistic function with the maximal gain of 29.3. 5) The increase of early NA correlated linearly with the initial heart rate (HR) reduction. 6) The late neurons did not contribute to the initial HR reduction. However, the late NA could be well correlated with HR reduction during the late phase. Altogether, our study demonstrated that the NTS excitatory neurons could be grouped into early and late neurons based on their firing patterns. The early neurons could be characterized by the sigmoid logistic function, and different neurons may differently contribute to HR regulation. Importantly, the grouping and quantitative methods used in this study may provide a useful tool for future assessment of functional changes of early and late neurons in disease models

Leveraging Prototype Patient Representations with Feature-Missing-Aware Calibration to Mitigate EHR Data Sparsity

Electronic Health Record (EHR) data frequently exhibits sparse characteristics, posing challenges for predictive modeling. Current direct imputation such as matrix imputation approaches hinge on referencing analogous rows or columns to complete raw missing data and do not differentiate between imputed and actual values. As a result, models may inadvertently incorporate irrelevant or deceptive information with respect to the prediction objective, thereby compromising the efficacy of downstream performance. While some methods strive to recalibrate or augment EHR embeddings after direct imputation, they often mistakenly prioritize imputed features. This misprioritization can introduce biases or inaccuracies into the model. To tackle these issues, our work resorts to indirect imputation, where we leverage prototype representations from similar patients to obtain a denser embedding. Recognizing the limitation that missing features are typically treated the same as present ones when measuring similar patients, our approach designs a feature confidence learner module. This module is sensitive to the missing feature status, enabling the model to better judge the reliability of each feature. Moreover, we propose a novel patient similarity metric that takes feature confidence into account, ensuring that evaluations are not based merely on potentially inaccurate imputed values. Consequently, our work captures dense prototype patient representations with feature-missing-aware calibration process. Comprehensive experiments demonstrate that designed model surpasses established EHR-focused models with a statistically significant improvement on MIMIC-III and MIMIC-IV datasets in-hospital mortality outcome prediction task. The code is publicly available at \url{https://github.com/yhzhu99/SparseEHR} to assure the reproducibility